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STATE OF THE GREAT LAKES 201 7
Environmental Condition (Combined Gradient)
Figure 6. Biotic response function (solid line) for total number of frog species in three seasonal surveys of coastal
wetlands throughout the Great Lakes Basin. Shown is the total number of species detected as a function of a
combined "human footprint" variable incorporating environmental condition due to agriculture, development, and
wetland area (0 = poor condition, 10 = good condition). Open circles represent binned data at 10 observations per
bin.
Source: Great Lakes Coastal Wetland Monitoring Program
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Sub-Indicator: Coastal Wetland Birds
Overall Assessment
Status: Fair
Trend: Unchanging
Rationale: Mean index of ecological condition (IEC), an objective biotic indicator summarizing standardized
observations of breeding birds in coastal wetlands was 3.9 (out of 10) in 2014 and did not significantly in-
crease or decrease from 1995-2014 or from 2011-2014.
Lake-by-Lake Assessment
Lake Superior
Status: Good
Trend: Unchanging
Rationale: Mean IEC in coastal wetlands was 4.7 in 2014 and did not significantly increase or decrease from 2002-
2014 or from 2011-2014.
Lake Michigan
Status: Fair
Trend: Unchanging
Rationale: Mean IEC in coastal wetlands was 3.9 in 2014 and did not significantly increase or decrease from 2002-
2014 or from 2011-2014.
Lake Huron
Status: Good
Trend: Unchanging
Rationale: Mean IEC in coastal wetlands was 4.6 in 2014 and did not significantly increase or decrease from 2002-
2014 or from 2011-2014.
Lake Erie
Status: Poor
Trend: Deteriorating
Rationale: Mean IEC in coastal wetlands was 3.0 in 2014 and significantly decreased by -1.6%/yr (-2.1, -0.9) [low-
er, upper 95% confidence limits] from 1995-2014 and by -3.9%/yr (-6.4, -0.9) from 2011-2014.
Lake Ontario
Status: Fair
Trend: Improving
Rationale: Mean IEC in coastal wetlands was 3.8 in 2014 and significantly increased by 1.1%/yr (0.2, 2.0) [lower,
upper 95% confidence limits] from 1995-2014 and by 2.9%/yr (0.5, 5.2) from 2011-2014.
Other Spatial Scales
Inland
Status and trend based on IECs were also calculated for inland wetlands for comparison with coastal wetlands. Re-
sults were similar to those described above for coastal wetlands, except that the status for Lake Superior and Lake
Huron was fair instead of good, and there were no significant increases or decreases at any scale over time.
Separate assessments for the connecting channels of the Great Lakes were not completed. Information for the chan-
nels is included with the adjacent down-stream lake, as shown on the maps of sample points.
Sub-Indicator Purpose
• To assess the status and trends of Great Lakes coastal wetland ecosystem health by directly measuring the
composition and relative abundance of wetland breeding birds, and thereby inferring the condition of
coastal wetland habitat as it relates to the health of this ecologically and culturally important component of
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wetland communities. To restore/maintain the overall biological integrity of Great Lakes coastal wetlands,
various ecological components including coastal wetland bird communities need to be addressed.
Ecosystem Objective
Coastal wetlands provide critical breeding and migratory habitat for wildlife such as birds. Conservation of remain-
ing coastal wetlands and restoration of previously degraded or destroyed wetlands are vital components of restoring
the Great Lakes ecosystem. Birds are effective ecological indicators and can be used to report progress toward such
an objective.
This sub-indicator best supports work towards General Objective #5 of the 2012 Great Lakes Water Quality Agree-
ment, which states that the Waters of the Great Lakes should "support healthy and productive wetlands and other
habitats to sustain resilient populations of native species."
Ecological Condition
Background
Wetland breeding birds are influenced by the physical, chemical, and biological components of wetlands and sur-
rounding landscapes. For example, the occurrence, abundance, and/or reproductive success of multiple bird species
in the Great Lakes Basin declines as (1) wetland size decreases; (2) wetland habitat and natural cover in the sur-
rounding landscape decreases or degrades in quality; (3) pollution from pesticides, herbicides, and sediment runoff
increases; and (4) generalist predators (e.g., northern raccoon [Procvon lotor]) associated with anthropogenic habi-
tats in the surrounding landscape increase (Brazner et al. 2007a, 2007b; Crosbie and Chow-Fraser 1999; Howe et al.
2007a; Grandmaison and Niemi 2007; Naugle et al. 2000; Smith and Chow-Fraser 2010 a, 2010b; Tozer et al.
2010). Thus, the occurrence or abundance of sensitive wetland breeding birds can be a valuable indicator of the
health of wetlands and the surrounding landscape.
Measures
Study design—Several initiatives monitor Great Lakes wetland breeding birds. One of the longest running is Bird
Studies Canada's Great Lakes Marsh Monitoring Program (GLMMP), which started in 1995 and has operated every
year since then at coastal and inland wetlands throughout much of the Great Lakes Basin (Tozer 2013, 2016). Previ-
ous reports for this sub-indicator are based solely on data from this ongoing broad scale program (e.g., Tozer 2014).
From 2001-2005, the University of Minnesota Duluth's Natural Resource Research Institute (NRRI) led an ambi-
tious multi-institutional Great Lakes Enviromnental Indicator project (GLEI) aimed at assessing the overall biotic
health of coastal wetlands in the U.S. portion of the Great Lakes (Howe et al. 2007a, 2007b; Hanowski et al. 2007a,
2007b). More recently, the Great Lakes Coastal Wetland Monitoring Program (CWMP) led by Central Michigan
University was initiated in 2011 and currently is scheduled to operate until at least 2020 throughout both the U.S.
and Canadian Great Lakes coastal zones (Cooper et al. 2014). These projects have somewhat different study designs,
but rely on standardized, fixed duration point counts that can be adjusted to maximize cross-project compatibility.
To garner large numbers of trained volunteer participants to achieve large sample sizes at relatively low cost, the
GLMMP allows participants to select sample points—a justifiable approach if one assumes that the sample points
are approximately representative of wetlands across a region of interest. By contrast, the GLEI and CWMP projects
select sample points via stratified random sampling of coastal wetlands and survey wetlands via paid professional
staff. Nonetheless, all of the projects target wetlands dominated by non-woody emergent plants such as cattails
{Tvpha spp.) and sedges (e.g., Carex spp.) with sample points located within wetlands. In this report the datasets
listed above were brought together for the first time to generate the most comprehensive analysis of the status and
trend of Great Lakes coastal wetland breeding birds and associated wetland health.
Bird sun'evs—Breeding birds were sampled to an unlimited distance from a point located at the edge or within a
wetland (hereafter "sample point"). In most large wetlands points were sampled both near the upland / wetland inter-
face (shoreline) and in the interior of the wetland, while in most small wetlands only shoreline points were sampled.
Each sample point was surveyed for 10 or 15 minutes on 1-3 visits separated by at least 10 or 15 days during the
main avian breeding season, typically between late May and early July. Surveys occurred in either the morning (30
minutes before local sunrise to 10:00 h local time) or evening (4 hours before local sunset to dark) or both and only
under weather conditions that were favourable for detecting all species and individuals present (little to no precipita-
tion; wind: Beaufort 0-3, 0-19 km/lir). Observers broadcasted calls during surveys to entice vocal response by indi-
viduals of especially secretive species. The broadcast calls occurred during a 5-minute portion of each 10- or 15-
minute survey and consisted of 30 seconds of vocalizations followed by 30 seconds of silence for each of the follow-
ing species: Least Bittern (Ixobrychus exilis), Sora (Porzana Carolina), Virginia Rail (Rallus limicola), a mixture of
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American Coot (Fulica americana) and Common Gallinule (Gallinula galeata), and Pied-billed Grebe (Podilymbus
podiceps), in that order. The survey protocols of each of the projects closely followed the Standardized North Amer-
ican Marsh Bird Monitoring Program protocol (Conway 2011).
Analyses—Numerous methods are available for analyzing Great Lakes coastal wetland breeding bird data. Previous
analyses for this report were based on the separate status and trend of the relative abundance of approximately 20
wetland dependent breeding bird species (e.g., Tozer 2014). Alternative approaches include various indices of wet-
land health, which combine data from suites of species (e.g.. Chin et al. 2014). The latter approach is likely more
objective and more practical for the purposes of State of the Great Lakes (previously known as SOLEC) because it
provides a single comprehensive metric that represents the collective responses of breeding bird species to wetland
condition. Multi-species metrics, like the widely used index of biotic integrity for fishes (Karr and Chu 1999) and
mean coefficient of conservatism for plants (Taft et al. 1997), tend to be robust because informative values are pro-
duced even when some species are absent due to factors outside the system of interest. For example, a wide-ranging
species might go undetected because, by chance, all individuals of that species happen to be located beyond survey
plots during the sampling period, even though these individuals are resident within the wetland. Similarly, a high
quality wetland might be missing a species because of a regional epidemic that affects individuals regardless of wet-
land condition.
In this report a new approach is introduced for assessing bird community health based on multi-species data from
wetland birds across the Great Lakes Basin (Howe et al. 2007a, 2007b; Hanowski et al. 2007a, 2007b; Tozer 2013,
2016). Quantitative data were used for breeding birds at approximately 4,000 sample points throughout the Great
Lakes in both the U.S. and Canada. At many of these sample points, information is available on three potential envi-
ronmental stressors: 1) agricultural intensity in the contributing watershed (i.e., the landscape draining into the wet-
land), 2) non-agricultural landscape development such as roads, buildings, and human population density in the con-
tributing watershed, and 3) wetland area and fragmentation, measured by the total wetland area within 1 km of the
sampled wetland's centroid. For convenience, these gradients are referred to in this report as agriculture, develop-
ment, and wetland area, respectively. Clearly, many other stressors affect bird communities in coastal wetlands, but
agricultural intensity, non-agricultural landscape development, and wetland area provide tractable quantitative yard-
sticks from which one can identify sensitive species and community variables (Brazner et al. 2007a, 2007b).
For birds, it was assumed that poor wetland condition was associated with high agriculture, high development, and
small wetland area. As such, values for the agriculture and development stressors were highly skewed in favour of
degraded or unhealthy wetlands, but values for the wetland area stressor suffered from the opposite issue. To allevi-
ate bias that these skewed distributions might cause in later analyses, i.e., to downplay the influence of the small but
highly influential number of sites with extreme values, the Yeo-Johnson transformation was applied (Yeo and John-
son 2000) in R (version 3.1.3, R Core Team 2015) with package "car" (Fox and Weisberg 2011). This normalizing
transformation resembles the general Box-Cox power transformation but allows for zero values in the data. To avoid
power transformations involving decimal values, values of the enviromnental gradient were first multiplied by a
large constant (e.g., 100). After transformation each stressor was converted to a standard scale with extreme values
representing the most impacted (0) and least impacted (10) sample points with respect to that stressor. Distributions
of the transformed and standardized variables for agriculture, development, and wetland area stressors resembled
normality and could be evaluated alone or in combination. To develop a comprehensive measure of ecosystem
health based on breeding birds, principal components analysis (PCA) was used to combine the agriculture, develop-
ment, and wetland area stressors into a single multi-variate "human footprint" (Gnass Giese et al. 2015), which was
used throughout the analysis described below. Scores from two of the three PCA axes could be ordered and scaled
from most stressed (condition = 0) to least stressed (condition = 10) based on correlations with the original stressor
variables. (The magnitude of scores on one axis was opposite in direction to that of the other axis, so values were
simply inverted to align with the 0-10 scale.) Scores from the two axes were weighted according to the percent vari-
ance explained (total = 61%), summed, and re-scaled from 0-10 to yield the multi-variate "human footprint" stressor
gradient.
The health of coastal wetlands was evaluated using the index of ecological condition (IEC), an objective biotic indi-
cator introduced by Howe et al. (2007a, 2007b), improved by Gnass Giese et al. (2015), and compared to other simi-
lar indices for wetland breeding birds by Chin et al. (2015). Existing data on breeding birds of Great Lakes coastal
wetlands described in more detail below were used for the first step in IEC development. The quantitative response
of a species or multi-species variable to a given stressor gradient can be modeled from presence/absence or abun-
dance of the species at wetlands where accompanying stressor data were available. Parameters of the best-fit math-
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ematical function were estimated by computer iteration in R (R Core Team 2015) with package "iec"
(https://github.com/ngwalton/iec). Results of this analysis yielded three parameters (mean, standard deviation, and
height) describing a bell-shaped or truncated Gaussian function within the range of 0-10. These biotic response (BR)
functions provide the basis for estimating the health of coastal wetlands based on bird observations (Figure 1). By
recording the species present at a wetland, one can essentially work backward to calculate an IEC. Species (or relat-
ed biotic variables) that have been shown previously to favour minimally-stressed wetlands will indicate ecological-
ly healthy conditions and high IEC scores. By contrast, species (or related biotic variables) that favour highly-
stressed wetlands will indicate ecologically unhealthy or degraded conditions and low IEC scores. This method re-
sembles other approaches to enviromnental indicator development, but the IEC framework establishes an explicit
connection between stressors and biotic variables, providing a clear picture about what our indicator truly "indi-
cates." A more detailed description of IEC methodology is available in a separate document (Howe et al. in prep.)
and at http://www.uwgb.edu/BIODIVERSITY/forest-index/iec.asp.
CWMP data were used (2011-2014) to build BR functions because these samples could be associated with site-
specific stressor data. Samples (n = 1,117) consisted of the maximum abundance of each bird species detected dur-
ing two field surveys at a single observation point within a single year (one morning sample and one evening sam-
ple). Although the distribution of some species varies across the region, all of the species used in this analysis occur
in each of the Great Lakes, so BR functions were generated using data from the entire Great Lakes Basin. Several
alternative approaches were considered for identifying the most informative bird-based indicator. For example, the
use of abundance data versus presence/absence data were compared, which are much less vulnerable to observer
variation or bias. Models using BR functions were also compared of all potentially occurring species versus models
using only BR functions of species that were present at the sample point. The latter is desirable because it avoids
quantitative "penalties" for the absence of species that were present but not detected or species that do not have suit-
able microhabitat conditions at the sample point. To avoid excessive zeros in the response variable, the data were
grouped into "bins" of 10 samples with similar stressor values. The response variable was then the average abun-
dance among the 10 samples or, in the case of presence/absence data, the frequency of occurrence in the 10 samples.
In addition to single species metrics, a number of multi-species metrics was also calculated, including variables such
as total number of individuals of wading birds and number of marsh-obligate bird species. For these variables,
"binned" data consisted of average values for each group of 10 samples. Data from the CWMP were used to derive a
final suite of BR functions, which in turn were used to derive IEC scores for wetlands from the GLMMP, GLEI, and
CWMP projects. The results presented in this report are based on presence/absence data using only BR functions of
individual species that were present at each sample point. Based on this examination of results from the many alter-
native approaches described above, this was the most informative and cost-effective approach for determining
coastal wetland health based on wetland breeding birds.
The final suite of species was identified for calculating BR functions and IECs via the following steps. The process
started with all species in the dataset, and then eliminated all non-wetland affiliated species (e.g., forest birds), mi-
grants, wintering species, unidentified species, and species present at fewer than five of the sample points. This left a
suite of candidate species that were associated at least partly with open wetlands during the spring and early sum-
mer, i.e., "wetland breeding birds". This definition includes "marsh obligates" (species that live and breed exclusive-
ly or almost exclusively in open marshes) and "marsh users" (species that forage, rest or roost, use, or occasionally
breed in an open marsh, but are more typical of other habitats, e.g., upland grasslands or woodlands). Species were
then eliminated for which the BR functions were uninfonnative (lowest 10% range between minimum and maxi-
mum predicted response) or highly variable (10% poorest goodness-of-fit). Non-native species were also excluded
that favoured minimally-stressed wetlands (e.g.. Mute Swan [Cvgnus o/or]) or species of conservation concern (e.g..
Common Tern [Sterna hirundo]) that favoured stressed sites where features like artificial nesting structures were
present. While these species are predictive of the gradient, they are likely to be present due to factors other than wet-
land health. The resulting 52 species used to generate BR functions for calculating IECs are shown in Table 1.
IECs for each sample point were calculated in each year based on species observed across either two field visits (for
CWMP and GLMMP) or a single visit (GLEI). Next the point-level IECs were averaged across all sample points
within each wetland or wetland complex in each year, which adjusted for wetlands containing differing numbers of
sample points. Means of these wetland-level IECs for coastal wetlands in each basin and throughout the entire Great
Lakes Basin were reported (hereafter "overall") in each year. These means form the basis for the status and trend
assessments, but comparable IEC metrics for inland wetlands are also reported. In addition, distributions of IECs for
coastal and inland wetlands in each basin and overall for recent years from 2011 -2014 were reported to illustrate
variation in the health of wetlands. In these calculations bird-based IEC values were averaged across years for wet-
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lands that were sampled in multiple years. Note that data from 2011-2014 were used in these calculations to increase
sample sizes for illustrating the distribution of IECs of inland wetlands, but assessments of current coastal wetland
status are based on 2014 data only.
Status—Dentitions of good, fair, and poor condition were assigned based on wetland-level IECs from all years and
all wetlands across all basins (;? = 4,938). IEC values greater than the 66th percentile were good, values between and
including the 33rd and 66th percentiles were fair, and values less than the 33rd percentile were poor. This translated
into the following definitions:
•	Good: IEC > 4.2
•	Fair: 3.1 < IEC < 4.2
•	Poor: IEC <3.1
Trend—The terms improving, unchanging, and deteriorating were applied based on geometric mean rates of change
(%/ yr) using equation 4 in Smith et al. (2014). The statistical significance of trends was assessed via parametric
bootstrapping in R (R Core Team 2015) with package "boot" (Canty and Ripley 2013). Bootstrapping in this manner
was necessary to account for the varying precision of the beginning annual estimate and the ending annual estimate
used to calculate each trend. Trend estimates with 95% confidence intervals that did not overlap zero were consid-
ered statistically significant. The short- and long-term trends were calculated but the trend assessments for the Great
Lakes Basin and each individual basin are based on short-term changes in bird assemblages. Short term was defined
as the period 2011-2014, whereas long term was 1995-2014 or 2002-2014 in cases where < 10 wetlands were sam-
pled in 1995. The following definitions were used to describe the status of bird assemblages at Great Lakes coastal
wetlands:
•	Improving: statistically significant short-term increase in IEC
•	Unchanging: no statistically significant short-term increase or decrease in IEC
•	Deteriorating: statistically significant short-term decrease in IEC
Endpoint— The endpoint of this sub-indicator was defined as the level when mean IECs were confidently above the
lower cutoff for good condition. In other words, the endpoint was reached when the lower 95% confidence limit for
mean IEC was > 4.2.
Status and trend of coastal wetland birds
Data coverage—The dataset available for scoring sites consisted of mean annual wetland-level IECs based on
30,252 point counts conducted at 3,932 sample points in 1,511 wetlands over 20 years from 1995-2014 throughout
the Great Lakes Basin (Figure 2). The number of years that each wetland was surveyed varied from 1 to 20, with a
mean of 3.3 ± 3.7 (SD), due mostly to large differences in observer participation in the long running, broad scale
GLMMP (Figure 2). The majority of the surveyed wetlands were coastal (;? = 1,078; 71%) rather than inland (;? =
433; 29%) because both the GLEI and CWMP projects focused entirely on coastal wetlands, whereas the GLMMP
surveyed both (Figure 2).
The number of wetlands surveyed per year (296 ± 127 [mean± SD]) ranged from 123 to 513 with substantially
more wetlands surveyed from 2002-2003 and from 2011-2014 due to the GLEI and CWMP projects operating dur-
ing those years (Figure 3). Annual coverage was also higher in Lake Erie and Lake Ontario compared to the upper
Great Lakes mostly because GLMMP coverage is more extensive in the lower lakes. Annual coverage also was
higher at coastal compared to inland wetlands (Figure 2).
Overall—Mean IEC in coastal wetlands ranged from 3.3 to 4.0 from 1995-2014, with no significant increase or de-
crease from 1995-2014, or more recently from 2011-2014, ending the period below the endpoint at 3.9 in 2014 (Fig-
ure 4). The distribution of coastal IECs across the degraded-pristine gradient from 2011-2014 approximated a nor-
mal distribution (Figure 5). Based on these patterns, the status of coastal wetland health in the Great Lakes overall is
fair and the trend is unchanging. Similar patterns occurred at inland wetlands (Figures 4, 5).
Lake Superior—Mean IEC in coastal wetlands ranged from 1.8 to 5.3 from 1995-2014, with no significant increase
or decrease from 1995-2014, or more recently from 2011-2014, ending the period above the endpoint at 4.7 in 2014
(Figure 4). The distribution of coastal IECs across the degraded-pristine gradient from 2011-2014 approximated a
normal distribution notably with no wetlands scoring less than 2.0 (Figure 5). Based on these patterns, the status of
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coastal wetland health in Lake Superior is good and the trend is unchanging. Similar patterns occurred at inland wet-
lands in the Lake Superior watershed, although the status of inland wetlands was fair rather than good and low sam-
ple sizes precluded trend estimates, or clear determination of the distribution of inland IECs from 2011-2014 (Fig-
ures 4, 5). Although landscapes in the coastal zone of Lake Superior are generally non-agricultural and minimally
developed compared with wetlands in the more southern lakes (Bourgeau-Chavez et al. 2015), it was calculated that
coastal wetlands of Lake Superior (with a few notable exceptions) are relatively small in area, accounting at least
partially for the modest scores in comparison with those from other lakes.
Lake Michigan—Mean IEC in coastal wetlands ranged from 2.8 to 4.3 from 1995-2014, with no significant increase
or decrease from 1995-2014, or more recently from 2011-2014, ending the period below the endpoint at 3.9 in 2014
(Figure 4). The distribution of coastal IECs across the degraded-pristine gradient from 2011-2014 approximated a
normal distribution (Figure 5). Based on these patterns, the status of coastal wetland health in Lake Michigan is fair
and the trend is unchanging. Similar patterns occurred at inland wetlands in the Lake Michigan watershed, although
low sample sizes precluded trend estimates, or clear determination of the distribution of inland IECs from 2011-
2014 (Figures 4, 5). Some of the highest quality wetlands with respect to birds occur in Lake Michigan, even though
development and agricultural stressors are fairly strong in parts of the coastal zones of this lake (Bourgeau-Chavez
etal. 2015).
Lake Huron—Mean IEC in coastal wetlands ranged from 3.8 to 5.0 from 1995-2014, with no significant increase or
decrease from 1995-2014, or more recently from 2011-2014, ending the period above the endpoint at 4.6 in 2014
(Figure 4). The distribution of coastal IECs across the degraded-pristine gradient from 2011-2014 deviated from a
normal distribution, with more wetlands located towards the degraded end of the gradient (Figure 5). Based on these
patterns, the status for coastal wetland health in Lake Huron is good and the trend is unchanging. Similar patterns
occurred at inland wetlands in the Lake Huron watershed, although the status of inland wetlands was fair rather than
good and low sample sizes precluded clear determination of the distribution of inland IECs from 2011-2014 (Figures
4, 5). Some of the highest quality wetlands with respect to birds occur in Lake Huron, even though development and
agricultural stressors are fairly strong in parts of the coastal zones of this lake (Bourgeau-Chavez et al. 2015).
Lake Erie—Mean IEC in coastal wetlands ranged from 2.8 to 4.1 from 1995-2014, with a significant decrease from
1995-2014, as well as more recently from 2011-2014, ending the period below the endpoint at 3.0 in 2014 (Figure
4). The distribution of coastal IECs across the degraded-pristine gradient from 2011-2014 deviated from a normal
distribution with more wetlands located towards the pristine end of the gradient (Figure 5). Based on these patterns,
the status of coastal wetland health in Lake Erie is poor and the trend is deteriorating. Similar patterns occurred at
inland wetlands in the Lake Erie watershed in terms of the distribution of IECs across the degraded-pristine gradient
from 2011-2014 (Figure 5). By contrast, there were no significant trends over time at inland wetlands, partly be-
cause mean IEC at inland wetlands started out relatively low in 1995, unlike the comparatively high scores at coastal
wetlands (Figure 4).
Lake Ontario—Mean IEC in coastal wetlands ranged from 3.1 to 3.9 from 1995-2014, with a significant increase
from 1995-2014, as well as more recently from 2011-2014, ending the period below the endpoint at 3.8 in 2014
(Figure 4). The distribution of coastal IECs across the degraded-pristine gradient from 2011-2014 approximated a
normal distribution (Figure 5). Based on these patterns, the status of coastal wetland health in Lake Ontario is fair
and the trend is improving. Similar patterns occurred at inland wetlands in the Lake Ontario watershed in terms of
the distribution of IECs across the degraded-pristine gradient from 2011-2014 (Figure 5). By contrast, there were no
significant trends over time at inland wetlands (Figure 4).
Discussion—Throughout the Great Lakes Basin, the current status of coastal wetland health based on wetland breed-
ing birds is fair, with current status of Lake Superior and Lake Huron being good. Lake Michigan and Lake Ontario
being fair, and Lake Erie being poor. In addition, we found that coastal IECs located towards the degraded end of
the degraded-pristine gradient are more common in Lake Michigan, Lake Erie, and Lake Ontario compared to Lake
Superior and Lake Huron. For instance, the proportion of coastal wetlands from 2011-2014 with IECs < 5 was 73-
94% in Lake Michigan, Lake Erie, and Lake Ontario, with degraded wetlands especially prevalent in Lake Erie and
Lake Ontario. By contrast, the proportion was 46-52% in Lake Superior and Lake Huron (Figure 5). These patterns
are probably due to greater anthropogenic stress from agriculture, development, and perhaps wetland loss in Lake
Michigan south of the Canadian Shield, and in all of Lake Erie and Lake Ontario compared to Lake Superior and
most parts of Lake Huron (Allan et al. 2013, Bourgeau-Chavez et al. 2015, Danz et al. 2007, Niemi et al. 2009).
Nonetheless, some high quality coastal wetlands are still present in all of the Great Lakes (Figure 5). By illustrating
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and documenting differences in wetland health in these ways, the analysis provides a unique baseline for assessing
long-term changes in wetland quality and for quantifying the success of restoration efforts in individual wetlands,
regions, and the entire Great Lakes basin. A more detailed analysis of species' responses to individual stressors is
available, but these results are beyond the scope of this report. The condition of sites based on a multi-variate "hu-
man footprint" stressor that incorporates measures of all three stressor variables (agriculture, development, and wet-
land area) was reported.
Throughout the Great Lakes Basin, coastal wetland health based on wetland breeding birds did not significantly in-
crease or decrease over the short term from 2011-2014, or over the long term from 1995-2014, with trends in most
individual lake basins showing no significant increase or decrease over the short or long term. Exceptions were in
Lake Ontario, where IECs significantly increased both over the short term from 2011-2014 and over the long term
from 1995-2014, and in Lake Erie, where IECs significantly decreased both over the short term from 2011-2014 and
over the long term from 1995-2014 (Figure 4). The cause of Lake Ontario's recent increase in IECs is unclear,
whereas the short- and long-term decreases in IECs in Lake Erie may be associated with increasing amounts of an-
thropogenic stress from agriculture, development, and perhaps wetland loss (e.g., Danz et al. 2007, Wolter et al.
2006). Thus, given that Lake Erie was the only lake basin where coastal IECs significantly decreased over time may
suggest that the health of Lake Erie's coastal wetlands are particularly compromised compared to coastal wetlands
in the remaining lake basins. The declining trends may also indicate that Lake Erie is experiencing unique stressors
or relatively high intensities of stressors compared with stressors in the other lake basins.
In addition to assessing status and trend of the health of coastal wetlands, status and trend of inland wetlands were
examined for comparison (Figures 4, 5). The ability to compare coastal and inland wetlands due to differences in
sample sizes was best for Lake Erie and Lake Ontario, whereas it was limited for the other lake basins. Similar pat-
terns across coastal and inland wetlands were found, with the following exceptions. In Lake Erie, coastal IECs sig-
nificantly decreased over the short term from 2011-2014, and over the long term from 1995-2014, but inland IECs
showed no significant corresponding short- or long-term decreases (Figure 4). In Lake Ontario, coastal IECs signif-
icantly increased over the short-term from 2011-2014, but inland IECs exhibited no significant corresponding in-
crease (Figure 4). Thus, wetland health as represented by wetland birds may be responding to different intensities of
stressors in coastal versus inland wetlands within the Lake Erie and Lake Ontario watersheds. Similarly, a previous
study using only the GLMMP dataset observed that mean abundance of certain wetland-dependent bird species was
lower at coastal marshes compared to inland marshes (Tozer 2013). Thus, continued sampling of both coastal and
inland wetlands throughout the Great Lakes Basin is needed to completely monitor and assess the health of wetlands
based on birds throughout the entire region.
The overall fair status and unchanging trend reported for coastal wetlands throughout the Great Lakes Basin con-
trasts with previous reports for this sub-indicator, which noted overall poor status and deteriorating trends based on
the prevalence of significant negative trends in abundance among approximately 20 wetland-dependent breeding
bird species using the GLMMP dataset alone (e.g., Tozer 2014). The apparent discrepancy in overall status and trend
between this report and previous reports is likely at least partially due to differences in sampling coverage, with pre-
vious reports summarizing the status and trend of predominantly the southern portion of the Great Lakes basin due
to reliance on the mostly southern GLMMP dataset; the current report provides a more balanced assessment
throughout the entire Great Lakes Basin by bringing GLMMP data together with data from the southern and north-
ern GLEI and CWMP projects. Thus, the overall poor status and deteriorating trend reported previously may have
only been most representative, for instance, of the current poor status and deteriorating trend reported for Lake Erie.
Nevertheless, it is important to note that the patterns summarized in this report are based on a comprehensive IEC
metric, which represents the collective responses of dozens of breeding bird species to wetland condition. Therefore,
one should not lose sight of the fact that there are particular species, including bitterns (e.g., Botaurus), shallow-
(e.g., Porzana) and deep-water rails (e.g., (railinula), and marsh-nesting terns (e.g., Chlidonias), which have experi-
enced long-term declines at various scales in the Great Lakes (e.g., Tozer 2013, 2016) that may be responding in
species-specific ways to enviromnental stressors that warrant unique management actions or present unique oppor-
tunities for improving wetland health.
Linkages
Coastal wetland breeding birds are influenced by numerous local and landscape-level characteristics, some of which
are monitored by other Great Lakes (previously known as SOLEC) indicators. For instance, coastal wetland breed-
ing birds are known to be influenced by changing water levels at local and individual Great Lakes Basin scales (e.g.,
Timmennans et al. 2008, Jobin et al. 2009). Thus, the Coastal Wetland Birds sub-indicator can be expected to co-
Page 169

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STATE OF THE GREAT LAKES 201 7
vary with the Water Levels sub-indicator (e.g.. Chin et al. 2014). Similarly, the Coastal Wetland Birds sub-indicator
can be expected to co-vary with sub-indicators that track the extent and spatial arrangement of wetland breeding bird
habitat (e.g.. Coastal Wetland Landscape Extent and Composition) and prey (Coastal Wetland Invertebrate Commu-
nities; Coastal Wetland Fish Community Health). It can also be expected to co-vary with invasive plant species (e.g.,
Phragmites australis) that encroach upon preferred native vegetation (e.g.. Aquatic and Terrestrial Non-Native Spe-
cies) and pollution that may reduce prey abundance and/or availability (e.g.. Contaminants in Sediments and Fish).
Comments from the Author(s)
This approach has been completed using the GLMMP component of the larger dataset analyzed in this report. Using
multi-season site occupancy models and data from 21,546 GLMMP point counts conducted at 2,149 sample points,
Tozer (2016) determined important local, wetland, and landscape-scale factors influencing occupancy of 15 wetland
breeding marsh bird species in wetlands throughout the southern portion of the Great Lakes Basin.
The status and trend assessment of coastal wetland health based on wetland breeding birds is based on BR functions
developed using CWMP data only. The BR functions were also developed based on information from three stressor
gradients: agriculture, development, and wetland area. The ability of the IEC to capture the health of coastal wet-
lands based on bird data might be improved by expanding the development of the BR functions to include all of the
marsh bird data that are available from the GLMMP, GLEI, and CWMP projects. The performance of the IEC might
also be improved by incorporating other known wetland bird stressors in the development of BR functions, particu-
larly within-wetland attributes like relative dominance of invasive plant species. These ideas are fruitful areas for
future expansion.
For the first time, three large marsh bird datasets were brought together, specifically the GLMMP, GLEI, and
CWMP project datasets to perform the analyses summarized in this report. This provided a tremendous improve-
ment in analytical power at many different scales compared to using only one of the datasets. However, it was evi-
dent that the combined dataset is lacking information from healthy wetlands. Future collection of bird data from
wetlands located towards the pristine end of the degraded-pristine gradient might improve the performance of the
IEC.
Assessing Data Quality
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not
Applicable
1. Data are documented, validated, or
quality-assured by a recognized agency or
organization
X





2. Data are traceable to original sources
X





3. The source of the data is a known,
reliable and respected generator of data
X





4. Geographic coverage and scale of data
are appropriate to the Great Lakes Basin
X





5. Data obtained from sources within the
U.S. are comparable to those from
Canada
X





6. Uncertainty and variability in the data
are documented and within acceptable
limits for this sub-indicator report
X





Acknowledgments
Authors
Douglas C. Tozer, Ontario Program Scientist, Bird Studies Canada, P.O. Box 160, 115 Front Street, Port Rowan,
ON NOE 1M0, dtozer@birdscanada.org.
Robert W. Howe, Professor and Director of the Cofrin Center for Biodiversity, University of Wisconsin - Green
Bay, 2420 Nicolet Drive, Green Bay, WI 54311, hower@uweb.edu
Gerald J. Niemi, Senior Research Associate and Professor, Natural Resources Research Institute, University of
Minnesota - Duluth, 5013 Miller Trunk Highway, Duluth MN 55811, gniemi@d.umn.edu
Page 170

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STATE OF THE GREAT LAKES 201 7
Erin E. Gnass Giese, Biodiversity Research Specialist, Cofrin Center for Biodiversity, University of Wisconsin -
Green Bay, 2420 Nicolet Drive, Green Bay, WI 54311, giesee@uwgb.edu
Nicholas G. Walton, Research Technician, Cofrin Center for Biodiversity, University of Wisconsin - Green Bay,
2420 Nicolet Drive, Green Bay, WI 54311, waltngl5@uwgb.edu
Annie M. Bracey, Research Fellow, Natural Resources Research Institute, University of Minnesota - Duluth, 5013
Miller Trunk Highway, Duluth, MN 55811, brace005@d.uinn.edu
Willson Gaul, Research Technician, Cofrin Center for Biodiversity, University of Wisconsin - Green Bay, 2420
Nicolet Drive, Green Bay, WI 54311, gaulw@uwgb.edu
Christopher J. Nonnent, Professor and Chair, Department of Environmental Science and Biology, The College at
Brockport, State University of New York, Brockport, NY 14420, cnonnent@brockport.edu
Thomas M. Gehring, Professor, Department of Biology and Institute for Great Lakes Research, Central Michigan
University, Mount Pleasant, MI 48859, gehri ltm@cmich.edu
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List of Tables
Table 1. Wetland bird species used to estimate indices of wetland health.
Source: Great Lakes Coastal Wetland Monitoring Program
List of Figures
Figure 1. Biotic response functions for selected bird species.
Source: Great Lakes Coastal Wetland Monitoring Program
Figure 2. Map of wetlands surveyed.
Source: Great Lakes Coastal Wetland Monitoring Program
Figure 3. Plots of number of wetlands surveyed per year.
Source: Great Lakes Coastal Wetland Monitoring Program
Figure 4. Plots of trends in mean IEC over time.
Source: Great Lakes Coastal Wetland Monitoring Program
Figure 5. Plots of distributions of recent IECs 2011-2014.
Source: Great Lakes Coastal Wetland Monitoring Program
Last Updated
State of the Great Lakes 2017 Technical Report
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STATE OF THE GREAT LAKES 201 7
No.
Common name
Scientific name
1
American Bittern
Botaunis lentiginosus
2
American Crow
Cotvus brachvrhvnchos
3
American Goldfinch
Spimis tristis
4
American Robin
Tiirdus migratorius
5
Bald Eagle
Haliaeetus leucocephalus
6
Barn Swallow
Hinmdo nistica
7
Belted Kingfisher
Megacervle alcvon
8
Black Tern
Chlidonias niger
9
Black-crowned Night-Heron
Nvcticorax m'cticorax
10
Blue-winged Teal
Anas discors
11
Bobolink
Dolichonvx orvzivorus
12
Brown-headed Cowbird
Molothrus ater
13
Canada Goose
Branta canadensis
14
Caspian Tern
Hvdroprogne caspia
15
Cliff Swallow
Petrochelidon pvrrhonota
16
Common Gallinule
Gallinula galeata
17
Common Goldeneye
Bucephala clangula
18
Common Grackle
Ouiscalus quiscida
19
Common Loon
Gavia immer
20
Common Merganser
Mergus merganser
21
Common Yellowthroat
Geothhpis trichas
22
Double-crested Cormorant
Phalacrocorax auritus
23
Eastern Kingbird
Tvrannus tvrannus
24
European Starling
Stimius vulgaris
25
Forster's Tern
Sterna forsteri
26
Green Heron
Butorides virescens
27
Herring Gull
Lams argentatus
28
Hooded Merganser
Lophodvtes cuciillatiis
29
House Sparrow
Passer domesticus
30
Killdeer
Charadrius vocifenis
31
Least Bittern
Ixobrvchus exilis
32
Mallard
Anas platvrhvnchos
33
Northern Harrier
Circus cvaneus
34
Norhtem Rough-winged Swallow
Stelgidoptervx serripennis
35
Osprey
Pandion haliaetus
36
Purple Martin
Progne subis
37
Red-breasted Merganser
Mergus serrator
38
Red-winged Blackbird
Agelaius phoeniceus
39
Ring-billed Gull
Larus delawarensis
40
Sandhill Crane
Gnis canadensis
41
Sedge Wren
Cistothonis platensis
42
Song Sparrow
Melospiza melodia
43
Sora
Porzana Carolina
44
Spotted Sandpiper
Actitis macularius
45
Swamp Sparrow
Melospiza georgiana
46
Traill's Flycatcher
Empidonax alnonim/traillii
47
Tree Swallow
Tachvcineta bicolor
48
Trumpeter Swan
Cvgnus buccinator
49
Virginia Rail
Rallus limicola
50
Wilson's Snipe
Gallinago delicata
51
Wood Duck
Aix sponsa
52
Yellow Warbler
Setophaga petechia
Table 1. Wetland breeding bird species (n = 52) used to generate biotic response functions for calculating indices of
wetland health for Great Lakes coastal wetlands.
Source: Great Lakes Coastal Wetland Monitoring Program
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STATE OF THE GREAT LAKES 201 7
American Bittern
o o
Q) OO OO OCD
o
o
0
2
4
6
8
10
ra
Sandhill Crane
o
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o
o
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European Starling
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Common Grackle
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o o
DD
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o
d
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Environmental condition
Figure 1. Biotic response functions (solid lines) for selected bird species from coastal wetlands throughout the Great
Lakes Basin. Shown is the probability of occurrence as a function of a combined "human footprint" variable incor-
porating environmental condition due to agriculture, development, and wetland area (0 = poor condition, 10 = good
condition). Open circles represent binned data at 10 observations per bin. See Table 1 for scientific names.
Source: Great Lakes Coastal Wetland Monitoring Program
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STATE OF THE GREAT LAKES 201 7
Number of years surveyed
- 1-2
•	3-5
•	6-10
•	11-15
•	16-20
• Coastal
© Inland
flftohtjjw
iKjiomcriBri
Figure 2. Wetlands sun eyed for birds from 1995-2014 throughout the Great Lakes Basin for the purpose of
estimating indices of wetland health. Shown are wetlands as a function of the number of years that each wetland was
surveyed (upper map) and as a function of coastal versus inland (lower map). Note that coastal wetlands (n = 1,078)
far outnumber inland wetlands (;? = 433), although this does not appear to be the case due to tightly overlapping
symbols.
Source: Great Lakes Coastal Wetland Monitoring Program
Page 176

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STATE OF THE GREAT LAKES 201 7
TJ
CD
>.
CD
£
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c
_ra
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_o
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400
300
200
100
150
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Michigan
100
q ***•$£

1995 1999 2003 2007 2011
1995 1999 2003 2007 2011
150
100
50
Erie
1
150
100
50
1995 1999 2003 2007 2011
Year
Figure 3. Number of wetlands surveyed for birds per year from 1995-2014 throughout the Great Lakes Basin for the
purpose of estimating indices of wetland health. Shown are wetlands surveyed as a function of the entire Great
Lakes Basin (overall) and each individual lake basin for coastal and inland wetlands.
Source: Great Lakes Coastal Wetland Monitoring Program
Overall
-•-Inland
-~-Coastal
150 -i Superior
100
50
0
1995 1999 2003 2007 2011
1501	Huron
Ontario
1995 1999 2003 2007 2011
Page 177

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STATE OF THE GREAT LAKES 201 7
10
3
6
4
2
0
	Coastal
1995-2014 0.3%jy (-0.1.0.8)
2011-2014-0.2%/yr {-1.3.1 6)
Overall
10
8
6
— 4
2
__ o
	Inland
1995-2014: -0 4%fjff (-0.8,00)
2011-2014 -1 3%/yr (-3.2,0,5)
1995 1999 2003 2007 2011
1995 1999 2003 2007 2011
10
8
6
4
2
0
2002-2014 -0.3%/yr (-1.5,0 9)
2011-2014 1.1%/yr (-3.8 S 9l
-t-
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-+-
-t-
Superior
10
8
6
4
2
0
Sample size loo small for
reliable trend analysis
-+-
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1995 1999 2003 2007 2011
1995 1999 2003 2007 2011
O
UJ
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10
3
6
4
2
0
2002-2014.
2011-2014-
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•0 4%/yr (-4 1,3.4)
Michigan
10
3

1995 1999 2003 2007 2011
10
8
6
4
2
0
2002-2014: 0 4%/yr (-0 6.1.4)
2011-2014 -1 3*/y» (-4 1.16)
-+-
-+-
1995 1999 2003 2007 2011
10
3
6
4
2
0
1995-2014; -1.6%^ (-2.1.-0.9)
2011-2014: -3.»%/yr (-6.4,-0.9)
1995 1999 2003 2907 2011
10
s
6
4
2
0
1995-2014:1,1%fyr ( 0.2,2.0)
2011-2014: 2.9%,y <0-6,5-2)
1995 1999 2003 2007 2011
6
4
2
- 0
1995
Huron
10
3	•
6 •
4
2
0
1995
Erie
10 -I
3	-
6	-
4	-
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-0-1^
1995
Ontario
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3
6
4
2
0
1995
Year
Sample size too small (o*
reliable irend analysis
-+•
-+-
1999 2003 2007 2011
1905-2014:0%/yf (-0.6.0.8)
2011-2014- -1.3%/yr (-5.9,3,0)
-t-

-t-

1999 2003 2007 2011
1995-2014 -0.6%/yr (-1.4,0-2)
2011-2014 -3 S%tyr (-8 0, 0 S)
-+-
-+-
1999 2003 2007 2011
1995-2014 -0.5Wyr{ -1 1,0,2)
201T-20T4 -0.8%/yr (-2.8.1.4)
1999 2003 2007 2011
Figure 4. Temporal trends in mean index of ecological condition (IEC) based on bird data from 1995-2014
throughout the Great Lakes Basin (solid lines). Shown are means across all surveyed wetlands in each year as a
function of the entire Great Lakes Basin (overall) and each individual lake basin for coastal and inland wetlands.
Dashed lines are 95% confidence limits. Also shown are geometric mean rates of change (%/yr) over the long or
short term. Short term was 2011-2014, whereas long term was 1995-2014 or 2002-2014 in cases where < 10
wetlands were sampled in 1995.
Source: Great Lakes Coastal Wetland Monitoring Program
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STATE OF THE GREAT LAKES 201 7
¦Coastal
Overall
Mean = 4-0
©
©
CM
©
©
I Inland
Mean = 3.5
01234 5 6789 10
Superior
Q -
©
cm
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O .
CM H
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012 3456789 10 0123456789 10
Michigan
Mean = 4 0 o ^	Mean-32
i i i i i—i—i—i—i—i
01234 56789 10 0123456769 10
Huron
 .
CO
Mean = 4 2
01234 56789 10
Erie
Mean = 32 §
©
CO
0123456789 10
Ontario
¦f—1—1	1
0123456789 10
-
Mean = 3 2
0 12 3 4
5 6 7 8 9 10
Mean ¦ 3 4
01234 56789 10
I EC
Figure 5. Distribution of index of ecological condition (IEC) scores based on bird data from 2011 -2014 throughout
the Great Lakes Basin. Shown are IECs for all surveyed wetlands as a function of the entire Great Lakes Basin
(overall) and each individual lake basin for coastal and inland wetlands. Note that prior to these calculations we
averaged across years for wetlands that were sampled in multiple years. We also note that the vertical axes differ
among overall and each lake for clarity of small sample sizes, but are the same within overall and each lake to
facilitate comparisons between coastal and inland.
Source: Great Lakes Coastal Wetland Monitoring Program
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STATE OF THE GREAT LAKES 201 7
Sub-Indicator: Coastal Wetland Fish
Overall Assessment
Status: Fair
Trend: Improving
Rationale: As of 2015, the majority of wetland sites were in the moderately degraded category based on the
health of coastal wetland fish communities. The trend is determined by comparing the current status of
coastal wetland fish to that of three years prior and whether the metric increased, decreased, or showed no
substantial change in score. Data are not currently available for long-term trend analysis. In 2012,17% of
wetland sites were in the degraded score category. In 2015, only 8% of wetlands were in the degraded score
category. Fair is defined as "the vast majority of the wetlands are not in the degraded category".
Lake-by-Lake Assessment
In an effort funded by the Great Lakes Restoration Initiative (GLRI) through 2020 (about $2 million per year),
approximately 200 wetlands were sampled annually since 2011. A total of 176 wetlands were sampled in 2011, 206
sampled in 2012, 201 in 2013, 216 in 2014, and 211 in 2015 for a total of 1010 Great Lakes coastal wetland
sampling events. As of 2015, nearly 100% of the medium and large (> 4 hectares), hydrologically-connected coastal
wetlands on the Great Lakes have been sampled. With respect to the entire Great Lakes, about 80% of coastal
wetlands by count and area have been sampled (Figure 1).
Individual lake basin assessments were not prepared for this report.
Sub-Indicator Purpose
• The purpose of this sub-indicator is to track the trends of Great Lakes coastal wetland ecosystem health by
measuring the composition and density of fish communities, and to infer suitability of habitat and water quality
for Great Lakes coastal wetland fish communities.
Ecosystem Objective
Coastal Wetland habitats are critical spawning and nursery areas for many fish species of ecological and economic
importance. Conservation of remaining coastal wetlands and restoration of previously destroyed wetlands are vital
components of restoring the Great Lakes ecosystem and this sub-indicator can be used to report progress toward
such an objective.
This sub-indicator best supports work towards General Objective #5 of the 2012 Great Lakes Water Quality
Agreement which states that the Waters of the Great Lakes should "support healthy and productive wetlands and
other habitats to sustain resilient populations of native species."
Restore and maintain the diversity of the fish community of Great Lakes coastal wetlands while indicating overall
ecosystem health (Annex 7 GLWQA). Significant wetland areas in the Great Lakes system that are threatened by
urban and agricultural development and waste disposal activities should be identified, preserved and, where
necessary, rehabilitated. This sub-indicator supports the restoration and maintenance of the chemical, physical and
biological integrity of the Great Lakes basin and beneficial uses dependent on healthy wetlands (Annexl GLWQA).
Ecological Condition
Coastal wetlands trap, process, and remove nutrients and sediment from Great Lakes nearshore waters and recharge
groundwater supplies. However, over half of all Great Lakes coastal wetlands have been destroyed by human
activities, and many remaining coastal wetlands suffer from anthropogenic stressors such as nutrient and sediment
loading, fragmentation, invasive species, shoreline alteration, and water level control, as documented by a binational
Great Lakes-wide mapping and attribution project (Albert and Simonson 2004; Ingram and Potter 2004).
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STATE OF THE GREAT LAKES 2017
In order to properly manage the Great Lakes coastal wetland fish community health there must be consistent
sampling methods. Sampling was conducted no earlier than mid-June and no later than August due to migration
patterns of the fish communities. Fish should be sampled using three replicate fyke nets of 4.8 mm mesh in each
major plant zone in each wetland for one net-night. Dominant vegetation zones were identified because different
zones support different fishes (Uzarski et al. 2005). There are two sizes of fyke nets that can be used: 0.5-m x 1-m
opening and 1-m x 1-m opening. The smaller nets are placed in water that is 0.25-0.5 m deep and the larger fyke
nets are placed in water that is greater than 0.50 m deep. The leads are 7.3 m long with 1.8 m long wings. Nets were
haphazardly placed a minimum of 20 m apart in each vegetation zone. The fyke nets are placed perpendicular to the
vegetation zone, therefore, fish swimming along the edge of the vegetation zone are captured. This sub-indicator can
only be used where there is sufficient water depth to use fyke nets and a minimum of 10 fish must be captured or the
sites must be fished another net-night.
Any fish collected that is greater than 25 mm were identified down to species. The number of the fish caught per
fyke net were recorded. Fish abundance by taxon is used to calculate the Great Lakes Coastal Wetland Monitoring
Program (GLCWMP) IBI scores (Uzarski et al. 2016). The GLCWC developed indices of biological integrity (IBIs)
in 2002 and protocols were finalized in 2008 (GLCWC 2008). These were further developed by the GLCWMP. The
Index of Biotic Integrity (IBI) was developed based on measures of richness and abundance, percent exotic species,
functional feeding groups, and other species-level parameters. Several different fish metrics are being utilized. See
GreatLakeswetlands.org 'Documents' for details on indicator metrics.
The IBI provides a rigorous approach to quantify the biological condition of fish communities within the Great
Lakes. It is based on reference conditions and is developed from a composite of specific measures used to describe
fish community, structure, function, individual health, and abundance. Specific parameters, termed "metrics," are
scored based on how similar they are to the reference condition. Individual IBIs are derived for each of the measures
and can be used independently as a measure of coastal wetland health, based on a percentage of points possible
reflected as 'reference conditions' to 'extremely degraded'. The IBI also provides a narrative characterization that
provides a measure of the enviromnental condition and will be calibrated for regional use.
From 2011 to 2015, an average of 10 to about 13 fish species were collected in Canadian and U.S. Great Lakes
coastal wetlands, respectively (Table 1). These data include sites in need of restoration, and some had very few
species. However, wetlands with the highest richness had as many as 23 (CA) or 28 (US) fish species. The average
number of non-native fish species per wetland was approximately one, though some wetlands had as many as 5
(U.S.). There are wetlands in which no non-native fish species were caught in fyke nets, although some non-native
fish are adept at net avoidance (e.g. common carp).
From 2011-2015, total fish species did not differ greatly by lake, averaging 12-14 species per wetland (Table 2).
Lake Ontario wetlands had the lowest maximum number of species, with the other lakes all having similar
maximums of 27-28 species. Lake Huron wetlands averaged the lowest mean number of non-native fish taxa. All
other lakes had a similar average number of non-native fish species per wetland, about 1.
When the fish communities of reference wetlands are compared across the entire Great Lakes, the most similar sites
come from the same ecological province rather than from any single Great Lake or specific wetland types. Data
from several studies indicate that the characteristic groups of fish species in reference wetlands from each ecological
province tend to have similar water temperature and aquatic productivity preferences.
There are a number of carp introductions that have the potential for substantial impact on Great Lakes fish
communities, including coastal wetlands. Goldfish (Carassius auratus) are common in some shallow habitats, and
they occurred along with common carp young-of-the-year in many of the wetlands sampled along Green Bay. In
addition, there are several other carp species, e.g., grass carp (Ctenopharvngodon idella), bighead carp
(Hypophthalmichthvs nobilis) and silver carp (Hypophthalmichthvs molitrix) that escaped aquaculture operations
and are now in the Illinois River and migrating toward the Great Lakes through the Chicago Sanitary and Ship
Canal. Most of these species attain large sizes. Some are planktivorous, but also eat phytoplankton, snails, and
mussels, while the grass carp eats vegetation. These species represent yet another substantial threat to food webs in
wetlands and nearshore habitats with macrophytes (U.S. Fish and Wildlife Service (USFWS) 2002).
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STATE OF THE GREAT LAKES 2017
Linkages
Pressures
Agriculture
Agriculture degrades wetlands in several ways, including nutrient enrichment from fertilizers, increased sediments
from erosion, increased rapid runoff from drainage ditches, introduction of agricultural non-native species (reed
canary grass), destruction of inland wet meadow zone by plowing and diking, and addition of herbicides. In the
southern lakes, Saginaw Bay, and Green Bay, agricultural sediments have resulted in highly turbid waters which
support few or no submergent plants.
Urban development
Physical modifications to the shoreline have disrupted coastal and nearshore processes, flow and littoral circulatory
patterns, altered or eliminated connectivity to coastal wetlands/dunes, and have altered nearshore and coastal habitat
structure. Urban development degrades wetlands by hardening shoreline, filling wetland, adding a broad diversity of
chemical pollutants, increasing stream runoff, adding sediments, and increased nutrient loading from sewage
treatment plants. In most urban settings, almost complete wetland loss has occurred along the shoreline. Thoma
(1999) and Johnson et al. (2006) were unable to find coastal wetlands on the U.S. side of Lake Erie that experienced
minimal anthropogenic disturbances. According to Seilheimer and Chow-Fraser (2006; 2007), there has been
accelerated loss of wetland fish habitat in Lake Ontario, Lake Erie and Lake Michigan near urban areas and
agriculture.
Residential shoreline development
Along many coastal wetlands, residential development has altered wetlands by nutrient enrichment from fertilizers
and septic systems, shoreline alterations for docks and boat slips, filling, and shoreline hardening. Agriculture and
urban development are usually less intense than local physical alteration which often results in the introduction of
non-native species. Shoreline hardening can completely eliminate wetland vegetation, which results in degradation
of fish habitat. It appears that when a wetland becomes affected by human development, the fish community
changes to that typical of a wanner, richer, more southerly wetland. This finding may help researchers anticipate the
likely effects of regional climate change on the fish communities of Great Lakes coastal wetlands.
Mechanical alteration of shoreline
Mechanical alteration takes a diversity of forms, including diking, ditching, dredging, filling, and shoreline
hardening. With all of these alterations, non-native species are introduced by construction equipment or in
introduced sediments. Changes in shoreline gradients and sediment conditions are often adequate to allow non-
native species to become established.
Introduction of non-native species
Non-native species are introduced in many ways. Some were purposefully introduced as agricultural crops or
ornamentals, later colonizing in native landscapes. Others came in as weeds in agricultural seed. Increased sediment
and nutrient enrichment allow many of the worst aquatic weeds to out-compete native species. Most of the worst
non-native species are either prolific seed producers or reproduce from fragments of root or rhizome. Non-native
animals have also been responsible for increased degradation of coastal wetlands. Common and grass carp
reproductive and feeding behaviour results in loss of submergent vegetation in shallow marsh waters.
Precipitation Amounts- change in atmospheric temperature will potentially affect the number of extreme storms in
the Great Lakes region which will, in turn, affect coastal wetlands
Water Levels - water level change has strong influences on Great Lakes habitat and biological communities
associated with Coastal Wetlands. Lake levels have a major influence on undiked coastal wetlands and are basic to
any analysis of wetland change trends
Pressures were also described in the Coastal Wetland Plants sub-indicator.
Comments from the Authors(s)
Individual IBIs can be used independently as a measure of coastal wetland health, based on a percentage of points
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STATE OF THE GREAT LAKES 2017
possible reflected as 'reference conditions' to 'extremely degraded'. The sub-indicator has been used basin wide
(U.S. and Canada) over the past four years and much longer in some regions. This sub-indicator can also be
evaluated as part of an overall analysis of biological communities of Great Lakes coastal wetlands and nearshore
aquatic systems. This can be done by by considering the coastal wetland sub-indicators in combination, because they
function and indicate anthropogenic disturbance at different spatial and temporal scales and have varying resolution
of detection. For example, fish tend to detect disturbance somewhere between the local and regional scale.
The sites sampled in 2015 are shown in Figure 2 and are colour coded by which taxonomic groups were sampled at
the sites. Many sites were sampled for all taxonomic groups. Sites not sampled for birds and amphibians typically
were sites that were impossible to access safely without a boat, and often related to private property access issues.
Most bird and amphibian crews do not operate from boats since they need to arrive at sites in the dark or stay until
well after dark. There are also a number of sites sampled only by bird and amphibian crews because these crews can
complete their site sampling more quickly and thus have the capacity to sample more sites than do the fish,
macroinvertebrate, and vegetation crews.
Assessing Data Quality
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not
Applicable
1. Data are documented, validated, or
quality-assured by a recognized agency or
organization
X





2. Data are traceable to original sources
X





3. The source of the data is a known,
reliable and respected generator of data
X





4. Geographic coverage and scale of data
are appropriate to the Great Lakes Basin
X





5. Data obtained from sources within the
U.S. are comparable to those from
Canada
X





6. Uncertainty and variability in the data
are documented and within acceptable
limits for this sub-indicator report
X





Acknowledgments
Authors:
Donald G. Uzarski, Institute for Great Lakes Research, CMU Biological Station, and Department of Biology,
Central Michigan University, Mount Pleasant, MI
Contributors:
Valerie J. Brady, Natural Resources Research Institute, University of Minnesota Duluth, Duluth, MN, USA
John Brazner, US Enviromnental Protection Agency, Mid-Continent Ecology Division Duluth, MN (2006)
Thomas M. Burton, Departments of Zoology and Fisheries and Wildlife, Michigan State University, East Lansing,
MI (2006)
Jan J.H. Ciborowski, Department of Biological Sciences, University of Windsor, Windsor, ON
Matthew J. Cooper, Burke Center for Freshwater Innovation, Northland College, Ashland, WI, USA
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STATE OF THE GREAT LAKES 2017
Joseph P. Gathman, University of Wisconsin-River Falls, River Falls, WI, USA
Greg P. Grabas, Environment and Climate Change Canada, Toronto, ON, Canada
David Jude, School of Natural Resources and the Enviromnent, University of Michigan, Ann Arbor, MI (2006)
Gary A. Lamberti, University of Notre Dame, Notre Dame, IN, USA
Ashley H. Moerke, Aquatic Research Laboratory, Lake Superior State University, Sault
Ste. Marie, MI, USA
Carl R. Ruetz III, Annis Water Resources Institute, Grand Valley State University,
Muskegon. MI, USA
Danielle J. Sass, Oak Ridge Institute of Science and Education (ORISE) Research Fellow, Appointed to the U.S.
Enviromnental Protection Agency (U.S. EPA), Great Lakes National Program Office (GLNPO) (2008)
Douglas A. Wilcox, Department of Enviromnental Science and Biology, SUNY College at Brockport, Brockport,
NY, USA
Information Sources
Albert, D. A., and L. Simonson. 2004. Coastal wetland inventory of the Great Lakes region (GIS coverage of U. S.
Great Lakes: www.glc.org/wtlands/inventory.html). Great Lakes Consortium, Great Lakes Commission, Ann Arbor,
MI.
Ingram, J. W., and B. Potter. 2004. Development of a Coastal Wetlands Database for the Great Lakes Canadian
Shoreline, http://www.glc.org/wetlands/inventory.html Great Lakes Consortium, Great Lakes Commission, Ann
Arbor, MI.
Johnson, L.B., Olker, J., Ciborowski, J.J.H., Host, G.E., Breneman, D., Brady, V., Brazner, J., and Danz, N. 2006.
Identifying Response of Fish Communities in Great Lakes Coastal Regions to Land Use and Local Scale Impacts.
Bull. N. Am. Benthol. Soc. [also in prep for submission to J. Great Lakes Research]
Seilheimer, T.S. and Chow-Fraser, P. 2006. Development and use of the Wetland Fish Index to assess the quality of
coastal wetlands in the Laurentian Great Lakes. Submitted to Can. J. Fish. Aquat. Sci. 63:354-366.
Seilheimer, T.S. and Chow-Fraser, P. 2007. Application of the Wetland Fish Index to Northern Great Lakes Marshes
with Emphasis on Georgian Bay Coastal Wetlands. Journal of Great Lakes Research. 33.3
Thoma. R.F. 1999. Biological monitoring and an index of biotic integrity for Lake Erie's nearshore waters. In
Assessing the sustainability and biological integrity of water resources using fish communities ed. T.P. Simon. CRC
Press, Boca Raton, FL. pp. 417-461.
U.S. Fish and Wildlife Service. 2002. Asian Carp, Key to Identification. Pamphlet. LaCross Fishery Resources
Office, Onalaska, WI. http://www.fws.gov/midwest/lacrossefisheries/reports/asian_carp_key.pdf
Uzarski, D.G., Burton, T.M., Cooper, M.J., Ingram, J., and Timmermans, S. 2005. Fish Habitat Use Within and
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STATE OF THE GREAT LAKES 2017
Across Wetland Classes in Coastal Wetlands of the Five Great Lakes: Development of a Fish Based Index of Biotic
Integrity. Journal of Great Lakes Research 31(1): 171-187.
Uzarski, D.G., V.J. Brady, M.J. Cooper, D.A. Wilcox, D.A. Albert, R. Axler, P. Bostwick, T.N. Brown, J.J.H.
Ciborowski, N.P. Danz, J. Gathman T. Gehring, G. Grabas, A. Garwood, R. Howe, L.B. Johnson, G.A. Lamberti,
A. Moerke, B. Murry, G. Niemi, C.J. Nonnent, C.R. Ruetz III, A.D. Steinman, D. Tozer, R. Wheeler, T.K.
O'Donnell, and J.P. Schneider. 2016. Standardized measures of coastal wetland condition: implementation at the
Laurentian Great Lakes basin-wide scale. Wetlands doi: 10.1007/sl3157-016-0835-7.
List of Tables
Table 1. Total fish species in wetlands, and non-native species; summary statistics by country for sites sampled
from 2011 through 2015.
Source: Great Lakes Coastal Wetland Monitoring Program (CWMP), Uzarski et al. 2016
Table 2. Fish total species and non-native species found in Great Lakes coastal wetlands by lake. Mean, minimum,
and minimum number of species per wetland. Data from 2011 through 2015.
Source: Great Lakes Coastal Wetland Monitoring Program (CWMP), Uzarski et al. 2016
List of Figures
Figure 1. Condition of coastal wetland fish communities based upon data from all sites sampled from 2011 through
2015.
Source: Great Lakes Coastal Wetland Monitoring Program (CWMP), Uzarski et al. 2016
Figure 2. The sites sampled in 2015 are color coded by which taxonomic groups were sampled at the sites.
Source: Great Lakes Coastal Wetland Monitoring Program (CWMP), Uzarski et al. 2016
Last Updated
State of the Great Lakes 2017 Technical Report
Country	Sites	Mean	Max	Min	St. Dev.
Overall





Canada
156
10.0
23
2
3.9
U.S.
365
13.3
28
2
5.2
Non-natives





Canada
156
0.7
3
0
0.7
U.S.
365
0.7
5
0
0.9
Table 1. Total fish species in wetlands, and non-native species; summary statistics by country for sites sampled
from 2011 through 2015.
Source: Great Lakes Coastal Wetland Monitoring Program (CWMP), Uzarski et al. 2016
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STATE OF THE GREAT LAKES 2017
Total Fish
Non-native Fish
Lake
Sites
Mean
Max
Min
Mean
Max
Min
Erie
66
12.2
27
2
1,1
4
0
Huron
180
11.5
27
2
0.4
2
0
Michigan
75
13.1
28
5
0.8
4
0
Ontario
135
12.3
23
4
0.8
3
0
Superior
65
14.1
28
3
0.9
5
0
Table 2. Fish total species and non-native species found in Great Lakes coastal wetlands by lake. Mean, minimum,
and minimum number of species per wetland. Data from 2011 through 2015.
Source: Great Lakes Coastal Wetland Monitoring Program (CWMP), Uzarski et al. 2016



Fish Indicator 2011-2015

ta
• 1
• -9
•• 'Si
• •
•
•
•
•
High Quality/Reference Condition
Mildly Impacted
Moderately Impacted
Moderately Degraded
Degraded


rs •
•
A . •
i
•
X

&
4 •



i

• * •
¦



•
t#
• §
c
•
m
4'
Figure 1. Condition of coastal wetland fish communities based upon data from all sites sampled from 2011 through
2015.
Source: Great Lakes Coastal Wetland Monitoring Program (CWMP), Uzarski et al. 2016
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STATE OF THE GREAT LAKES 2017


Coastal Wetland Monitoring
Sites Sampled - 2015



• All Crews



+ Fish/invertA/eg
~ %
•
*¦+
•
+ 4>
•
, \
•
•
Bird/Amphib
+
' * \

*
MP
0
1
+ . - *
••
#
•
s
+
230 500
a J



KlSofTfltBrc
Figure 2. The sites sampled in 2015 are color coded by which taxonoinic groups were sampled at the sites.
Source: Great Lakes Coastal Wetland Monitoring Program (CWMP), Uzarski et al. 2016
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STATE OF THE GREAT LAKES 201 7

Sub-Indicator: Coastal Wetland Invertebrates
Overall Assessment
Status: Fair
Trend: Deteriorating
Rationale: As of 2015, the vast majority of wetland sites are not in the degraded category based on the health
of coastal wetland invertebrate communities. However, in the southern portion of the basin most sites fall
within the moderately impacted category or worse. In the norther region, most fall within the moderately
impacted category or better. The trend is determined by comparing the current status of invertebrate
communities in coastal wetlands to that of three years prior and whether the metric increased, decreased, or
showed no substantial change in score. Data are not currently available for long-term trend analysis. In 2012,
17% of wetland sites were in the degraded score category. In 2012,15% of wetland sites were in the degraded
score category. In 2015,19% of wetlands were in the degraded score category. Fair is defined as "the vast
majority of the wetlands are not in the degraded category".
Lake-by-Lake Assessment
In an effort funded by the Great Lakes Restoration Initiative (GLRI) through 2020 (about $2 million per year),
approximately 200 wetlands were sampled annually since 2011. A total of 176 wetlands were sampled in 2011, 206
sampled in 2012, 201 in 2013, 216 in 2014, and 211 in 2015 for a total of 1010 Great Lakes coastal wetland
sampling events. As of 2015, nearly 100% of the medium and large (> 4 hectares), hydrologically-connected coastal
wetlands on the Great Lakes have been sampled. With respect to the entire Great Lakes, about 80% of coastal
wetlands by count and area have been sampled, however the most recent sub-indicator map includes data from years
2011 through 2014 as data from 2015 are still being processed into map configuration (Table 1).
Individual lake basin assessments were not prepared for this report.
Sub-Indicator Purpose
• The purpose of this sub-indicator is to assess the diversity of the invertebrate community, especially aquatic
insects; to track the trends of Great Lakes coastal wetland ecosystem health by measuring the composition
and density of macroinvertebrates; and to infer water quality, habitat suitability, and biological integrity of
Great Lakes coastal wetlands.
Ecosystem Objective
Coastal Wetland habitats are critical spawning and nursery areas for many invertebrate species of ecological and
economic importance. Conservation of remaining coastal wetlands and restoration of previously destroyed wetlands
are vital components of restoring the Great Lakes ecosystem and this sub-indicator can be used to report progress
toward such an objective.
This sub-indicator best supports work towards General Objective #5 of the 2012 Great Lakes Water Quality
Agreement which states that the Waters of the Great Lakes should "support healthy and productive wetlands and
other habitats to sustain resilient populations of native species."
Significant wetland areas in the Great Lakes system that are threatened by urban and agricultural development and
waste disposal activities should be identified preserved and, where necessary, rehabilitated. Conducting monitoring
and surveillance activities will gather definitive information on the location, severity, aerial or volume extent, and
frequency of the monitoring of Great Lakes coastal wetlands. This sub-indicator supports the restoration and
maintenance of the chemical, physical and biological integrity of the Great Lakes Basin and beneficial uses
dependent on healthy wetlands (Annex 1 GLWQA).
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STATE OF THE GREAT LAKES 201 7
Ecological Condition
Coastal wetlands trap, process, and remove nutrients and sediment from Great Lakes nearshore waters; and recharge
groundwater supplies. However, over half of all Great Lakes coastal wetlands have been destroyed by human
activities and many remaining coastal wetlands suffer from anthropogenic stressors such as nutrient and sediment
loading, fragmentation, invasive species, shoreline alteration, and water level control, as documented by a binational
Great Lakes-wide mapping and attribution project (Albert and Simonson 2004; Ingram and Potter 2004).
To restore/maintain the overall biological integrity of Great Lakes coastal wetlands, the various ecological
components need to be adequately represented. The Great Lakes Coastal Wetland Consortium (GLCWC)-adopted
Index of Biotic Integrity (IBI, Uzarski et al. 2004) and further developed by the Great Lakes Coastal Wetland
Monitoring Program (GLCWMP) offers information on overall diversity of the invertebrate community and trends
over time (Uzarski et al. 2016). The presence, diversity and abundance of invertebrates tend to correlate with factors
such as water depth, vegetation, and sediment type. Such localized conditions influence the invertebrate community
present in each wetland. Therefore, a sufficient number of representative wetlands were needed to characterize each
lake basin adequately. The SOLEC 98 Biodiversity Investment Areas paper on Coastal Wetland Ecosystems
identified the eco-reaches from which representative wetlands were selected.
Macroinvertebrate samples should be collected annually from the dominant plant zones in each wetland using dip
nets in accordance with standard protocols initially developed by the GLCWC and further developed by the
GLCWMP. Plant zones are defined as patches of vegetation in which a particular plant type or growth form
dominates the plant community based on visual coverage estimates. Numerous replicate samples are collected from
each plant zone within each wetland. Samples should be collected annually and depending on latitude and wetland
type during either June, July, or August when vegetation has developed. Southern drowned river mouths should be
sampled during June while lacustrine sites should be sampled during July in the south latitudes and during August in
the northern latitudes.
The invertebrate IBI has been applied to coastal wetlands basin-wide by a syndicate of universities from 2011 to
2015. IBI scores were primarily based on richness and relative abundance of Odonata; richness and relative
abundance of Crustacea plus Mollusca taxa; total genera richness; relative abundance of Gastropoda; relative
abundance of Sphaeriidae; richness of Ephemeroptera plus Trichoptera taxa; relative abundance Isopoda; relative
abundance of Amphipoda; Evenness; Shannon Diversity Index; and Simpson Index. See GreatLakeswetlands.org
'Documents' for details on indicator metrics.
As of 2014, the average number of macroinvertebrate taxa (taxa richness) per site was about 40, but some wetlands
had more than twice this number (Table 1). Sites scheduled for restoration and other taxonomically poor wetlands
had fewer taxa, as little as 10 at a Canadian site and as little as zero at restoration sites in the US. However, the
average number of non-native invertebrate taxa in coastal wetlands was less than 1, with a maximum of no more
than 5. It is important to note that the one-time sampling method used at coastal wetland sites may not be capturing
all of the non-native taxa and it is not necessarily intended to. Furthermore, some non-native macroinvertebrates are
very cryptic, may resemble native taxa, and may not yet be recognized as invaders to the Great Lakes.
There is some variability among lakes in the mean number of macroinvertebrate taxa per wetland. Lake Ontario and
Erie wetlands averaged 32 and 35 taxa, respectively (Table 2), while Lakes Huron and, Superior, and Michigan
about 42-47 taxa. The maximum number of invertebrate taxa was higher in lakes Huron and Michigan wetlands
(>80) than for the most invertebrate-rich wetlands in other lakes, which have a maximum of 60-70 taxa. Wetlands
with the fewest taxa are sites in need of restoration and have as few as no taxa found at all (in both Erie and
Ontario). Patterns are likely driven by differences in habitat complexity, which may in part be due to the loss of
wetland habitats on lakes Erie and Ontario from diking and water level control, respectively. There is little
variability among lakes in non-native taxa occurrence, although Erie and Huron had wetlands with 4-5 non-native
taxa. In each lake, a portion of wetlands had zero non-native taxa; however, as noted above, this does not necessarily
mean that these sites do not harbor non-native macroinvertebrates.
Linkages
Pressures
Physical alteration and eutrophication of wetland ecosystems continue to be a threat to invertebrates of Great Lakes
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STATE OF THE GREAT LAKES 201 7
coastal wetlands. Both can promote establishment of non-native vegetation, and physical alteration can destroy plant
communities altogether while changing the natural hydrology to the system. Invertebrate community composition is
directly related to vegetation type and densities; changing either of these components will negatively impact the
invertebrate communities.
Agriculture
Agriculture degrades wetlands in several ways, including nutrient enrichment from fertilizers, increased sediments
from erosion, increased rapid runoff from drainage ditches, introduction of agricultural non-native species (reed
canary grass), destruction of inland wet meadow zone by plowing and diking, and addition of herbicides.
Urban development
Physical modifications to the shoreline have disrupted coastal and nearshore processes, flow and littoral circulatory
patterns, altered or eliminated connectivity to coastal wetlands/dunes, and have altered nearshore and coastal habitat
structure. Urban development degrades wetlands by hardening shoreline, filling wetland, adding a broad diversity of
chemical pollutants, increasing stream runoff, adding sediments, and increased nutrient loading from sewage
treatment plants. In most urban settings, almost complete wetland loss has occurred along the shoreline.
Residential shoreline development
Along many coastal wetlands, residential development has altered wetlands by nutrient enrichment from fertilizers
and septic systems, shoreline alterations for docks and boat slips, filling, and shoreline hardening. Agriculture and
urban development are usually less intense than local physical alteration which often results in the introduction of
non-native species.
Mechanical alteration of shoreline
Mechanical alteration takes a diversity of forms, including diking, ditching, dredging, filling, and shoreline
hardening. With all of these alterations, non-native species are introduced by construction equipment or in
introduced sediments.
Introduction of non-native species
Non-native species are introduced in many ways. Some were purposefully introduced as agricultural crops or
ornamentals, later colonizing in native landscapes. Others came in as weeds in agricultural seed. Increased sediment
and nutrient enrichment allow many of the worst aquatic weeds to out-compete native species. Most of the worst
non-native species are either prolific seed producers or reproduce from fragments of root or rhizome. Non-native
animals have also been responsible for increased degradation of coastal wetlands. The faucet snail (Bithynia
tentaculata) is an example of a prolific macroinvertebrate invader of particular interest to USFWS and others
because it carries parasites that can cause disease and die-offs of waterfowl.
Pressures were also described in the Coastal Wetland Plants sub-indicator.
Precipitation Amounts - change in atmospheric temperature will potentially affect the number of extreme storms in
the Great Lakes region which will, in turn, affect coastal wetlands
Water Levels - water level change has strong influences on Great Lakes habitat and biological communities
associated with Coastal Wetlands. Lake levels have a major influence on undiked coastal wetlands and are basic to
any analysis of wetland change trends
Comments from the Author(s)
The invertebrate IBI is a multi-indicator, developed from a composite of specific parameters, termed "metrics," used
to describe the invertebrate community, structure, function, and abundance. The IBI provides a rigorous approach
that quantifies the biological condition of the invertebrate community of Great Lakes coastal wetlands based on data
from least-impacted sites that are representative of Great Lakes coastal wetlands, referred to as a reference
condition. These are then compared to sites experiencing a gradient of the amount and type of anthropogenic
disturbance and stratified by region and wetland type. It is important to note that the invertebrate IBI has been
developed for coastal wetlands that are directly connected to the Great Lakes, not for those wetlands that are only
connected hydrologically via groundwater.
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STATE OF THE GREAT LAKES 201 7
This sub-indicator can also be evaluated as part of an overall analysis of biological communities of Great Lakes
coastal wetlands and nearshore aquatic systems. This can be done by considering the coastal wetland sub-indicators
in combination, because they function and indicate anthropogenic disturbance at different spatial and temporal
scales and have varying resolution of detection. For example, invertebrates detect much more local disturbance of
the lakeward portion of the wetland within regions.
The sites sampled in 2015 are shown in Figure 2 and is colour coded by which taxonomic groups were sampled at
the sites. Many sites were sampled for all taxonomic groups. Sites not sampled for birds and amphibians typically
were sites that were impossible to access safely, and often related to private property access issues. Most bird and
amphibian crews do not operate from boats since they need to arrive at sites in the dark or stay until well after dark.
There are also a number of sites sampled only by bird and amphibian crews because these crews can complete their
site sampling more quickly and thus have the capacity to sample more sites than do the fish, macroinvertebrate, and
vegetation crews.
Assessing Data Quality
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not
Applicable
1. Data are documented, validated, or
quality-assured by a recognized agency or
organization
X





2. Data are traceable to original sources
X





3. The source of the data is a known,
reliable and respected generator of data
X





4. Geographic coverage and scale of data
are appropriate to the Great Lakes Basin
X





5. Data obtained from sources within the
U.S. are comparable to those from
Canada
X





6. Uncertainty and variability in the data
are documented and within acceptable
limits for this sub-indicator report
X





Acknowledgments
Authors:
Donald G. Uzarski, Institute for Great Lakes Research, CMU Biological Station, and Department of Biology,
Central Michigan University, Mount Pleasant, MI.
Contributors:
Valerie J. Brady, Natural Resources Research Institute, University of Minnesota Duluth, Duluth, MN, USA
John Brazner, US Enviromnental Protection Agency, Mid-Continent Ecology Division Duluth, MN (2006)
Thomas M. Burton Departments of Zoology and Fisheries and Wildlife, Michigan State University, East Lansing,
MI (2006)
Jan J.H. Ciborowski, Department of Biological Sciences, University of Windsor, Windsor, ON
Matthew J. Cooper, Burke Center for Freshwater Innovation, Northland College, Ashland, WI, USA
Joseph P. Gathman, University of Wisconsin-River Falls, River Falls, WI, USA
Page 191

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STATE OF THE GREAT LAKES 201 7
Greg P. Grabas, Environment and Climate Change Canada, Toronto, ON, Canada
David Jude, School of Natural Resources and the Enviromnent, University of Michigan, Ann Arbor, MI (2006)
Gary A. Lamberti, University of Notre Dame, Notre Dame, IN, USA
Ashley H. Moerke, Aquatic Research Laboratory, Lake Superior State University, Sault
Ste. Marie, MI, USA
Carl R. Ruetz III, Annis Water Resources Institute, Grand Valley State University,
Muskegon MI, USA
Danielle J. Sass, Oak Ridge Institute of Science and Education (ORISE) Research Fellow, Appointed to the U.S.
Enviromnental Protection Agency (U.S. EPA), Great Lakes National Program Office (GLNPO) (2008)
Douglas A. Wilcox, Department of Enviromnental Science and Biology, SUNY College at Brockport, Brockport,
NY, USA
Information Sources
Albert, D. A., and L. Simonson. 2004. Coastal wetland inventory of the Great Lakes region (GIS coverage of U. S.
Great Lakes: www.glc.org/wtlands/inventory.html). Great Lakes Consortium, Great Lakes Commission, Ann Arbor,
MI.
Environment Canada and Central Lake Ontario Conservation Authority. 2004. Durham Region Coastal Wetland
Monitoring Project: year 2 technical report. Downsview, ON. ECB-OR.
Ingram, J. W., and B. Potter. 2004. Development of a Coastal Wetlands Database for the Great Lakes Canadian
Shoreline, http://www.glc.org/wetlands/inventory.html Great Lakes Consortium, Great Lakes Commission, Ann
Arbor, MI.
Uzarski, D.G., Burton, T.M., and Genet, J.A. 2004. Validation and performance of an invertebrate index of biotic
integrity for Lakes Huron and Michigan fringing wetlands during a period of lake level decline. Aquat. Ecosystem
Health & Manage. 7(2):269-288.
Uzarski, D.G., V.J. Brady, M.J. Cooper, D.A. Wilcox, D.A. Albert, R. Axler, P. Bostwick, T.N. Brown, J.J.H.
Ciborowski, N.P. Danz, J. Gathman T. Gehring, G. Grabas, A. Garwood, R. Howe, L.B. Johnson, G.A. Lamberti,
A. Moerke, B. Murry, G. Niemi, C.J. Nonnent, C.R. Ruetz III, A.D. Steinman, D. Tozer, R. Wheeler, T.K.
O'Donnell, and J.P. Schneider. 2016. Standardized measures of coastal wetland condition: implementation at the
Laurentian Great Lakes basin-wide scale. Wetlands doi: 10.1007/sl3157-016-0835-7.
List of Tables
Table 1. Total macroinvertebrate taxa in Great Lakes coastal wetlands, and non-native species; summary statistics
by country. Data from 2011 through 2014.
Source: Uzarski, D.G., V.J. Brady, M.J. Cooper, D.A. Wilcox, D.A. Albert, R. Axler, P. Bostwick, T.N. Brown
J.J.H. Ciborowski, N.P. Danz, J. Gatlunan, T. Gehring, G. Grabas, A. Garwood, R. Howe, L.B. Johnson, G.A.
Lamberti, A. Moerke, B. Murry, G. Niemi, C.J. Nonnent, C.R. Ruetz III, A.D. Steimnan, D. Tozer, R. Wheeler,
T.K. O'Donnell, and J.P. Schneider. 2016. Standardized measures of coastal wetland condition: implementation at
the Laurentian Great Lakes basin-wide scale. Wetlands doi: 10.1007/sl3157-016-0835-7.
Table 2. Macroinvertebrate total taxa and non-native species found in Great Lakes coastal wetlands by lake. Mean,
maximum, and minimum number of taxa per wetland. Data from wetlands sampled in 2011 through 2014.
Page 192

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STATE OF THE GREAT LAKES 201 7
Source: Uzarski, D.G., V.J. Brady, M.J. Cooper, D.A. Wilcox, D.A. Albert, R. Axler, P. Bostwick, T.N. Brown,
J.J.H. Ciborowski, N.P. Danz, J. Gathman, T. Gehring, G. Grabas, A. Garwood, R. Howe, L.B. Johnson, G.A.
Lamberti, A. Moerke, B. Murry, G. Nieini, C.J. Nonnent, C.R. Ruetz III, A.D. Steinman, D. Tozer, R. Wheeler,
T.K. O'Donnell, and J.P. Schneider. 2016. Standardized measures of coastal wetland condition: implementation at
the Laurentian Great Lakes basin-wide scale. Wetlands doi: 10.1007/sl3157-016-0835-7.
List of Figures
Figure 1. Condition of coastal wetland macroinvertebrate communities based upon data from all sites sampled from
2011 through 2014.
Source: Uzarski, D.G., V.J. Brady, M.J. Cooper, D.A. Wilcox, D.A. Albert, R. Axler, P. Bostwick, T.N. Brown,
J.J.H. Ciborowski, N.P. Danz, J. Gathman, T. Gehring, G. Grabas, A. Garwood, R. Howe, L.B. Johnson, G.A.
Lamberti, A. Moerke, B. Murry, G. Niemi, C.J. Nonnent, C.R. Ruetz III, A.D. Steimnan, D. Tozer, R. Wheeler,
T.K. O'Donnell, and J.P. Schneider. 2016. Standardized measures of coastal wetland condition: implementation at
the Laurentian Great Lakes basin-wide scale. Wetlands doi: 10.1007/sl3157-016-0835-7.
Figure 2. The sites sampled in 2015 are color coded by which taxonomic groups were sampled at the sites.
Source: Uzarski, D.G., V.J. Brady, M.J. Cooper, D.A. Wilcox, D.A. Albert, R. Axler, P. Bostwick, T.N. Brown,
J.J.H. Ciborowski, N.P. Danz, J. Gatlunan, T. Gehring, G. Grabas, A. Garwood, R. Howe, L.B. Johnson, G.A.
Lamberti, A. Moerke, B. Murry, G. Niemi, C.J. Nonnent, C.R. Ruetz III, A.D. Steimnan, D. Tozer, R. Wheeler,
T.K. O'Donnell, and J.P. Schneider. 2016. Standardized measures of coastal wetland condition: implementation at
the Laurentian Great Lakes basin-wide scale. Wetlands doi: 10.1007/sl3157-016-0835-7.
Last Updated
State of the Great Lakes 2017 Technical Report
Country
Sites
Mean
Max
Min
St. Dev.
Overall





Canada
149
39.8
76
10
13.5
U.S.
326
40.7
85
0
5.2
Non-natives





Canada
149
0.8
3
0
0.9
U.S.
326
0.7
5
0
1.0
Table 1. Total macroinvertebrate taxa in Great Lakes coastal wetlands, and non-native species; summary statistics
by country. Data from 2011 through 2014.
Source: Uzarski, D.G., V.J. Brady, M.J. Cooper, D.A. Wilcox, D.A. Albert, R. Axler, P. Bostwick, T.N. Brown,
J.J.H. Ciborowski, N.P. Danz, J. Gatlunan, T. Gehring, G. Grabas, A. Garwood, R. Howe, L.B. Johnson, G.A.
Lamberti, A. Moerke, B. Murry, G. Niemi, C.J. Nonnent, C.R. Ruetz III, A.D. Steimnan, D. Tozer, R. Wheeler,
T.K. O'Donnell, and J.P. Schneider. 2016. Standardized measures of coastal wetland condition: implementation at
the Laurentian Great Lakes basin-wide scale. Wetlands doi: 10.1007/sl3157-016-0835-7.
Page 193

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STATE OF THE GREAT LAKES 201 7
Total Macroinvertebrates
Non-native Macroinvertebrates
Lake
Sites
Mean
Max
Min
Mean
Max
Min
Erie
58
34,9
70
0
1.1
4
0
Huron
168
44.7
81
13
0.7
5
0
Michigan
66
42.1
85
19
0.7
3
0
Ontario
114
32.3
63
0
0.8
3
0
Superior
67
46.7
69
15
0.1
2
0
Table 2. Macroinvertebrate total taxa and non-native species found in Great Lakes coastal wetlands by lake. Mean,
maximum, and minimum number of taxa per wetland. Data from wetlands sampled in 2011 through 2014.
Source: Uzarski, D.G., V.J. Brady, M.J. Cooper, D.A. Wilcox, D.A. Albert, R. Axler, P. Bostwick, T.N. Brown,
J.J.H. Ciborowski, N.P. Danz, J. Gathman. T. Gehring. G. Grabas, A. Garwood, R. Howe, L.B. Johnson, G.A.
Lamberti, A. Moerke, B. Murry, G. Niemi, C.J. Nonnent, C.R. Ruetz III, A.D. Steinman. D. Tozer. R. Wheeler,
T.K. O'Donnell, and J.P. Schneider. 2016. Standardized measures of coastal wetland condition: implementation at
the Laurentian Great Lakes basin-wide scale. Wetlands doi:10.1007/sl3157-016-0835-7.
%
• •
Aquatic Macroinvertebrate Indicator 2011-2014
•	Reference Conditions
Mildly Impacted
Moderately Impacted
'1 Moderately Degraded
•	Degraded

%
•<>¦¦
Figure 1. Condition of coastal wetland macroinvertebrate communities based upon data from all sites sampled from
2011 through 2014.
Source: Uzarski, D.G., V.J. Brady, M.J. Cooper, D.A. Wilcox, D.A. Albert, R. Axler, P. Bostwick, T.N. Brown,
J.J.H. Ciborowski, N.P. Danz, J. Gatlunan, T. Gehring. G. Grabas, A. Garwood, R. Howe, L.B. Johnson, G.A. Lam-
berti, A. Moerke, B. Murry, G. Niemi, C.J. Nonnent. C.R. Ruetz III, A.D. Steinman. D. Tozer, R. Wheeler, T.K.
O'Donnell, and J.P. Schneider. 2016. Standardized measures of coastal wetland condition: implementation at the
Laurentian Great Lakes basin-wide scale. Wetlands doi: 10.1007/sl3157-016-0835-7.
Page 194

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STATE OF THE GREAT LAKES 201 7

Coastal Wetland Monitoring
Sites Sampled - 2015


* All Crews


Hh Fish/invertA/eg
~ t
+ dL
+ *5* 4*
*+ *
A • *
Bird/Amphib
+
*4
. -v ~

i j i
*
#
+ . * *
|
#

*. +
•
0
1
•
K
25Q 500
i I


KPamabara
iomm&is
Figure 2. The sites sampled in 2015 are color coded by which taxonomic groups were sampled at the sites.
Source: Uzarski, D.G., V.J. Brady, M.J. Cooper. D.A. Wilcox, I).A. Albert, R. Axler, P. Bostwick, T.N. Brown,
J.J.H. Ciborowski, N.P. Danz, J. Gathman. T. Gehring. G. Grabas. A. Garwood, R. Howe, L B. Johnson, G.A.
Lamberti, A. Moerke, B. Murry, G. Niemi, C.J. Nonnent, C.R. Ruetz III, A.D Steimnan. D. Tozer, R. Wheeler,
T.K. O'Donnell, and J.P. Schneider. 2016. Standardized measures of coastal wetland condition: implementation at
the Laurentian Great Lakes basin-wide scale. Wetlands doi: 10.1007/sl3157-016-0835-7.
Page 195

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STATE OF THE GREAT LAKES 201 7
Sub-Indicator: Coastal Wetland Plants
Overall Assessment
Status: Fair
Trend: Undetermined
Rationale: Based on scores of three plant community measures from the Coastal Wetland Monitoring* in-
ventory between 2011 and 2014 (Tables 1 and 2, Figures 1 and 2), status of the coastal wetland plant commu-
nity in the Great Lakes is fair. The three measures tell a similar story, although IBI scores (Albert 2008) are
consistently higher than Mean C (Herman et al. 2001) and weighted Mean C (wC) (Bourdaghs et al. 2006)
scores. On average, wetlands in Lakes Huron, Michigan, and Superior generally harbour fair or good wet-
land plant communities with some very high quality sites and lower numbers of poor sites. Wetlands in
Lakes Erie and Ontario tend to be of more uniformly low quality, with only scattered high quality sites.
Lake-by-Lake Assessment
Lake Superior
Status: Good
Trend: Undetermined
Rationale: Lakewide average values for the three plant community measures all fall in the 'good' category. Over
half the surveyed wetland sites in Lake Superior have overall site scores categorized as good. While there are low-
quality sites adjacent to urban centers and in other scattered locations, most wetlands in Lake Superior have good
quality plant communities. The highest quality wetlands in Lake Superior tend to be barrier-protected poor fens (av-
erage mean C and wC >5), since many species in these wetlands are habitat specialists with high conservatism val-
ues. Trends cannot be determined because of the lack of comparable pre-existing data of the measures. Benchmark
and lake-wide data for all of the Great Lakes will limit the use of the undetermined category in the future.
Lake Michigan
Status: Fair
Trend: Undetermined
Rationale: Among all Great Lakes, Lake Michigan has the widest distribution of sites across the gradients. On aver-
age, most wetland plant communities are considered having fair condition, with the higher quality wetlands general-
ly occurring in the northern part of the lake. Riverine wetlands have lower average scores, especially those in the
south with extreme urban and agricultural nutrient enrichment, while open lacustrine and barrier wetlands farther
north have higher scores associated with surrounding forest cover. Many wetlands in the Green Bay, WI region
have experienced severe wetland degradation resulting from long-term agricultural and urban nutrient enrichment
and more recent low water levels and associated invasion by reed (Phragmites australis). Restoration efforts in this
region are improving wetland plant condition. Trends cannot be determined because of the lack of comparable pre-
existing data of the measures. Benclunark and lake-wide data for all of the Great Lakes will limit the use of the un-
determined category in the future.
Lake Huron
Status: Fair
Trend: Deteriorating
Rationale: The overall status of Lake Huron wetlands is fair based on Mean C and wC scores, and good based on
IBI scores. Wetlands in Lake Huron occur across a wide gradient in plant community condition, with some very
poor and high quality sites and many good sites. Sites in the northern and eastern portion of Lake Huron tend to be
of higher quality for barrier (protected), lacustrine, and riverine wetlands that reflect surrounding forest cover and
management. Extensive plowing, raking, and mowing during recent low water periods has led to vast areas of na-
tive wetland vegetation in open lacustrine wetlands being replaced by Phragmites australis and Tvpha x glauca.
particularly in the Saginaw Bay region. This long-term change was documented by observed changes between sur-
veys conducted in the mid-1990s and those conducted between 2011-2015. During the recent extended low-water
conditions, Phragmites australis lias expanded lakeward beyond native emergent vegetation on Ontario's Bruce
Peninsula and eastern shoreline of Lake Huron, although perhaps recent high water conditions will erode these ex-
tensive Phragmites beds. Loss of emergent vegetation has also occurred in wetlands bordering the St. Marys River,
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STATE OF THE GREAT LAKES 2017
the connecting river between Lakes Superior and Huron during the 1999 to 2013 low-water conditions, probably the
result of both winter ice and ship wakes on exposed sediments and vegetation beds. This long-term change is based
on surveys conducted in the late 1980s, mid 1990s (summarized in Mine 1997), and between 2011 and 2015. Wet-
lands in eastern Georgian Bay are susceptible to nutrient enrichment from runoff through shallow soils or on ex-
posed bedrock; in this area, increasing pressures from development and changing water levels are expected to have
the greatest impacts in the near future. Overall, wetland quality in this lake is considered deteriorating.
Lake Erie
Status: Poor
Trend: Deteriorating
Rationale: Wetlands of Lake Erie have plant communities of generally poor status. Some high quality sites exist at
Presque Isle, Pennsylvania and at several large Ontario sites along the north shore, including Long Point, Turkey
Point, Rondeau, and Point Pelee, while restoration activities have recently improved Metzger Marsh Ohio. Overall,
the coastal wetland plant communities of Lake Erie are also classified as deteriorating based on historical data from
1975 in Lake Erie (Stuckey 1989). In Lake Erie, riverine wetlands have slightly lower average quality than barrier
or lacustrine wetlands. Mean C scores are consistently higher than Weighted Mean C, indicating widespread domi-
nance by species with low Conservatism values, including cattails and invasive species.
Lake Ontario
Status: Fair
Trend: Unchanging
Rationale: The overall status of Lake Ontario's coastal wetlands is fair. There are very few high quality coastal wet-
lands in Lake Ontario, whereas there are many wetlands of moderately low quality. Riverine wetlands have lower
average quality than barrier or lacustrine wetlands. Substantially lower scores for Weighted Mean C compared to
Mean C indicate Lake Ontario wetlands tend to be dominated by species with low Conservatism scores, including
cattails and invasive species. There is a strong east to west gradient in condition, due largely from high levels of
urbanization in the western portion of the basin.
Sub-Indicator Purpose
The purpose of this sub-indicator is to assess the quality of the vegetation as an integral component of the condition
of coastal wetlands.
Ecosystem Objective
Coastal wetlands throughout the Great Lakes basin are influenced by coastal manipulations and the input of sedi-
ments, nutrients, and pollutants. About half of coastal wetlands have been lost basinwide. Remaining wetlands
should be dominated by native vegetation with low numbers of invasive plant species at low levels of coverage.
Conservation of these wetlands and restoration of previously destroyed wetlands are vital components of restoring
the Great Lakes ecosystem and this sub-indicator can be used to report progress toward such objectives.
This sub-indicator best supports work towards General Objective #5 of the 2012 Great Lakes Water Quality Agree-
ment that states the Waters of the Great Lakes should "support healthy and productive wetlands and other habitats to
sustain resilient populations of native species."
Ecological Condition
Across the entire Great Lakes basin, the state of the wetland plant community is quite variable, ranging from good to
poor depending primarily on local land use history, nearshore management, and the prevalence of invasive plant
species. Plant communities in some wetlands have deteriorated rapidly in recent years due to extremely low water
levels that have allowed invasion and dominance by exotic species. With water levels rebounding in 2014-2015, it
will be critical to evaluate how these wetlands respond. In other wetlands, there have been recent improvements to
plant community condition. For example, the turbidity of the southern Great Lakes has reduced with expansion of
zebra mussels, resulting in improved submergent plant diversity in many wetlands. Moreover, wetland restoration
activities have been undertaken throughout the basin over the past 5 years, especially targeting wetlands dominated
by invasive plants.
Short- and long-term trends in wetland condition based on plants have not been well-established in the Great Lakes.
In the southern lakes (Lake Erie, Lake Ontario, and the Upper St. Lawrence River), almost all wetlands are degraded
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STATE OF THE GREAT LAKES 2017
by water-level control, nutrient enrichment sedimentation, or a combination of these factors. Probably the strongest
demonstration of this is the prevalence of broad zones of cattails, reduced submergent diversity and coverage, and
prevalence of non-native plants, including reed (Phragmites australis), reed canary grass (Phalaris arundinacea),
purple loosestrife (Lvthrum salicaria), curly pondweed (Potamogeton crispus), Eurasian milfoil (Myriophvllum spi-
catum), frog bit (Hvdrocharis morsus-ranae), and water chestnut (Trapa natans).
In the remaining Great Lakes (Lake St. Clair, Lake Huron, Lake Michigan, Georgian Bay, Lake Superior, and their
connecting rivers), intact, diverse wetlands can be found for most geomorphic wetland types. However, low water
conditions have resulted in the explosive expansion of reed in many wetlands, especially in Lake St. Clair and
southern Lake Huron, including Saginaw Bay (Albert and Brown 2008) as well as Green Bay in Lake Michigan. As
water levels rise, the response of reed should be monitored.
One of the disturbing trends is the expansion of frog bit, a floating plant that forms dense mats capable of eliminat-
ing submergent plants, from the St. Lawrence River and Lake Ontario into Lake Erie, Lake St. Clair, Lake Huron,
and the St. Mary's River. This expansion will probably continue into all of the remaining Great Lakes. In addition,
our sampling has shown water chestnut to be expanding rapidly in Lake Ontario—increasing in both distribution and
density.
Studies in the northern Great Lakes have demonstrated that non-native invasive species like reed, reed canary grass,
and purple loosestrife have become established throughout the Great Lakes but that the abundance of these species is
low, often restricted to only local disturbances such as docks and boat channels. It appears that undisturbed marshes
are not easily colonized by these species. However, as these species become locally established, seeds or fragments
of plants may be able to establish themselves when water-level changes create appropriate sediment conditions.
Hybrid cattail (Typha x glauca) expansion has also been recently documented in northern Lakes Michigan and Hu-
ron and the St. Marys River (Lishawa et al. 2010).
Regional Wetland Types
The conditions of the plant community in coastal wetlands naturally differ across the Great Lakes basin, due to dif-
ferences in geomorphic and climatic conditions. The characteristic size and plant diversity of coastal wetlands vary
by wetland type, lake, and latitude; in this document these differences will be described broadly as "regional wetland
types."
Coastal wetlands are divided into three main categories based on the hydrology of the area. Lacustrine wetlands are
connected to the Great Lakes, and they are largely impacted by fluctuations in lake levels. Riverine wetlands occur
in the lower reaches of rivers that flow into the Great Lakes basin. Typically, the quality of riverine wetlands is in-
fluenced by the river drainage system; however, coastal processes cause lakes to flood back into these wetlands,
which control water levels. The last type of coastal wetlands is barrier protected. Barrier protected wetlands are de-
rived from coastal processes that deposit sediment to create barrier beaches that separate wetlands from the Great
Lakes. Coastal wetlands contain different vegetation zones (treed or shrub swamp, meadow, emergent, submergent
and floating), some of which may be absent in certain types of wetlands and under different water-level conditions.
Great Lakes wetlands were classified and mapped in 2004 (see http://glc.org/wetlands/inventorv.html) with coastal
wetland inventory maps developed for the United States (see http://glc.org/wetlands/us_mapping.html) and Canada
(see http://glc.org/wetlands/can_mapping.html).
Lake Variations
Physical properties such as the type of shoreline, substrate, bedrock, and chemical and physical water quality param-
eters vary between Great Lakes. Variation in nutrient levels creates both a north to south gradient, and an increase in
nutrient levels from Lake Erie in the west to Lake Ontario and the upper St. Lawrence River in the east. Lake Supe-
rior is the most distinct Great Lake due to its low alkalinity and prevalence of bedrock shoreline.
Differences in Latitude
Latitudinal variations result in different climatic conditions based on the location of the coastal wetlands. Tempera-
ture differences between the north and south lead to differences in the species of plants found in coastal wetlands.
Watersheds in the southern portion of the Great Lakes also have increased agricultural activity, resulting in in-
creased nutrient loads, sedimentation, and non-native species introductions.
Linkages
There are characteristics of coastal wetlands that make use of plants as indicators difficult in certain conditions.
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STATE OF THE GREAT LAKES 2017
Among these are:
Water-level fluctuation
Great Lakes water levels fluctuate greatly from year to year. Either an increase or decrease in water level can result
in changes in numbers of species or overall species composition in the entire wetland or in specific zones with
change in level of human disturbance. Such changes make it difficult to monitor change over time. Changes are
great in two zones: the wet meadow, where grasses and sedges may disappear in high water or new annuals may
appear in low water, and in shallow emergent or submergent zones, where submergent and floating plants may dis-
appear when water levels drop rapidly. Recent studies indicate that prolonged periods of low water favor rapid ex-
pansion of invasive species like Phragmites australis (Albert and Brown 2008, Lishawa etal. 2010, Wilcox 2012).
In addition, water levels of Lakes Superior and Ontario are regulated, which has altered plant community dynamics.
This is most obvious in Lake Ontario, where cattails have displaced sedge/grass meadow (Wilcox et al. 2008).
Pressures
Lake-wide alterations
For the southern lakes, most wetlands have been dramatically altered by both intensive agriculture and urban devel-
opment of the shoreline. Alterations of coastal wetland especially in the wet meadow and upper emergent zone will
lead to drier conditions which may allow invasive species to establish.
Agriculture
Agriculture degrades wetlands in several ways, including nutrient enrichment from fertilizers, increased sediments
from erosion, increased rapid runoff from drainage ditches, introduction of agricultural non-native species (reed ca-
nary grass), destruction of inland wet meadow zone by plowing and diking, and addition of herbicides. In the south-
ern lakes, Saginaw Bay, and Green Bay, agricultural sediments have resulted in highly turbid waters that support
few or no submergent plants.
Lake-level regulation
Regulation of Lake Ontario water levels since 1960 has reduced the range of fluctuations. The most evident effect
has been the elimination of low lake-level periods, even when water supplies are low. The competitive advantage of
sedges and grasses at higher elevations due to their tolerance of low water levels and low soil moisture has been lost,
and they have been displaced by larger cattails that are no longer limited by their need for more water.
Urban development
Urban development degrades wetlands by hardening shoreline, filling wetland, adding a broad diversity of chemical
pollutants, increasing stream runoff, adding sediments, and increased nutrient loading from sewage treatment plants.
In most urban settings, almost complete wetland loss has occurred along the shoreline.
Residential shoreline development
Along many coastal wetlands, residential development has altered wetlands by nutrient enrichment from fertilizers
and septic systems, shoreline alterations for docks and boat slips, filling, and shoreline hardening. Agriculture and
urban development are usually less intense than local physical alteration which often results in the introduction of
non-native species. Shoreline hardening can completely eliminate wetland vegetation.
Mechanical alteration of shoreline
Mechanical alteration takes a diversity of forms, including diking, ditching, dredging, filling, shoreline hardening,
and disking and plowing of coastal vegetation by private landowners. With all of these alterations, non-native spe-
cies are introduced by construction equipment or in introduced sediments. Changes in shoreline gradients and sedi-
ment conditions are often adequate to allow non-native species to become established. Disking and plowing of
coastal wetlands continued through 2011 in exposed coastal marshes along Saginaw Bay, Grand Traverse Bay, and
on islands within the St. Clair River delta.
Introduction of non-native species
Non-native species are introduced in many ways. Some were purposefully introduced as agricultural crops or orna-
mentals, later colonizing in native landscapes. Others came in as weeds in agricultural seed. Increased sediment and
nutrient enrichment allow many of the worst aquatic weeds to out-compete native species. Most of the worst non-
native species are either prolific seed producers or reproduce from fragments of root or rhizome. Non-native animals
have also been responsible for increased degradation of coastal wetlands. One of the worst invasive species has been
common carp, whose mating and feeding habits result in loss of submergent vegetation in shallow marsh waters.
The most prevalent non-native plants including common reed (Phragmites australis), reed canary grass (Phalaris
arundinacea), purple loosestrife (Lvthrum salicaria), curly pondweed (Potamogeton crispus), and Eurasian milfoil
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STATE OF THE GREAT LAKES 2017
(Mvriophyllum spicatum). Low water conditions have resulted in the almost explosive expansion of common reed in
many wetlands, especially in Lake St. Clair and southern Lake Huron including Saginaw Bay (Albert and Brown
2008). One of the disturbing recent trends is the expansion of frog bit, a free floating plant that forms dense mats
along the emergent margin capable of eliminating submergent and emergent plants, from the St. Lawrence River and
Lake Ontario into Lake Erie, Lake St. Clair, Lake Huron, and the St. Mary's River. This expansion will likely con-
tinue to all of the remaining Great Lakes. In addition, our sampling has shown water chestnut to be expanding rap-
idly in Lake Ontario—increasing in both distribution and density. The recent rediscovery of a non-native macroal-
gae, starry stonewort (Nitellopsis obtusa), is of conservation concern because of its long-term establishment since
the 1970s and its current distribution within better quality wetlands in northeastern Lake Ontario as well as wetlands
in Saginaw Bay, Lake St. Clair, and the Detroit River.
Comments from the Authors
*The Coastal Wetland Monitoring program was funded by the Great Lakes Restoration Initiative 2011-2015 to im-
plement statistically sound basin-wide monitoring of select physical and biotic components (Uzarski et al.). This
binational program involved a consortium of universities and agencies with the goal of producing scientifically-
defensible information on status and trends of Great Lakes coastal wetlands. As of 2015, the majority of coastal
wetlands >4 ha with a surface water connection to the lakes have been surveyed at least once since 2011. Data from
2011-2014 were included in the analysis reported here. In each wetland, data from up to three wetland zones (wet
meadow, emergent, submergent) are included if all zones are present.
The tables in this document summarize data collected between 2011 and 2014 on three broad hydrogeomorphic wet-
land types: barrier, lacustrine, and coastal wetlands that were characterized for each separate Great Lake. In subse-
quent analyses these types will be further divided into recognized subtypes (Albert et al. 2006) that are subject to
different enviromnental and human stresses, and thus characterized by different status and potential for restoration.
This sub-indicator incorporates information on the presence, abundance, and diversity of aquatic macrophytes within
Great Lakes coastal wetlands. Plant abundance data are used to calculate three measures of wetland plant quality
including: l.Mean Coefficient of Conservatism (C); 2.Weighted Mean Coefficient of Conservatism (wC); and 3.
Vegetation Index of Biological Integrity (IBI). The Mean C approach is preferred by many, because it provides a
nice, neat, easily computed number however, it provides little understanding of the overall diversity of the wetlands
within the lake. In both Lake Michigan and Lake Huron there is an extreme enviromnental gradient [climate and
hydro-geomorphology] that are reflected in land use and vegetation response, and a single FAIR designation ignores
that gradient. One number or condition cannot reflect these lakes. The IBI better demonstrates the breadth of types
and conditions. However, for the purposes of this sub-indicator report, if calculation results fall into different as-
sessment categories than the conservative score is used. More information on these calculations can be found in the
Coastal Wetlands Plant sub-indicator description.
It has been estimated that approximately half of the coastal wetlands have been lost basinwide, but this estimate does
not include degraded wetlands, but just those that have been lost by shoreline hardening or complete erosion of veg-
etation from an area. There is no agreed on approach to providing a more accurate estimate for several reasons, the
most important of which are 1) The original land surveys, the basis of many original plant community area esti-
mates, did not consistently reference herbaceous wetland vegetation along the shoreline, 2) Emergent wetland vege-
tation is not easily seen in aerial photos limiting the use of 1930s and 1940s early aerial photos to estimate original
wetland sites, and 3) the earliest Great Lakes-wide surveys of coastal wetlands were conducted in the late 1970s and
early 1980s, well after most of the coastal wetland destruction had occurred due to a combination of shoreline hard-
ening, dredging, agricultural planting, and destruction by invasive fish [carp].
While no Great Lake-wide surveys of coastal wetland vegetation were conducted before the 1980s, cluster analyses
of physical and vegetation data from field surveys conducted in the 1980s and 1990s identify several distinct native
plant communities, as well as some plant communities dominated by invasive plants, that show strong relationships
to regional climatic, sediment, and hydro-geomorphic conditions (Mine 1997, Albert and Mine 2001, Albert et al.
2006) that can justifiably be used as the basis for assuming there are predictable regional wetland vegetation types or
communities.
Cattails have been noted as a major source of degradation because the expansion of cattails into wetlands following
nutrient enrichment and water-level manipulation had been documented in numerous studies (Prince and D"Itri
1985, Stuckey 1989, Wilcox 1993, Mine 1997, Wilcox et al. 2008, Lishawa et al. 2010, and Robert Humphreys
(refuge manager for MI DNR), personal communications). The native cattail in Great Lakes coastal wetlands was
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STATE OF THE GREAT LAKES 2017
Typha latifolia (common or wide-leaved cattail) a species that was limited in distribution by characteristic fluctua-
tions in Great Lakes water levels. Typha angustifolia (narrow-leaved cattail) has expanded into Great Lakes wet-
lands, where it tolerates deeper water levels than common cattail, expanding its range rapidly through the eastern
U.S. and the Midwest along roadside ditches. Common and narrow-leaved cattails hybridized, forming Typha x
glauca (hybrid cattail), a larger and more aggressive plant that along with narrow-leaved cattail created broad, dense
monocultures that did not meet the habitat needs of many native waterbirds and waterfowl. Their dense mats were
also able to float in drown river mouth wetlands, eliminating important fish habitat as well.
Damage to Great Lakes wetlands by exotic invasive plants during the most recent low-water event (1999-2013 in
Lakes Michigan and Huron) is considered to be linked to anthropogenic degradation because all of the invasive
plants that have expanded dramatically into Great Lakes coastal wetlands were introduced into the Great Lakes by
humans and respond aggressively to agricultural and urban nutrient enrichment and/or sedimentation. Earlier sur-
veys of Great Lakes wetlands in low-water conditions in the 1980s and 1990s documented existing large-scale or
localized expansions of these invasive plants in Lakes Ontario, Erie, and Lake St. Clair, but the expansion of these
same plants was much greater than the extended low-water conditions in Lakes Huron and Michigan between 1999
and 2013. Prior to the 1970s, our most aggressive invasive plants (Phragmites australis, Typha angustifolia, Typha
x glauca, Lvthrum salicaria, Hvdrocharis morsus-ranae, etc.) that respond to low-water conditions were not wide-
spread along the Great Lakes shoreline, but since then and into the future prolonged periods of low-water can be
expected to result in at least localized expansions of invasive wetland plants.
Baseline condition in biological or restoration studies has typically been based on characteristic native flora and fau-
na in an ecosystem. Several examples of wetlands with no extensive populations of invasive plants were inventoried
during the 2011-2015 of invasive plants and animals (Uzarski et al. 2016) is the definition of baseline condition and
the goal of restoration. These high quality wetlands will remain the basis for monitoring wetland condition and guid-
ing restoration efforts, even if it is determined in the future that returning degraded wetlands to these conditions is
impossible.
Assessing Data Quality
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not
Applicable
1. Data are documented, validated, or
quality-assured by a recognized agency or
organization
X





2. Data are traceable to original sources
X





3. The source of the data is a known,
reliable and respected generator of data
X





4. Geographic coverage and scale of data
are appropriate to the Great Lakes basin
X





5. Data obtained from sources within the
U.S. are comparable to those from
Canada
X





6. Uncertainty and variability in the data
are documented and within acceptable
limits for this sub-indicator report


X



Acknowledgments
Authors: Dennis Albert, Department of Horticulture, Oregon State University
Nicholas P. Danz, Department of Natural Sciences, University of Wisconsin-Superior
Douglas A. Wilcox, Department of Enviromnental Science and Biology, SUNY College at Brockport
Daniel Rokitnicki-Wojcik, Canadian Wildlife Service, Enviromnent and Climate Change Canada
Contributors and previous authors: Joseph Gathman, University of Wisconsin-River Falls
Greg Grabas, Enviromnent and Climate Change Canada
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STATE OF THE GREAT LAKES 2017
Brad Mudrzynski, Department of Environmental Science and Biology, SUNY College at Brockport
Danielle J. Sass, Great Lakes National Program Office, US EPA
Information Sources
Albert, D. A., and P. Brown. Coastal wetlands in Michigan: Effect of isolation on Pragmites australis expansion.
Michigan Natural Features Inventory report 2008-14.
Albert, D.A., and Mine, L.D. 2001. Abiotic and floristic characterization of Laurentian Great Lakes' coastal
wetlands. Stuttgart, Germany. Verb. Internal. Verein. Limnol. 27:3413-3419.
Albert, D.A., Wilcox, D.A., Ingram, J.W., and Thompson, T.A. 2006. Hydrogeomorphic Classification for
Great Lakes Coastal Wetlands. J. Great lakes Res 31(1): 129-146..
Albert, D.A., March 2008. Great Lakes Coastal Wetlands Monitoring Plan, Chapter Three Vegetation Com-
munity Indicators. Developed by the Great Lakes Coastal Wetlands Consortium, A project of the Great Lakes
Commission.
Bourdaghs, M„ C. A. Johnston, and R. R. Regal. 2006. Properties and performance of the floristic quality
index in Great Lakes coastal wetlands. Wetlands 26 (3): 718-735.
Enviromnent Canada and Central Lake Ontario Conservation Authority. 2004. Durham Region Coastal Wet-
land Monitoring Project: Year 2 Technical Report. Enviromnent Canada, Downsview, ON: ECB-OR.
Great Lakes Commission. Great lakes Coastal Wetlands Consortium. Inventory and Classification. Last up-
dated: June 30, 2007. http://glc.org/wetlands/inventorv.html
Great Lakes Commission. Great lakes Coastal Wetlands Consortium. Coastal Wetlands Inventory-Great Lakes
Region. Last updated: April 20, 2004. http://glc.org/wetlands/us mapping.html
Great Lakes Commission. Great lakes Coastal Wetlands Consortium. Canadian Mapping Resources. Last updated:
April 20, 2004. http://glc.org/wetlands/can mapping.html
Herdendorf, C.E. 1988. Classification of geological features in Great lakes nearshore and coastal areas. Protect-
ing Great lakes Nearshore and Coastal Diversity Project. International Joint Commission and The Nature Con-
servancy, Windsor, ON.
Herdendorf, C.E., Hakanson L„ Jude, D.J., and Sly, P.G. 1992. A review of the physical and chemical compo-
nents of the Great Lakes: a basis for classification and inventory of aquatic habitats. In The development of an
aquatic habitat classification system for lakes eds. W.-D. N. Busch and P. G. Sly, pp. 109-160. Ann Arbor, MI:
CRC Press.
Herdendorf, C.E., Hartley, S.M., and Barnes, M.D. (eds.). 1981a. Fish and wildlife resources of the Great lakes
coastal wetlands within the United States, Vol. 1: Overview. U.S. Fish and Wildlife Service, Washington, DC.
FWS/OBS- 81/02-vl.
Herman, K. D„ L. A. Masters, M. R. Penskar, A. A. Reznicek, G. S. Wilhelm, W. W. Brodovich,and K. P.
Gardiner. 2001. Floristic Quality Assessment with Wetland Categories and Examples of Computer Applications for
the State of Michigan.
Jaworski, E., Raphael, C.N., Mansfield, P. J., and Williamson, B.B. 1979. Impact of Great lakes water level fluc-
tuations on coastal wetlands. U.S. Department of Interior, Office of Water Resources and Technology, Contract
Report 14-0001-7163, from Institute of Water Research, Michigan State University, East Lansing, MI, 3 51pp.
Keough J.R., Thompson, T.A., Guntenspergen, G.R., and Wilcox, D.A. 1999. Hydrogeomorphic factors and eco-
system responses in coastal wetlands of the Great Lakes. Wetlands 19:821-834.
Lishawa, S.C., D.A. Albert, and N.C. Tuchman. 2010. Natural water level decline drives invasive species estab-
lishment and vegetation change in Great Lakes coastal wetlands. Wetlands: 30(6) 1085-1097.
Mine, L.D. 1997. Great lakes coastal wetlands: An over\>iew of abiotic factors affecting their distribution,
form, and species composition. Michigan Natural Features Inventory, Lansing, MI.
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STATE OF THE GREAT LAKES 2017
Mine, L.D., and Albert, D.A. 1998. Great Lakes coastal wetlands: abiotic and floristic characterization. Michigan
Natural Features Inventory, Lansing, MI.
Prince, H.H., and D"Itri, F.M. (eds.) 1985. Coastal Wetlands. Lewis Publishers, Inc., Chelsea, MI.
Stuckey, R.L. 1989. Western Lake Erie aquatic and wetland vascular plant flora: its origin and change. In Lake
Erie Estuarine Systems: Issues, Reources, Status, and Management, pp. 205-256. Estuary-of-the-Month Seminar
Series No. 14. Washington, D.C.: NOAA.
Swink, F„ and Wilhelm, G. 1994. Plants of the Chicago Region 41'1 Edition. Lisle, Illinois. The Indiana Academy
of Science.
United States and Canada. 1987. Great Lakes Water Quality Agreement of 1978, as amended by Protocol signed
November 18, 1987. Ottawa and Washington.
Uzarski, D.G., V.J. Brady, M.J. Cooper, D.A. Wilcox, D.A. Albert, R. Axler, P. Bostwick, T.N. Brown, J.J.H.
Ciborowski, N.P. Danz, J. Gathman, T. Gehring, G. Grabas, A. Garwood, R. Howe, L.B. Johnson G.A. Lamberti,
A. Moerke, B. Murry, G. Niemi, C.J. Nonnent, C.R. Ruetz III, A.D. Steimnan, D. Tozer, R. Wheeler, T.K.
O'Donnell, and J.P. Schneider. In press. Standardized measures of coastal wetland condition: implementation at
the Laurentian Great Lakes basin-wide scale. Wetlands doi: 10.1007/sl3157-016-0835-7.
Wilcox, D.A. 1993. Effects of water level regulation on wetlands of the Great Lakes. Great Lakes Wetlands 4: 1-2,
11.
Wilcox, D.A., K.P. Kowalski, H. Hoare, M.L. Carlson, and H. Morgan. 2008. Cattail invasion of sedge/grass
meadows and regulation of Lake Ontario water levels: photointerpretation analysis of sixteen wetlands over five
decades. Journal of Great Lakes Research 34:301-323.
Wilcox, D.A., and Whillans, T.H. 1999. Techniques for restoration of disturbed coastal wetlands of the Great
Lakes .Wetlands 19:835-857.
Wilcox, D.A. 2012. Response of wetland vegetation to the post-1986 decrease in Lake St. Clair water levels: seed-
bank emergence and beginnings of the Phragmites australis invasion. Journal of Great Lakes Research 38:270-277.
List of Tables
Table 1. Lakewide means and 95% confidence intervals for three measures of Great Lakes coastal wetland plant
community condition observed 2011-2014. Some sites with missing vegetation zones were not used in calculations
for the vegetation IBI, resulting in slightly lower sample size. Mean C and wC scores are based on a maximum score
of 10, while Veg IBI scores are based on a maximum score of 5. Vegetation IBI scores must be doubled to be equiv-
alent of Mean C and wC scores.
Source: GLRI Coastal Wetland Monitoring Program, analysis by Nicholas Danz
Table 2. Condition class categories based on sub-indicator definitions for three measures of coastal wetland plant
communities.
Source: GLRI Coastal Wetland Monitoring Program, analysis by Nicholas Danz
Table 3. Lakewide and wetland-type means and 95% confidence intervals for three measures of Great Lakes coastal
wetland plant community condition observed 2011-2014. Some sites with missing vegetation zones were not used
in calculations for the vegetation IBI, resulting in slightly lower sample size. Mean C and wC have a maximum
score of 10, while Vegetation IBI has a maximum score of 5 and must be doubled to be the equivalent of Mean C
and wC.
Source: GLRI Coastal Wetland Monitoring Program, analysis by Nicholas Danz
List of Figures
Figure 1. Frequency histogram of overall site Mean C (blue) and Weighted Mean C (red) values for 451 Great
Lakes coastal wetland sites surveyed between 2011 and 2014.
Source: GLRI Coastal Wetland Monitoring Program, analysis by Nicholas Danz
Figure 2. Frequency histogram of overall site Vegetation IBI values for 415 Great Lakes coastal wetland sites sur-
veyed between 2011 and 2014.
Source: GLRI Coastal Wetland Monitoring Program, analysis by Nicholas Danz
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STATE OF THE GREAT LAKES 2017
Last Updated
State of the Great Lakes 2017 Technical Report


Mean C and wC

Veg IBI
Veg IBI x2
Lake
n
Mean
C
95%
CI
wC
95%
CI
n
Veg
IBI
95%
CI

Lake Erie
52
2.53
0.19
2.22
0.25
50
1.6
0.15
3.2
Lake Huron
162
4.33
0.17
4.21
0.19
140
3.0
0.15
6.0
Lake Michigan
65
3.57
0.26
3.46
0.30
61
2.9
0.20
5.8
Lake Ontario
107
3.02
0.13
2.53
0.16
104
1.9
0.10
3.8
Lake Superior
65
5.18
0.30
5.19
0.34
60
3.7
0.23
7.4
Table 1. Lakewide means and 95% confidence intervals for three measures of Great Lakes coastal wetland plant
community condition observed 2011-2014. Some sites with missing vegetation zones were not used in calculations
for the vegetation IBI, resulting in slightly lower sample size. Mean C and wC scores are based on a maximum score
of 10, while Veg IBI scores are based on a maximum score of 5. Vegetation IBI scores must be doubled to be equiv-
alent of Mean C and wC scores.
Source: Great Lakes Coastal Wetlands Consortium

Measures

Lake
Mean C
Weighted
Mean C
Veg IBI
Overall
Lake Erie
Poor
Poor
Fair
Poor
Lake Huron
Fair
Fair
Good
Fair
Lake Michigan
Fair
Fair
Good
Fair
Lake Ontario
Fair
Poor
Fair
Fair
Lake Superior
Good
Good
Good
Good
Table 2. Condition class categories based on sub-indicator definitions for three measures of coastal wetland plant
communities.
Source: Great Lakes Coastal Wetlands Consortium
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STATE OF THE GREAT LAKES 2017


Mean C and wC
Veg IBI
Lake
Hydrogeomorphic
Type
n
Mean C
95% CI
wC
95% CI
n
IBI
95%
CI
Erie
Barrier (protected)
10
2.61
0.25
2.34
0.44
5
1.78
0.49

Lacustrine (coastal)
22
2.66
0.33
2.40
0.41
17
1.61
0.22

Riverine
31
2.40
0.30
2.06
0.39
28
1.45
0.21










Huron
Barrier (protected)
16
4.60
0.70
4.58
0.76
12
3.46
0.57

Lacustrine (coastal)
113
4.23
0.23
4.07
0.26
82
2.95
0.18

Riverine
62
4.46
0.25
4.36
0.30
46
3.03
0.27










Michigan
Barrier (protected)
11
3.75
0.65
3.69
0.80
10
3.32
0.68

Lacustrine (coastal)
37
3.74
0.39
3.67
0.42
30
2.88
0.27

Riverine
26
3.25
0.39
3.07
0.47
21
2.67
0.28










Ontario
Barrier (protected)
27
3.39
0.41
2.95
0.48
23
1.99
0.27

Lacustrine (coastal)
28
3.04
0.20
2.49
0.24
24
1.88
0.18

Riverine
68
2.87
0.15
2.38
0.20
57
1.81
0.13










Superior
Barrier (protected)
17
6.29
0.55
6.48
0.55
15
4.35
0.29

Lacustrine (coastal)
9
5.12
0.51
4.99
0.78
7
3.63
0.56

Riverine
42
4.75
0.33
4.71
0.39
38
3.48
0.29
Table 3. Lakewide and wetland-type means and 95% confidence intervals for three measures of Great Lakes coastal
wetland plant community condition observed 2011-2014. Some sites with missing vegetation zones were not used
in calculations for the vegetation IBI, resulting in slightly lower sample size. Mean C and wC have a maximum
score of 10, while Vegetation IBI has a maximum score of 5 and the value noted above must be doubled to be the
equivalent of Mean C and wC.
Source: Great Lakes Coastal Wetlands Consortium
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STATE OF THE GREAT LAKES 2017
Erie
60
40
20
0 "
Huron
Michigan
Ontario
60-
40-
20-
0
60
40
20
0
Superior
4 5
Mean C or wC
~ Mean C ~ wC
Figure 1. Frequency histogram of overall site Mean C (blue) and Weighted Mean C (red) values for 451 Great
Lakes coastal wetland sites surveyed between 2011 and 2014. Lake Assessment Scale for Mean C and wC are
Good: 5.0 and above; Fair: 3.0 - 4.9 and Poor: 0.0 - 2.9. Please note the difference in scale between Figure 1 and 2
on the x-axis.
Source: Great Lakes Coastal Wetlands Consortium
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STATE OF THE GREAT LAKES 2017
Erie
60
40
20
0
60
40
20
Huron
Ui
1 60
03
Michigan
0.1
5
a
a>
p
a.i
CL
40
20
0
60
40
20
0
60
40
20
0
Ontario
Superior
"i i i iiIIIiir
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
Veg IBI
Figure 2. Frequency histogram of overall site Vegetation IBI values for 415 Great Lakes coastal wetland sites sur-
veyed between 2011 and 2014. Lake Assessment Scale for IBI are Good: 5.0 and above; Fair: 3.0 - 4.9; and Poor:
0.0 - 2.9. Please note the difference in scale between Figure 1 and 2 on the x-axis.
Source: Great Lakes Coastal Wetlands Consortium
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STATE OF THE GREAT LAKES 201 7
Sub-Indicator: Coastal Wetlands: Extent and Composition
Overall Assessment
Status: Undetermined
Trend: Undetermined
Rationale: Mapping and estimation of the areal coverage of Great Lakes coastal wetlands was done in 2004.
An update is underway but has not yet been completed. Because there has not been an update to the estima-
tion of areal extent in over 10 years, the status and trend are undetermined.
Lake-by-Lake Assessment
Lake-by-lake assessments are not available for the same reason the basin-wide assessments are not available.
Sub-Indicator Purpose
• To assess the periodic changes in area (particularly losses) of coastal wetland types, taking into account
natural lake level variations. Coastal wetlands provide critical breeding and migratory habitat for wildlife
such as birds, mammals, reptiles, and amphibians. These habitats are also critical spawning and nursery ar-
eas for many fish species of ecologic and economic importance.
Ecosystem Objective
Maintain total areal extent of Great Lakes coastal wetlands, ensuring adequate representation of coastal wetland
types across their historical range. Conservation of remaining coastal wetlands and restoration of previously de-
stroyed wetlands are vital components of restoring the Great Lakes ecosystem and this sub-indicator can be used to
report progress toward such an objective.
This sub-indicator best supports work towards General Objective #5 of the 2012 Great Lakes Water Quality Agree-
ment which states that the Waters of the Great Lakes should "support healthy and productive wetlands and other
habitats to sustain resilient populations of native species."
Ecological Condition
This sub-indicator will measure areal extent of coastal wetlands by hydro geomorphic type for a specific time period
based on data sources/imagery available. Coastal wetlands trap, process, and remove nutrients and sediment from
Great Lakes nearshore waters, and recharge groundwater supplies. However, over half of all Great Lakes coastal
wetlands have been destroyed by human activities and many remaining coastal wetlands suffer from anthropogenic
stressors such as nutrient and sediment loading, fragmentation, invasive species, shoreline alteration, and water level
control (Albert and Simonson 2004; Ingram and Potter, 2004).
An existing baseline map circa 2004 of the binational coastal wetland occurrence and general boundaries was pro-
duced from available data sources on wetland occurrence including the USFWS National Wetland Inventory, Mich-
igan National Wetland Inventory, Ohio Wetlands inventory, Wisconsin DNR Wetlands Inventory, and best profes-
sional judgement (Figures 1, 2, and 3). There has not yet been a complete update to this map, so current areal extent
and composition of coastal wetlands across the entire Great Lakes basin cannot be reported.
New data sets have been produced that allow the circa 2004 data set to be reexamined and refined, which will ulti-
mately allow determination of a more current status and trend over time. For example, a multi-season (spring, sum-
mer and fall) satellite optical and L-band radar data with a minimum mapping unit of 0.2 ha (Bourgeau-Chavez el al.
2015) for wetland plant communities and other landuse classes was produced (Figure 4). This map delineates eco-
system type (i.e. emergent, shrub and forested wetland) as well wetland monocultures (Typha, Phragmites, Schoe-
noplectus) and peatland types (fens and bogs). In addition, upland and landuse classes, potential wetland stressors,
are mapped. An overall accuracy of 94% was documented by this effort when the map was compared to vegetation
types identified in field studies between 2008 and 2011. The bands found most important for wetland mapping were
the thermal, NIR and L-band SAR and should be integrated into any map update to maintain the integrity and level
of accuracy. Optical data alone may be used but woody wetlands in particular are not mapped as accurately with
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STATE OF THE GREAT LAKES 201 7
optical data alone (e.g. forested wetlands, scrub shrub, bogs, fens). This map could be updated on an incremental
basis, such as a five year cycle, using change detection methods.
Updating maps in a standardized way across the whole Great Lakes Basin is now planned, and there are efforts un-
derway to use the 2008-2011 field study data set and update the circa 2004 coastal wetland data set in select geo-
graphic areas (e.g., Saginaw Bay to Western Lake Erie Basin - US side only, state of Michigan.)
It should be noted that the assessment in the State of the Great Lakes 2011, 2009, 2007 and 2005 reports was Fair
(Mixed) and Deteriorating for this sub-indicator, based on historical data, 1981-1997.
Linkages
Linkages to other sub-indicators in the indicator suite include:
•	Hardened Shorelines - physical modifications to the shoreline have disrupted coastal and nearshore processes,
flow and littoral circulatory patterns, altered or eliminated connectivity to coastal wetlands/dunes, and have al-
tered nearshore and coastal habitat structure
•	Precipitation Events - change in atmospheric temperature will potentially affect the number of extreme storms
in the Great Lakes region which will, in turn, affect coastal wetlands
•	Terrestrial Invasive Species - many terrestrial invaders are found in Great Lakes coastal wetlands and can dis-
place native vegetation as they spread
•	Water Levels - water level change has strong influences on Great Lakes habitat and biological communities
associated with Coastal Wetlands. Water levels have a major influence on un-diked coastal wetlands and are
basic to any analysis of wetland change trends
This sub-indicator links directly to the other sub-indicators in the Habitats and Species indicator, particularly the
other coastal wetlands-related sub-indicators.
Comments from the Author(s)
This sub-indicator needs to be evaluated in terms of both wetland quality and extent. While some wetlands may de-
crease in both area and quality due to the lack of water level fluctuation, as on Lake Ontario, the area of other wet-
lands could remain within the range determined by natural water level fluctuations, but be degraded by other factors,
such as sedimentation, excessive nutrients, invasive species or land use pressures. When interpreting the data, the
other coastal wetland sub-indicators that evaluate wetland quality need to be considered. Measurement should be
based upon total area of inventoried coastal wetlands where known. Where areal extent is not known efforts should
be focused on collecting that baseline data. Total change can be roughly determined on a lake basin basis and for
scientifically-based sampling, priority sites should be established where regular ground-truthing facilitates a statisti-
cal analysis.
An overall view of wetland health can be derived by considering the 6 Coastal Wetland sub-indicators in combina-
tion, because they function and indicate anthropogenic disturbance at different spatial and temporal scales and have
varying resolution of detection. For example, landscape measures are used to determine loss, transformation and
restoration of wetland types experiencing varying degrees of anthropogenic disturbance. However, landscape
measures have been challenging due to data gaps and because coastal wetlands are extremely dynamic systems; they
migrate, disappear, and appear with changing water levels not necessarily related to anthropogenic disturbance.
Assessing Data Quality
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not
Applicable
1. Data are documented, validated, or
quality-assured by a recognized agency or
organization
X





2. Data are traceable to original sources
X





3. The source of the data is a known,
reliable and respected generator of data
X





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STATE OF THE GREAT LAKES 201 7
4. Geographic coverage and scale of data
are appropriate to the Great Lakes Basin
X





5. Data obtained from sources within the
U.S. are comparable to those from
Canada




X

6. Uncertainty and variability in the data
are documented and within acceptable
limits for this sub-indicator report
X





Clarifying Notes:
The Data Quality assessment given here is copied from the State of the Great Lakes (previously known as SOLEC) 2009 data quality
assessment, which was based on the State of the Great Lakes (SOLEC) 2005 report. This is done because the majority of this report
still refers to the SOGL 2005 report.
Acknowledgments
Author of 2016 update: Kevin O'Donnell, US EPA Great Lakes National Program Office
Authors of original 2007 report:
Joel Ingrain, Canadian Wildlife Service, Environment Canada
Lesley Dunn, Canadian Wildlife Service, Enviromnent Canada
Krista Holmes, Canadian Wildlife Service, Enviromnent Canada
Dennis Albert, Michigan Natural Features Inventory, Michigan State University Extension
Information Sources
Bourgeau-Chavez, Laura. 2015. Implementation of Great Lakes Coastal Wetlands Consortium Protocol. USEPA
Grant GL-00E00559-0 Final Report.
Bourgeau-Chavez, L.; Endres, S.; Battaglia, M.; Miller, M.E.; Banda, E.; Laubach, Z.; Higman, P.; Chow-Fraser, P.;
Marcaccio, J. Development of a Bi-National Great Lakes Coastal Wetland and Land Use Map Using Three-Season
PALSAR and Landsat Imagery. Remote Sens. 2015, 7, 8655-8682.
Great Lakes Wetlands Consortium. 2004. Great Lakes Coastal Wetland GIS Shapefile. Available at:
http://glc.org/proiects/liabitat/coastal-wetlands/cwc-mapping/
Albert, D.A., Wilcox, D.A., Ingram, J.W., and Thompson, T.A. 2005. Hydrogeomorphic classification for Great
Lakes coastal wetlands. J. Great Lakes Res 31(1):129-146.
Enviromnent Canada and Ontario Ministry of Natural Resources. 2003. The Ontario Great Lakes Coastal Wetland
Atlas: a summary of information (1983 -1997). Canadian Wildlife Service (CWS), Ontario Region,
Enviromnent Canada; Conservation and Planning Section-Lands and Waters Branch, and Natural Heritage Infor-
mation Center, Ontario Ministry of Natural Resources.
Herdendorf, C.E., Hartley, S.M., and Barnes, M.D. (eds.). 1981a. Fish and wildlife resources of the Great Lakes
coastal wetlands within the United States, Vol. 1: Overview. U.S. Fish and Wildlife Service, Washington, DC.
FWS/OBS-81/02-vl.
Herdendorf, C.E., Hartley, S.M., and Barnes, M.D. (eds.). 1981b. Fish andwildlife resources of the Great Lakes
coastal wetlands within the United States, Vol. 2: Lake Ontario. U.S. Fish and Wildlife Service, Washington, DC.
FWS/OBS-81/02-v2.
Herdendorf, C.E., Hartley, S.M., and Barnes, M.D. (eds.). 1981c. Fish andwildlife resources of the Great Lakes
coastal wetlands within the United States, Vol. 3: Lake Erie. U.S. Fish and Wildlife Service, Washington, DC.
FWS/OBS-81/02-v3.
Page 210

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STATE OF THE GREAT LAKES 201 7
Herdendorf, C.E., Hartley, S.M., and Barnes, M.D. (eds.). 198 Id. Fish and wildlife resources of the Great Lakes
coastal wetlands within the United States, Vol. 4: Lake Huron. U.S. Fish and Wildlife Service, Washington. DC.
FW S/OB S-8 l/02-v4.
Herdendorf, C.E., Hartley. S.M.. and Barnes, M.D. (eds.). 1981e. Fish and wildlife resources of the Great Lakes
coastal wetlands within the United States, Vol. 5: Lake Michigan. U.S. Fish and Wildlife Service, Washington, DC.
F WS/OB S-81/02-v5.
Herdendorf, C.E., Hartley, S.M., and Barnes, M.D. (eds.). 198 If. Fish and wildlife resources of the Great Lakes
coastal wetlands within the United States, Vol. 6: Lake Superior. U.S. Fish and Wildlife Service, Washington. DC.
FW S/OB S-8 l/02-v6.
List of Figures
Figure 1. Great Lakes coastal wetland distribution and total area by lake and river.
Source: Great Lakes Coastal Wetlands Consortium
Figure 2. Coastal wetland area by geomorpliic type within lakes of the Great Lakes system.
Source: Great Lakes Coastal Wetlands Consortium
Figure 3. Coastal wetland area by geomorpliic type within connecting rivers of the Great Lakes system.
Source: Great Lakes Coastal Wetlands Consortium
Figure 4: Wetlands and land use land cover (LULC) classes within a 10 km buffer of the Great Lakes coastline in
both the United States and Canada.
Source: Bourgeau-Chavez. Laura. 2015
Last Updated
State of the Great Lakes 2017 Technical Report
'*¦
\
61.461"
13.642
!:? J •
: ,:i?
DglroiT Ewer
¦W.v	¦	¦-: :¦
Figure 1. Great Lakes coastal wetland distribution and total area by lake and river.
Source: Great Lakes Coastal Wetlands Consortium, from SOGL 2007 report
Page 211

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STATE OF THE GREAT LAKES 201 7
<0
0)
k*
m
0
0)
1
<
lu-
ck:
<
25,000
22,500
20,000
17,500
15,000
12.500
10.000
7.500
5,000
2,500
0
i Barrier Protected
i Open Embayment
~	Protected Embayment
i Drowned River-Mouth
~	Delta
Superior Huron Michigan St. Clair Erie Ontario
LAKE
Figure 2. Coastal wetland area by geomorphic type within lakes of the Great Lakes system.
Source: Great Lakes Coastal Wetlands Consortium, from SOGL 2007 report
V)
03
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STATE OF THE GREAT LAKES 201 7
KWW

40'UIJH"
4S 0TTN-
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-------
STATE OF THE GREAT LAKES 201 7
Sub-Indicator: Aquatic Habitat Connectivity
Overall Assessment
Status: Fair
Trend: Improving
Rationale: Dams and barriers have been impacting the health of aquatic ecosystems in the Great Lakes Basin
for over a century and are limiting the recovery of some fish populations. In addition to limiting access of
fishes to spawning and nursery habitats, loss of aquatic connectivity impacts nutrient flows, and riparian and
coastal processes. The construction of new dams and barriers on Great Lakes tributaries peaked over a cen-
tury ago when water power was primary energy source in the basin. Many of the larger dams were built in
the 20th century for hydro-electric power generation. Over the last few decades very few new dams have been
built, and there has been a recent trend to remove old dams. The potential impacts of road-stream crossings
are now better understood, and there have been several regional initiatives to identify and mitigate culverts
that act as barriers. The assessments are based on expert opinion and data review, and are largely based on
Biodiversity Conservation Strategies developed for each lake.
Lake-by-Lake Assessment
Lake Superior
Status: Fair
Trend: Improving
Rationale: Dams and barriers are identified as a high threat to migratory fishes (Lake Superior LAMP 2013) and are
considered an impediment to the recovery of some fishes, such as Lake Sturgeon, Brook Trout and Walleye (Horns
et al. 2003). There are several projects that have been completed or are exploring options to improve connectivity
(http://greatlakes.fisliliabitat.org/proiects) such as the Camp 43 dam on the Black Sturgeon River. A collaborative
geo-database of inventoried connectivity barriers within the South Central Superior Basin will be used to prioritize
restoration for approximately 1,800 inventoried road-stream crossings and is an example of the efforts to address
connectivity (https://www.fws. gov/glri/documents/GLRIBook2014.pdf).
Lake Michigan
Status: Poor
Trend: Improving
Rationale: Approximately 83% of tributary stream habitat is unavailable to migratory fish due to fragmentation
caused by dams and dams are ranked as a high threat to migratory fishes (Pearsall et. al 2012a). Several dam remov-
al and mitigation projects have been initiated through the Great Lakes Restoration Initiative (e.g. Boardman River
dam removal projects will connect over 250 km of stream habitat back to Lake Michigan - the dam closest to the
river mouth will be modified to allow for fish passage while blocking access for sea lamprey.)
Lake Huron
Status: Poor
Trend: Improving
Rationale: Approximately 86% of major tributaries are no longer connected to the Lake Huron basin (Gebhardt et al.
2005) and dams are ranked as a high threat to migratory fishes (Franks Taylor et al. 2010). Aquatic habitat connec-
tivity varies in the basin. Franks Taylor et al. (2010) identified that Eastern Georgian Bay has sufficient access to
spawning habitat to maintain fish population while in Saginaw Bay access to spawning habitat is severely limiting
fish populations.
Lake Erie
Status: Fair
Trend: Improving
Rationale: Approximately 64% of tributary stream habitat is unavailable to migratory fish due to fragmentation
caused by dams, and dams are ranked as a medium threat to migratory fishes (Pearsall et. al 2012b) Several dam
removal and mitigation projects have been initiated in the last few years through the Great Lakes Restoration Initia-
tive (e.g. Ballville Dam on the Sandusky River will open up 35 km of river habitat for walleye).
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STATE OF THE GREAT LAKES 2017
Lake Ontario
Status: Fair
Trend: Improving
Rationale: The Lake Ontario Biodiversity Conservation Strategy identified dams and barriers as critical threat to the
health of the lake (Lake Ontario Biodiversity Conservation Strategy Working Group, 2009). In addition to dams on
Lake Ontario tributaries, the Moses-Saunders Power Dam on the St. Lawrence River impacts habitat connectivity,
particularly for the migration of the American Eel (MacGregor et. al 2013). The Eel Passage Research Center was
established in 2013 to address this issue. Several dam mitigation projects have been initiated including dam removal
in the Duffins Creek watershed by the Toronto Region Conservation Authority to improve access for Atlantic Salm-
on and removal of the Hogansburg dam to restore connectivity in the St. Regis River.
Other Spatial Scales
To assist in targeting these investments to reconnect habitats and barrier removal, spatial data on the location and
attributes of barriers (dams and road-stream crossings) throughout the Great Lakes Basin is being synthesized and
used to analyze the optimal strategy for enhancing connectivity to restore fish migrations by the University of Wis-
consin. The project will provide the basis for a decision-support tool to guide restoration at scales from individual
watersheds to the entire basin, and provide a systematic framework for comparing costs (direct economic costs, spe-
cies invasions) and benefits (connectivity, focal fish species) of barrier removal (Januchowski-Hartley et al. 2013).
Sub-Indicator Purpose
•	To determine the amount of accessible tributary habitat for migratory Great Lakes fishes;
•	To summarize key initiatives to improve the connectivity of aquatic habitat; and
•	To highlight some of the issues related to barrier mitigation.
Ecosystem Objective
Maintaining or increasing the aquatic habitat/connectivity to native fish would be considered desirable. Conversely,
decreases in aquatic habitat connectivity would be considered undesirable.
This sub-indicator best supports work towards General Objective #5 of the 2012 Great Lakes Water Quality Agree-
ment which states that the Waters of the Great Lakes should "support healthy and productive wetlands and other
habitats to sustain resilient populations of native species."
Ecological Condition
The installation and management of dams threatens the diversity of native Great Lakes fishes by restricting or elimi-
nating connectivity between the lake and critical spawning, nursery, and overwintering habitats (Januchowski-
Hartley et al. 2013). For example, in Lake Huron before the 1800's, over 10,000 km (more than 6,000 miles) of trib-
utary habitats were accessible to Lake Huron fish (Liskauskas et al. 2004, LHBP 2008). In 2005, 86% of major trib-
utaries were no longer connected to the Lake Huron basin (Gebhardt et al. 2003). This loss of tributary habitat has
resulted in significant declines in native fish populations in the lake, such as Lake Herring, Yellow Perch, Walleye,
Lake Sturgeon, River Redhorse, Black Redhorse, Eastern Sand Darter, and Channel Darter (Great Lakes Fishery
Commission. 2007, Bredin 2002).
Linkages
Linkages to other sub-indicators in the indicator suite include:
•	Aquatic Invasive Species - There are examples in all of the Great Lakes where dams and barriers, in some
instances, are protecting the native stream assemblages from competition and physical disturbance of sub-
strates from non-native salmonids (Bredin 2002). Hence, decisions about removal of dams and barriers in
Lake Huron must balance competing interests and goals, which may not always be explicit. Some dams and
barriers may also play a role in limiting the spread, of other invasive species such as Round Gobies,
Tubenose Gobies, and Viral Hemorrhagic Septicemia
•	Lake Sturgeon - Loss of aquatic connectivity lias contributed to the decline of the species
•	Lake Trout - Removed barriers that result in more parasitic Sea Lampreys would likely cause declines in
numbers of lake trout and slow progress towards restoration.
•	Sea Lamprey - Barrier removal is not straightforward as there are also potential ecological benefits to
some dams and barriers. For example, dams and barriers currently limit the spread of some Great Lakes in-
Page 215
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STATE OF THE GREAT LAKES 2017
vaders. Lake Huron supports the largest population of sea lamprey in the Great Lakes (Liskauskas et al.
2007), and dams and low-head barriers are a major control mechanism used by managers
•	Walleye - Loss of aquatic connectivity has contributed to the decline of the species
•	Water Quality in Tributaries - Barrier removal could improve water quality as natural flow patterns are
restored and stream temperatures are reduced.
This sub-indicator also links directly to the other sub-indicators in the Habitats and Species indicator.
Comments from the Author(s)
Aquatic habitat connectivity is defined for the purposes of this report as the direct connection between the
Great Lakes and waterways that are used by migratory fishes.
Aquatic connectivity provides chemically and physically unobstructed routes to fulfill life history requirements of
aquatic species, including access to intact refugia and opportunities for genetic exchange. Certain migratory fish
species (e.g. Atlantic Salmon and Walleye) depend on unimpeded access to spawning habitats in streams. In many
cases dams and other obstructions (e.g. perched culverts) prevent mature fish from reaching spawning habitat and
thus compromise stock and species diversity, losses in annual recruitment and reduced production and harvests. For
some fishes (e.g. Walleye, Lake Sturgeon) passage facilities will mitigate these effects, because these species cannot
jump. In addition to impacting the fishes that migrate from the Great Lakes into tributaries, many stream-dwelling
species of fish (e.g. suckers and minnows) suffer discontinuity in their ranges because of barriers.
Although there have been significant improvements in the cataloging of dams and barriers across the basin in the
last few years, some dams are undocumented. Spatial analysis of connectivity can be challenging if dams coordi-
nates do not intersect with the hydrology layer. Road stream crossing can highlight potential barriers, but these need
to be ground-truthed to assess their impact. Recent efforts to relicense hydropower dams in the United States have
led to a reconsideration of the habitat losses associated with these dams and a useful picture is emerging which al-
lows an assessment of the adverse impacts of habitat fragmentation on migratory and resident stream-fish communi-
ties. Data for tributary habitat are being developed in connection with Federal Energy Regulatory Commission
(FERC) dam relicensing procedures in the United States. Data are presently available for Michigan, New York
State, and Wisconsin. The identification of new projects will require research and contact with agencies.
The Upper Midwest and Great Lakes Landscape Conservation Cooperative has established an Aquatic Connectivity
Collaborative to provide tools for strategic planning and optimization of efforts to connect habitats. The Collabora-
tive will develop, prioritize, review, recommend and fund research that supports connectivity in the Great Lakes.
This effort should increase the amount of habitat connected in each of the Great Lakes in the future.
(https://lccnetwork.org/group/great-lakes-aquatic-connectivity-collaborative)
Assessing Data Quality
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not
Applicable
1. Data are documented, validated, or
quality-assured by a recognized agency or
organization






2. Data are traceable to original sources






3. The source of the data is a known,
reliable and respected generator of data
X





4. Geographic coverage and scale of data
are appropriate to the Great Lakes Basin
X





5. Data obtained from sources within the
U.S. are comparable to those from
Canada

X




Page 216
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STATE OF THE GREAT LAKES 2017
6. Uncertainty and variability in the data
are documented and within acceptable
limits for this sub-indicator report

X




Acknowledgments
Authors: DanKraus, Nature Conservancy of Canada
Contributors: Joseph Sheahan, U.S Fish and Wildlife Service
Information Sources
Bredin J. 2002. Lake Huron Initiative Action Plan. Michigan Department of Environmental Quaity Eagle, A.C.,
E.M. Hay-Chmielewski, K.T. Cleveland, A.L. Derosier, M.E. Herbert, and R.A. Rustem, eds.
Franks Taylor, R., A. Derosier, K. Dinse, P. Doran, D. Ewert, K. Hall, M. Herbert, M. Khoury, D. Kraus, A.
Lapenna, G. Mayne, D. Pearsall, J. Read, and B. Schroeder. 2010. The Sweetwater Sea: An International Biodiversi-
ty Conservation Strategy for Lake Huron - Technical Report. A joint publication of The Nature Conservancy, Envi-
ronment Canada, Ontario Ministry of Natural Resources Michigan Department of Natural Resources and Environ-
ment, Michigan Natural Features Inventory Michigan Sea Grant, and The Nature Conservancy of Canada. 264 pp.
with Appendices.
Gebhardt, K., J. Bredin, R. Day, T.G. Zom A. Cottrill, D. McLeish, and M.A. MacKay. 2003. Status of the near-
shore fish community. In The State of Lake Huron in 1999. Edited by M.P. Ebener. Great Lakes
Fish. Comm. Spec. Pub. 03-XX. pp. 22-37
Great Lakes Fishery Commission. 2007. A Report of the Enviromnental Objectives Working Group of the Lake
Huron Technical Committee. Great Lakes Fishery Commission.
Horns, W.H., C.R. Bronte, T.R. Busiahn, M.P. Ebener, R.L. Eshenroder, T. Gorenflo, N. Kmiecik, W. Mattes, J.W.
Peck, M. Petzold, and D.R. Schreiner, 2003. Fish-community objectives for Lake Superior. Great Lakes Fish.
Comm. Spec. Pub. 03-01, Ann Arbor, MI. 78p. Available at http://www.glfc.org/pubs/SpecialPubs/Sp03 l.pdf
Januchowski-Hartley S, Mclntyre PB, Diebel M, & Doran PJ. 2013. Restoring aquatic ecosystem connectivity re-
quires expanding barrier inventories. Frontiers in Ecology & Enviromnentll: 211-217.
Lake Huron Binational Partnership. 2008. Lake Huron Binational Partnership 2008-2010 Action Plan.
Available from http://www.epa.gov/glnpo/lamp/lh 2008/lh 2008.pdf
Lake Ontario Biodiversity Conservation Strategy Working Group . (2009). The Beautiful Lake - A Bi-national
Biodiversity Conser\>ation Strategy for Lake Ontario. U.S. Enviromnental Protection Agency and Enviromnent
Canada.
Lake Superior Lakewide Action and Management Plan (LAMP) - Superior Work Group. 2013. Lake
Superior Biodiversity Conservation Assessment. 130 pp. (Updated March 2015). Available at:
http://www.natureconservancv.ca/assets/documents/on/lake-superior/A-Biodiversitv-Conservation-Assessment-for-
Lake-Superior-Vol-1 -Final-Draft-Updated-Marcli2015 .pdf
Liskauskas J. Johnson, M. McKay, T. Gorenflo, A. Woldt, and Bredin 2004. DRAFT Enviromnental Objectives for
Lake Huron: Draft Report of the Enviromnental Objectives Working Group of the Lake Huron Technical Commit-
tee. Great Lakes Fishery Commission.
MacGregor, R„ J. Casselman, L. Greig, J. Dettmers, W. A. Allen, L. McDennott, and T. Haxton. 2013. Recovery
Strategy for the American Eel (Anguilla rostrata) in Ontario. Ontario Recovery Strategy Series. Prepared for Ontario
Ministry of Natural Resources, Peterborough, Ontario, x + 119 pp.
Pearsall, D„ P. Carton de Grammont, C. Cavalieri, P. Doran., L. Elbing, D. Ewert, K. Hall, M. Herbert, M.
Page 217
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STATE OF THE GREAT LAKES 2017
Khoury., S. Mysorekar., S. Neville., J. Paskus., and A. Sasson. 2012a. Michigami: Great Water. Strategies to
Conserve the Biodiversity of Lake Michigan. Technical Report. A joint publication of The Nature
Conservancy and Michigan Natural Features Inventory. 309 pp. with Appendices.
Pearsall, D.. P. Carton de Grammont, C. Cavalieri, C. Chu, P. Doran, L. Elbing, D. Ewert, K. Hall, M.
Herbert, M. Khoury, D. Kraus, S. Mysorekar, J. Paskus and A. Sasson 2012b. Returning to a Healthy Lake:
Lake Erie Biodiversity Conservation Strategy. Technical Report. A joint publication of The Nature
Conservancy, Nature Conservancy of Canada, and Michigan Natural Features Inventory. 340 pp. with
Appendices.
List of Figures
Figure 1. Lake Superior Aquatic Habitat Connectivity - Location of Dams and Barriers
Source: Lake Superior Lakewide Action and Management Plan - Superior Work Group (2013)
Figure 2. Lake Michigan Aquatic Habitat Connectivity - Stream Accessibility
Source: Pearsall et al. (2012a)
Figure 3. Lake Huron Aquatic Habitat Connectivity - Stream Accessibility
Source: Franks Taylor et al. (2010)
Figure 4. Lake Erie Aquatic Habitat Connectivity - Stream Accessibility
Source: Pearsall et al. (2012b)
Figure 5. Lake Ontario Aquatic Habitat Connectivity - Tributary Connectivity
Source: Lake Ontario Biodiversity Conservation Strategy Working Group (2009)
Last Updated
State of the Great Lakes 2017 Technical Report
Page 218
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STATE OF THE GREAT LAKES 2017
THBIAT
Dam* ah d Bat f iefi
WISCONSIN
Figure 1. Lake Superior Aquatic Habitat Connectivity - Location of Dams and Barriers
Source: Lake Superior Lakewide Action and Management Plan - Superior Work Group (2013)
Page 219
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STATE OF THE GREAT LAKES 2017

Mative Migratory Fish
Stream Accessibility
	YES
River/Streams- USGS -1:100k National
Hydrography Plus Dataset, Analysis by: Michigan Department of
Natural Resources - Institute of Fisheries and Research (2004)
Figure 2. Lake Michigan Aquatic Habitat Connectivity - Stream Accessibility
Source: Pearsall et al. (2012a)
Page 220
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STATE OF THE GREAT LAKES 2017
Dam Locations and
Connected Stream Reaches
Stream Accessibility
YEs "" Tr,«r*
Figure 3. Lake Huron Aquatic Habitat Connectivity - Stream Accessibility
Source: Franks Taylor et. al (2010)
Page 221
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STATE OF THE GREAT LAKES 2017

Matiue Migratory Fish
Stream Accessibility
	V€S
Streams: 1:100K National Hydrography Dataset
PLUS, 24k Ontario Ministry of Natural Resources,
Analysis by: The Nature Conservancy's Michigan
and Great Lakes Project (2011).
Figure 4. Lake Erie Aquatic Habitat Connectivity - Stream Accessibility
Source: Pearsall et al. (2012b)
Page 222
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STATE OF THE GREAT LAKES 2017
l^akc Ontario IHotiivrrcjEy Strategy
Lrtr ft>-fttfiuWy QaWhSSMQ.'
Figure 5. Lake Ontario Aquatic Habitat Connectivity - Tributary Connectivity
Source: Lake Ontario Biodiversity Conservation Strategy Working Group (2009)
Page 223
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STATE OF THE GREAT LAKES 201 7
Sub-Indicator: Phytoplankton
Open water
Overall Assessment
Status: Fair
Trend: Deteriorating
Rationale: Phytoplankton are a critical food resource for zooplankton and small fish. Invasive mussels have
caused algal reductions in Lake Michigan and Lake Huron, negatively impacting food webs of those lakes.
Re-eutrophication has occurred in Lake Erie. Changes in Lake Superior and Lake Ontario are more subtle.
Lake-by-Lake Assessment
Lake Superior
Status: Good
Trend: Unchanging
Rationale: The lake has maintained a phytoplankton assemblage reflecting oligotrophic conditions. Invasive species
are not notably affecting phytoplankton, but there is evidence from paleolimnological data of gradual assemblage
reorganization due to recent climate changes.
Lake Michigan
Status: Fair
Trend: Deteriorating
Rationale: The lake has a phytoplankton assemblage reflecting oligotrophic conditions. A reduction in phytoplank-
ton and consequent diminution in seasonality lias occurred. Lower levels of primary production could be reducing
resources for higher trophic levels.
Lake Huron
Status: Fair
Trend: Deteriorating
Rationale: The lake lias a phytoplankton assemblage reflecting oligotrophic conditions, more so due to the recent
invasion by mussels that have reduced pelagic primary producers (negatively affecting invertebrate grazers).
Lake Erie
Status: Poor
Trend: Deteriorating
Rationale: Re-eutrophication and proliferation of undesirable cyanobacteria is an increasing problem, particularly in
the western basin. The central basin exhibits substantial spring diatom blooms indicating periodic eutropliic or
mesotropliic conditions.
Lake Ontario
Status: Good
Trend: Unchanging
Rationale: The lake has a phytoplankton assemblage reflecting mesotropliic to oligotrophic conditions. There is
some evidence of assemblage changes due to invasive dreissenids.
Sub-Indicator Purpose
The purpose of this indicator is to directly assess phytoplankton species composition, biomass, and primary produc-
tivity in the Great Lakes, and to indirectly assess the impact of stressors on Great Lakes lower food webs. This in-
cludes inferring impacts from water quality changes, invasive non-native species and climate change.
Ecosystem Objective
(1)	Maintain trophic states with phytoplankton biomass and composition consistent with a healthy aquatic
Page 224
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STATE OF THE GREAT LAKES 2017
ecosystem in open waters of the Great Lakes. Desired objectives are phytoplankton biomass and
community structure indicative of oligotrophic conditions (i.e. a state of low biological productivity, as
is generally found in the cold open waters of large lakes) for Lakes Superior, Huron and Michigan; and
of mesotrophic (or better) conditions for Lakes Erie and Ontario.
(2)	Qualitatively and quantitatively detect and predict changes in phytoplankton biomass and composition
and apply those changes to stressor impacts or recovery. Desired outcomes are maintenance of good
condition over several years or a detectable transition to better conditions.
(3)	This indicator best supports work towards General Objective #5 of the 2012 Great Lakes Water Quality
Agreement which states that the Waters of the Great Lakes should "support healthy and productive
wetlands and other habitats to sustain resilient populations of native species." Also, as an indicator at
the bottom of the food chain phytoplankton are capable of detecting subtler ecosystem changes, so
Article 2(1 )(b) of the GLWQA ("develop programs, practices and technology necessary for a better
understanding of the Great Lakes Basin Ecosystem") applies.
Ecological Condition
The amount and taxonomic structure of phytoplankton populations can be related to anthropogenic stressors, thereby
permitting inferences to be made about lake condition and change (Stoenner 1978). Recently, the most important,
comprehensive data sources for phytoplankton-based assessments have been time series data on phytoplankton
community size and composition (e.g. Reavie et al. 2014a; Figure 1), satellite-based measurements of chlorophyll
(e.g. Barbiero et al. 2012) and recent paleolimnological studies of fossil phytoplankton (e.g. Chraibi et al. 2014).
Additional phytoplankton data have been collected by Canadian agencies, such as that for Lake Erie winter condi-
tions (Twiss et al. 2012; Enviromnent and Climate Change Canada 2015).
Status of the Great Lakes ecosystem as whole is characterized as fair although condition and trends vary significant-
ly among lakes. Invasive mussels have caused reductions in algae in Lake Michigan and Lake Huron, negatively
impacting food webs of those lakes. Re-eutrophication has occurred in Lake Erie in the last decade, mainly indicated
by cyanobacterial blooms that are occurring with greater frequency in the western basin of Lake Erie. Slower, long-
term changes are occurring in Lake Superior and Lake Ontario, but these changes are not yet well understood. How-
ever, with the exception of Lake Erie, trophic status across the basin would generally be considered good. For the
most part, trends herein reflect compiled datasets from 2001 through 2014 ("long-term"), as well as some long-term
inferences from previous collections.
Assigning firm condition assessments was also complicated in individual lakes. Consider Lake Michigan and Lake
Huron, for instance: if trophic status was the only factor considered their low phytoplankton abundance would su-
perficially reflect good conditions. However, the periodic, mussel-driven depletion of phytoplankton in these lakes
represents food web stress. From an ecological perspective that simultaneously considers multiple parameters fair is
a more appropriate assessment.
The 2011 State of the Great Lakes report noted the rapid changes that occurred in the phytoplankton community of
several Great Lakes in the decade prior. In general, these changes are continuing, or the lakes remain in the
"changed" state reported in 2011. In association with the dreissenid advance, the spring phytoplankton bloom in
Lake Huron, which practically disappeared in 2003 (Barbiero et al. 2011), remains absent. Declines in the spring
bloom were also seen in Lake Michigan (Reavie et al. 2014a). Such trends of oligotrophication can be viewed posi-
tively, but it likely also represents an overall reduction in the carrying capacity of the two lakes, as evidenced by
coinciding losses of invertebrates and reductions in fish energy content (Pothoven and Fahnenstiel 2014).
Lake Superior will always be oligotrophic, so in that context it will remain in good condition. But, it is noteworthy
that the lake's phytoplankton assemblage continues to change over decadal timescales, likely associated with atmos-
pheric wanning that is changing the physical properties of the lake (Chraibi et al. 2014). Such a shift has now been
recognized across all of the Great Lakes and their sub-basins (Reavie, unpublished data), so such longer-term
changes in primary producers should continue to be observed to determine future impacts.
In the western basin of Lake Erie, blooms of the nuisance algae Microcystis (among other cyanobacteria) have con-
tinued to occur (Michalak et al. 2013). The spring algal bloom in the central basin, largely attributed to filamentous
Page 225
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STATE OF THE GREAT LAKES 2017
diatoms (Reavie et al. 2014a, Twiss et al. 2012) is likely contributing substantial biomass to the hypolimnion and
exacerbating hypoxia.
Over the last decade in Lake Ontario spring chlorophyll levels have remained stable, but there is evidence of a slight
summer chlorophyll increase (USEPA, unpublished data) since declines seen in the 1980s (Johengen et al. 1994).
This corresponds with recent changes in Lake Erie, albeit at a smaller scale. Future conditions in Lake Ontario
should be observed carefully.
Linkages
Linkages to other indicators include:
(1)	Nutrients and Dreissenid Mussels - it is well known that the phytoplankton population and its productivity
changes with anthropogenic pollution. The ecosystem changes are reflected by the change of phytoplankton compo-
sition and productivity. For example. Lake Superior represents an oligotrophic ecosystem and is widely considered
to be in the best condition of the Great Lakes. Similarly, Lake Erie's phytoplankton composition, which was once
eutrophic, dramatically changed to meso-oligotrophic status due to phosphorous abatement and the invasion of zebra
mussels, a trophic trend that has since reversed to indicate re-eutrophication. A great deal of recent data are available
for phytoplankton biomass, composition and primary productivity which will reflect the overall ecosystem health
including grazing pressures of non-native filter-feeders and bottom-up influences from nutrients.
(2)	This sub-indicator also links directly to the other sub-indicators in the Habitat and Species indicator, such as in-
vertebrate grazers that rely on phytoplankton as a primary food resource. The cycling of phosphorus is being driven
by catchment inputs and sedimentary processes, impacting the food web and having implications on many forms of
aquatic life, especially benthos, zooplankton and phytoplankton. Effects on fish communities are less direct, but
must also be considered.
Comments from the Author(s)
Objective, quantitative mechanisms for evaluating ecosystem health from phytoplankton are gradually being devel-
oped. For instance, nutrient optima and tolerances for indicator species are now available for the Great Lakes
(Reavie et al. 2014b), thereby allowing quantitative reconstructions of water quality variables from assemblage data.
Several qualitative indicators also exist: the abundance of cyanobacteria is a clear indicator for nutrient stress; reduc-
tions in algal abundance signal dreissenid-driven oligotrophication; and phytoplankton assemblage changes reflect
changes in pelagic ecology due to climate change and other factors. The U.S. Enviromnental Protection Agency has
an active program for phytoplankton collection and analysis in the pelagic regions of all Great Lakes in spring and
summer, and other, more localized programs are ongoing (e.g. Fahnenstiel et al. 2010). Satellite imagery has also
enabled the detection of chlorophyll trends in the surface waters of the Great Lakes (e.g., Kerfoot et al., 2010), and
these data can provide a broad overview of algal abundance.
To date the main purposes of this indicator have been to (1) measure biological responses of primary producers to
changing water quality and invasive species abundance; (2) evaluate direct problems (e.g. blooms) associated with
phytoplankton; (3) indirectly evaluate the trophic efficiency of the food web at transferring algal production to fish.
As a sensitive indicator of changes in primary producers due to various drivers (invasive species effects, nutrients,
climate, etc.), phytoplankton provide information on the effects of multiple stressors. As a newly-recognized driver
of phytoplankton assemblages in Lake Superior (Chraibi et al. 2014), climate change effects on phytoplankton and
their potential impacts on food webs will be tracked.
Assessing Data Quality
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not
Applicable
1. Data are documented, validated, or
quality-assured by a recognized agency or
organization
X





2. Data are traceable to original sources

X




3. The source of the data is a known,
reliable and respected generator of data

X




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STATE OF THE GREAT LAKES 2017
4. Geographic coverage and scale of data
are appropriate to the Great Lakes Basin
X





5. Data obtained from sources within the
U.S. are comparable to those from
Canada


X



6. Uncertainty and variability in the data
are documented and within acceptable
limits for this sub-indicator report

X




Clarifying Notes: These data have been derived from many sources, including scientific literature, satellite data, and unpublished
data.
Acknowledgments
Author: Euan D. Reavie, University of Minnesota Duluth, Natural Resources Research Institute, Duluth, MN,
ercavieVvd.umn.cdu
Information Sources
Barbiero, R.P., Lesht, B.M., and Warren, G.J. (2011). Evidence for bottom-up control of recent shifts in the pelagic
food web of Lake Huron. Journal of Great Lakes Research, 37:78-85.
Barbiero, R.P., Lesht, B.M., & Warren, G.J. (2012). Convergence of trophic state and the lower food web in Lakes
Huron, Michigan and Superior. Journal of Great Lakes Research, 38: 368-380.
Bridgeman, T.B., Chaffin, J.D., Kane, D.D., Conroy, J.D., Panek, S.E., & Annenio, P.M. (2012). From River to
Lake: Phosphorus partitioning and algal community compositional changes in Western Lake Erie. Journal of Great
Lakes Research, 38: 90-97.
Chraibi, V.L.S., Kireta, A.R., Reavie, E.D., Cai, M„ & Brown, T.N. (2014). A paleolimnological assessment of hu-
man impacts on Lake Superior. Journal of Great Lakes Research, 40: 886-897.
Enviromnent and Climate Change Canada (2015). Great Lakes Surveillance Program.
http://www.ec.gc.ca/scitech/default.asp?lang=en&n=3F61CB56-l (Accessed 22 September 2015).
Fahnenstiel, G., Pothoven, S., Vanderploeg, H„ Klarer, D„ Nalepa, T., & Scavia, D. (2010). Recent changes in pri-
mary production and phytoplankton in the offshore region of southeastern Lake Michigan. Journal of Great Lakes
Research, 36: 20-29.
Johengen, T.H., Johannsson, O.E., Pernie, G.L., and Millard, E.S. (1994). Temporal and seasonal trends in nutrient
dynamics and biomass measures in Lakes Michigan and Ontario in response to phosphorus control. Canadian Jour-
nal of Fisheries and Aquatic Sciences, 51: 2470-2578.
Kerfoot, W.C., Yousef, F„ Green, S.A., Budd, J.W., Schwab, D.J., Vanderploeg, H.A., (2010). Approaching storm:
Disappearing winter bloom in Lake Michigan. Journal of Great Lakes Research, 36: 30-41.
Michalak, A.M., Anderson, E.J., Beletsky, D., Boland, S., Bosch, N.S., Bridgeman, T.B., et al. (2013). Record-
setting algal bloom in Lake Erie caused by agricultural and meteorological trends consistent with expected future
conditions. Proceedings of the National Academy of Sciences, 110: 6448-6452.
Pothoven, S.A., Fahnenstiel, G.L. (2014). Declines in the energy content of yearling non-native alewife associated
with lower food web changes in Lake Michigan. Fisheries Management and Ecology, 21: 439-447.
Page 227
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STATE OF THE GREAT LAKES 2017
Reavie, E.D., Barbiero, R.P., Allinger, L.E., Warren, G.J. (2014a). Phytoplankton trends in the Laurentian Great
Lakes: 2001-2011. Journal of Great Lakes Research, 40: 618-639.
Reavie, E.D., Heathcote, A.J., Chraibi, V.L.S. (2014b). Laurentian Great Lakes phytoplankton and their water quali-
ty characteristics, including a diatom-based model for paleoreconstruction of phosphorus. PLoS ONE, 9: el04705.
Stoenner, E.F. (1978). Phytoplankton assemblages as indicators of water quality in the Laurentian Great Lakes.
Transactions of the American Microscopical Society 97: 2-16.
Twiss, M.R., McKay, R.M.L., Bourbonniere, R.A., Bulleijahn, G.S., Carrick, H.J., Smith, R.E.H., Winter, J.G.,
D'souza, N.A., Furey, P.C., Lashaway, A.R., Saxton, M.A., Wilhelm, S.W. (2012). Diatoms abound in ice-covered
Lake Erie: An investigation of offshore winter limnology in Lake Erie over the period 2007 to 2010. Journal of
Great Lakes Research, 3: 18-30.
List of Figures
Figure 1. Histograms of phytoplankton biovolume and community composition in the Great Lakes basins from
2001 through 2013. Spring and summer assemblages are provided from offshore, surface waters. Small numbers at
the bottom of each bar indicate the number of samples averaged. Major noteworthy trends include: declines in
phytoplankton abundance in Lake Huron and Lake Michigan (particularly in spring and attributed to diatom loss);
and increases in spring and summer phytoplankton in central and western Lake Erie (mainly attributed to increases
in spring diatoms and summer cyanophytes).
Source: U.S. Enviromnental Protection Agency, Great Lakes National Program Office. Modified from Reavie et al.
(2014a).
Last Updated
State of the Great Lakes 2017 Technical Report
Page 228
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STATE OF THE GREAT LAKES 2017
Superior
Huron North
Huron Smiih
Figure 1. Histograms of phytoplankton biovolume and community composition in the Great Lakes basins from
2001 through 2013. Spring and summer assemblages are provided from offshore, surface waters. Small numbers at
the bottom of each bar indicate the number of samples averaged. Major noteworthy trends include: declines in
phytoplankton abimdance in Lake Huron and Lake Michigan (particularly in spring and attributed to diatom loss);
and increases in spring and summer phytoplankton in central and western Lake Erie (mainly attributed to increases
in spring diatoms and summer cyanophytes).
Source: U.S. Environmental Protection Agency, Great Lakes National Program Office. Modified from Reavie et al.
(2014a).
¦	UlltdfcjnlrfkKl
¦	DmoFlaofcliciiies
¦	Cyaiwpbytes
BCryplojE'tiyles.
aOirysophytes
¦	Ohlorophyles
Pennate dialoma
W Centric diatoms
Erie Wesl
Ontario
Erie East
Erie Central
Michigan North	M idliga r¦ S< >u i •.
Page 229
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STATE OF THE GREAT LAKES 201 7
Sub-Indicator: Zooplankton
Open water
Overall Assessment
Status: Good
Trend: Unchanging
Rationale: Zooplankton biomass levels and community composition are consistent with the oligotrophic state
of the four deepest Great Lakes. Lake Erie has more cladocerans which is typical of a shallow productive
lake. The 14 year trends are declining in Lake Huron and perhaps Lake Ontario, unchanging in Lakes Supe-
rior and Michigan and perhaps increasing in Lake Erie. The proportion of calanoid copepods, an index of
oligotrophication has increased in Lakes Michigan, Huron and Ontario. Shorter term trends are largely un-
changing (2006-2011).
Lake-by-Lake Assessment
Lake Superior
Status: Good
Trend: Unchanging
Rationale: Zooplankton biomass stable and near 3 g m~2. Community composition also stable with high prevalence
of calanoid copepods including the large copepod Limnocalcmus, an indicator of cold deep oligotrophic lakes.
Lake Michigan
Status: Good
Trend: Unchanging
Rationale: Zooplankton biomass higher than Lake Superior near 5-6 g m~2. No overall decline in zooplankton de-
spite observed declines in primary productivity. Shift in zooplankton community was apparent around 2001-2004
with reduction of daphnid cladoceran biomass by 50%, and increased prevalence of calanoid copepods particularly
Limnocalcmus. However since that time, there has been no change in community composition.
Lake Huron
Status: Fair (low)
Trend: Unchanging
Rationale: Zooplankton biomass has remained low in Lake Huron since 2003. In 2003, zooplankton biomass de-
creased from 4-8 g m"2 to 2 g m"2, falling below Lake Superior biomass levels. Sharp declines in cladoceran bio-
mass, particularly daphnids, yielded a community dominated by calanoid copepods. Zooplankton biomass decrease
coincided with decline in primary productivity and fishery indicators (Riley et al. 2008, Barbiero et al. 2011). How-
ever, since that decline, there has been no further change in biomass or community composition. Although the cur-
rent status is similar to Lake Superior, the abrupt change that the zooplankton community underwent in 2003 has
had ecosystem implications.
Lake Erie
Status: Good
Trend: Unchanging
Rationale: Note: Areal biomass goals are lower for shallow Lake Erie relative to the deeper Great Lakes.
Lake Erie has three distinct basins- Western, Central, and Eastern. Biomass in shallow (10 m depth) Western Basin
has increased from 0.5 g m"2 to 1.0 g m"2 with persistent cyclopoid copepods and cladocerans and a small but in-
creasing calanoid copepod component. Deeper Central (20 m) and Eastern (50 m) Basins have similar overall zoo-
plankton biomass at 2-4 g m"2. Although areal (total water column) biomass levels are similar to oligotrophic Lake
Superior, zooplankton are more concentrated (more individuals per unit volume) in the shallower basins of Lake
Erie. Some evidence of increased overall biomass in later years, 2010-2011. Lake Erie has the highest zooplankton
diversity rich in cladoceran species. Deep-dwelling Limnocalcmus, increasingly important in other Great Lakes, is
Page 230
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STATE OF THE GREAT LAKES 201 7
rare in Lake Erie due to limited hypolimnetic habitat. Limnocalcmus copepodites can be washed into Western Lake
Erie from Lake Huron in the spring.
Lake Ontario
Status: Good
Trend: Unchanging
Rationale: Zooplankton biomass levels are intermediate between Lake Michigan and Lake Superior at levels around
4-5 g m~2. Some recovery occurred after a biomass minimum during the time period 2004-2007, however, for the
most part, the biomass in Lake Ontario has not changed significantly since 2000. Community shift away from cy-
clopoid copepods toward calanoid copepods suggests oligotrophication. Some signs of recovery in daphnid cladoc-
eran biomass in 2010-2011. Predation by alewife is high relative to other Great Lakes.
Sub-Indicator Purpose
•	The offshore zooplankton biomass sub-indicator assesses the standing stock and community composition of
zooplankton in the Great Lakes over time and space.
•	Changes in the offshore zooplankton biomass sub-indicator track forcing from both bottom-up (primary
production) and top-down (vertebrate or invertebrate predation) mechanisms as well as energy transfer
across trophic levels. The purpose of this sub-indicator is to measure the trophic efficiency of the food web
at transferring algal production to fish.
•	Zooplankton biomass has often been used to explain deviations in the relationship of nutrients (total phos-
phorus, TP) and phytoplankton biomass (chl a) (Taylor and Carter 1997).
•	Mean body size and species composition of zooplankton are also sensitive indicators of predatory pressure
by planktivorous fish and large invertebrates (Mvsis and predatory cladocerans). Such indicators need fur-
ther development.
Ecosystem Objective
Maintain and support a healthy and diverse fishery; maintain trophic states consistent with the lake-specific goals -
oligotrophic Lake Superior, Huron, Michigan, and Ontario, and mesotrophic Lake Erie. Zooplankton represent an
important trophic link from primary production to fish and abundant zooplankton tend to improve water quality and
fish production capacity.
This sub-indicator best supports work towards General Objective #5 of the 2012 Great Lakes Water Quality Agree-
ment that states that the Waters of the Great Lakes should "support healthy and productive wetlands and other habi-
tats to sustain resilient populations of native species."
Ecological Condition
Lakes with lower target Total Phosphorus (TP) concentrations (e.g. Lake Superior and Huron at 5 (ig P l"1 and Lake
Michigan at 7 (ig P l"1) will have a lower target offshore zooplankton biomass of 3 g m~2 than lakes with higher tar-
get TP concentrations (e.g. Lake Ontario at 10 |ig P l"1) having a target offshore zooplankton biomass of 5 g m"2.
Although Lake Erie has a similar TP target as Lake Ontario, a shallower habitat suggests a lower zooplankton bio-
mass goal of 3 g m"2 for the central (20 m) and eastern (40 m) basins and 1 g m"2 for the western basin (10 m).
Summer biomass of crustacean zooplankton communities in the offshore waters of Lake Superior lias remained at a
relatively low but stable level near 3 g m"2 since at least 1998 (Figure 1). The plankton community is dominated by
large calanoid copepods (Leptodiaptomus sicilis and Limnocalcmus macrurus) that are characteristic of oligotrophic,
coldwater ecosystems. In 2003, the biomass of cladocerans and cyclopoid copepods in Lake Huron declined dra-
matically, with total biomass falling below that of Lake Superior (Barbiero et al. 2011). Our updated time series
shows that there has been little additional change since 2003 in Lake Huron. Similar declines of cladocerans oc-
curred in Lake Michigan, although this decline has been offset by the increase in L. macrurus (Barbiero et al. 2009).
Our time series suggest overall zooplankton biomass levels near 5-6 g m"2 have been maintained. Summer zoo-
plankton communities in Lakes Huron and Michigan have become increasingly similar to that of Lake Superior,
with composition characteristic of cold oligotrophic systems (Barbiero et al. 2012).
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STATE OF THE GREAT LAKES 201 7
Overall zooplankton biomass of Lake Ontario (4-5 g m~2) is between that of Lake Michigan and Lake Superior. Cy-
clopoid copepods comprised a large part of the zooplankton community before decreasing in 2004. Cladocerans
biomass was also important but has varied over time. Decreases in cyclopoid and cladoceran biomass have been
offset by increases in calanoid copepods including L. macrurus. Thus, changes in the zooplankton community of
Lake Ontario mirror that of lakes Superior, Michigan, and Huron although cyclopoid copepods and cladocerans re-
main higher than in the other deep lakes (Barbiero et al. 2014, Rudstam et al. 2015).
Zooplankton biomass of shallow Western Lake Erie has slightly increased to levels near 1-2 g m~2. Zooplankton
biomass in the deeper central and eastern basins has maintained levels near 3 g m"2 and community composition has
remained diverse and rich in native and non-native cladoceran species.
The proportion of biomass represented by calanoid copepods in Lake Superior has remained fairly stable at 85%,
indicating oligotrophic conditions. Summer zooplankton communities in lakes Huron, Michigan, and Ontario have
shown an increasing proportion of calanoid copepods in recent years, which suggests increased oligotrophication. It
has been a result primarily of substantial declines in cladoceran and cyclopoid copepod populations. This had led to
decreased overall zooplankton biomass in Lake Huron to levels that may be limiting to alewife, although other fish
species have increased (Riley et al. 2008). In contrast, calanoid biomass has made up for the decrease in cladocer-
ans in Lakes Michigan and Ontario. Limnocalanus is a large deep dwelling copepod so that, although overall bio-
mass has been maintained, the zooplankton community has shifted toward less dense, larger organisms that live
deep in colder water. Therefore, zooplankton production decreases following these species changes even though
biomass does not change. Some fish species (e.g. native coregonids) may benefit from this change but others (e.g.
alewife) may not. Primary production, and in particular the spring phytoplankton bloom, has indeed declined nota-
bly in lakes Huron and Michigan coincident with the shifts in the zooplankton communities. Lake Ontario has not
experienced recent declines in primary production, suggesting that top-down control from alewife and predatory
cladocerans (particularly Bvthotrephes) may better explain observed zooplankton community shifts in this lake
(Barbiero et al. 2014, Rudstam, et al. 2015). Maintenance of cladoceran fauna relative to calanoids in Lake Erie can
be attributed to shallow habitat as well as a mesotrophic state.
Linkages
Linkages to other sub-indicators in the indicator suite include:
•	Other Habitat and Species sub-indicators (phytoplankton and benthos).
•	Nutrients in Lakes (open water) - phosphorus levels regulate primary productivity by phytoplankton and
thus food levels for zooplankton.
•	Dreissenid Mussels - filter feeding of phytoplankton by mussels competes with zooplankton grazers.
Smaller zooplankton may be ingested by mussels. Increased water clarity shifts primary production to
deeper depths in the form of deep chlorophyll layers (DCL).
•	The connection of the zooplankton sub-indicator to other trophic levels provides a test of the principle de-
veloped in marine settings that pelagic communities, on average, have approximately equal biomass in ex-
ponentially widening size classes (Sheldon et al. 1972). Material and energy flow up this size spectrum
from bacteria and phytoplankton via zooplankton to fish with varying efficiency (Borgmann 1987). Some
of this production sinks from the surface euphotic zone to nourish the benthos. It may flow efficiently, with
high productivity across the size-spectrum, or it may accumulate as algae, negatively affecting water quali-
ty while little energy reaches top predators.
Comments from the Author(s)
Changes in the zooplankton communities of Lake Huron and Lake Michigan, and to a lesser extent Lake Ontario,
are consistent with reductions in nutrient levels, which have been seen in all three lakes, and could represent a con-
sequence of nutrient reduction activities, perhaps compounded by effects of dreissenid mussels. The reductions in
cladocerans in the former two lakes, along with continued declines in populations of the benthic amphipod Diporeia,
could represent a decreasing food base for forage fish. However, exact mechanisms of these declines, and the rela-
tive strength of bottom-up versus top-down forcing, have yet to be fully determined.
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STATE OF THE GREAT LAKES 201 7
An important threat to the zooplankton communities of the Great Lakes is posed by invasive species. The continued
proliferation of dreissenid populations can be expected to impact zooplankton communities through the alteration of
the structure and abundance of the phytoplankton community that many zooplankton depend on for food. Predation
from the non-native cladocerans Bvthotrephes longimamts and Cercopagis pengoi may also have an impact on zoo-
plankton abundance and community composition. Invasive predatory cladocerans have been shown to have had a
major impact on zooplankton community structure in the Great Lakes (Lehman 1991; Barbiero and Tuchman 2004;
Warner et al. 2006).
Currently U.S. EPA monitoring data for crustaceans are available through 2011. Details on methods for zooplank-
ton sampling and analysis can be found in Barbiero et al. (2001). Summer offshore crustacean zooplankton biomass
is the main indicator reported this year.
Note that unlike previous indicator reports, we use areal biomass (g m"2) rather than volumetric (mg m~3) units to
better evaluate the overall standing biomass of these lakes for connecting to fish production potential (Bunnell et al.
2014). Whole water column (in this case maximum of 100 m) tows in deep lakes include large strata of hypolimni-
on that have few zooplankton. Volumetric biomass estimates are thus "diluted" relative to shallower lakes that have
less hypolimnion. Areal biomass is calculated by summing the zooplankton biomass found within one meter
squared of lake water column. Note that for Lakes Superior, Michigan, and Ontario most offshore GLNPO sites are
> 100 m but many of the sites for Lake Huron are < 100 m. In Lake Erie, depths range from 10 m in the Western to
20 m in the Central to 50 m in the Eastern basins.
The length-weight coefficients have been updated for calanoid copepods based on recent studies to better reflect
their contribution (Watkins et al. 2011, Burgess et al. 2015). This update leads to an increase in estimated calanoid
biomass by a factor of 2 compared to previous State of the Great Lakes (previously known as SOLEC) indicator
reports and Bunnell et al. 2014.
Assessing Data Quality
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not
Applicable
1. Data are documented, validated, or
quality-assured by a recognized agency or
organization

X




2. Data are traceable to original sources

X




3. The source of the data is a known,
reliable and respected generator of data

X




4. Geographic coverage and scale of data
are appropriate to the Great Lakes Basin

X




5. Data obtained from sources within the
U.S. are comparable to those from
Canada





X
6. Uncertainty and variability in the data
are documented and within acceptable
limits for this sub-indicator report

X




Acknowledgments
Authors:
James Watkins, Cornell University, jmw237@cornell.edu
Richard P. Barbiero, CSC, Chicago, IL, gloeotri@sbcglobal.net
Lars Rudstam, Cornell University, lgrl@cornell.edu
Page 233
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STATE OF THE GREAT LAKES 201 7
Data Source:
U.S. EPA Great Lakes National Program Office
Glenn J. Warren, Great Lakes National Program Office (U.S. EPA), Chicago, IL
Richard P. Barbiero, CSC, Chicago, IL, gloeotri@sbcglobal.net
Information Sources
Barbiero, R.P., Little, R.E., Tuchman, M.L. 2001. Results from the U.S. EPA's biological open water surveillance
program of the Laurentian Great Lakes: III. Zooplankton. J. Great Lakes Res. 27: 167-184.
Barbiero, R.P., and Tuchman, M.L. 2004. Changes in the crustacean communities of Lakes Michigan Huron, and
Erie following the invasion of the predatory cladoceran Bvthotrephes longimanus. Can. J. Fish. Aquat. Sci. 61:2111-
2125.
Barbiero, R. P., Bunnell, D. B., Rockwell, D. C., Tuclunan, M.L., 2009. Recent increases in the large glacial-relict
calanoid Limnocalcmus macrurus in Lake Michigan. J. Great Lakes Res. 35:285-292.
Barbiero, R.P., Lesht, B.M., and Warren, G.J. 2011. Evidence for bottom-up control of recent shifts in the pelagic
food web of Lake Huron. J. Great Lakes Res. 37:78-85.
Barbiero, R. P., Lesht, B. M., Warren, G. J., 2012. Convergence of trophic state and the lower food web in Lakes
Huron, Michigan and Superior. J. Great Lakes Res. 38:368-380.
Barbiero, R. P., Lesht, B. M., and Warren G. J., 2014. Recent changes in the crustacean zooplankton community of
Lake Ontario. J. Great Lakes Res. 40:898-910.
Bunnell, D. B„ R. P. Barbiero, S. A. Ludsin, C. P. Madenjian, G. J. Warren, D. M. Dolan, T.O. Brenden, R. Briland,
O.T. Gorman, J. X. He, T. H. Johengen, B. F. Lantry, T. F. Nalepa, S. C. Riley, C. M. Riseng, T. J. Treska, I.
Tsehaye, M. G. Walsh, D. M. Warner, and B. C. Weidel. 2014. Changing ecosystem dynamics in the Laurentian
Great Lakes: bottom-up and top-down regulation. Bioscience 64: 26-39.
Burgess, S., Jackson, E.W., Schwarzman, L„ Gezon, N. and Lehman, J.T. 2015. Improved Estimates of Calanoid
Copepod Biomass in the St. Lawrence Great Lakes. J. Great Lakes Res. 41:484-491.
Lelunan, J. T. 1991. Causes and consequences of cladoceran dynamics in Lake Michigan: implications of species
invasion by Bvthotrephes. J. Great Lakes Res. 17:437-445.
Riley, S. C., Roseman, E. F., Nichols, S. J., O'Brien, T. P.JCiley, C. S., Schaeffer, J. S., 2008. Deepwater demersal
fish community collapse in Lake Huron. Trans. Am. Fish. Soc. 137: 1879-1890.
Rudstam, L. G., Holeck, K. T„ Bowen, K. L„ Watkins, J.M., Weidel, B. C., and Luckey, F. J., 2015. Lake Ontario
zooplankton in 2003 and 2008: Community changes and vertical redistribution. Aquat. Ecosyst. Health Mgmt. 18:
43-62.
Taylor, W.D., and Carter, J.C.H. 1997. Zooplankton size and its relationship to trophic status in deep Ontario lakes.
Can. J. Fish. Aquat. Sci. 54: 2691-2699.
Warner, D. M„ Rudstam, L.G., Benoit, H. Johannsson,O.E. and Mills. E.L. 2006. Changes in seasonal
nearshore zooplankton abundance patterns in Lake Ontario following establishment of the exotic
predator Cercopagispengoi. J. Great Lakes Res. 32:531-542.
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STATE OF THE GREAT LAKES 2017
Watkins, J. M., Rudstam, L. G., and Holeck, K. T„ 2011. Length-weight regressions for zooplanktonbiomass calcu-
lations - A review and a suggestion for standard equations. eCommons Cornell http://hdl.handle.net/1813/24566.
List of Figures
Figure 1. Areal biomass (g in2) calculated from U.S. EPA's GLNPO summer survey D100 tows (100 m or 2 m
above bottom for shallower sites) 153-um tows for each lake. Length-weight coefficients used are from Watkins et
al. 2011.
Data Source: Rick Barbiero
Last Updated
State of the Great Lakes 2017 Technical Report
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STATE OF THE GREAT LAKES 2017
M
i
3 li
liliilllill
SI 6
~ *
t
S 3
I I
111
Jllu
i
Figure 1. Areal biomass (g nf ) calculated
from U.S. EPA's GLNPO summer survey
D100 tows (100 m or 2 m above bottom for
shallower sites) 153-(xmtows for each lake.
Length-weight coefficients used are from
Watkins et al. 2011.
Data Source: Rick Barbiero
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STATE OF THE GREAT LAKES 201 7
Sub-Indicator: Benthos
Open water
Overall Assessment
Status: Good
Trend: Unchanging
Rationale: Based on the benthic community, both the long-term (1997 - 2012) and short-term (2010-2012) trends in
the trophic condition of the lakes are generally considered to be good and unchanging, except for the Lake Erie
where the long-term trends are indicative of increased eutrophication. Overall, an increasing Oligochaete Trophic
Index (OTI) means increasing eutrophication or increasing trophic conditions.
Lake-by-Lake Assessment
Lake Superior
Status: Good
Trend: Unchanging
Rationale: All sites in Lake Superior were classified as oligotrophic based on the oligochaete community index both
long-term (since 1997) and in the recent years. The endpoint for this sub-indicator is to maintain oligotrophic condi-
tions in the open waters of Lake Superior.
Lake Michigan
Status: Good
Trend: Unchanging
Rationale: All sites in northern and central Lake Michigan as well as deep sites in the southern part of the lake have
a trophic index value below 0.6 indicating an oligotrophic condition. Overall, no significant negative trends were
found in the trophic condition of the lake since 1997 and in the last few years. Poor OTI (> 1.0) scores were found in
recent years at two nearshore sites (of 16 total) in the southeastern part of the lake, and significant trends of increas-
ing eutrophication are evident at one of these two sites (near the Grand River outlet) since 2002. The endpoint for
this sub-indicator is to maintain an oligotrophic state in the open waters of Lake Michigan.
Lake Huron
Status: Good
Trend: Unchanging
Rationale: Almost all sites in northern, southern and central Huron are oligotrophic, except for one mesotrophic site
in the southern part and two eutrophic sites: on the eastern shore near the outlet of Saugeen River in Ontario, Cana-
da, and in Saginaw Bay. The trophic state of the lake has not changed significantly in the last 16 years. The endpoint
for this sub-indicator is to maintain an oligotrophic state in the open waters of Lake Huron.
Lake Erie
Status: Poor
Trend: Deteriorating
Rationale: All sites on Lake Erie are eutrophic, and several have a long-term trend of increasing OTI. The highest
OTI values are found in the eastern basin. The endpoint for this sub-indicator is to maintain mesotrophic conditions
in the open waters of the western and central basins of Lake Erie, and oligotrophic conditions in the eastern basin of
Lake Erie.
Lake Ontario
Status: Fair
Trend: Unchanging
Rationale: All deep-water sites (>80 m) in both basins of Lake Ontario are oligotrophic, and one shallow site is eu-
trophic. Most of the nearshore sites are mesotrophic and two sites in western basin showed trends toward eutrophi-
cation in the last decade. Overall, no significant negative trends were found in the trophic condition of the lake since
1997 and in the last few years.
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STATE OF THE GREAT LAKES 2017
There are no permanent stations on connecting channels, so they are not assessed as part of this sub-indicator report.
Sub-Indicator Purpose
• The purpose of this sub-indicator is to assess trends in trophic conditions in the Great Lakes using oligo-
chaete diversity, abundances, and the individual species responses to organic enrichment and to infer health
of the benthic community.
Ecosystem Objective
The Ecosystem Objective is that the benthic community in the Great Lakes should remain relatively constant over
time and be comparable to unimpaired waters with similar depth and substrate. One estimate is based on the Oligo-
chaete Trophic Index which uses oligochaete diversity, trophic classifications and abundance to compute trophic
status of a body of water.
This sub-indicator best supports work towards General Objective #5 of the 2012 Great Lakes Water Quality Agree-
ment which states that the Waters of the Great Lakes should "support healthy and productive wetlands and other
habitats to sustain resilient populations of native species."
This sub-indicator will evaluate trophic conditions in the Great Lakes using oligochaete diversity, abundances, and
the individual species responses to organic enrichment.
Calculation of the Oligochaete Trophic Index (OTI)
To evaluate trends in the benthic community of the Great Lakes, an Oligochaete Trophic Index (OTI) is used. The
OTI was initially described by Mosley and Howmiller (1977) with subsequent modifications by Howmiller and
Scott (1977), Milbrink (1983), and Lauritsen et al. (1985). This sub-indicator primarily follows Milbrink's formula
(Riseng et al. 2014). Milbrink classifies Tubificids and Lumbriculids oligochaetes into four ecological classes rela-
tive to trophic status of the lake. The values range from 0 indicating intolerant of enrichment (oligotrophic condi-
tions) to 3 indicating tolerant of enrichment (highly eutrophic conditions). The index is calculated as:
n0 + Z ni + 2 £ n2 + 3 £ n3
0TI =c x v—	
2 n0 + 2 nx + 2 n2 + £ n3
where n„. n2. and n3 indicate the abundances of organisms in each of the four trophic categories (Table 1) and c is
a density coefficient that scales the index to absolute densities of Tubificids and Lumbriculids. The c coefficient is
calculated as follows (Milbrink 1983):
c = 1 if n > 3,600
c = 0.75 if 1,200  1 then Tubtubi is classified as a 0; however, if the ratio is close to one (0.75 to 1.25) then
Tubtubi is a 3 if c > 0.5 and a 0 if c < 0.5;
if Limhoff density is zero and n0 is relatively high and/or total density is low, then Tubtubi is 0, otherwise 3; and,
finally, if the total density of oligochaetes is zero, then the index is zero.
Trophic classifications were obtained from literature for the Great Lakes and are shown in Table 1.
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STATE OF THE GREAT LAKES 2017
Ecological Condition
In the 2012 Great Lakes Water Quality Agreement (GLWQA), the Areas of Concern (AOC) Annex's purpose is to
contribute to the achievement of the General and Objectives of the Agreement by restoring the beneficial uses that
have become impaired due to location conditions. Beneficial Use Impairments are the measures of the environmen-
tal, human health or economic impact of poor water quality. The GLWQA defines 14 Beneficial Use Impairments
that contribute to a location's designation as an AOC. Degradation of Benthos is one of the BUIs for the Great Lakes
and further emphasizes the importance of the sub-indicator in the suite.
State of the Great Lakes reporting (previously knowns as SOLEC) uses the modified oligochaete-based trophic con-
dition index (OTI, Milbrink 1983; Howmiller and Scott 1977) to assess trophic status of each site. The trophic con-
dition index is calculated based on known organic enrichment tolerances and abundances of oligochaete taxa (see
attached summary of calculation procedure). The index ranges from 0 - 3: scores less than 0.6 (the lower blue line
in Figure 1) indicate oligotrophic conditions; scores above 1 (the top black line in Figure 1) indicate eutrophic con-
ditions; and scores between 0.6 and 1.0 suggest mesotrophic conditions. Scores approaching 3 indicate high densi-
ties of oligochaetes dominated by the pollution tolerant tubificidae including Limnoclrilus hoffineisteri. Overall, an
increasing OTI means increasing eutrophication or increasing trophic conditions.
A consistent difference in trophic conditions among and within Great Lakes was found during the study period
(1997- 2012) (Figure 1). Trophic state was significantly inversely related to site depth (r = -0.58), with Lake Erie
being the most eutrophic lake, followed in order of decreasing trophic state by lakes Ontario, Michigan, Huron and
Superior. To assess the temporal trends in OTI at each site we used linear regression. The only significant lake-wide
long-term trend of increasing trophic conditions or becoming more eutrophic (P < 0.005) was observed in Lake Erie,
where significant trends were found in half of the sampled sites. Localized increases in OTI over time were found at
nearshore sites in southeastern Lake Michigan, eastern Lake Huron, and western Lake Ontario (Figure 2).
The most eutrophic sites in Lake Erie were found in the eastern basin, where OTI at deep sites doubled since the
early 2000s as a result of drastic decrease in pollution-intolerant species. Significant trends of OTI increase were
found here at 4 of 5 sampled sites (Figure 3). One more site that showed a significant trend of increasing trophic
conditions was a nearshore site in the central basin located between Ashtabula and Erie, PA (Figure 3). The average
OTI forthe eastern basin (1.96±0.45, mean± standard deviation) exceeded those for both the western (1.41±0.51)
and the central basins (1.39±0.36). The overall phytoplankton biomass in the lake has increased since the mid-
1990s (Conroy et al. 2005b), potentially a result of the dramatic increase in dissolved reactive phosphorus loads
from tributaries (Richards et al. 2010), in contrast to the relatively constant Total Phosphorus loads (Scavia et al.
2014). In addition, dreissenid populations declined in the central basin in early 2000s (Patterson et al. 2005; Karata-
yev et al. 2014) most likely due to hypoxia events. Considering that the eastern basin is the main region of sediment
and organic matter deposition in Lake Erie, the increase in basin- and lake-wide OTI may be indicative of increasing
trophic state of the lake.
Deepwater sites in Lake Ontario continue to be oligotrophic throughout the whole study period. In contrast, the
nearshore sites, especially along the southern shore, are mesotrophic or eutrophic (Figure 2). Two nearshore sites in
the western basin showed a trend toward increasing eutrophication since 2001 (Figure 3), likely being affected in the
southern shore by the outlet of the Niagara River, and on the northern shore by the Toronto metropolitan area.
All sites in northern and central Lake Michigan, as well as deep sites in the southern part of the lake are oligotrophic
(Figure 2). Two nearshore sites in southeastern Michigan (near the Grand and Kalamazoo River outlets) are eu-
trophic and one of them (at the mouth of Grand River) had a significant trend of increasing eutrophication (P <
0.001). One site in northern Michigan and one in Green Bay showed opposite trends of increasing oligotrophication
(Figure 3).
Almost all sites in northern, southern and central Huron are oligotrophic; one site in the southern part is mesotrophic
(Figure 2). Only two sites in Lake Huron are eutrophic: one on the central-eastern shore (near the outlet of Saugeen
River in Ontario, Canada) where the total density of Oligochaeta increased 20-fold since the early 2000s, and eu-
trophication is significantly increased (P = 0.004), and the other in Saginaw Bay, which was highly eutrophic in late
1990s, improved to mesotrophic in 2002, but has trended towards eutrophic again starting in 2007.
All sites in Lake Superior were oligotrophic based on OTI values since 1997, and one easternmost site even showed
trends of decreasing OTI in the last four years (Figure 3). There was an increase in OTI at one western site north of
Duluth (Figure 3) but the change was minimal (from 0 to 0.125).
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STATE OF THE GREAT LAKES 2017
Linkages
Linkages to other sub-indicators in the indicator suite include:
•	Dreissenid Mussels - the relative abundance of non-native benthos such as zebra and quagga mussels can change
dramatically the structure of aquatic communities including the benthos, affect ecosystem functioning and lake
trophic state. In addition to direct local effects, dreissenid mussels also interact indirectly withbenthic community
by affecting other sub-indicators such as Nutrients in Lakes, therefore decreasing the amount of available food.
There are strong interactions between these sub-indicators although not well understood and require further investi-
gation.
•	Nutrients in Lakes (open water) - nutrients impact the food web and are important for many forms of aquatic life,
especially benthos, zooplankton and phytoplankton. Addition of nutrients affects the structure and abundance of
benthic community, changing the share of tolerant and intolerant species, but the magnitude of changes varies de-
pending on the depth and lake trophic status. Since the OTI was designed to reflect community changes following
organic enrichment, it can be expected to co-vary with increase in nutrients. Indeed, OTI positively correlates with
the amount of Total Phosphorus and Total Soluble Phosphorus measured at the bottom (Burlakova et al. in prepara-
tion).
•	Diporeia (open water) - Diporeia is a benthic macroinvertebrate in the cold, deep-water habitats of all the Great
Lakes (except Lake Erie), an indicator of oligotrophic conditions, and an important fish food item. Historically Di-
poreia has been a dominant benthic macroinvertebrate in profundal regions of all five of the Great Lakes (Cook and
Johnson, 1974). Proliferation of dreissenid mussels coincided with significant declines in Diporeia in Lakes Ontario,
Michigan and Huron, but the nature of these interactions is not yet well understood. While the abundance of Di-
poreia is not considered by the current index (OTI), a significant increase in organic enrichment may negatively
affect Diporeia.
This sub-indicator also links directly to the other sub-indicators in the Habitat and Species indicator.
Comments from the Author(s)
The oligochaete sub-indicator used for the State of the Great Lakes (previously known as SOLEC) assesses trophic
status of the lakes and may suggest pressures due to organic enrichment. Most of the sites that showed increasing
eutrophication are located near large river mouths, suggesting that pollution abatement mitigation in the upland wa-
tersheds could help to improve water quality and sediment conditions at these sites. Other pressures not accounted
for in the oligochaete trophic index include invasive species, regional climate change, water level changes, toxic or
other contaminants. The tendency of decreasing OTI with depth (due to the lack of pollution tolerant species at
depths over 60m) may affect the lake-wide index depending on the ratio of deep to shallow sites sampled in each
lake. The regular benthic monitoring program of U.S. EPA Great Lakes National Program Office (U.S. EPA
GLNPO) has a relatively small number of stations, with poor representation of nearshore areas, and complement-
ing these annual surveys with a wider range of sites during CSMI years will aid greatly in identifying trends in
benthic community.
Invasive species that strongly affect freshwater ecosystems (e.g., Dreissena spp.) can alter the composition and
abundance of benthic communities, affecting behavior of benthic indices, including OTI. Even though mussel bio-
mass has been declining in the 30-90m depth zones in some of the lakes, dreissenids are still a dominant compo-
nent of the benthos.
There is an emerging realization of the importance of benthic processes and pathways within whole-lake context
(Vander Zanden and Vadeboncoeur 2002). Recent analysis of long-term dynamics of major trophic levels in Lauren-
tian Great Lakes revealed a far greater prevalence of bottom-up regulation since 1998, emanated from long-term
declines in TP inputs and the more recent proliferation of nonindigenous dreissenid mussels (Bunnell et al. 2013).
Filter feeding Ponto-Caspian bivalves Dreissenapolvmorpha and D. rostriformis bugensis are powerful ecosystem
engineers that affect both abiotic (e.g., enhance water clarity and alter nutrient cycling) and biotic (e.g., reduce
abundance of phytoplankton and microzooplankton. enhance benthic algae and macrophytes, induce changes in ben-
thic community) components of the ecosystem (Karatayev et al. 1997, 2002; Higgins and Vander Zanden 2010).
Filter-feeding activity, sediment deposition and habitat provided by dreissenids directly affect benthic macroinverte-
brate community abundance and composition by promoting epifaunal predators, scavengers and collectors while
replacing native filter feeders (e.g., Karatayev et al. 1997; 2002; Burlakova et al. 2012; Ward and Ricciardi 2007;
Higgins and Vander Zanden 2010). However, most of the changes in benthic community following dreissenid inva-
sion are described for the littoral zone rich in epifaunal species while changes in profundal infaunal community are
poorly understood (Burlakova et al. 2014; Karatayev et al. 2015). The abundance of non-dreissenid taxa (e.g., Di-
poreia., Sphaeriidae) declined in profundal habitats after Dreissena invasion (Higgins and Vander Zanden 2010;
Page 240
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STATE OF THE GREAT LAKES 2017
Nalepa et al. 2007, 2009; reviewed in Karatayev et al. 2015) where quagga mussels compete for space and food re-
sources with most of native invertebrates. This may be a result of system-wide (e.g. food interception effect, result-
ing in strong decline of spring phytoplankton blooms) r.v. local Dreissena effects (e.g. enrichment of sediments with
biodeposits). The resulting effect of Dreissena on oligochaete community may induce changes in the OTI that will
not reflect the changes in the trophic status of the ecosystem. Therefore, more data on the effect of dreissenids on
species composition and abundance of benthic invertebrates in profundal r.v. nearshore zone are needed to fully un-
derstand their impact on benthic communities.
Assessing Data Quality
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not
Applicable
1. Data are documented, validated, or
quality-assured by a recognized agency or
organization
X





2. Data are traceable to original sources
X





3. The source of the data is a known,
reliable and respected generator of data
X





4. Geographic coverage and scale of data
are appropriate to the Great Lakes Basin


X*



5. Data obtained from sources within the
U.S. are comparable to those from
Canada





X
6. Uncertainty and variability in the data
are documented and within acceptable
limits for this sub-indicator report

X




Clarifying Notes:
*The regular benthic monitoring program of U.S. EPA GLNPO has a relatively small number of stations, with poor
representation of nearshore areas and thus it provides limited information.
Acknowledgments
Authors:
Lyubov Burlakova, Great Lakes Center, SUNY Buffalo State, Buffalo, NY
Alexander Karatayev, Great Lakes Center, SUNY Buffalo State, Buffalo, NY
Richard Barbiero, CSC, Chicago, IL
Susan Daniel, Great Lakes Center, SUNY Buffalo State, Buffalo, NY
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Bunnell, D. B„ R. P. Barbiero, S. A. Ludsin, C. P. Madenjian, G. J. Warren, D. M. Dolan, T. O. Brenden, R.
Briland, O. T. Gorman J. X. He, T. H. Johengen, B. F. Lantry, B. M. Lesht, T. F. Nalepa, S. C. Riley, C. M. Riseng,
T. J. Treska, I. Tsehaye, M. G. Walsh, D. M. Warner, andB. C. Weidel. 2013. Changing Ecosystem Dynamics in
the Laurentian Great Lakes: Bottom-Up and Top-Down Regulation. Bioscience.
Burlakova, L. E„ A. Y. Karatayev, and V. A. Karatayev. 2012. Invasive mussels induce community changes by
increasing habitat complexity. Hydrobiologia 685: 121-134.
Burlakova, L. E„ A. Y. Karatayev, C. Pennuto, and C. M. Mayer. 2014. Changes in Lake Erie benthos over the last
50 years: historical perspectives, current status, and main drivers. J. Great Lakes Res. 40: 560-573.
Burlakova, L. E„ E. Kovalenko, K. Schmude, A. Y. Karatayev and R. Barbiero. In preparation. Indices of water
quality based on the profundal benthic communities of the Great Lakes. J. Great Lakes Res.
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Cook, D. G. and M. G. Johnson. 1974. Benthic macroinvertebrates of the St. Lawrence Great Lakes. J. Fish. Res.
Bd. Can. 31: 763-782.
Conroy, J. D., D. D. Kane, D. M. Dolan, W. J. Edwards, M. N. Charlton, and D. A. Culver. 2005. Temporal trends
in Lake Erie plankton biomass: roles of external phosphorus loading and dreissenid mussels. J. Great Lakes Res. 31:
89-110.
Howmiller, R. P. and M. A. Scott. 1977. An enviromnental index based on relative abundance of oligochaete
species. J. Water Pollut. Con. F. 46: 809-815.
Higgins, S. N„ and M. J. Vander Zanden. 2010. What a difference a species makes: a meta-analysis of dreissenid
mussel impacts on freshwater ecosystems. Ecol. Monogr. 80: 179-196.
Karatayev, A. Y„ L. E. Burlakova, and D. K. Padilla. 1997. The effects of Dreissena polvmorpha (Pallas) invasion
on aquatic communities in eastern Europe. J. Great Lakes Res. 16: 187-203.
Karatayev, A. Y„ L. E. Burlakova, and D. K. Padilla. 2002. Impacts of zebra mussels on aquatic communities and
their role as ecosystem engineers. Pages 433-446 in E. Leppakoski, S. Gollach, and S. Olenin, editors. Invasive
Aquatic Species of Europe: Distribution, Impacts and Management. Kluwer Academic Publishers, Dordreicht, The
Netherlands.
Karatayev, A. Y„ L. E. Burlakova, C. Pennuto, J. Ciborowski, V. A. Karatayev, P. Juette, andM. Clapsadl. 2014.
Twenty five years of changes in Dreissena spp. populations in Lake Erie. J. Great Lakes Res. 40: 550-559.
Karatayev, A. Y„ L. E. Burlakova, and D. K. Padilla. 2015. Zebra versus quagga mussels: A review of their spread,
population dynamics and ecosystem impacts. Hydrobiologia 746: 97-112.
Kreiger, K. A. 1984. Benthic macroinvertebrates as indicators of enviromnental degradation in the southern
nearshore zone of the central basin of Lake Erie. J. Great Lakes Res. 10(2): 197-209.
Lauritsen, D. D., S. C. Mozley, and D. S. White. 1985. Distribution of oligochaetes in Lake Michigan and
comments on their use as indices of pollution. J. Great Lakes Res. 11(1): 67-76.
Milbrink, G. A. 1983. An improved environmental index based on the relative abundance of oligochaete species.
Hydrobiologia 102:89-97.
Mozley, S. C, and Howmiller, R. P. 1977. Benthos of Lake Michigan. Argonne Nat. Lab. Rept. ANL/ES-40, 6, 148
pp.
Nalepa, T. F„ D. L. Fanslow, S. A. Pothoven, A. J. Foley, and G. A. Lang. 2007. Long-term trends in benthic
macroinvertebrate populations in Lake Huron over the past four decades. J. Great Lakes Res. 33: 421-436.
Nalepa, T. F„ D. L. Fanslow, and G. A. Lang. 2009. Transformation of the offshore benthic community in Lake
Michigan: recent shift from the native amphipod Diporeia spp. to the invasive mussel Dreissena rostriformis
bugensis. Freshwater Biol. 54: 466-479.
Patterson, M. W. R., J. J. H. Ciborowski, and D. R. Barton. 2005. The distribution and abundance of Dreissena
species (Dreissenidae) in Lake Erie, 2002. J. Great Lakes Res. 31, Suppl. 2: 223-237.
Richards, R.P., Baker, D.B., Crumrine, J.P., Stearns, A.M., 2010. Unusually large loads in 2007 from the Maumee
and Sandusky Rivers, tributaries to Lake Erie. J. Soil Water Conserv. 65: 450-462.
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Riseng, C., G. Carter, K. Schmude, S. Adlerstein, and R. Barbiero. 2014. Benthos Diversity and Abundance. In:
State of the Great Lakes 2011. Environment Canada and the U.S. Environmental Protection Agency. Cat No. Enl61-
3/1-201 IE-PDF. EPA 950-R-13-002. Available at http://binational.net
Schloesser, D. W., K. A. Krieger, J. J. H. Ciborowski, and L. D. Corkum. 2000. Recolonization and possible
recovery of burrowing mayflies (Ephemeroptera: Ephemeridae: Hexagenia spp.) in Lake Erie of the Laurentian
Great Lakes. J. Aquatic Ecosyst. Stress Recovery 8: 125-141.)
Scavia, D., J. David Allan, K. K. Arend, et al. 2014. Assessing and addressing the re-eutrophication of Lake Erie:
Central basin hypoxia. J. Great Lakes Res. 40: 226-246.
Vander Zanden, M. J. and Y. Vadeboncoeur. 2002. Fishes as integrators of benthic and pelagic food webs in lakes.
Ecology 83:2152-2161.
Ward, J. M„ and A. Ricciardi. 2007. Impacts of Dreissena invasions on benthic macroinvertebrate communities: a
meta-analysis. Divers. Distrib. 13: 155-165.
List of Tables
Table 1. Trophic classifications for select mature lumbriculids and tubificids taken from Howmiller and Scott
(1977), Milbrink (1983) with additions from Kreiger (1984), Lauritsen et al. (1985). If Milbrink classifications dif-
fered from Howmiller and Scott, Howmiller and Scott was used.
Source: Riseng et al. 2014.
List of Figures
Figure 1. Scatterplot of the index values for Milbrink's (1983) Modified Enviromnental Index, applied to data from
GLNPO's 1997 through 2012 summer surveys. Values ranging from 0 to less than 0.6 indicate oligotrophic condi-
tions (blue line); values from 0.6 to 1.0 indicate mesotrophic conditions (black line); and values greater than 1.0
indicate eutrophic conditions. Data points represent the average of triplicate samples taken at each sampling site;
immature specimens were included in the analysis for calculation of overall density used to establish the coefficient
c but only mature specimens were used to calculate the number belonging to each ecological group of oligochaetes
(see attached description of index calculation).
Source: 1997-2012 U.S. EPA GLNPO benthic data collected from permanent stations.
Figure 2. Map of the Great Lakes showing the mean trophic status at each sampling site calculated for 2010-2012.
Trophic status was based on the modified trophic index for oligochaete worms from Milbrink (1983).
Source: 2010-2012 U.S. EPA GLNPO benthic data.
Figure 3. Maps of the Great Lakes showing sites with significant temporal trend in trophic status between 1997 and
2012. Sites without significant changes in oligochaete trophic index with time ("no change", P > 0.10, linear regres-
sion), with marginally significant trends ("eutrophication or oligotrophication", 0.05 < P < 0.10) and with significant
trends ("strong eutrophication or oligotrophication", P < 0.05) are indicated.
Source: 1997-2012 U.S. EPA GLNPO benthic data.
Last Updated
State of the Great Lakes 2017 Technical Report
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STATE OF THE GREAT LAKES 2017
SPEC-
CODE
GENUS
SPECIES
Trophic
Class
Source
Comment
RHY-
COCC
Rhyacodrillus
coccineus
0
Howmiller &
Scott 1977
Same classification as Krieger 1984 & Lauri-
tsen et al. 1985
TASA-
MER
Tasserkidrilus
americanus
0
Howmiller &
Scott 1977
Formerly T. Kessler \ in both Lauritsen et al.
1985 and Krieger
LIM-
PROF
Limnodrilus
profundicola
0
Howmiller &
Scott 1977
Same classification as Krieger 1984 & Lauri-
tsen et al. 1985
RHYMO
NT
Rhyacodrilus
montana
0
Krieger 1984
Same classification as Lauritsen et al. 1985
RHYSP
Rhyacodrilus
spp.
0
Krieger 1984
Same classification as Lauritsen et al. 1985
SPINIK
0
Spirosperma
nikolskyi
0
Krieger 1984
Same classification as Lauritsen et al. 1985
STYHER
1
Stylodrilus
heringianus
0
Howmiller &
Scott 1977
General agreement from all sources for this
taxon
TAS-
SUPE
Tasserkidrilus
superiorensis
0
Krieger 1984
Same classification as Lauritsen et al. 1985
AU-
LAMER
Aulodrilus
americanus
1
Howmiller &
Scott 1977
Classification based on Aulodrilus sp.
AULL-
IMN
Aulodrilus
limnobius
1
Milbrink 1983

AULPIG
U
Aulodrilus
pigueti
1
Milbrink 1983

ILYTEMP
llyodrilus
templetoni
1
Krieger 1984
Same classification as Milbrink 1983 & Lauri-
tsen et al. 1985
ISOFRE
Y
Isochaetides
freyi
1
Krieger 1984
Same classification as Lauritsen et al. 1985
SPIFER
0
Spirosperma
ferox
1
Howmiller &
Scott 1977
Same classification as Krieger 1984 & Lauri-
tsen et al. 1985
AULPLU
R
Aulodrilus
pluriseta
2
Milbrink 1983

Ll-
MANGU
Limnodrilus
angustipenis
2
Howmiller &
Scott 1977

LIMCER
V
Limnodrilus
cervix
2
Howmiller &
Scott 1977
Same as Milbrink 1983
LIMCEC
L
Limnodrilus
cer-
vix/claparedeianus
2
Howmiller &
Scott 1977
Same as Milbrink 1983
LIMCLAP
Limnodrilus
claparedeianus
2
Howmiller &
Scott 1977
Same as Milbrink 1983
LIM-
MAUM
Limnodrilus
maumeensis
2
Howmiller &
Scott 1977

LIMUDE
K
Limnodrilus
udekemianus
2
Howmiller &
Scott 1977
Same as Milbrink 1983
POT-
BEDO
Potamothrix
betodi
2
Milbrink 1983

POT-
MOLD
Potamothrix
moldaviensis
2
Milbrink 1983
Same classification as Lauritsen et al. 1985
POT-
VEJD
Potamothrix
vejdovskyi
2
Milbrink 1983
Same classification as Lauritsen et al. 1985
QUIM-
ULT
Quistadrilus
multisetosus
2
Howmiller &
Scott 1977

LIM-
HOFF
Limnodrilus
hoffmeisteri
2
Milbrink 1983
Differs from classification in Lauritsen et al.
1985
TUBTUBI
Tubifex
tubifex
0 OR 3
Milbrink 1983
Depends on densities of LIMHOFF and
STYHERI and total oligochaete density
Table 1. Trophic classifications for select mature lumbriculids and tubificids taken from Howmiller and Scott
(1977), Milbrink (1983) with additions from Kreiger (1984), Lauritsen et al. (1985). If Milbrink classifications dif-
fered from Howmiller and Scott, Howmiller and Scott was used.
Source: Riseng et al. 2014.
Page 244
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STATE OF THE GREAT LAKES 2017
X
0)
"O
c
(_>
Ic
Q.
O
 J .!:;: *11''!5: J;
t » » i ; » , . *
,c	U h I 1 ' i ;	r--	1-4	r
A	A	*
Q.5j	0-5- •	4
Q.0!	0.0
1996 1998 2000 2002 2004 2006 2006 2010 2012	1995 1998 2000 2002 2004 2006 2008 2010 2012
Lake Michigan	Lake Ontario
34*1	1 IlOv
BfcSta
2 5 * C*"r"	I 15-t
* Or«n |fy
P Northern
2j(Ji- » Seuthwn	I	A	• *
~	*	f
l 51	- # *	*	* fe l i"	. 1 t * 1 A * A • A
t * *— " 11A;	;—. -
* •	~ • *	j	•• A	* . A I 4
0.5 • i f t * 7 *	I ¦ ~	o.i 7 J I •	¦	* a •
9si*tiiitiii!n8 41 i j 1 j t • I * * • t# i •
o.o- ~DDDQ*DDDaDDDa* I W ; TAAAAA,iA4AA*A"*
1996 199® KKK> 2002 ZOOS 2006 2008 2010 2012	1995	2000 2002 2004 2005 2006 2010 2012
Lake Huron
3.0	—|
Bavin
• Central
3 5	4	O Northern
9	• Saginaw Sty
2 o	#	• Southern
g W-	. ~	.	*	• ~
® ~ ~
• . . * ~
i.o-	 ? j	«	
	m	*	m	»	ft	a	
¦ ¦!•••• ' i * ! i S i ~
0.0	• • »	•
19» 1998 2000 2002 2004 2006 2008 2010 2012
Figure 1. Scatterplot of the index values for Milbrink's (1983) Modified Environmental Index, applied to data from
GLNPO's 1997 through 2012 summer surveys. Values ranging from 0 to less than 0.6 indicate oligotrophy condi-
tions (blue line); values from 0.6 to 1.0 indicate mesotropliic conditions (red line); and values greater than 1.0 indi-
cate eutropliic conditions. Data points represent the average of triplicate samples taken at each sampling site; imma-
ture specimens were included in the analysis for calculation of overall density used to establish the coefficient c but
only mature specimens were used to calculate the number belonging to each ecological group of oligochaetes.
Source: 1997-2012 U.S. EPA GLNPO benthic data collected from permanent stations.
Page 245
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STATE OF THE GREAT LAKES 2017

Trophic Status
9 ctgoyophic
C Mesotrophe
9 EairopWc
N


i •
:•

•/
% •
0 70 HO
••
280
420
560
I Kilometers
Figure 2. Map of the Great Lakes showing the mean trophic status at each sampling site calculated for 2010-2012.
Trophic status was based on the modified trophic index for oligochaete worms from Milbrink (1983).
Source: 2010-2012 U.S. EPA GLNPO benthic data.
Change in Trophic Status
$ ©
c
pi
C
c
©
oc
Oc
c
. -®
6
: •
70 140

Slrcoff CM^olraphKalcn
QlflCtJOpbica&on
Ma Qianpe
futrciptofjrtiun
Su ortfl Eyifopfiicanofi
r> ~
^ •
V
%
E®
280
420
560
I Kilometers
Figure 3. Map of the Great Lakes showing sites with significant temporal trend in trophic status between 1997 and
2012. Sites without significant changes in oligochaete trophic index with time ("no change", P > 0.10, linear regres-
sion), with marginally significant trends ("eutrophication or oligotrophication", 0.05 < P < 0.10) and with significant
trends ("strong eutrophication or oligotrophication", P < 0.05) are indicated.
Source: 1997-2012 U.S. EPA GLNPO benthic data.
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STATE OF THE GREAT LAKES 201 7
Sub-Indicator: Diporeia
Open Water
Overall Assessment
Status: Poor
Trend: Deteriorating
Rationale: Abundances of the benthic amphipod Diporeia spp. continue to decline in Lakes Michigan, Huron
and Ontario. Abundances in Lake Superior are variable but overall trends are not apparent. Diporeia is cur-
rently extremely rare in Lake Erie and has likely been extirpated. In all the lakes where Diporeia has de-
clined, lower abundances first became apparent a few years after dreissenid mussels became established.
Because of high variability at depths < 30 m and a preference of Diporeia for offshore regions, trends in popu-
lations are best assessed at depths > 30 m. Assessments are restricted to the main basins of each of the lakes
since Diporeia, being a cold -water stenotherm, is not found in the shallow-warm bays and basins, nor in the
connecting channels. Since lake-wide assessments are mostly based on surveys every 5 years, temporal trends
can be considered mainly at this level of detail. Some regional assessments are made on an annual basis, and
these are included if data are available.
Lake-by-Lake Assessment
Lake Superior
Status: Good
Trend: Unchanging
Rationale: Long term monitoring and studies of distribution patterns indicate that, although substantial temporal
variability can occur, there are no directional trends in abundances of Diporeia in the lake.
Lake Michigan
Status: Poor
Trend: Deteriorating
Rationale: Diporeia abundances continue to decline in Lake Michigan. A lakewide survey in 2010 indicated that
Diporeia is now extremely rare at depths < 90 m (297 ft.) over the entire lake (Figure 1). At depths > 90 m, this taxa
can still be found, but abundances were lower by 66 % compared to abundances found in 2005 (Figure 2). Recent
annual surveys (2012-2014) conducted in just the southern basin of Lake Michigan reveal continued declines since
2010 (Figure 4). A lakewide survey of the population occurred again in 2015 but results are not yet available.
Lake Huron
Status: Poor
Trend: Deteriorating
Rationale: Diporeia abundances continue to decline in Lake Huron. The most recent lakewide survey occurred in
2012, and abundances were lower compared to a similar survey in 2007 (Figures 1, 2, 3). Abundances are now <
100 m~2 at depths 31-90 m and < 300 m~2 at depths > 90 m.
Lake Erie
Status: Poor
Trend: Deteriorating
Rationale: Because of shallow, warm waters, Diporeia are naturally not present in the western basin and most of the
central basin. Diporeia declined in the eastern basin beginning in the early 1990s and have not been found in that
basin since 1998.
Lake Ontario
Status: Poor
Trend: Deteriorating
Rationale: Diporeia abundances continue to decline in Lake Ontario (Figures 1 and 2). The last lake-wide survey in
Lake Ontario occurred in 2013 and, of the 45 sites sampled, only a single individual was found. That individual
occurred at a 140-m site. Based on these results, this organism is near extirpation in Lake Ontario.
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STATE OF THE GREAT LAKES 201 7
Sub-Indicator Purpose
•	The purpose of this sub-indicator is to show the status and trends in Diporeia populations, and to infer the
basic structure of cold-water benthic communities and the general health of the Great Lakes ecosystem.
Ecosystem Objective
The cold, deep-water regions of the Great Lakes should be maintained as a balanced, stable, and productive oligo-
trophic ecosystem with Diporeia as one of the key organisms in the food chain.
This sub-indicator best supports work towards General Objective #5 of the 2012 Great Lakes Water Quality Agree-
ment (GLWQA) which states that the Waters of the Great Lakes should "support healthy and productive wetlands
and other habitats to sustain resilient populations of native species."
Ecological Condition
This glacial-marine relic was once the most abundant benthic organism in cold, offshore regions (greater than 30 m
(98 ft) of each of the lakes. It was present, but less abundant in nearshore regions of the open lake basins, but natu-
rally absent from shallow, warm bays, basins, and river mouths. Diporeia occurs in the upper few centimetres of
bottom sediment and feeds on algal material that freshly settles to the bottom from the water column (i.e., mostly
diatoms). In turn, it is fed upon by most species of Great Lakes fish; in particular by many forage fish species, which
themselves serve as prey for the larger piscivores such as trout and salmon. For example, sculpin feed almost exclu-
sively upon Diporeia, and sculpin are eaten by lake trout. Also, lake whitefish, an important commercial species,
feeds heavily on Diporeia. Thus, Diporeia was an important pathway by which energy was cycled through the eco-
system, and a key component in the food web of offshore regions.
On a broad scale, abundances are directly related to the amount of food settling to the bottom, and population trends
reflect the overall productivity of the ecosystem. Abundances can also vary somewhat relative to shifts in predation
pressure from changing fish populations. In nearshore regions, this species is sensitive to local sources of pollution,
but because of varying conditions such as temperature fluctuations, substrate heterogeneity, and wave-induced tur-
bulence, it is difficult to assess population trends in this region.
Methods for estimating abundances of Diporeia are generally similar across the Great Lakes. Samples of bottom
substrates are collected with a Ponar grab and contents are washed through a screen (or net mesh) of 0.5-mm open-
ings. All Diporeia retained on the screen are immediately preserved, and later counted and identified. Densities are
reported as numbers per square metre. Nalepa et al. (2009) provides additional details on sampling methods and
abundances.
Diporeia populations are currently in a state of dramatic decline in all the lakes except Lake Superior (Figures 1 and
2). Based on the most recent surveys, Diporeia are present but continue to decline in lakes Michigan and Huron,
while it has likely been extirpated from Lake Erie and is near extirpation in Lake Ontario. The population in Lake
Superior, although highly variable, remains unchanged. Initial declines were first observed in all lake areas within
two to three years after zebra mussels (Dreissena polvmorpha) or quagga mussel (Dreissena bugensis) first became
established. These two species were introduced into the Great Lakes in the late 1980s via the ballast water of ocean-
going ships. Reasons for the negative response of Diporeia to these mussel species are not entirely clear. One hy-
pothesis is that dreissenid mussels are out-competing Diporeia for available food. That is, large mussel populations
filter food material before it reaches the bottom, thereby decreasing amounts available to Diporeia. However, evi-
dence suggests that the reason for the decline is more complex than a simple decline in food because Diporeia have
completely disappeared from areas where food is still settling to the bottom and where there are no local populations
of mussels. Also, individual Diporeia show no signs of starvation before or during population declines. Further,
Diporeia and Dreissena apparently coexist in some lakes outside of the Great Lakes (i.e.. Finger Lakes in New
York). Some studies suggest that the decline in Diporeia could be related to disease/parasites, but the findings are
often inconclusive and further work is needed in this area. Given the decline and disappearance of Diporeia in near-
shore regions, and very low abundances of Diporeia in offshore regions in each of the lakes except Lake Superior, it
seems that these present monitoring programs are adequate to detect population changes.
Linkages
Linkages of this sub-indicator to other sub-indicators in the indicator suite include:
•	Impacts of Aquatic Invasive Species
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STATE OF THE GREAT LAKES 201 7
•	Dreissenid Mussels
•	Toxic Chemicals in Sediment
This sub-indicator also links directly to the other sub-indicators in the Habitat and Species indicator, particularly
Lake Trout, as lake trout are among the fish species that are energetically linked to Diporeia. Young lake trout feed
on Diporeia directly, while adult lake trout feed on sculpin, and sculpin feed heavily on Diporeia. Lake trout are a
top predator in the deep-water habitat, and therefore assessments of both Diporeia and lake trout provide an evalua-
tion of lower and upper trophic levels in the cold, deep-water habitat.
Comments from the Author(s)
The continuing decline of Diporeia has strong implications to the Great Lakes food web. As noted, many fish spe-
cies rely on Diporeia as a major prey item, and the loss of Diporeia has impacted many of these species. Fish re-
sponses include changes in diet, movement to areas with more food, or a reduction in weight or energy content. Im-
plications to fish populations include changes in distribution, abundance, growth, recruitment, and condition. Recent
evidence suggests that fish are already being affected. Studies have shown that populations of lake whitefish, an
important commercial species, have been affected, as well as fish species that serve as prey for salmon and trout
such as alewife, sculpin, and bloater.
Because of the rapid rate at which Diporeia has declined in many areas, and its significance to the food web, agen-
cies committed to documenting trends should report data in a timely manner. The population decline has a defined
natural pattern, and studies of food web impacts should be spatially well coordinated. Also, studies to define the
cause of the negative response of Diporeia to Dreissena should continue and build upon existing information. Po-
tential areas of study are physiological and biochemical responses of Diporeia to Dreissena, and influence of poten-
tial pathogens, including bacteria and viruses. With an understanding of exactly why Diporeia populations are de-
clining, one may better predict what additional areas of the lakes are at risk. Also, by better understanding the cause,
one can better assess the potential for population recovery if dreissenid populations significantly decline.
Assessing Data Quality
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not
Applicable
1. Data are documented, validated, or
quality-assured by a recognized agency or
organization
X





2. Data are traceable to original sources
X





3. The source of the data is a known,
reliable and respected generator of data
X





4. Geographic coverage and scale of data
are appropriate to the Great Lakes Basin
X





5. Data obtained from sources within the
U.S. are comparable to those from
Canada





X
6. Uncertainty and variability in the data
are documented and within acceptable
limits for this sub-indicator report
X





Acknowledgments
Authors:
T. F. Nalepa, Water Center, Graham Sustainability Institute, University of Michigan, Ann Arbor, MI
A. K. Elgin, Great Lakes Enviromnental Research Laboratory, National Oceanic and Atmospheric Administration
Ann Arbor, MI
Page 249
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STATE OF THE GREAT LAKES 201 7
Information Sources
Nalepa, T. F.. Fanslow, D. F.. and Lang, G. A. 2009. Transformation of the offshore benthic community in Lake
Michigan: recent shift from the native amphipod Diporeia spp. to the invasive mussel Dreissena rostriformis
bugensis. Freshwater Biology 54:466-479.
Supportive Information Sources:
Auer, M. T.. N. A. Auer, N. R. Urban, and T. Auer. 2013. Distribution of the amphipod Diporeia in Lake Superior:
the ring of fire. Journal of Great Lakes Research 39:33-46.
Barbiero, R. P., K. Schmude, B. M. Lesht, C. M. Riseng, J. Glenn, G. J. Warren, and M. L. Tuchman. 2011. Trends
inDiporeia populations across the Great Lakes., 1997-2009. Journal of Great Lakes Research 37: 9-17.
Birkett, K„ S. J. Lozano, and L. G. Rudstam. 2015. Long-term trends in Lake Ontario's benthic macroinvertebrate
community from 1994-2008. Aquatic Ecosystem Health & Management Society. 18: 76-88.
Nalepa, T. F„ D. L. Fanslow, G. A. Lang, K. Mabrey, and M. Rowe. 2014. Lake-wide benthic surveys in Lake
Michigan in 1994-95, 2000, 2005, and 2010: abundances of the amphipod Diporeia spp. and abundances and
biomass of the mussels Dreissena polymorpha and Dreissena rostriformis bugensis. NOAA Technical
Memorandum GLERL-164. Great Lakes Enviromnental Research Laboratory, Ann Arbor, MI.
List of Figures
Figure 1. Mean densities (number per square metre) of the amphipod Diporeia spp. from sites at 31-90 m in lakes
Michigan, Huron, and Ontario, 1995 - 2014. Data are from lake-wide surveys conducted mostly at 5-year intervals.
Lake Michigan = triangles, dashed line (blue); Lake Huron = squares, dot-dash line (red); Lake Ontario = circles,
solid line (black).
Sources: Watkins et al. 2007; Birkett et al. 2015; Great Lakes Enviromnental Research Lab, NOAA
Figure 2. Mean densities (number per square metre) of the amphipod Diporeia spp. from sites at > 90 m in lakes
Michigan, Huron, and Ontario, 1995 - 2014. Data are from lake-wide surveys conducted mostly at 5-year intervals.
Lake Michigan = triangles, dashed line (blue); Lake Huron = squares, dot-dash line (red); Lake Ontario = circles,
solid line (black).
Sources: Watkins et al. 2007; Birkett et al. 2015; Great Lakes Enviromnental Research Lab, NOAA
Figure 3. Diporeia population density (No. m"2 x 103) declines in Lake Huron, 2000 - 2012.
Source: Great Lakes Enviromnental Research Lab, NOAA
Figure 4. Mean densities (number per square metre) of the amphipod Diporeia spp. in southern Lake Michigan,
reported by depth: < 30 m (squares, solid line); 31-90 m (triangles, long dashed line); and > 90 m (circles, short
dashed line), 2010-2014. Note that the axis scale is greatly reduced compared to Figures 1 and 2.
Source: Great Lakes Enviromnental Research Lab, NOAA
Last Updated
State of the Great Lakes 2017 Technical Report
Page 250
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STATE OF THE GREAT LAKES 201 7
7000
6000
5000 ¦
a
4000
3000
2000 •
1000 -
1990
1995
2000
2005
2010
2015
Year
Figure 1. Mean densities (number per square metre) of the amphipod Diporeia spp. from sites at 31-90 m in lakes
Michigan, Huron, and Ontario, 1995 - 2014.
Data are from lake-wide surveys conducted mostly at 5-year intervals. Lake Michigan = triangles, dashed line
(blue); Lake Huron = squares, dot-dash line (red); Lake Ontario = circles, solid line (black).
Sources: Watkins et al. 2007; Birkett et al. 2015: Great Lakes Environmental Research Lab. NOAA
Page 251
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STATE OF THE GREAT LAKES 201 7
5000
4000
CN
Lake Qnlario
CL
3000 ¦
2000
§. 1000
1995
2000
2005
2010
2015
1990
Year
Figure 2. Mean densities (number per square metre) of the amphipod Diporeia spp. from sites at > 90 m in lakes
Michigan. Huron and Ontario, 1994 - 2013
Data are from lake-wide surveys conducted mostly at 5-year intervals. Lake Michigan = triangles, dashed line
(blue); Lake Huron = squares, dot-dash line (red); Lake Ontario = circles, solid line (black).
Sources: Watkins et al. 2007; Birkett et al. 2015: Great Lakes Enviromnental Research Lab. NOAA
2000	2003	2007	2012
Figure 3. Diporeia population density (No. m x 103) declines in Lake Huron, 2000 - 2012.
Source: Great Lakes Enviromnental Research Lab. NOAA
Page 252
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STATE OF THE GREAT LAKES 201 7
250
— 200
£
 90 m (circles, short
dashed line), 2010 - 2014. Note that the axis scale is greatly reduced compared to Figures. 1 and 2.
Source: Great Lakes Environmental Research Lab. NOAA

<30 m
"31-90 m
>90 m
a	
¦a-
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STATE OF THE GREAT LAKES 2017
/
Sub-Indicator: Prey fish
Open water
Overall Assessment:
Status: Fair
Trend: Undetermined
Rationale: Prey fish communities across the Great Lakes continue to change, although the direction and
magnitude of those changes are not consistent across the lakes. The metrics used to categorize prey fish status
in this and previous periods are based on elements that are common among each of the lake's Fish
Community Objectives and include diversity and the relative role of native species in the prey fish
communities. The diversity index categorized three of lakes as \fair', while Superior and Erie were 'good'
(Table 1). The short term trend, from the previous period (2008-2010) to the current period (2011-2014)
found diversity in Erie and Superior to be unchanging, but the other three lakes to be 'deteriorating',
resulting in an overall trend categorization of'undetermined' (Table 1). The long term diversity trend
suggested Lakes Superior and Erie have the most diverse prey communities although the index for those prey
fish have been quite variable over time (Figure 1). In Lake Huron, where non-native alewife have
substantially declined, the diversity index has also declined. The continued dominance of alewife in Lake
Ontario (96% of the prey fish biomass) resulted in the lowest diversity index value (Figure 1). The
proportion of native species within the community was judged as 'good' in Lakes Superior and Huron, 'fair'
in Michigan and Erie and 'poor' in Ontario (Table 2). The short term trend was improving in in all lakes
except Michigan {'deteriorating') and Ontario {'unchanging'), resulting in an overall short term trend of
'undetermined' (Table 2). Over the current period, Lake Superior consistently had the highest proportion
native prey fish (87%) while Lake Ontario had the lowest (1%) (Figure 2). Lake Michigan's percent native
has declined as round goby increase and comprises a greater proportion of the community. Native prey fish
make up 51% of Lake Erie, although basin-specific values differed (Figure 2). Most notably, native species in
Lake Huron comprised less than 10% of the community in 1970, but since alewife have declined, now
represent nearly 80% of the community (Figure 2). Prey fish data are most consistent for in-lake populations,
which are reported here; data from connecting channels was not consistently available across the basin.
Abundance was not used to judge prey fish status since successful, basin-wide management actions, including
mineral nutrient input reductions and piscivore restoration, both inherently reduce prey fish abundance.
However, recent abundance trends as they relate to predator prey balance are referenced, such as in Lakes
Michigan and Huron where piscivore stocking is being reduced to lower predation demand on prey fish
populations and maintain sport fisheries.
Lake-by-Lake Assessment:
Lake Superior
Status: Good
Trend: Unchanging
Rationale: The average prey fish diversity index of the current reporting period (2011-2014) was 79% of the
maximum value in the time series and the proportion of native species by biomass in the prey fish community was
87%. As these values are greater than 75%, the status of Lake Superior was categorized as 'goodThere was little
change in the metrics between the current reporting period and the previous period (2008-2010). Despite
fluctuations and current lower overall density, the Lake Superior prey fish community is considered healthy due to
the high number of different native species present, the high proportion of biomass of native versus non-native
species, and the ability of the prey fish community to support a health sustaining predator fish population. More
recently biologists have become concerned that Lake Superior prey fish abundance is declining and may potentially
influence native, sport and commercial fisheries.
Lake Michigan
Status: Fair
Trend: Deteriorating
Rationale: The average prey fish diversity index of the current reporting period (2011-2014) was 72% of the
maximum value in the time series and the proportion of native species by biomass in the prey fish community was
Page 254
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STATE OF THE GREAT LAKES 201 7
48%. As these values are between 75% and 25%, the status of Lake Michigan is categorized as 'fairBoth metrics
were lower in the current reporting period relative to the previous reporting period (2008-2010) resulting a trend of
'deteriorating'.
Lake Huron
Status: Fair
Trend: Undetermined
Rationale: The average prey fish diversity index of the current reporting period (2011-2014) was 47% of the
maximum value in the time series and the proportion of native species by biomass in the prey fish community was
77%. These values are categorized as 'fair' and 'good', respectively, and the final status was conservatively based
on the lowest status. The trend was 'undetermined' since between the current and previous reporting periods the
proportion of native species increased but the diversity index declined slightly.
Lake Erie
Status: Fair
Trend: Improving
Rationale: The average prey fish diversity index of the current reporting period (2011-2014) was 77% of the
maximum value in the time series and the proportion of native species by biomass in the prey fish community was
49%. These values are both categorized as 'good' and 'fair', respectively, based on our sub-indicator description.
The overall trend was judged to be Improving' since the variable diversity index was similar to the overall trend
from the previous reporting period, but the proportion of native prey fish has continued to increase over the time
series.
Lake Ontario
Status: Poor
Trend: Deteriorating
Rationale: The average prey fish diversity index of the current reporting period (2011-2014) was 25% of the
maximum value in the time series, a value determined to be at the lowest end of the 'fair categorization, while the
proportion of native species was judged as 'poor representing only 1% of the total. The overall status of Lake
Ontario was categorized as 'poor' while the unchanging trend in proportion native and declining diversity trend
resulted in an overall trend assessment of'deteriorating
Sub-Indicator Purpose:
The purpose of this sub-indicator is to report on the status of the Great Lakes' prey fish communities as they relate
to community diversity and proportion of native species.
Ecosystem Objective:
Ecosystem objectives are based on the lake-specific Fish Community Objectives (FCO) that pertain to prey fish.
These FCOs are developed bv each of the respective Lake Committees and the Great Lakes Fishery Commission
(GLFC).
Lake Superior: Fish Community Goal - "To rehabilitate and maintain a diverse, healthy, and self-regulating fish
community, dominated by indigenous species and supporting sustainable fisheries". Additional principals note:
"Preser\'ation of indigenous species is of the highest concern " (Horns et al. 2003).
Lake Michigan: Planktivore Objective - "Maintain a diversity of planktivore (prey) species at population levels
matched to primary production and to predator demands. Expectations are for a lakewide planktivore biomass of
0.5 to 0.8 billion kg. " (Eshenroder et al. 1995).
Lake Huron: Prey Objective - "Maintain a diversity of prey species at population levels matched to primary
production and to predator demands. Emphasis is placed on species diversity and self-regulation of the fish
community" (DesJardine et al. 1995).
Lake Erie: Forage Fish Objective - "Maintain a diversity offorage fishes to support terminal predators and to sus-
tain human use" (Ryan et al. 2003).
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STATE OF THE GREAT LAKES 201 7
Lake Ontario: Offshore Pelagic Zone Objective- "Increase prev-fish diversity - maintain and restore a diverseprev-
fish community that includes Alewife, Lake Herring (Cisco), Rainbow Smelt, Emerald Shiner, and Three spine Stick-
leback. Status and trend indicators are 1) maintaining or increasing populations and increasing species diversity of
the pelagic prey fish community including introduced species (Alewife, Rainbow Smelt) and selected native prey fish
species (Threespine Stickleback, Emerald Shiner and Lake Herring (Cisco)); and 2) increasing spawning popula-
tions of native Lake Herring (Cisco) in the Bay of Ouinte, Hamilton Harbor, and Chaumont Bay" (Stewart et al.
2013).
Ecological Condition:
Lake Superior, Status: Good, Trend: Unchanging
Observations from Lake Superior suggest the prey fish community is both diverse and primarily composed of native
species resulting in a status categorization of good and an unchanging trend. These metrics support the idea that the
Lake Superior food web and fish community is the least-impacted of the five lakes. Unlike the other Great Lakes
that have a variety of non-native prey fish. Rainbow Smelt are the only non-native prey that contributes to the Lake
Superior community. Diversity changes illustrated across the time series are primarily driven by fluctuations in the
coregonid populations which are known to exhibit variable year class strength.
Lake Michigan, Status: Fair, Trend: Deteriorating
Based on the two metrics of this sub-indicator. Lake Michigan prey fish status remains fair, however trends suggest
the community is changing in ways that are inconsistent with the stated fish community objectives. The decline in
proportion of native species was primarily driven by decreased proportions of bloater and increased proportions of
non-native round goby. Diversity index declines were the result of round goby and alewife comprising
proportionally more of the catch and proportional declines in bloater and slimy sculpin, although the current
diversity index is similar to the long term average. Recently, declines in Lake Michigan prey fish abundance
(primarily alewife) have caused resource management to reduce native and sport fish stocking levels in an effort to
reduce predation on prey fish populations and maintain sport fisheries (Tsehaye et al., 2014).
Lake Huron Status: Fair, Trend: Undetermined
Across the entire period of observation the Lake Huron prey fish community has arguably seen the most change.
The prey fish community was dominated by non-native alewife and rainbow smelt from the 1970s through the early
2000s then abruptly shifted to a community dominated by native bloater after alewife populations severely declined
(Dunlop and Riley, 2013). This change has been attributed to physical factors, bottom-up influences of reduced
mineral nutrients, proliferation of dreissenids mussels, as well as top-down forces by increasing populations of
naturally reproduced piscivorous lake trout and Pacific Salmon (Dunlop and Riley, 2013; Kao et al., 2016).
Interestingly, this shift towards a more native community has also resulted in an overall decline in prey fish diversity
as measured by the index used in this analysis. The diversity decline is also partly driven by the decline of
deepwater sculpin in bottom trawls. This species historically comprised approximately 5% of the community
biomass but has declined to 1% of the total.
Lake Erie Status: Fair, Trend: Improving
Lake Erie status, as the wannest and most nutrient-rich Great Lake, likely explains the high prey fish diversity
observed since 1990. Although variable, the proportion of native species observed in bottom trawls has generally
increased over the period of observation although some specific native species are generally in decline such as Silver
Chub (McKenna Jr and Castiglione, 2014). It is important to note that bottom trawl observations of prey fish from
Lake Erie are based on basin-specific surveys by various agencies. Results are reported according to a lake-wide
standardized numerical density as opposed to surveys from other lakes that are reported as biomass density.
Lake Ontario, Status: Poor, Trend: Unchanging
Over the period of observation the Lake Ontario prey fish community has been dominated by a single, non-native
species, alewife. This results in low and unchanging metrics for prey fish diversity and proportion of native species
between this and the previous reporting periods. Across the time series the proportional importance of alewife
increased from 50-65% of the community to more recently over 96% of the prey fish community. This change was
primarily driven by a steady decrease in the proportional importance of non-native rainbow smelt. The benthic prey
fish community, once dominated by native slimy sculpin, is now primarily composed of non-native round goby with
lower abundances of slimy sculpin and the rebounding native deepwater sculpin. Alewife's dominance drives both
reported metrics to low values but their high abundance supports abundant and fast-growing populations of stocked
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STATE OF THE GREAT LAKES 201 7
lake trout and Pacific Salmon. Active management efforts to improve Lake Ontario prey fish diversity and restore
native species began in 2012. Efforts included reintroducing previously-extirpated bloater from Lake Michigan to
Lake Ontario and enhancing the remnant native Cisco population by stocking historically-important spawning
locations.
Linkages:
As an intermediate trophic level within Great Lakes food webs, prey fish are closely linked with many of the other
sub-indicators including those addressing nutrients, physical properties, lower tropic levels and predators. Some
examples of those linkages include:
•	Nutrients in Lakes - fuels the food web supporting prey fish
•	Zooplankton -primary food of most prey fish
•	Benthos - benthic invertebrates are primary food of some prey fish
•	Diporeia - important food of some prey fishes, generally declining
•	Dreissenid mussels -provide food for round goby, alter lower trophic levels that support prey fish
•	Surface Water Temperature - drives prey fish energetics and behavior
•	Water Levels - regulator of habitat and spawning habitat
•	Lake Trout - native predator of prey fish
•	Walleye - native predator of prey fish
Comments from the Author(s)
This sub-indicator report is one of the first to provide readily-interpretable, consistent metrics that illustrate prey fish
status across all five Great Lakes. Focusing on prey fish diversity and the proportion of native species across the
basin, this report builds on our understanding of Great Lakes prey fish dynamics such as those illustrated in
aggregate across lakes (Bunnell et al. 2014) or by individual species in each lake such as those illustrated in Gorman
and Weidel (2015). Diversity in both prey fish communities and how they are surveyed across the basin make it
difficult to compare their status along a common gradient. The metrics reported herein were selected based on the
availability of similar data from each lake and common elements found in each of the Lake Committee-created Fish
Community Objectives. For example, the terms diverse or diversity appear in each of the respective lake Fish
Community Objectives. Similarly, the importance of native or indigenous prey fish species is directly referenced or
mentioned in supporting principals of four of the five Fish Community Objectives. In contrast to previous prey fish
indicator reports, prey fish abundance was not directly used as a specific judging metric. Prey fish abundance
depends heavily on intentionally-implemented management actions, specifically nutrient load reductions and
piscivore stocking. These actions improved Great Lakes ecosystems and their services however their success
naturally resulted in reduced prey fish abundance, confounding the utility of abundance as an indicator.
A number of factors likely influence the data and results used to judge this sub-indicator including how the data
were collected, the use of raw or model-based estimates, the metrics chosen, and the thresholds used to create
categories. Data used to judge this sub-indicator came from bottom trawls, however these gears do not catch all
species in equal proportion to their true abundance (catchability) and that catchability can be altered by the
enviromnent (Kocovsky and Stapanian, 2011). Most notably the proportional importance of pelagic species
including alewife, rainbow smelt, bloater and cisco is likely under represented by these gear types. Warner et al.
2015 noted that yearly Lake Michigan alewife biomass estimates generated by acoustic surveys were 4.5 times
greater than bottom trawl-based estimates across a 20+ year time series. In Lake Superior acoustic surveys yielded
greater abundances and more precise estimates of Cisco as compared to bottom trawls (Stockwell et al., 2006). In
addition, seasonal survey timing and methodologies likely influence interpretations. Weidel et al. (2015) illustrated
the biomass density of round goby in Lake Ontario differed by an order of magnitude (lOx) between a spring survey
that used a trawl designed to avoid Dreissena mussels and a fall survey that employed the more traditional bottom
trawl. Admittedly, the choice of metrics to illustrate prey fish community diversity is imperfect and intended serve
as a starting point from which to improve. While the Shannon index is commonly applied to describe "diversity" it
has both notable flaws and utility (Hurlbert, 1971; Jost, 2006). Finally, theoretical or widely-agreed upon thresholds
for what constitutes a prey fish community as 'good', 'fair', or 'poor' do not exist. Future indicator-type reports
would benefit from thoughtful discussion and thorough examination of how these potential sources of bias and
threshold choices influence this sub-indicator and our understanding of prey fish in the Great Lakes.
An important component missing from this sub-indicator but conspicuous across the prey fish-related Fish
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STATE OF THE GREAT LAKES 201 7
Community Objectives is the idea of managing prey fish in balance with their food supply or the number of
predators. Potential metrics that could be used in future reports to 'judge' this balance include predatonprey biomass
ratios or a simpler approach that uses the condition (fatness) or relative weights of prey fish and predators as
integrated indicators of predator prey balance.
Assessing Data Quality:
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not
Applicable
1. Data are documented, validated, or
quality-assured by a recognized agency or
organization
X





2. Data are traceable to original sources

X




3. The source of the data is a known,
reliable and respected generator of data
X





4. Geographic coverage and scale of data
are appropriate to the Great Lakes basin


X



5. Data obtained from sources within the
U.S. are comparable to those from
Canada



X


6. Uncertainty and variability in the data
are documented and within acceptable
limits for this sub-indicator report


X



Acknowledgments:
Principal Authors:
Brian C. Weidel, USGS, Great Lakes Science Center, Lake Ontario Biological Station
Erin Dunlop, Ontario Ministry of Natural Resources and Forestry
Contributing Authors:
Maureen G. Walsh, USGS, Great Lakes Science Center, Lake Ontario Biological Station
James Markham, Michael Connerton, New York Department of Environmental Conservation
Jeremy Holden, Ontario Ministry of Natural Resources and Forestry
Edward F. Roseman, Timothy O'Brien, David B. Bunnell, Dave Warner, and Charles P. Madenjian, USGS, Great
Lakes Science Center, Ann Arbor
Mark Vinson, Dan Yule, Owen Gorman. USGS, Great Lakes Science Center, Lake Superior Biological Station
Patrick Kocovsky, Richard Kraus, Mark Rogers- USGS, Great Lakes Science Center, Lake Erie Biological Station
JohnDeller, Eric Wiemer- Ohio Department of Natural Resources
Contributors:
U.S. Geological Survey
Ontario Ministry of Natural Resources and Forestry
Ohio Department of Natural Resources, Division of Wildlife
New York State Department of Enviromnental Conservation
Lake Erie Forage Task Group of the Lake Erie Committee
Information Sources:
Bunnell, D.B., Barbiero, R.P., Ludsin, S.A., Madenjian, C.P., Warren, G.J., Dolan, D.M., Brenden, T.O., Briland,
R., Gorman, O.T., He, J.X., Johengen, T.H., Lantry, B.F., Lesht, B.M., Nalepa, T.F., Riley, S.C., Riseng, C.M.,
Treska, T.J., Tsehaye, I., Walsh, M.G., Warner, D.M., Weidel, B.C., 2014. Changing Ecosystem Dynamics in the
Laurentian Great Lakes: Bottom-Up and Top-Down Regulation. Bioscience 64, 26-39. doi: 10.1093/biosci/bit001
Chapra, S.C., Dolan, D.M., 2012. Great Lakes total phosphorus revisited: 2. Mass balance modeling. J. Gt. Lakes
Page 258
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STATE OF THE GREAT LAKES 201 7
Res. 38, 741-754.
Dunlop, E.S., Riley, S.C., 2013. The contribution of cold winter temperatures to the 2003 alewife population
collapse in Lake Huron. J. Gt. Lakes Res. 39, 682-689. doi:10.1016/j.jglr.2013.08.001
Fisher, J.P., Fitzsimons, J.D., Combs Jr, G.F., Spitsbergen, J.M., 1996. Naturally occurring thiamine deficiency
causing reproductive failure in Finger Lakes Atlantic salmon and Great Lakes lake trout. Trans. Am. Fish. Soc. 125,
167-178.
Fitzsimons, J.D., Williston, B., Williston, G., Brown, L„ El-Shaarawi, A., Vandenbyllaardt, L„ Honeyfeld, D„
Tillitt, D„ Wolgamood, M„ Brown, S.B., 2007. Egg thiamine status of Lake Ontario salmonines 1995-2004 with
emphasis on lake trout. J. Gt. Lakes Res. 33, 93-103.
Gorman O.T., Weidel, B.C., 2015. Cross Lakes Prey Fish Report.
Honeyfield, D.C., Hinterkopf, J.P., Fitzsimons, J.D., Tillitt, D.E., Zajicek, J.L., Brown, S.B., 2005. Development of
thiamine deficiencies and early mortality syndrome in lake trout by feeding experimental and feral fish diets
containing thiaminase. J. Aquat. Anim. Health 17, 4-12.
Hurlbert, S.H., 1971. The Nonconcept of Species Diversity: A Critique and Alternative Parameters. Ecology 52,
577-586. doi:10.2307/1934145
Jost, L„ 2006. Entropy and diversity. Oikos 113, 363-375. doi:10.1111/j.2006.0030-1299.14714.x
Kao, Y.-C., Adlerstein, S.A., Rutherford, E.S., 2016. Assessment of Top-Down and Bottom-Up Controls on the
Collapse of Alewives (Alosa pseudoharengus) in Lake Huron. Ecosystems. doi:10.1007/sl0021-016-9969-y
Kocovsky, P.M., Stapanian, M.A., 2011. Influence of Dreissenid Mussels on Catchability of Benthic Fishes in
Bottom Trawls. Trans. Am. Fish. Soc. 140, 1565-1573. doi: 10.1080/00028487.2011.639271
Kowalski, K.P., Wiley, M.J., Wilcox, D.A., 2014. Fish assemblages, connectivity, and habitat rehabilitation in a
diked Great Lakes coastal wetland complex. Trans. Am. Fish. Soc. 143, 1130-1142.
McKenna Jr, J.E., Castiglione, C., 2014. Model Distribution of Silver Chub (Macrhybopsis storeriana) in Western
Lake Erie. Am. Midi. Nat. 171, 301-310.
Tillitt, D.E., Zajicek, J.L., Brown, S.B., Brown, L.R., Fitzsimons, J.D., Honeyfield, D.C., Holey, M.E., Wright,
G.M., 2005. Thiamine and thiaminase status in forage fish of salmonines from Lake Michigan. J. Aquat. Anim.
Health 17, 13-25.
Tsehaye, I., Jones, M.L., Brenden, T.O., Bence, J.R., Claramunt, R.M., 2014. Changes in the Salmonine Community
of Lake Michigan and Their Implications for Predator-Prey Balance. Trans. Am. Fish. Soc. 143, 420-437.
doi: 10.1080/00028487.2013.862176
List of Tables:
Table 1. Diversity index status and trends for Great Lakes prey fish. Diversity is represented by the Shannon
index and status categories are based on the average value of the current reporting period (2011-2014) relative
to the maximum value observed in the time series for a given lake. To attain as status of 'Good' the current
period average diversity index must be 75% or more of the maximum value observed in the time series;
similarly, the 'Poor' status represents average values that are less than 25% of the maximum observed index
value. Trend judgement is based on comparisons between the current and previous period (2008-2010) average.
Table 2. The proportion of native species in the bottom trawl prey fish samples describes the status and trends
for Great Lakes prey fish. For this sub-indicator's categorization, status categories are 'Good' if the average
proportion native for the current period (2011-2014) is equal to or greater than 75% and 'Poor' if that value is
less than 25%, and 'Fair' otherwise.
Table 3. Overall assessment for prey fish communities of the Great Lakes as determined by the community diversity
index and proportion native species.
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STATE OF THE GREAT LAKES 201 7
List of Figures:
Figure 1. Shannon Diversity index values for Great Lakes prey fish communities.
Source: Data primarily derive from bottom trawl surveys conducted by US federal and state and Canadian provincial
agencies.
Figure 2. Proportion of native species in Great Lakes prey fish communities.
Source: Data primarily derive from bottom trawl surveys conducted by US federal and state and Canadian provincial
agencies.
Last Updated:
State of the Great Lakes 2017 Technical Report
Lake
Percent of
Current Avg.
Previous Avg.
Long term
Status
Trend

maximum
(2011-2014)
(2008-2010)
Avg.


Superior
79%
1.33
1.27
1.26
Good
Unchanging
Michigan
72%
1.23
1.60
1.17
Fair
Deteriorating
Huron
47%
0.73
0.76
1.08
Fair
Deteriorating
Erie
77%
1.60
1.70
1.60
Good
Unchanging
Ontario
25%
0.25
0.31
0.57
Fair
Deteriorating
Table 1. Diversity index status and trends for Great Lakes prey fish. Diversity is represented by the Shannon
index and status categories are based on the average value of the current reporting period (2011-2014) relative
to the maximum value observed in the time series for a given lake. To attain as status of 'Good' the current
period average diversity index must be 75% or more of the maximum value observed in the time series;
similarly, the 'Poor' status represents average values that are less than 25% of the maximum observed index
value. Trend judgement is based on comparisons between the current and previous period (2008-2010) average.
Lake
Current
Previous
Long term
Status
Trend
Superior
87%
83%
83%
Good
Improving
Michigan
48%
64%
64%
Fair
Deteriorating
Huron
77%
69%
36%
Good
Improving
Erie
49%
30%
35%
Fair
Improving
Ontario
1%
1%
5%
Poor
Unchanging
Table 2. The proportion of native species in the bottom trawl prey fish samples
describes the status and trends for Great Lakes prey fish. For this sub-
indicator's categorization, status categories are 'Good' if the average
proportion native for the current period (2011-2014) is equal to or greater than
75% and 'Poor' if that value is less than 25%, and 'Fair' otherwise.
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STATE OF THE GREAT LAKES 201 7
Lake
Status
Trend
Superior
Good
Unchanging
Michigan
Fair
Deteriorating
Huron
Fair
Undetermined
Erie
Fair
Improving
Ontario
Poor
Deteriorating
Table 3. Overall assessment for prey fish communities of
the Great Lakes as determined by the community
diversity index and proportion native species.
	Su perior
	Michigan
—Huron
Erie
Ontario
0
1972 1977 19S2 1987 1992 1997 2002 2007 2012
Figure 1. Shannon Diversity index values for Great Lakes prey fish communities.
Source: Data primarily derive from bottom trawl surveys conducted by US federal and state and Canadian provincial
agencies.
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STATE OF THE GREAT LAKES 201 7
100%
90%
80%
•§ 70%
| 60%
1	50%
c
¦|	40%
£	30%
CL
20%
10%
1982
1992
2002
2012
0%
1972
	Superior
Michigan
Huron
Erie
Ontario
Figure 2. Proportion of native species in Great Lakes prey fish communities.
Source: Data primarily derive from bottom trawl surveys conducted by US federal and state and Canadian provincial
agencies.
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STATE OF THE GREAT LAKES 201 7
Sub-Indicator: Lake Sturgeon
Overall Assessment
Status: Poor
Trend: Improving
Rationale: There are remnant populations of Lake Sturgeon in each basin of the Great Lakes, but few of
these populations are large. Progress continues as agencies learn more about population status in many trib-
utaries and the Great Lakes proper. Confirmed observations and captures of Lake Sturgeon continue to in-
crease in all lakes. Stocking is contributing to increased abundance in some areas. The trend for the overall
and lake-by-lake assessments are improving over the last ten years based on increased observations, stocking,
and habitat restoration efforts. There remains a need for information on some remnant spawning popula-
tions. In many areas habitat restoration is needed because spawning and rearing habitat has been destroyed
or altered, or access to it has been blocked.
Lake-by-Lake Assessment
Lake Superior
Status: Poor
Trend: Improving
Rationale: Populations meet all rehabilitation criteria in two Lake Superior tributaries and most criteria in four other
rivers. Reproduction occurs in at least 10 tributaries and Lake Nipigon. Abundance is increasing through natural
reproduction and limited stocking.
Lake Michigan
Status: Poor
Trend: Improving
Rationale: Remnant populations persist in at least nine tributaries. Natural recruitment supports stable or growing
populations in at least four of these. Streamside hatcheries are being used to rear and stock fingerlings to help reha-
bilitate two populations and reintroduce populations to four other rivers.
Lake Huron (including St. Mary's River)
Status: Poor
Trend: Improving
Rationale: Consistent Lake Sturgeon spawning occurs in five tributaries, the Garden, Mississaugi, Spanish and
Nottawasaga Rivers, as well as at the upper St. Clair River. Stocks of mixed sizes are consistently captured in the
North Channel, Georgian Bay, southern Lake Huron and Saginaw Bay.
Lake Erie (including the St. Clair, Detroit, and Niagara rivers)
Status: Poor
Trend: Improving
Rationale: Lakewide incidental catches since 1992 indicate a possible improvement in their status in Lake Erie.
Spawning occurs in the Detroit and St. Clair Rivers, connecting Lakes Huron and Erie and habitat restoration efforts
in this system have created an additional five spawning locations over the last ten years. Spawning is suspected in
Buffalo Harbor and the upper Niagara River, connecting Lakes Ontario and Erie. A restoration plan and stocking
program are being developed for the Maumee River.
Lake Ontario (including the Niagara and St. Lawrence rivers)
Status: Poor
Trend: Improving
Rationale: Lakewide incidental catches since 1995 indicate a possible improvement in their status. Spawning oc-
curs in the lower Niagara River, Trent River, and Black River. There are sizeable populations within the Ottawa and
St. Lawrence River systems. Stocking for restoration began in 1995 in New York.
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STATE OF THE GREAT LAKES 2017
Sub-Indicator Purpose
• The purpose of this sub-indicator is to measures status and trends in population abundance of key life
stages, distribution, habitat utilization, and recruitment of Lake Sturgeon in the Great Lakes and their
connecting waterways and tributaries. Lake Sturgeon are representative of healthy fish communities in
major habitats of the Great Lakes and support valuable fisheries in the Great Lakes and that reflect
ecosystem health through their roles in the aquatic food web.
Ecosystem Objective
Conserve, enhance, or rehabilitate self-sustaining populations of Lake Sturgeon where the species historically oc-
curred and at a level that will permit all state, provincial and federal de-listings of classifications that derive from
degraded or impaired populations (e.g. threatened, endangered or at risk species).
This sub-indicator best supports work towards General Objective #5 of the 2012 Great Lakes Water Quality Agree-
ment which states that the Waters of the Great Lakes should "support healthy and productive wetlands and other
habitats to sustain resilient populations of native species."
Ecological Condition
Background
Lake Sturgeon (Acipenser fulvescens) were historically abundant in the Great Lakes with spawning populations us-
ing many of the major tributaries, connecting waters, and shoal areas across the basin. Prior to European settlement
of the region, they were a dominant component of the nearshore benthivore fish community, with populations esti-
mated in the millions in each of the Great Lakes (Baldwin el al. 1979). In the mid- to late 1800s, they contributed
significantly as a commercial species ranking among the five most abundant species in the commercial catch (Bald-
win el al. 1979, Figure 1).
The decline of Lake Sturgeon populations in the Great Lakes was rapid and commensurate with habitat destruction,
degraded water quality, and intensive fishing associated with settlement and development of the region. Sturgeon
were initially considered a nuisance species of little value by European settlers, but by the mid-1800s, their value as
a commercial species began to be recognized and a lucrative fishery developed. In less than 50 years, their abun-
dance had declined sharply, and since 1900, they have remained a highly depleted species of little consequence to
the commercial fishery. Sturgeon are now extirpated from many tributaries and waters where they once spawned and
flourished (Figures 2-7). They are considered rare, endangered, threatened, or of watch or special concern status by
the various Great Lakes fisheries management agencies. Their harvest is currently prohibited or highly regulated in
waters of the Great Lakes.
Status of Lake Sturgeon
Efforts continue by many agencies and organizations to gather information on remnant spawning populations in the
Great Lakes. Most sturgeon populations continue to sustain themselves at a small fraction of their historical abun-
dance. In many systems, access to spawning habitat lias been blocked and other habitats have been altered. Howev-
er, there are remnant populations in each basin of the Great Lakes and some of these populations are large in number
(tens of thousands of fish Figures 3-7). Genetic analysis has shown that Great Lakes populations are regionally
structured and show significant diversity within and among lakes (DeHaan el al. 2006, Welsh el al. 2008).
Lake Superior
The fish community of Lake Superior remains relatively intact in comparison to the other Great Lakes (Bronte et al.
2003). Historic and current information indicate that at least 21 Lake Superior tributaries supported spawning Lake
Sturgeon populations (Holey et al 2000; Quinlan 2007). Successful reproduction was confirmed in the St. Louis
River in spring 2011 through capture of larval sturgeon. In the White River, Ontario successful spawning was im-
plied through the identification of a staging and spawning location (C. Avery, AOFRC, pers. comm.). Lake Stur-
geons currently reproduce in 11 Lake Superior tributaries. The Lake Sturgeon Rehabilitation Plan for Lake Superior
(Auer 2003) serves as the guiding document for agency activities. Populations in the Sturgeon River, Michigan, and
Bad River, Wisconsin, meet rehabilitation plan criteria for self-sustaining populations (Auer 2003, Auer and Baker
2007, GLIFWC unpublished data, Quinlan 2007, Quinlan et al. 2010). Improvements in assessment techniques have
provided better estimates of lakewide abundance (Auer and Baker 2007, Scliram 2007, and GLIFWC unpublished
data). The estimated combined annual spawning run population size in the Bad and White rivers, Wisconsin, was
844 individuals, 666 in the Bad River and 178 in the White River (Schloesser and Quinlan 2011). The estimated
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STATE OF THE GREAT LAKES 2017
number of Lake Sturgeon in annual spawning runs in the Sturgeon River, Michigan range from 350 to 400 adults
(Auer and Baker 2007). The abundance of juvenile Lake Sturgeon was estimated at 4,977 (95% CI 3,295-7,517) in
Goulais Bay, eastern Lake Superior (Pratt et al. 2014). Lake Sturgeon abundance in Goulais Bay is the highest
measured in Lake Superior (Schloesser 2014). Stocking in the St. Louis River, Minnesota and Ontonagon River,
Michigan have resulted in increases in abundance in localized areas. Genetic analysis has shown that Lake Sturgeon
populations in most areas of Lake Superior, except eastern waters, are distinct from one another and significantly
different from those in the other Great Lakes (Welsh et al. 2008).
Studies and assessments continue in embayments and nearshore waters associated with each of the 21 historic
spawning tributaries. A key study on the Kaministiquia River, Ontario, examined the effect of controlled flow re-
gimes at Kakabeka Falls on the migratory behavior and reproductive response of Lake Sturgeon from 2002-2009
(Friday 2009). Habitat (substrate type and water depth) for adult and juvenile fish was geo-referenced and quantified
using hydroacoustics in the Kaministiquia River, Ontario (Biberhofer and Prokopec 2005) and Bad River (Cholwek
et al. 2005). Habitat preference of stocked sturgeon in the Ontonagon and St. Louis rivers was described using radio
telemetry (Fillmore 2003, 1854 Treaty Authority unpublished data). Due to potential for overexploitation, sport fish-
ing regulations in Ontario waters have been changed to eliminate harvest. There remains a prohibition of commer-
cial harvest of Lake Sturgeon in Lake Superior. Regulation of recreational and subsistence/home use harvest in Lake
Superior varies by agency.
In 2011 and 2016, fishery agencies conducted coordinated lakewide Lake Sturgeon index surveys to evaluate trends
in abundance and biological characteristics associated with all known current and historic Lake Sturgeon popula-
tions. Despite progress, challenges remain. Spawning runs are absent in 10 of 21 historic spawning tributaries, and
data gathered lias provided evidence for only two populations to meet targets identified in the 2003 Rehabilitation
Plan. Overall, Lake Sturgeon abundance remains a small fraction of historical abundance, estimated at 870,000
(Hay-Climielewski and Whelan 1997).
Lake Michigan
Sturgeon populations in Lake Michigan continue to sustain themselves at a small fraction of their historical abun-
dance. An optimistic estimate of the lakewide adult abundance is less than 10,000 fish, well below 1% of the most
conservative estimates of historic abundance (Hay-Climielewski and Whelan 1997). Remnant populations currently
are known to spawn in waters of at least nine tributaries having unimpeded connections to Lake Michigan
(Sclineeberger et al. 2005, Elliott 2008, Clapp et al. 2012). Two rivers, the Menominee and Peshtigo, appear to sup-
port annual spawning runs of 200 or more adults. Six rivers, the Manistee, Muskegon, Grand, Kalamazoo, Fox
and Oconto, appear to support annual spawning runs of between 20 and 100 adults, and smaller numbers of sturgeon
in spawning condition have been captured or observed in the lower Mansitique and St. Joseph rivers (Baker 2006;
Elliott and Gundennan 2008; K. Smith, unpublished data). Successful reproduction has been documented in eight of
these rivers, and age 0 juveniles can be captured regularly in many of these rivers. Recent recruitment estimates
have been made from research efforts in the Peshtigo River indicating that in some years, several hundred fall re-
cruits are produced from that system (Caroffino et al. 2007), and research and assessment efforts in the Manistee
and Muskegon rivers indicate significant recruitment from those systems as well (K. Smith, MDNR, personal com-
munication). In addition, abundance of spawners in some rivers appears to have increased in the last decade, indicat-
ing that increased recruitment may have been occurring for several years in some rivers. Some Lake Sturgeon have
been observed during spawning times in a few other Lake Michigan tributaries such as the Cedar, Millecoquins and
Boardman rivers, and near some shoal areas where sturgeon are thought to have spawned historically, but it is not
known if spawning occurs in these systems. A large self-sustaining population exists in the Lake Winnebago system
upstream of the lower Fox River. This population spawns in the Wolf and Upper Fox rivers and supports an active
winter recreational spear fishery. The upper Menominee River also supports two self-sustaining populations which
are separated from each other and from the lower Menominee River population by several dams. These populations
also support a limited hook and line fishery in the fall of each year.
Active management in the form of reintroduction and rearing assistance stocking has been implemented in seven
Lake Michigan basin tributaries. To date, over 30,000 fingerling sturgeon have been stocked into these rivers using
Streamside Rearing Facilities. Since 2005, Lake Sturgeon have been reared from eggs to fingerling size using
streamside hatcheries and stocked into the Milwaukee, Kewaunee, Cedar and Wliitefish rivers, all rivers where stur-
geon were considered extirpated for some time. Streamside rearing facilities have also been used on the Manistee
River (since 2003, Holtgren et al 2007) and the Kalamazoo River (since 2011) to rear fingerling sturgeon from wild
fertilized eggs and larva collected from these rivers to help increase survival during early development and boost
population growth. Over the next 20-25 years, these stocking efforts are intended to rebuild self-sustaining popula-
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STATE OF THE GREAT LAKES 2017
tions that use these rivers to spawn. Stocking has also occurred in the upper Menominee River for many years and in
portions of the Winnebago system. Though limited recreational harvest is allowed in both the upper Menominee
River and the Winnebago system, no harvest is allowed from other Lake Michigan tributaries or from Lake Michi-
gan. Habitat evaluations have been conducted in many sturgeon tributaries within the Lake Michigan basin (Daugh-
erty et al. 2008) and have guided habitat and flow restoration projects and fish passage via dam removal and instal-
lation of fish passage facilities. A fish elevator for upstream passage and downstream bypass facilities began opera-
tion on the lower Menominee River in 2015.
Lake Huron
Lake Sturgeon populations continue to be well below estimated historical levels. Spawning has been identified in the
Garden, Mississaugi and Spanish rivers in the North Channel, and in the Moon, Musquash, and Nottawasaga rivers
in Georgian Bay. Spawning also continues to occur at the mouth of the St. Clair River in southern Lake Huron.
Spawning surveys in the Mississaugi and Nottawasaga Rivers have consistently captured hundreds of Lake Sturgeon
while over 50 fish are commonly captured during surveys in the Spanish River. The spawning population at the
mouth of the St. Clair River in southern Lake Huron contains one of the largest populations of Lake Sturgeon in the
Great Lakes with an estimated population near 30,000 individuals (Chiotti et al. 2013). Research in the Saginaw
River watershed in 2005 - 2007 indicated that Lake Sturgeon are no longer spawning in that watershed, although
sufficient spawning habitat does exist below the Dow Dam (Midland, MI) on the Tittabawassee River, and below
the Hamilton Dam (Flint, MI) on the Flint River (Boase 2007). Also, creation of rock ramps at the Chesaning Dam
(Chesaning, MI) on the Shiawassee River and Frankemnuth Dam (Frankenmuth, MI) on the Cass River now allows
Lake Sturgeon passage and access to approximately 40 miles (64 kilometres) and 73 miles (117 kilometres), respec-
tively above each former dam site. Research since 2007 on the St. Marys River system has yet to determine a
spawning stock of Lake Sturgeon, however anecdotal evidence of spawning behavior exists (A. Moerke, LSSU,
personal communication) Spawning activity has been observed in a number of new locations including the Moon
and Musquash rivers in eastern Georgian Bay and the Manitou River on Manitoulin Island. Barriers and habitat
degradation in Michigan's and Ontario's tributaries to Lake Huron continue to be a major impediment to successful
rehabilitation in Lake Huron.
Stocks of Lake Sturgeon in Lake Huron are monitored by various resource management agencies along with the
volunteer efforts of commercial fishers. To date the combined efforts of researchers in U.S. and Canadian waters
have resulted in over 7,000 sturgeon tagged in Saginaw Bay, southern Lake Huron, Georgian Bay and the North
Channel, with relatively large stocks of mixed sizes being captured at each of these general locations. Tag recover-
ies, telemetry studies, and genetic collections indicate that Lake Sturgeon are moving within and between jurisdic-
tional boundaries and between lake basins. There is currently no commercial or recreational harvest of Lake Stur-
geon in Lake Huron. Regulation of subsistence harvest in Lake Huron varies by agency and is largely unknown.
In an effort to assess basin-wide juvenile abundance in Lake Huron, eleven tributaries were sampled in 2012 and
2013 using a protocol successful in capturing juvenile Lake Sturgeon in Lake Superior (Schloesser el al. 2014).
Nine of tributaries were sampled in Ontario and two in Michigan. Juvenile Lake Sturgeon were captured at four of
these tributaries including the Blind, Echo, Serpent, and Spanish rivers all located in the North Channel. The devel-
opment of a juvenile index to assess the status of Lake Sturgeon in Lake Huron continues to be of interest to man-
agement agencies.
In an effort to understand the migration patterns of Lake Sturgeon in southern Lake Huron and the St. Clair River,
126 adult Lake Sturgeon have been implanted with acoustic transmitters. Utilizing the Great Lakes Acoustic Te-
lemetry Observation System (GLATOS) over four million detections have been documented since 2011, providing
valuable information regarding the movements of adult Lake Sturgeon in Lake Huron and the St. Clair River system
(Hondorp et al. 2015).
Lake Erie
Lake Sturgeon populations continue to be well below historical levels with the exception of the stocks located in the
St. Clair - Detroit River System. Spawning lias been identified at seven locations in the connecting waters between
lakes Huron and Erie (Caswell et al. 2004; Manny and Kennedy 2002; Roseman et al. 2011) and is likely occurring
in Buffalo Harbor and the upper Niagara River (Legard 2015). Three new spawning sites have been identified in the
St. Clair River resulting from artificial reef restoration projects aimed at removing the loss of fish and wildlife habi-
tat beneficial use impairment (BUI) in this river (E. Roseman, USGS, personal communication). Tag recovery data
and telemetry research indicate that a robust Lake Sturgeon stock of approximately 11,000 fish reside in the North
Channel of the St. Clair River and Lake St. Clair (Thomas and Haas 2002; Chiotti et al. 2013). The spawning popu-
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STATE OF THE GREAT LAKES 2017
lation in the upper St. Clair River near Port Huron, Michigan contains one of the largest populations of Lake Stur-
geon in the Great Lakes with an estimated population near 30,000 individuals (Chiotti et al. 2013). The North Chan-
nel of the St. Clair River, Anchor Bay in Lake St. Clair, the Detroit River (East of Fighting Island), and the western
basin of Lake Erie have been identified as nursery areas as indicated by consistent catches in commercial and survey
fishing gears. The upper Niagara River is a suspected nursery area based on reports from anglers and divers (C. Le-
gard NYSDEC, personal communication). However, a dedicated Lake Sturgeon survey has not been done in the
upper Niagara River to confirm to these reports. In the central and eastern basins of Lake Erie, Lake Sturgeon are
scarcer with only occasional catches of sub-adult or adult Lake Sturgeon in commercial and research fishing nets.
Survey work conducted in 2005 and 2006 indicated that no Lake Sturgeon spawning is taking place in the Maumee
River, Ohio (J. Boase, USFWS, personal communication). A habitat suitability model and restoration plan is cur-
rently being developed for the Maumee River to assess reintroduction efforts (Sherman et al. 2015). An observed
concentration of sturgeon in the spring of 2009 and subsequent sampling through 2015 in Buffalo Harbor has yield-
ed maturing and sexually mature adult and sub-adult Lake Sturgeon suggesting spawning is occurring in the area.
Sidescan sonar imagery for a roughly seven square mile (18 square kilometres) section of Buffalo Harbor has been
collected to develop a categorical habitat map intended to identify potential sturgeon spawning habitat.
In an effort to understand the migration patterns of Lake Sturgeon in the St. Clair - Detroit River System, nearly 300
adult Lake Sturgeon have been implanted with acoustic transmitters. Utilizing the Great Lakes Acoustic Telemetry
Observation System (GLATOS) over four million detections have been documented since 2011, providing valuable
information regarding the movements of adult Lake Sturgeon in this system as well as lakes Huron and Erie (Hon-
dorp et al. 2015). In Buffalo Harbor, a total of 19 Lake Sturgeon were implanted with acoustic transmitters, nine of
which were equipped with satellite transmitters, in the spring of 2015. To date GLATOS has provided nearly five
million detections for sturgeon acoustically tagged in Buffalo Harbor.
In an effort to assess basin-wide juvenile abundance in Lake Erie, 14 tributaries were sampled in 2013 and 2014
using a protocol successful in capturing juvenile Lake Sturgeon in Lake Superior (Schloesser et al. 2014). A total of
176 nets were set and a total of 15 Lake Sturgeon were captured, all in the St. Clair - Detroit River System.
Research efforts will continue to focus on identifying rivers with suitable habitat for reintroduction efforts, identifi-
cation of spawning locations, habitat requirements, and migration patterns.
Lake Ontario/ Upper St. Lawrence River
The numbers of mature sturgeon are not well quantified for most of the spawning areas surrounding Lake Ontario;
however, some data is available to address the long term restoration indicator. Biesinger et al. (2013) reported a
mark-recapture population estimate of 2,856 (95% confidence interval of 1,637 to 5,093) mature and immature fish
for the lower Niagara River. Also, numbers of sturgeon counted at or near the two artificial spawning beds con-
structed in the vicinity of Iroquois Dam in the upper St. Lawrence River ranged between 122 and 395 at the peak of
spawning activity during 2008-2012 (NYSDEC 2013). Spawning populations also exist at Black River, NY (Klindt
and Gordon 2014), and the Trent River, ON (A. Mathers, OMNR, personal, communication); however, these popu-
lations are small - likely in the 10s to 100s of fish.
Several management actions have been taken to promote sturgeon recovery. Commercial harvest of sturgeon in
Lake Ontario and upper St. Lawrence River was banned in 1976 in New York and in 1984 in Ontario. In addition,
all recreational fishing has been closed since 1979. During the past decade artificial spawning shoals for sturgeon
have been created in the upper St. Lawrence River and their success has been evaluated showing egg deposition and
emergence of larvae (NYSDEC 2013).
Between 1993 and 2013, NYSDEC in collaboration with U.S. FWS, have stocked 85,814 (0 to 14,047 fish per year)
sturgeon into the Lake Ontario system. Gametes for these efforts were collected in St. Lawrence River (below Mo-
ses-Saunders power dam since 1996). Stocking locations extend from the Genesee River east to Lake St. Francis
tributaries. Research on sturgeon stocked in the lower Genesee River documented high level of survival and good
growth suggesting these types of habitats are highly suitable for sturgeon and also that stocking has the potential to
increase sturgeon abundance substantially (Dittman and Zollweg 2006). It is expected that spawning populations
based on stocked fish will develop in the Genesee River, as well as the Oswego River, in the next few years (Cha-
lupnicki et al. 2011).
Research will continue assessing the Lake Sturgeon spawning shoals for aggregations of adults, egg deposition and
fry emergence. Monitoring of sturgeon catches and population age structure via agency fish community assessment
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STATE OF THE GREAT LAKES 2017
programs will provide an index of population status in, and recruitment to, eastern Lake Ontario. Targeted surveys
of sturgeon in the lower Niagara River appears to be required to monitor this population. Efforts to stock sturgeon
by agencies appear to be highly successful and monitoring of its effects should continue. Because sturgeon become
sexual mature at an advanced age, a decade or more may be needed to observe responses to restoration efforts.
Linkages
•	Aquatic Habitat Connectivity - loss of aquatic connectivity has contributed to the decline of Lake Sturgeon.
Research and development are needed to determine ways for Lake Sturgeon to pass man-made barriers on
rivers.
•	Aquatic Invasive Species and Dreissenid Mussels - An additional concern for Lake Sturgeon in many of the
Great Lakes is the ecosystem changes that have resulted from high densities of invasive species such as
Dreissenid Mussels and round gobies and the presumed related exposure to Botulism Type E which has
produced measurable die-offs of Lake Sturgeon in several years since 2001.
Comments from the Author(s)
Research and development is needed to determine ways for Lake Sturgeon to pass man-made barriers on rivers. In
addition, there are significant, legal, logistical, and financial hurdles to overcome in order to restore degraded
spawning habitats in connecting waterways and tributaries to the Great Lakes. More monitoring is needed to deter-
mine the current status of Great Lakes Lake Sturgeon populations, particularly the juvenile life stage.
As monitoring programs and techniques are refined, our ability to detect Lake Sturgeon has likely increased making
it difficult to determine whether changes observed are a result of increasing populations or reflect more efficient
monitoring. Long-term standardized monitoring programs need to be developed in order to effectively assess the
status of Lake Sturgeon stocks in each lake.
It should also be noted that the overall assessment for each lake changed from fair and improving in 2011 to poor
and improving in 2016, but this is not due to deteriorating populations. Based on the status assessment measures
used in both the 2011 and 2016 reports, all of the lakes should have been assessed as poor and improving in 2011.
Assessing Data Quality
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not
Applicable
1. Data are documented, validated, or
quality-assured by a recognized agency or
organization

X




2. Data are traceable to original sources
X





3. The source of the data is a known,
reliable and respected generator of data

X




4. Geographic coverage and scale of data
are appropriate to the Great Lakes Basin

X




5. Data obtained from sources within the
U.S. are comparable to those from
Canada

X




6. Uncertainty and variability in the data
are documented and within acceptable
limits for this sub-indicator report

X




Clarifying Notes: Since the status assessment is highly dependent upon the number of self-sustaining populations within each lake
basin, the source of the data for the historical population status is currently being assessed for Lakes Huron and Erie.
For some of the Great Lakes, the 4. "Geographic coverage and scale of data" may not be appropriate.
Acknowledgments
Authors:
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STATE OF THE GREAT LAKES 2017
Justin Chiotti, U.S. FWS, Alpena Fish and Wildlife Conservation Office, Alpena, MI 49707
Robert Elliott, U.S. FWS, Green Bay Fish and Wildlife Conservation Office, New Franken, WI 54229
Henry Quinlan, U.S. FWS, Ashland Fish and Wildlife Conservation Office, Ashland, WI 54806
James Boase, U.S. FWS, Alpena Fish and Wildlife Conservation Office, Alpena, MI, 49707
Lloyd Molir, OMNRF, Upper Great Lakes Management Unit - Lake Huron, Owen Sound, ON N4K 2Z1
Dimitry Gorsky, Lower Great Lakes Fish and Wildlife Conservation Office, Basom, NY 14013
Rich Drouin OMNRF, Lake Erie Management Unit, London, ON N6E 1L3
Josh Schloesser, U.S. FWS, Ashland Fish and Wildlife Conservation Office, Ashland, WI 54806
Zy Biesinger, U. S. FWS, Lower Great Lakes Fish and Wildlife Conservation Office, Basom, NY 14013
Contributors:
Darryl Hondorp, U.S. GS, Great Lakes Science Center
Edward Roseman, U.S. GS, Great Lakes Science Center
Jessica Sherman, University of Toledo, Lake Erie Center
Michael Thomas, MDNR, Lake St. Clair Research Station
Rachel Neuenhoff, U.S. FWS, Northeast Fishery Center
Jonah Withers, U.S. FWS, Northeast Fishery Center
Lori Davis, U.S. FWS, Northeast Fishery Center
Alastair Mathers, OMNRF, Lake Ontario Management Unit
Christopher Legard, New York State Department of Enviromnental Conservation Region 9
DawnDittman, U.S. GS, Great Lakes Science Center
Ashley Moerke, Lake Superior State University, School of Biological Sciences
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B. May. 2008. Genetic assessment of Lake Sturgeon population structure in the Laurentian Great Lakes. North
Amer. J. of Fish. Manage. Vol. 28: 572-591.
Welsh, A.B., Elliott, R.F., Scribner, K.T., Quinlan, H.R., Baker, E.A., Eggold, B.T., Holtgren, J.M., Krueger, C.C.,
May, B. Genetic guidelines for the stocking of Lake Sturgeon (,Acipenser fulvescens) in the Great Lakes basin. Great
Lakes Fish. Comm. Misc. Publ. 2010-01
Welsh A., Hill, T„ Quinlan, H„ Robinson, C., and May, B. 2008. Genetic assessment of Lake Sturgeon population
structure in the Laurentian Great Lakes. North Am. J. Fish. Manage. 28: 572-591.
Zollweg, E.C., Elliott, R.F., Hill, T.D., Quinlan, H.R., Trometer, E., and Weisser, J.W. (eds.). 2003. Great Lakes
Lake Sturgeon Coordination Meeting. In Proceedings of the December 11-12, 2002 Workshop, Sault Ste. Marie, MI.
List of Figures
Figure 1. Historic Lake Sturgeon harvest from each of the Great Lakes.
Source: Baldwin et al. 1979
Figure 2. Historic distribution of Lake Sturgeon.
Source: Zollweg et al. 2003
Figure 3. Lake Sturgeon population status in Lake Superior, 2012.
Source: Lake Superior Lake Sturgeon Work Group
Figure 4. Lake Sturgeon population status in Lake Michigan, 2012.
Source: Lake Michigan Lake Sturgeon Task Group
Figure 5. Lake Sturgeon population status in Lake Huron, 2012.
Source: Lake Huron Lake Sturgeon Task Group
Figure 6. Lake Sturgeon population status in Lake Erie, 2012.
Source: Lake Erie Lake Sturgeon Working Group
Last Updated
State of the Great Lakes 2017 Technical Report
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STATE OF THE GREAT LAKES 2017
Superior
Michigan
Huron
Erie
Ontario
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c 5000
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J 4000
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Source: Baldwin etal. 1979
o
Legend
• historic distribution
— Great Lakes Shoreline
0 6?5 125
Figure 2. Historic distribution of Lake Sturgeon.
Source: Zollweg et al. 2003
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STATE OF THE GREAT LAKES 2017
Lake Sturgeon Status
Lake Superior
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Figure 3. Lake Sturgeon population status in Lake Superior, 2012.
Source: Lake Superior Lake Sturgeon Work Group


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STATE OF THE GREAT LAKES 2017
Lake Sturgeon Status
Lake Michigan
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Legend
•	G*tanit
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~	unknown
| LaKe MlcNgan snorelm*
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Lakfl Michigan stream:*
Figure 4. Lake Sturgeon population status in Lake Michigan, 2012.
Source: Lake Michigan Lake Sturgeon Task Group
Page 275
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STATE OF THE GREAT LAKES 2017
Legend
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a extirpated > reintroduced > extant
~	extirpated > reintroduced
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Inland lakes
Lake Huron streams
Lake Sturgeon Status
Lake Huron
Figure 5. Lake Sturgeon population status in Lake Huron, 2012.
Source: Lake Huron Lake Sturgeon Task Group
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STATE OF THE GREAT LAKES 2017


Lake Sturgeon Status
Lake Erie



m
y
 supplementation
a extii paled > remboduced > extant
~	extirpated > reintroduced
¦ •xlnpated
unknown > reintroduced
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Lake Erie shoreline
Luke Erie streams
Figure 6. Lake Sturgeon population status in Lake Erie, 2012.
Source: Lake Erie Lake Sturgeon Working Group
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STATE OF THE GREAT LAKES 2017
Lake Sturgeon Status
Lake Ontario
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Lake Ontario shoreline
Inland lakes
Lake Ontaiio and St, Lawience sttearns
Figure 7. Lake Sturgeon population status in Lake Ontario, Ottawa River and St. Lawrence River, 2012.
Source: New York Lake Sturgeon Working Group, and Tim Haxton. OMNRF
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STATE OF THE GREAT LAKES 201 7
Sub-Indicator: Walleye
Overall Assessment
Status: Good
Trend: Unchanging
Rationale: The health of native Walleye populations in the Great Lakes is quite variable; however, the over-
all trend is that populations are unchanging. In lakes where non-native species have been on the decline (in-
cluding alewife) and increases in productivity have been beneficial (i.e., excluding harmful algal blooms),
Walleye populations have responded favorably. Where productivity increases or other factors have been dele-
terious to ecosystem health, Walleye populations have struggled to maintain the robust levels attained previ-
ously. Recruitment trends in each Great Lake or in each localized sub-population (i.e., river, embayment or
basin) continue to play a large part in the overall health of Walleye populations. Consistent years of good re-
cruitment have helped fortify some populations, while poor recruitment trends in others have resulted in low-
er than desirable population levels over the short-term. Overall, population trends in Erie, Huron and Supe-
rior appear to be consistent (i.e., based on reported harvest) over the long-term, whereas Walleye population
has decreased in Lake Ontario but increased in Lake Michigan.
Lake-by-Lake Assessment
Lake Superior
Status: Fair
Trend: Unchanging
Rationale: Assessment efforts are continuing throughout the lake, showing signs of improving conditions in one of
the areas, but static population trends in the others. Efforts have been made throughout the lake to address manage-
ment concerns for Walleye populations including limiting commercial and recreational harvest, nearshore habitat
rehabilitation, shoreline remediation and assessment programs to identify other actions. Assessments in the connect-
ing waters have not been included due to lack of monitoring.
Lake Michigan
Status: Good
Trend: Unchanging
Rationale: On a lake-wide basis. Walleye harvest levels have met the target range set by the Lake Michigan Fish
Community Objectives (FCOs) for a sustainable harvest of 200,000 to 400,000 pounds since 2011. The average
Walleye harvest (biomass) was 311,722 pounds during 2011-2014, with a high of 357,322 pounds in 2012. This
includes a 9,357 pound average commercial harvest by the Tribal commercial fishers for the time period, as well as
the sport-caught Walleye from the four state jurisdictions. Assessments in the connecting waters have not been in-
cluded due to lack of monitoring.
Lake Huron
Status: Good
Trend: Unchanging
Rationale: The largest source of Walleye in Lake Huron is the Saginaw Bay stock which achieved recovery targets
in 2009. The recovery was fueled by the disappearance of Alewives in the lake beginning in 2003 stemming from
profound food web shifts. Walleye reproductive success soared in the absence of Alewives and recovery of this im-
portant stock was achieved. In Ontario waters, particularly Georgian Bay and to a lesser extent in the North Channel,
most Walleye stocks continue to be depressed; a situation that is being addressed with the initiation of the develop-
ment of a Walleye Management Plan for Ontario waters.
Lake Erie
Status: Good
Trend: Improving
Rationale: The Walleye population and associated fisheries in Lake Erie are managed individually by four United
States state agencies and one Canadian provincial agency. Under the auspices of the Great Lakes Fishery Commis-
sion's Lake Erie Committee (LEC), a new stakeholder process, known as the Lake Erie Percid Management Adviso-
ry Group (LEPMAG), was initiated in 2010. The purpose of the LEPMAG was to provide Lake Erie managers ad-
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STATE OF THE GREAT LAKES 2017
vice on fisheries management objectives and associated harvest policies. The work of the LEPMAG resulted in a
revised Walleye Management Plan for 2015-2019 (Kayle et al. 2015).
Lake Ontario
Status: Fair
Trend: Unchanging
Rationale: Following declines in juvenile and adult Walleye abundance in the 1990s, associated with reduced
young-of-the-year production in the mid-1990s, the Walleye population stabilized in Bay of Quinte and in Ontario
and New York waters of eastern Lake Ontario. Walleye performance targets, identified in the Bay of Quinte Fisher-
ies Management Plan (2010) and based on a post-dreissenid time-period (2002-2006), are currently being met or
exceeded. Recent hatches should keep the population at current or improved levels of abundance for the next sever-
al years. Assessments in the connecting waters have not been included due to lack of monitoring.
Other Spatial Scales
Huron-Erie Corridor (St. Clair River-Lake St. Clair-Detroit River)
Status: Fair
Trend: Unchanging
Rationale: Walleye are an important part of the recreational fishery in the Huron-Erie Corridor. This fishery has
been evaluated on an inconsistent basis and no continuous fishery data are available. The most recent Ontario creel
survey in 2009 showed that the Walleye catch and catch rate in Lake St. Clair were lower than the early 2000s and
the 1980s. However the catch and catch rates in the Detroit River remained high in the 2009 creel compared to the
2000s and early 1990s. Recent (2011-2014) catch rates in the annual voluntary angler diary program remain near the
long term average in Lake St. Clair, Detroit River, and St. Clair River.
Sub-Indicator Purpose
• The purpose of this indicator is to measure status and trends in Walleye population abundance and
recruitment in various Great Lakes habitats; to infer the status of cool water predator communities; and to
infer ecosystem health, particularly in moderately-productive (mesotrophic) areas of the Great Lakes and
through their roles in the aquatic food web.
Ecosystem Objective
Protection enhancement and restoration of historically important, mesotrophic habitats that support natural stocks of
Walleye as the top fish predator. These habitats are necessary for a stable, balanced, and properly-functioning Great
Lakes ecosystem.
This indicator best supports work towards General Objective #5 of the 2012 Great Lakes Water Quality Agreement
which states that the Waters of the Great Lakes should "support healthy and productive wetlands and other habitats
to sustain resilient populations of native species."
Ecological Condition
The historical dominance of Walleye in mesotrophic habitats in the Great Lakes provides a good basis for a basin-
wide evaluation of ecosystem health. Maintaining or re-establishing historical levels of relative abundance, biomass,
or production of self-sustaining Walleye populations throughout their native range in the Great Lakes Basin will
help ensure dominance of this species in the ecosystem and the maintenance of a desirable and balanced aquatic
community in cool water, mesotrophic habitats. Historical data can be used to develop status and trend information
on Walleye populations. Commercial catch records for Walleye in the Great Lakes extend back to the late 1800s;
recreational catch data and assessment fishing data supplement these commercial catch records in some areas in re-
cent decades and sport fishing data are especially useful in areas where the commercial fishery for the species has
been closed.
The "mesotrophic" cool-water fish community is associated with more productive waters in nearshore areas. Meso-
trophic communities, along with oligotrophic and eutrophic communities are found to varying degrees in all five of
the Great Lakes with more than half of Lake Erie represented by mesotrophic habitat.
The Walleye is the top predator in the cool nearshore and offshore waters of the Great Lakes and is selected as an
indicator because they represent one of the original fish communities in the different habitats, they have value to the
ecosystem and to fisheries, and they are the focus of fisheries management and restoration efforts. Being co-evolved
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STATE OF THE GREAT LAKES 2017
with the rest of the fish community and the natural ecosystem of the Great Lakes, Walleye represent the natural bio-
diversity of the lakes. They have been subjected to the full slate of other enviromnental effects resulted from human
disruption of the Great Lakes including habitat loss, nutrient pollution, and persistent toxic pollutants. While restora-
tion efforts like stocking can complicate interpretation of their status, the successes of these species are indicative of
progress toward the goals of the GLWQA. Walleye support large commercial and recreational fisheries throughout
Lakes Erie and Huron; consequently, trends in harvest are useful for assessing ecosystem health. However, in Lakes
Michigan, Ontario, and Superior, where Walleye are constrained to coolwater habitats, harvest information may not
be as reflective of ecosystem health as in Lakes Erie and Huron due to their limited spatial distribution. Rather, har-
vest trends may only reflect the ecosystem health of particular areas in Lakes Michigan, Ontario or Superior because
of the limited data available.
Lake Superior
Thunder Bay-Kaministiquia River contains a small but healthy self-sustaining population, with evidence of con-
sistent recruitment. In Black Bay, assessment work is showing an increase in relative abundance of Walleye (creel
results are pending). In Nipigon Bay and Nipigon River, Walleye are low in abundance, but assessment work is
showing signs of increasing density (high growth rates and low mortality). Due to limited assessment surveys, it is
difficult to assess if population targets in the St. Louis River, Bad River and Chequamegen Bay were met during this
reporting period.
Lake Michigan
Michigan and Wisconsin sport anglers are the two main user groups contributing to the sport harvest, primarily in
the northern end of the lake and Green Bay. Most of the Walleye harvested from Lake Michigan were from the wa-
ters of Green Bay. In northern Green Bay, Walleye harvest has shown a declining trend the past four years although
harvest has been steady the past two years. In southern Green Bay, harvest has increased during this period because
of good recruitment from above average young of year production in most years from 2007-2014. Walleye pro-
duced in 2013, the strongest young of year production measured in southern Green Bay since 2003, have just begun
to enter the fishery. The harvest trend in Lake Michigan appears to be steady, although data is limited.
Lake Huron
Considerable insights have been gained about the status and behavior of the Saginaw Bay stock since the resurgence
in reproductive success. A telemetry study confirmed that about half of the adult Walleyes make an annual migra-
tion to the main basin of the lake outside the bay from about May or June until returning in the fall. Bioenergetics
modeling indicates that Walleyes account for about 10% of the total prey fish consumption demand in the main ba-
sin of Lake Huron since recovery. Advanced stock assessment of the population and fisheries were conducted lead-
ing to an improved understanding of the stock's population metrics and dynamics. Models indicate the recovered
Saginaw Bay stock of Walleye ranges from 2.5 to nearly 4 million age-2 and older Walleyes in most years. From
this, a simulation model was developed enabling the evaluation of new management objectives and strategies. The
Michigan DNR, used these tools to shift management of Saginaw Bay Walleyes from a recovery strategy to one that
is based on the state of the stock with goals of achieving more full utilization within the recreational fishery and to
try and manage Walleye predation for the betterment of Yellow Perch in the bay.
Other sources of Walleye in Lake Huron trace to individual localized reproductive sources usually associated with
tributaries. In the Ontario waters, these span the watershed across Georgian Bay and the North Channel. The Ontario
Ministry of Natural Resources and Forestry has recently initiated efforts to develop a Walleye Management Plan for
Ontario waters of Lake Huron which includes a review and synthesis of historic and contemporary Walleye popula-
tion assessment data. Preliminary reviews have indicated that the status of individual Walleye stocks is variable with
a majority of stocks currently depressed compared to historic levels of abundance. Georgian Bay stocks appear to be
more depressed than those in the North Channel. In spite of the disappearance of Alewives, these localized popula-
tions have not demonstrated the same sort of recovery that was seen in Saginaw Bay. Factors limiting the abundance
of these stocks are uncertain. In some instances it may be recruitment limitations but in others it may be suppression
by high rates of total mortality. The status of Walleye in the Ontario waters of the southern main basin appear to be
stable as a consequence of these stocks being of mixed origin, primarily immigrants from Saginaw Bay and western
Lake Erie.
Overall the trend appears to be unchanging. The overall status of the Lake Huron Walleye population and fisheries
has to be characterized as "Good" given the recovery of the Saginaw Bay stock, although there is likely further im-
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STATE OF THE GREAT LAKES 2017
provement possible particularly in Ontario waters. Generally yield across all sources has not fully achieved this his-
toric average or the Fish Community Objective of 0.7 million kgs/year.
Lake Erie
Since 2011, the annual Total Allowable Catch (TAC, or fishery quota) set for the west and central basins of Lake
Erie has been gradually increasing (no TAC is set for the east basin), resulting in increased Walleye harvest in both
the sport and commercial fisheries. The commercial harvest has annually exceeded the 4 million pound manage-
ment objective as identified in the Walleye Management Plan (Kayle et al. 2015). In 2015 the projected spawner
biomass was estimated at 25.858 million kilograms, well above the 11 million kilogram limit reference point of 20%
of the unfished spawner biomass.
Across Lake Erie, the annual sport fishing effort remains below the long-term mean, but has been trending upwards
since 2011. Similar increasing trends have been observed in the sport fishing catch rates, with catch rates for all
management units at or above the long-term mean and meeting the current Walleye management objective of 0.4
Walleye/hour.
Commercial effort across the lake lias also been trending upwards over the last five years with the most dramatic
increase in effort observed in the 2014. However, effort for all management units remains below the long-term
mean. Commercial lake-wide catch rates have been trending down since 2010. The downward trends are strongest
in the west with 2014 catch rates falling below the long-term mean while catch rates in the east-end of the lake re-
main above the long-term mean and appear to be stable.
Lake Erie Walleye fisheries remain largely dependent on older fish from the 2003 and 2007 cohorts, with more re-
cent contributions by the 2010 and 2011 cohorts. Mean age of Walleye in the sport and commercial harvest contin-
ues to rise with the average age for Walleye in the commercial harvest at 7 years of age and the sport harvest at 6
years of age.
Walleye recruitment has improved since 2011 with two of the last four cohorts (2013 and 2014) being moderate to
strong year classes. It is expected that these year classes will make strong contributions to the fishery over the next
few years. The earlier 2011 and 2012 cohorts were assessed as weak and are expected to contribute little to the fish-
ery.
Some recovery and expansion is apparent in eastern basin Walleye stocks. Sport and commercial harvest and catch
rates in the east end of the lake are currently above the long-term mean. This may be the result of recent recruitment
patterns as well as the abundance of older, highly migratory stocks of Walleye from the western and central basins
of Lake Erie (Kayle et al. 2015).
Lake Ontario
Smaller, local Walleye populations exist in other areas of Lake Ontario. Some embayment areas support small but
healthy and self-sustaining populations (e.g., Wellers Bay, West Lake) while other areas with degraded habitat re-
quire on-going rehabilitation efforts (e.g., Hamilton Harbour), including Walleye stocking. Stocking to restore
Walleye populations in waters they formerly occupied serves to help diversify fish community trophic structure and
to enhance recreational fishing.
Huron-Erie Corridor (St. Clair River-Lake St. Clair-Detroit River)
The Ontario Ministry of Natural Resources and Forestry fall trap net survey shows no trend in the catch rate of
Walleye in recent years, however the catch rate has declined since the 1970s and 1980s. Similarly the Michigan
spring trap net survey shows no trend the catch rate of Walleye in recent years. The growth rate of Walleye in the
Ontario fall trap net survey has increased each decade since the survey began. The highest growth rate of Walleye
occurred from 2011-2014. Recent recruitment of Walleye in Lake St. Clair has been poor. The last year-class of
even moderate strength that was produced in Lake St. Clair was in 1986. Since then, very few age-1 Walleye have
been caught in the Ontario fall trap net survey.
Linkages
Linkages to other sub-indicators in the indicator suite include:
• Aquatic Habitat Connectivity
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STATE OF THE GREAT LAKES 2017
One of the impediments identified as a potential impediment to the continued health of Walleye populations in the
Great Lakes Basin is the connectivity between riverine spawning grounds and juvenile habitat. Often this phenome-
non may be the result of human-induced alterations (e.g., dam construction) to the landscape.
This sub-indicator also links directly to the other sub-indicators in the Habitat and Species indicator.
Comments from the Author(s)
Fishery yields (Figure 1) are appropriate indicators of Walleye health but only in a general sense. Yield was estimat-
ed for the recreational fisheries by multiplying the number of fish harvested by estimating the average size of fish
harvested and extrapolating an estimated weight of harvested fish to the total number harvested. Fishery (i.e., de-
pendent and independent) assessments are lacking for some fisheries (recreational, commercial, or tribal) in some
years for all of the studied areas. Moreover, measurement units are not standardized among fishery types (i.e., com-
mercial fisheries are measured by mass while recreational fisheries are typically measured in numbers of fish),
which means additional conversions are necessary which reduce accuracy. Also, "zero" values need to be differenti-
ated from "missing" data in any figures. Therefore, trends in fishery yields across time (blocks of years) are proba-
bly better indicators than absolute values within any year, assuming that any introduced bias is relatively constant
over time. Abundance, spawner biomass, recruitment, age/length at maturity, and fishery performance (effort, catch
rate, yield) are useful metrics for describing Great Lakes ecosystem and fishery health. However in the absence of
absolute abundance and spawner biomass estimates for all lakes, relative measures from fishery dependent (i.e., har-
vest) and independent (i.e., population assessments) are suitable metrics for reporting on Walleye population health
in the event population estimates are lacking.
Many agencies have developed, or are developing, population estimates for many Great Lakes fishes. Walleye popu-
lation estimates for selected areas (i.e.. Lakes Erie, Huron, Michigan and Ontario) would probably be a better as-
sessment of Walleye population health than harvest estimates, thus to the extent that it is possible, future efforts
should focus on developing these capabilities.
Assessing Data Quality
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not
Applicable
1. Data are documented, validated, or
quality-assured by a recognized agency or
organization

LE, LH,
LM, LO,
HEC
LS



2. Data are traceable to original sources
LE
LH, LM,
LO,
HEC
LS



3. The source of the data is a known,
reliable and respected generator of data
LE
LM, LO,
HEC
LH, LS



4. Geographic coverage and scale of data
are appropriate to the Great Lakes Basin

LE, LM,
LO
LH, LS,
HEC



5. Data obtained from sources within the
U.S. are comparable to those from
Canada

LE, LM,
HEC
LO, LS
LH


6. Uncertainty and variability in the data
are documented and within acceptable
limits for this sub-indicator report
LE
LM, LO
LH, LS,
HEC



Acknowledgments
Authors:
Jenn Richards, Ontario Ministry of Natural Resources and Forestry (OMNRF), co-editor
Christopher Vandergoot, Ohio Department of Natural Resources, co-editor
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STATE OF THE GREAT LAKES 2017
Contributors:
Lake Superior: Eric Berglund, OMNRF, eric.berglund@ontario.ca
Lake Michigan: Steve Hogler, Wisconsin Department of Natural Resources, Steven.hogler@wisconsin. gov
Lake Michigan: Troy Zorn, Michigan Department of Natural Resources (MDNR), zornt@michigan. gov
Lake Michigan: Brian Breidart, Indiana Department of Natural Resources, BBreidert@dnr.IN. gov
Lake Huron: Arunas Liskauskas, OMNRF, arunas.liskauskas@ontario.ca
Lake Huron: David Fielder, MDNR, fielderd@michigan. gov
Lake Erie: Richard Drouin, OMNRF, richard.drouina Ontario.ca
Lake Erie: Todd Wills, MDNR, willst(@,michigan.gov
Lake Ontario: Jim Hoyle, OMNRF. iim.hovle@ontario.ca
Lake Ontario: Jana Lantry, New York Department of Enviromnental Conservation, irlantrv a ,g\v. dec, state.in.us
Huron-Erie Corridor: Megan Belore, OMNR, megan.belore@ontario.ca
Information Sources
Kayle, K„ K. Oldenburg, C. Murray, J. Francis, and J. Markham. 2015. Lake Erie Walleye Management Plan
2015-2019. Great Lakes Fishery Commission,
http://www.glfc.org/lakecom/lec/LEC docs/position statements/Walleve managment planpdf.
Fishery harvest and population assessment data were obtained from the following sources:
Lake Superior: Eric Berglund, OMNRF, eric.berglund@ontario.ca
Lake Michigan: Steve Hogler, Wisconsin Department of Natural Resources, Steven.hogler@wisconsin. gov
Lake Michigan: Troy Zorn, MDNR, zornt@michigan. gov
Lake Michigan: Brian Breidart, Indiana Department of Natural Resources, BBreidert@dnr.IN. gov
Lake Huron: Arunas Liskauskas, OMNRF, arunas.liskauskas@ontario.ca
Lake Huron: David Fielder, MDNR, fielderd@michigan. gov
Lake Erie: Richard Drouin, OMNRF, ri c ha rd. d ro u i n a Ontario. ca
Lake Erie: Todd Wills, MDNR, willst(@,michigan.gov
Lake Ontario: Jim Hoyle, OMNRF. iim.hovle@ontario.ca
Lake Ontario: Jana Lantry, New York Department of Enviromnental Conservation, i rla nt rv a gw. dec, state. in. us
Huron-Erie Corridor: Megan Belore, OMNRF, m e g a n. b e 1 o re Vv. o n t a ri o. c a
List of Figures
Figure 1. Walleye harvest, reported in metric tonnes, split into contributions from tribal, recreational and
commercial fisheries in the five Great Lakes, 1975 - 2014. Fish Community Goals and Objectives are: Lake Michi-
gan 100-200 metric tonnes; Lake Huron, 700 metric tonnes; Lake Erie, sustainable harvest in all basins; Lake On-
tario, maintain early 1990s populations and expand populations into favorable habitats.
Source: Chippewa Ottawa Resource Authority, Michigan Department of Natural Resources, Minnesota Department
of Natural Resources. New York State Department of Enviromnental Conservation, Ontario Ministry of Natural
Resources, Ohio Department of Natural Resources, Pennsylvania Fish and Boat Commission, Wisconsin Depart-
ment of Natural Resources.
Last Updated
State of the Great Lakes 2017 Technical Report
Page 284
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STATE OF THE GREAT LAKES 2017
Lake Erie
s
8000
600 0
4000
2000
I ORrtrMlioftAl
¦CufttffiaiciM
75 73 81 84 87 50 93 96 99 02 05 03 11 14
Year
Lake Michigan
130
160
*140
1120
£ioo
£ 80
I so
40
20
o J-pi
75
D Tribal
BCowitnwrcwl
78 81 84 87 90 93 96
Year
02 OS 08 11 14
Lake Huron
600
500
400
¦Commamai
iiillill 1
75 78 81 84 87 90 93 96 99 02 05 08 11 14
Year
Lake Ontario
to
a
c
c
o
250

100
50
«TrlW
~RecFMtKHiri
• CommHClil
75 78 81 84 87 90 93 96 99 02 05 08 11 14
Year
Lake Superior
20
15
10
5
0
75 78 81 84 87 90 93 96 99 02 05 OS 11 14
Year
Figure 1. Walleye harvest reported in metric tonnes, split into contributions from tribal, recreational and
commercial fisheries in the five Great Lakes, 1975 - 2014. Fish Community Goals and Objectives are: Lake Michi-
gan 100-200 metric tonnes; Lake Huron, 700 metric tonnes; Lake Erie, sustainable harvest in all basins; Lake On-
tario, maintain early 1990s populations and expand populations into favorable habitats.
Source: Chippewa Ottawa Resource Authority, Michigan Department of Natural Resources, Minnesota Department
of Natural Resources. New York State Department of Enviromnental Conservation, Ontario Ministry of Natural
Resources, Ohio Department of Natural Resources, Pennsylvania Fish and Boat Commission, Wisconsin Depart-
ment of Natural Resources.
Page 285
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STATE OF THE GREAT LAKES 201 7
Sub-Indicator: Lake Trout
Overall Assessment
Status: Fair
Trend: Improving
Rationale: Self-reproducing populations are present in Lake Superior and natural reproduction is wide-
spread and increasing in Lake Huron. Populations in lakes Michigan, Erie, and Ontario are mostly below
Great Lakes Fishery Commission Lake Committee target levels for relative abundance and natural reproduc-
tion is low. Some population increases are being observed with support of stocking and other rehabilitation
efforts.
Lake-by-Lake Assessment
Lake Superior
Status: Good
Trend: Unchanging
Rationale: Natural reproduction of both nearshore (lean) and offshore (siscowet) populations is widespread and
supports all populations. Populations have likely reached carrying capacity given the current available forage base.
Overall lake-wide abundance is stabilizing with eastern Michigan populations declining from peak abundance levels
and western Lake Superior populations continuing to build. Most stocking has been discontinued. Excessive fish-
ing is occurring in eastern Wisconsin, western Michigan, and in eastern Ontario waters. Sea Lamprey mortality has
been increasing. Most agencies are committed to further restoration and conservation.
Lake Michigan
Status: Poor
Trend: Improving
Rationale: Lake-wide densities are stable but well below target. Some natural reproduction being detected in areas
with low mortality, older age compositions and higher parental densities; significant recruitment of wild fish to the
general population remains elusive. Survival of stocked fish in northern Lake Michigan is poor due to high Sea
Lamprey mortality and fishing resulting in inadequate parental stocks. Most agencies are committed to rehabilita-
tion.
Lake Huron
Status: Good
Trend: Improving
Rational: More than 15 year classes of wild Lake Trout have been observed lake wide, and represent more than
50% of survey catches and 50-90% of harvest in recent years. Abundant year classes of wild Lake Trout have en-
tered the adult portion of the population and wild juvenile abundance reached a new high level since the 2010 year
class. Post-release survival of stocked fish is low and stocking reductions are being considered. All agencies com-
mitted to further rehabilitation and conservation.
Lake Erie
Status: Fair
Trend: Improving
Rationale: Increased stocking levels in recent years and success of the Lake Champlain strain lias increased adult
stocks to near rehabilitation targets outlined in the rehabilitation plan. Sea Lampreys predation continues to be an
issue, and natural reproduction has still not been detected. All agencies remain committed to further rehabilitation
and conservation.
Lake Ontario
Status: Fair
Trend: Improving
Rationale: Sea Lamprey predation was strongly related to a collapse in adult stocks during 2004-2005; however
abundance increased each year during 2008 - 2014 following improved Sea Lamprey control. Post-release survival
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STATE OF THE GREAT LAKES 2017
of stocked fish was low from the early 1990s through 2010, but increased 3.4 fold during 2011 - 2014, and the catch
of ages-1 and -2 naturally reproduced Lake Trout in 2014 was more than 14 times greater than any previous year
since 1994. All agencies remain committed to further rehabilitation and conservation.
Sub-Indicator Purpose
•	To estimate the relative abundance of both stocked and wild (naturally reproduced) Lake Trout.
•	To measure the success of rehabilitation through catch rates of wild fish
•	To infer the control measures on fishing and Sea Lamprey predation through the age structure and
abundance of mature fish.
•	To infer the basic structure of the cold water predator community and the general health of the ecosystem
Ecosystem Objective
Self-sustaining, naturally reproducing populations that support target yields to fisheries are the goal of the Lake
Trout rehabilitation program. Target yields approximate historical levels of Lake Trout harvest or levels adjusted to
accommodate stocked naturalized introduced predators such as Pacific salmon. Targets, most centered on desired
harvest expectations, are set by Lake Committees of the Great Lakes Fishery Commission in Fish Community Ob-
jectives (Horns et al. 2003, Eshenroder et al. 1999, DesJardin et al.1995, Ryan et al. 2003., Stewart et al. 1999), and
are revised periodically. These targets are 1.8 million kg (4 million pounds) from Lake Superior, 1.1 millionkg (2.5
million pounds) from Lake Michigan 0.9 million kg (2.0 million pounds) from Lake Huron and 50 thousand kg (0.1
million pounds) from Lake Erie. Lake Ontario has no specific yield objective but has a population objective of 0.5 to
1.0 million adult fish that produce 100,000 yearling recruits annually through natural reproduction. The desired state
will be for Lake Trout to serve as the primary top predator in Lake Superior and share this status with other native
and established non-native predators in lakes Michigan, Huron, Erie and Ontario.
Ecological Condition
Measure
Trends in the relative abundance of stocked lean Lake Trout in lakes Huron, Michigan, Erie and Ontario, and wild
lean Lake Trout in Lake Superior are displayed in Figure 1. Targets are set for most populations of lean Lake Trout
as these are perceived to be biologically important to increase the probability of natural reproduction in lakes Huron,
Michigan, Erie and Ontario and to maintain wild populations in Lake Superior. Target values are measured and
expressed by relative abundances of all or a portion of the population in multiagency gill net surveys that are stand-
ardized within each lake. These measures are superior to harvest objectives, which are harder to evaluate and repre-
sent desired states that cannot be easily tested for sustainability. Lake Trout abundance dramatically increased in all
the Great Lakes after initiation of Sea Lamprey control, stocking, and harvest control. Success to achieve popula-
tion targets and ultimately to self-sustaining naturally reproducing populations has been mixed among the lakes.
Endpoint
Desired states are populations that are self-sustaining through natural reproduction with minimal or no hatchery sup-
plementation required, that support a sustainable harvest, and serve as a top predator. The resulting population size
and sustainable yield compared to historical levels will likely be lower in most lakes since this apex trophic level is
now shared by naturalized non-native predators that support a multi-billion dollar fishery.
Background
Historically Lake Trout were the keystone salmonine predator for most of the Great Lakes. Overfishing and preda-
tion by non-native sea lamprey, and to a limited extent other factors, destroyed nearshore lean populations and deep
water siscowet Lake Trout populations, but many survived in Lake Superior and a few lean Lake Trout populations
in Lake Huron (Lawrie and Ralirer 1972, Berst and Spangler 1972, Wells and McLain 1972, Hartman 1972, Christie
1972). Rehabilitation efforts through stocking and controls on fisheries and sea lamprey have been ongoing since
the early 1960s (Hansen et al. 1995, Eshenroder et al. 1995, Holey et al. 1995, Cornelius et al. 1995, Elrod et al.
1995). '
Status of Lake Trout
Lake Superior
Wild lean Lake Trout populations have recovered from collapse in the 1950s due to an aggressive recovery program
employing Sea Lamprey suppression, stocking of hatchery fish, and fishery restrictions (Bronte et al. 2003). Recov-
ery began with the buildup of large populations of hatchery Lake Trout, which were superseded by wild fish. The
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STATE OF THE GREAT LAKES 2017
transition to wild Lake Trout dominance began in the 1980s in Michigan waters and was subsequently followed in
Wisconsin, then most recently in Minnesota. Little or no recovery lias been observed in the Ontario waters of east-
ern Lake Superior. In Michigan waters, abundance and recruitment of most Lake Trout populations are near historic
high levels with some indications of density-dependent growth declines (Wilberg et al. 2003; Richards et al. 2004;
Sitar et al. 2010). The latest progress in recovery was the cessation of most stocking in Minnesota waters.
Siscowet, a deep water morphotype, is the most abundant form of Lake Trout in Lake Superior occupying deep wa-
ter areas and have recovered from depressed levels in the 1940s (Bronte and Sitar 2008; Ebener et al. 2010). Recent
harvest is low, though emerging industrial interest in extracting omega-3 fatty acid from siscowet may develop a
demand. Sea Lamprey wounding rates on siscowet are high, though the mortality inflicted may not be higher than
that experienced by lean Lake Trout (Moody et al. 2010). Similar to leans, siscowet are at high levels and experi-
encing density-dependent effects.
Currently, wild Lake Trout abundance has declined in recent years, but remains higher on average than the prior
State of the Great Lakes (previously known as SOLEC) reporting period. Fishing mortality has been controlled in
most areas of Lake Superior through regulations. Despite continued Sea Lamprey management, wounding rates on
Lake Trout in some areas have increased above target levels since 1995 (Sitar et al. 2010). In the near-term, further
decline in Lake Trout abundance is expected due to density-dependent effects.
Lake Huron
Lake Trout rehabilitation efforts continue to show signs of success over the past several years. Over 3 million year-
lings are stocked annually in the lake, split almost equally in Ontario and Michigan waters. Relative abundance of
Lake Trout lias increased in recent years (Ji et al. 2013), primarily in the North Channel and the Main Basin. Unfor-
tunately the opposite has occurred in Georgian Bay.
Similarly, Sea Lamprey wounding has decreased significantly since 2000 in the main basin and in particular in the
North Channel but have increased in Georgian Bay. However, the relative abundance of age-7 hatchery Lake
Trout, corrected for stocking, lias decreased since 2002 year class from an average 0.92 to a range of 0.05-0.27. The
major food of Lake Trout has switched since 2002 from alewives and rainbow smelt to round gobies and rainbow
smelt. The relative abundance of juvenile Lake Trout appears to be negatively influenced by the dominance of adult
fish in the population, while a dramatic decline in the recruitment of stocked fish is apparent. The oldest age ob-
served has rapidly increased from less than 10 years in 2002 to more than 25 years recently, and suggests that the
combination of natural mortality and Sea Lamprey mortality may be substantially lower now.
Lake wide wild recruitment of Lake Trout has occurred since 2004, after the collapse of alewives and their suspect-
ed adverse effects on reproduction via Thiamine Deficiency Syndrome and predation on Lake Trout eggs and fry.
The first pulse of wild recruitment did not fully compensate the decline in the recruitment of hatchery fish, but wild
recruitment has reached a new high level since 2010 year class. Sufficiently low mortality, relatively stable spawn-
ing stock biomass, and continuing increases in the abundance of wild adults have contributed to the recent progress
of Lake Trout rehabilitation in Lake Huron.
Lake Michigan
Stocking continues in all jurisdictions. Lake Trout densities measured by spring assessment surveys remain below
target in most areas and lakewide. Few wild fish were recovered in assessment surveys (Bronte et al. 2007, Lake
Trout Task Group 2015), which indicates that natural reproduction remains low even though fry from reproduction
by stocked Lake Trout have been recovered (Jannsen et al. 2006). However, areas (Illinois, Indiana, and southern
Wisconsin waters) with advanced age compositions and densities of Lake Trout approaching target levels show
some evidence of sustained natural reproduction (Hanson et al. 2012). Northern Lake Michigan is plagued by high
fishing and Sea Lamprey mortality that is resulting in very low spawning stock biomass. Recent events that should
increase the probability of achieving the Lake Trout rehabilitation objectives include: 1) a revised implementation
strategy for the rehabilitation of Lake Trout in Lake Michigan that concentrates stocking and other management
efforts in the best habitat areas, 2) egg thiamin levels, thought to be inadequate for hatching success and fry survival,
have recently increased lakewide, and 3) Sea Lamprey numbers, which were above the targets levels for many years,
have declined.
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STATE OF THE GREAT LAKES 2017
Sea Lamprey induced mortality, low adult stock size, and lack of sustainable reproduction (Bronte et al. 2003,
2007), continues to limit Lake Trout rehabilitation. Recommendations to advance recovery include minimizing adult
mortality from fishing and lamprey, focus hatchery production in refuge areas, restore a native forage base of core-
gonines and recast FCOs for desired population characteristics rather than harvest levels.
Lake Erie
Directed efforts to restore Lake Trout in Lake Erie began in 1982. Recruitment of stocked fish was good but their
survival to adulthood was poor due to excessive Sea Lamprey predation. Adoption of the original Lake Trout reha-
bilitation plan in 1985 (Lake Trout Task Group 1985) brought higher annual stocking targets. Sea Lamprey control,
and standardized assessment programs to monitor the population. The Lake Trout responded quickly to the imple-
mentation of Sea Lamprey suppression and increased stocking, building a large population by 1990. However, these
accomplishments were short lived as stocking numbers were reduced in 1996 due to concerns about a shortage of
forage fishes (Einhouse et al. 1999) while at the same time Sea Lamprey control was relaxed (Sullivan et al. 2003).
Adult Lake Trout abundance was quickly reduced to low levels by 2000 where it has since remained.
Overall Lake Trout abundance in Lake Erie has increased in more recent years due to adoption of a revised rehabili-
tation plan (Markham et al. 2008) that increased stocking numbers back to their original level. Stocking has recently
shifted to include all areas of the lake, including the western basin. Recruitment of stocked fish, especially the Lake
Champlain strain, has been high, and adult abundance is near targets established in the rehabilitation plan. Sea
Lamprey abundance has declined in recent years but still remains well above targets despite increased lampricide
treatments, and this continues to suppress the adult Lake Trout population. Achievement of Lake Trout rehabilita-
tion goals will continue to be hampered if Sea Lamprey abundance and wounding rates remain above target levels.
Natural reproduction has yet to be detected in Lake Erie.
Lake Ontario
The abundance of hatchery-reared adult lake Trout in Lake Ontario was relatively high during 1986-1998, but de-
clined by more than 30% in 1999 due to reduced stocking and poor survival of stocked yearlings since the early
1990s (Elrod et al. 1995, Lantry and Lantry 2015). Adult abundance remained relatively stable during 1999-2004,
but again declined by 54% in 2005 likely due to ongoing poor recruitment and increased mortality from sea lamprey
predation. Enhanced control of Sea Lampreys and subsequent decreases in wounding on Lake Trout during 2008-
2014 were followed by a sharp recovery in adult Lake Trout numbers, which in 2010-2014 rose to levels similar to
those observed during 1999-2004.
Although the abundance of adults reached a peak in 1986, appearance of naturally reproduced Lake Trout in as-
sessment surveys occurred later after the abundance of large adult females exceeded target levels in 1992 (Lantry
and Lantry 2015). Despite widespread catches of small numbers of natural recruits nearly every year during 1993-
2013, a failure to achieve self-sustaining stocks has been attributed to the dense populations of alewives in Lake
Ontario and an associated diet of Lake Trout that favors alewives (leading to Early Mortality Syndrome), the ab-
sence of suitable alternative deepwater preyfishes, and colonization of spawning reefs by invasive round gobies
(Fitzsimons et al. 2003, Lantry et al. 2003, Schneider et al. 1997, Walsh et al. 2015). Recent meager prospects for
restoration have improved with the reappearance of deepwater sculpin in assessment catches (their abundance stead-
ily increased during 2002-2014) (Lantry et al. 2007, Weidel et al. 2015), with the joint US and Canadian efforts cur-
rently underway to reestablish cisco and bloater, and with the inclusion of round gobies in Lake Trout diets
(Diertrich et al. 2006; Rush et al. 2012). Signs of improving conditions for natural reproduction were realized in
2014 when assessment catches of naturally reproduced age-1 and -2 Lake Trout rose sharply to a level 14.2 times
greater than the 1994-2013 mean.
Linkages
The rehabilitation of Lake Trout populations in the Great Lakes has linkages to Sea Lamprey, prey fish, and non-
native species. Lake Trout stocking and the building parental stocks would not be possible without sustained levels
of Sea Lamprey control, as well as controls on fisheries. Non-indigenous alewives, while at lower levels now, still
effect wild recruitment through predation on Lake Trout fry. Alewives also contain high levels of thaiminase that
lowers egg viability and fry survival in LakeTrout that consume mostly alewives. The lack of native pelagic and
benthopelagic coregonines, lost to overfishing, habitat degradation and non-native invasions, is also hampering re-
covery as these lost species were conduits for offshore benthic and pelagic production to the nearshore enviromnent
and to Lake Trout as prey.
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STATE OF THE GREAT LAKES 2017
Comments from the Author(s)
Reporting frequency should be every five years. Monitoring systems are in place, but in most lakes the measures do
not directly relate to stated harvest objectives. Lake Trout population-objectives need to be redefined as endpoints in
units measured by the monitoring activities, are relevant to population characteristics required for restoration to pro-
ceed, and should be incorporated into restoration guides and plans. The data time series we present are based on
important population targets that can be measured with current assessment activities.
Assessing Data Quality
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not
Applicable
1. Data are documented, validated, or
quality-assured by a recognized
agency or organization

X




2. Data are traceable to original
sources

X




3. The source of the data is a known,
reliable and respected generator of
data

X




4. Geographic coverage and scale of
data are appropriate to the Great
Lakes Basin

X




5. Data obtained from sources within
the U.S. are comparable to those
from Canada



X


6. Uncertainty and variability in the
data are documented and within
acceptable limits for this sub-
indicator report

X




Acknowledgments
Authors
Charles R. Bronte, S. Dale Hanson, U.S. Fish and Wildlife Service, New Franken, WI
Ji X. He, Michigan Department of Natural Resources, Alpena, MI
BrianF. Lantry, U.S. Geological Survey, Oswego, NY
James L. Markham, New York Department of Enviromnental Conservation Dunkirk, NY.
Shawn P. Sitar, Michigan Department of Natural Resources, Marquette, MI
Lloyd Molir, Ontario Ministry of Natural Resources and Forestry, Owen Sound, ON.
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B. McDonald, K.M. Mullett, C.K. Murray, and P.A. Ryan. 2003. The sea lamprey in Lake Erie: a case history.
Journal of Great Lakes Research 29 (Supplement l):615-636.
Walsh, M.G., and M.J. Connerton. 2015. Status of alewife in the U.S. waters of Lake Ontario, 2014. In NYDEC
Annual Report to the Great Lakes Fishery Commission's Lake Ontario Committee, March, 2011.
Weidel, B.C., M.G. Walsh, and M.J. Connerton. 2015. Section 12 In Status of sculpins and round goby in the U.S.
waters of Lake Ontario, 2014. Section 12 In NYDEC Annual Report to the Great Lakes Fishery Commission's
Lake Ontario Committee, March 2011.
Wells, L. and A.L. McLain. Lake Michigan: effects of exploitation, introductions, and eutrophication on the
salmonid community. Journal of the Fisheries Research Board of Canada 29:889-898.
Wilberg, M.J., M.J. Hansen, and C.R. Bronte. 2003. Historic and modern abundance of wild lean lake trout in
Michigan waters of Lake Superior: implications for restoration goals. North American Journal of Fisheries
Management 23:100-108.
List of Figures
Figure 1. Relative abundance of stocked Lake Trout (wild fish in Lake Superior) in the Great Lakes from 1975 -
2015. The measurements reported vary from lake to lake, as shown on the vertical scale, and comparisons among
lakes may be misleading. Overall trends over time provide information on relative abundances for all or part of the
population.
Source: Data sources are from biological assessments conducted cooperatively by state, federal, tribal and provincial
agencies, and are largely contained in non-peered reviewed reports to the Great Lakes Fishery Commission, Lake
Committees , New York Department of Enviromnental Conservation, Ontario Ministry of Natural Resources, U.S.
Fish and Wildlife Service and U.S. Geological Survey.
Last Updated
State of the Great Lakes 2017 Technical Report
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STATE OF THE GREAT LAKES 2017
Lake Superior
5 20 -
1975 1980 1985 1990 1995 2000 2005 2010 2015
Lake Huron
-*-Wild fish -*-Hatchery fish
35.00
30.00 1
| 25.00 -I
E 20.00 -
« 15.00
£
E
I 10.00 -I
5.00 -
0.00
1975 1980 1985 1990 1995 2000 2005 2010 2015
Lake Ontario
~-Mature females —Mature female target
Lake Michigan
-Lakewide mean —Target
30.00
25.00
r 20.00
o 15.00
€ 10.00
9.00
1975 1980 1985 1990 1995 2000 2005 2010 2015
Lake Erie
—All fish Adult fish 	All fish target — Adult target
6.00 -
5.00 -
4.00 -
2.00 -
1.00 -
1975 1980 1985 1990 1995 2000 2005 2010 2015
1975 1980 1985 1990 1995 2000 2005 2010 2015
Figure 1. Relative abundance of stocked Lake Trout (wild fish in Lake Superior) in the Great Lakes from 1975 -
2015. The measurements reported vary from lake to lake, as shown on the vertical scale, and comparisons among
lakes may be misleading. Overall trends over time provide information on relative abundances for all or part of the
population.
Source: Data sources are from biological assessments conducted cooperatively by state, federal, tribal and provincial
agencies, and are largely contained in non-peered reviewed reports to the Great Lakes Fishery Commission, Lake
Committees , New York Department of Environmental Conservation. Ontario Ministry of Natural Resources, U.S.
Fish and Wildlife Service and U.S. Geological Survey.
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STATE OF THE GREAT LAKES 201 7
Sub-Indicator: Fish-Eating and Colonial Nesting Waterbirds
Overall Assessment
Status: Fair
Trend: Unchanging
Rationale: Four of eight species are less numerous now than when systematic monitoring began (1976-80;
Great Blue Heron, Black-crowned Night-Heron, Herring Gul, Common Tern), although rates of decline have
slowed for all over the last decade. Twenty-year (1989-91 to 2007-09) population trends for six of eight species
have been assessed as stable. Great Blue Herons exhibited a moderate 20-year decline (-40%). Double-crested
Cormorant nests increased 385% since 1989-91, although the rate of increase has slowed over the last decade
(a 30% increase since 1997-99).
Lake-by-Lake Assessment
Lake Superior
Status: Fair
Trend: Unchanging.
Rationale: Two species are less numerous now than when systematic monitoring began (1976-80; Great Blue Heron,
Common Tern), although rates of decline have slowed for both species over the last decade. Since 1989-91, one
species has exhibited a stable trend (Common Terns), two have undergone moderate declines (Great Blue Heron,
Herring Gull), one species lias had a large decline (Ring-billed Gulls) and one a large increase (cormorants). Unable
to calculate trends for night-herons or Caspian Terns; egrets have never nested on this water body.
Lake Michigan
Status: Fair
Trend: Unchanging.
Rationale: Two species are less numerous now than when systematic monitoring began (1976-80; Black-crowned
Night-Heron, Common Tern), although rates of decline have slowed for both species over the last decade. Twenty-
year populations trends: two species have experienced large declines (Common Tern Great Blue Heron), one had a
moderate decline (Black-crowned Night-Heron), three species were stable (Herring Gull, Ring-billed Gull, Caspian
Tern) and one species exhibited a large (> six-fold) increase (cormorants). Unable to calculate a trend for egrets.
Lake Huron
Status: Fair
Trend: Unchanging.
Rationale: Five species are less numerous now than when systematic monitoring began (1976-80; Great Blue
Heron, Herring Gull, Ring-billed Gull, Common Tern, Caspian Tern); rates of decline have slowed for all species,
except Great Blue Heron over the last decade. Since 1989-91, one species has undergone a large decline (Great
Blue Heron), one had a moderate decline (Caspian Tern), three species were stable (Herring Gull, Ring-billed Gull,
Common Tern), one species had a moderate increase (Black-crowned Night-Heron) and two exhibited large increas-
es (cormorants, 2.5x; egrets, 7.8x).
Lake Erie
Status: Fair
Trend: Unchanging.
Rationale: Three species are less numerous now than when systematic monitoring began (1976-80; Great Blue
Heron, Black-crowned Night-Heron, Common Tern); rates of decline have slowed for all these species over the last
decade. Since 1989-91, three species exhibited a moderate decline (Great Egret, Black-crowned Night-Heron, Her-
ring Gull), three species were stable (Great Blue Heron, Ring-billed Gull, Common Tern) and one species had a
large increase (cormorants, 7.5x). Unable to calculate trend for Caspian Tern (colonized water body during the past
decade).
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STATE OF THE GREAT LAKES 2017
Lake Ontario
Status: Fair
Trend: Unchanging.
Rationale: One species is less numerous now than when systematic monitoring began (1976-80; Common Tern); the
rate of decline has increased over the last decade. Since 1989-91, two species exhibited a moderate decline (Ring-
billed Gull, Common Tern), three species were stable (Great Blue Heron, Black-crowned Night-Heron, Herring
Gull) and two species had a large increase (cormorants, 2.3x; Caspian Tern, 1.7x). Unable to calculate trend for
Great Egret (colonized this water body during the 1997-99 census).
Sub-Indicator Purpose
•	Assessment of ecosystem health by examining long-term trends in the abundance and distribution of colonial
waterbird populations breeding on the Great Lakes.
•	The sub-indicator tracks changes in the number of breeding pairs (nests), breeding colonies, and populations
of nine species of fish-eating birds since the mid-1970s, at multiple geographic scales
•	Secondarily, some ecological endpoints will be assessed for representative colonial waterbird species at se-
lected sites on the Great Lakes.
Ecosystem Objective
Conservation of critical island breeding habitat, and maintenance of self-sustaining populations (i.e. no further de-
clines in abundance or reductions in distribution) of each of the eleven waterbird species that comprise that avian
community. Fish-eating, colonial waterbirds are distributed across all five Great Lakes, their connecting channels,
and the St. Lawrence River, both in Canadian and US waters.
This sub-indicator best supports work towards General Objective #5 of the 2012 Great Lakes Water Quality Agree-
ment which states that the Waters of the Great Lakes should "support healthy and productive wetlands and other
habitats to sustain resilient populations of native species."
Ecological Condition
Fish-eating, colonial waterbirds are distributed across all five Great Lakes, their connecting channels, and the St.
Lawrence River, both in Canadian and US waters. Colonial waterbirds function as apex predators in freshwater sys-
tems, and provide an important linkage between aquatic and terrestrial habitats. As a guild, waterbirds derive a large
proportion of their diet from fish and other aquatic prey (species range from obligate piscivores to having a mix of
aquatic and terrestrial prey). On the Great Lakes, waterbird species differ in the foraging strategies they employ, and
thus, differ in the aquatic habitats and trophic levels they utilize (e.g. surface feeders or pursuit divers in open water;
sit-and-wait predators in littoral zones and wetlands, surface feeders in wetlands). Another life-history trait that wa-
terbirds share is that they nest in dense aggregations (i.e. colonially), almost exclusively on islands (except for For-
ster's and Black terns, which nest in wetlands). As such, they can also serve as an important indicator of change in
status of this unique habitat within the Great Lakes system.
Changes in waterbird population abundance, distribution and demography can reflect changes in ecosystem trophic
structure and/or island or wetland nesting habitat and, therefore, are important metrics for assessing the health of a
variety of Great Lakes ecosystem components. Inter-specific differences in foraging and nesting strategies make it
possible to assess and integrate trend information across a variety of temporal, spatial and ecosystem scales. Declin-
ing waterbird populations (number of breeding pairs or nests) or vital rates (hatching success, fledging success, mor-
tality rates, etc.) can be indicators of local enviromnental stress. The Great Lakes-wide population of colonial water-
birds has been censused jointly, by the Canadian Wildlife Service and the U.S. Fish and Wildlife Service since the
1970s, approximately every 10 years; four "decadal" censuses have been conducted to date: in the 1970s, 1980s,
1990s and 2000s. For this sub-indicator, population change (over the last 20 years) is defined as: large decline =
>50% decline; moderate decline = >25% to <50% decline; stable: <25% decline to <33% increase; moderate in-
crease: >33% to <100% increase; large increase: >100% increase.Briefly, in the long-term (1976-2009), these cen-
suses have shown that the breeding numbers of four species have undergone large increases: Double-crested Cormo-
rants, Great Egrets, Ring-billed Gulls and Caspian Terns (population growth has slowed since the 2nd census for the
latter two species; Figure 1). Three species. Great Blue Heron, Herring Gull and Great Black-backed Gull (GBBG,
an uncommon breeder on the Great Lakes, trend not shown), exhibited a period of population growth followed by a
decline; current numbers of breeding pairs for these species are similar to 1970s levels and are considered stable. In
contrast. Common Terns and Black-crowned Night-Herons have both undergone long-term declines, although the
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STATE OF THE GREAT LAKES 2017
rates of decline have slowed over the last decade and these populations are currently considered stable. For the six
species that have undergone declines since the 1989-91 census (Figure 1; GBBG trend not shown), continued moni-
toring will determine whether these populations have in fact stabilized or if there is evidence for concern. Currently,
drivers such as habitat change and loss, changes in trophic structure and abundance of fish prey (Hebert et al. 2008,
2009), reduced access to alternate sources of food (for gulls, due to changes in agricultural and waste disposal prac-
tices), inter-specific competition for nesting space (e.g. increased pressure from hyper-abundant species such as
cormorants and Ring-billed Gulls) and stressors in overwintering areas likely play a larger role in regulating water-
bird populations than contaminant-related impairments.
Measure
Nine focal species of colonial waterbirds breed at sites (predominantly islands) distributed across all of the Great
Lakes: Herring, Ring-billed and Great Black-backed gulls, Caspian, and Common terns. Great Blue Herons, Great
Egrets, Black-crowned Night-Herons and Double-crested Cormorants. A complete census of all waterbird colonies
on the Great Lakes, their connecting channels and the St. Lawrence River (up to 1 km inland from shorelines) has
been conducted, jointly, by the Canadian Wildlife Service and the U.S. Fish and Wildlife Service, approximately
every 10 years, since the mid-1970s (four complete census periods; the most recent was completed in 2009; the next
comparable survey is planned for 2020). Survey timing and methodologies were coordinated between Canada and
the USA. Measures include:
•	Nest counts of colonial waterbird species across all water bodies and connecting channels at relevant tem-
poral and spatial scales:
•	Annual: Counts for Herring Gull (13 focal colonies distributed across the Great Lakes) and Double-
crested Cormorant (Lake Ontario and the St. Lawrence River to Cornwall, ON) since the late 1970s.
Methods are consistent with 'decadal' survey efforts.
•	Decadal: All breeding sites for the nine focal colonial waterbird species are censused at 10-year in-
tervals.
•	Periodic measurement of waterbird demographic parameters known to be directly or indirectly impacted by
enviromnental stressors, including (but not limited to): clutch size, egg volume, hatching and fledging suc-
cess, natal and breeding site fidelity, age at first breeding and age-specific survivorship.
•	Additional monitoring considerations include: avian disease surveillance (e.g. botulism type E) and studies
tracking adults through the full annual cycle to establish connectivity between breeding and wintering areas.
Endpoints
•	Healthy, self-sustaining populations of each waterbird species.
•	Populations of stable or declining species remain stable or increase, respectively
•	Populations of hyper-abundant species (cormorants and Ring-billed Gulls) either remain stable or decline
•	Critical island breeding habitat is conserved
There are no specific population objectives for these species, other than within a few Great Lakes Areas of Concern
(e.g. Hamilton Harbour, ON).
Linkages
Linkages to other sub-indicators in the indicator suite include:
•	Toxic Chemicals in Great Lakes Herring Gull Eggs
•	Toxic Chemicals in Great Lakes Whole Fish
•	Water Levels
This sub-indicator also links directly to the other indicators in the Habitat and Species indicator, particularly:
•	Lake Sturgeon
•	Lake Trout
•	Walleye
•	Preyfish (open water)
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STATE OF THE GREAT LAKES 2017
Waterbird population trends and breeding habitat are indicators in some of the AOCs, and are used for delisting cri-
teria.
Comments from the Author(s)
This newly-developed sub-indicator, previously reported in conjunction with the historic State of the Great Lakes
(previously known as SOLEC) "Contaminants in Waterbirds" sub-indicator, which described trends in chemical
contaminants found in the eggs of fish-eating, colonial waterbirds is now being assessed separately to report on pro-
gress towards two different general objectives under the 2012 GLWQA.
Data Limitations
•	Most waterbird species are migratory. Changes in population status or trends could reflect enviromnental or
anthropogenic stressors experienced during the non-breeding period (or cumulative effects over the full an-
nual cycle, inside and outside of the Great Lakes region)
•	Inferences on the effects of climate change on population trends are beyond the scope of this sub-indicator
as they would have to include changing food webs and energy cycling though them. In addition, birds could
be moving out of the Great Lakes region (i.e. a shift in distribution) in response to climate-related effects,
with no net change in abundance at larger spatial scales.
•	Data are collected at 10-year intervals, which is longer than the reporting cycle for State of the Great Lakes
Reporting (previously known as SOLEC).
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not
Applicable
1. Data are documented, validated, or
quality-assured by a recognized agency or
organization
X





2. Data are traceable to original sources
X





3. The source of the data is a known,
reliable and respected generator of data
X





4. Geographic coverage and scale of data
are appropriate to the Great Lakes Basin
X





5. Data obtained from sources within the
U.S. are comparable to those from
Canada
X





6. Uncertainty and variability in the data
are documented and within acceptable
limits for this sub-indicator report
X





Acknowledgments
Author: Dave Moore, Enviromnent and Climate Change Canada, Canadian Wildlife Service - Ontario Region,
Canada Centre for Inland Waters, Burlington Ontario L7R 4A6. (dave.moore2@canada.ca)
Contributors:
Dr. Francesca Cuthbert, Dept. Fisheries, Wildlife and Conservation Biology, 2003 Buford Circle, 135 Skok,
University of Minnesota, St. Paul, MN 55108 USA. cuthbOO 1 @umn.edu
Dr. Linda Wires, Dept. Fisheries, Wildlife and Conservation Biology, 2003 Buford Circle, 135 Skok,
University of Minnesota, St. Paul, MN 55108 USA. wiresOO 1 @umn.edu
Shane deSolla, Enviromnent and Climate Change Canada, Wildlife and Landscape Science Directorate -
Ecotoxicology and Wildlife Health, Canada Centre for Inland Waters, Burlington, Ontario L7R 4A6.
Shane.de So 11 a a ca nada.ca
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STATE OF THE GREAT LAKES 2017
Dr. Craig Hebert, National Wildlife Research Centre, Environment and Climate Change Canada, Wildlife and
Landscape Science Directorate - Ecotoxicology and Wildlife Health, Carleton University, 1125 Colonel By Drive,
Ottawa, Ontario K1A 0H3. Cra i a. Hebe rt a ca nada. ca
Doug Crump, National Wildlife Research Centre, Enviromnent and Climate Change Canada, Wildlife and
Landscape Science Directorate - Ecotoxicology and Wildlife Health, Carleton University, 1125 Colonel By Drive,
Ottawa, Ontario K1A 0H3. Do im. C rump a ca nada. ca
Information Sources
Cuthbert, F.J. and L. Wires. 2013. The fourth decadal U.S. Great Lakes colonial waterbird survey (2007-2010):
Results and recommendations to improve the scientific basis for conservation and management (Final Report).
University of Minnesota.
Enviromnent and Climate Change Canada, Canadian Wildlife Service, unpublished data.
Hebert, C.E., D.V.C. Weseloh, A. Idrissi, M.T. Arts, R. O'Gonnan, O.T. Gorman, B. Locke, C.P. Madenjianand
E.F. Roseman. 2008. Restoring piscivorous fish populations in the Lurentian Great Lakes causes seabird dietary
change. Ecology, 89: 891-897
Hebert, C.E., D.V.C. Weseloh, A. Idrissi, M.T. Arts and E.F. Roseman. 2009. Diets of aquatic birds reflect changes
in the Lake Huron ecosystem. Aquatic Ecosystem Health & Management, 12:37-44.
Morris, R.D., D.V.C. Weseloh, F.C. Cuthbert, C. Pekarik, L.R. Wires andL. Harper. 2010. Distribution and
abudance of nesting common and Caspian terns on the North American Great Lakes, 1976 to 1999. J. of Great
Lakes Research 36: 44-56.
Morris, R.D., D.V.C. Weseloh, L.R. Wires, C. Pekarik, F.C. Cuthbert and D.J. Moore. 2011. Population Trends of
Ring-billed Gulls Breeding on the North American Great Lakes, 1976 to 2009. Waterbirds 34: 202-212.
Morris, R.D., C. Pekarik and D.J. Moore. 2012. Current Status and Abundance Trends of Common Terns Breeding
at Known Coastal and Inland Nesting Regions in Canada. Waterbirds 35: 194-207.
Rush, S.A., C. Pekarik, D.V.C. Weseloh, F.C. Cuthbert, D.J. Moore and L.R. Wires. 2015. Changes in heron and
egret populations on the Laurentian Great Lakes and connecting channels, 1977-2009. Avian Conservation and
Ecology 10(1): 7. http://dx.doi.org/10.5751/ACE-00742-1001Q7
List of Figures
Figure 1. Population trends for the entire Great Lakes region (black line) and by water body (coloured lines, see
legend) for eight species of colonial waterbirds censused during four 'decadal' periods, 1976-2009.
Sources: Canadian Wildlife Service- Ontario Region, Enviromnent and Climate Change Canada, Burlington, ON;
Cuthbert and Wires (2013).
Last Updated
State of the Great Lakes 2017 Technical Report
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STATE OF THE GREAT LAKES 2017
120,000
80,000
40,000
Pursuit divers
Double-crested Cormorant
Great Blue Heron
12,000
B,000
6,000
4,000
2,000
4,000
Surface foragers
Common Tern	Caspian Tern
8,000
4,000
Littoral zone/wetland foragers
Great Egret
Black-crowned NightHeron
2,000
1,000
6,000
4,000
2,000
'76-80 '89-91 '97-00 '07-09
>	Lake Superior
•	Lake Michigan
•	Lake Huron (ind. St. Marys River)
Lake Erie (ind. Lake St. Clair,
Detroit & Niagara rivers)
>	Lake Ontario
St. Lawrence River
>	Total, Great Lakes
Figure 1. Population trends for the entire Great Lakes region (black line) and by water body (coloured lines, see
legend) for eight species of colonial waterbirds censused during four 'decadal' periods, 1976-2009.
Sources: Canadian Wildlife Sendee- Ontario Region, Environment and Climate Change Canada, Burlington, ON;
Cuthbert and Wires (2013).
Aquatic /terrestrial predators/scavengers
Herring Gull	Ring-billed Gull
800,000'	
80,000
40,000
400,000
'76-80 '89-91 '97-00 '07-09
o	—		
'76-80 '89-91 '97-00 '07-O9
Census period
Page 300
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Nutrients and Algae
Status: Fair Trend: Unchanging to Deteriorating
The 2012 Great Lakes Water Quality Agreement states that "the Waters of the Great Lakes should be free from nutrients
that directly or indirectly enter the water as a result of human activityj in amounts that promote growth of algae and
cyanobacteria that interfere with aquatic ecosystem health, or human use of the ecosystem"
Algae occur naturally in freshwater systems and are essential to
a healthy aquatic food web. Phosphorus is a key nutrient for the
growth of aquatic plants. However, too much phosphorus can
lead to too much algae in the water, which can be harmful to the
environment, the economy and human health. Excessive nutrient
loadings to Lake Erie, some nearshore areas, and embayments of
the Great Lakes contribute to harmful and nuisance algal blooms.
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Nutrients and Algae
Assessment Highlights
The 1972 GLWQA focused on phosphorus reductions. In the
1980s and early 1990s, basin-wide restoration efforts were
successful in reducing nutrient-related runoff and conditions
in the lakes improved. These efforts included the regulation
of phosphorus concentrations in detergents, investments
in sewage treatment, and the implementation of best
management practices on agriculture lands and in expanding
urban areas. Despite these efforts, there is a nutrient
imbalance in the Great Lakes. With the recent resurgence of
the nearshore algal problem in some areas and with other
changes in the ecosystem, the problem has become more
complicated. Overall, the conditions result in a status of Fair
and a trend of Unchanging to Deteriorating for this indicator.
Many offshore regions of some of the Great Lakes have
nutrient levels below desired concentrations. In fact,
concentrations may be too low in some areas, resulting
in insufficient growth of key phytoplankton species which
form the base of the food chain. Only in Lake Superior are
offshore phosphorus concentrations considered in acceptable
condition. Conversely, there are excess nutrients in many
nearshore areas. While a certain level of nutrients is good,
too much may lead to the development of nuisance and
harmful algal blooms (HABs) and hypoxic zones (areas with
low oxygen levels). This issue is primarily a concern in Lake
Erie, parts of Lake Ontario, Saginaw Bay and Green Bay, along
with other nearshore areas that experience elevated nutrient
levels. Algal blooms can be harmful to both ecosystem and
human health. The western basin of Lake Erie and some
parts of Lake Ontario have experienced a resurgence of HABs
since 2008, adversely impacting ecosystem health as well as
commercial fishing, municipal drinking water systems and
recreational activities. Algal blooms are particularly harmful
when they are dominated by cyanobacteria (or "blue-
green" algae) which can produce toxins such as microcystin.
These toxins can impact drinking water safety or can cause
gastrointestinal upsets, skin rashes and at elevated levels can
be fatal to many organisms.
Total Phosphorus Concentrations in the Great Lakes
Total Phosphorus (mg/L)
Spring 2013 (Lakes Onfarioand Superic
Spring 2014 (Lakes Erie, Michigan, Huron and Ge
Cladophora is a nuisance algae that is broadly distributed
over large areas of the nearshore regions of Lakes Erie,
Ontario, Huron and Michigan. Large mats of Cladophora
give the impression that nutrient concentrations are high
in the nearshore. However, in some areas, these mats of
nuisance algae persist despite low nutrient concentrations
in the surrounding water, which is why the management
of Cladophora has become such a challenge. Excessive
Cladophora poses many problems including beach and
shoreline fouling, clogging of municipal water intakes and
unpleasant aesthetics, as well as tourism and recreational
fishing impacts. There are also significant ecological impacts
of excessive Cladophora growth and, when washed up on the
shoreline, Cladophora may harbour pathogens and create
an environment conducive to the development of botulism
outbreaks which pose a risk for fish and wildlife.
Warmer temperatures, higher frequency and intensity of
precipitation events, and invasive species, in particular Zebra
and Quagga Mussels, are confounding factors in the cycling
and uptake of nutrients in the lakes. These factors may lead
to increased frequency, distribution and severity of HABs,
hypoxic zones and Cladophora.
Sub-Indicators Supporting the Indicator Assessment
Sub-Indicator
Lake Superior
Lake Michigan
Lake Huron
Lake Erie
Lake Ontario
Nutrients in Lakes
Unchanging
Deteriorating
Deteriorating
Deteriorating
Deteriorating
Cladophora
Unchanging
Undetermined
Undetermined
Undetermined
Undetermined
Harmful Algal Blooms
Undetermined
Undetermined
Undetermined
Deteriorating
Deteriorating
Water Quality in Tributaries
Unchanging
Undetermined
Unchanging
Unchanging
Unchanging
Status:
GOOD
FAIR
POOR
UNDETERMINED
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STATE OF THE GREAT LAKES 201 7
Sub-Indicator: Nutrients in Lakes
Open water
Overall Assessment
Status: Fair
Trend: Deteriorating
Rationale: Phosphorus remains the growth-limiting nutrient in the Great Lakes. In the past, phosphorus con-
centrations were elevated throughout many of the lakes. Presently, the problems of excess phosphorus are
confined primarily to some nearshore areas and parts of Lake Erie. In Lakes Michigan, Huron and Ontario,
offshore total phosphorus concentrations are currently below objectives and may be too low, negatively im-
pacting lake productivity (phytoplankton, zooplankton and fish production). Nearshore, symptoms of nutri-
ent enrichment persist in some locations. In Lake Erie, objectives are frequently exceeded and conditions are
deteriorating. Only in Lake Superior are offshore objectives being met and conditions acceptable.
Lake-by-Lake Assessment
Lake Superior
Status: Good
Trend: Unchanging
Rationale: Objectives have consistently been met, and offshore total phosphorus concentrations are similar to histor-
ic values, indicating acceptable conditions. There is a very slow rate of decrease over time that is observed in the
data.
Lake Michigan
Status: Fair (below objective)
Trend: Deteriorating (further below objective)
Rationale: Offshore phosphorus concentrations are continuing to decrease below objectives. Concentrations have
fallen to low levels and may be negatively affecting lake productivity. In some nearshore areas, elevated phosphorus
is observed and may be supporting nuisance algae growth.
Lake Huron
Status: Fair (below objective)
Trend: Deteriorating (further below objective)
Rationale: Offshore phosphorus concentrations are continuing to decrease to values that are well below objective.
Concentrations may be too low to support a healthy level of lake productivity. In some nearshore areas, elevated
nutrients may be contributing to nuisance algae growth.
Lake Erie
Status: Poor (above objective)
Trend: Deteriorating
Rationale: Total phosphorus objectives continue to be exceeded and trends indicate possibly increasing concentra-
tions. Harmful algal blooms have recently plagued the western basin and parts of the central basin, and nuisance
benthic algae have resurged in the eastern basin.
Lake Ontario
Status: Fair (below objective)
Trend: Deteriorating (further below objective)
Rationale: Offshore phosphorus concentrations are continuing to decrease to levels too low to support healthy off-
shore lake productivity. Certain nearshore areas are experiencing recurrent nuisance algae, possibly fueled by local-
ly-high phosphorus discharges or in-lake nutrient cycling.
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STATE OF THE GREAT LAKES 201 7
Sub-Indicator Purpose
•	To assess nutrient concentrations in the Great Lakes
•	To assess progress in meeting GLWQA General Objective #6, Lake ecosystem Objectives and Substance
Objectives for nutrient concentrations for the Waters of the Great Lakes
•	To infer progress in meeting nutrient loading targets and allocations
•	To support the evaluation of trophic status and food web dynamics in the Great Lakes
•	To support assessment of the state of the nearshore waters for the nearshore framework
Ecosystem Objective
General Objective #6 of the 2012 Great Lakes Water Quality Protocol states that the Waters of the Great Lakes
should "be free from nutrients that directly or indirectly enter the water as a result of human activity, in amounts that
promote growth of algae and cyanobacteria that interfere with aquatic ecosystem health, or human use of the ecosys-
tem."
Annex 4 of the 2012 GLWQA Protocol includes Lake Ecosystem Objectives to: maintain an oligotrophic state, rela-
tive algal biomass, and algal species consistent with healthy aquatic ecosystems, in the open waters of Lakes Superi-
or, Michigan, Huron and Ontario; maintain mesotrophic conditions in the open waters of the western and central
basins of Lake Erie, and oligotrophic conditions in the eastern basin of Lake Erie.
Interim Substance Objectives for Total Phosphorus concentrations in open waters are additionally established in
Annex 4 for each of the Great Lakes. These interim objectives are shown in Table 1, and comprise objectives for
both spring total phosphorus concentrations and summer chlorophyll a concentrations. The resultant nutrient
(trophic) states corresponding to the objective concentrations are also displayed. There are no objectives for near-
shore nutrient concentrations; Provincial and/or State nutrient objectives will be considered here as benchmarks on-
ly.
The establishment of Substance Objectives for phosphorus concentrations and loading targets take into account the
bioavailability of phosphorus (and seasonality); therefore, status and trends of the bioavailable phosphorus fraction
(soluble reactive phosphorus) and seasonal information are provided here where possible.
There are no current ecosystem objectives for nitrogen. There is a requirement in Annex 4 to establish Substance
Objectives for other nutrients, as required, to control the growth of nuisance and toxic algae to achieve Lake Ecosys-
tem Objectives. As an interim measure, and as discussed in Dove and Chapra (2015), the Redfield ratio of 7.2
mgN/mgP is used as a benchmark to assess nitrogen levels; above this level, lakes would tend to be phosphorus lim-
ited, below this level, lakes would tend to be nitrogen limited, with nitrogen limitation favoring harmful cyanobacte-
ria. The goal would be to maintain ratios well above this level.
Ecological Condition
The condition of the Great Lakes with respect to nutrients is determined using data collected by the federal agencies
Enviromnent and Climate Change Canada and the United States Enviromnental Protection Agency. The determina-
tion of the lakes' current status is based on samples collected during recent spring (late March-May) or summer
(generally July-August with some September data) seasons. Data for the determination of trends are restricted to
offshore stations (see Dove and Chapra, 2015) sampled at the surface during spring cruises.
Current Status
The current status of spring total phosphorus concentrations in 2013-14 is shown graphically in Figure 1. The objec-
tive concentration of 5 |ig TP/L is achieved in lakes Superior and Huron as well as Georgian Bay, with the exception
of some embayments although it should be noted that these exceedances are single values and in other years the ob-
jectives have been met at these sites. In Lake Michigan, current concentrations are well below the objective of 7 |ig
TP/L. Concentrations in Lake Erie are highly variable. In some years, a majority of the lake at the time of the spring
cruise is meeting objectives (e.g., 2012); in other years (e.g., 2011, 2013) all stations exceed objectives, indicating
elevated nutrient concentrations. In Lake Ontario, concentrations meet the objectives at most offshore stations and in
the northeast portion of the lake, but concentrations in the west, along much of the southern shore and parts of the
northern shore exceed the objective. The current status of the bioavailable portion of phosphorus (soluble reactive
phosphorus) is very similar to that for total phosphorus, with SRP comprising between 15 - 25% of total phospho-
rus, depending on location. There is no objective for SRP against which to compare current values.
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STATE OF THE GREAT LAKES 201 7
Temporal Trends
The long-term trends of offshore total phosphorus are shown in Figure 2. All of the lakes show statistically signifi-
cant long-term declining trends. For Lake Superior, the rate of change is very slow and the statistical significance of
the trend relies on the inclusion of certain data points and further time is needed to confirm this result. In Lake Hu-
ron, no significant change is noted until the mid- to late-1990s, with a significant and dramatic decline noted since
that time. Georgian Bay data are not shown here but the temporal trends closely match those in Lake Huron. In Lake
Ontario, two periods of decline are observed. The first occurred in response to the phosphorus management im-
provements legislated in the 1970s, resulting in dramatic declines of TP in Lake Ontario from approximately 23
|ig/L in 1972 to 10 |ig/L in 1988. Since that time, concentrations have declined more gradually to approximately 6
|ig/L in 2013. In Lake Erie, high spatial and inter-annual variability is observed. The central basin is shown in Fig-
ure 2 to represent the lake and we interpret the trends to indicate high concentrations in the 1970s (in the range of 18
|igP/L) and lower concentrations in the 2000s (roughly 12 |igP/L). The variable nature of the data obscure any re-
cent trends.
The long-term trends of offshore soluble reactive phosphorus are similar to those for total phosphorus, but the values
are lower. In Lake Ontario, the ratio of SRP:TP also shows a striking linear decline. In the 1970s, about 70% of the
total phosphorus in the offshore comprised the soluble reactive fraction. By 2012 the ratio had declined to only 20%.
Together, these trends indicate a shortage of phosphorus in offshore regions of the lake.
Trends of spring total oxidized nitrogen (TON) are represented as nitrate (N03) in Figure 3 (note that nitrate com-
prises more than 95% of TON in the Great Lakes). Unlike phosphorus, concentrations of nitrate have increased over
time, but those increases have slowed and even reversed in recent years, especially in the lower Great Lakes. Con-
centrations of nitrate are lowest in Lake Erie, the most productive of the lakes, where it is taken up by algae, phyto-
plankton and other consumers. High nitrate is protective against blue-green algae blooms, because these algae have
a competitive advantage in their ability to use atmospheric nitrogen when nitrogen is low in water. Total nitrogen
can be estimated for offshore waters using nitrate concentrations (Dove and Chapra, 2015), and there is an excellent,
long-term record of nitrate available. Because nitrate has increased over time and phosphorus has declined, it is
therefore phosphorus, not nitrogen, which is increasingly limited in recent years. Currently, all of the lakes are phos-
phorus limited, with the most extreme limitation occurring in the upper Great Lakes. The ecosystem objective to
maintain ratios above the Redfield ratio of 7.2 is currently being met in all of the lakes, with Lake Erie showing
greatest risk (ratios closest to the objective; Figure 4).
Inferred Nutrient Loadings
The offshore nutrient objectives represent expected conditions when tributary nutrient loadings targets are achieved.
The most recent loadings estimates (obtained by summing all reported sources, scaling these to the lake-wide scale
and estimating between-lake transfers) show that loading targets are only occasionally exceeded and that there are
no significant temporal trends since the 1980s with the exception of declines noted for Lakes Ontario and Huron
(Dolan and Chapra 2012; Maccoux et al. accepted). Despite the recent success of largely meeting the loading tar-
gets, there is increasing evidence of nutrient imbalances in the lakes; that is, eutrophic (nutrient-rich) nearshore con-
ditions may be persisting (or resurging) despite low offshore nutrient concentrations. In this way, the existing objec-
tives may not be sufficient to protect all areas of the lakes.
Both the Substance Objectives for Total Phosphorus Concentration in Open Waters and the Phosphorus Load Tar-
gets are due for assessment and revision as necessary. Loadings targets have recently been adopted for Lake Erie;
these call for a 40% reduction in annual total phosphorus loads to the western and central basins of Lake Erie and a
40% reduction in spring total and soluble reactive phosphorus loads from certain tributaries (Enviromnent and Cli-
mate Change Canada and U.S. EPA, 2015). Work on the other lakes is being initiated, where the need to maintain or
even enhance offshore nutrients will need to be considered.
Lake trophic status
A lake's trophic state describes its nutritional or growth status. Ranges of phosphorus, together with the response
variables of chlorophyll a (an indicator of the amount of algae and phytoplankton in a sample) and Secclii disk depth
(an indicator of water clarity) are used in combination to determine the trophic status. The objectives vary between
each of the Great Lakes and for Lake Erie the objectives vary by basin. Collectively, the information shows that the
open portions of lakes Superior, Michigan and Huron are in the ultraoligotrophic range (i.e., very low in nutrients
and below the objective of oligotrophy). Lake Ontario is in the oligotrophic range (i.e., nutrient poor and below the
objective) and Lake Erie ranges from eutrophic in the west (nutrient rich and exceeding the objective) to meso-
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STATE OF THE GREAT LAKES 201 7
trophic in the central basin (exceeding the objective) and oligotrophic in the east (below objective). This indicates
that the offshore regions of the Great Lakes are nutrient deficient with the exception of Lake Erie which suffers from
elevated nutrient conditions.
Other Spatial Scales - Nearshore Regions
This sub-indicator report mainly on total phosphorus (TP) concentrations in the offshore. These offshore waters best
indicate long-term trends because, in contrast to shallower, nearshore waters, they are less influenced by local pollu-
tant discharges. As demonstrated here, offshore nutrient concentrations in most lakes have declined over time, are
below objectives, and may now be too low to support healthy levels of lake productivity.
At the same time as offshore TP concentrations are reaching unprecedented lows, many nearshore regions of the
Great Lakes are experiencing nuisance algae problems. The extent of the algae problem seems to be of similar
magnitude as was experienced in the 1970s (GLWI2005), despite significantly lower phosphorus loads since that
time (Chapra and Dolan 2012). In Lake Michigan, growth of the benthic alga Cladophora remains a problem, mak-
ing some beaches unswimmable (Bootsma et al. 2015). Cladophora blooms appear to be most extensive in eastern
Lake Erie, while the western Lake Erie basin is also plagued by the more toxic Microcystis algal blooms (Stumpf et
al. 2012). In Lake Huron, the benthic alga Chara is flourishing on the east side and additional algal species are as-
sociated with other fouling issues in the lake; however Cladophora alone is not the only contributor (E.T. Howell,
personal communication). In Lake Ontario, nearshore regions on both the south and north shores routinely experi-
ence nuisance benthic algae blooms.
The causes of the nearshore algae resurgence are not clear. For example, in Lake Erie, loadings of phosphorus ex-
hibit high inter-annual variability but have decreased since the 1970s and show no temporal trend since the late
1980s (Maccoux et al. 2016). The invasion and proliferation of non-indigenous mussels (Dreissena spp.) may be
altering nutrient dynamics, simultaneously depleting offshore nutrients and elevating concentrations in nearshore
regions, resulting in a "feast and famine" dichotomy that is unbalanced, especially for lakes Ontario, Michigan and
Huron. Lake Erie is an exception, where phosphorus concentrations are above objectives throughout the western
basin and much of the central basin and there is no sign of a decline. Symptoms of nearshore eutrophication (elevat-
ed nutrients) are observed.
Linkages
•	Benthos - nutrient concentrations impact benthic community abundance and composition
•	Cladophora - high nutrients in the nearshore favour the proliferation of nuisance benthic algae
•	Dreissenid Mussels - Dreissenids influence the cycling of phosphorus, which may alter in-lake concentra-
tions, their relationships with loads and may enhance the growth of Cladophora
•	Harmful Algal Blooms -nutrient concentrations impact the development, timing and severity of harmful
algal blooms
•	Phytoplankton (open water) - nutrient concentrations impact phytoplankton community abundance and
composition
•	Wastewater treatment can reduce the nutrient loading to the lakes.
•	Water Quality in Tributaries - tributary nutrient concentrations impact nutrient concentrations in Great
Lakes Waters
•	Zooplankton - nutrient concentrations impact zooplankton community abundance and composition via the
food web
Comments from the Author(s)
Continued water quality monitoring in the Great Lakes and measurements of nutrient loads are required in order to
inform management, track progress and update status and trend information.
This sub-indicator provides both the long term record and recent trends (where statistically apparent). The emphasis
is on recent trends as these are most relevant for contemporary nutrient management. Continued monitoring and
reporting of offshore conditions is critical to maintain our ability to assess Great Lakes status and trends.
Possible improvements for future reporting include the incorporation of additional information from the Great Lakes
connecting channels because these rivers can be primary drivers of water quality in the lakes. For some of these
channels (e.g., St. Clair River, Niagara River, St. Lawrence River), long-term, high-frequency and high-quality in-
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STATE OF THE GREAT LAKES 201 7
formation is available and may be used to inform nutrient status and trends and to assess shorter-term (e.g., seasonal,
cyclical) fluctuations that cannot be assessed with other available data.
We also aim to incorporate data collected by other State and Provincial environmental agencies in order to report
more fully on nearshore nutrient status and trends, including coverage in Green Bay. This will require data integra-
tion and further consideration of interagency laboratory comparability.
Integrating nutrient loading information to this sub-indicator will be a challenge without concerted efforts to im-
prove load monitoring in the basin. Important work to coordinate, collect and manage such information has been
initiated for Lake Erie
Assessing Data Quality
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not
Applicable
1. Data are documented, validated, or
quality-assured by a recognized agency or
organization
X





2. Data are traceable to original sources
X





3. The source of the data is a known,
reliable and respected generator of data
X





4. Geographic coverage and scale of data
are appropriate to the Great Lakes basin
X





5. Data obtained from sources within the
U.S. are comparable to those from
Canada

X




6. Uncertainty and variability in the data
are documented and within acceptable
limits for this sub-indicator report
X





Clarifying Notes:
Comparison of US and Canadian TP data indicates consistently lower values are obtained by the U.S. EPA relative to Environment
and Climate Change Canada. Statistical tests were performed for lakes Ontario and Huron, where some shared stations permit paired
t-test comparisons. The results indicated significantly higher values obtained by ECCC compared to the U.S. EPA (p<0.001). The
differences amount to approximately 1.9 and 1.6 (.ig P/L for lakes Ontario and Huron, respectively. No significant difference was
observed for laboratory quality assurance (filtered) samples over many years (1999-2008), indicating agreement between laboratory
instruments used. The difference occurs independently of field sampling date and location and is likely due to differing sample
digestion durations. Samples collected by Environment and Climate Change Canada are digested for a minimum of 30 minutes once
digestor temperature has reached 121°C. Samples collected by the U.S. EPA are digested for 30 minutes with the oven set to 121°C,
but this includes time for the oven to reach high temperature. The longer digestion of ECCC samples may result in more complete
breakdown of nutrients attached to particles and higher concentrations are measured.
Acknowledgments
Authors: Alice Dove, Enviromnental Scientist, Water Quality Monitoring and Surveillance, Enviromnent and
Climate Change Canada
Eric Osantowski, Physical Scientist, Great Lakes Remediation and Restoration Branch, U.S. EPA Great Lakes
National Program Office
Contributors: E.T. Howell, Enviromnental Monitoring and Reporting Branch, Ontario Ministry of the Enviromnent
and Climate Change.
Information Sources
Bootsma, H.A., Rowe, M.D., Brooks, C.N., Vanderploeg, H.A. 2015. Commentary: The need for model
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STATE OF THE GREAT LAKES 201 7
development related to Cladophora and nutrient management in Lake Michigan. J. Great Lakes Res. Doi:
10.1016/j .jglr.2015.03.023.
Dolan, D.M. and Chapra, S.C. 2012. Great Lakes total phosphorus revisited: 1. Loading analysis and update (1994-
2008). Journal of Great Lakes Research 38(4), 730-740.
Dove, A. and Chapra, S.C. 2015. Long-term trends of nutrients and trophic response variables for the Great Lakes.
Limnology and Oceanography. 60(2): 696-721. http://dx.doi.org/10.1002/lno.10055.
Maccoux, M.J., A. Dove, S.M. Backus and D.M. Dolan. 2016. Total and Soluble Reactive Phosphorus Loadings to
Lake Erie: a detailed accounting by year, basin country and tributary. Journal of Great Lakes Research.
Stumpf, R.P., Wynne, T.T., Baker, D.B., Fahnenstiel, G.L., 2012. Interannual variability of cyanobacterial blooms in
Lake Erie. PLoS One 7, e42444.
List of Tables
Table 1. Interim Substance Objectives for Spring Total Phosphorus and Summer Chlorophyll a Concentrations, with
resultant Trophic State
List of Figures
Figure 1. Spatial distribution of total phosphorus (|ig/L) in the Great Lakes based on lake-wide cruises conducted
during the spring 2013 and 2014. Sampling stations are shown as black dots. Environment and Climate Change
Canada and U.S. Enviromnental Protection Agency programs do not monitor all locations.
Data source: Enviromnent and Climate Change Canada and the United States Enviromnental Protection Agency
Figure 2. Long-term trends of offshore, spring (April - May) total phosphorus in the Great Lakes (|ig/L). The inter-
im GLWQA TP objectives are shown as the horizontal dashed lines. The additional data points (circles) for Lake
Michigan prior to 1983 are from Chapra and Dobson (1981), Scavia et al. (1986) and Lesht et al. (1991). Statistical-
ly significant temporal trends are shown as solid lines. After Dove and Chapra (2015).
Data source: Enviromnent and Climate Change Canada and U.S. Enviromnental Protection Agency
Figure 3. Long-term trends of Open Lake, Spring (April - May) Nitrate-plus-Nitrite (N03+N02) concentrations
(|ig/L) in the Great Lakes. Additional data (circles) for Lake Michigan prior to 1983 are from Canale et al (1976),
Rockwell et al. (1980), Mortimer (1981) and Schelske et al. (2006). Statistically significant temporal trends are
shown as solid lines. After Dove and Chapra (2015).
Data source: Enviromnent and Climate Change Canada and U.S. Enviromnental Protection Agency
Figure 4. Trends of open lake, spring ratios of median N03:TP for the Great Lakes. The Redfield ratio of 7.2
mgN/mgP is superimposed as an estimate of the level above which lakes would tend to be phosphorus limited.
Phosphorus limitation is beneficial because nitrogen limitation would favor potentially toxic blue-green algae (cya-
nobacteria). After Dove and Chapra (2015).
Data source: Enviromnent and Climate Change Canada and U.S. Enviromnental Protection Agency
Last Updated
State of the Great Lakes 2017 Technical Report
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STATE OF THE GREAT LAKES 201 7

Total Phosphorus
Chlorophyll a
Trophic
Basin
(H2P/L)
(ugChla/L)
state
Lake Superior
5
1.3
Oligotrophic
Lake Michigan
7
1.8
Oligotrophic
Lake Huron
5
1.3
Oligotrophic
Western Lake Erie
15
3.6
Mesotrophic
Central Lake Erie
10
2.6
Oligomesotrophic
Eastern Lake Erie
10
2.6
Oligomesotrophic
Lake Ontario
10
2.6
Oligomesotrophic
Table 1. Interim Substance Objectives for Spring Total Phosphoras and Summer Chlorophyll a Concentrations, with
resultant Trophic State
Total Phosphorus
Spring 2013 (Lakes Oniano and Superior)
Spring 2014 (Lake Erie. Michigan, Huron and Georgian Bay!
Figure 1. Spatial distribution of total phosphorus ((.ig/L) in the Great Lakes based on lake-wide cruises conducted
during the spring 2013 and 2014. Sampling stations are shown as black dots. Environment and Climate Change
Canada and U.S. Environmental Protection Agency programs do not monitor all locations.
Data Source: Environment and Climate Change Canada and the United States Environmental Protection Agency
Page 309
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STATE OF THE GREAT LAKES 201 7
Lake Superior
Centra Lake Erie
111111111111111111 m 111111111111111111 m 1111
Lake Michigan
Lake Huron
o m o m o 10
N N CO CO O) O)
0)0)0)0)0)0)
omoioomoioo
I^I^COOOOJOJOOx-
0)0)0)0)0)0) ooo
rrrrrrMMM
50
40 —
30 —
Lake Ontario
1111111111111II I | I I 1111111111111II I I | I
o m
1^ 1^
0)0)0)0)0)0)
o m o io
CO CO O) O)
rrrrrrMMM
Figure 2. Long-term trends of offshore, spring (April - May) total phosphorus in the Great Lakes (|ig/L). The inter-
im GLWQA TP objectives are shown as the horizontal dashed lines. The additional data points (circles) for Lake
Michigan prior to 1983 are from Chapra and Dobson (1981), Scavia et al. (1986) and Lesht et al. (1991). Statistical-
ly significant temporal trends are shown as solid lines. After Dove and Chapra (2015).
Data source: Enviromnent and Climate Change Canada and U.S. Enviromnental Protection Agency
Page 310
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STATE OF THE GREAT LAKES 201 7
400 —
300 —
200 —
100 —
Lake Superior
400
800
300 —
Central Lake Erie
200 —
600 —
100 —
Lake Michigan
400 —
400
300 —
200 —
200 —
100 —
Lake Huron
800
Lake Ontario
600 —
400 —
200 —
o w o w o w
S S 00 0O O) (J)
o w o
O O T-
Figure 3. Long-term trends of Open Lake, Spring (April-May) Nitrate-plus-Nitrite (N03+N02) concentrations
(|ig/L) in the Great Lakes. Additional data (circles) for Lake Michigan prior to 1983 are from Canale et al (1976),
Rockwell et al. (1980), Mortimer (1981) and Schelske et al. (2006). Statistically significant temporal trends are
shown as solid lines. After Dove and Chapra (2015).
Data source: Enviromnent and Climate Change Canada and U.S. Enviromnental Protection Agency
Page 311
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STATE OF THE GREAT LAKES 201 7
100
Western Erie
90
A Central Erie
Eastern Erie
Q_
I-
« *
o
z
A A
7.2
A A M"
1965
1975
1985
1995
2005
200
— Superior
-ft.. Michigan
- Huron
—o- Ontario
180
160 -
140 -
O.120 -
40 -
7.2
1975
1985
1965
1995
2005
Figure 4. Trends of open lake, spring ratios of median N03:TP for the Great Lakes. The Redfield ratio of 7.2
mgN/mgP is superimposed as an estimate of the level above which lakes would tend to be phosphorus limited.
Phosphorus limitation is beneficial because nitrogen limitation would favor potentially toxic blue-green algae (cya-
nobacteria). After Dove and Chapra (2015).
Data source: Environment and Climate Change Canada and U.S. Environmental Protection Agency.
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STATE OF THE GREAT LAKES 201 7
Sub-Indicator: Cladophora
Overall Assessment
Status: Poor
Trend: Undetermined
Rationale: Cladophora is broadly distributed over large areas of the near shore regions of Lakes Erie, Ontar-
io, Huron and Michigan. Accrual of biomass to nuisance levels occurs across broad regions of the littoral
zones of Lake Ontario, Lake Michigan and the eastern basin of Lake Erie. Nuisance conditions in Lake Hu-
ron are limited to isolated locations. No recent information exists for Lake Superior. Temporal trends are
difficult to determine because of the lack of binationally consistent monitoring with sufficient spatial and
temporal scope to assess trends in distribution or biomass in all lakes. Empirical and anecdotal evidence sug-
gests that biomass levels in Lakes Erie, Ontario, Huron and Michigan are comparable to those observed in
the 1960s and 1970s, with lower levels observed in the 1980s and 1990s.
Lake-by-Lake Assessment
Lake Superior
Status: Good
Trend: Unchanging
Rationale: Fouling of shorelines by Cladophora has not historically been an issue in Lake Superior. There is no evi-
dence that this status has changed since the last update.
Lake Michigan
Status: Poor
Trend: Undetermined
Rationale: In the two regions of Lake Michigan where regular monitoring of Cladophora is conducted (Milwaukee
and Sleeping Bear Dunes), biomass varies significantly from year-to-year but peak biomass remains above nuisance
thresholds. There is some evidence for a possible declining trend, but this is confounded by high inter-annual varia-
bility. Accumulation of Cladophora on beaches indicates that algal growth rates remain high in many parts of the
lake; however an unchanging trend over the previous 3 years has been seen.
Lake Huron
Status: Fair
Trend: Undetermined
Rationale: Cladophora biomass approaches nuisance thresholds in localized areas over the Canadian shoreline of the
main basin. Cladophora biomass over broader areas of the nearshore zone is generally below nuisance conditions
and occurs in waters deeper than 10 metres. Periodic fouling of shorelines can occur but is generally comprised of
other macroalgae (e.g. Charophytes) and periphyton Cladophora is not found at macroscopically visible levels in
the nearshore of eastern Georgian Bay.
Lake Erie
Status: Poor
Trend: Undetermined
Rationale: Cladophora remains broadly distributed along much of the north shore of the eastern basin. Biomass is
variable from year-to-year but remains at or above nuisance conditions at most sites sampled. Substantial inter-
annual variability in biomass confounds assessment of trends at regional and local scales.
Lake Ontario
Status: Poor
Trend: Undetermined
Rationale: Cladophora is widely distributed in Lake Ontario. Biomass routinely exceeds nuisance conditions in the
western end of the lake where hard substrate dominates the nearshore lake bottom. Surveys from recent years indi-
cate nuisance conditions both in the vicinity of point source inputs, and also in regions remote from any known
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STATE OF THE GREAT LAKES 2017
sources. Inter-annual variability is comparable to that observed in Lakes Erie and Michigan and the lack of con-
sistent monitoring hinders assessment of trends.
Other Spatial Scales
Saginaw Bay
Cladophora is part of a cosmopolitan assemblage of benthic macroalgae in Saginaw Bay linked to episodic fouling
of beaches with decaying organic matter.
Sub-Indicator Purpose
The purpose of this sub-indicator is to evaluate spatial and temporal trends in biomass of Cladophora in the Great
Lakes. Data can be used to infer the availability of Cladophora to be transported to the lake shore where it may foul
beaches and clog water intakes, as well as its potential contribution to other negative impacts such as avian botulism.
Cladophora is also useful as an integrative measure of nutrient loading and nutrient cycling processes within the
Great Lakes.
Ecosystem Objective
Waters and beaches should be safe for recreational use and be free from nuisance algae which may negatively im-
pact drinking water infrastructure and beach use, and which may contribute to negative impacts on ecosystem
health, such as avian botulism. This sub-indicator best supports work towards General Objective #6 of the 2012
Great Lakes Water Quality Agreement which states that the Waters of the Great Lakes should "be free from nutri-
ents that directly or indirectly enter the water as a result of human activity, in amounts that promote growth of algae
and cyanobacteria that interfere with aquatic ecosystem health, or human use of the ecosystem." This sub-indicator
also supports General Objective #2 of the 2012 Great Lakes Water Quality Agreement which states that the Waters
of the Great Lakes should "allow for swimming and other recreational use, unrestricted by enviromnental quality
concerns."
Ecological Condition
Background
Algae occur naturally in freshwater systems. They are essential to the aquatic food web and healthy ecosystems.
However, too much algae can lead to the development of algal blooms, which can be harmful to human health and
the enviromnent.
The fouling of shorelines by large rotting mats of filamentous algae (primarily Cladophora) in the summer months
was a common phenomenon in the lower Great Lakes as far back as the mid-20th century (Taft and Kishler 1973).
Cladophora is a filamentous green algae that grows on hard substrates in all of the Great Lakes. Generally attributed
to excess phosphorus pollution, these blooms elicited public outcry and were identified as an emerging issue under
the 1978 Great Lakes Water Quality Agreement. Targeted research in the late 1970s generally concluded that phos-
phorus (P) load reductions being implemented under the GLWQA would contribute to a reduction of nuisance
Cladophora growth (Auer 1982). A brief research and monitoring interlude from the mid-1980s to mid-1990s cou-
pled with a small number of documented (Canale and Auer 1982, Painter and Kamaitis 1987) and anecdotal reports
(e.g.. Painter and McCabe 1987) has been interpreted as an indicator of success of P control programs in reducing
Cladophora growth. In the 1980s and early 1990s, basin-wide restoration efforts were successful in reducing nutri-
ent-related runoff, and conditions in the lakes improved. These efforts included the regulation of phosphorus con-
centrations in detergents, investments in sewage treatment, and the development and implementation of best man-
agement practices on agriculture lands and in expanding urban areas. However, by the mid-1990s, reports of shore
fouling began to appear in Lake Erie (Howell 1998) and by the early 2000s, had extended to Lakes Ontario (DeJong
2000, Malkin et al. 2008) and Michigan (Bootsma et al. 2005). With the recent resurgence of the nearshore algal
problem in some areas and with other changes in the ecosystem, the problem has become more complicated. A more
detailed and considered history of Cladophora in the Great Lakes is provided in Higgins et al. (2008) and Auer and
Bootsma (2009).
The negative economic, aesthetic and recreational use impacts of excessive Cladophora growth and biomass are
well documented and include the fouling of beaches and residential shorelines, clogging of municipal and industrial
water intakes, and unpleasant aesthetics associated with rafts of decaying organic matter along the lake shore (Hig-
gins et al., 2008, Peller et al. 2014). The ecological impacts of excessive Cladophora growth and biomass are less
well understood, but may nonetheless be important. Cladophora is generally considered to be a poor food resource
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STATE OF THE GREAT LAKES 2017
for grazers (Dodds and Gudder 1992), thus expansive standing crops may represent a substantial (albeit perhaps
temporary) nutrient sink over much of the growing season (Higgins et al. 2005). Accumulation of attached or drift-
ing mats can result in transient hypoxic conditions in shallow littoral regions (Gubelit and Berezina, 2010) which
may have deleterious impacts on invertebrate communities (Berezina and Golubkov 2008), while Cladophora that is
deposited on the shoreline may harbour pathogenic organisms and create an enviromnent conducive to the develop-
ment of botulism, thus creating a risk for fish and wildlife (Chun et al. 2015).
Current conditions
Locations affected by excessive Cladophora biomass continue to be found across much of Lake Ontario and Michi-
gan, as well as the northern shore of eastern Lake Erie. In Lake Huron reports of excessive biomass are generally
restricted to isolated locations along the south-eastern shore of Lake Huron (Figure 1). A recent assessment of satel-
lite imagery from 2008-2011 indicated that Cladophora and other submerged aquatic vegetation cover up to 40 % of
the neashore lake bottom visible to satellites (Lake Huron -15%, Lake Erie - 23%, Lake Michigan - 28%, Lake On-
tario - 40 %; Brooks et al. 2014).
Lake Michigan
In Lake Michigan, anecdotal evidence (primarily observations of accumulation on beaches and fouling of water in-
takes) indicates that Cladophora has been growing at nuisance levels since the mid-to late 1990s. Biomass has been
monitored at one location about 7 km north of Milwaukee Harbor since 2006. These dry weight measurements indi-
cate that peak biomass varies from year-to-year ranging from a high of 268 g m~2 in 2008 to a low of 38 g m~2 in
2014 (Figure 2). Highest biomass levels were observed between 2006 and 2011. Since 2012, peak biomass levels
have been more moderate, but there continue to be problems with fouling of beaches and water intakes. The 10-year
record suggests that there may be a trend toward lower peak summer biomass, but the time series is not long enough
to confirm whether this is a real trend or simply inter-annual variation.
Lake Huron
Cladophora biomass can reach nuisance conditions in the vicinity of local nutrient inputs in isolated regions along
the south-eastern shore. Episodic fouling of beaches has occurred sporadically since 2004 although the degree of
shore fouling is considerably less severe than that experienced in Lakes Michigan, Erie and Ontario. In 2013 and
2014, limited measurements were made at a depth of 1 metre near Goderich ON (affected by a municipal WWTP
discharge) and Kincardine ON (affected by a small inflowing agricultural drain). Biomass at Goderich was 46 g m"2
and 49 g m"2 respectively, while at Kincardine biomass was 21 g m"2 and 33 g m"2 respectively. Similar observations
of localized growth of Cladophora directly adjacent to nutrient discharge points have been observed over the coast-
line in recent years as in the past (Barton et al. 2013; Howell personal observations). The spatial extent of growth at
these locations was limited. Over broader stretches of the eastern shoreline, Cladophora grows to depths of 20 m,
although biomass rarely exceeds 10 - 20 g m"2 (Barton et al. 2013). A 2014 study by the Ontario Ministry of the
Enviromnent and Climate Change of 48 sites in eastern Georgian Bay found little Cladophora over the hard and
mostly bare substrate surveyed (Figure 1).
Lake Erie
In Lake Erie, Cladophora has reached nuisance levels since the mid-1990s, primarily along the northern shore of the
eastern basin (Howell 1998). Biomass has been measured infrequently since 1995, with significant effort in 2001 -
2002 (Higgins et al. 2005) comprising the most spatially comprehensive dataset. Since 2010, regular assessment of
biomass has occurred at 4-5 transects in the vicinity of the Grand River, extending eastward to Port Colborne, ON
by the Ontario Ministry of Enviromnent and Climate Change and Enviromnent and Climate Change Canada. Recent
measurements at sites in shallow water (~ 3 m) indicate that inter-annual variability is substantial, and peak seasonal
biomass in July ranged from a high of 308 g DW m"2 in 2012 to a low of 34.4 g DW m"2 in 2014 (Figure 3).
Lake Ontario
It has been apparent for many years that portions of the shallow lakebed of Lake Ontario are widely and extensively
colonized by Cladophora (Wilson et al. 2006; Malkin et al. 2008; Higgins et al. 2012). The trajectory of changes
over the years is broadly similar to Lake Erie and Lake Michigan. The onset of the recent high levels of Cladophora
by about year 2000 at the latest has been persistent. Measurements of Cladophora have been made at a wide range
of locations and sporadically over the years, but with no systematic monitoring over time. General features of Clad-
ophora over hard substrate, confirmed in more recent surveys by the Ontario Ministry of the Environment and Cli-
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mate Change and Environment and Climate Change Canada in 2012, 2013 and 2015, include high surface coverage
to the point of blanketing substrate, strong attenuation of biomass with depth but persisting cover to depths > 10-20
m, typically co-occurring with high dreissenid mussel cover, and frequently with other filamentous green algae, no-
tably Spirogvra. Biomass levels of >50 g m~2 have been observed at sites surveyed on the eastern central and west-
ern shores of the main basin of the lake, however, there appears to be less data on the occurrence of Cladophora in
the eastern basin of the lake. High short-term and spatial variability in biomass levels make inferences on difference
among areas or over the years challenging. The finding of Higgins et al. (2012) indicating higher Cladophora levels
in areas of urbanized shoreline remains a central and significant hypothesis influencing the direction of recent stud-
ies (e.g., Auer 2014) given the needs for nearshore phosphorus management over the developed shoreline of the
lake.
Summary
The proximal drivers of Cladophora growth are reasonably well understood. Numerical models that are driven pri-
marily by three variables - temperature, irradiance, and soluble reactive phosphorus (SRP) concentration - perform
moderately well in simulating Cladophora growth (Higgins et al. 2006, Malkin et al. 2008, Tomlinson et al. 2010,
Auer et al. 2010). However, there remains some uncertainty about the processes that ultimately regulate these driv-
ers. There is strong evidence that dreissenid mussels play an important role, due both to their ability to clear the
water column (and hence increase in situ irradiance) by removing particulate material, and their recycling of phos-
phorus, making dissolved phosphorus more available in the near-bottom layer where Cladophora grows (e.g., Oz-
ersky et al. 2009, Martin 2010, Dayton et al. 2014). It is unclear at present if the enhanced phosphorus near the
lakebed is derived from excretion of soluble nutrients as metabolic wastes (i.e. Conroy et al. 2005) or perhaps en-
hanced remineralization of non-edible algae and other detritus that accumulates within mussel beds. The role of
dreissenids is highlighted by observations of increased production rates of Cladophora in the presence of mussels
(e.g., Davies and Hecky 2005) and the presence of high Cladophora biomass even in regions where there are no
major nutrient inputs (Wilson et al. 2006, Depew et al. 2011), unlike the 1960s and 1970s when Cladophora was
associated primarily with point sources of nutrients. For example, in 2015 Cladophora biomass in Good Harbor Bay
(near Sleeping Bear Dunes) in Lake Michigan, where there are no major tributary sources of nutrients, peaked at 186
g DW m~2, while peak biomass several kilometres north of Milwaukee Harbor, which is a major nutrient source, was
38 g DW m"2. However, in other regions, (i.e. Lake Ontario) there is evidence that local nutrient inputs do indeed
have a local influence on Cladophora biomass (Higgins et al. 2012).
Biomass and Phosphorus status as indicators
Monitoring of biomass has been and remains a favored metric for assessing the status of Cladophora. Peak standing
crops are usually achieved in mid-summer, although the exact timing varies between years and locations. Growth
rates and loss processes (i.e. sloughing) are known to vary over short term periods (hours to days) in response to
enviromnental conditions (i.e. wind and wave action, turbidity, nutrient supply, thermal regime). This generally
leads to significant spatial and temporal variability in attached biomass at a given point in time (e.g.. Figure 3).
Comparisons of point-in-time measurements of biomass across spatial and temporal gradients may be misleading
without appropriate consideration of enviromnental conditions.
Approaches for monitoring Cladophora were reviewed in the previous status report. These include collection of
grab samples at selected monitoring locations (Higgins et al. 2005), hydro-acoustic methods (Depew et al. 2009),
and remote sensing (Schuchman et al. 2013). Recent studies suggest that in situ monitoring using time lapse image-
ry may also be a useful method for monitoring Cladophora biomass (Bootsma et al. 2015). Each of these approaches
has advantages and disadvantages related to spatial coverage, quantitative accuracy and precision, technical difficul-
ty, and cost. For example, remote imaging and acoustic survey methods offer potential to expand the geographic
scope of assessment and subsume some of the variability in biomass induced by processes operating on the metre to
sub-kilometre scale (i.e. substrate patchiness, degree of exposure, variation in light climate), however they suffer
from precision and accuracy issues when estimating biomass. Even among quantitative studies, differences in proto-
cols and approaches to collecting biomass may add additional uncertainty. Specific challenges that remain include:
1) Determining the accuracy with which areal biomass can be determined with satellite imagery; 2) Development of
protocols for selection of sentinel sites; 3) Development of sampling / measurement methods and approaches that
are relatively simple while accounting for spatial and temporal variability.
The P content (or P status) of algal filaments has long been considered a useful metric for assessing the status of
Cladophora and the potential for P management to be effective in controlling growth. Expressed most commonly as
the proportion of dry weight (% DW; QP), the P content of the alga is directly related to its capacity for future
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STATE OF THE GREAT LAKES 2017
growth (Auer et al. 2010). QP is thought to provide a time-integrated measure of algal exposure to P that a) removes
uncertainty in P supply created by point in time measures of SRP from the overlying water column (which are fre-
quently near or below the detection limit) and b) represents exposure and uptake of P by the alga in its physical habi-
tat (i.e. at the lakebed). In general, values exceeding 1.6 mg g"1 (0.16 %) are considered P saturated, values between
1.6 and 0.6 mg g"1 (0.16 - 0.06 %) are considered P limited, while values below 0.06 % are considered critically
limiting and insufficient to sustain net positive growth rates.
QP may be influenced to a large degree by light availability (i.e. water clarity), as the lower growth rates associated
with lower irradiance allow for greater P accumulation in Cladophora tissue. Research conducted in Lake Michigan
in 2015 revealed that Cladophora biomass can vary by more than 10 fold within a distance of 10 km. In the same
survey, Cladophora P content was found to vary more than 3 fold and Cladophora biomass was negatively correlat-
ed with QP, suggesting that QP alone is not a good indicator of P availability and growth potential. Similar observa-
tions were documented in eastern Lake Erie over 2012-2014, with QP increasing (and biomass decreasing) along a
gradient toward the Grand River, which is a significant source of turbidity and P to the Lake Erie nearshore (Figure
4).	These observations underscore the important role of light as a regulator of Cladophora growth, and the im-
portance of considering light climate when interpreting QP.
A further question when considering biomass level as an indicator of shore fouling is uncertainty in the degree to
which high levels of biomass on the lakebed manifest as shore fouling when biomass extends deeper than the shore-
line fringe. In a broad sense, shore fouling concerns map to biomass levels on the lakebed yet the specifics of foul-
ing problems in an area may not. For example, Riley et al. (2015) found that structural development of beaches (i.e.
breakwalls, jetties and piers) were important predictors of the degree of Cladophora fouling on Lake Michigan
beaches and Barton et al. (2013) found that accumulation of algae on Lake Huron beaches was greatest where shore-
line features intercepted nearshore currents. Despite these and other limitations, the presence of excessive biomass at
a given location is likely to indicate the potential for shore fouling and other negative impacts.
Monitoring
The lack of a framework for Cladophora monitoring has been repeatedly cited as a major impediment to understand-
ing the status and trends of Cladophora in the Great Lakes. Since the early 2000s, much if not most of the infor-
mation on Cladophora has been generated as a result of targeted research efforts by academic institutions and/or
occasional and opportunistic ad-hoc surveys conducted by government agencies. As a result, there is limited ability
to extrapolate results from a particular study site to larger areas or assess differences among studies/surveys as an
indicator of spatial variability.
A recent assessment of available historical and contemporary biomass data from Lakes Huron, Erie and Ontario
indicates that inter-annual variability is considerably greater than spatial variability (site to site variability) (Figure
5).	Such structure in variance implies that, for management relevant time scales (i.e. 5-10 years), a large sampling
effort would be required to detect trends unless the change in biomass is substantial (Figure 6). This does not mean
that current survey approaches are unimportant, as spatial surveys can generally provide information on the spatial
extent of nuisance conditions. On the other hand, if temporal trends are of interest, targeted study at a smaller num-
ber of sites may be better suited to determining the presence of a trend. Regardless of the approach taken, it will be
important that monitoring plans clearly define their objectives as well as the magnitude and type of change that
needs to be detected. With this in mind, it may be prudent to consider a tiered or nested approach to monitoring. For
example, recently developed approaches (i.e. remote sensing or acoustic measurements) or simple surveillance with
underwater video may prove useful for defining broader regions of interest where accumulation of nuisance biomass
is an issue, or assessing the extent of problem conditions. Representative sentinel sites can be nested within these
broader regions and monitored with sufficient frequency to establish confidence in trends that may be observed and
then help to inform programs and policies affecting a larger geographic area. Using this approach can reduce the
amount of monitoring that needs to occur. No such framework currently exists, but would be an important develop-
ment toward management of the Cladophora problem.
Linkages
Linkages to other sub-indicators in the indicator suite include:
•	Benthos (open water) - benthos diversity and abundance may be correlated with the occurrence levels of
Cladophora and connected by indirect mechanisms that are poorly understood.
•	Dreissenid Mussels - Cladophora is significantly influenced by the state of water clarity and nutrients in
the Great Lakes, which are influenced by dreissenid mussel populations.
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STATE OF THE GREAT LAKES 2017
• Water Quality in Tributaries - Nutrient loading from tributaries can have both an immediate and long-term
effect on Cladophora growth. Likewise, tributary loads of suspended sediment and coloured dissolved or-
ganic material affect water clarity in the nearshore zone, which in turn affects light availability for Clado-
phora growth.
This sub-indicator also links directly to the other sub-indicators in the Harmful and Nuisance Algae indicator.
Improved wastewater treatment and sustainable agriculture practices which result in decreased nutrient loadings to
the Great Lakes may also result in decreases in Cladophora biomass.
Comments from the Author(s)
The issue of Cladophora in the Great Lakes merits sustained integrated research and monitoring because the symp-
toms of coastal impairment cannot be easily ignored given the proximity of the problem to recreational and industri-
al users. Given the apparent sensitivity of Cladophora to very low levels of SRP (Auer et al. 2010), the principal
challenge is a better understanding of the relative contributions of nutrient supply from both lake-wide and local
sources, as well as the internal processes that regulate phosphorus supply to Cladophora growth.
Following a robust binational science-based process and extensive public consultation Canada and the U.S. have
adopted phosphorus reduction targets (compared to a 2008 baseline) for the Western and Central basins of Lake Erie
to address algal toxins and low -oxygen (hypoxic) areas.
For the Eastern Basin, a target has not been recommended to address nuisance algae (Cladophora) at this time.
Nonetheless, it is important to note that targets have been recommended for the Western and Central Basins and
work in concert, not in isolation. Because all tributaries to Lake Erie, including the Detroit River and the Huron-Erie
Corridor, contribute phosphorus loads to the Eastern Basin, the reductions needed to address algal blooms and hy-
poxia may lower the phosphorus concentrations in the Eastern Basin as well. This may help address nuisance algal
issues in the Eastern Basin, while maintaining enough nutrients to support the fisheries. Further work to establish
targets that will minimize impacts from nuisance algae in the eastern basin of Lake Erie continues.
Evaluating the current status of Cladophora is a somewhat subjective exercise, based on measurements of biomass
when and where available, the frequency and magnitude of accumulation on beaches, and the fouling of water in-
takes. From a management perspective, it would be ideal to designate a biomass target, which would be useful not
only for the purpose of assigning a status, but also for developing management strategies with specific, quantitative
objectives, the most obvious being nutrient loading targets. As discussed in the previous status report, and in the
commentary by Bootsma et al. (2015), a dry biomass of 50 g m~2, which was suggested as a nuisance threshold for
Lake Huron in the early 1980's (Canale and Auer 1982), may now be well above the level that leads to a "nuisance"
and beneficial use impairment, because nuisance growth is no longer restricted to nearshore regions adjacent to point
nutrient sources, and the depth range of Cladophora has increased due to greater water clarity. Other factors also
confound the use of a single biomass target. In nearshore regions with sparse rocky substrate, biomass on rocks may
exceed 50 g m~2, but spatially averaged biomass may be well below that level, resulting in little accumulation on
shore. Also, standing biomass may be a poor indicator of the actual amount of biomass available for accumulation
on shorelines, because biomass is not necessarily correlated to production. A significant portion of Cladophora pro-
duction may be lost to sloughing (Canale and Auer 1982), and in summers when sloughing rates are high (due to
wave-induced turbulence or high temperatures), standing biomass may remain low while the availability of Clado-
phora for accumulation on beaches is high. While this might suggest that the frequency and magnitude of accumu-
lation on the shoreline is a more useful measure, this can also be misleading, as shoreline accumulation is stochastic
and subject to the vagaries of nearshore currents and waves. Reliable evaluations of the status of Cladophora will
ideally depend on measurement of more than one variable, such as biomass. Additional measurements that will
support evaluation, and lead to a better understanding of the factors and mechanisms that regulate Cladophora in-
clude tissue P content, water clarity (along with solar radiation), and growth rate. While direct measurement of
growth rate is technically more challenging than measurement of biomass, it may be possible to use a proxy for
growth rate, such as the 13C:12C ratio of Cladophora.
The designation of Cladophora as a nuisance is based primarily on its impact on shoreline conditions, which are the
most visible to the public. As discussed above, there are a number of less obvious, and less well understood ways in
which Cladophora affects nutrient and trophic dynamics (Turschak et al. 2014) and contaminant transfer (e.g. Lepak
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STATE OF THE GREAT LAKES 2017
et al. 2015). These processes ultimately influence ecosystem integrity and beneficial uses, so a rigorous assessment
of the status of Cladophora will require these factors be increasingly understood.
Assessing Data Quality
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not
Applicable
1. Data are documented, validated, or
quality-assured by a recognized agency or
organization

X




2. Data are traceable to original sources

X




3. The source of the data is a know n,
reliable and respected generator of data

X




4. Geographic coverage and scale of data
are appropriate to the Great Lakes Basin


X



5. Data obtained from sources within the
U.S. are comparable to those from
Canada

X
X



6. Uncertainty and variability in the data
are documented and within acceptable
limits for this sub-indicator report

X
X



Acknowledgments
Authors:
David Depew, Enviromnent and Climate Change Canada. 867 Lakeshore Rd, Burlington. ON. L7S 1A1.
David.Depew@canada.ca
Harvey Bootsma, University of Wisconsin-Milwaukee, School of Freshwater Sciences. 600 E. Greenfield
Ave., Milwaukee, WI. 53204. hbootsma@uwm.edu
Todd Howell, Ontario Ministry of Enviromnent and Climate Change, 125 Resources Rd. Etobicoke, ON.
To dd. H o we 1 lV/o n t a ri o. ca
Alice Dove, Water Quality Monitoring and Surveillance, Enviromnent and Climate Change Canada, 867 Lakeshore
Rd., Burlington, ON. L7S 1A1. Alice.Dove@canada.ca
Veronique Hiriart-Baer, Enviromnent and Climate Change Canada. 867 Lakeshore Rd, Burlington, ON. L7S 1A1.
Veronique.Hiriart-Baer@canada.ca
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List of Figures
Figure 1. Locations within the Great Lakes where Cladophora has been reported since the year 2000. Empty circles
indicate biomass below nuisance threshold of 50 g m~2 DW while filled circles indicate biomass above the nuisance
threshold. Inset panel denotes higher resolution regions of eastern Georgian Bay where Ontario Ministry of
Enviromnent and Climate Change monitoring in 2014 took place.
Data sources: Lake Ontario - Malkin et al. 2008, S. Malkin unpubl.data, Higgins et al. 2012, D. Depew - unpubl.
data. Lake Huron - Barton et al. 2013, d.Depew unpubl.data, T. Howell, unpubl. data. Lake Erie - Enviromnent and
Climate Change Canada unpubl. data, Higgins et al,. 2005, Lake Michigan - Garrison et al.2008, Tomlinson et al.
2010, H. Bootsma unpubl. data, Dayton et al. 2014.
Figure 2. Seasonal biomass of Cladophora from 2006 to 2015 in the near shore of Lake Michigan (~ 7 km north of
Milwaukee, depth = 9m).
Source: H. Bootsma, unpubl. data.
Figure 3. Seasonal plot of Cladophora biomass at 3 m depth from 5 transects in eastern Lake Erie (2012 - 2015).
Panels are arranged in increasing distance from the Grand River, starting with the western most transect and
proceeding eastward (top to bottom). Note different y axis scales on each panel. Notation in upper right corner of
each panel indicates approximate distance from Grand River confluence.
Source - Enviromnent and Climate Change Canada, unpubl.data.
Figure 4. Plot of attached biomass and QP for sites in eastern Lake Erie for the same stations in Figure 3.
Source: Enviromnent and Climate Change Canada, unpubl. data.
Figure 5. Estimated percent of total variation attributed to spatial (site to site), coherent temporal (inter-annual),
ephemeral temporal (intra-annual at a given site) and residual (error or umneasured) variation. Estimates are from a
mixed model for log10 (Cladophora biomass; g DW m~2) versus time for the period 1971 - 2014.
Source: Depew et al. (in prep).
Figure 6. Power curves for detecting temporal trends in Cladophora biomass with increasing number of fixed sam-
ple sites sampled per year and increasing trend magnitude a) -5 % per year, b) -10 % per year, c) -20 % per year, and
d) -40 % per year. Variance components estimated from available data for Lake Erie since 1990, for depths of 0.5 -
3 m during June 1 - Aug 15.
Source: Depew et al. in prep.
Last Updated
State of the Great Lakes 2017 Technical Report
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STATE OF THE GREAT LAKES 2017
Figure 1. Locations within the Great Lakes where Cladophora has been reported since the year 2000. Empty circles
indicate biomass below nuisance threshold of 50 g m2 DW while filled circles indicate biomass above the nuisance
threshold. Inset panel denotes higher resolution regions of eastern Georgian Bay where Ontario Ministry of
Environment and Climate Change monitoring in 2014 took place.
Data sources - Lake Ontario - Malkin et al. 2008, S. Malkin unpubl.data, Higgins et al. 2012, D. Depew - unpubl.
data. Lake Huron - Barton et al. 2013. d.Depew unpubl.data, T. Iiowell, unpubl. data. Lake Erie - Environment and
Climate Change Canada unpubl. data. Higgins et al,. 2005, Lake Michigan - Garrison et al. 2008, Tomlinson et al.
2010, H. Bootsma unpubl. data, Dayton et al. 2014.
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STATE OF THE GREAT LAKES 2017
400
350 -
2 150
M 1
JX

2006 2007 2008 2009 2010
11 2012 2013 2014 2015
Figure 2. Seasonal and long-term Cladophora biomass trends from 2006 to 2015 in the nearshore of Lake Michigan
(~ 7 km north of Milwaukee, depth = 9m).
Source - H. Bootsma, unputfl. data.
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STATE OF THE GREAT LAKES 2017
g
£
Q
00
o
s
4 km W
200 -
100 -
0
20(1
150
100 -
50 -
0
250
200 -
150
100
50
0
400
300
200
100
w/^\ . . .
V
J
~ 2 km E
r
-

K
\.
~ 4 km E
	P-t	r	.	MM 	1	,	,	M-.	,	
6 km E

~ 16 km E
. 1
.. ,
o
01-2012 07-2012 01-2013 07-2013 01-2014 07-2014 01-2015 07-2015 01-2016
Figure 3. Seasonal plot of Cladophora biomass at 3 m depth from 5 transects in eastern Lake Erie (2012 - 2015).
Panels are arranged in increasing distance from the Grand River, starting with the western most transect and
proceeding eastward (top to bottom). Note different y axis scales on each panel. Notation in upper right corner of
each panel indicates approximate distance from Grand River confluence.
Source: Environment and Climate Change Canada, unpubl.data.
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STATE OF THE GREAT LAKES 2017

~
0 2 km E
300 -
~
| 4 km E


~ 6 km E
250 -

16 km E


2 km W
V
V
V
0.00 0.05 0.10 0.15 0.20 0.25
QP(% DW)
0.30
0.35
0.40
Figure 4. Plot of attached biomass and QP (tissue phosphorus concentration, expressed as percent of dry weight) for
sites in eastern Lake Erie for the same stations in Figure 3.
Source: Environment and Climate Change Canada, unpubl. data.
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STATE OF THE GREAT LAKES 2017
CJ
u
.2
C3
C9
**--
O
Spatial Coherent Ephemeral Residual
Temporal Temporal
Figure 5. Estimated percent of total variation attributed to spatial (site to site), coherent temporal (inter-annual),
ephemeral temporal (intra-annual at a given site) and residual (error or unmeasured) variation. Estimates are from a
mixed model for logw(Cladophora biomass; g DW m"2) versus time for the period 1971-2014.
Source: Depew et al. (in prep).
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STATE OF THE GREAT LAKES 2017
— - 30 *i «lcs
-	- -	20 siles
•	.10 SilCi
20
25
30
V'caft
Figure 6. Power curves for detecting temporal trends in Cladophora biomass with increasing number of fixed sam-
ple sites sampled per year and increasing trend magnitude a) -5 % per year, b) -10 % per year, c) -20 % per year, and
d) -40 % per year. Variance components estimated from available datasets from Lake Erie (since 1990) for depths of
0.5 - 3 m between June 1 and Aug 15.
Source: Depew et si. in prep.
— 5 sites
	10 silo
• • • 20 site*
¦ — * 3D sites
T	1	1
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STATE OF THE GREAT LAKES 201 7
Sub-Indicator: Harmful Algal Blooms
Nearshore
Overall Assessment
Status: Fair
Trend: Undetermined
Rationale: There is little systematic monitoring outside of Lake Erie and Lake Ontario to enable a rigorous
evaluation of HABs in the Great Lakes. HABs (toxic and nuisance) have become a major issue for the western
basin of Lake Erie and some eutrophic inshore embayments in lakes Michigan, Huron and Ontario and
recently, Lake St. Clair. Based on available data and best professional judgement, the overall status of the
Great Lakes in deep offshore waters is generally good and although the trend is deteriorating in the
embayments, shallower basins or nearshore areas, the overall trend for the Great Lakes is noted as
Undetermined.
Lake-by-Lake Assessment
Lake Superior
Status: Good
Trend: Undetermined
Rationale: There is little systematic monitoring for HABs (toxic and nuisance) in Lake Superior; however this wa-
terbody is dominated by pico-cyanobacteria that are less likely to produce toxins than the larger cyanobacteria that
typically dominate many of the blooms in the Great Lakes. An occasional local impairment may occur near the
shoreline or in connecting channels.
Lake Michigan
Status: Fair
Trend: Undetermined
Rationale: Offshore waters are generally good but cyanobacteria blooms have been reported in some coastal regions
and eutrophic embayments such as Green Bay, Muskegon Bay and in many of the drowned river mouths along the
western shore. Nuisance algal blooms and beach fouling by Cladophora remains a problem for many of the
beaches and nearshore regions; this issue is assessed further in a separate sub-indictor report.
Lake Huron
Status: Fair
Trend: Undetermined
Rationale: Lake Huron is generally oligotrophic in most areas, but experiences toxic and nuisance blooms in some
nearshore areas, notably Saginaw Bay and Sturgeon Bay (Georgian Bay).
Lake Erie
Status: Poor
Trend: Deteriorating
Rationale: Lake Erie continues to experience toxic and nuisance cyanobacteria blooms throughout the western basin.
Blooms in 2013, 2014 and 2015 were ranked as severe in a number of categories, and the 2014 event caused the
closure of the City of Toledo water supply system. These blooms often expand into the central basin, and have
resulted in loss of economic and ecosystem services provided by the lake. Southwest nearshore areas experience
benthic proliferation of the nuisance cyanobacteria Lyngbva which has been documented elsewhere as a potential
source of toxins.
Lake Ontario
Status: Fair
Trend: Deteriorating
Rationale: Offshore waters remain good with very little cyanobacterial abundance and no reported blooms.
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STATE OF THE GREAT LAKES 2017
However, toxic and nuisance planktonic blooms have been reported in several of the embayments on the New York
side (Sodus Bay, Port Bay), and continue to occur in Hamilton Harbour and the Bay of Quinte on the Canadian side.
Nearshore waters continue to experience nuisance algal blooms of Cladophora.
Connecting Channels
St. Clair River/Lake St. Clair/Detroit River
Status: Fair-Poor
Trend: Deteriorating
Rationale: Lake St. Clair offshore sites have a low plankton biomass representative of upstream Lake Huron
assemblages. Some inshore sites are now experiencing toxic and nuisance planktonic HABs (Thames River mouth
and south shore), and benthic Lvngbva proliferation (southeast shoreline).
Detroit, Niagara and St. Lawrence Rivers
Status: Undetermined
Trend: Undetermined
Rationale: Monitoring for HAB rarely occurs in riverine systems but the occurrence of pelagic blooms is expected to
be low due to the higher flow conditions. Benthic and attached algae are an increasing issue in the St. Lawrence
River and have been associated with toxicity though the extent of this issue is currently unknown. Information on
benthic algal abundance is sparse in the other connecting channels such as the Detroit and Niagara rivers.
Sub-Indicator Purpose
The purpose of this sub-indicator is to assess potential harm to human health, livestock, pets, and other organisms or
ecosystems from harmful algal blooms (HABs). This includes: i) cyanobacteria-based harmful algal blooms
(cHABs): e.g. blooms that are documented to contain cyanobacterial toxins or are dominated by cyanobacteria spe-
cies with the genetic potential to produce toxins, and ii) non-toxic nuisance algal blooms (NABs) e.g. episodes of
high algal/cyanobacterial biomass e that, while not documented to contain toxins, disrupt ecosystem services pro-
vided by the water body.
Ecosystem Objective
Waters should be safe for drinking and recreational use and substantially free from toxic and/or high abundances of
noxious cyanobacteria or algae that may harm human, animals or ecosystem health or have other significant adverse
effects.
This sub-indicator best supports work towards General Objective #6 of the 2012 Great Lakes Water Quality Agree-
ment which states that the Waters of the Great Lakes should "be free from nutrients that directly or indirectly enter
the water as a result of human activity, in amounts that promote growth of algae and cyanobacteria that interfere
with aquatic ecosystem health, or human use of the ecosystem."
Ecological Condition
Background
Harmful cyanobacterial and/or algal blooms (HABs) are a global issue in eutrophic waters with high nutrient load-
ings. HABs can be differentiated from 'non-harmful' (i.e. nuisance) blooms by their impacts on water quality and
the associated biota, generally associated with the production of toxins. Nuisance algal blooms (NABs) are a sepa-
rate subclass of algal blooms whose impact on the ecosystem is generally associated with elevated levels of biomass
and not with the production of toxin. HABs and NABs can have detrimental impacts on ecosystem services provided
by the lake and negatively impact aesthetics or recreation use of the water body. Prior to remediation in the late
1970s, HABs and NABs were a major problem in many offshore and nearshore areas in the Great Lakes (e.g. Wat-
son et al. 2008) and at that time, the risk of toxins had not been widely recognized and concerns focused on reduced
aesthetics, taste and odour (T&O), foodweb structure, beach/intake/net fouling and economic impacts. Lake-wide
remediation efforts initiated in the 1980s were mainly directed towards the reduction of point-source nutrient load-
ing, and successfully mitigated many toxic and nuisance algal bloom impairments with progress largely gauged
against the management reduction targets for Total Phosphorus (TP) and chlorophyll a (chl-a). This progress
changed in 2000 with the identification of the toxins produced by blooms of Microcystis in western Lake Erie (Brit-
tain et al. 2000). Because toxin production was not generally recognized as a threat to the Great Lakes in the 1970s,
there are no historical data on their occurrence prior to 2000. It is now recognized that many genera of bloom-
forming cyanobacteria contain both toxic and non-toxic species and that differentiation between the toxic and non-
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STATE OF THE GREAT LAKES 2017
toxic counterparts may not be possible at the level of light microscopy. Current management approaches which tar-
get planktonic (subsurface) chl-a as a measure of total algal biomass and productivity may be using a poor metric for
these events. Newer analytical techniques specifically targeting the pigments produced by cyanobacteria (phycocy-
anin and phycoerythrin) provide a better measure of cyanobacteria biomass but again cannot differentiate between
toxic and non-toxic species. Recognizing this issue, many agencies now specifically test for the hepatotoxin micro-
cystins, a family produced by toxic members of the genus Microcystis, Planktothrix and Dolichospermum (syn. An-
abaena) and extensive monitoring and bloom forecasting programs now exist for Lake Erie, Lake St. Clair and
some of the embayments in lakes Huron and Ontario (e.g.
http://www.glerl.noaa.gov/res/HABs and Hvpoxia/habsMon.html: https://www.hamilton.ca/parks-
recreation/parks-trails-and-beaches/beach-water-quality-in-hamilton).
Most efforts are focused on visible HABs caused by planktonic toxic cyanobacteria, but HABs also can be caused
by benthic/littoral macroalgae. These benthic mats, along with planktonic outbreaks, have shown an apparent resur-
gence, particularly in the lower Great Lakes. Because these events are often episodic, and vary seasonally and inter-
annually in severity and spatial coverage, it is difficult to implement appropriate research, monitoring and manage-
ment programs, particularly in large and complex waterbodies such as the Great Lakes where sampling is often sub-
ject to weather and vessel access. These blooms are not restricted to the lakes themselves and have been reported in
major embayments, tributaries and connecting channels.
Most algal blooms in the Great Lakes are reported in the nearshore areas, which are most prone to shoreline devel-
opment issues, greater influx of nutrients and to some extent, increased public vigilance. The size of nearshore zones
varies from approximately 1-10% in Superior to 60-90% in Erie, as does the influence of physical and climatic fac-
tors (runoff, erosion, thermal bar formation, upwelling/down-welling,alongshore/nearshore/offshore currents, circu-
lation patterns, surface/ground water inputs, lake level regulation, ice formation, etc). As a result, the nearshore
zones are highly dynamic, and there is significant spatial-temporal variance in the areas supporting littoral and
planktonic communities and offshore-nearshore material exchange.
Key aspects of HABs
These are summarized in detail in Watson and Boyer 2008, but some key points are below:
•	HABs cause significant economic harm. Annual estimates vary, but range up to annual 4.6 billion
USD/year in the USA including monitoring, fisheries, tourism, public health & advisory, lost revenue and
property value (Anderson et al. 2000). For the Lake Erie basin alone, a recent report estimated that the ma-
jor 2011 HAB event cost approximately 71million USD; the smaller 2014 HAB event approximately 65
million USD (Bingham et al. 2015). Lost benefits of 1.3-2.2 billion dollars are predicted over the next 30
years with no management action to control the blooms, a cost which could be reduced by 60-75% if reme-
dial action is taken (Bingham et al. 2015; Smith and Sawyer 2015).
•	Not all HABs resemble green paint or pea soup. They are caused by many species, and vary in colour from
green to red and brown. Algal blooms do not always appear as surface scums, and can be difficult to identi-
fy or anticipate. Some blooms are mixed through the water column, or grow in deep water layers, under ice
or as benthic/attached mats.
•	Cyanobacteria produce many toxins which fall into three major categories, based on their activity: liver
toxins (hepatotoxins), neurotoxins and dermal irritants. These toxins vary greatly in their chemical proper-
ties, stability and toxicity. Microcystins (MCs; hepatoxins) are the most stable and prevalent across the
Great Lakes. These toxins can persist in the water column after a bloom has died and disappeared. Many
cyanobacteria and several classes of algae produce volatile organic compounds (VOCs) that cause unpleas-
ant taste and odour in drinking water supplies, but measures of toxins, taste and odour, visible blooms, cya-
nobacteria and algal biomass, and chl-a may or may not be correlated. Toxins are odourless and colourless
and are often poorly related to malodorous VOCs, which are derived from different biochemical pathways.
Both classes of compounds are produced by a diversity of cyanobacteria and algal taxa, and vary in cell
production with enviromnental conditions and growth stage, both among and within species.
•	The term 'algal bloom' is a non-quantitative descriptor for visible increases in free-floating or attached al-
gal/cyanobacterial density, often manifested as scums, mats or water colour, (see e.g. Watson and Molot
2012). Blooms are difficult to define, measure and predict. Blooms can show rapid changes in their spatial
location and abundance. With calm conditions (or overnight), buoyancy-regulating cyanobacteria can float
to the surface and be carried large distances by wind/waves. These may wash onshore, creating patches of
very high toxin levels along beaches. Variations in analytical and sampling methods can lead to inconsist-
encies in the reported levels of these compounds.
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STATE OF THE GREAT LAKES 2017
•	Fluorescence-based, cell counts and other abundance measures (e.g. molecular, biochemical) are often
poorly correlated with each other and actual cell biomass, due to wide variance in pigment content, photo-
acclimation and cell composition. Taxonomic identification of many of the responsible species may be
complex, leading to differences between analysts.
•	Harmful algal blooms are differentiated as having harmful socioeconomic or ecological effects, and may be
caused by algal/cyanobacteria species belonging to many major taxonomic groups. The greatest concern is
with HABs caused by cyanobacteria (cHABs), which include toxic blooms, caused by a subset of cyano-
bacterial species with the capacity to produce one or more toxins (neurotoxins, hepatotoxins or dermatotox-
ins) and currently are the only known sources of algal toxins in inland waters that directly affect humans.
Detrimental health effects from benthic algal accumulations on the shore are more difficult to quantify but
may result in significant socioeconomic and ecological damage.
Current State of HABs in Individual Lakes
Although a HABs Index has been developed for this sub-indicator, there are not enough data available to use this
index to assess status and trends. There are few long term data or rigorous monitoring programs in place outside of
Lake Erie and Lake Ontario, and only a qualitative assessment of the current status in each lake can be made at this
time. Recent efforts to use satellite imagery for measuring and quantifying HABs including NABs are increasing
(e.g. Stumpf et al. 2012), and offer one potential approach that could help to address this issue. The One Health
Harmful Algal Bloom System (OHHABS) will collect data to help health officials understand the severity and ex-
tent of illnesses caused by harmful algal blooms both in people and animals and the occurrence of harmful algal
blooms. OHHABS development began in 2014 as a collaborative effort between state and federal partners. It has
leveraged existing technical capacity for electronic reporting at the Center for Disease Control, lessons learned from
a previous HAB-associated illness surveillance effort that ended in 2012, and support from the Great Lakes Restora-
tion Initiative (GLRI), which will use OHHABS data to evaluate and inform restoration efforts for the Great Lakes
ecosystem.
Lake Superior: There is very little quantitative current information on HABs in Lake Superior. To our knowledge,
severe HABs outbreaks have not been documented recently in this lake and the offshore waters are generally domi-
nated by non-toxic pico-cyanobacteria. Algal biomass, especially for potentially toxic cyanobacterial species re-
mains mostly at low levels, although there may be some local impairment near shoreline development. Localized,
low toxicity blooms have been observed in the connecting channels across the Keweenaw Peninsula
Lake Michigan: Cyanobacteria blooms are reported in many of the river mouths along the eastern shore of Lake
Michigan and eutrophic embayments such as Muskegon Bay and Green Bay, where there has been an increase in
cyanobacterial blooms and hypoxia (e.g. de Stasio et al. 2014). Nuisance algal blooms and beach fouling by Clado-
phora remains an issue for many of the beaches and nearshore regions, especially along the western shoreline and in
the area of Sleeping Bear dunes.
Lake Huron: Lake Huron is generally oligotrophic in most areas, but experiences potentially toxic cHABs in some
nearshore embayments, notably Saginaw Bay which develops toxic summer outbreaks of Microcystis aeruginosa
(see http://www.glerl.noaa.gov/res/HABs_and_Hypoxia/SBMicrocystin.html) and Sturgeon Bay (Georgian Bay)
where blooms have been reported since the early 2000s, and are largely dominated by N-fixing cyanobacteria; tox-
in levels in this embayment are generally low or undetected to date (see Township of the Archipelago Sturgeon Bay
Project Reports).
St. Clair River/Lake St. Clair/Detroit River's status is Fair-Poor. Seasonal sampling along the south shore from
the Thames River to the outflow of the Detroit River into Lake Erie showed high microcystin levels near the Thames
mouth, from blooms dominated by Microcystis (Davis et al. 2014). Lvngbva mats were reported in 2015 along the
Eastern shoreline (Vijayavel et al. 2013). NASA and NOAA Coast Watch satellite imagery showed extensive algal
blooms again in 2015 that covered much of the southern areas of Lake St. Clair. Nearshore sites vulnerable to HABs
have been recently incorporated into the NOAA/GLERL/ECCC tracking and forecasting system; see
http://www.glerl.noaa.gov/res/HABs_and_Hypoxia/STCMicrocystin.html.
Lake Erie: Lake Erie is the most heavily impaired by planktonic cHABs, particularly over the last few years when
satellite images of extensive surface blooms of Microcystis and other cHAB species such as Dolichospermum have
been widely posted (e.g. NOAA; http://coastwatch.glerl.noaa.gov). Toxic cHABs and their causes/management are a
major focus of the IJC and US-Canada working groups and a number of recent studies and initiatives (e.g. IJC Sci-
ence Advisory Board 2013; MERHAB-LGL, Stumpf et al. 2012; Steffen et al. 2014; Watson et al. 2016). Currently,
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STATE OF THE GREAT LAKES 2017
highly resolved data on chlorophyll and toxin levels in the west basin are available online
(http://www.glerl.noaa.gov/res/HABs and Hypoxia/WLEMicrocvstin.html) along with a 'HAB tracker' and weekly
HAB bulletins posted (http://www2.nccos.noaa.gov/coast/lakeerie/bulletin/bulletin current.pdf).
General trends for Lake Erie: Data indicates a high year-to-year variability in bloom intensity, coverage and timing
with a general deterioration in overall status since 2008. Shifts in the physical/chemical/ biological regimes (e.g.
Michalek et al. 2013; Watson et al. 2016) are evident - notably in the western basin. Overall, satellite imagery indi-
cates an increase in the severity of cyanobacteria blooms in the western basin (Figure 1) and some nearshore areas of
the north shore (Point Pelee, Rondeau Bay, Long Point), and a general decline or no change in overall chl-a and total
and/or eutrophic species biomass in the offshore regions of the central and eastern basins. Immense surface blooms
(>20 km2) are now annually recorded in the western basin of Lake Erie near the Maumee and Sandusky rivers (e.g.
Stumpf et al. 2012; Michalek et al. 2013; Steffen et al. 2014;Watson et al. 2016 ). Microcystins (MCs) are the most
common cyanobacterial toxins measured in Lake Erie. Data from 2000-2004 measured a wide range in MC levels
from detection limits (in 2002) to >20 |ig/L (in 2003). Toxicity is not restricted to the western basin and has been
reported in Sandusky Harbor, Presque Isle and in Long Point Bay. Neurotoxins (anatoxin-a, saxitoxin, neosaxitoxin)
occurred at or near detection limits in the open lake waters. Samples collected across the lake between 2003 and
2015 showed the greatest proportion of samples with detectable MC levels from the western basin, although only a
small fraction of these samples exceed the drinking water guidelines of 1.5 |ig/L and even fewer exceed the recrea-
tional contact level of 20 |ig/L.
Wind driven material from west basin blooms intermittently impair central and northern shorelines (e.g. Figure 2) -
although some of these events may be of local origin e.g. near Point Pelee. Blooms are frequently dominated by
potentially toxic non nitrogen-fixers, notably Microcystis and Planktothrix spp., suggesting increased Nitrogen load-
ing or dreissenid activity, although significant blooms of nitrogen-fixers (Dolichospermum and. Iplianizoiiienonj
also occur in both western and eastern basins (Allinger and Reavie 2013). Severe impairments by thick mats of the
cyanobacterium Lvngbva wollei reported in the mouth of the Maumee River between 2006-2009 appear to have
abated (Western Lake Erie Waterkeeper Association unpublished). However, extensive mats of attached green al-
gae, notably Cladophora are showing an increase in abundance along some northern shorelines (Depew et al. 2011;
Watson et al. 2016).
Most impairment occurs at shorelines and beaches and can be manifested as fish/bird kills. Lyngbyatoxins (inflam-
matory/vesicatory and tumour-promoting) were not detected in the mats of Lvngbva wollei proliferating in the
Maumee and Detroit rivers. Geosmin and 2-methylisoborneol (MIB) occur in several areas of the lake (Kutovaya
and Watson 2014) and are likely the cause of annual musty-muddy odour problems in drinking water in supplies in
the western basin (e.g. Toledo). Significant odour is also produced by extensive rotting mats of shoreline attached
algae. In 2014, a Microcystis bloom in the western basin of Lake Erie near the Collins Park Water Treatment facili-
ty serving the City of Toledo resulted in measureable levels of microcystin toxin in the finished drinking water in
excess of 2.5 |ig/L. which is significantly higher than the 1.5 |ig/L drinking water guideline. This resulted in the City
of Toledo being placed under an emergency drinking water degree and severely disrupted city services for nearly
500,000 residents for a period of 5 days.
Lake Ontario: Blooms of cyanobacteria and related impairments (toxins, shoreline fouling, taste and odour) occur
on an annual basis in some nearshore areas, notably Areas of Concerns (AOCs) of Lake Ontario. Outbreaks of high
MC levels and cyanobacteria blooms have been recorded most years in Hamilton Harbour, Bay of Quinte, Oswego
Harbor and the southern shore embayments of New York (Watson and Boyer 2008, Perri et al. 2015). Toxic cHAB-
related beach closures occur annually in Hamilton Harbour, where the Health Agency lias established a systematic
beach monitoring program which includes toxin testing (City of Hamilton 2014).
Spatial and temporal levels of MCs in the Bay of Quinte, Hamilton Harbour, Oswego Harbor (now delisted), Sodus
Bay, and the Rochester Embayments continue to indicate periods of severe impairment of nearshore sites by wind-
blown accumulations of toxic material, where MC levels can reach levels in excess of 500 |ig/L (Watson et al. 2009;
Figure 3). Microcystins and toxigenic Microcystis are also commonly found in many of the nearshore regions and
embayments that span the northern coast of New York State (Perri et al. 2015). While microcystins are certainly the
toxin of most concern in Lake Ontario, recent surveys indicate the widespread occurrence of low concentrations of
anatoxin-a in nearshore embayments (Boyer 2007). The organism responsible for anatoxin-a production is currently
unidentified. Cylindrospennopsins have not been detected (Figure 3).
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STATE OF THE GREAT LAKES 2017
Connecting Channels
There are few studies or reports of HABs in connecting channels although a number of papers report significant
blooms in tributaries to Lake Erie (Maumee, Sandusky; e.g. Kutovaya et al. 2012; Davis et al. 2015). Toxin-
producing A licrocystis blooms have been reported recently in the Detroit River, likely derived from upstream
blooms in Lake St. Clair (Davis et al. 2014) although MC levels reported to date have been below the WHO guide-
line. Toxin-producing (saxitoxin analogues) and taste and odour producing Lyngbva has been reported in the St.
Lawrence River (e.g. Lajeunesse et al. 2012), along with frequent impairments of drinking water and shoreline
odour produced by benthic and epiphytic cyanobacteria (Watson et al. 2008).
Linkages
Increasing nutrient inputs from diffuse and point sources, climate change (severe storm events, differences in insula-
tion/harmful irradiation, ice-cover and mixing), and invasive species (e.g. dreissenid mussels) in the Great Lakes
may lead to an increased risk of more frequent, widespread and severe nearshore (attached/benthic) and offshore
algal blooms and favour the predominance of cyanobacteria, particularly in the more eutrophic areas of the lower
lakes.
Comments from the Author(s)
There are few long term data collected on HABs and more specifically, toxins, in the Great Lakes, making trend
analysis difficult. Differences in sampling regimes and analytical protocols (e.g. surface or integrated sampling;
taxa enumeration; toxin analyses) utilized in past studies affects the ability to compare data and determine long term
trends in toxins and bloom occurrences. Event or response-based sampling also tends to inflate the severity of the
issue by only focusing on times when blooms are in high abundance.
Attention is most often focused on shoreline scums or algal material visible at the surface, particularly for inland
waters where many reported blooms are caused by attached macroalgae (Cladophora, Lvngbva) or large, buoyancy-
regulating cyanobacteria. These buoyancy-regulating taxa can produce rapid surface accumulations from popula-
tions through the mixed layer or deep living/benthic populations. Concentrated surface scums appear, disappear and
migrate rapidly with changes in vertical mixing, currents and wind activity. These can produce rapid changes in
toxin levels along a waterfront or cover extensive areas in large lakes, and are difficult to sample, quantify or pre-
dict.
Beach and shoreline sampling programs require multiple subsites to capture this envelope of spatial/temporal vari-
ance in risk and impairment, which are poorly represented by basin-wide seasonal means. Sampling regimes in the
Great Lakes are often sparse (both temporally and spatially) and are likely to miss spatial and temporal peaks in cy-
anobacterial/algal abundance.
Potential new sources of data that could be used in future evaluations of this sub-indicator, with the application of
the developed index to assess status and trends, include: i) the expanded HABtracker data, available online
(http://www.glerl.noaa.gov/res/HABs and Hypoxia/habsTracker.html); ii) the increased number of drinking water
treatment plants now monitor toxins in the raw water in compliance with state or provincial regulations; ii) beach
closure statistics iii); and more specific data from proactive beach monitoring programmes which are now incorpo-
rating HAB or toxin measures into colifonn surveys.
Assessing Data Quality
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not
Applicable
1. Data are documented,
validated, or quality-assured
by a recognized agency or
organization


X*



2. Data are traceable to
original sources

X




3. The source of the data is a
known, reliable and respected
generator of data


X



Page 334
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STATE OF THE GREAT LAKES 2017
4. Geographic coverage and
scale of data are appropriate
to the Great Lakes basin




X

5. Data obtained from sources
within the U.S. are
comparable to those from
Canada

X




6. Uncertainty and variability
in the data are documented
and within acceptable limits
for this sub-indicator report



X


Clarifying Notes: The sources of data are varied and in many cases, use different sampling and analytical methods.
Monitoring in the lower lakes is generally good but monitoring in the upper lakes Michigan, Huron and Superior is
sparse and largely reactive. * Increasingly, the data are validated and quality controlled by recognized agencies e.g.
NOAA-GLERL, SYNY, ECCC, USGS.'
Acknowledgments
Susan Watson, Enviromnent and Climate Change Canada, Burlington, ON (sue.watson@canada.ca)
Greg Boyer, State University of New York (glbover@esf.edu)
Information Sources
Allinger, L. E., Reavie, E. D„ 2013. The ecological history of Lake Erie as recorded by the phytoplankton communi-
ty. J. Great Lakes Res. 39(3), 365-382
Brittain, S.M., Wang, J., Babcock-Jackson, L., Cannichael, W.W., Rinehart, K.L., Culver, D.A., 2000. Isolation and
characterization of microcystins, cyclic heptapeptide hepatotoxins from a Lake Erie strain of Microcystis aerugino-
sa. J. Great Lakes Res. 26 (3), 241-249
City of Hamilton, 2015; http://www.hamilton.ca/sites/default/files/media/browser/2015-02-
18/annual beach monitoring report 2014.pdf.
City of Toledo, 2015. Algal Toxin Tap level Reports, available online at http://toledo.oh.gov/services/public-
utilities/water-treatment/algal-toxin-tap-level-reports/ Accessed March 2015
Davis, T.W., Watson, S.B., Rozmarynowycz, M.J., Ciborowski, J.J.H., McKay, R.M., Bulleijahn, G.S., 2014. Phy-
togenies of microcystin-producing cyanobacteria in the Lower Laurentian Great Lakes suggest extensive genetic
connectivity. PLoS ONE 9(9): el06093. doi:10.1371/journal.pone.0106093
Davis, T.W., Bulleijahn, G.S., Tuttle, T., McKay, R.M., Watson, S.B., 2015. Effects of increasing nitrogen and
phosphorus concentrations on phytoplankton community growth and toxicity during Planktothrix blooms in
Sandusky Bay, Lake Erie. Environ. Sci. Technol. 49(12), 7197-7207.
De Stasio, B„ Schrimpf, M., and Cornwell, B. 2014. Phytoplankton communities in Green Bay, Lake Michigan after
invasion by dreissenid mussels: increased dominance by cyanobacteria. Diversity 6(4): 681.
Depew, D.C., Houben, A. J., Guildford, S.J., Hecky, R.E., 2011. Distribution of nuisance Cladophora in the lower
Great Lakes: Patterns with land use, near shore water quality and dreissenid abundance. J. Great Lakes Res. 37(4),
656-671
IJC, 2013. Taking Action on Lake Erie; The IJC Science Advisory Board TAcLE Work Group Science Summary
Report. http://www.iic.org/files/tinymce/uploaded/TAcLE%20Summarv%20Report%20FINAL.pdf
Kutovaya, O.A., McKay, R.M.L., Beall, B.F.N., Wilhelm, S.W., Kane, D.D., Chaffin, J.D., Bridgeman, T.B., and
Bulleijahn, G.S., 2012. Evidence against fluvial seeding of recurrent toxic blooms of Microcystis spp. in Lake Erie's
western basin. Harmful Algae 15: 71-77.
Page 335
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STATE OF THE GREAT LAKES 2017
Kutovaya, O. A., and Watson, S.B., 2014. Development and application of a molecular assay to detect and monitor
geosmin-producing cyanobacteria and actinomycetes in the Great Lakes. J. Great Lakes Res.
http://dx.doi.Org/10.1016/i.iglr.2014.03.016
Lajeunesse, A., Segura, P.A., Gelinas, M„ Hudon, C., Thomas, K„ Quilliam, M.A., and Gagnon C., 2012. Detec-
tion and confirmation of saxitoxin analogues in freshwater benthic Lyngbva wollei algae collected in the St. Law-
rence River (Canada) by liquid chromatography-tandem mass spectrometry. J. Chromatogr.A 1219: 93-103.
Michalak, A.M., Anderson, E.J., Beletsky, D., Boland, S., Bosch, N.S., Bridgeman T.B., Chaffin, J.D., Cho, K.,
Confesor, R„ Daloglu, I., DePinto, J.V., Evans, M.A., Fahnenstiel, G.L., He, L., Ho, J.C., Jenkins, L., Johengen,
T.H., Kuo, K.C., LaPorte, E„ Liu, X., McWilliams, M.R., Moore, M.R., Posselt, D.J., Richards, R.P., Scavia, D„
Steiner, A.L., Verhamme, E„ Wright, D.M., and Zagorski, M.A. 2013. Record-setting algal bloom in Lake Erie
caused by agricultural and meteorological trends consistent with expected future conditions. Proceedings of the Na-
tional Academy of Sciences 110(16): 6448-6452.
Perri, K.A., Sullivan, J.M., Boyer, G.L., 2015. Harmful algal blooms in Sodus Bay, Lake Ontario: A comparison of
nutrients, marina presence, and cyanobacterial toxins. J. Great Lakes Res. 41(2), 326-337
Township of the Archipelago Sturgeon Bay Projects see
http://www.thearcliipelago.on.ca/index.php/enviromnent/water/sturgeon-bav-proiect (accessed Dec 2015)
Steffen, M.M., Belisle, S., Watson, S.B., Boyer, G.L., Wilhelm S.W., 2014. Status, causes and controls of cyanobac-
terial blooms in Lake Erie. J. Great Lakes Res. 40(2), 215- 225
Stumpf, R.P., Wynne, T.T., Baker, D.B., Fahnenstiel, G.L., 2012. Interannual variability of cyanobacterial blooms in
Lake Erie. PloS ONE, 7(8), e42444 doi: 10.1371/journal.pone.0042444
Vijayavel, K„ Sadowsky, M.J., Ferguson, J.A., and Kashian, D.R., 2013. The establishment of the nuisance cyano-
bacteria Lvngbva wollei in Lake St. Clair and its potential to harbor fecal indicator bacteria. J. Great Lakes Res
39(4): 560-568.
Watson, S.B & Boyer, G.L., Nearshore Areas of the Great Lakes 2009. State of the Lakes Ecosystem Conference
2008 Background Paper. Harmful Algal Blooms (HABs) in the Great Lakes: Current Status and Concerns.
Watson, S B„ Ridal, J. & Boyer, G.L. 2008. Taste and odour and cyanobacterial toxins: impairment, prediction, and
management in the Great Lakes. Can. J. Fish. Aquat. Sci. 65(8): 1779-1796
Watson S.B., Burley, M„ Borisko, J., Lalor, J., 2009. Bay of Quinte Harmful Bloom Programme Phase 1. Bay of
Quinte RAP Annual Report http://www.barap.ca/publications/documentlibrarv/getfile.cfm?id=260
Watson, S.B., Miller, C., Arhonditsis, G., Boyer, G.L., Cannichael, W., Charlton, M.N., Confesor, R„ Depew, D.C.,
Hook, T.O., Ludsin, S.A., Matisoff, G., McElmurry, S.P., Murray, M.W., Peter Richards, R., Rao, Y.R., Steffen,
M.M., and Wilhelm, S.W. 2016. The re-eutrophication of Lake Erie: Harmful algal blooms and hypoxia. Harmful
Algae 56: 44-66.
List of Figures
Figure 1. Bloom severity index for 2002-2015, based on the amount of biomass over the peak 30-days.
Source: NOAA-GLERL Experimental Harmful Algal Bloom Bulletin;
https://www.glerl.noaa.gOv//res/HABs_and_Hypoxia/lakeErieHABArcliive/bulletin_2015-027.pdf
Figure 2. The maximum extent of the bloom on 6 September 6, 2015 shown as a true colour image. The bloom was
less concentrated at this time than in August.
Source: Raw data was obtained from NASA's Modis-Terra sensor:
https://www.glerl.noaa.g0v//pubs/brocl1ures/bluegreenalgae_factsheet.pdf
Figure 3. Seasonal (June-September) average (±standard deviation) levels of microcystin and geosmin from 2009 in
the Bay of Quinte (lm) grouped by station.
Source: Watson et al. (2009)
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STATE OF THE GREAT LAKES 2017
Last Updated
State of the Great Lakes 2017 Technical Report
Lake Erie Severity Index
Forecast 8.7
2003 2005 2007 2009 2011 2013 2015
Year
Figure 1. Bloom severity index for 2002-2015, based on the amount of bio mass over the peak 30-days.
Source: NOAA-GLERL Experimental Harmful Algal Bloom Bulletin;
https://www.glerl.noaa.gOv//res/HABs and Hypoxia/lakeErieHABArchive/bulletin 2015-027.pdf
Figure 2. The maximum extent of the bloom on September 6,2015 shown as a true colour image. The bloom
was less concentrated at this time than in August.
Source: Raw data was obtained from NASA's Modis-Terra sensor:
https://www.glerl.noaa.gOv//pubs/brochures/bluegreenalgae factsheet.pdf
Page 33/
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STATE OF THE GREAT LAKES 2017
Bay of Quinte microcystin 2009
by station
1000-1
800 =
250-
200-
150-
100-
50-
-1	
Trenton Belleville Deseronto Hay Bay Picton Conway
Location
Bay of Quinte geosmin 2009
by station
~r
~r
~r

Trenton Belleville Deseronto Hay Bay Picton Conway
Figure 3. Seasonal (June-September) average (±standard deviation) levels of microcystin and geosmin from 2009 in
the Bay of Quinte (lm) grouped by station.
Source: Watson et al. 2009
Page 338
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STATE OF THE GREAT LAKES 201 7
Sub-Indicator: Water Quality in Tributaries
Overall Assessment
Status: Fair
Trend: Unchanging
Rationale: The overall water quality status of tributaries to the Great Lakes was described as Fair which is
unchanged since the previous indicator report in 2011. The average Water Quality Index (WQI) score for 92
Canadian tributaries to the Great Lakes was 67/100. The WQI scores ranged from 11 to 100 (Poor to Good).
Overall, 30% of the tributaries were categorized as having Good water quality, 51% as Fair, and 19% were
Poor (Figures 1 and 2). Good water quality was found in certain tributaries to lakes Superior, Huron, and
Ontario, and the St. Lawrence River. Poor water quality was found in certain tributaries of lakes Erie and
Ontario and in one tributary of Lake Huron.
Lake-by-Lake Assessment
Lake Superior
Status: Fair
Trend: Unchanging
Rationale: The average WQI score for 9 tributaries was 78/100. WQI scores ranged from 65 to 100 (Fair to Good).
There were only a few sites monitored, therefore those sites assessed as fair may be under-represented. In 2011, the
average WQI value was 80/100.
Lake Michigan
Status: Undetermined
Trend: Undetermined
Rationale: No tributaries to Lake Michigan are monitored by the Ontario Provincial Water Quality Monitoring
Network (PWQMN).
Lake Huron
Status: Good
Trend: Unchanging
Rationale: The average WQI score for 28 tributaries was 81/100. WQI scores ranged from 44 to 100 (Poor to Good).
In 2011, the average WQI value was 83/100, noting one less tributary was used for the 2016 assessment.
Lake Erie
Status: Poor
Trend: Unchanging
Rationale: The average WQI score for 18 tributaries was 43/100. WQI scores ranged from 11 to 75 (Poor to Fair). In
2011, the average WQI value was 45/100.
Lake Ontario
Status: Fair
Trend: Unchanging
Rationale: The average WQI score for 31 tributaries was 65/100. WQI scores ranged from 29 to 93 (Poor to Good).
In 2011, the average WQI value was 66/100, noting that 2 less tributaries were included in the 2016 assessment.
Other Spatial Scales
St. Lawrence River
Status: Fair
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STATE OF THE GREAT LAKES 201 7
Trend: Deteriorating
Rationale: The average WQI score for 6 tributaries was 73/100. WQI scores ranged from 55 to 85 (Poor to Good). In
2011, the average WQI value was 81/100.
Sub-Indicator Purpose
The purpose of this sub-indicator is to communicate water quality status relative to guidelines and support the evalu-
ation of aquatic ecosystem health in Great Lakes tributaries.
Ecosystem Objective
The surface waters in the Great Lakes Basin should be of a quality that is protective of aquatic life and healthy
aquatic ecosystems.
This sub-indicator best supports work towards General Objective #6 and # 4 of the 2012 Great Lakes Water Quality
Agreement. General Objective # 6 states that the Waters of the Great Lakes should "be free from from nutrients that
directly or indirectly enter the water as a result of human activity, in amounts that promote growth of algae and cya-
nobacteria that interfere with aquatic ecosystem health, or human use of the ecosystem" and General Objective # 4
states that the Waters of the Great Lakes should " be free from pollutants in quantities or concentrations that could
be harmful to human health, wildlife, or aquatic organisms, through direct exposure or indirect exposure through the
food chain."
Ecological Condition
Measure
Inland water quality is evaluated using the Water Quality Index (WQI). The WQI provides a mathematical frame-
work for synthesizing water quality monitoring results for multiple samples and parameters into one value that rep-
resents overall water quality for the protection of aquatic life at a given site. The WQI uses three measures of com-
pliance with water quality criteria (guidelines and objectives) to assess water quality:
1.	Scope: measures the percentage of the number of parameters that comply with water quality criteria;
2.	Frequency: measures the percentage of individual water quality tests that comply with criteria; and
3.	Magnitude: measures by how much criteria are exceeded.
The three factors are combined into a single unit-less value between 0 and 100 where higher numbers indicate better
water quality. The WQI is computed using the Canadian Council of Ministers of the Enviromnent's Water Quality
Index (v. 1.2; CCME 201 la), which is described in detail in CCME (2001a, b). The sensitivity of the WQI to varia-
tions in its formulation and application has been studied extensively (e.g. Davies, 2006; Gartner Lee Limited, 2006;
de Rosemond et al. 2009; Kilgour and Associates Limited, 2009; etc.).
For the Canadian tributaries assessed for this report, the WQI values were calculated at sites with four years of data
and a minimum of 10 observations for total concentrations of the following eight (8) site-relevant parameters: am-
monia (un-ionized), chloride, copper, iron, nitrates, nitrites, phosphorus, and zinc. Inland stream water quality re-
sults for these parameters were acquired from the Ontario Provincial Water Quality Monitoring Network (PWQMN)
(OMOE, 2013). For the calculation of the WQI, the water quality results are compared with guidelines from the
Canadian Council of Ministers of the Enviromnent (CCME)'s Water Quality Guidelines for the Protection of Aquat-
ic Life (CCME, 201 lb) or, in the absence of CCME Guidelines, the Ontario Interim Provincial Water Quality Ob-
jectives (PWQO) (i.e. for total phosphorus) (OMOE, 1994) (Table 1).
The WQI was calculated for the most downstream monitoring site for streams draining to the Great Lakes, including
tributaries to the Great Lake connecting channels and the St. Lawrence River as an indication of water quality enter-
ing the Great Lakes. The most recent four years of water quality monitoring results that are available online (as of
Winter 2015; OMOE, 2013) were used for the index calculations. For most (81/92) sites, the WQI was computed
using monitoring results from 2009-2012 but for sites that were monitored infrequently (< 10 samples) between
2009 and 2012, results from 2002-2005 or 2006-2009 were used (11/92 sites).
Background
The Ontario Ministry of the Enviromnent and Climate Change's Provincial (Stream) Water Quality Monitoring
Network (PWQMN) measures water quality in rivers and streams at hundreds of sites across Ontario in partnership
Page 340
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STATE OF THE GREAT LAKES 201 7
with Ontario's Conservation Authorities. Most of these sites are located in the Great Lakes Basin, and many are lo-
cated at or near the outlets of tributaries to the Great Lakes. Stream water samples are collected on an approximately
monthly basis and delivered to the Ministry of the Enviromnent and Climate Change's laboratory where they are
analyzed using consistent analytical methods for a consistent suite of water quality indicators. Water quality indica-
tors are selected to indicate the influence of land-use activities on stream water quality. For example, chloride is
measured as an indicator of the influence of salt loading from winter de-icing. Field measurements including water
temperature and pH are also taken at the time of sample collection using portable water quality meters. Water quali-
ty data for all stream monitoring sites is available on the Ontario Ministry of the Enviromnent and Climate Change
public website (https://www.ontario.ca/data/provincial-stream-water-quality-monitoring-network).
Targets or Endpoint
Desirable outcomes are the absence of undesirable water quality conditions in streams. The Water Quality Index
(WQI) score is from 0 to 100 with rankings for poor, fair and good. The category ranges describe sites where the
water quality complies with criteria virtually all of the time (Good) or hardly any of the time (Poor).
Status Assessment and Justification
The calculated values fit into five categories that describe water quality conditions as used by the CCME:
Excellent (95-100);
Good (80-94);
Fair (65-79);
Marginal (45-64); and
Poor (0-44).
For this sub-indicator, the five original categories developed by CCME were dissolved into three descriptive
categories:
Good: 80-100
Fair: 45-79
Poor: 0-44
Status of Water Quality in the Great Lakes Tributaries
The WQI was computed for 92 Canadian tributaries to the Great Lakes. The overall water quality status of tributar-
ies to the Great Lakes can be described as Fair (WQIavg=67, WQIrailge=l 1-100). 30% of the tributaries were catego-
rized as having Good water quality, 51% as Fair, and 19% were Poor (Figures 1 and 2).
Good water quality was found in certain tributaries to lakes Superior, Huron, and Ontario, and the St. Lawrence
River. Poor water quality was found in certain tributaries of lakes Erie and Ontario and in one tributary of Lake Hu-
ron. The WQI scores ranged from 11 (Sturgeon River, Lake Erie) to 100 (Montreal and Michipicoten rivers. Lake
Superior; Mississagi and Serpent rivers. Lake Huron).
On a lake-by-lake basis, tributaries to Lake Huron can be described as having Good water quality (WQIavg=81,
WQIrange=44-100, n=28). Tributaries to Lake Superior (WQIavg=78, WQIrange=65-100, n=9). Lake Ontario
(WQIavg=65, WQIrange=29-93, n=31), and the St. Lawrence River (WQIavg=73, WQIrange=55-85, n=6) had Fair water
quality. Tributaries to Lake Erie (WQIavg=43, WQIrailge=l 1-75, n=18) were categorized as having Poor water quality.
The overall water quality status of tributaries to the Great Lakes was described as Fair which is unchanged since the
previous State of the Great Lakes (previously known as SOLEC) report (EC and USEPA, 2014). On a lake-by-lake
basis, the description of the water quality for tributaries to lakes Huron and Ontario has not changed since the previ-
ous State of the Great Lakes report but the status of tributaries to lakes Superior and Erie and the St. Lawrence River
has changed. For tributaries to lakes Superior and Erie, the average WQI scores reported in 2011 were at the lower
boundary of the Good and Fair categories, respectively. In this current report, the average WQI scores for tributaries
to these lakes decreased by 2 and are described as Fair (Lake Superior) and Poor (Lake Erie). However, since the
WQI score only decreased by 2, the trend was reported as 'unchanging' irrespective of the change in status. The
water quality of tributaries to the St. Lawrence River was Good in 2011 and Fair in this current report. This change
in status for the St. Lawrence River is likely attributed to the WQI scores in certain tributaries where more recent
water quality results showed non-compliance of water quality criteria for multiple parameters whereas non-
compliance in earlier results was for one parameter only (i.e. phosphorus).
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STATE OF THE GREAT LAKES 201 7
Linkages
The WQI values for the 92 tributaries show a statistically significant negative relationship with percent of the water-
shed occupied by human land uses (Figure 3). This relationship suggests that overall water quality in the Great
Lakes tributaries is influenced by human land use where minimally developed watersheds have higher WQI scores
than the more heavily developed watersheds.
The WQI scores suggest the potential for substances in stream water to impact aquatic life based on compliance with
water quality criteria. However, the WQI values are not a direct measure of impacts to aquatic communities, such as
changes in fish and benthic invertebrate communities. The WQI values (and the water quality in tributaries) also
infer the potential for discharge of nutrients or other substances from tributaries into the Great Lakes and the associ-
ated impacts of these discharges, particularly at the tributary mouths and nearby nearshore areas.
However, it should be noted that there are some linkages that can be made to impacts on aquatic life. For example,
freshwater mussels are particularly sensitive to chloride (a component of road salt) exposure compared to other
aquatic life, especially during their early life stages. Chloride concentrations in many of our rivers and streams have
been increasing since the mid-1990s. (Water Quality in Ontario, 2014 Report).
Comments from the Author(s)
The WQI is a communication tool that was designed to report complex water quality information about multiple
variables in a simplified format. While the WQI can provide a broad overview of water quality, it is not intended to
replace rigorous technical analyses of water quality data for water resource management.
Although the water quality of many Great Lakes tributaries has been monitored since the 1960s, assessing long-term
trends in water quality is challenging due to inconsistent laboratory methods and detection limits over time and in-
complete datasets. At this time, the utility of using the WQI for the statistical analysis of trends in water quality in
Ontario tributaries continues to be explored.
For this Water Quality in Tributaries report, the WQI has been computed only for Canadian tributaries. The applica-
tion of the WQI to assess water quality in U.S. tributaries to the Great Lakes depends on the availability of monitor-
ing data. An anticipated challenge is that WQI results are not directly comparable between jurisdictions where dif-
ferent water quality parameters and criteria are used.
Most of the PWQMN's monitoring sites are purposefully located where water quality impacts are known or
expected, such as areas with a high population or where land is used for agriculture. Minimally-impacted reference
watersheds are likely under-represented in this sub-indicator. The sub-indicator may also under-represent tributaries
to the upper Great Lakes (especially Lake Superior). For future reports, a redundancy or other analysis could be
undertaken to eliminate some sites from the lower Great Lakes to ensure all lakes are more equally represented.
Water quality criteria can be exceeded in areas that are naturally rich in a given nutrient or metal. The calculation of
the WQI does not take into account naturally-occurring elevated concentrations of some parameters.
This current Water Quality in Tributaries report is a status update from 2011 (EC and USEPA, 2014). This report
uses the same eight (8) site-relevant parameters as the previous report. The WQI was recalculated for this report
using the most recent water quality monitoring results for these parameters with current water quality criteria for the
protection of aquatic life. For chloride, the guideline for the protection of aquatic life is now 120 mg L-l (CCME,
2011) whereas a guideline value of 110 mg L-l was used previously (EC and USEPA, 2014). For this current report,
WQI scores are computed for 92 tributaries whereas scores were computed for 95 tributaries used previously (EC
and USEPA, 2014). Although fewer tributaries to Lakes Ontario (n2on=33, n20i7=31) and Huron (n20ii=29, n20i7=28)
were included in this current report, there continues to be ample representation of these lakes.
Because the WQI can be influenced by factors other than water quality (i.e. the particular parameters selected for the
calculation, the number of parameters included, the specific sites used, and the water quality criteria for a given ju-
risdiction), using changes in the WQI scores over time to identify trends can be potentially more indicative of
changes based on how the index was calculated than changes in the quality of the water. However, in this case, the
locations and criteria used the same 8 criteria and for the most part, the same locations.
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STATE OF THE GREAT LAKES 201 7
Assessing Data Quality
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not
Applicable
1. Data are documented, validated, or
quality-assured by a recognized agency or
organization
X





2. Data are traceable to original sources
X





3. The source of the data is a known,
reliable and respected generator of data
X





4. Geographic coverage and scale of data
are appropriate to the Great Lakes Basin

X




5. Data obtained from sources within the
U.S. are comparable to those from
Canada





X
6. Uncertainty and variability in the data
are documented and within acceptable
limits for this sub-indicator report
X





Clarifying Notes: WQI calculations for Ontario tributaries to the Great Lakes were computed using monitoring data from the
PWQMN (https://www.ontario.ca/data/provincial-stream-water-aualitv-monitorins-network). The WQI may be calculated
independently for U.S. tributaries if data are available.
Acknowledgments
Authors: Ontario Ministry of the Environment and Climate Change, Water Monitoring Section
Contributors: Jillian Kingston, Aaron Todd, Georgina Kaltenecker, Katrina Estacio, and Kimberly Summers,
Enviromnental Monitoring and Reporting Branch, Ontario Ministry of the Enviromnent and Climate Change,
Toronto, ON
Information Sources
Canadian Council of Ministers of the Enviromnent (CCME). 200 la. Canadian water quality guidelines for the pro-
tection of aquatic life: CCME Water Quality Index 1.0, Technical Report. In: Canadian enviromnental quality guide-
lines, 1999, Canadian Council of Ministers of the Enviromnent, Winnipeg.
(http://www.ccme.ca/files/Resources/calculators/WQI%20Technical%20Report%20(en).pdf)
Canadian Council of Ministers of the Enviromnent (CCME). 2001b. Canadian water quality guidelines for the pro-
tection of aquatic life: CCME Water Quality Index 1.0, User's Manual. In: Canadian enviromnental quality guide-
lines, 1999, Canadian Council of Ministers of the Enviromnent, Winnipeg.
(http://www.ccme.ca/files/Resources/calculators/WQI%20User's%20Manual%20(en).pdf)
Canadian Council of Ministers of the Enviromnent (CCME). 2006. Sensitivity Analysis of the Canadian Water
Quality Index. Prepared by Gartner Lee.
Canadian Council of Ministers of the Enviromnent (CCME). 2011a. CCME Water Quality Index 1.2.
(http://www.ccme.ca/en/resources/canadian enviromnental quality guide lines/calculators, html)
Canadian Council of Ministers of the Enviromnent (CCME). 2011b. Canadian water quality guidelines for the pro-
tection of aquatic life: CCME Summary Table. Updated 2011. http://st-ts.ccme.ca/en/index.html
Davies, J-M., 2006. Application and Tests of the Canadian Water Quality Index for Assessing Changes
in Water Quality in Lakes and Rivers of Central North America. Lake and Reservoir Management 22(4): 308-320.
De Rosemond, S., Duro, D.C., and Dube, M. 2009. Comparative analysis of regional water quality in Canada using
the Water Quality Index. Enviromnental Monitoring and Assessment 156(1-4): 223-240.
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STATE OF THE GREAT LAKES 201 7
Environment Canada, 2015. Canadian Environmental Sustainability Indicators, (https://www.ec.gc.ca/indicateurs-
indicators/default.asp?lang=En)
Enviromnent Canada and the U.S. Enviromnental Protection Agency (EC and USEPA). 2014. State of the Great
Lakes 2011. Cat No. Enl61-3/l-201 IE-PDF. EPA 950-R-13-002. (http://binational.net/wp-
content/uploads/2014/1 l/sogl-201 l-technical-report-en.pdf)
Gartner Lee Limited. 2006. A sensitivity analysis of the Canadian Water Quality Index.
http://www.ccme.ca/files/Resources/water/water qualitv/pn 1355 wqi sensitivity analysis rpt.pdf
Kilgour and Associates Limited. 2009. Reducing the sensitivity of the Water Quality Index to episodic events.
(http://www.ccme.ca/files/Resources/water/water qualitv/pn 1435 wqi sensitivitv.pdf)
Ontario Ministry of the Enviromnent (OMOE). 1994. Water management, policies, guidelines and provincial water
quality objectives of the Ministry of the Enviromnent.
http://www.ontario.ca/document/water-management-policies-guidelines-provincial-water-qualitv-obiectives
Ontario Ministry of the Enviromnent (OMOE). 2013. Provincial Water Quality Monitoring Network data (2002-
2012). (https://www.ontario.ca/data/provincial-stream-water-qualitv-monitoring-network)
Ontario Ministry of the Enviromnent and Climate Change, 2016. Water Quality in Ontario 2014 Report. Published
online March 18, 2016: https://www.ontario.ca/page/water-qualitv-ontario-2014-report
Ontario Ministry of the Enviromnent and Climate Change, 2016. Ontario's Great Lakes Strategy First Progress Re-
port 2016. PIBS 9934e ©Queen's Printer for Ontario
List of Tables
Table 1. Water quality criteria for the eight indicators used in the CCME Water Quality Index (WQI) calculations.
Source: Ontario Ministry of the Environment and Climate Change
List of Figures
Figure 1. CCME Water Quality Index (WQI) values for 92 Canadian tributaries to the Great Lakes.
Source: Ontario Ministry of the Environment and Climate Change
Figure 2. CCME Water Quality Index (WQI) values for Canadian Great Lakes tributaries by lake basin.
Source: Ontario Ministry of the Environment and Climate Change
Figure 3. CCME Water Quality Index (WQI) values for Canadian Great Lakes tributaries (n=92) versus percent
watershed occupied by human land uses.
Source: Ontario Ministry of the Environment and Climate Change
Last Updated
State of the Great Lakes 2017 Technical Report
Page 344
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STATE OF THE GREAT LAKES 201 7
Parameter
Criterion
Source
Ammonia (un-ionized)
0.0152 mgL-i-N
CCME
Chloride
120 mg L-i
CCME
Copper
2	|.ig L-i at water hardness of 0-120 mg L-i-CaC03
3	|.ig L-i at water hardness of 120-180 mg L-i-CaC03
4	|.ig L-i at water hardness of >180 mg L-i-CaC03
CCME
Iron
300 (.ig L-i
CCME
Nitrate
2.9 mg L-i-N
CCME
Nitrite
0.06 mgL-i-N
CCME
Phosphorus
0.03 mg L-i
OMOE
Zinc
30 ^ig L-i
CCME
Sources: CCME = Water quality guidelines for the protection of aquatic life (CCME 201 la); OMOE = Interim provincial water
quality objective (OMOE 1994).
Table 1. Water quality criteria for the eight parameters used in the CCME Water Quality Index (WQI) calculations.
Source: Ontario Ministry of the Environment and Climate Change
Water Quality IndBX
¦ Good ¦: SO-1001
0 Fair (45*79)
• Poor (0-44)
KiP 1

m

i -




. «c +*-
t * J
£
— •
« » i»
I	1	1	1	4-
Figure 1. CCME Water Quality Index (WQI) values for 92 Canadian tributaries to the Great Lakes.
Source: Ontario Ministry of the Environment and Climate Change
Page 345
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STATE OF THE GREAT LAKES 201 7
• Good (80-100)	Fair (46-79) • Poor (0-44) 	Average WQl
tf>
* £
35
si
X £
¦S w
c ®
^ re
<3 ra
3 ay
o3
t, c
« .2
3 73
£ 
-------
STATE OF THE GREAT LAKES 201 7
100*-*
CO
 CO
r
LU
2
o
o
1	1	1	1	1	1	1—
t	• •
-i	1	1	1	1	1	1	1	1	1	r
80,
60
40 -
20
r = 0.45
pO.0001
J	I	I	I	I	I	I	I	I	I	I	I	I	I	I—
20
40
60
80
100
Percent Watershed Occupied by Human Land Uses
Figure 3. CCME Water Quality Index (WQI) values for Canadian Great Lakes tributaries (n=92) versus percent
watershed occupied by human land uses.
Source: Ontario Ministry of the Environment and Climate Change
Page 347
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Invasive Species
Status: Poor Trend: Deteriorating
The 2012 Great Lakes Water Quality Agreement states that "the Waters of the Great Lakes should be free from the
introduction and spread of aquatic invasive species and free from the introduction and spread of terrestrial invasive species
that adversely impact the quality of the Waters of the Great Lakes"
The number of new invasive species entering
the Great Lakes has been significantly reduced;
however, those invasive species already in the
Great Lakes such as Sea Lamprey, Zebra Mussels
and Purple Loosestrife continue to cause more than
$100 million annually in economic impacts in the
U.S. alone.
Page 348
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Invasive Species
Assessment Highlights
The Invasive Species indicator highlights that the spread and
impact of aquatic and terrestrial invasive species continues to
be a significant stress to biodiversity in the Great Lakes region.
As such, the Invasive Species indicator is assessed as Poor and
the trend is Deteriorating.
To date, over 180 aquatic non-native species have become
established in the Great Lakes Basin. Only one new non-
native species has been discovered since 2006, a zooplankton
called Thermocyclops crassus. This tremendous success
in reducing the introduction of invasive species is largely
due to the regulation of ballast water from trans-oceanic
ships. Additionally, the Asian carp species established in the
Mississippi River, which are threatening the Great Lakes, have
not become established. This success is attributed to the
important prevention efforts in both countries, including the
U.S. Army Corps of Engineers electrical barrier on the Chicago
Sanitary and Ship Canal.
Despite the significant slowdown in recent introductions, the
impacts of established invaders persist and their ranges within
the lakes are expanding. It is believed that at least 30% of
the aquatic non-native species found in the Great Lakes have
significant environmental impact.
For several decades, Sea Lamprey have been causing severe
ecological impacts. However, Sea Lamprey abundance has
been reduced significantly in the five lakes through active, on-
going, and basin-wide control measures. But, native fish such
as Lake Trout, Walleye and Lake Sturgeon are still subject to
Sea Lamprey predation. Sea Lamprey remain an impediment
to achieving critical fish community and ecosystem objectives
and therefore continuation of and improvements to Sea
Lamprey control are required.
Dreissenid mussels, also known as Zebra and Quagga Mussels,
are prominent invasive species in the Great Lakes as well. In
many offshore regions, Zebra Mussels have been displaced
Sub-Indicators Supporting the Indicator Assessment
Sub-Indicator
Lake Superior
Lake Michigan
Lake Huron
Lake Erie
Lake Ontario
Impacts of Aquatic Invasive Species
Deteriorating
Deteriorating
Deteriorating
Deteriorating
Deteriorating
Dreissenid Mussels
Unchanging
Deteriorating
Deteriorating
Improving
Deteriorating
Sea Lamprey
Improving
Improving
Improving
Improving
Unchanging
Terrestrial Invasive Species
Deteriorating
Deteriorating
Deteriorating
Deteriorating
Deteriorating
Status:
GOOD
FAIR
POOR
UNDETERMINED
Aquatic Invasive Species -
Establishments Have Slowed Down
¦	Live Wells/Recreational Boating
¦	Bait Release
¦	Escaped Culture
¦	Hitchhiker with Organisms in Trade
¦	Aquarium
¦	Canals
¦	Plant ed/Stocked
¦	Unknown
¦	Shipping
1839 1864 1889 1914 1939 1964 1989 2014
Year
by increasing populations of Quagga Mussels. While in some
nearshore regions, populations of both species seem to
be stable or declining. Overall, dreissenids are a dominant
component of the bottom-dwelling community. Consequently,
they have played an instrumental role in the alteration of
the zooplankton and phytoplankton communities as well as
disrupting the nutrient cycle and increasing water clarity.
On the land, terrestrial invasive species have a significant
impact and continue to spread throughout the Great
Lakes Basin. Five terrestrial invasive species were assessed
collectively—Phragmites, Purple Loosestrife, Garlic Mustard,
Emerald Ash Borer and Asian Long-horned Beetle. These
species are widely distributed and their ranges appear to be
expanding. All five of these species have a detrimental impact
on the surrounding ecosystem, including degrading habitat
and water quality.
Limiting the impact of existing invaders is critical. However,
binational prevention efforts, including continuing early
detection and rapid response programs, are where the biggest
difference can be made to ensure the Great Lakes are healthy,
safe and sustainable.
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STATE OF THE GREAT LAKES 201 7
Sub-Indicator: Impacts of Aquatic Invasive Species
Overall Assessment
Status: Poor
Trend: Deteriorating
Rationale: While new species have been prevented from entering the Great Lakes, those which are
established are continuing to expand within the basin. Although no new aquatic nonindigenous species (ANS)
have become established in the Great Lakes in nearly a decade, the impacts of established invaders persist
and their ranges within the lakes are expanding resulting in a Poor status and Deteriorating trend. Great
Lakes Aquatic Nonindigenous Species Information (GLANSIS) includes more than 15,000 records for species
in new locations in the last decade. To date, 185 nonindigenous species have become established in the Great
Lakes basin, however no new species are reported to have become established since 2006 (GLANSIS 2015).
Parrot feather (Myriophyllum aquaticum) was established in Meserve Lake, IN (within the Lake Michigan
drainage) in 2006, but has not been reported elsewhere within the basin. This species was an escaped
ornamental pond plant. Bloody red shrimp (Hemimysis anomala) was reported for the first time in the Great
Lakes in 2006 in Lake Michigan, however surveys done that same year found the species to already be
widespread (with populations in Lakes Michigan, Erie and Ontario) throughout the Great Lakes;
introduction of this species is attributed to ballast water. Note that one new species, Thermocyclops crassus,
was discovered in Lake Erie in 2016, after the analysis done for this report. Examination of archived samples
by EPA scientists place our current best estimate for the introduction of Thermocyclops crassus as 2014 and
revises the total number of species to 186.
Lake-by-Lake Assessment
Lake Superior
Status: Poor
Trend: Deteriorating
Rationale: Lake Superior is the site of greatest ballast water discharge in the Great Lakes, but this pathway has led to
comparatively few direct ANS establishments (Grigorovich et al. 2003). Intrabasin movement of ANS is likely to be
of greater consequence. Species established within the Great Lakes basin continue to expand their ranges into and
within Lake Superior. GLANSIS records 19 species as new introductions to Lake Superior within the last decade
although these species were already present elsewhere in the Great Lakes (some likely reflect reporting time lags).
Records indicate range expansion within the Superior basin accounting for 67 species in the same period. Many of
these represent significant expansions of high impact species. Since 2010, only two new nonindigenous species have
been identified in Lake Superior; the deadly infectious fish disease (i.e. VHS) was discovered in 2010; the Banded
Mystery Snail was detected and reported in 2015. Note that addition of Banded mystery snail (Viviparus
georgianus) in Lake Superior in 2016 (back dated to a 2014 introduction), reported after the analysis for this report,
would revise the number of new introductions to Lake Superior within the last decade to 20 - but the entire dataset
was not re-analyzed systematically (additional species may also have expanded ranges and/or introduction dates
may have been revised).
Lake Michigan
Status: Poor
Trend: Deteriorating
Rationale: Species established within the Great Lakes basin continue to expand their ranges into and within Lake
Michigan. No new species have been reported for Lake Michigan since 2009. GLANSIS records more than 30
species as first reported in the Lake Michigan watershed within the last decade (some likely reflect reporting time
lags), most recently Brittle Waternymph (Najas minor) and red swamp crayfish (Procambarus clarkia) in 2009.
Records indicate range expansion within the Lake Michigan basin recording 86 species in the same period. Many of
these represent significant expansions of high impact species.
Lake Huron
Status: Poor
Trend: Deteriorating
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STATE OF THE GREAT LAKES 201 7
Rationale: Species established within the Great Lakes basin continue to expand their ranges into and within Lake
Huron. GLANSIS records 23 species as first reported in Lake Huron within the last decade (some likely reflect
reporting time lags), most recently Chain Pickerel (Esox nigef) in 2015 and Tubenose Goby (Proterorhinus
semilunaris) in 2012. Records indicate range expansion within the Lake Huron basin (including the St. Marys
River) recording 54 species in the same period. Many of these represent significant expansions of high impact
species.
Lake Erie
Status: Poor
Trend: Deteriorating
Rationale: Species established within the Great Lakes basin continue to expand their ranges into and within Lake
Erie. GLANSIS records 29 species as first reported in Lake Erie within the last decade (some likely reflect
reporting time lags), most recently a parasitic copepod (Neoergasilus japonicus) in 2011. Records indicate range
expansion within the Lake Erie basin recording 76 species in the same period. Many of these represent significant
expansions of high impact species. Note that the addition of Thermocvclops crassus (back-dated to 2014, but
discovered after the analysis for this report was complete) would revise the number of species first reported in Lake
Erie within the last decade to 30 - but the entire dataset was not re-analyzed systematically (additional species may
also have expanded ranges and/or introduction dates may have been revised).
Lake Ontario
Status: Poor
Trend: Deteriorating
Rationale: Species established within the Great Lakes basin continue to expand their ranges into and within Lake
Ontario. GLANSIS records 19 species as first reported in Lake Ontario within the last decade (some likely reflect
reporting time lags), most recently Tubenose goby (Proterorhinus semilunaris) in 2011. Records indicating range
expansion within the Lake Ontario basin (including the Niagara River) have been recorded for 79 species in the
same period. Many of these represent significant expansions of high impact species.
Lake St-Clair, Detroit and St. Clair Rivers
Status: Poor
Trend: Deteriorating
Rationale: Species established within the Great Lakes basin continue to expand their ranges into and within Lake St.
Clair (including the Detroit and St. Clair Rivers). GLANSIS records 26 species as first reported in this corridor
within the last decade (some likely reflect reporting time lags), most recently Yellow Floating Heart (Nvmphoides
peltata) in 2015, Western Mosquitofish (Gambusia affinis) in 2013, Chinese Mystery Snail (Cipangopaludina
chinensis) in 2012, and faucet snail (Bithvnia tentaculata) in 2011. Records indicating range expansion within the
Lake St. Clair corridor have been recorded for 48 species in the same period. Many of these represent significant
expansions of high impact species.
Sub-Indicator Purpose
The purpose of this sub-indicator is to assess the presence, number, distribution and impact of aquatic invasive
species (AIS) in the Laurentian Great Lakes. The rate of invasion will also be measured as the number of new AIS
arriving in the Great Lakes since the last assessment, a retrospective analysis to identify the likely pathway by which
the species arrived, and an evaluation of the longer record to quantify any trend in the rate of invasion.
Ecosystem Objective
The goal of the Great Lakes Water Quality Agreement is to restore and maintain the biological integrity of the Great
Lakes Ecosystem. Fundamental to this goal is to control existing, and prevent further introduction of, aquatic
invasive species.
This sub-indicator best supports work towards General Objective #7 of the 2012 Great Lakes Water Quality
Agreement which states that the Waters of the Great Lakes should "be free from the introduction and spread of
aquatic invasive species and free from the introduction and spread of terrestrial invasive species that adversely
impact the quality of the Waters of the Great Lakes."
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STATE OF THE GREAT LAKES 201 7
Ecological Condition
Background
The National Oceanic and Atmospheric Administration (NOAA) currently reports a total of 186 established Great
Lakes ANS (plus at least 17 species native to some part of the basin which have expanded their ranges to other
parts).
In the Great Lakes, transoceanic ships (including solid ballast, packing materials, ballast water and ballast residuals)
have been the primary invasion vector responsible for 44% of the total established ANS. Historically, deliberate
introductions (stocking fish and agricultural/horticultural plants) have been a significant vector (21%) and both
accidental releases and hitchhikers with such organisms in trade (e.g., parasites, diseases, contaminants in
shipments) have also been significant vectors (10% and 5%, respectively).
During the 1980s, the importance of ship ballast water as a vector for ANS introductions was recognized, prompting
ballast management measures in the Great Lakes. In the wake of Eurasian ruffe and zebra mussel introductions,
Canada introduced voluntary ballast exchange guidelines in 1989 for ships declaring "ballast onboard" (BOB)
following transoceanic voyages; this action followed recommendations by the Great Lakes Fishery Commission and
the International Joint Commission. In 1990, the United States Congress passed the Nonindigenous Aquatic
Nuisance Prevention and Control Act, producing the Great Lakes' first ballast exchange and management
regulations in May of 1993. The National Invasive Species Act (NISA) followed in 1996. Following initiation of
voluntary guidelines in 1989 and mandated regulations in 1993, the overall rate of Great Lakes invasion did not
decline until recently (Grigorovich et al. 2003; Holeck et al. 2004; Ricciardi 2006). However, more than 90% of
transoceanic ships that entered the Great Lakes during the 1990s declared "no ballast onboard"
(NOBOB; Colautti et al. 2003; Grigorovich et al. 2003; Holeck et al. 2004) and were not required to
exchange ballast, despite their tanks containing residual sediments and water that could be discharged in the Great
Lakes. Residual water and sediment in these ships were found to contain several species previously unrecorded in
the basin; such species could be discharged after the ship undergoes sequential ballasting operations as it travels
between ports within the Great Lakes to offload and take on cargo (Duggan et al. 2005, Ricciardi and Maclsaac
2008). In June 2006, Canada implemented new regulations for the management of residuals contained within
NOBOB tanks and requires the salinity of all incoming ballast water to be at least 30 parts per trillion (Government
of Canada 2006). In the decade since, we have seen no new ballast water ANS introductions (the last being
Hemimvsis cmomala, collected in May 2006) despite a fairly steady number of NOBOB transits. Ballast water
regulation appears to have been largely successful in preventing new introductions from this vector - there lias been
only one new introduction attributed to this vector in the last decade (2006-2015); in comparison there were 9
introductions attributed to this vector in the previous decade (1996-2005) and 18 in the decade prior to that (1986-
1995). However, ballast water movement within the basin, which is not currently regulated, may pose a relatively
high risk of spreading ANS (Casas-Monroy et al. 2014).
Second to shipping, release, transfer, and escape have introduced ANS into the Great Lakes. Of particular concern
are private sector activities related to aquaria, garden ponds, baitfish, and live food fish markets. Silver and bighead
carp escapees from southern United States fish farms have developed large populations in the middle and lower
segments of the Illinois River, which connects the Mississippi River to Lake Michigan via the Chicago Sanitary and
Ship Canal (CSSC). A prototype electric barrier on the CSSC was activated in April 2002 to block the
transmigration of species between the Mississippi River system and the Great Lakes basin. The U.S. Army Corps of
Engineers (partnered with the State of Illinois) completed construction of the second and third permanent barriers in
2005 and 2011, respectively. Since 2009, enviromnental DNA (eDNA) surveillance has been used to complement
the use of traditional monitoring and suppression tools. Between 2009 and 2010, DNA of both bighead and silver
carp was detected past the electric barriers; however, only a single bighead carp was subsequently found
(Lake Calumet, June 2010). As of August of the 2011 monitoring year, only silver carp DNA had been detected on
the lake side of these barriers for that year; despite an intensive sampling effort in response to three consecutive
rounds of positive eDNA tests in the Lake Calumet area, no Asian carp were seen or captured. Nearly a million
Asian carp, including bighead and black carp, are sold annually at fish markets within the Great Lakes basin. Until
recently, most of these fish were sold live. All eight Great Lakes states and the province of Ontario now have some
restriction on the sale of live Asian carp. Enforcement of many private transactions, however, remains a challenge.
The U.S. Fish and Wildlife Service published a final rule in March 2011, officially adding the bighead carp to the
federal injurious wildlife list and codifying the Asian Carp Prevention and Control Act. Bighead, silver, and black
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STATE OF THE GREAT LAKES 201 7
carp are now listed as nuisance species under the Lacey Act, prohibiting interstate transport. There are currently
numerous shortcomings in legal safeguards relating to commerce in exotic live fish in Great Lakes and Mississippi
River states, Quebec, and Ontario, as identified by Alexander (2003). These include: express and de facto
exemptions for the aquarium pet trade; de facto exemptions for the live food fish trade; inability to proactively
enforce import bans; lack of inspections at aquaculture facilities; allowing aquaculture in public waters; inadequate
triploidy (sterilization) requirements; failure to regulate species of concern (e.g., Asian carp); regulation through
"dirty lists" only (e.g., banning known nuisance species); and failure to regulate transportation.
Status
The total number of ANS introduced and established in the Great Lakes increased steadily from the 1830s to 2006,
but lias stabilized in the last decade (Figure 1). Although there have been 34 invasions since the GLWQA was
signed in 1987, no new species have been discovered since 2006. However, species introduced in the previous
decades continue to spread - each of the Great Lakes has seen new species become established in its waters in the
last decade (ranging from 19 new species for Superior to 30 for Lake Michigan) and nearly every watershed in the
entire basin has seen at least one new species in that decade.
A NOAA-developed impact assessment tool (NOAA 2014 was applied to 182 of the Great Lakes'
established ANS. Briefly, this questionnaire-style assessment considered three main categories of impact:
enviromnental, socio-economic, and beneficial. Scores under criteria for each impact category were determined
based on literature review and expert evaluation, with the results assigned a qualitative score of High, Moderate,
Low, or Unknown. At least 31% of the nonindigenous species found in the Great Lakes have significant (moderate
to high) enviromnental impact, as seen in Figure 3. While substantially higher than the often cited estimate of' 10%
of established nonindigenous species have significant impacts' this estimate is likely also an under-estimate of the
true enviromnental impact. If the 88 species which are currently unable to be fully assessed (due to lack of data)
follow the trends of the assessed species this number will be closer to 60%. While less substantial, socio-economic
impacts are also likely higher than the 10% figure-we estimate between 14 and 16% of the nonindigenous species
found in the Great Lakes have moderate to high socio-economic impact (NOAA 2014).
The overall economic impact of ANS on the Great Lakes region—spanning direct operating costs, decreased
productivity, and reduced demand within sport and commercial fishing, power generation, industrial facilities,
tourism and recreation, water treatment, and households—is estimated at well over $100 million annually (Rosaen et
al. 2012). This figure includes both basinwide efforts such as that of Great Lakes Fishery Commission's sea lamprey
control program, with an annual budget of about $18 million and local responses, such as the $l,040-$26,000 cost
per acre of Eurasian watennilfoil removal (Rosaen et al. 2012). Economic impacts from dreissenid mussel control
and monitoring are estimated at $1.2 million annually per power plant, $1.97 million for removal of 400 cubic yards
at a paper plant, and $480,000-$540,000 annually at a water treatment plant (Rosaen et al. 2012).
Linkages
Invasion Meltdown: Evidence indicates that newly invading species may benefit from the presence of previously
established invaders. That is, the presence of one ANS may facilitate the establishment or population growth of
another (Ricciardi 2001). For example, the sea lamprey (Petromyzon marinus) may have created enemy-free space
that facilitated the alewife's (Alosapseudoharengus) invasion, and the round goby (Neogobius melcmostomus) and
Echinogammarus ischnus (amphipod) have thrived in the presence of previously established zebra (Dreissena
polvmorphd) and quagga mussels (Dreissena bugensis). In effect, dreissenids have set the stage to increase the
number of successful invasions, particularly those of co-evolved species in the Ponto-Caspian assemblage. This
result may be a critical factor contributing to the continued spread of species across lakes within the Great Lakes
system.
Multi-stressors: Changes in water quality, global climate change, and land use also may make the Great Lakes more
hospitable for the arrival of new invaders. We are particularly concerned that climate change may be facilitating the
northward spread of both invasive species and the spread of native species into adjacent habitats to which they are
not native (e.g., range expansion).
Secondary Shifts in Native Populations: ANS may exert significant direct and indirect pressures upon native species
including facilitation of parasitism, transmission of viral/bacterial infections, magnification of toxins, competition,
food-web alteration, genetic introgression, degradation of water quality, and degradation of physical habitat. ANS
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STATE OF THE GREAT LAKES 201 7
have promoted the proliferation of native nuisance species, including green algae (Cladophora); cyanobacteria
(Skubinna et al. 1995; Vanderploeg et al. 2001), and bacteria (botulism).
The potential for ANS to colonize new locations is increased with removal of dams. In contrast, ecological
separation of the Great Lakes from the Mississippi River basin is currently being discussed as a way to limit transfer
of ANS between these basins.
Many nonindigenous plants are capable of forming dense mats that may exclude fish from nearshore habitats.
Colonization of lakebed areas by dreissenid mussels and the consequent filling of remaining interstitial spaces with
pseudofeces and fine-grained sediments led to the exclusion of lake trout from some of their native spawning
grounds (S. Mackey, Habitat Solutions NA, pers. comm.).
Comments from the Author(s)
ANS have invaded the Great Lakes basin from regions around the globe. Increasing world trade and travel elevates
the risk that additional species will continue to gain access to the Great Lakes. Existing connections between the
Great Lakes watershed and systems outside the watershed, such as the Chicago Sanitary and Ship Canal, and growth
of industries such as aquaculture, live food markets, and aquarium retail stores will also increase the risk that new
ANS will be introduced. New vectors may arise as the face of industry in the region changes. Climate change may
also facilitate the northward migration of species as well as altering habitat in a way that favors some invaders over
natives or alters their impacts. Increasing lake temperatures associated with climate change will lead to increased
potential for ANS introduced from wanner climates to establish overwintering populations (Adebayo et al. 2011;
Mandrak 1989). The rate of invasion may increase if positive interactions involving established ANS or native
species facilitate the establishment of new ANS. Each new invader can interact in unpredictable ways with
previously established invaders, potentially creating synergistic impacts (Ricciardi 2001, 2005). For example,
recurring outbreaks of avian botulism in the lower Great Lakes are thought to result from the effects of dreissenid
mussels and round gobies, in which the mussels create environmental conditions that promote the pathogenic
bacterium and the gobies transfer bacterial toxin from the mussels to higher levels of the food web.
Data on range expansion populations (those native or cryptogenic to a portion of the basin but introduced to other
areas of the basin) is currently still lacking - GLANSIS tracks only 12 such species (mostly those that invaded the
upper lakes via the Welland Canal. More monitoring data will be needed to assess potential expansion of these
populations due to climate change.
Authors of the previous report recommended additional discussion of prevention, spread and control options for
ANS. We have made a preliminary attempt to include information here on spread and impact as indicators of
ecosystem pressure. While GLANSIS has begun to serve information on regulation and control options (pending
NOAA Tech Memo 2015) that remains beyond the scope of this report in that it would shift the focus to one of
response.
Assessing Data Quality
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not
Applicable
1. Data are documented, validated, or
quality-assured by a recognized agency or
organization

X




2. Data are traceable to original sources

X




3. The source of the data is a known,
reliable and respected generator of data

X




4. Geographic coverage and scale of data
are appropriate to the Great Lakes basin

X




5. Data obtained from sources within the
U.S. are comparable to those from
Canada

X




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STATE OF THE GREAT LAKES 201 7
6. Uncertainty and variability in the data
are documented and within acceptable
limits for this sub-indicator report

X




Acknowledgements
Author:
Dr. Rochelle Sturtevant
Regional Sea Grant Specialist - Outreach
NOAA Great Lakes Environmental Research Laboratory
4840 South State Road, Ann Arbor, MI 48108
Kristen T. Holeck, Department of Natural Resources, Cornell University, Bridgeport, NY
Contributors:
Abigail J. Fusaro, NOAA Great Lakes Enviromnental Research Laboratory, Ann Arbor, MI
Jeff Brinsmead, Ontario Ministry of Natural Resources and Forestry
Information Sources
Adebayo, AA„ Briski, E., Kalaci, O., Hernandez, M„ Ghabooli, S., Beric, B., et al. 2011. Water hyacinth
(Eichhornia crassipes) and water lettuce (Pistia stratiotes) in the Great Lakes: playing with fire? Aquatic Invasions
6:91-96.
Alexander, A. 2003. Legal tools and gaps relating to commerce in exotic live fish: phase 1 report to the Great Lakes
Fishery Commission bv the Environmental Law and Policy Center. Enviromnental Law and Policy Center, Chicago,
IL.
Bailey, S.A., Duggan, I.C., Jenkins, P.T., and Maclsaac, H.J. 2005. Invertebrate resting stages in residual ballast
sediment of transoceanic ships. Can. J. Fish. Aquat. Sci. 62:1090-1103.
Casas-Monroy, O., Linley, R.D., Adams, J.K., Chan, F.T., Drake, D.A.R., and Bailey, S.A. 2014. National Risk
Assessment for Introduction of Aquatic Nonindigenous Species to Canada by Ballast Water. DFO Can. Sci. Advis.
Sec. Res. Doc. 2013/128. vi + 73 p.
Colautti, R.I., Niimi, A.J., van Overdijk, C.D.A., Mills, E.L., Holeck, K.T., and Maclsaac, H.J. 2003. Spatial and
temporal analysis of transoceanic shipping vectors to the Great Lakes. In Invasion Species: Vectors and
Management Strategies. G.M. Ruiz and J.T. Carlton, eds., pp. 227-246. Washington, DC: Island Press.
Cole, R. 2001. USGS Factsheet: Exotic parasite causes large scale mortality in American coots. Available from
http://www.nwhc.usgs.gov/publications/fact_sheets/pdfs/fact_lpolyoon.pdf
Cudmore-Vokey, B„ Crossman, E.J. 2000. Checklists of the fish fauna of the Laurentian Great Lakes and their
connecting channels. Canadian Manuscript Report of Fisheries and Aquatic Sciences 2250:v + 39.
Duggan, I.C., van Overdijk, C.D.A., Bailey, S.A., Jenkins, P.T., Limen, H„ and Maclsaac, H.J. 2005. Invertebrates
associated with residual ballast water and sediments of cargo carrying ships entering the Great Lakes. Can. J. Fish.
Aquat. Sci. 62:2463-2474.
Government of Canada. 2006. Ballast water control and management regulations. Canada Gazette, vol. 140, no. 13
(June 28, 2006).
Grigorovich LA.. Colautti, R.I., Mills, E.L., Holeck, K.T., Ballert, A.G., and Maclsaac, H.J. 2003. Ballast-mediated
animal introductions in the Laurentian Great Lakes: retrospective and prospective analyses. Can. J. Fish. Aquat. Sci.
60:740-756.
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STATE OF THE GREAT LAKES 201 7
Herborg, L.-M., Mandrak, N.E., Cudmore, B.C., and Maclsaac, H.J. 2007. Comparative distribution and invasion
risk of snakehead (Channidae) and Asian carp (Cyprinidae) species in North America. Can. J. Fish. Aquat. Sci.
64:1723-1735.
Holeck, K.T., Mills, E.L., Maclsaac, H.J., Dochoda, M.R., Colautti, R.I., and Ricciardi, A. 2004. Bridging troubled
waters: understanding links between biological invasions, transoceanic shipping, and other entry vectors in the
Laurentian Great Lakes. Bioscience 54:919-929.
Johengen, T„ Reid, D.F., Fahnenstiel, G.L., Maclsaac, H.J., Dobbs, F.C., Doblin, M., Ruiz, G., and Jenkins, P.T.
2005. A Final Report for the Project: Assessment of Transoceanic NOBOB Vessels and Low-Salinity Ballast Water
as Vectors for Non-indigenous Species Introductions to the Great Lakes. National Oceanic and Atmospheric
Administration, Great Lakes Enviromnental Research Laboratory, and University of Michigan, Cooperative Institute
for Limnology and Ecosystems Research, Ann Arbor. 287 pp. Available at
http://www.glerl.noaa.gov/res/projects/nobob/products/NOBOBFinalReport.pdf
Kipp, R„ Bailey, S.A., Maclsaac, H., and Ricciardi, A. 2010. Transoceanic ships as vectors for nonindigenous
freshwater bryozoans. Diversity and Distributions 16:77-83.
Kolar, C.S., and Lodge, D.M. 2002. Ecological predictions and risk assessment for alien fishes in North America.
Science 298:1233-1236.
Kolar, C.S., Chapman, D.C., Courtenay, W.R., Housel, C.M., Williams, J.D., and Jennings, D.P. 2005. Asian carps
of the genus Hypophthalmichthvs (Pisces, Cyprinidae) - A biological synopsis and enviromnental risk assessment.
Report to US Fish and Wildlife Service per Interagency Agreement 94400-3-0128.
Mandrak, N.E. 1989. Potential invasion of the Great Lakes by fish species associated with climatic wanning. J.
Great Lakes Res. 15:306-316.
Mendoza-Alfaro, R.E., Cudmore, B., Orr, R„ Fisher, J.P., Balderas, S.C., Courtenay, W.R., Osorio, P.K., Mandrak,
N., Torres, P.A., Damian M.A., Gallardo, C.E., Sanguines, A.G., Greene, G., Lee, D., Orbe-Mendoza, A., Martinez,
C.R., and Arana, O.S.. 2009. Trinational risk assessment guidelines for aquatic alien invasive species: test cases for
the snakeheads (Channidae) and armored catfishes (Loricariidae) in North American inland waters. Commission
for Enviromnental Cooperation.
Mills, E.L., Leach, J.H., Carlton, J.T., and Secor, C.L. 1993. Exotic species in the Great Lakes: A history of biotic
crises and anthropogenic introductions. J. Great Lakes Res. 19(1): 1-54.
Mills, E.L., Scheuerell, M.D., Carlton, J.T., and Strayer, D.L. 1997. Biological invasions in the Hudson River. NYS
Museum Circular No. 57. Albany, NY.
Ricciardi, A. 2001. Facilitative interactions among aquatic invaders: is an "invasional meltdown" occurring in the
Great Lakes? Can. J. Fish. Aquat. Sci. 58:2513-2525.
Ricciardi, A. 2005. Facilitation and synergistic interactions among introduced aquatic species. In Invasive Alien
Species: A New Synthesis. H.A. Mooney, R.N. Mack, J. McNeely, L.E. Neville, P.J. Schei, and J.K. Waage, eds.,
pp. 162-178. Washington, DC: Island Press.
Ricciardi, A. 2006. Patterns of invasions in the Laurentian Great Lakes in relation to changes in vector activity.
Diversity and Distributions 12:425-433.
Ricciardi, A. and Maclsaac, H.J. 2008. Evaluating the effectiveness of ballast water exchange policy in the Great
Lakes. Ecol. Appl. 18(5): 1321-1323.
Ricciardi, A., and Rasmussen, J.B. 1998. Predicting the identity and impact of future biological invaders: a priority
for aquatic resource management. Can. J. Fish. Aquat. Sci. 55:1759-1765.
Rixon, C.A.M., Duggan, I.C., Bergeron, N.M.N., Ricciardi, A., and Maclsaac, H.J. 2005. Invasion risks posed by
the aquarium trade and live fish markets on the Laurentian Great Lakes. Biodiversity and Conser\>. 14:1365-1381.
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STATE OF THE GREAT LAKES 201 7
Rosaen, A.L., Grover, E.A., Spencer, C.W., and Anderson, P.L. 2012. The costs of aquatic invasive species to Great
Lakes states. Report prepared by the Anderson Economic Group LLC.
Simberloff, D. 2006. Invasional meltdown 6 years later: important phenomenon unfortunate metaphor, or both?
Ecol. Letters 9:912-919.
Skubinna, J.P., Coon, T.G., and Batterson, T.R. 1995. Increased abundance and depth of submersed macrophytes in
response to decreased turbidity in Saginaw Bay, Lake Huron. J. Great Lakes Res. 21:476-488.
Stepien, C.A., and Tumeo, M.A.. 2006. Invasion genetics of Ponto-Caspian gobies in the Great Lakes: a 'cryptic'
species, absence of founder effects, and comparative risk analysis. Biological Lnvasions 8:61-78.
U.S. EPA (United States Enviromnental Protection Agency). 2008. Predicting future introductions of nonindigenous
species to the Great Lakes. National Center for Enviromnental Assessment, Washington DC; EPA/600/R-08/066F.
Available from the National Technical Information Service, Springfield, VA, and http://www.epa.gov/ncea.
Vanderploeg, H.A., Liebig, J.R., Cannichael, W.W., Agy, M.A., Johengen, T.H., Fahnenstiel, G.L., andNalepa,
T.F. 2001. Zebra mussel (Dreissena polymorpha) selective filtration promoted toxic Microcystis blooms in Saginaw
Bay (Lake Huron) and Lake Erie. Can. J. Fish. Aquat. Sci. 58:1208-1221
Williamson, M.H., and Brown, K.C. 1986. The analysis and modeling of British invasions. Philosophical
Transactions of the Royal Society of London, Series B. 314:505-522.
List of Figures
Figure 1. Cumulative Invasions to the Great Lakes Basin by Vector
Source: GLANSIS
Figure 2. Number of AIS Present in the Great Lakes Basin
Source: GLANSIS
Figure 3. Enviromnental and Socio-Economic Impact and Benefit of AIS
Source: GLANSIS
Last Updated
State of the Great Lakes 2017 Technical Report
Page 357
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STATE OF THE GREAT LAKES 201 7
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STATE OF THE GREAT LAKES 201 7
Figure 2. Number of AIS Present in the Great Lakes Basin for each lake.
Source: GLANSIS
Environmental
Impact
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Low
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Figure 3. Environmental and Socio-Economic Impact and Benefit of AIS
Source: GLANSIS
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STATE OF THE GREAT LAKES 201 7
Sub-Indicator: Dreissenid Mussels
Overall Assessment
Status: Poor
Trend: Deteriorating
Rationale: Over all the lakes, the status of dreissenid mussel populations varies depending upon the water
depth and particular lake region. In general, populations in lakes Michigan, Huron, and Ontario appear to
have stabilized or are decreasing at depths < 90, but are gradually increasing in offshore regions at depths >
90 m. The deep zone appears to be a continuing invasion front for quagga mussels, though the rate of popula-
tion growth is slower than what was observed in more shallow depths. These assessments are mostly based on
lake-wide surveys conducted every 5 years. In these three lakes, quagga mussels have displaced zebra mus-
sels except in shallow, nearshore areas and bays. Because the offshore region of these lakes (> 90 m) compris-
es a relatively large proportion of total lake area and quagga mussels are still expanding in this region, the
overall status would indicate a deteriorating status. It is also worth noting that although mussel biomass has
been declining in some of the lakes in the 30-90 m depth zone, dreissenid mussels remain a dominant compo-
nent of the benthos. Depending on the lake basin, dreissenid populations in Lake Erie are stable or declining,
while populations in Lake Superior remain at low levels. In regions of all the lakes where populations are sta-
ble or declining, it is not clear if mussel impacts are becoming less severe. Population trends are mostly de-
rived from density estimates, but biomass estimates give a better evaluation of trends. However, biomass es-
timates are often not available, or methods of determination are not consistent. Herein, trends in biomass are
given only when estimates are temporally consistent. Further, assessments are limited to the main basins of
the lakes and exclude the connecting channels. There are few, if any, regular monitoring programs in the
connecting channels and, even so, physical factors such substrate variability, current patterns, etc. do not
provide the best conditions to assess temporal trends in populations. In the main lake basins, emphasis will be
placed on trends at depths > 30 m. Wide variations in populations occur at shallower depths making assess-
ments of temporal trends difficult. Finally, since lake-wide assessments are mostly based on surveys every 5
years, temporal trends can be considered mainly at this level of detail. Some regional assessments are made
on an annual basis, and these are included if data are available.
Lake-by-Lake Assessment
Lake Superior
Status: Good
Trend: Unchanging
Rationale: Zebra mussels were first found in Duluth-Superior Harbor in 1989, and quagga mussels were subse-
quently found in the same area in 2005. Since then, the spread and population growth of both dreissenid species has
been minimal. Both species are most abundant in the Duluth Harbor area or just outside the harbor in the immediate
vicinity of nearshore Lake Superior. Mussels have spread from the Duluth Harbor region. Some zebra mussels
were found in the east side of the lake in Whitefish Bay in 2002, and in a bay of Isle Royale in 2009. It is believed
that calcium concentrations in Lake Superior are too low to support high abundances.
Lake Michigan
Status: Poor
Trend: Deteriorating
Rationale: The status of dreissenid populations in Lake Michigan is routinely assessed by two major surveys. One
survey is conducted over the entire lake every 5 years, while the other survey is conducted in the southern basin on a
yearly basis. The last 5-year survey with reported results was in 2010. When densities in 2010 were compared to
densities in 2005, the dreissenid population (all quagga) at 31-90 m appeared to have stabilized (Figure 1), but the
population at > 90 m continued to increase (Figure 2). More recent data on density and biomass from the annual
survey in the southern basin indicate populations are now in the state of decline at 30-90 m (Figures 3 and 4). Den-
sity also shows a slight decline at > 90 m since 2012 (Figure 3), but biomass seems to be holding relatively steady
(Figure 4), indicating that mean biomass per mussel (i.e., mean size) is increasing at these greater depths. Despite
declines at sites in the 31-90 m interval, the quagga mussel population still well exceeds maximum densities previ-
ously reached by zebra mussels in that interval. A lake-wide survey was conducted in 2015 (5 years since 2010) and
future results will confirm whether these patterns are apparent throughout the entire lake.
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STATE OF THE GREAT LAKES 201 7
Lake Huron
Status: Poor
Trend: Deteriorating
Rationale: The last lake-wide survey of dreissenid populations in Lake Huron occurred in 2012. Between 2007 and
2012, dreissenid densities (all quagga) appear to have stabilized at 31-90 m, but are still increasing at > 90 m (Fig-
ures 1 and 2). At the former depth interval, decreased densities at 31-50 m were compensated by increased densities
at 51-90 m (Figure 5). In Georgian Bay, densities at 31-90 m decreased two-fold between 2007 and 2012, while
mussels were not present in North Channel at the sites sampled, which is similar to the finding in 2007.
Lake Erie
Status: Fair
Trend: Improving
Rationale: An update on trends in dreissenid populations in Lake Erie was provided by Karatayev et al. (2014). In
2009-2012, zebra mussels were rarely found outside of the western basin, and even there it comprised only 30 % of
the total dreissenid density. Overall, lake-wide densities of dreissenids were lower in 2009-2012 compared to 2002,
which is a continuation of a trend first observed between the late 1990s and 2002.
The lake-wide decrease was mostly a function of decreases in the eastern basin. In this basin, mean densities were
about 9,000 m~2 in 2002 but only 442 m"2 in 2009-2012. Historically, densities in the eastern basin tended to be
greater than in the western and central basins, but in 2012, the eastern basin densities dropped below those observed
in the western basin. Potential explanations included food limitation, predation by round goby, and sampling site
bias, but none have been demonstrated definitively (Karatayev et al. 2014).
Populations in the central basin are limited because of seasonal hypoxia.
Populations in the western basin are limited because of poor food quality (cyanophytes, inorganic particulates).
Based on annual USGS surveys in just the western basin, the dreissenid population appears to be stable, with annual
densities fluctuating around 1,000 m"2 since 2006 (Figure 6). Also, while quagga mussels have displaced zebra
mussels as the dominant dreissenid species, the percentage of sampled sites with dreissenids present has fluctuated
around a mean level since the early 2000s, indicating the total population is not spatially expanding within the west-
ern basin (Figure 6).
Lake Ontario
Status: Poor
Trend: Deteriorating
Rationale: The last lake-wide survey of dreissenid populations in Lake Ontario occurred in 2013. Dreissenid densi-
ties (all quagga) at 31-90 m were lower in 2013 compared to densities in 2008 (Figure 1). Densities at this depth
interval appear to have peaked in 2003. On the other hand, the population at > 90 m still seems to be expanding as
densities at these deep depths in 2013 were the highest ever recorded (Figure 2). While densities at 31-90 m were
lower in 2013 compared to 2008, biomass was slightly higher. Mean biomass was 31.2 g m"2 in 2013 compared to
19.3 g m"2 in 2008. This can be attributed to the greater mean size of mussels in the former year.
Sub-Indicator Purpose
• The purpose of this sub-indicator is to assess the population status of the invading Dreissena rostriformis
bugensis (quagga mussel) and Dreissena polvmorpha (zebra mussel) in the Great Lakes.
Ecosystem Objective
Dreissenids are actively changing the integrity of Great Lakes ecosystems by altering nutrient and energy cycling,
promoting nuisance algal blooms and benthic algae, and negatively impacting native species of invertebrates and
fish. Such changes to ecosystem integrity create uncertainty in effective resource management. Thus, the sub-
indicator addresses the objective of maintaining healthy and sustainable ecosystems.
This sub-indicator best supports work towards General Objective #7 of the 2012 Great Lakes Water Quality Agree-
ment which states that the Waters of the Great Lakes should "be free from the introduction and spread of aquatic
invasive species and free from the introduction and spread of terrestrial invasive species that adversely impact the
quality of the Waters of the Great Lakes."
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STATE OF THE GREAT LAKES 201 7
Ecological Condition
Dreissenid populations in the Great Lakes are presently in various stages of change. In many offshore regions, pop-
ulations are increasing, but in some nearshore regions populations seem to be stable or declining. While some year-
to-year variability can be expected, a goal of this sub-indicator is to determine at what level of abundance/biomass
populations become stable and at equilibrium with the surrounding enviromnent. Such levels, along with associated
degrees of uncertainty, can then be used in predictive models to better manage Great Lakes resources.
Many sampling efforts have sought to provide data on population abundances and biomass. While abundances are
the most common reporting measure of population status, biomass is more valuable for assessing ecological impacts
and for input to predictive models. Biomass is calculated from the soft tissue of these organisms. Some protocols
call for separating soft tissue from shell and directly determining soft tissue weight, while others determine the size
frequency of the populations (shell length) and infer tissue biomass based upon a predetermined relationship be-
tween shell length and soft tissue weight. Data used to obtain biomass with the latter protocol can also be used to
assess population dynamics and predict the direction of populations over time. For example, a population with a
large number of individuals and a size distribution skewed toward smaller individuals demonstrates high recruitment
and possibly low survivability (or if survivability is not compromised then it may illustrate recent colonization). In
contrast, populations showing a size-frequency distribution skewed towards larger individuals with fewer numbers
suggests an aging population with relatively lower recruitment and greater survivability. Traditional population
ecology suggests that stable populations move from a size-frequency distribution of low mean biomass towards one
of higher mean biomass. As a population colonizes a new area, high resource availability promotes high recruit-
ment. As resources are sequestered into the population recruitment decreases with decreasing resource availability
and mean biomass increases as fewer new (low biomass) individuals are added to the population and surviving
members continue to grow.
Linkages
Linkages to other sub-indicators in the indicator suite include:
•	Benthos (open water) - the relative abundance of the benthic community other than dreissenids can be af-
fected by dreissenids.
•	Cladophora - Cladophora is significantly influenced by increases in mussel populations and the corre-
sponding state of water clarity and nutrients in the Great Lakes.
•	Diporeia (open water) - Diporeia is an important component of the native benthic community that has
been affected by dreissenids.
•	Harmful Algal Blooms - the filtering and nutrient excretion activities of dreissenids may lead to increased
frequency, distribution and severity of both inshore (attached/benthic) and offshore algal blooms and fa-
vour the predominance of cyanobacteria.
•	Phytoplankton - the abundance and composition of phytoplankton has dramatically changed in areas of the
Great Lakes where dreissenids have become abundant.
This sub-indicator also links directly to the other sub-indicators in the Invasive Species category, particularly Aquat-
ic Invasive Species.
Comments from the Author(s)
Dreissenid mussels may be responsible for adverse impacts to several other indicators. Dreissenid mussels have
directly or indirectly impaired native species and therefore have negatively impacted biological integrity. Further
they have impaired several beneficial uses listed under Annex 2, (1) of the Great Lakes Water Quality Agreement
including fish and wildlife consumption, and fish and wildlife populations. Aquatic invasive species, including
dreissenid mussels, have been given a high priority in the renewed Water Quality Agreement. In 2014, the U.S.
Invasive Mussel Collaborative (http://invasivemusselcollaborative.net/) was formed to advance scientifically-sound
technologies to control invasive mussels. The Collaborative also aims to improve communication and coordination
among researchers and resource managers.
Because of the rapid rate at which Dreissena populations have expanded in many areas, and because of the ability of
dreissenids to cause ecosystem-wide changes, agencies committed to documenting trends should report data in a
timely manner. Besides abundance, biomass should be routinely monitored. This allows comparisons across lakes
and other food web components, and is most useful for predictive models. Since dreissenids are found on hard as
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STATE OF THE GREAT LAKES 201 7
well as on soft substrates, various sampling methods may be needed to truly assess population mass in a given lake
or lake region particularly in the nearshore.
Assessing Data Quality
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not
Applicable
1. Data are documented, validated, or
quality-assured by a recognized agency or
organization
X





2. Data are traceable to original sources
X





3. The source of the data is a known,
reliable and respected generator of data
X





4. Geographic coverage and scale of data
are appropriate to the Great Lakes Basin

X




5. Data obtained from sources within the
U.S. are comparable to those from
Canada





X
6. Uncertainty and variability in the data
are documented and within acceptable
limits for this sub-indicator report
X





Acknowledgments
Authors:
T. F. Nalepa, Water Center, Graham Sustainability Institute, University of Michigan, Ann Arbor, MI
A. K. Elgin, Great Lakes Enviromnental Research Laboratory, National Oceanic and Atmospheric Administration
Ann Arbor, MI
D. W. Schloesser, Great Lakes Science Center, U. S. Geological Survey, Ann Arbor, MI
Information Sources
Birkett, K„ S. J. Lozano, andL. G. Rudstam. 2015. Long-term trends in Lake Ontario's benthic macroinvertebrate
community from 1994-2008. Aquatic Ecosystem Health & Management 18:76-88.
Karatayev, A. V., L. E. Burlakova, C. Pennuto, J. Ciborowski, V. A. Karatayev, P. Juette, andM. Clapsadl. 2014.
Twenty years of changes in Dreissena spp. populations in Lake Erie. J. Great Lakes Res. 40: 550-559.
Nalepa, T. F„ D. W. Schloesser, C. M. Riseng, and A. K. Elgin. Abundance and distribution of benthic
macroinvertebrates in the Lake Huron system: Saginaw Bay, 2006-2009, and Lake Huron, including Georgian
Bay and North Channel, 2007 and 2012. NOAA Technical Memorandum, Great Lakes Enviromnental
Research Laboratory, Ann Arbor, MI. (in prep).
Watkins, J. M., R. Dennott, S. J. Lozano, E. L. Mills, L. G. Rudstam, and J. V. Scharold. 2007. Evidence for
Remote Effects of Dreissenid Mussels on the Amphipod Diporeia: Analysis of Lake Ontario Benthic Surveys,
1972-2003. Journal of Great Lakes Research 33:642.
List of Figures
Figure 1. Mean densities (number per square metre) of Dreissena from sites at 31-90 m in lakes Michigan, Huron,
and Ontario. Data are from lake-wide surveys conducted mostly at 5-year intervals. Lake Michigan = blue
triangles, dashed line; Lake Huron = red squares, dot-dash line; Lake Ontario = black circles, solid line.
Sources: Watkins et al. 2007; Birkett et al. 2015; Great Lakes Enviromnental Research Lab, NOAA
Page 363
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STATE OF THE GREAT LAKES 201 7
Figure 2. Mean densities (number per square metre) of Dreissena from sites at > 90 m in lakes Michigan, Huron,
and Ontario. Data are from lake-wide surveys conducted mostly at 5-year intervals. Lake Michigan = blue
triangles, dashed line; Lake Huron = red squares, dot-dash line; Lake Ontario = black circles, solid line.
Sources: Watkins et al. 2007; Birkett et al. 2015; Great Lakes Environmental Research Lab. NOAA
Figure 3. Mean (± SE) density (number per square metre) of dreissenids at each of four depth intervals in southern
Lake Michigan 1992-2014. The number of sites in each depth interval was 16-30 m = 9-12, 31-50 = 11-
13, 51-90 m = 11, > 90 m = 6. Zebra mussels = black; quagga mussels = blue. Two outlier stations were
removed: H-14 in 2012 (31-50 m interval, density = 50201/m2); and H-18 in 2013 (16-30 m interval,
density = 45403/nr). In both cases, one of the replicates contained >5000 newly settled mussels (length
 90 m = 6. Zebra mussels = black; quagga mussels = blue.
Source: Great Lakes Environmental Research Lab, NOAA
Figure 5. Densities (No. m3) of zebra (top) and quagga (bottom) mussels in Lake Huron from 2000-2012.
Source: Nalepa et al. (in prep)
Figure 6. Percentage of sites with Dreissena (top panel) and mean density of Dreissena (number per square metre)
(bottom panel) in western Lake Erie, 1991-2013; n=30. Zebra mussels = blue squares; quagga mussels =
black circles.
Source: Great Lakes Science Center, USGS
Last Updated
State of the Great Lakes 2017 Technical Report
14000
Lake Michigan
12000
Lake Huron
£ 10000 «
Lake Ontario
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6000
4000
2000
1990
1995
2000
2005
2010
2015
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Figure 1. Mean densities (number per square metre) of Dreissena from sites at 31 -90 m in lakes Michigan. Huron,
and Ontario. Data are from lake-wide surveys conducted mostly at 5-year intervals. Lake Michigan = blue
triangles, dashed line; Lake Huron = red squares, dot-dash line; Lake Ontario = black circles, solid line.
Sources: Watkins et al. 2007; Birkett et al. 2015; Great Lakes Environmental Research Lab, NOAA
Page 364
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STATE OF THE GREAT LAKES 201 7
Lake Michigan
Lake Huron
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Lake Ontario
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Figure 2. Mean densities (number per square metre) of Dreissena from sites at > 90 m in lakes Michigan, Huron,
and Ontario. Data are from lake-wide surveys conducted mostly at 5-year intervals. Lake Michigan = blue
triangles, dashed line; Lake Huron = red squares, dot-dash line; Lake Ontario = black circles, solid line.
Sources: Watkins et al. 2007; Birkett et al. 2015; Great Lakes Enviromnental Research Lab, NOAA
Page 365
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STATE OF THE GREAT LAKES 201 7
20000
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5000
) +* m i *»'i **** —i 1 ¦ ¦ *••••
1980 1985 1990 1995 2000 2005 2010 2015
)	m I n I «**——***» mt«
1980 1985 1990 1995 2000 2005 2010 2015
Year	Year
Figure 3. Mean (± SE) density (number per square metre) of dreissenids at each of four depth intervals in southern
Lake Michigan 1992-2014. The number of sites in each depth interval was 16-30 m= 9-12, 31-50 = 11-13, 51-90
m = 11, > 90 m = 6. Zebra mussels = black; quagga mussels = blue. Two outlier stations were removed: H-14 in
2012 (31-50 m interval, density = 50201/nr); and H-18 in 2013 (16-30 m interval, density = 45403/m2). In both
cases, one of the three replicates contained >5000 newly settled mussels (length 
-------
STATE OF THE GREAT LAKES 201 7
60
60
51-90 m
16-30 m
Quagga
Zebra
50 -
40 -
40 -
30 -
20 -
a 20 "
10 -
¦5 10 "
0 4	*	1			1	«—~»•*¥ P			*—*	1			*			P—»—
1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015
60
60
31-50 m
> 90 m
50 -
40 -
40 -
30 -
20 "
20 -
=5 io -
10 -
1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015
Year	Year
Figure 4. Mean (± SE) biomass (shell-free grams per square metre) of dreissenids at each of four depth intervals in
southern Lake Michigan, 1998-2014. The number of sites in each depth interval was 16-30 m = 9-12, 31-50 =11-
13, 51-90 m = 11, > 90 m = 6. Zebra mussels = black; quagga mussels = blue.
Source: Great Lakes Enviromnental Research Lab, NOAA
Page 367
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STATE OF THE GREAT LAKES 201 7
2000
2003
2007
2012
2000
2003
2007
2012
:%
II
Figure 5. Densities (No. m3) of zebra (top) and quagga (bottom) mussels in Lake Huron from 2000-2012.
Source: Nalepa et al. (in prep)
Page 368
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STATE OF THE GREAT LAKES 201 7
-¦—Zebra mussels
• ¦ ¦ Quagga mussels
/ ^ ^ ^ / / / / / / / /
Year
-Zebra mussels
• ¦ ¦ Quagga mussels
1S00
^ 1600
E 1400
1200
= 1000
^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^ ^
Year
Figure 6. Percentage of sites with Dreissena (top panel) and mean density of Dreissena (number per square metre)
(bottom panel) in western Lake Erie, 1991-2013; n=30. Zebra mussels = blue squares; quagga mussels = black
circles.
Source: Great Lakes Science Center, USGS.
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STATE OF THE GREAT LAKES 201 7
Sub-Indicator: Sea Lamprey
Overall Assessment
Status: Fair
Trend: Improving
Rationale: Annual sea lamprey control activities in the Great Lakes have successfully suppressed sea lam-
prey populations from peak levels by about 90%. Currently, index estimates of adult sea lamprey abundance
are meeting targets in Lakes Huron, Michigan, and Ontario and are above targets, but declining in Lakes
Superior and Erie. More suppression is needed to bring adult indices to targets in all lakes.
Lake-by-Lake Assessment
Lake Superior
Status: Fair
Trend: Improving
Rationale: Index estimates of adult sea lamprey abundance are above the target, but have declined since 2012.
Lake Michigan
Status: Good
Trend: Improving
Rationale: Index estimates of adult sea lamprey abundance are meeting the target and have declined since 2012.
Lake Huron
Status: Good
Trend: Improving
Rationale: Index estimates of adult sea lamprey abundance are meeting the target and have declined since 2012.
Lake Erie
Status: Fair
Trend: Improving
Rationale: Index estimates of adult sea lamprey abundance are above the target, but have declined since 2010.
Lake Ontario
Status: Good
Trend: Unchanging
Rationale: Index estimates of adult sea lamprey abundance are meeting the target and have been holding steady
since 2013.
Sub-Indicator Purpose
•	To estimate and track the relative adult sea lamprey abundance for each lake.
•	To monitor the damage caused by sea lamprey to the aquatic ecosystem.
•	To monitor the success of sea lamprey control actions.
Ecosystem Objective
This sub- indicator supports Great Lakes Fishery Commission (GLFC) and fishery management agencies fish com-
munity objectives that were established under A Joint Strategic Plan for the Management of Great Lakes Fisheries.
Fish community objectives call for suppressing sea lamprey populations to levels that cause only insignificant mor-
tality on fish to achieve objectives for Lake Trout and other members of the fish community.
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STATE OF THE GREAT LAKES 201 7
This sub-indicator best supports work towards General Objective #7 of the 2012 Great Lakes Water Quality Agree-
ment, which states that the Waters of the Great Lakes should "be free from the introduction and spread of aquatic
invasive species and free from the introduction and spread of terrestrial invasive species that adversely impact the
quality of the Waters of the Great Lakes."
Ecological Condition
The sea lamprey is a non-native species and a lethal parasite of many fish species in the Great Lakes (e.g. Bergstedt
and Schneider 1988; Kitchell 1990), and has caused ecologic and economic tragedy in terms of its impact on the
Great Lakes fish communities and ecosystem (Smith and Tibbies 1980). Before control, sea lampreys killed an esti-
mated 103 million pounds (47 million kilograms) of fish per year with the average sea lamprey killing up to 40
pounds (18 kg) of fish during its parasitic stage. Sea lampreys prefer trout, salmon, whitefish, and Lake Sturgeon but
they also attack smaller fish like cisco. Walleye, and perch (GLFC). The first complete round of stream treatments
with the lampricide TFM (as early as 1960 in Lake Superior) successfully suppressed sea lamprey populations to
less than 10% of pre-control abundances in all of the Great Lakes except Lake Erie, and subsequent lampricide
treatments conducted on a regular basis across the Great Lakes have successfully maintained sea lamprey popula-
tions at this level in all lakes except Lake Erie. The sea lamprey, however, continues to be a significant source of
mortality for many fish species (Bergstedt and Schneider 1988; Kitchell 1990) and its continued control is needed to
restore and maintain the Great Lakes fish communities and ecosystem.
Index estimates of adult sea lamprey abundance relative to lake-specific targets is the primary performance indicator
of the sea lamprey control program (Figure 1). Index estimates are calculated as the sum of the spawning run esti-
mates for a subset of streams in a given lake basin. The numbers of adult sea lampreys migrating into each index
stream are estimated with traps using mark/recapture methods. Index estimates are updated on an annual basis.
On all lakes except Huron and Michigan, index targets are the average index estimate in each lake during times
when whole-lake sea lamprey wounding rates on Lake Trout were tolerable, that is, causing less than 5% annual
mortality (or when Lake Trout wounding rates were less than or equal to five wounds per 100 fish). For Lake Huron,
Lake Trout wounding rates have not been at tolerable levels, so the index target is set at 25% of the average index
estimate during the late 1980s. For Lake Michigan, sea lamprey index estimates are not available during times when
Lake Trout wounding rates were tolerable, so the index target is set using index data from late 1990s corrected for
the higher than tolerable Lake Trout wounding rates. Index targets are only updated when an index stream is either
added and/or removed from the estimation procedure.
In past years, the sea lamprey sub-indicator encompassed whole-lake adult sea lamprey abundances calculated as the
sum of spawning run estimates for all sea lamprey-producing streams in a given basin. Abundances were obtained in
streams with traps using mark/recapture estimates or extrapolation from previous trap capture efficiency estimates,
and in streams without traps using a model that relates spawning run size to stream discharge, larval abundance, and
year since last treatment (spawner model; Mullett et al., 2003). The majority of the abundances were obtained using
the spawner model. Recently, the GLFC changed their adult sea lamprey monitoring protocols, moving away from
the spawner model in favor of an adult sea lamprey index on a subset of streams in a given basin. The change was
made because of the high amount of uncertainty inherent to the spawner model. The index provides a means to track
adult sea lamprey populations using the best available data - actual population assessment data, reducing uncertainty
and providing a better method to track adult sea lamprey populations and assess the impacts of the sea lamprey con-
trol program. Indices have been back calculated so that historical data matches the new data. It is important to note
that the previous indicator report (2011) would not have significantly changed if the adult index method was used.
Therefore, the change in trends from the previous indicator report are not due to the change in methodology, but are
a result from increased sea lamprey control efforts in all lakes, especially Lakes Huron, Michigan, and Erie.
Sea lamprey wounding rates on Lake Trout have also been previously included as another measure of the abundance
of sea lamprey in relation to their prey. However, wounding rates were not used directly to assess sea lamprey
abundance in previous sea lamprey indicator reports. Lake Trout wounding rate trends do not always match sea
lamprey abundance trends. Lake Trout wounding rates are dependent on sea lamprey abundance and abundances of
ALL host fish. These relationships are hard to reconcile because of the lack of abundance data on hosts other than
Lake Trout, which leads to inconsistencies between sea lamprey abundance and Lake Trout wounding rates (e.g., a
Lake Trout wounding rate can increase in the presence of a steady sea lamprey population if the abundance of other
host fish declines). However, sea lamprey wounding rates on Lake Trout for each lake along with their targets are
Page 371
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STATE OF THE GREAT LAKES 201 7
graphically summarized in Figure 2 as additional information to show some of the impact sea lamprey have on Great
Lakes fish, specifically Lake Trout.
Lake Superior
In Lake Superior, the adult index estimate is above the target, but has been decreasing since 2012. Sources of sea
lampreys that are of concern include the Bad River and lentic populations in the Kaministiquia, Nipigon, Gravel,
and Batchawana rivers where populations are sparsely distributed and granular Bayluscide treatment is less effective
than conventional TFM applications. Overall lampricide control effort has increased since 2005 with additional trib-
utary and lentic (estuaries, bays, and slower moving tributaries) areas being treated, likely leading to a reduction in
the adult sea lampreys.
Lake Michigan
In Lake Michigan, the adult index estimate is meeting the target. Sources of sea lampreys that are of concern include
the Manistique River, other productive tributaries in the northern part of the lake, and the St. Marys River (Lake
Huron). Lampricide control effort has increased starting in 2006 with additional treatments. In addition, the Man-
istique River has been treated six times since 2003 with the most recent treatment during 2014. Reductions in sea
lamprey abundance during the past nine years are likely due to the repeated treatment of the Manistique River.
Lake Huron
In Lake Huron, the adult index estimate is meeting the target. Sources of sea lampreys that are of concern include
the St. Marys River, other productive tributaries in the northern part of the lake (e.g. Cheboygan and Mississagi riv-
ers), and the Manistique River (Lake Michigan). Lampricide control effort has increased starting in 2006 with addi-
tional treatments. Additionally, a large-scale effort to treat the North Channel area of Lake Huron (including the St.
Marys River) occurred from 2010-2011 along with geographically expanded treatment in the northern parts of Lakes
Huron and Michigan in 2012-2013 and 2014-2015. Application of this strategy successfully reduced larval sea lam-
preys in the St. Marys River to all-time lows and the adult index estimate for Lake Huron to target levels.
Lake Erie
In Lake Erie, the adult index estimate is above the target, but has been decreasing since 2010. Sources of sea lam-
preys that are of concern include hard-to-treat tributaries (e.g. Cattaraugus Creek), tributaries with non-target species
of concern (Conneaut Creek), and the St. Clair and Detroit River System. Lampricide control effort dramatically
increased during 2008-2010 with the implementation of a large-scale treatment strategy where all known sea lam-
prey-producing tributaries to Lake Erie were treated in consecutive years. Increased control effort was also applied
during 2013 with the treatment of twelve tributaries. The adult sea lamprey index has yet to meet the target as ex-
pected. Assessment and treatment strategies are being developed for the Huron-Erie Corridor.
Lake Ontario
In Lake Ontario, the adult index estimate is meeting the target. A source of sea lampreys that is of concern is the
Niagara River - the larval sea lamprey population is currently small, but could become an issue with improved habi-
tat and water quality. Steady lampricide control effort on Lake Ontario has maintained the adult sea lamprey index at
or near the target.
Linkages
Lake Trout; Walleye; and Lake Sturgeon;
Sea lampreys remain a significant source of mortality on many fish species of the Great Lakes including Atlantic,
Chinook, and Coho Salmon, Burbot, Ciscoes, Lake Sturgeon (threatened in some parts of the Great Lakes basin).
Lake Trout, Steelhead, Walleye, Whitefish, etc. Short lapses in sea lamprey control can result in rapid increases in
sea lamprey abundance and the damage they inflict on fish. Continued stream and lentic area treatments are neces-
sary to overcome the reproductive potential of the sea lamprey and to ensure the achievement of population man-
agement objectives for many different species, and to preserve functioning ecosystems.
Aquatic Habitat Connectivity; Water Quality:
The potential for sea lamprey to colonize new locations is increased with improved aquatic habitat connectivity
through the removal of dams and improved water quality. The failure of the Manistique River dam to block sea lam-
preys and the subsequent sea lamprey production from the river is an example of the linkages between sea lamprey
and aquatic habitat connectivity. Additionally, as water quality improves, streams and lentic areas once inhospitable
Page 372
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STATE OF THE GREAT LAKES 201 7
to sea lampreys may become viable spawning and nursery habitats. As examples, during the mid-2000s, a significant
larval population requiring regular lampricide treatment was established for the first time in the estuary of the Ka-
ministiquia River (Lake Superior) after a local paper mill began tertiary treatment of its effluent. The establishment
of larval populations in the St. Marys, St. Clair, and Lower Niagara rivers followed concerted efforts to improve
water quality, and with observations of successful reproduction by lake sturgeon, whitefish, and brindled madtom, it
is likely only a matter of time before sea lamprey reproduction is documented in the Detroit River.
Climate Change:
Rising temperatures in the Great Lakes have recently been associated with increasing size of adult sea lampreys
(Kitchell et al. 2014). As temperatures rise, sea lampreys may grow larger increasing metabolism and becoming
more fecund, which may increase the number of sea lampreys and the damage they cause to host fish.
Comments from the Author(s)
Increases in lampricide treatments have reduced index estimates of adult sea lamprey abundance to within target
ranges in three of the five Great Lakes (Huron, Michigan, and Ontario). The effects of increased lampricide treat-
ments are observed in index estimates beginning two years after they occur. Efforts to identify new/unidentified
sources of sea lampreys also need to continue. In addition, research to better understand sea lamprey/prey interac-
tions, recruitment dynamics, population dynamics of sea lampreys that survive treatment, and refinement of and
research into other control methods are all keys to achieving and maintaining index estimates at targets.
Assessing Data Quality
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not
Applicable
1. Data are documented, validated, or
quality-assured by a recognized agency or
organization
X





2. Data are traceable to original sources
X





3. The source of the data is a known,
reliable and respected generator of data
X





4. Geographic coverage and scale of data
are appropriate to the Great Lakes basin
X





5. Data obtained from sources within the
U.S. are comparable to those from
Canada
X





6. Uncertainty and variability in the data
are documented and within acceptable
limits for this sub-indicator report
X





Acknowledgments
Authors: Michael J. Siefkes, Great Lakes Fishery Commission, 2100 Commonwealth Blvd., Suite 100, Ann Arbor,
MI 48105. Phone: (734) 669-3013. Email: msiefkes@glfc.org
Contributors: Jean V. Adams, U.S. Geological Survey, Great Lakes Science Center, 223 East Steinfest Road,
Antigo, WI 54409. Phone: (715) 627-4317 ext. 3125. Email: ivadams@usgs.gov
Jessica M. Barber, U.S. Fish and Wildlife Service, Marquette Biological Station, 3090 Wright Street, Marquette, MI
49855. Phone: (906) 226-1241. Email: iessica barber@fws.gov
Gale Bravener, Department of Fisheries and Oceans Canada, Sea Lamprey Control Centre, 1219 Queen Street East,
Sault Ste. Marie ONP6A 2E5. Phone: (705) 941-2625. Email: gale,bravener@dfo-mpo.gc.ca
Page 373
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STATE OF THE GREAT LAKES 201 7
Information Sources
Bergstedt, R.A., and Schneider, C.P. 1988. Assessment of sea lamprey (Petromyzon marinus) predationby recovery
of dead lake trout (Salvelinus namaycush) from Lake Ontario, 1982-85. Can. J. Fish. Aquat. Sci. 45:1406-1410.
DesJardine, R.L., Gorenflo, T.K., Payne, R.N., and Schrouder, J.D. 1995. Fish-community objectives for Lake Hu-
ron. Great Lakes Fish. Comm. Spec. Publ. 95-1.
Eshenroder, R.L., Holey, M.E., Gorenflo, T.K., and Clark, R.D., Jr. 1995. Fish-community objectives for Lake
Michigan. Great Lakes Fish. Comm. Spec. Publ. 95-3.
Great Lakes Fishery Commission (GLFC), Sea Lampreys Reach Thirty-Year Low in Lake Huron. Reach Twenty-
Year Low in Lake Michigan, and Trend Downward in the Other Lakes.
http://www.glfc.org/pressrel/sea%201amprey%20abundances%209-23-15.pdf
Heinrich, J.W., Mullett, K.M, Hansen, M.J., Adams, J.V., Klar, G.T., Johnson, D.A., Christie, G.C., and Young, R.J.
2003. Sea lamprey abundance and management in Lake Superior, 1957-1999. J. Great Lakes Res. 29 (l):566-583.
Horns, W.H., Bronte, C.R., Busiahn, T.R., Ebener, M.P., Eshenroder, R.L., Greenfly, T., Kmiecik, N„ Mattes, W.,
Peck, J.W., Petzold, M„ and Schneider, D.R. 2003. Fish-community objectives for Lake Superior. Great Lakes Fish.
Comm. Spec. Publ. 03-01.
Kitchell, J.F. 1990. The scope for mortality caused by sea lamprey. Trans. Amer. Fish. Soc. 119:642-648.
Kitchell, J.F., T.J. Cline, V. Bennington, and G. McKinley. 2014. Climate change challenges management of inva-
sive sea lamprey in Lake Superior. In: Keller, R„ M. Cadotte, and G.Sandiford (editors). Invasive Species in a Glob-
alized World. Univ. of Chicago Press.
Larson, G.L., Christie, G.C., Johnson, D.A., Koonce, J.F., Mullett, K.M., and Sullivan, W.P. 2003. The history of
sea lamprey control in Lake Ontario and updated estimates of suppression targets. J. Great Lakes Res. 29 (1):637-
654.
Lavis, D.S., Hallett, A., Koon, E.M., and McAuley, T. 2003. History of and advances in barriers as an alternative
method to suppress sea lampreys in the Great Lakes. J. Great Lakes Res. 29 (l):584-598.
Morse, T.J., Ebener, M.P., Koon, E.M., Morkert, S.B., Johnson, D.A., Cuddy, D.W., Weisser, J.W., Mullet, K.M.,
and Genovese, J.H. 2003. A case history of sea lamprey control in Lake Huron: 1979-1999. J. Great Lakes Res. 29
(1):599-614.
Mullett, K M., Heinrich, J.W., Adams, J.V. Young, R. J., Henson, M.P., McDonald, R.B., and Fodale, M.F. 2003.
Estimating lakewide abundance of spawning-phase sea lampreys (Petromyzon marinus) in the Great Lakes: extrapo-
lating from sampled streams using regression models. J. Great Lakes Res. 29 (l):240-253.
Ryan, P.S., Knight, R., MacGregor, R., Towns, G., Hoopes, R., and Culligan, W. 2003. Fish-community goals and
objectives for Lake Erie. Great Lakes Fish. Comm. Spec. Publ. 03-02.
Schleen, L.P., Christie, G.C., Heinrich, J.W., Bergstedt, R.A., Young, R.J., Morse, T.J., Lavis, D.S., Bills, T.D.,
Johnson J., and Ebener, M.P. 2003. In press. Development and implementation of an integrated program for control
of sea lampreys in the St. Marys River. J. Great Lakes Res. 29 (l):677-693.
Scholefield, R.J., Slaght, K.S., and Stephens, B.E. 2008. Seasonal variation in sensitivity of larval sea lampreys to
the lampricide 3-trifluoromethyl-4-nitrophenol. North Am. J. Fish. Manage. 28: 1609-1617.
Page 374
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STATE OF THE GREAT LAKES 201 7
Smith, B.R., and Tibbies, J.J. 1980. Sea lamprey (Petromyzon marinus) in lakes Huron, Michigan and Superior:
history of invasion and control, 1936-78. Can. J. Fish. Aquat. Sci. 37:1780-1801.
Stewart, T.J., Lange, R.E., Orsatti, S.D., Schneider, C.P., Mathers, A., and Daniels M.E. 1999. Fish-community ob-
jectives for Lake Ontario. Great Lakes Fish. Comm. Spec. Publ. 99-1.
Sullivan, W.P., Christie, G.C., Cornelius, F.C., Fodale, M.F., Johnson, D.A., Koonce, J.F., Larson, G.L., McDonald,
R.B., Mullet, K.M., Murray, C.K., and Ryan, P. A. 2003. The sea lamprey in Lake Erie: a case history. J. Great
Lakes Res. 29 (l):615-637.
List of Figures
Figure 1. Index estimates of adult sea lamprey abundance plotted on sea lamprey spawning year. Horizontal lines
represent the targets for each lake. Note the scale differences for each lake.
Source: Great Lakes Fishery Commission
Figure 2. Number of A1-A3 sea lamprey wounds per 100 Lake Trout > 532 mm (Superior, Huron Michigan, and
Erie) and number of A1 sea lamprey wounds per 100 Lake Trout > 432 mm (Ontario) from standardized
assessments. Horizontal lines represent the wounding rate target for each lake. Note the scale differences for each
lake.
Source: Great Lakes Fishery Commission
Last Updated
State of the Great Lakes 2017 Technical Report
Page 375
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STATE OF THE GREAT LAKES 201 7
Superior
Huron
1980 1985 1990 1995 2000 2005 2010 2015 2020
200 -
50 -
100 -
1980 1985 1990 1995 2000 2005 2010 2015 2020
Spawning Year
Michigan
Erie
-sr 100
x
0)
¦a
c
>
0)
Q.
E
re
_i
rz
0)
ifi
3
5
1980 1985 1990 1995 2000 2005 2010 2015 2020
1980 1985 1990 1995 2000 2005 2010 2015 2020
Ontario
1980 1985 1990 1995 2000 2005 2010 2015 2020
Year
Figure 1. Index estimates of adult sea lamprey abundance plotted on sea lamprey spawning year. Horizontal lines
represent the targets for each lake. Note the scale differences for each lake.
Source: Great Lakes Fishery Commission
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STATE OF THE GREAT LAKES 201 7
Superior	Huron
30
25 -
20 -
10 -
1985 1990 1995 2000 2005 2010 2015 2020
30
25 -
20 -
1985 1990 1995 2000 2005 2010 2015 2020
Michigan	Erie
30
-4—1
3
O
H 25 -
v
re 20 -
o
o
V
a.
V)
¦a
c
3
O
S
1985 1990 1995 2000 2005 2010 2015 2020
30
25 -
20 -
10 -
1985 1990 1995 2000 2005 2010 2015 2020
Ontario
10
8
6
4
2
0
1985 1990 1995 2000 2005 2010 2015 2020
Year
Figure 2. Number of A1-A3 sea lamprey wounds per 100 Lake Trout > 532 mm (Superior, Huron. Michigan, and
Erie) and number of A1 sea lamprey wounds per 100 Lake Trout > 432 mm (Ontario) from standardized
assessments. Horizontal lines represent the wounding rate target for each lake. Note the scale differences for each
lake.
Source: Great Lakes Fishery Commission
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STATE OF THE GREAT LAKES 201 7
Sub-Indicator: Terrestrial Invasive Species
Overall Assessment
Status: Poor
Trend: Deteriorating
Rationale: Based on the five species of interest - Asian longhorned beetle, emerald ash borer, garlic mustard,
Phragmites and purple loosestrife, terrestrial invasive species are having significant negative impacts and con-
tinue to spread throughout the Great Lakes Basin ecosystem.
Lake-by-Lake Assessment
Lake Superior
Status: Fair
Trend: Deteriorating
Rationale: The impact of the five species of interest has been less significant in the Lake Superior basin than in the
other Great Lakes basins. The limited amount of impact and number of introductions of the five species may be at-
tributed to the fact that the basin has few major population centres, which reduces the potential for anthropogenic
movement of terrestrial invasive species. Nevertheless, the threat of the five terrestrial invasive species remains high
and wanning temperatures due to climate change may increase the habitat range for invasive species. Garlic mustard
and purple loosestrife threaten to further spread their ranges in the basin. There have also been a few sites south of
Lake Superior with confirmed emerald ash borer infestations and strict regulation is important to limit its spread.
Furthermore, data from the Early Detection and Distribution Mapping System (EDDMapS) suggest that since 2003,
there has been an increase in Phragmites observations in the lake basin. No infestations of the Asian longhorned
beetle have been reported in the Lake Superior basin.
Lake Michigan
Status: Poor
Trend: Deteriorating
Rationale: The five species of interest continue to have considerable negative impacts on the Lake Michigan basin
ecosystem. It appears that emerald ash borer, garlic mustard and Phragmites continue to spread through the Lake
Michigan basin. In the Lake Michigan basin, over 6000 hectares of monotypic Phragmites stands were detected by
satellite imagery in 2010. The vast range of Phragmites is likely impacting the quality of both wetland and riparian
habitat. However, the Asian longhorned beetle was declared eradicated from the Chicago, Illinois area after 10 years
of eradication efforts beginning in 1998. The magnitude of purple loosestrife infestations has also been successfully
limited by biocontrol programs, but they are not capable of complete eradication.
Lake Huron
Status: Poor
Trend: Deteriorating
Rationale: Emerald ash borer, garlic mustard and Phragmites are having significant negative impacts on the Lake
Huron basin. The emerald ash borer exists in numerous locations along the south shore of Lake Huron near Sarnia,
Ontario. Based on volunteered geographic information (VGI) observations from the Early Detection and Distribu-
tion Mapping System, garlic mustard and purple loosestrife have been spreading in the basin. Though it is difficult
to discern the magnitude of infestations based on VGI data (an observation could represent one plant or hundreds of
plants), it provides insight into potential distribution and spread of the two plant invasive species. In the U.S. Lake
Huron basin, over 10 000 hectares of dense Phragmites stands were detected by radar imagery in 2010. The exten-
sive range of Phragmites likely impacts the habitat and populations of wildlife. No infestations of the Asian long-
horned beetle have been reported in the Lake Huron basin.
Lake Erie
Status: Poor
Trend: Deteriorating
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STATE OF THE GREAT LAKES 201 7
Rationale: Garlic mustard continues to have considerable negative impact on the Lake Erie basin ecosystem. Fur-
thermore, the impacts of emerald ash borer on forests in southwestern Ontario have been particularly devastating;
from 2004-2012, over 66 000 hectares of forests in the Aylmer and Guelph Ministry of Natural Resource Districts
have experienced moderate- to-severe defoliation and decline. Quarantine areas exist throughout the Lake Erie basin
and education and eradication campaigns have been crucial in slowing the spread of the emerald ash borer. Phrag-
mites lias also had considerable negative impact on the U.S. Lake Erie basin; more than 8200 hectares of dense
Phragmites stands were detected by satellite imagery in 2010. A study by the Canadian Wildlife Service of Envi-
ronment and Climate Change Canada suggests that Phragmites continued to spread in the Canadian Lake Huron-
Erie corridor in the areas around the St. Clair River, Lake St. Clair and Detroit River from 2006-2010. Meanwhile,
the extent and severity of purple loosestrife infestations has been controlled by two leaf-eating beetles, Galerucella
calmariensis and Galerucella pusilla. which feed exclusively on this wetland perennial. No infestations of the Asian
longhorned beetle have been reported in the Lake Erie basin.
Lake Ontario
Status: Poor
Trend: Deteriorating
Rationale: Garlic mustard and Phragmites have continued to negatively impact the Lake Ontario basin ecosystem
and the extent of these 2 species has spread across this basin. By comparison, purple loosestrife has been effectively
controlled by the two leaf-eating beetles (noted above) in the Lake Ontario basin. This perennial plant may continue
to spread through the basin, but the beetles can limit the severity of infestations. In the Lake Ontario basin, emerald
ash borer was detected in the Niagara Region in 2012. While its impacts have not been as severe as the Lake Erie
basin, there are large areas in the region that are experiencing moderate-to-severe decline and mortality in ash trees.
The emerald ash borer has the ability to spread quickly and negatively impact forest ecosystems. For the Asian
longhorned beetle, two infestation areas exist in the basin; the infestation in the Toronto-Vaughan area was declared
eradicated. The other infestation in Toronto-Mississauga is under quarantine and the pest will be declared eradicated
if there are no detections after 5 years of surveys.
Sub-Indicator Purpose
•	The purpose of this sub-indicator is to assess the presence, number, and distribution of five selected
terrestrial invasive species (TIS) in the Laurentian Great Lakes watershed, and to understand the means by
which these species are introduced and persist.
•	It is also to aid in the assessment of the status of biotic communities, as invasive species alter both the
structure and function of ecosystems thereby compromising the biological integrity of these systems.
•	This sub-indicator provides insight into the complex relationships between land and water that impact
Great Lakes water quality.
Ecosystem Objective
To reduce and further prevent expansion of five selected terrestrial invasive species: Asian Long-horned Beetle,
Emerald Ash Borer, Garlic Mustard, Phragmites (Common Reed) and Purple Loosestrife, in the Great Lakes be-
cause they can negatively impact the biodiversity, habitat, chemical loads, nutrient cycling, and hydrogeology of
terrestrial and other ecosystems within the Great Lakes watershed.
This sub-indicator best supports work towards General Objective #7 of the 2012 Great Lakes Water Quality Agree-
ment which states that the Waters of the Great Lakes should "be free from the introduction and spread of aquatic
invasive species and free from the introduction and spread of terrestrial invasive species that adversely impact the
quality of the Waters of the Great Lakes."
Ecological Condition
The proliferation of terrestrial invasive species in the Great Lakes Basin has occurred as an unintended consequence
of global trade and movement of people. As the movement of goods and people continues to grow, a greater number
of species are transported from their native ranges to introduced ranges. Though not all alien species are a threat to
their introduced range, a small number of these alien species are invasive and have the ability to significantly disturb
ecosystems (Canadian Food Inspection Agency, 2005; Great Lakes Regional Collaboration Strategy, 2005). Since
the 1800s, there has been a dramatic increase in the number of invasive alien plant species introduced into Canada
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STATE OF THE GREAT LAKES 201 7
(Canadian Food Inspection Agency, 2005; Figure 1). This trend is also supported by data analyzed from the World
Wildlife Fund's Invasive Species database, which indicates that there has been a 55% increase in the number of ter-
restrial invasive species introductions in the Great Lakes Basin from 1900 to 2000 (World Wildlife Fund 2003).
The status and trends of terrestrial invasive species will be assessed based on the impacts and distribution of the
Asian longhorned beetle, emerald ash borer, garlic mustard, Phragmites and purple loosestrife. These species were
selected because of their significant and widespread impact on the Great Lakes Basin. It must be noted that though
the species selected would predominantly result in poor and deteriorating assessments, there is an opportunity to
realize improvement through limiting their spread and impact.
Asian Longhorned Beetle
Asian longhorned beetles (ALB) are native to China and Korea and have been discovered in Ontario and Illinois.
The preferred hosts of ALB are maple trees, but they have been known to infest and kill other hardwood trees, such
as poplars, willows, birches, horse chestnuts and elms. Because the ALB has no natural predators in North America,
they pose a substantial threat to millions of trees. The United States Department of Agriculture (2006) estimates that
the ALB could have a potential market economic impact of more than $41 billion in the United States. However, the
intangible economic losses associated with ALB, such as ecosystem service and aesthetic losses, are estimated to
have a greater impact than market economic losses (Animal and Plant Health Inspection Service [APHIS], 2015). In
North America, extensive eradication measures have been introduced to ensure that the number and spread of ALB
is limited. Treatment options include strict regulation of quarantine areas and tree removal. In Ontario, susceptible
and infested trees located in the two identified infestation sites were removed; one of the infestations was declared
eradicated as no beetles or infested trees were discovered after 5 years of surveys. The other infestation site is being
monitored to ensure that further eradication measures are implemented if warranted. Since 2008, the ALB has been
eradicated from Illinois. Outside the Great Lakes Basin, efforts continue in nearby southern Ohio and New York to
limit the spread of the ALB.
Emerald Ash Borer
The emerald ash borer (EAB) was first discovered in North America in the Detroit-Windsor area in the early 2000s.
These wood-boring pests are believed to have arrived from East Asia in wood shipping containers. EAB feeds on
green, red, white, black and blue ash and is responsible for the destruction of millions of ash trees across Ontario and
all eight Great Lakes States. In Ontario, Canadian Forest Service scientists estimate that $2 billion over a 30-year
horizon will be required to remove and replace trees (Natural Resources Canada [NRCAN], 2015). Moreover, high
mortality rates are typical once an infestation occurs; after 6 years of initial infestation, roughly 99% of ash trees are
killed in the woodlot (NRCAN, 2015). In 2001, Toronto had approximately 860,000 ash trees throughout the city.
In 2016, approximately 9,500 viable trees remain. The effects of EAB on the Great Lakes ecosystem are wide-
ranging, particularly in areas that are dominated by ash trees. It is also expected that urban areas will be affected
since ash trees are often planted for their quick-growing nature. The loss of ash trees will increase the amount of
stonnwater runoff and exacerbate the urban heat island effect (Wisconsin Department of Natural Resources, 2015).
Forests play a key role in stabilizing soil and limiting the amount of sediment-bound pollutants into receiving waters
(Turner & Rabalais 2003). These forests also protect water quality as well as the habitats of a number of native spe-
cies (The Nature Conservancy). The emerald ash borer is significantly impacting the Lake Erie ecosystem (Figure
2); it is estimated that more than 65 000 acres across southern Ontario have experienced moderate-to-severe decline
and mortality in ash trees (Figure 3). The impacts of EAB have also been having a severe impact in the Lake Huron
basin in the area surrounding Sarnia, Ontario (Figure 2). Areas west of London, Ontario have been particularly af-
fected and there is concern that the emerald ash borer will continue to spread east into the Lake Ontario basin and
north into the Lake Superior basin. The Canadian Council of Forest Ministers [CCFM] (2015) predicts that EAB
will spread to Thunder Bay, Ontario and into other parts of Northern Ontario due to the lack of biological prevention
tools and regulation. However, the rate of spread for EAB in Northern Ontario will be slower than in the south be-
cause of cooler climatic conditions (CCFM 2015). Quarantine areas, strict regulation, education programs and re-
moval of ash trees in infested areas are some important measures to limit the spread of emerald ash borer in the
Great Lakes Basin and beyond.
Garlic Mustard
Garlic mustard was likely introduced to North America from Europe in the late 19th century for culinary or medici-
nal purposes. It is considered one of the most invasive exotic species in North America as it out-competes native
plants and disrupts natural understory growth (Yates & Murphy, 2008). The invasive nature of garlic mustard results
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STATE OF THE GREAT LAKES 201 7
in altered forest composition since garlic mustard can control the nutrient supply in soil, making it difficult for tree
seedlings to germinate (Rodgers, Stinson & Finzi, 2008). Further, two native species - the wood poppy and wood
aster have been designated as endangered and threatened, respectively, by the Committee on the Status of Endan-
gered Wildlife in Canada in part due to the spread of garlic mustard. This invasive species is also toxic to the larvae
of some butterflies, which results in a reduction of plant pollination (Lake Huron Centre for Coastal Conservation
n.d.). Tracking the distribution of garlic mustard is an important step in eradication efforts as it highlights areas that
require management. The Early Detection & Distribution Mapping System (EDDMapS) is one important platform
that collects volunteered geographic information about garlic mustard observations in Canada and the United States.
Data derived from EDDMapS indicates that there has been a spread of garlic mustard in the Great Lakes Basin as it
has now been observed across Ontario and in all eight Great Lakes States (Figures 5 and 6). The Greater Toronto
Area and southern and western Michigan appear to have a number of garlic mustard observations. Over time, the
range of garlic mustard in Ontario has spread to the northern shores of Lake Superior. It is predicted that garlic mus-
tard will continue its spread across North America as it possesses a specific combination of traits, making it a suc-
cessful competitor in a number of different ecosystems (Rodgers et al. 2008). Because garlic mustard can grow in
numerous diverse ecosystems, unique management options are required for each site (The Nature Conservancy of
Canada, 2007).
Phragmites
Two varieties of Phragmites exist in the Great Lakes Basin, the native subspecies (americanus) and the invasive
subspecies (australis). Phragmites australis subsp. australis form dense stands in roadside ditches, along the water's
edge and in wetlands, decreasing biodiversity by choking out native plant species. In 2005, Agriculture and Agri-
food Canada declared that Phragmites australis subsp. australis (herein Phragmites) was the worst invasive plant
species in Canada. Invasive Phragmites is responsible for changes in the hydrologic cycle, alterations to the nutrient
cycle as well as losses to biodiversity and habitat (Ontario Ministry of Natural Resources [MNR], 2011). The spread
of Phragmites occurs quickly as it can grow up to 4 centimetres a day vertically and can establish root systems that
measure several metres (MNR, 2011). The rhizomes of its roots release toxins that inhibit the growth of native spe-
cies, resulting in the formation of a dense monoculture. Invasive Phragmites seeds are easily transported by the
wind, water or birds and can rapidly colonize disturbed enviromnents. Once it is established in an area, multiple
management controls are typically required for eradication because of their large root systems (MNR, 2011). Based
on data analyzed from EDDMapS, Lake Ontario and Lake Erie have the most observations of Phragmites in the
Great Lakes Basin. In Ontario, this perennial grass has begun to migrate north to Georgian Bay and Lake Superior.
It appears that over a period of 67 years beginning in 1948, the distribution of observations has expanded to multiple
locations in Ontario and into five of the eight Great Lakes States (Figures 7 and 8). The presence of Phragmites
across the Great Lakes Basin has been supported by research undertaken by a team at the Michigan Tech Research
Institute, led by Laura Bourgeau-Chavez. The locations of mature stands of invasive Phragmites on the U.S. side of
the Great Lakes Basin were mapped using satellite imagery (Figure 4). Data collected in 2008-2010 was used to
detect invasive Phragmites that dominated 90% of 0.2 hectare mapping units. Significant stands were mapped in
Lake Huron (10 395 ha). Lake Erie (8233 ha) and Lake Michigan (6002 ha), while little to none were mapped in the
Lake Ontario and Lake Superior basins (Bourgeau-Chavez 2011). It should be noted that the radar imagery can only
detect large, dense stands of Phragmites. It can also be difficult for the researchers to determine whether the imagery
depicted Phragmites as other monotypic aquatic plants. The overall accuracy of the basin-wide map was 87%, illus-
trating that radar imagery is an effective means to detect the presence of large, mono-typic invasive Phragmites
stands in the Great Lakes Basin. Wilcox et al. (2010) investigated the change in plant communities on the northern
shores of Lake Erie in Long Point, Ontario and found that areas of predominantly cattails and marsh were replaced
by Phragmites. Long Point is noted as an important staging area for waterfowl, which may be negatively impacted
by dense monocultures of Phragmites (Wilcox et al. 2010).
Purple Loosestrife
Purple loosestrife is a perennial plant native to Asia and Europe, which was initially introduced in North America as
a decorative plant. It has spread extensively to wetlands and disturbed enviromnents due to its small, easily-
transported seeds. Purple loosestrife weaves thick mats of roots that cover vast areas, impacting the quality of habitat
for birds, insects and other plants (Government of Ontario, 2012). Furthermore, purple loosestrife threatens wetland
ecosystems by altering water levels and reducing food sources for both aquatic and terrestrial native species
(Thompson, Stuckey & Thompson, 1987). According to data collected by EDDMapS, purple loosestrife is present
across Ontario and in all eight Great Lakes States. It appears that beginning in 1900, purple loosestrife has expanded
its range over a 115-year period as it is now ubiquitous along the shorelines of all five Great Lakes (Figures 9-10).
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STATE OF THE GREAT LAKES 201 7
However, there is an effective control measure to combat the spread of purple loosestrife, which has been the use of
their natural predators, Galerucella calmariensis and G. pusilla beetles. Multiple studies were carried out to ensure
that the use of Galerucella calmariensis and G. pusilla would not impact native species (Michigan Sea Grant). It
was determined that these particular varieties of beetle only target purple loosestrife, making it a viable biocontrol
option. They have been used at multiple sites in the Great Lakes Basin and can significantly reduce purple loose-
strife populations (Government of Ontario, 2012). In 2006, the Ontario Federation of Anglers and Hunters reported
that this perennial invasive has been successfully controlled by Galerucella calmariensis and G. pusilla in more than
80% of the 300 control sites located in Ontario. It must be noted that the beetles can only reduce purple loosestrife to
manageable populations and are not capable of complete eradication.
Linkages
Climate change may expand the current habitat ranges of terrestrial invasive species as temperatures warm and
growing seasons become longer (Clements & DiTommaso, 2012). Based on spatial data from EDDMapS, it appears
that invasive Phragmites has begun to move north into the Lake Superior basin, perhaps as a result of wanner tem-
peratures. Smith et al. (2012) have described the need to better study the impacts of climate change on terrestrial
invasive species and stressed the need to bridge the gap between policy and science.
Forest cover can be negatively impacted by the Asian longhorned beetle and the emerald ash borer, which increases
the amount of runoff and sediment-bound pollutants into the Great Lakes and its tributaries (Turner & Rabalais
2003). Forests also play a key role in carbon sequestration by absorbing and removing greenhouse gas emissions
(Natural Resources Canada 2015).
The invasion of purple loosestrife and Phragmites in the Great Lakes can alter the structure and function of coastal
wetland ecosystems (Keil & Hickman 2015). Wetlands provide ecosystem services that are significant to the Great
Lakes Basin including filtering nutrients that stimulate algal growth and limiting eutrophication in lakes and tributar-
ies (Zedler & Kercher 2005). Furthermore, wetlands are unique habitats for plants and animals and help store large
quantities of carbon in their soils (Zedler & Kercher 2005).
Comments from the Author(s)
Because EDDMapS is a repository of volunteered geographic information, it may not provide a perfect picture of
the extent of terrestrial invasive species in the Great Lakes region. However, the assessments strive to depict the
status and trend of terrestrial invasive species in each lake basin as accurately as possible given the data available.
This report was supported by data from both EDDMapS and qualitative information from government reports, non-
governmental agencies and journal articles.
EDDMapS is an important platform that gathers volunteered geographic information about terrestrial and aquatic
invasive species in Canada and the United States. It is currently supported by a number of organizations, including
the National Park Service, U.S. Forest Service, U.S. Fish & Wildlife Service, Nature Conservancy, U.S. Department
of Agriculture and Ontario Federation of Anglers and Hunters. The data is validated by one of their partner organi-
zations to ensure its accuracy. Some limitations must be noted since the data is volunteered geographic information.
The maps may not depict a complete portrait of the spatial distribution of terrestrial invasive species in the Great
Lakes Basin since monitoring efforts are not uniform. Also, the locations of the observations are contingent upon the
users who submit the data and the amount of resources expended in an area (a greater amount of resources will gen-
erally result in a greater amount of observations). The data only reflects observations that were made and does not
reflect treatment options that have been applied; for instance, a strand of Phragmites may have been eradicated after
the observation was submitted to EDDMapS. An observation may also represent one plant or hundreds of plants.
EDDMapS does however provide some spatial data that helps ecosystem managers to track the spread of terrestrial
invasive species and to identify areas that require greater intervention.
It is also important to note that agencies have increased their level of public education and outreach for the time pe-
riod shown in the species-specific figures and thus the public is far better informed about invasive species. The de-
velopment of tools such as EDDMapS and associated Applications has made it much easier for the public to report
observations of invasives. While there is evidence that garlic mustard and others is increasing on the landscape, the
frequency of reports and the distribution of the reports may have considerably outpaced the actual spread of the spe-
cies. EDDMapS is likely the best information available and it is a great tool, but the limitations should be carefully
considered and explained so that the information is not misrepresented - especially for tracking spread and trends.
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STATE OF THE GREAT LAKES 201 7
It is difficult to fully appreciate the status and spread of terrestrial invasive species in the Great Lakes region due to
the extent of the region, the number of terrestrial invasive species and the differences in monitoring efforts across
space and time. The management of invasive species is essential as they are one of the greatest threats to biodiversi-
ty in the Great Lakes region. Consequently, a greater amount of research is required to not only understand where
terrestrial invasive species are located, but to also understand what impact terrestrial invasive species are having on
different habitats and water quality.
Assessing Data Quality
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not
Applicable
1. Data are documented, validated, or
quality-assured by a recognized agency or
organization

X




2. Data are traceable to original sources

X




3. The source of the data is a known,
reliable and respected generator of data

X




4. Geographic coverage and scale of data
are appropriate to the Great Lakes Basin

X




5. Data obtained from sources within the
U.S. are comparable to those from
Canada

X




6. Uncertainty and variability in the data
are documented and within acceptable
limits for this indicator report

X




Acknowledgments
Author: Lindsay Wong, Enviromnent and Climate Change Canada
Contributors: Stacey Cherwaty-Pergentile, Enviromnent and Climate Change Canada
Information Sources
Anderson, Hayley. 2012. Invasive Garlic Mustard (Alliaria petiolata) Best Management Practices in Ontario. Ontar-
io Invasive Plant Council. Peterborough, ON.
Animal and Plant Health Inspection Service. 2015. Asian longhorned beetle eradication program.
. Last accessed 1 September 2015.
Bourgeau-Chavez, L. L„ Kowalski, K. P., Mazur, M. L. C., Scarbrough K. A., Powell, R. B„ Brooks, C. N„ ... &
Riordan, K. 2013. Mapping invasive Pliragmites australis in the coastal Great Lakes with ALOS PALSAR satellite
imagery for decision support. Journal of Great Lakes Research, 39, 65-77.
Canadian Council of Forest Ministers. 2015. Emerald ash borer pest risk analysis for Northern Ontario and Manito-
ba. . Last accessed 1 September 2015.
Canadian Food Inspection Agency. 2008. Invasive Alien Plants in Canada - Technical Report, .
Last accessed 1 September 2015.
City of Toronto, Forest's Health Care Unit, http://toronto.ctvnews.ca/hot-dry-weather-accelerated-toronto-s-
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STATE OF THE GREAT LAKES 201 7
emerald-ash-borer-tree-crisis-1.3044828.
Clements, D. R.. & DiTommaso, A. 2012. Predicting weed invasion in Canada under climate change: Evaluating
evolutionary potential. Canadian Journal of Plant Science, 92(6), 1013-1020.
Emerald ash borer. N.d. Maps and State EAB Information, . Last accessed 1
September 2015.
Enviromnent Canada - Canadian Wildlife Service. 2014. Extent of Non-Native Phragmites australis in Coastal
Wetlands in the Canadian Huron-Erie Corridor. . Last accessed 1 September 2015.
Finneran, R. 2011. Garlic mustard may be Michigan's worst woodland weed.
. Last accessed 1 Sep-
tember 2015.
Government of Canada. 2015. Asian long-horned beetle, .
Last accessed 1 September 2015.
Government of Canada. 2014. Purple loosestrife, .
Last accessed 1 September 2015.
Government of Canada. 2004. An invasive alien species strategy for Canada.
. Last accessed 1 September 2015.
Great Lakes Regional Collaboration. 2005. Great Lakes Regional Collaboration Strategy.
. Last accessed 1 September 2015.
Henns, D.A. and McCullough, D.G., 2014. Emerald ash borer invasion of North America: history, biology, ecology,
impacts, and management. Annual Review of Entomology, 59, 13-30.
Keil, K.E. & Hickman, K.R. Mapping Distribution in Oklahoma and Raising Awareness: Purple Loosestrife (Lyth-
rum salicaria), Multiflora Rose (Rosa multiflora), and Japanese Honeysuckle (Lonicera japonica). Oklahoma Native
Plant Record, 14(1).
Lake Huron Centre for Coastal Conservation. N.d. Garlic Mustard: Threatening the Huron Fringe Forest. < >. Last
accessed 1 September 2015.
Mika, A. M„ & Newman, J. A. 2010. Climate change scenarios and models yield conflicting predictions about the
future risk of an invasive species in North America. Agricultural and Forest Entomology, 12(3), 213-221.
National Invasive Species Council. 2008. 2008-2012 National Invasive Species Management Plan.
. Last accessed 1 September 2015.
Natural Resources Canada. 2015. Asian longhorned beetle, . Last accessed 1 September 2015.
Natural Resources Canada. 2015. Emerald ash borer, . Last accessed 1 September 2015.
Natural Resources Canada. 2015. Forest carbon, 
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STATE OF THE GREAT LAKES 201 7
carbon/13085 >. Last accessed 1 September 2015.
Ontario Federation of Anglers and Hunters. 2006. Purple loosestrife control saves Ontario wetlands.
. Last accessed 1 September
2015.
Ontario Ministry of Natural Resources. 2012. Ontario Invasives Species Strategic Plan. Toronto: Queen's Printer for
Ontario. 58 pp.
Ontario Ministry of Natural Resources. 2012. State of resources reporting - Phragmites in Ontario.
. Last accessed 1 September 2015.
Ontario Ministry of Natural Resources, Invasive Phragmites - Best Management Practices, Ontario Ministry of Nat-
ural Resources, Peterborough, Ontario. Version 2011. 15p.
Rodgers, V. L„ Stinson, K. A., & Finzi, A. C. 2008. Ready or not, garlic mustard is moving in: Alliaria petiolata as a
member of eastern North American forests. Bioscience, 58(5), 426-436.
The Nature Conservancy of Canada. Great Lakes Mixed Forest, . Last accessed 1 September 2015.
The Nature Conservancy of Canada - Southwestern Ontario. 2007. Control Methods for the Invasive Plant Garlic
Mustard (Alliaria petiolata) within Ontario Natural Areas. Document VI.0
Thompson, D. Q., Stuckey, R. L., & Thompson. E. B. 1987. Spread, impact, and control of purple loosestrife (Lyth-
rum salicaria) in North American wetlands.
Turner, R.E. & Rabalais, N.N. 2003. Linking landscape and water quality in the Mississippi River basin for 200
years. Bioscience, 53(6), 563-572.
United States Department of Agriculture. 2015. Asian Longhorned Beetle Eradication Program.
. Last accessed 1 September 2015.
United States Forest Service. 2013. Forest Service National Strategic Framework for Invasive Species Management.
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September 2015.
Wilcox, K.L., Petrie, S.A., Maynard, L.A. and Meyer, S.W., 2003. Historical distribution and abundance of Phrag-
mites australis at long point. Lake Erie, Ontario. Journal of Great Lakes Research, 29(4), 664-680.
Wisconsin Department of Natural Resources. 2015. Reducing the Impact of Emerald Ash Borer Guidelines for
Managing Ash in Wisconsin's Urban Forests, . Last accessed 1
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Yates, C. N., & Murphy, S. D. 2008. Observations of herbivore attack on garlic mustard (Alliaria petiolata) in
Southwestern Ontario, Canada. Biological Invasions, 10(5), 757-760.
Page 385
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STATE OF THE GREAT LAKES 201 7
List of Figures
Figure 1. Cumulative Number of Invasive Alien Plant Species Introduced into Canada from 1600 to 2005 -
Estimated.
Source: Canadian Food Inspection Agency
Figure 2. Areas where emerald ash borer has caused moderate-to-severe decline and mortality to ash species.
Source: Ontario Ministry of Natural Resources
Figure 3. Cumulative amount of area where emerald ash borer has caused moderate-to-severe decline and mortality
to ash species.
Source: Ontario Ministry of Natural Resources
Figure 4. Potential distribution of invasive phragmites in the U.S. Great Lakes Basin, 2008-2010.
Source: Bourgeau-Chavez et al.
Figure 5. Garlic Mustard Observations in the Great Lakes Basin (2002-2005).
Source: EDDMapS
Figure 6. Garlic Mustard Observations in the Great Lakes Basin (2002-2015).
Source: EDDMapS
Figure 7. Phragmites Observations in the Great Lakes Basin (1948-1961).
Source: EDDMapS
Figure 8. Phragmites Observations in the Great Lakes Basin (1948-2015).
Source: EDDMapS
Figure 9. Purple Loosestrife Observations in the Great Lakes Basin (1900-1979).
Source: EDDMapS
Figure 10. Purple Loosestrife Observations in the Great Lakes Basin (1900-2015).
Source: EDDMapS
Last Updated
State of the Great Lakes 2017 Technical Report
Page 386
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STATE OF THE GREAT LAKES 201 7
300
HJ 250
150
R 100
SO
0 r r T - I	I	I	I	I	I	I
1600 1650 1700 1750 1800 1850 1900 1950 2000
YEAR
Figure 1. Cumulative Number of Invasive Alien Plant Species Introduced
into Canada from 1600 to 2005 - Estimated.
Source: Canadian Food Inspection Agency
Figure 2. Areas where emerald ash borer has caused moderate-to-severe decline and
mortality to ash species.
Source: Ontario Ministry of Natural Resources
Page 387
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STATE OF THE GREAT LAKES 201 7
Area Summary
Total area *mhm wh»cb ©moraId ash bor«f caused moderate-to-severe defoliation and
decline 2004 - 2012 by MNR District (area in hectares)
District
2OO4-20O7
2ciii9
2010
2011
2012
Total
Southern






Aytmcf
7,122
11.734
19,436
24,324
1,4 in
M.ora
Gueiph
0
0
a
177
2,812
2,989
~CpmpfOTlk*
(I
0
41

m
thf*
Prnvinri.il lnl.il
7,1*2
11.7J4

?M29
4, SMI
67.9/1
'Arej cjtcutiticns for 2r*/9 Nxrkxfe damage thjt occurred tn JrXtS
Cumulative area within which emerald ash Ivorer caused moderate to severe do-dine
and mortality 2004 2012 {area in hectares)
aOOoo
7Q.Q0Q
jg 'iO.OPO
1™* 50,000
40 000
30,000
30.000
ro.ooo
0
Yoar	3004 ?5 ?OOfi TOO?	2010 Ml'1 ?01?
Figure 3. Cumulative amount of area in specific regions of the province of
Ontario where emerald ash borer has caused moderate-to-severe decline and
mortality to ash species.
Source: Ontario Ministry of Natural Resources
Potential invasive Phragmites
350 Kilometers
Figure 4. Potential distribution of invasive phragmites (may include dense, mono-typic stands
of other wetland plants) in the U.S. Great Lakes Basin using remotely sensed data. 2008-
2010.
Source: Bourgeau-Chavez et al.
Page 388
ill
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STATE OF THE GREAT LAKES 201 7
A
N
A
Page 389
Figure 5. Garlic Mustard Observations in the Great Lakes Basin (2002-2005).
Source: EDDMapS
Figure 6. Garlic Mustard Observations in the Great Lakes Basin (2002-2015).
Source: EDDMapS
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STATE OF THE GREAT LAKES 201 7
A
Figure 7. Phragmites Observations in the Great Lakes Basin (1948-1961).
Source: EDDMapS
A
Figure 8. Phragmites Observations in the Great Lakes Basin (1948-2015).
Source: EDDMapS
Page 390
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STATE OF THE GREAT LAKES 201 7
A
Figure 9. Purple Loosestrife Observations in the Great Lakes Basin (1900-1979).
Source: EDDMapS
A
Figure 10. Purple Loosestrife Observations in the Great Lakes Basin (1900-2015).
Source: EDDMapS
Page 391
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Groundwater
Status: Fair Trend: Undetermined
Groundwater can enhance surface water
quality and quantity and provide essential
aquatic habitats. Groundwater can also
transmit contaminants and excessive
loads of nutrients to the Great Lakes.
The 2012 Great Lakes Water Quality Agreement states that "the Waters of the Great Lakes should be free from the harmful
impact of contaminated groundwater "
Assessment Highlights
The Groundwater Quality indicator is assessed as Fair
but the trend is Undetermined due to insufficient long-
term data. The concentrations of nitrate, primarily
from agricultural practices, and chloride, mainly from
the urban use of de-icing salt, are being used to assess
groundwater quality. Elevated concentrations of both of
these constituents in water can have detrimental impacts
to ecosystem and human health.
Portions of the Great Lakes Basin with more intense
development, such as areas within the basins of Lakes
Michigan, Erie and Ontario, are generally assessed as
fair. Groundwater quality is generally assessed as good
in the less developed areas, such as portions of the Lake
Huron basin. A better understanding about the impacts
of contaminated groundwater and its interaction with the
waters of the Great Lakes is needed, particularly for the
nearshore zone.
Groundwater Quality Assessment by Lake Basin
Quality Status
Sub-Indicators Supporting the Indicator Assessment
Sub-indicator
Lake Superior
Lake Michigan
Lake Huron
Lake Erie
Lake Ontario
Groundwater Quality
Undetermined
Undetermined
Undetermined
Undetermined
Undetermined
Status:
GOOD
FAIR
POOR
UNDETERMINED
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STATE OF THE GREAT LAKES 201 7
Sub-Indicator: Groundwater Quality
Regional-scale assessment
Overall Assessment
Status: Fair
Trend: Undetermined
Rationale: The overall status of groundwater quality, based on current knowledge and data in the Great
Lakes Basin, is assessed as fair. Of the 670 monitoring wells in the basin that were included, the groundwater
quality was assessed to be Poor in 203 (30%), Fair in 173 (26%), and Good in 294 (44%). Given that no
single category captured more than 50% of wells, the overall assessment is fair (following the criteria as
explained on page 3). Caution must be used when interpreting and applying this overall assessment, due to
the large spatial gaps in the data, particularly in the northern and central portions of the basin, as shown in
Figure 1 where hundreds of kilometres/miles exist between wells in the Lake Superior basin, for example. In
large areas of the basin where groundwater quality data are missing (Figure 1), the status of groundwater
quality should be considered to be undetermined. The overall trend in groundwater quality in the basin is
undetermined for two reasons: (1) a lack of available long-term sample analysis for many of the monitoring
locations; (2) a statistical analysis of the available data has not yet been completed. However, as noted in this
report, trends of increasing (or upward trends in) chloride and nitrate concentrations in groundwater have
been reported previously for various watersheds within the basin.
Lake-by-Lake Assessment
Lake Superior
Status: Undetermined
Trend: Undetermined
Rationale: Data (22 wells) are insufficient for assessing overall groundwater quality in the Lake Superior basin
(Figure 2).
Lake Michigan
Status: Fair
Trend: Undetermined
Rationale: The status assessment as fair should only be considered valid within the western portion of the Lake
Michigan basin, given that this is where almost all of the available data were collected (Figure 3). Of the 136 wells
that were assessed, the groundwater quality was poor in 64 (47%), fair in 29 (21%), and good in 43 (32%). On the
eastern side of Lake Michigan (Figure 3), the status should be considered as undetermined. Trend analysis is not
part of this initial assessment (2016-17), but is anticipated to be a component of future assessments.
Lake Huron
Status: Good
Trend: Undetermined
Rationale: The status assessment as good should only be considered valid within the southeast portion of the Lake
Huron basin (Ontario) for which data were available (Figure 4). Of the 77 wells that were assessed, the groundwater
quality was poor in 14 (18%), fair in 16 (21%), and good in 47 (61%). Where data gaps exist (Figure 4), the status
should be considered as undetermined. Trend analysis is not part of this initial assessment (2016-17), but is
anticipated to be a component of future assessments.
Lake Erie
Status: Fair
Trend: Undetermined
Rationale: The status assessment as fair should only be considered valid within the areas of the basin for which data
were available (Figure 5). Of the 177 wells that were assessed, the groundwater quality was poor in 50 (28%), fair
in 49 (28%), and good in 78 (44%). Where data gaps exist (Figure 5), the status should be considered as
undetermined. Trend analysis is not part of this initial assessment (2016-17), but is anticipated to be a component of
future assessments.
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STATE OF THE GREAT LAKES 201 7
Lake Ontario
Status: Fair
Trend: Undetermined
Rationale: Of the 258 wells that were assessed, the groundwater quality was poor in 74 (29%) fair in 78 (30%), and
good in 106 (41%) (Figure 6). Trend analysis is not part of this initial assessment (2016-17), but is anticipated to be
a component of future assessments.
Sub-Indicator Purpose
•	The purpose of this sub-indicator is to assess the general status of the quality of shallow groundwater in the
Great Lakes Basin (GLB), which is interactive with other components of the water cycle, and has potential
to impact the quality of the Great Lakes waters. Select chemical constituents of groundwater can be used to
provide information about ecosystem health and potential risks to the waters of the Great Lakes Basin.
Ecosystem Objective
This sub-indicator supports work towards General Objective #8 of the 2012 Great Lakes Water Quality Agreement
(GLWQA), which states that the waters of the Great Lakes should be "free from the harmful impact of contaminated
groundwater."
Ecological Condition
Groundwater can become contaminated with various substances including nutrients, salts, metals, pesticides,
pharmaceuticals and other contaminants. Groundwater plays an important role as a reservoir of water that, if
contaminated, can become a continuous source of contamination to the Great Lakes. Chemical parameters, such as
nitrate and chloride, can be used to assess groundwater quality and to provide information about ecosystem health
and potential risk to Great Lakes water quality. Nitrate is mainly from agricultural practices and chloride is mainly
an urban contaminant as a result of de-icing road salt.
Elevated concentrations of nitrate in water have been shown to have detrimental effects on aquatic organisms and
aquatic ecosystems (e.g., direct toxicity and increasing the risk of algal blooms and eutrophication; CCME, 2012),
and human health (Health Canada, 2013). Elevated concentrations of chloride in water have been shown to have
detrimental effects on aquatic organisms and aquatic ecosystems (e.g., toxicity; CCME, 2012).
Nitrate and chloride are considered to be key indicator contaminants in groundwater for the following reasons:
•	They are two of the most prevalent and widespread contaminants in groundwater that have been measured
and reported in the GLB (and elsewhere);
•	They both are derived from multiple contaminant sources in both rural (agricultural) and urban areas;
•	As anions, they are both extremely mobile (soluble) in water, including the subsurface enviromnent;
•	They are stable contaminants that do not have much physical or chemical interaction with the material they
flow through;
•	Chloride in particular, is persistent - chloride is not subject to attenuation in the subsurface by processes
such as biodegradation, sorption or precipitation, and therefore may have an adverse effect on the water
quality of streams, rivers and lakes in the GLB;
•	Although nitrate is potentially reduced or eliminated by denitrification in some subsurface enviromnents,
nitrate also may have an adverse effect on surface-water quality in the GLB; and
•	Even though some natural sources of these compounds exist in the enviromnent (e.g., geological), nitrate
and chloride are considered as general indicators of anthropogenic impact to aquatic systems.
As noted in a recent report on "Groundwater science relevant to the Great Lakes Water Quality Agreement"
(Grannemann and Van Stempvoort, 2016):
"The natural flux of groundwater to the Great Lakes and their tributaries can enhance water quality and water
quantity and provide essential habitats for Great Lakes ecosystems. Groundwater can also be a transmitter
(vector) of contaminants and excessive loads of nutrients, which are derived from both non-point sources and point
sources, to the Great Lakes. In addition to the direct flux of groundwater that transports contaminants and nutrients
to the Great Lakes, the flux of groundwater to streams flowing into the Great Lakes also must be considered because
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STATE OF THE GREAT LAKES 201 7
the ecology and habitats of streams are interconnected with ecology of the Great Lakes (for example, fish spawning
and migration)".
This sub-indicator regional-scale assessment was based on measurements (2000-2015) of the dissolved
concentrations of two water quality constituents in groundwater in the GLB, nitrate and chloride, as part of ongoing
monitoring of groundwater quality. For this initial assessment, the data were obtained from groundwater monitoring
networks maintained by (1) the U.S. Geological Survey (USGS) and its partners, and (2) the Ontario Ministry of the
Enviromnent and Climate Change (MOECC) and its partners.
For each monitoring location/well, the groundwater quality was assessed, on the basis of concentrations of chloride
(CI") in milligrams per litre (mg/L) and nitrate (N03) in milligrams nitrogen per litre (mg N/L) as being:
Good: less than or equal to (<) 0.8 mg N/L N03" AND < 30 mg/L CI"
Fair: greater than (>) 0.8 BUT less than (<) 3 mg N/L N03" AND/OR >30 BUT < 120 mg/L CI"
Poor: greater than or equal to (>) 3 mg N/L N03" AND/OR >120 mg/L CI"
In this approach, the distinction between fair and poor is based on the CCME (2012) water quality guidelines for the
protection of aquatic life (120 mg/L for chloride; 3 mg N/L for nitrate) and the distinction between good and fair is
based on concentrations equivalent to one-quarter (25%) of these guidelines (i.e., 30 mg/L for chloride; 0.75,
rounded up to 0.8 mg N/L for nitrate) (see sub-indicator description for additional information). These "25% of
guideline" criteria provide an interim, protective approach for this sub-indicator assessment, based on judgement
rather than directly on previously established criteria. They may be modified in future, if sufficient support for
alternative criteria becomes available.
For each individual lake or entire Great Lakes Basin, the overall groundwater quality is assessed as follows:
•	GOOD: If the percentage of wells assessed as GOOD is more than 50 (>50), THEN the basin is assessed as
GOOD.
•	POOR: If the percentage of wells assessed as POOR is more than 50 (>50), THEN the basin is assessed as
POOR.
•	FAIR: If the percentage of wells assessed as FAIR is more than 50 (>50), then the basin is assessed as
FAIR
•	If the basin is not assessed as Good, Fair, or Poor by the above definitions, THEN by default the basin is
assessed as FAIR.
•	UNDETERMINED: Data are not available or are insufficient to assess conditions of the ecosystem.
As illustrated in Figure 7, the definition of FAIR is more inclusive than POOR or GOOD, because FAIR includes all
cases where there is no majority of individual wells assessed as poor, fair, or good (i.e., the central portion of this
diagram, coloured in orange, where each of these three classifications is < 50%).
Only "shallow" groundwater samples (collected from wells screened at depths less than (<) 40 metre) were included
in this assessment, given that shallow groundwater is the most interactive with the rest of the hydrologic system,
including surface water in the Great Lakes Basin (Conant et al. 2016). Most of the shallow groundwater in the basin
flows towards and will eventually discharge into the Great Lakes. This connection has many implications for water
quality. Shallow groundwater tends to be "younger," or in other words, more recently recharged, and therefore it
better reflects the groundwater quality impacts of recent activities in the recharge area (e.g., land use practices).
That said, it should be understood that it can sometimes take years or decades for changes in land management
practices to measurably impact the shallow groundwater (e.g., Zebarth et al. 2015).
The spatial distribution of data used in this assessment was uneven (Figures 1-6). Given that there were very few
data points for the Lake Superior basin the groundwater quality in this basin was assessed as "undetermined." The
Lake Michigan basin was assessed using data from only the western portion of the basin given the lack of data on
the east side. Likewise, the Lake Huron basin was assessed using data from the southeast portion of the basin, given
that there were only two scattered data points throughout the northern and western portions of this basin. In the
Lake Erie basin data were concentrated in the north, resulting in large areas (especially the southwest portion)
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STATE OF THE GREAT LAKES 201 7
where groundwater quality data were limited. The Lake Ontario basin had sufficient, distributed data and thus has
no caveats to note on the whole basin assessment for this lake.
There was a stronger tendency for groundwater quality to range from poor to fair in those portions of the basin that
had more intense development, including urbanization (e.g., areas within the Michigan, Erie, and Ontario basins),
and a tendency for groundwater quality to range from fair to good in the less developed areas (e.g., Huron and
Superior basins) (Figure 1). For example, although only 22 data points (wells) were available for the Lake Superior
basin, 90.9% of these (20) had good groundwater quality.
For the Canadian monitoring wells included in this study, statistical tests indicated no significant difference
(probability value less than 0.05) between the depths of the wells and the concentrations of nitrate and chloride
(Figure 8). The lack of correlation may reflect differences in the settings of the well sites (e.g., differences in land
uses and in nitrate loadings from surface, and differences in subsurface conditions such as permeability of geologic
units and in intensity of microbial activity).
It is important to note that if only one of the two constituents that were combined for this sub-indicator (chloride and
nitrate) was assessed individually, the results would be very different. For example, in the western portion of the
Lake Michigan basin, many of the wells have excessive nitrate concentrations resulting in the overall assessment of
poor water quality. But, if only the chloride concentrations were considered, many of these wells would have been
assessed as having fair to good groundwater quality (data not shown). This example illustrates that different areas in
the basin have different contaminant issues that may drive the overall assessment when combined into a multi-
contaminant approach. However, for reasons noted on page 2 of this report, it is informative to analyze both
contaminants together, in particular, as it provides a fairly representative assessment of ambient groundwater quality
in the Great Lakes Basin with the inclusion of these two contaminants from multiple sources. Consequently, the
addition of other chemicals/constituents in the future would likely affect the assessments. This may require
explanation when comparing updated results (that include additional constituents) to earlier sub-indicator reports.
Reported Trends of Chloride and Nitrate Concentrations in Groundwater in the Great Lakes Basin
Over the past several decades, various studies and status reports have provided information about trends of chloride
and nitrate in surface water and groundwater in the Great Lakes Basin. For example, in a recent national study in
the United States, which included the Great Lakes Basin, DeSimone et al. (2014) stated that "concentrations of..
chloride, and (or) nitrate in groundwater increased in two-thirds of groundwater well networks that were sampled at
10-year intervals between the early 1990s and 2010" (Figure 9). Similarly, on the Canadian side of the Great Lakes
Basin, Sawyer (2009) reported that "increasing concentrations of nitrate and chloride are obvious" in groundwater
throughout the Grand River watershed. Ongoing monitoring of water quality in Ontario has shown that chloride
concentrations have increased in lakes and streams over the past several decades (Ontario Ministry of the
Enviromnent and Climate Change, 2016).
Chloride
One of the early studies to document chloride trends in the Great Lakes Basin was Bubeck et al. (1971), who
reported that "salt used for deicing streets near Rochester, New York, had increased the concentration of chloride in
Irondequoit Bay at least fivefold over two decades."
Thomas (2000a) investigated groundwater quality in the Detroit metropolitan area, and found that "young, shallow
waters.... had significantly higher median concentrations of ... .chloride ... .than older, deeper waters." Based on
analysis of chloride/bromide ratios, Thomas (2000a) concluded that the elevated salinities were "due to human
activities rather than natural factors, such as upward migration of brine."
Kelly and Wilson (2008) reported that the majority of shallow public supply wells in some counties in northeastern
Illinois have had increasing chloride concentrations since the 1960s. The increases were attributed primarily to
"road salt runoff." DeSimone et al. (2014) reported that in the glacial aquifer system, which extends across the
northern United States, including the Great Lakes Basin, "chloride concentrations were highest in shallow
groundwater beneath urban areas, reflecting the use of deicing salt and the many other mamnade sources of chloride
in urban and suburban areas." Similarly, Mullaney et al. (2009) also reported evidence for increasing chloride
concentrations in streams in urbanized and urbanizing areas of the United States, including the Great Lakes Basin.
On the Canadian side of the Great Lakes Basin, Howard and Beck (1993) reported that background concentrations
of chloride in groundwater in glacial deposits in southern Ontario were in the range of 15-20 mg/L, but chloride
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STATE OF THE GREAT LAKES 201 7
concentrations were as great as 700 mg/L in domestic wells, as great as 2,840 mg/L in urban springs, and as great as
13,700 mg/L in pore waters extracted from beneath shallow urban areas. The potential sources of the chloride
included road salts, landfill leachates, agricultural fertilizers, and saline bedrock waters. There was "extensive
association of high chloride concentrations with urbanization in Metropolitan Toronto" (Howard and Beck, 1993).
Bowen and Hinton (1998) reported that long-term monitoring of surface water in the Greater Toronto area showed a
"gradual increase in chloride concentrations" and that "detailed baseflow water chemistry surveys... .confirm that
lower chloride concentrations occur predominantly in the rural portions of the watersheds."
In a more detailed study of a watershed in Metropolitan Toronto, Howard and Haynes (1993) estimated that 55% of
the deicing salt applied each winter to roads, highways, and parking lots entered "temporary storage in shallow sub-
surface waters." Howard and Haynes (1993) predicted that if salt application was maintained at the same rate, the
average steady-state chloride concentrations in groundwater discharging as springs in the basin would exceed 400
mg/L, possibly within 20 years. In a follow-up study of the same watershed (20 years later), Perera et al. (2013)
reported that chloride concentrations in base flow ranged widely, peaking at 500-600 mg/L in late spring, and then
declining to around 250-300 mg/L. This was evidence that "a component of the groundwater, elevated in salinity
due to the prior season's salting activity, moves.... rapidly to the stream via relatively shallow, preferential flow
zones within the aquifer". Perera et al. (2013) reported that if "current road salt application rates are continued, late
summer baseflow chloride concentrations will reach around 505 mg/L, almost double present levels," and would be
above the drinking water guideline and the CCME (2012) aquatic chronic toxicity guideline. Similarly, Eyles and
Meriano (2010) reported that in an urbanized watershed at Pickering, Ontario (Lake Ontario basin), 52% of the
deicing salt applied "accumulates in groundwater where it continues to be released as brackish baseflow to creeks in
summer."
By 1998, the Ontario Ministry of the Enviromnent (1998) reported that a high percentage of water quality
monitoring stations along Lake Erie had increasing chloride concentrations "indicative of the significant amount of
urbanization and development that has occurred in watersheds in southern Ontario since the early 1980s." In the
2009 "State of the Great Lakes" report. Sawyer et al. (2009) noted that chloride levels in groundwater in the Grand
River watershed of Ontario "can be linked to urban growth and its associated land uses." Sawyer et al. (2009)
reported that increasing chloride concentrations have been observed in most municipal wells in the Grand River
watershed, and that this increase has been attributed to winter deicing of roads with sodium chloride. Similarly, at
Barrie, Ontario, one of the municipal wells has an upward trend in chloride concentrations that has become a
drinking water issue (South Georgian Bay-Lake Simcoe Source Protection Committee., 2015). Another example is
in the town of Orangeville, Ontario, where increasing chloride concentrations were documented in 5 of the 12
municipal supply wells for the 1982-2012 period (Credit Valley Conservation Authority, 2015).
Nitrate
Nitrogen is an essential nutrient for plants and animals. Nitrogen promotes rapid growth, increases seed and fruit
production, and improves the quality of leaf and forage crops. Nitrogen exists in the enviromnent in many forms as a
part of the nitrogen cycle, with nitrate (N03~) and ammonium (NH4+) being important inorganic species in water
systems.
Nitrate is highly soluble in water and weakly absorbed by soil particles so it easily infiltrates through the soil profile
and subsequently enters the groundwater system. The ability of nitrate to move through the soil can depend on the
biological activity in the soil, the type of soil, and on the concentration of nitrate in the infiltrating water
(Mikolajkow, 2003).
A study of groundwater quality in agricultural areas of Ontario was initiated by Agriculture Canada in 1991.
Approximately 1,300 domestic farm wells were sampled in 1991 and 1992 and the groundwater samples were
analyzed for nitrate, total and fecal colifonns, and several pesticides (Rudolph and Goss, 1993). Nitrate
concentrations exceeded the drinking water quality standard of 10 mg N/L in samples from 15% of the domestic
farm wells. The occurrence and concentration of nitrate in groundwater were found to be associated with the
following:
•	Most of the nitrate contaminated wells were shallow dug or bored wells;
•	The nitrate concentrations tended to be higher in areas where the soils had high permeability;
•	The nitrate concentrations were consistent at the same location and did not show a seasonal variation; and
•	The nitrate concentrations decreased linearly with depth.
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STATE OF THE GREAT LAKES 201 7
Similarly, a survey in Ontario in the late 1990s showed 14% of drinking water supply wells on farms had nitrate
concentrations above the drinking water quality limit (Goss et al. 1998). Nitrate concentrations in groundwater are
often elevated in urban and agricultural areas (Dubrovsky et al. 2010; IJC, 2010).
Sawyer et al. (2009) noted a linkage between "increased agricultural activity and groundwater contamination and its
impact on surface water quality." Some elevated nitrate concentrations are linked to agricultural practices, but some
may also be linked to "rural communities with a high density of septic systems that leach nutrients to the
subsurface."
In a study of nitrate concentrations in groundwater in an agricultural region within the western Lake Erie basin
Thomas (2000b found that 37% of the samples had elevated nitrate concentrations that indicated human effects (e.g.,
fertilizer, manure, septic systems), and that 7% of the samples had nitrate concentrations that exceeded the U.S.
Enviromnental Protection Agency's Maximum Contaminant Level of 10 mg N/L (U.S. Enviromnental Protection
Agency, 2015).
Similar to chloride, increasing nitrate concentrations in some of the municipal supply wells in the Town of
Orangeville have also been seen from 1982-2012 (Credit Valley Conservation Authority, 2015).
Linkages
Linkages to other Great Lakes sub-indicators include:
•	Treated Drinking Water
•	Water Quality in Tributaries. This sub-indicator is based the Water Quality Index (WQI), which is
calculated using a total of eight parameters, including both chloride and nitrate concentrations.
•	Coastal Wetlands: Extent and Composition
•	Aquatic Habitat Connectivity
•	Nutrients in Lakes (open water)
•	Watershed Stressors - to some extent, the pattern of groundwater quality status appears to be
associated with land-use and development patterns. Poorer groundwater quality tends to be in areas of
more intense land use, including urban and agricultural land use. Additional statistical analysis is
warranted to confirm linkages in future reports.
•	Human Population - Similarly, poorer groundwater quality tends to be in areas that are more densely
populated. Additional statistical analysis is warranted to confirm linkages in future reports.
Future consideration of the above linkages may be useful in terms of demonstrating how regional groundwater
quality patterns are related to surface water quality, habitats and various stressors.
Comments from the Author(s)
The new "USGS Online Mapper" (https://www.usgs.gov/news/usgs-online-mapper-provides-decadal-look-
groundwater-qualitv. release date: June 2, 2016) is a "first of its kind" online interactive mapping tool that provides
"summaries of decadal-scale changes in groundwater quality" across the United States, including areas in the Great
Lakes Basin (U.S. side only).
Limitations
This sub-indicator takes into account only two contaminants (nitrate and chloride) and is therefore not meant to
capture all possible groundwater contamination issues or problems at a given location. Groundwater can be
contaminated by many other substances including natural chemicals (e.g., petroleum hydrocarbons and arsenic),
synthetic chemicals (e.g., organic pesticides and pharmaceuticals), or other substances, such as pathogenic
microorganisms. Also, the impact of some sectors on groundwater quality is not well assessed by the two chemicals
that have been selected (e.g., mining sector).
In future assessments, where sufficient data are available, the basic spatial (geographic) unit of observation for this
sub-indicator should be sub-watersheds within the Great Lakes Basin. However, due to large gaps in spatial
distribution of currently (2016) available groundwater quality data (USGS and MOECC monitoring networks), this
sub-watershed component was not included in this initial status report.
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STATE OF THE GREAT LAKES 201 7
Also, how the contaminated groundwater impacts and interacts with the water of the Great Lakes, in particular in the
nearshore zone, requires a better understanding.
Assessing Data Quality
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not
Applicable
1. Data are documented, validated, or
quality-assured by a recognized agency or
organization
X





2. Data are traceable to original sources
X





3. The source of the data is a known,
reliable and respected generator of data
X





4. Geographic coverage and scale of data
are appropriate to the Great Lakes Basin

X




5. Data obtained from sources within the
United States are comparable to those
from Canada



X


6. Uncertainty and variability in the data
are documented and within acceptable
limits for this sub-indicator report

X




Clarifying Notes:
The networks of monitoring wells that were used for this assessment differ on the U.S. and Canadian sides of the border.
Specifically, the ages of the wells, their construction methods, and the criteria that were used for selecting these monitoring wells
were different. Although the methods of analyses used for the U.S. and Canadian data also differed, this is not likely to have had a
substantial effect on the outcome of the assessment.
Acknowledgments
Authors: Dale Van Stempvoort, Enviromnent and Climate Change Canada; George Zhang, Ontario Ministry of the
Environment and Climate Change; Chris Hoard, U.S. Geological Survey; John Spoelstra, Enviromnent and Climate
Change Canada; Norman Grannemann, U.S. Geological Survey; Scott MacRitchie, Ontario Ministry of the
Environment and Climate Change.
Contributor:
Stacey Cherwaty-Pergentile, Enviromnent and Climate Change Canada
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Sawyer, A., Cooke, S., Pitcher, J., and Lapcevic, P. 2009. Natural groundwater quality and human-induced changes.
Indicator #7100. In "State of the Great Lakes 2009", Enviromnent Canada and the U.S. Enviromnental Protection
Agency, p. 329-334.
South Georgian Bay-Lake Simcoe Source Protection Committee. 2015. Chapter 8, City of Barrie. Approved
Assessment Report: Lakes Simcoe and Couchiching-Black River Source Protection Area Part 1.
Stone, M„ Emelko, M.B., Marsalek, J., Price, J.S., Rudolph, D.L., Saini, H„ and Tighe, S.L. 2010. Assessing the
efficacy of current road salt management programs. Report by University of Waterloo and the National Water
Research Institute.
Thomas MA. 2000a. The effect of residential development on ground-water quality near Detroit, Michigan. J
Enviromnental Quality 36:1023-1038.
Thomas, M.A. 2000b. Ground-water quality and vulnerability to contamination in selected agricultural areas of
southeastern Michigan northwestern Ohio, and northeastern Indiana. U.S. Geological Survey Water-Resources
Investigations Report 00-4146, 22 p.
U.S. Enviromnental Protection Agency. 2015. National Primary Drinking Water Regulations. Accessible at:
http://water.epa.gOv/drink/contaminants/index.cfm#List (accessed on November 19, 2015).
Zebarth, B.J., Ryan, M.C., Graham, G., Forge, T.A., and Neilsen, D. Groundwater monitoring to support
development of BMPs for groundwater protection: The Abbotsford-Sumas aquifer case study. Groundwater
Monitoring & Remediation 35(l):82-96. doi: 10.1111/gwmr. 12092.
List of Tables
Table 1. Summary of well data assessments for each Great Lake.
Source: Ontario Ministry of the Enviromnent and Climate Change and U.S. Geological Survey
List of Figures
Figure 1. Groundwater quality status in Great Lakes Basin based on nitrate and chloride concentrations in shallow
groundwater (based on measurements from 2000-2015 for wells < 40 m below ground). A total of 670 monitoring
wells in the basin were included in the analysis with groundwater quality being assessed as Good in 294 (a/). Fair in
Page 401
-------
STATE OF THE GREAT LAKES 201 7
173 (+), and Poor in 203 (x). Symbols indicate the results for individual monitoring wells, and shaded areas indicate
the results for each lake basin.
Source: Ontario Ministry of the Enviromnent and Climate Change and U.S. Geological Survey
Figure 2. Assessment results for the groundwater quality sub-indicator for the Lake Superior basin. Symbols
indicate the results for individual monitoring wells. The U.S. data plot very close together near the international
border, having the appearance of one (overlapped) symbol.
Source: Ontario Ministry of the Enviromnent and Climate Change and U.S. Geological Survey
Figure 3. Assessment results for the groundwater quality sub-indicator for the Lake Michigan basin (based on
measurements from 2000-2015 for wells < 40 m below ground). Symbols indicate the results for individual
monitoring wells.
Source of data: U.S. Geological Survey
Figure 4. Assessment results for the groundwater quality sub-indicator for the Lake Huron basin (based on
measurements from 2000-2015 for wells < 40 m below ground). Symbols indicate the results for individual
monitoring wells.
Source of data: Ontario Ministry of the Enviromnent and Climate Change and U.S. Geological Survey
Figure 5. Assessment results for the groundwater quality sub-indicator for the Lake Erie basin (based on
measurements from 2000-2015 for wells < 40 m below ground). Symbols indicate the results for individual
monitoring wells.
Source of data: Ontario Ministry of the Enviromnent and Climate Change and U.S. Geological Survey
Figure 6. Assessment results for the groundwater quality sub-indicator for the Lake Ontario basin (based on
measurements from 2000-2015 for wells < 40 m below ground). Symbols indicate the results for individual
monitoring wells.
Source of data: Ontario Ministry of the Enviromnent and Climate Change and U.S. Geological Survey
Figure 7. Ternary diagram showing the lake- by- lake groundwater quality assessments. Lake Superior is assessed
as Undetermined and therefore is not included on this ternary diagram.
Source of data: Ontario Ministry of the Enviromnent and Climate Change and U.S. Geological Survey
Figure 8. Plots of nitrate and chloride concentrations versus depth for the Canadian monitoring wells included in
this study. (Pearson correlations for these: nitrate vs depth: -0.092, p = 0.146; chloride vs depth: 0.038, p = 0.543,
i.e., not significant at the 0.05 level).
Source of data: Ontario Ministry of the Enviromnent and Climate Change
Figure 9. Maps illustrating decadal changes (from early 1990s to 2010) in chloride and nitrate concentrations in
groundwater in the United States, including increasing chloride and nitrate concentrations in the vicinity of the Great
Lakes.
Source: DeSimone et al. (2014)
Last Updated
State of the Great Lakes 2017 Technical Report
Page 402
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STATE OF THE GREAT LAKES 201 7


Poor groundwater
quality
Fair groundwater
quality
Good groundwater
quality
Canadian data
Total # wells
# wells
%
# wells
%
# wells
%
Superior
9
1
11%
1
11%
7
78%
Huron
75
12
16%
16
21%
47
63%
Erie
54
15
28%
8
15%
31
57%
Ontario
114
35
31%
34
30%
45
39%
All basins
252
63
25%
59
23%
130
52%



Poor groundwater
quality
Fair groundwater
quality
Good groundwater
quality
US data
Total # wells
# wells
%
# wells
%
# wells
%
Superior
13
0
0%

0%
13
100%
Michigan
136
64
47%
29
21%
43
32%
Huron
2
2
100%
0
0%
0
0%
Erie
123
35
28%
41
33%
47
38%
Ontario
144
39
27%
44
31%
61
42%
All basins
418
140
33%
114
27%
164
39%



Poor groundwater
quality
Fair groundwater
quality
Good groundwater
quality
Binational data
Total # wells
# wells
%
# wells
%
# wells
%
Superior
22
1
5%
1
5%
20
91%
Michigan
136
64
47%
29
21%
43
32%
Huron
77
14
18%
16
21%
47
61%
Erie
177
50
28%
49
28%
78
44%
Ontario
258
74
29%
78
30%
106
41%
Entire Great
Lakes Basin
670
203
30%
173
26%
294
44%
Table 1. Summary of well data assessments for each Great Lake.
Source: Ontario Ministry of the Enviromnent and Climate Change and U.S. Geological Survey
Page 403
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STATE OF THE GREAT LAKES 2017
Groundwater Qualify Status In fho Great Lakes Drainage Basin
o SO 100
xn
Figure 1. Groundwater quality status in Great Lakes basin based on nitrate and chloride concentrations in shallow
groundwater (based on measurements from 2000-2015 for wells < 40 m below ground). A total of 670 monitoring
wells in the basin were included in the analysis with groundwater quality being assessed as Good in 294 (a/). Fair in
173 (+), and Poor in 203(x). Symbols indicate the results for individual monitoring wells, and shaded areas indicate
the results for each lake basin.
Source of data: Ontario Ministry of the Environment and Climate Change and U.S. Geological Survey
Page 404
-------
STATE OF THE GREAT LAKES 2017

VtelUlWrQuiUT tiaflrt
IMDH1KUMS
6000

WOfl
Groundwater Quality Status in the Lake Superior Drainage Basin
a » ao	m		wa
jfi> Ontario
Figure 2. Assessment results for the groundwater quality sub-indicator for the Lake Superior basin (based on
measurements from 2000-2015 for wells < 40 m below ground). Symbols indicate the results for individual
monitoring wells. The U.S. data plot very close together near the international border, having the appearance of one
(overlapped) sy mbol.
Source of data: Ontario Ministry of the Environment and Climate Change and U.S. Geological Survey
Page 405
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STATE OF THE GREAT LAKES 2017
Groundwater Quality Status in the Lake Michigan Drainage Basin
0 M as	136	110	MO
jfi> Ontario
UuAiwWil WJtn aiming Ujtui
m UNOfUmBHEO
O oooo
0	wn
01	w»
Figure 3. Assessment results for the groundwater quality sub-indicator for the Lake Michigan basin (based on
measurements from 2000-2015 for wells < 40 m below ground). Symbols indicate the results for individual
monitoring wells.
Source of data: U.S. Geological Survey
Page 406
-------
STATE OF THE GREAT LAKES 2017
Gtoundwator Quality Status In iho Lako Huron Drainage Basin
s a 40 so m
•W W*W QuHf,
'jNwmmwQ
QCXX3
LJ ***
POO*
£> Ontario
Figure 4. Assessment results for the groundwater quality sub-indicator for the Lake Huron basin (based on
measurements from 2000-2015 for wells < 40 m below ground). Symbols indicate the results for individual
monitoring wells.
Source of data: Ontario Ministry of the Environment and Climate Change and U.S. Geological Survey
Page 407
-------
STATE OF THE GREAT LAKES 2017
Groundwater Quality Stdlus irk tho Lake Eriti Drainage Basin
ft » 40	«c 	tn	HO
-4
Figure 5. Assessment results for the groundwater quality sub-indicator for the Lake Erie basin (based on
measurements from 2000-2015 for wells < 40 m below ground). Symbols indicate the results for individual
monitoring wells.
Source of data: Ontario Ministry of the Environment and Climate Change and U.S. Geological Survey
Page 408
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STATE OF THE GREAT LAKES 2017
Groundwalor Qua Idly Status In tho Lake Ontario Drainage Basin
£> Ontario
Figure 6. Assessment results for the groundwater quality sub-indicator for the Lake Ontario basin (based on
measurements from 2000-2015 for wells < 40 m below ground). Symbols indicate the results for individual
monitoring wells.
Source of data: Ontario Ministry of the Environment and Climate Change and U.S. Geological Survey
Page 409
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STATE OF THE GREAT LAKES 2017
GOOD
100%
Binational Data
25%
50%
^ Erie Basin
Huron Basin
100%
POOR
Ontario Basin
Figure 7. Ternary diagram showing the lake- by- lake groundwater quality assessments. Lake Superior is assessed
as Undetermined and therefore is not included on this ternary diagram.
Source of data: Ontario Ministry of the Environment and Climate Change and U.S. Geological Survey.
Page 410
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STATE OF THE GREAT LAKES 2017
100
10
nitrate
(mg/L 1
as N)
0.01
~v*i .~* ~;/ ~~
J*~~ ~~ ~
20
Well depth (m)
40
10000
1000
100
chloride
(mg/L)
10
0.1
~
~*

20
Well depth (mi)
40
Figure 8. Plots of nitrate and chloride concentrations versus depth for the Canadian monitoring wells included in
this study. (Pearson correlations for these: nitrate vs depth: -0.092, p = 0.146; chloride vs depth: 0.038, p = 0.543,
i.e., not significant at the 0.05 level).
Source of data: Ontario Ministry of the Environment and Climate Change.
Page 411
-------
Chloride
STATE OF THE GREAT LAKES 2017
if.
„ ' * •
V
i
\I
.+ ~ t *r
m * * 4
4
*~
V
*.
EXPLANATION
Ckangt in ciitanifo csn«ntmio*.
wtdisn of ch*TQ*i .n uudf ittfcmrfc,
in fnilh ornrftT pit liter
D»cip»m
*	~ -cl
+ ~ Ha 30
~	~>»
¦ Wu n&fiilic ¦¦! ch»g«
Mitrnli?

V
\

EXPLANATION
ChnoflD la nitrate cwictntraiioa.
mad III! «t ck»n«H in Ua*r titrwmrk.
lit »illja<>m pai liter» N
Incraain
*
*
Oacn»n
~	
-------
Watershed Impacts and
Climate Trends
Status: Fair Trend: Unchanging
The 2012 Great Lakes Water Quality Agreement states that "the Waters of the Great Lakes should be free from other
substances, materials or conditions that may negatively impact the chemical, physical or biological integrity of the Waters of
the Great Lakes"

Between 1971 and 2011 the number of
people living in the Great Lakes Basin
increased by almost 20 percent, resulting
in significant changes to land use in many
Great Lakes watersheds. Shifting climate
trends are also being experienced across
the Great Lakes Basin, including warming
temperatures, changing precipitation
patterns, decreased ice coverage, and
more extreme fluctuations of water levels.
Changes in land use and shifting climate
trends can have a profound effect on Great
Lakes water quality.
Page 413
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Watershed Impacts and Climate Trends
Assessment Highlights
Overall, the Watershed Impacts and Climate Trends indicator
is assessed as Fair and Unchanging. This indicator includes
ail "other substances, materials or conditions" that are not
highlighted in the eight other indicators noted on page 2,
but are important with respect to the state of the Great
Lakes. The indicator currently includes an array of land-based
conditions which can affect water quality as well as climate
trends which can impact all parts of the ecosystem.
Watershed Impacts
Population, development, agriculture and road density can
cause land-based pressures on the Great Lakes ecosystem,
especially in areas with larger population centres. Although
urban and agricultural lands are important to the Great Lakes
region because they help support people and the economy,
the water quality in these areas, in particular the lower
lake basins, is more susceptible to impairments or threats.
Conversely, the northern part of the Great Lakes Basin has
lower relative amount of stress since it remains largely
undeveloped and is dominated by natural cover.
Agricultural Lands in the Southern
Parts of the Great Lakes Basin
Across the entire basin, almost 400 square kilometres (154
square miles) or 40,000 hectares of natural lands were
converted to developed land cover between 2001 and 2011.
The latest analysis shows a growing trend of increasing
development, resulting in a ioss of agricultural, forested and
natural lands.
Research has shown that an increase in forest cover
improves water quality. In particular, forest cover within a
riparian zone (i.e. land along a lake, river or stream), plays a
key role in stabilizing soil and can help reduce the amount of
runoff from the land and reduce nutrient loadings and other
non-point source pollutants. Forest cover in the riparian
zones varies with the Lake Superior watershed having the
highest amount at 96% and the Lake Erie watershed having
the least with 31%. With half of the Great Lakes Basin
currently in agricultural or developed land use, and with
much less forest cover in the more southern parts of the
Great Lakes Basin, it is evident that land-based pressures can
significantly impact water quality.
Forest Cover Helps to Improve Water Quality
Sub-Indicators Supporting the Indicator Assessment
Sub-Indicator
Lake Superior
Lake Michigan
Lake Huron
Lake Erie
Lake Ontario
Forest Cover
Unchanging
Unchanging
Unchanging
Improving
Deteriorating
Land Cover
Unchanging
Unchanging
Unchanging
Unchanging
Unchanging
Watershed Stressors
Unchanging
Unchanging
Unchanging
Unchanging
Unchanging
Hardened Shorelines
Undetermined
Undetermined
Undetermined
Undetermined
Deteriorating
Tributary Flashiness
No lake was assessed separately
Great Lakes Basin trend is Unchanging
Human Population
Decreasing
Increasing
Increasing
Increasing
Increasing
Status:
GOOD
FAIR
POOR
UNDETERMINED
kilometres
Riparian Forest Rating
Poor
	i Fair
Good
Page 414
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Watershed Impacts and Climate Trends
Climate Trends
Data collected over the past 30-40 years in the Great
Lakes Basin show increases in the amount of precipitation,
increases in summer surface water temperature and a
reduction in ice cover. Lake levels have also generally
decreased, although there has been a recent rebound in
water levels in the past few years. It is not yet possible to say
with any certainty, however, if changes in water levels are
due to human activity or natural long-term cycles.
These changes can affect the health of the Great Lakes Basin
including impacts to spawning and other habitats for fish
species, the amount and quality of coastal wetlands and
changes in forest composition. Shifts in climate trends can
also lead to the northward migration of invasive species and
alter habitat in a way that favours some invaders over native
species. An extended growing season, increases in runoff and
nutrient loads and changes to contaminant cycling could also
result from a shift in climate trends.
Assessing Climate Trends
Climate information is not assessed in the same
manner as other indicators in this report. For example,
the ecosystem has adapted to and needs both high
and low water levels and neither condition can be
assessed as Good or Poor. However, prolonged
periods of high or low water levels may cause stress
to the ecosystem. Therefore, climate trends are simply
assessed as Increasing, Unchanging or Decreasing
over a defined period of time.
Sub-Indicators Supporting the Indicator Assessment
Sub-Indicator
Lake Superior
Lake Michigan
Lake Huron
Lake Erie
Lake Ontario
Precipitation Amounts
(1948-2015)
No lake was assessed separately
Great Lakes Basin trend is
Surface Water Temperature
(1979/1980-2014)
~


Undetermined
Undetermined
Ice Cover
(1973-2015)
*
*
*
*
*
Water Levels
(1985-2015)
*
*
*
*
No significant
change
Baseflow Due to Groundwater
No lake was assessed separately
Great Lakes Basin trend is Undetermined
Surface Water Temperatures are Increasing
O
o
Surface Water Temperature
data from 3 locations in Lake Superior
-------
STATE OF THE GREAT LAKES 201 7
Sub-Indicator: Forest Cover
Forest Cover in the Riparian Zone
Overall Assessment
Status: Fair
Trend: Unchanging
Rationale: Forested cover in the riparian zone of water bodies is high in the Lake Superior basin (96%),
moderate in the Michigan, Huron and Ontario basins (61 - 73%) and low in the Lake Erie basin (31%) based
on satellite imagery. Trends in forested cover (2006 - 2011 in U.S. and 2002 - 2011 in Canada) in riparian
zone are showing unchanging conditions in the Lake Superior, Michigan and Huron basins, small decrease in
the Ontario basin (-1.7%) and an increase in the Erie basin (+4.5%). The northern watersheds have much
higher rates of forested riparian zones than watersheds in the south, where there is much greater develop-
ment and agriculture.
Similarly, forested lands are a large percentage of land area within the Lake Superior basin (93%), a moder-
ate amount in the Lake Michigan, Huron and Ontario basins (48 - 65%) and low in the Lake Erie basin
(19%) based on satellite imagery. Trends in forest cover across the lake basins are very similar to the riparian
zone assessments, showing unchanging conditions in the Superior and Huron basins. However, losses in forest
cover were seen in Lakes Michigan (-1.2%), Erie (-1.2%) with the largest losses in the Ontario basin (-3.9%).
Lake-by-Lake Assessment
Lake Superior
Status: Good
Trend: Unchanging
Rationale: With riparian zones of water bodies in this basin having high forest cover, these waters are likely to be
well protected. The Lake Superior basin also has a high overall forest cover. These data suggest that there is unlike-
ly to be long-term impairment of water quality due to forest cover change.
Lake Michigan
Status: Fair
Trend: Unchanging
Rationale: Northerly watersheds within this basin have high forest cover in riparian zones, while southern water-
sheds have reduced cover that may decrease water quality and ecosystem integrity. There is a similar pattern for
forest cover in this basin, with high forest cover in the northern watersheds, while southern watersheds have low
forest cover. These data suggest there is some potential in southerly watersheds to have impairments in water quality
and ecosystem integrity due to forest cover change.
Lake Huron
Status: Fair
Trend: Unchanging
Rationale: Northerly watersheds within this basin have high forest cover in riparian zones, while southern water-
sheds have reduced cover that may decrease water quality and ecosystem integrity. There is a similar pattern for
forest cover in this basin, with high forest cover in the northern watersheds, while southern watersheds have low
forest cover. These data suggest there is some potential in southerly watersheds to have impairments in water quality
and ecosystem integrity due to forest cover change.
Lake Erie
Status: Poor
Trend: Improving
Rationale: A low proportion of forest cover in riparian zones suggests heightened threat to water quality and ecosys-
tem integrity. However, the trend (between 2002 and 2011) is improving on the Canadian-side of the basin. This
basin also has a low coverage by forests, which has declined over the 2002 to 2011 period on the Canadian-side of
Page 416
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STATE OF THE GREAT LAKES 2017
the basin despite increase in riparian forest cover. These data suggest that there is a large potential for water quality
problems and risks to ecological integrity due to forest cover change.
Lake Ontario
Status: Fair
Trend: Deteriorating
Rationale: There is a moderate level of forest cover in riparian zones in this basin which suggests there a moderate
risk to water quality and ecosystem integrity. Similarly, most watersheds in the Lake Ontario basin have moderate
forest cover, which has declined over the 2002-2011 period on the Canadian-side of the basin. These data suggest
there is a potential for water quality problems and risks to ecological integrity due to forest cover, particularly in
Canada where losses have been larger while the U.S. has remained unchanged.
Sub-Indicator Purpose
The purpose of this sub-indicator is to quantify forest cover in riparian zones in relation to its role in performing
hydrologic functions, providing essential processes (e.g., evapotranspiration and nutrient transport), and protecting
the physical integrity of the watershed (e.g., erosion control), all of which are necessary for supplying high quality
water.
Ecosystem Objective
To have a forest composition and structure that reflects the natural ecological diversity (i.e., under present climate
conditions) of the region.
This sub-indicator best supports work towards General Objective #9 of the 2012 Great Lakes Water Quality Agree-
ment which states that the Waters of the Great Lakes should "be free from other substances, materials, or conditions
that may negatively impact the chemical, physical, or biological integrity of the Waters of the Great Lakes."
Ecological Condition
This sub-indicator includes the percent of forested lands within riparian zones by watershed, over time as the main
component being assessed. The percent of forested lands within watershed by lake basin, over time is also included
to support and provide context for the lake-by-lake and overall assessments.
Decades of research and monitoring have shown that water draining forested watersheds is of high quality, as meas-
ured by sediment yields, nutrient loadings, contaminant concentrations and temperatures. Forest cover also contrib-
ute to many other ecosystem services, including controlling soil erosion, increasing groundwater infiltration, stabi-
lizing shorelines and mitigating storm run-off. Leaf litter and woody debris provide critical food and habitat for fish
and other aquatic wildlife. Although there are different roles of non-forest vegetation in maintaining water
quality and quantity, forest cover in riparian areas is a good representation of water protection.
In general, an increase in forest cover improves water quality. Ernst (2004), in a small survey of municipal water
systems, showed that water treatment costs can be directly related to the degree of forest cover in the source water-
shed. The function she developed suggests that treatment costs are lowest at levels of forest cover above -60%. Oth-
er studies have been less successful in discovering empirical relationships between forest cover and the economics
of municipal water supplies.
Where watersheds have experienced large land-use changes due to agricultural activities or urban and suburban de-
velopment, increased forest coverage within a riparian zone can mitigate many of the potentially harmful impacts on
water bodies. Forested riparian zones can decrease the amount of surface runoff to water bodies (reducing erosion),
mitigate nutrient loadings from fertilizer application and other non-point source pollutants and increases the capacity
of the ecosystem to store water. Riparian zones are also important sources of energy and material to aquatic systems
and help regulate water temperatures. Thus the amount of forest in riparian zones (30 metre buffer around all water
bodies which includes water polygons, rivers, streams and intermittent streams where identified) within each lake
basin is the component being used to assess the conditions within this sub-indicator. The status assessment is deter-
mined using the following criteria: Good = >80% forest cover in riparian zones; Fair = 50 - 80% forest cover in
riparian zones; and Poor = <50% forest cover in riparian zones. For trends, a trend is considered unchanging if
change is < ± 1% and changing if >± 1%. Overall forest cover in a lake basin is used as additional information to
provide a larger context.
Page 417
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STATE OF THE GREAT LAKES 2017
The riparian zone was assessed by creating a 30 metre buffer around all waterbodies and using it as a mask on the
forest cover data layers. On a lake basin level (Figure 1), the Lake Superior basin has 96% of its riparian zones iden-
tified as forested, with moderate level of forest in riparian areas for Michigan (63%), Huron (73%), and Ontario
(61%). Only 31% of riparian zones in the Lake Erie basin is forested (Table 1). There is also substantial variation at
the tertiary watershed level with each of the lake basins (Figure 2). The northern watersheds have much higher rates
of forested riparian zones than watersheds in the south, where there is much greater development and agriculture.
Assessing trends in the forest cover within the riparian zone sub-indicator has proven difficult. Whereas the status of
forest cover can be readily assessed through analysis of carefully checked and referenced satellite data, these data
are usually available for single points in time. For this report, satellite imagery data was employed for the U.S. por-
tions of the lake basins from 2006 to 2011 and for the Canadian portions of the basins from 2002 and 2011. Trend
analysis showed that riparian forest is unchanging for northern basins, a small increase in Erie (4.5%) and small de-
crease in Ontario (-1.7%) basins. Changes were small in the U.S. (<1.1%) and larger in Ontario (range from -3.3%
to +16.3%) (Table 2). These trends should be interpreted with some caution, realizing the short span of time in
which they are calculated (5 years for U.S. and 9 years for Canada). A longer record (>20 years) is required in order
to identify trends with any degree of reliability.
Patterns in forest cover within watersheds show similar findings to the forest cover in riparian areas. Figure 3 shows
the tertiary watersheds draining into the Great Lakes and their level of forest cover. There is a strong N-S gradient
evident in the degree of forest cover as would be expected given a similar gradient in population and agricultural
activity. In the Lake Superior basin, 93% of the land area is forested (Table 2). In all the other basins, forests have
been replaced by development and agriculture, leaving forest to occupy 49% (Michigan), 65% (Huron), 19% (Erie)
and 48% (Ontario) of the basins (Table 2). However, it must be noted that within any given basin, there are water-
sheds with fair to good forest cover (Figure 4). Table 2 shows that in the U.S. portion of all lake basins, there is a
trend of unchanging (Erie, Ontario) or small declines (Superior, Huron Michigan), whereas in Canadian basins there
are unchanging (Superior, Huron) to some larger declines (Erie, Ontario) in forest cover (Table 2).
Linkages
The well-documented ability of forested lands to produce high quality water and in particular for forested riparian
areas to protect water resources has linkages to many other sub-indicators. In particular, forest cover within riparian
areas contribute directly to reducing nutrient, and other non-point source pollutant, loadings to the tributaries and
lakes and help to improve the negative effects of atmospheric deposition. Indirectly, the high quality water emanat-
ing for forested areas supports diverse aquatic communities. Climate change, through its effects on forest composi-
tion and function and on local hydrological processes is likely to affect the ability of forests to produce high quality
water, although the magnitude and direction of these affects are not well known. For example, the decline to total
annual runoff in many Great Lakes basins may lead to increased concentrations of nutrients and contaminants in
tributary waters. Also, changes in forest composition, due human activities (e.g., forest management) or natural
agents (e.g., emerald ash borer), may affect water quality and/or quantity.
Comments from the Author(s)
Estimating forest cover by remote sensing is widely used and generally reliable. However, many of the available
datasets do not contain the long time series needed to adequately assess trends. Regular assembly of cross-border
data sets are needed to measure changes in forest cover and to understand the drivers of change. Forest inventory
data (e.g., USFS FIADB) is also useful but Canada lacks an equivalent system. There also remains the challenge of
integrating both forest inventory systems and remote sensing data across jurisdictions due to differences in goals and
methodologies.
It is acknowledged that forest type and the age structure and composition of forests as a function of types and inten-
sity of disturbance influence water quality and quantity. Although it may be desirable to expand the analysis to in-
clude these factors, devising a way compile and calculate indicators given the different sources of data will be a
challenge. It is also recognized that a standard 30 m buffer may not be sufficient to protect water bodies and as-
sessing different or variable buffer sizes might be more beneficial.
Page 418
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STATE OF THE GREAT LAKES 2017
Assessing Data Quality
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not Appli-
cable
1. Data are documented, validated, or
quality-assured by a recognized agen-
cy or organization

X




2. Data are traceable to original
sources

X




3. The source of the data is a known,
reliable and respected generator of data

X




4. Geographic coverage and scale of
data are appropriate to the Great Lakes
Basin



X


5. Data obtained from sources within
the U.S. are comparable to those from
Canada


X



6. Uncertainty and variability in the
data are documented and within ac-
ceptable limits for this sub-indicator
report

X




Acknowledgments
Authors: Kara Webster, Natural Resources Canada, Canadian Forest Service, Great Lakes Forestry Centre, 1219
Queen St E, Sault Ste. Marie, ON, P6A 2E5; email: kara.webster@canada.ca; phone 705 541-5520
Contributors: Charles Perry and Dale Gormanson. USDA Forest Service, 1992 Folwell Avenue, St. Paul, MN
55108; Larry Watkins, Ontario Ministry of Natural Resources and Forestry (OMNRF), 70 Foster Dr., Suite 400,
Sault Ste. Marie, ON, P6A 6V5
Information Sources
Fry, J., Xian, G., Jin, S., Dewitz, J., Homer, C., Yang, L„ Barnes, C., Herold, N., and Wickham, J., 2011. Comple-
tion of the 2006 National Land Cover Database for the Conterminous United States, PE&RS, Vol. 77(9):858-864.
Data Link: http://www.mrlc.gov/nlcd2006.php (accessed October 10, 2011).
Homer, C.G., Dewitz, J.A., Yang, L„ Jin, S., Danielson, P., Xian, G., Coulston, J., Herold, N.D., Wickham, J.D.,
and Megown, K„ 2015, Completion of the 2011 National Land Cover Database for the conterminous United States-
Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing, v. 81,
no. 5, p. 345-354. Data link: http://www.mrlc.gov/nlcdl l data.php (accessed 27 October 2015).
List of Tables
Table 1. Percent of forest cover in riparian zones and percent change by basin for U.S. (2006 and 2011) and Canada
(2002 and 2011) and combined U.S. and Canada Great Lakes region. Data was based on summing forest cover types
in a 30 m buffer around all water bodies. Forest cover was identified from Landsat satellite imagery for U.S. and
Canada (Ontario).
Sources: U.S. National Land Cover Database 2006 (Fry et al. 2006), 2011 (Homer et al. 2015) and Ontario Land-
cover 2002 and SOLRIS 2002 (OMNRF 2006, Forest Sustainability and Information Section, unpublished data) and
Landcover 2008 and SOLRIS 2011(OMNRF 2015, Forest Sustainability and Information Section, unpublished data)
Table 2. Percentage of forest cover and percent change by lake basin for U.S. (2006 and 2011) and Canada (2002
and 2011) and combined U.S. and Canada Great Lakes region. Forest cover was identified from Landsat satellite
imagery for U.S. and Canada (Ontario).
Sources: U.S. National Land Cover Database 2006 (Fry et al. 2006), 2011 (Homer et al. 2015) and Ontario Land-
cover 2002 and SOLRIS 2002 (OMNRF 2006, Forest Sustainability and Information Section, unpublished data) and
Landcover 2008 and SOLRIS 2011(QMNRF 2015, Forest Sustainability and Information Section, unpublished data)
Page 419
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STATE OF THE GREAT LAKES 2017
List of Figures
Figure 1. Percentage of forest cover within riparian zone (30 m buffer around water bodies) for tertiary watersheds
(HUC8 in U.S. and 4 digit in Ontario) of the Great Lakes. Forest cover was estimated from satellite imagery and
includes a variety of forest types (i.e. deciduous, conifer, mixed) and treed wetlands.
Source: U.S. National Land Cover Database NLCD 2011 (Homer et al. 2015) and Ontario Landcover 2008 and
SOLRIS 2011(OMNRF 2015, Forest Sustainability and Information Section, unpublished data)
Figure 2. Forest cover within riparian zone (30 m buffer around water bodies) rating for tertiary watersheds (HUC8
in U.S. and 4 digit in Ontario) of the Great Lakes. Forest cover was estimated from satellite imagery and includes a
variety of forest types (i.e. deciduous, conifer, mixed) and treed wetlands.
Source: U.S. National Land Cover Database NLCD 2011 (Homer et al. 2015) and Ontario Landcover 2008 and
SOLRIS 2011(OMNRF 2015, Forest Sustainability and Information Section, unpublished data)
Figure 3. Percentage of forest cover in tertiary watersheds (HUC8 in U.S. and 4 digit in Ontario) of the Great Lakes.
Forest cover was estimated from satellite imagery and includes a variety of forest types (i.e. deciduous, conifer,
mixed) and treed wetlands.
Source: U.S. National Land Cover Database NLCD 2011 (Homer et al. 2015) and Ontario Landcover 2008 and
SOLRIS 2011(OMNRF 2015, Forest Sustainability and Information Section, unpublished data)
Figure 4. Forest cover rating in tertiary watersheds (HUC8 in U.S. and 4 digit in Ontario) of the Great Lakes.
Source: U.S. National Land Cover Database NLCD 2011 (Homer et al. 2015) and Ontario Landcover 2008 and
SOLRIS 2011(OMNRF 2015, Forest Sustainability and Information Section, unpublished data)
Last Updated
State of the Great Lakes 2017 Technical Report
Page 420
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STATE OF THE GREAT LAKES 2017


% Change


Riparian
Riparian
in
Amount of

Forest 2006
Forest 2011
Riparian
Riparian
Class
U.S. Basin (ha)
(ha)
Forest
Forest 2011
Value



% Change
Amount of


Riparian
Riparian
in
Riparian

Canada
Forest 2002
Forest 2011
Riparian
Forest
Class
Basin
(ha)
(ha)
Forest
2011
Value

Riparian
Riparian
% Change
Amount of

Great
Forest
Forest
in
Riparian

Lake
2006/02
2011/11
Riparian
Forest
Class
Basin
(ha)
(ha)
Forest
2011
Value
Superior
172,927
171,014
-1.1%
85.9%
Good
Superior
619,980
626,200
1.0%
98.8%
Good
Superior
792,907
797,214
0.5%
95.7% Good
Michigan
270,484
268,988
-0.6%
62.5%
Fair
Michigan





Michigan
270,484
268,988
-0.6%
62.5% Fair
Huron
93,021
92,367
-0.7%
56.4%
Fair
Huron
607,694
611,857
0.7%
75.8%
Fair
Huron
700,715
704,224
0.5%
72.5% Fair
Erie
95,593
95,421
-0.2%
35.1%
Poor
Erie
37,571
43,689
16.3%
23.8%
Poor
Erie
133,164
139,110
4.5%
30.5% Poor
Ontario
95,857
96,807
1.0%
58.0%
Fair
Ontario
163,564
158,216
-3.3%
62.1%
Fair
Ontario
259,421
255,023
-1.7%
60.5% Fair
Total:
727,882
724,597
-0.5%
58.8%
Fair
Total:
1,428,808
1,439,963
0.8%
76.6%
Fair
Total:
2,156,690
2,164,560
0.4%
69.6% Fair
Table 1. Percent of forest cover in riparian zones and percent change by basin for U.S. (2006 and 2011) and Canada (2002 and 2011) and combined U.S. and
Canada Great Lakes region. Data was based on summing forest cover types in a 30 m buffer around all water bodies. Forest cover was identified from Landsat
satellite imagery for U.S. and Canada (Ontario).
Sources: U.S. National Land Cover Database 2006 (Fry et al. 2006), 2011 (Homer et al. 2015) and Ontario Landcover 2002 and SOLRIS 2002 (OMNRF 2006,
Forest Sustainability and Information Section, unpublished data) and Landcover 2008 and SOLRIS 201 l(OMNRF 2015, Forest Sustainability and Information
Section, unpublished data)
All Forest
All Forest
% Change
Amount of
Class
U.S. Basin 2006 (ha)
2011 (ha)
in Forest
Forest 2011
Value




Amount of
Great
All Forest
All Forest

Amount of

Canada
All Forest
All Forest
% Change
Forest
Class Lake
2006/02
2011/11
% Change
Forest
Class
Basin
2002 (ha)
2011 (ha)
in Forest
2011
Value Basin
(ha)
(ha)
in Forest
2011
Value
Superior
3,539,252
3,483,919
-1.6%
83.5%
Good
Superior
7,038,011
7,037,552
0.0%
98.9%
Good
Superior
10,577,263
10,521,471
-0.5%
93.2% Good
Michigan
5,577,078
5,507,977
-1.2%
48.9%
Fair
Michigan





Michigan
5,577,078
5,507,977
-1.2%
48.9% Fair
Huron
2,048,628
2,006,615
-2.1%
49.8%
Fair
Huron
6,278,642
6,289,194
0.2%
72.3%
Good
Huron
8,327,270
8,295,809
-0.4%
65.2% Good
Erie
1,107,959
1,100,254
-0.7%
20.7%
Poor
Erie
296,517
287,027
-3.2%
14.2%
Poor
Erie
1,404,476
1,387,281
-1.2%
18.9% Poor
Ontario
1,533,078
1,537,099
0.3%
46.1%
Fair
Ontario
1,330,982
1,215,674
-8.7%
49.3%
Fair
Ontario
2,864,060
2,752,773
-3.9%
47.5% Fair
Total:
13,805,995
13,635,864
-1.2%
48.5%
Fair
Total:
14,944,151
14,829,448
-0.8%
73.0%
Good
Total:
28,750,146
28,465,312
-1.0%
58.8% Fair
Table 2. Percentage of forest cover and percent change by lake basin for U.S. (2006 and 2011) and Canada (2002 and 2011) and combined U.S. and Canada
Great Lakes region. Forest cover was identified from Landsat satellite imagery for U.S. and Canada (Ontario).
Sources: U.S. National Land Cover Database 2006 (Fry et al. 2006), 2011 (Homer et al. 2015) and Ontario Landcover 2002 and SOLRIS 2002 (OMNRF 2006,
Forest Sustainability and Information Section, unpublished data) and Landcover 2008 and SOLRIS 201 l(OMNRF 2015, Forest Sustainability and Information
Section unpublished data)
Page 421
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STATE OF THE GREAT LAKES 2017
Percent riparian forest
I I Under 25
kilometres
Figure 1. Percentage of forest cover within riparian zone (30 m buffer around water bodies) for tertiary watersheds
(HUC8 in U.S. and 4 digit in Ontario) of the Great Lakes. Forest cover was estimated from satellite imagery and
includes a variety of forest types (i.e. deciduous, conifer, mixed) and treed wetlands.
Source: U.S. National Land Cover Database NLCD 2011 (Homer et al. 2015) and Ontario Landcover 2008 and
SOLR1S 2011(OMNRF 2015, Forest Sustainability and Information Section, unpublished data)
Page 422
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STATE OF THE GREAT LAKES 2017
Riparian forest rating
¦I Poor (Under 50 percent)
Fair (50 to 79 percent)
Good (80 percent and over)
0 75 150
kilometres
Figure 2. Forest cover within riparian zone (30 m buffer around water bodies) rating for tertiary watersheds (HUC8
in U.S. and 4 digit in Ontario) of the Great Lakes. Forest cover was estimated from satellite imagery and includes a
variety of forest types (i.e. deciduous, conifer, mixed) and treed wetlands.
Source: U.S. National Land Cover Database NLCD 2011 (Homer et al. 2015) and Ontario Landcover 2008 and
SOLRJS 201 l(OMNRF 2015, Forest Sustainability and Information Section, unpublished data)
Page 423
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STATE OF THE GREAT LAKES 2017
Percent forest cover
I I Under 10
~ 10 to 20
r 121 to 40
IE 41 to 60
H 61 to 70
M 71 to 30
Over 30
kilometres
Figure 3. Percentage of forest cover in tertiary watersheds (HUC8 in U.S. and 4 digit in Ontario) of the Great Lakes.
Forest cover was estimated from satellite imagery and includes a variety of forest types (i.e. deciduous, conifer,,
mixed) and treed wetlands.
Source: U.S. National Land Cover Database NLCD 2011 (Homer et al. 2015) and Ontario Landcover 2008 and
SOLRIS 201 KOMNRF 2015, Forest Sustainability and Information Section, unpublished data)
Page 424
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STATE OF THE GREAT LAKES 2017
Forest rating
¦I Poor (Under 30 percent}
Fair (30 to 60 percent)
Good (Over 60 percent)
0 75 150
kilometres
Figure 4. Forest cover rating in tertiary watersheds (HUC8 in U.S. and 4 digit in Ontario) of the Great Lakes.
Source: U.S. National Land Cover Database NLCD 2011 (Homer et al. 2015) and Ontario Landcover 2008 and
SOLRIS 201 l(OMNRF 2015, Forest Sustainability and Information Section, unpublished data)
Page 425
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STATE OF THE GREAT LAKES 201 7
Sub-Indicator: Land Cover
Overall Assessment
Status: Fair
Trend: Unchanging
Rationale: Across the entire basin, between 2001 and 2011, there was a net conversion of 393 km2 from
natural land cover to developed land cover. This decrease in natural land cover constituted 0.05% of the
assessed land area (see explanation of the geographic extent considered under "Ecological Condition),
resulting in a determination of "unchanging". With 50% of the basin in agricultural or developed land use,
the status by definition is "Poor" however, this percentage is straddling the Fair - Poor threshold. Based on
the lake-by-lake assessments below, the overall sub-indicator assessment will remain as "Fair" for this
reporting cycle.
Lake-by-Lake Assessment
Lake Superior
Status: Good
Trend: Unchanging
Rationale: Land cover change was assessed only on the U.S. side of the basin as there are no 2012 era land use data
available for Canada in the Lake Superior basin. Forest land in the U. S Lake Superior basin decreased by approxi-
mately 400 km2 or 0.93% of the watershed, but this conversion was predominately to grass/shrub land cover, which
increased by 384 km2 or 0.089%. With 93% natural land cover, the status is "Good" and the trend is "Unchanging".
Lake Michigan
Status: Fair
Trend: Unchanging
Rationale: Forest land cover decreased by 600 km2 and developed land increased by 450 km2. However the predom-
inant transition of forest was to grass/shrub, whereas the increase in developed land came from conversion of agri-
cultural land. For this reason the trend is "Unchanging". With 42% of the watershed in agriculture and 11% in de-
veloped land, the status is "Fair".
Lake Huron
Status: Fair
Trend: Unchanging
Rationale: Land cover data were unavailable for the Canadian portion of the basin outside of the SOLRIS coverage.
Forest land cover decreased by 450 km2, with much of this converting to grass/shrub. Developed land increased by
117 km2 but agricultural lands decreased by 90 km2. With essentially no net change between developed and natural
land covers, the trend is unchanging. With 42% of the watershed in agriculture and 8% in developed land, the status
is "Fair".
Lake Erie
Status: Poor
Trend: Unchanging
Rationale: Land cover data were unavailable for the Canadian portion of the basin outside of the SOLRIS coverage.
Lake Erie's largest land use change was an 458 km2 increase in developed land, largely due to the conversion of
agricultural land, which decreased by almost 300 km2. Forest land decreased by 225 km2, primarily by conversion to
developed land or agriculture. With 62% of the watershed in agriculture and 17 % in developed land, the status is
characterized as Poor and the trend is Unchanging.
Lake Ontario
Status: Fair
Trend: Unchanging
Page 426
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STATE OF THE GREAT LAKES 2017
Rationale: Land cover data were unavailable for the Canadian portion of the basin outside of the SOLRIS coverage.
The largest land use change was a 300 km2 increase in developed land, due to conversion from agricultural land, and
to a lesser degree, forestland. Forest land cover decreased overall by 100 km2. With 42% of the watershed in agricul-
ture and 11% in developed land, the status is characterized as Fair and the trend is Unchanging.
Other Spatial Scales
This sub-indicator pertains primarily to risk of degradation of the coastal margins and nearshore waters. The im-
portance of land use condition (especially as a source of nutrients and contaminants) declines with increasing dis-
tance away from the coastal margin since substances are typically transported by the water contributed by tributar-
ies.
Sub-Indicator Purpose
•	Assess the status of natural land cover within the Great Lakes Basin
•	Inform inferences about the major proximate causes of changes and trends in other biological communities,
physical habitat, and water quality indicators that are more directly reflective of the health of the Great
Lakes ecosystem
Ecosystem Objective
Sustainable development is a generally accepted land use goal for the Great Lakes Basin. This sub-indicator best
supports work towards General Objective #9 of the 2012 Great Lakes Water Quality Agreement, which states that
the Waters of the Great Lakes should "be free from other substances, materials, or conditions that may negatively
impact the chemical, physical, or biological integrity of the Waters of the Great Lakes."
Ecological Condition
For the previous analysis, a common land cover classification was developed to allow an integrated comparison of
land use in both Canada and the U.S. This involved integrating the detailed but distinct classifications of the U.S.
system (24 land use classes as delineated by Wolter et al. 2006) with the Canadian system (The Ontario Ministry of
Natural Resources' Ontario Provincial Land Cover, consisting of 27 (in 1990) or 28 (in 2000) classes). The resulting
unified assessment consisted of six land classes (Developed, Agriculture, Grassland/ Shrubland, Forest, Wetland,
and Water (Ciborowski et al. 2011)). Using this common land cover classification for the year 2000, we calculated
the total and proportional amounts of each land cover class by lake and across the Great Lakes Basin.
In the present assessment, temporally comparable (i.e., 2000-2002 era) datasets derived from U.S. National Land
Cover Dataset (NLCD) and Ontario Land Cover Compilation v.2.0 were merged into a single binational land cover
product by the Great Lakes Aquatic Habitat Framework project (GLAHF; http://ifr.snre.umich.edu) (Wang et al.
2015). Subsequently, a more contemporary product was created utilizing 2011 NLCD (Homer et al. 2015) and 2012
(SOLRIS v2.0) data. The SOLRIS land cover dataset however, is not a complete coverage of the Canadian side of
the Great Lakes Basin - it excludes approximately 175,000 km2 of the largely forested northern regions of the Lake
Superior and Lake Huron watersheds. This is 34% of the land area within the Great Lakes basin watershed. It is ex-
pected that outside of forest harvest activities, this region has experience relatively little land use change. The as-
sessments of land cover change presented below however, only reflect that portion of the basin where 2001 and
2011 data are directly comparable.
Over the extent of our study area there was a net conversion of 393 km2 from natural to human-modified land cover.
This change came largely at the expense of forest land, which decreased by 1780 km2. The area of the basin in agri-
cultural land use also decreased by 948 km2. Increases were seen in the amount of developed land (1341 km2) and
grass/shrub land cover (1257 km2). Rates of land use change provide an important integrated indicator of the degree
and location of both loss and gain of natural lands, representing increases and reductions in the risks of degradation.
These latest analysis reflect a growing trend of increasing developed lands, at the expense of both agricultural and
forest lands.
As might be expected, the large variations in land cover across the Great Lakes noted in the previous report has re-
mained constant, with the Lake Superior basin continuing to be predominately forested (Figure 1) and Lake Erie
predominantly agricultural (Figure 4). Forest and Agricultural land uses are more evenly distributed in lakes Michi-
gan and Ontario (Figures 2 and 5). This large variation in land use among lakes reflects the underlying climatic and
Page 427
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STATE OF THE GREAT LAKES 2017
soil gradients across the Great Lakes Basin that have historically constrained the conversion of the native vegetation
(forest or grassland) to agricultural land use.
The distribution of land use classes for each Great Lake is shown in Figures 1-5. The greatest change toward human-
modified land use uses on both an absolute and percentage basis occurred in the Lake Erie basin, with a net change
of 165 km2. This change was entirely due to increases in developed lands, which increased by 458 km, largely due to
the conversion of agricultural (-292 km2) and forested (-225 km2) lands. Similar changes occurred in the Lake On-
tario basin, which saw a 298 km2 increase in developed land, again due to loss of agricultural land (-226 km2) and
forest land (-106 km2). In fact, with the exception of Lake Superior, all lakes experienced declines in agricultural
land and increases in developed lands (Table 1). The row totals in Table 1 show the total area in a land use class in
2001, whereas the column totals show the total area by class in 2010. The barren land class was too uncommon to
show in the figures but included in Table 1 for completeness.
Linkages
The importance of land use condition (especially as a source of nutrients and contaminants) is greatest at shorelines
and coastal margins, and declines with increasing distance away from the shore since substances are typically trans-
ported by the water contributed by tributaries. Natural land cover is an indicator of good conditions because it incor-
porates nutrients into biomass and slows the rate of water runoff into the lakes, together with materials (sediments,
pollutants) that the water transports. This sub-indicator also relates to the Forest Cover sub-indicator, and indirectly
to Tributary Flashiness, which is influenced by conversion to human-modified land covers.
Comments from the Author(s)
Issues with data registration and classification criteria between 1992 and 2000-era data precluded meaningful land
cover change analysis in the 2011 report, as noted in Ciborowski 2011.
Assessing Data Quality
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not
Applicable
1. Data are documented, validated, or
quality-assured by a recognized agency or
organization
X





2. Data are traceable to original sources
X





3. The source of the data is a known,
reliable and respected generator of data
X





4. Geographic coverage and scale of data
are appropriate to the Great Lakes Basin

X




5. Data obtained from sources within the
U.S. are comparable to those from
Canada
X





6. Uncertainty and variability in the data
are documented and within acceptable
limits for this sub-indicator report

X




Clarifying Notes: Re: geographic coverage - the SOLRIS land cover dataset is not a complete coverage of the Canadian
side of the Great Lakes Basin. It excludes the largely forested northern regions of the Lake Superior and Lake Huron
watersheds, north of N 45.88334 and west of W83.10000.
Page 428
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STATE OF THE GREAT LAKES 2017
Acknowledgments
Authors: George E. Host, Terry N. Brown, Paul Meysembourg, and Lucinda B. Johnson, Natural Resources Re-
search Institute, University of Minnesota Duluth, 5013 Miller Trunk Highway, Duluth, MN, 55811;
Jan J.H. Ciborowski, Department of Biological Sciences, University of Windsor, 401 Sunset Avenue, Windsor, ON,
Canada. N9B 3P4;
Catherine Riseng, G170 Dana, School of Natural Resources and Environment, University of Michigan, 440 Church
St, Ann Arbor, MI 48109.
Contributors: Members of the Great Lakes Enviromnental Indicators project - Gerald L. Niemi (Senior PI; NRRI,
University of Minnesota Duluth), Nicholas P. Danz (University of Wisconsin - Superior), and Thomas Hollenhorst
(U.S. EPA, Mid-Continent Ecology Division National Health and Enviromnental Effects Research Laboratory, Du-
luth, MN 55804) contributed to formation of the research group that identified the need for this database. The
SOLEC coordinators Rob Hyde, Nancy Stadler-Salt, Stacey Cherwaty-Pergentile (Enviromnent and Climate Change
Canada, Burlington, ON), and Paul Horvatin and Karen Rodriguez (U.S. EPA GLNPO, Chicago, IL) provided the
impetus for developing the concept paper on land cover that allowed to assess the status of land cover within the
Great Lakes basin, and to infer the potential impact (risk of degradation) of land cover and land cover change on
Great Lakes ecosystem health.
The project on which these data were based was originally funded by the U.S. Enviromnental Protection Agency
Science to Achieve Results (STAR) Estuarine and Great Lakes (EaGLe) program through funding to the Great
Lakes Enviromnental Indicators (GLEI) and Reference Condition projects (U.S. EPA Agreements EPA/R-8286750
and EPA/R-82877701, respectively). More recent work was supported by the second stage GLEI-II Indicator Test-
ing and Refinement project funded by a Great Lakes Restoration Initiative grant from the U.S. Enviromnental Pro-
tection Agency Great Lakes National Program Office to Lucinda.B. Johnson (GL-00E00623-0). Although the re-
search described in this work has been partly funded by the U.S. EPA, it lias not been subjected to the agency's re-
quired peer and policy review and therefore does not necessarily reflect the views of the agency and no official en-
dorsement should be inferred. The GLAHF project was funded by the Great Lakes Fisheries Trust.
Information Sources
Literature Cited
Ciborowski, J.J.H., G.E. Host, T.A. Brown, P. Meysembourg and L.B. Johnson. 2011. Linking Land to the Lakes:
the linkages between land-based stresses and conditions of the Great Lakes. Background Technical Paper prepared
for Enviromnent Canada in support of the 2011 State of the Lakes Ecosystem Conference (SOLEC), Erie, PA. 47 p
+ Appendices.
Homer, C.G., Dewitz, J.A., Yang, L„ Jin, S., Danielson, P., Xian, G., Coulston, J., Herold, N.D., Wickham, J.D.,
and Megown, K„ 2015, Completion of the 2011 National Land Cover Database for the conterminous United States-
Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing, v. 81,
no. 5, p. 345-354
Wang, L., C.M. Riseng, L.A. Mason, K.E. Wehrly, E.S. Rutherford, J.E. McKenna, Jr, C. Castiglione6, L. B. John-
son, D. M. Infante, S. Sowa, M. Robertson J. Schaeffer, M. Khoury, J. Gaiot, T. Hollenhorst, C. Brooks, M.
Coscarelli. 2015. A Hierarchical Spatial Classification and Database for Management, Research, and Policy Mak-
ing: the Great Lakes Aquatic Habitat Framework. Journal of Great Lakes Research 41:584-596.
Wolter, P.T., C.A. Johnston, G.J. Niemi. 2006. Land Use Land Cover Change in the U.S. Great Lakes Basin 1992 to
2001. Journal of Great Lakes Research 32(3): 607-628.
Data Sources
The integrated and reclassified NLCD 2011 and SOLRIS 2012 land use/land cover data were obtained from the
Great Lakes Aquatic Habitat Framework; http://ifr.snre.umich.edu
The following credits for land cover circa 2000 and 2010 were posted with on the GLAPH metadata page:
• National Land Cover Dataset, 2001 vll http://www.mrlc.gov/nlcd01_data.php:
Page 429
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STATE OF THE GREAT LAKES 2017
•	The 2000 Provincial Landcover Ontario PLO
https://ww\i\javacoeapp.lrc.gov.on.ca/geonet\i>ork/srv/en/main.home;
•	2000 Southern Ontario Land Resource Information System (SOLRIS) v 1.2
https:/7www.jm'acoeapp. lrc.gov. on. ca/geonet\vork/srv/en/main.home;
•	Anderson, J.R., Hardy, E. E„ Roach, J.T., Witmer, R. E., 1976. A Land Use and Land Cover Classification
System for Use with Remote Sensor Data. United States Department of the Interior. Geological Survey
Professional Paper 964. A revision of the land use classification system as presented in U.S. Geological
Survey Circular 671. Conversion to Digital 2001. United States Government Printing Office, Washington.
1976.;
•	Hollenhorst, T. P., Johnson, L.B., and Ciborowski, J., 2011. Monitoring land cover change in the Lake Su-
perior Basin. Aquatic Ecosystem Health and Management, 14(4):433-442.;
•	Wolter, P.T., Johnston, C. A., Niemi, G.J., 2006. Land Use Land Cover Change in the U.S. Great Lakes
Basin 1992 to 2001. Journal of Great Lakes Research. 32:607-628.:
List of Tables
Table 1. Transitions among land use/land cover classes between 2001 (rows) and 2011 (columns); values are area in
square kilometres.
Data Source: 2011 NLCD and 2012 SOLRIS; integrated classification by Wang et al. 2015; regions north of the
SOLRIS demarcation line represent 2001-era data.
List of Figures
Figure 1. Distribution of land use/land cover across the Lake Superior Basin.
Source: GLAHF 2001 are an integration of the National Land Cover Dataset (NLCD) and the Ontario Land Cover
Compilation v 2.0 data from 2001, whereas GLAPH 2011 incorporate 2011 NLCD and 2012 SOLRIS data (Wang et
al. 2015); the GLAPH 2011 dataset does not cover the area north of the demarcation line.
Figure 2. Distribution of land use/land cover across the Lake Michigan Basin in 2011.
Source: 2011 NLCD and 2012 SOLRIS; integrated classification by Wang et al. 2015; regions north of the SOLRIS
demarcation line represent 2001-era data.
Figure 3. Distribution of land use/land cover across the Lake Huron Basin.
Source: GLAHF 2001 are an integration of the National Land Cover Dataset (NLCD) and the Ontario Land Cover
Compilation v 2.0 data from 2001, whereas GLAPH 2011 incorporate 2011 NLCD and 2012 SOLRIS data (Wang et
al. 2015); the GLAPH 2011 dataset does not cover the area north of the demarcation line.
Figure 4. Distribution of land use/land cover across the Lake Erie Basin in 2011.
Source: 2011 NLCD and 2012 SOLRIS; integrated classification by Wang et al. 2015; regions north of the SOLRIS
demarcation line represent 2001-era data).
Figure 5. Distribution of land use/land cover across the Lake Ontario Basin.
Source: GLAHF 2001 are an integration of the National Land Cover Dataset (NLCD) and the Ontario Land Cover
Compilation v 2.0 data from 2001, whereas GLAPH 2011 incorporate 2011 NLCD and 2012 SOLRIS data (Wang et
al. 2015); the GLAPH 2011 dataset does not cover the area north of the demarcation line.
Last Updated
State of the Great Lakes 2017 Technical Report
Page 430
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STATE OF THE GREAT LAKES 2017
Lake Erie
Agrlcultu re
Barren
Developed
Forest
Grass/Shrub
Wetland
Water
Totals
Agriculture
'16311
38
487
192
8
95
18
47151
Barren
23
172
8
1
3
2
4
213
Developed
214
1
12234
23
0
13
3
12488
Forest
200
7
149
10320
55
123
7
10860
Grass/Shrub
4
2
27
5
886
2
1
927
Wetland
99
2
38
92
2
3939
6
4178
Water
4
1
3
2
0
16
615
641
Totals
46858
222
12945
10635
955
4190
653
76457
Lake Huron
Agriculture
Barren
Developed
Forest
Grass/Shrub
Wetland
Water
Totals
Agriculture
26284
34
270
276
25
122
13
27024
Barren
31
175
4
1
5
4
5
227
Developed
190
1
4727
48
0
21
3
4990
Forest
287
10
71
16682
474
172
9
17706
Grass/Shrub
11
3
4
95
2538
1
1
2652
Wetland
124
5
29
147
5
10690
10
11010
Water
7
3
2
5
0
18
852
888
Totals
26934
231
5107
17255
3048
11029
893
64497
Lake Michigan
Agriculture
Barren
Developed
Forest
Grass/Shrub
Wetland
Water
Totals
Agriculture
37112
38
308
11
34
15
14
37532
Barren
15
379
7
0
3
1
10
415
Developed
0
0
11486
0
0
0
0
11486
Forest
33
17
61
34731
768
7
4
35621
Grass/Shrub
25
g
46
281
4581
5
2
4949
Wetland
3
5
24
1
8
23086
10
23138
Water
1
6
2
1
1
12
3448
3470
Totals
37189
453
11934
35024
5395
23127
3488
116611
Lake Ontario
Agriculture
Barren
Developed
Forest
Grass/Shrub
Wetland
Water
Totals
Agriculture
22617
23
497
429
10
193
15
23783
Barren
32
110
5
1
1
1
1
151
Developed
263
2
5393
79
0
35
5
5777
Forest
442
7
120
16077
43
305
16
17010
Grass/Shrub
6
3
8
28
2102
6
3
2156
Wetland
186
3
49
275
3
6217
12
6745
Water
9
2
4
15
0
27
1216
1272
Totals
23556
149
6075
16904
2159
6783
1267
56893
Lake Superior
Agriculture
Barren
Developed
Forest
Grass/Shrub
Wetland
Water
Totals
Agriculture
1411
1
2
2
5
1
0
1420
Barren
0
153
1
0
6
1
5
167
Developed
0
0
1535
0
0
0
0
1535
Forest
10
13
12
25303
895
7
1
26241
Grass/Shrub
2
6
3
529
472
2
0
1015
Wetland
1
3
3
4
20
11346
1
11378
Water
0
10
0
0
3
5
1379
1397
Totals
1423
188
1556
25839
1400
11361
1387
43153
Table 1. Changes in area of land use/land cover classes between 2001 and 2011. Row totals show the total area in a
land use class in 2001, column totals show the total area by class in 2010. Values are area in square kilometres.
Data Source: 2011 NLCD and 2012 SOLR1S; integrated classification by Wang et al. 2015; regions north of the
SOLRJS demarcation line represent 2001-era data.
Page 431
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STATE OF THE GREAT LAKES 2017
| Developed
] Agrteufture
| Grassland I ShruDlana
| Forest
| Wetland
Q Water
Resources
Research Institute
tftriMmm « MflOtfacttA 1^:1 ini
I*
Figure 1. Distribution of land use/land cover across the Lake Superior Basin.
Source: GLAHF 2001 are an integration of the National Land Cover Dataset (NLCD) and the Ontario Land Cover
Compilation v 2.0 data from 2001, whereas GLAPH 2011 incorporate 2011 NLCD and 2012 SOLRIS data (Wang et
al. 2015); the GLAPH 2011 dataset does not cover the area north of the demarcation line.
Page 432
-------
STATE OF THE GREAT LAKES 2017
Lake Michigan
Developed
Agriculture
Grassland I Shmfaland
Forest
Wetland
Water
souree flata GLAHF 2011
UmiVBttsrrt <« MHNtson Dm ith
DrJv«n In DUcovir
Naturaf Resources
Research Institute
Figure 2. Distribution of land use/land cover across the Lake Michigan Basin in 2011.
Source: 2011 NLCD and 2012 SOLRIS; integrated classification by Wang et al. 2015; regions north of the SOLRIS
demarcation line represent 2001-era data.
Page 433
-------
STATE OF THE GREAT LAKES 2017
L'MMHSin rx Mism.sctta Du
Drivtn to Diiio*df
Developed
Agriculture
Grassland / Scrubland
Forest
Westend
Water
Natural Resources
Research Institute
* GLAHF2°11
Figure 3. Distribution of land use/land cover across the Lake Huron Basin.
Source: GLAHF 2001 are an integration of the National Land Cover Dataset (NLCD) and the Ontario Land Cover
Compilation v 2.0 data from 2001, whereas GLAPH 2011 incorporate 2011 NLCD and 2012 SOLRIS data (Wang et
al. 2015); the GLAPH 2011 dataset does not cover the area north of the demarcation line.
Page 434
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STATE OF THE GREAT LAKES 2017
Developed
Agriculture
Grassland I Shrubland
Forest
Wetland
Water
L\mxsm op Minnlm.'Ta DuLirtM
Drlvifi ta Diicftvar
tilSU
soureedats GL-ABF2011
N
A
0 25 50	tOO
KJom«ter*
Natural Resources
Research institute
Figure 4. Distribution of land use/land cover across the Lake Erie Basin in 2011.
Source: 2011 NLCD and 2012 SOLR1S; integrated classification by Wang et al. 2015; regions north of the SOLRIS
demarcation line represent 2001-era data).
Page 435
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STATE OF THE GREAT LAKES 2017
glahF
GtfrHP
N
A
0 25 50	100
K4om«lfcf»
Natural Resources
Research Institute
UNM'ssrrv os Minnesota Duujto
Drlrtn to Dlito'tf
| Developed
! Agncutlure
| Grassland I ShruMand
| FOffcst
| Wetland
I wat«»
Figure 5. Distribution of land use/land cover across the Lake Ontario Basin.
Source: GLAHF 2001 are an integration of the National Land Cover Dataset (NLCD) and the Ontario Land Cover
Compilation v 2.0 data from 2001. whereas GLAPH 2011 incorporate 2011 NLCD and 2012 SOLRIS data (Wang et
al. 2015); the GLAPH 2011 dataset does not cover the area north of the demarcation line.
Page 436
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STATE OF THE GREAT LAKES 201 7
Sub-Indicator: Watershed Stressors
Overall Assessment
Status: Fair
Trend: Unchanging
Rationale: The status can also be described as MIXED, with GOOD condition found in 19.4% of the water-
sheds in the Basin, FAIR condition found in 60.1% of the Basin's watersheds, and 20.5% of the Basin in
POOR condition, see Tables 1,2. This sub-indicator reports long term trends at 5-10 year intervals, as data
becomes available. The sub-indicator currently reports for the period 2000-2010.
The basin is a globally unique entity subject to moderate or large amounts of development within its water-
shed. The spatial arrangement of watershed-based stress reflects the basin's geomorphology. Much of the
southern part of the basin, which is underlain by rich soils and naturally supports deciduous forest, has been
developed for agriculture or dwelling, whereas the northern (Canadian Shield) part of the basin remains
largely undeveloped. When the combined stresses of population density, road density, urban development,
and agricultural development are considered, two of the five Great Lakes (Erie and Ontario) are individually
assessed as having a status of 'Poor', Lake Michigan is assessed as 'Fair', and Lakes Huron and Superior are
classified as 'Good' (Table la; Figure 1). Consequently, the status of the Great Lakes Basin overall is opera-
tionally defined as 'Fair' (Table la; Figure 1), since the majority of its watersheds are in fair condition.
When the operational definition of condition is based on the percent area, (Table lb), the majority of the ba-
sin's area is in Fair condition.
Across the Basin, roads were ubiquitous and represent the largest source of potential risk of degradation in
largely undeveloped areas (Figure 7). Basin wide, condition category shifts were relatively rare with only 81
of 5583 watersheds changing condition categories from 2000 to 2010. Changes from FAIR (generally) to
POOR classes were most common (38 watershed transitions), followed by transitions from POOR to FAIR
condition (26), and GOOD to FAIR (12). Due to the small number of watershed transitions, which together
represent only 1.4% of the total watersheds, and 0.13% percent of the basin area, the trend is listed as UN-
CHANGING. See author's notes for further explanation of data interpretation issues.
Lake-by-Lake Assessment
Note: impacts from watersheds draining into connecting channels are assigned to the downstream lake.
Lake Superior
Status: Good
Trend: Unchanging
Rationale: Of the 1,534 watersheds in the Lake Superior basin, 595 were classified as GOOD (38.8%), 917 were
classified as FAIR (59.8%), and only 22 were classified as POOR (1.4%) (Table 2, Figure 9). The interval from
2000 to 2010 saw a very minor shift in the condition of Lake Superior's watersheds, with a change of 0.6% of total
watershed numbers (10 of 1,534) from the GOOD to FAIR category. Five watersheds transitioned from FAIR to
GOOD. No watersheds shifted from FAIR to POOR. This suggests that conditions are largely unchanged (Table 2;
Figure 8). A portion of the Lake Superior Basin in Canada did not have 2010 era land use data; to derive an estimate
of change the assumption was made that there was no change in percent agricultural or developed land. The basin-
wide trend is therefore to be regarded as a conservative estimate. Lake Superior has the lowest percentage of
agricultural land in the basin, the lowest road density and lowest population density. This basin was second in terms
of the number of watersheds in the lowest quintile for percent developed land, behind Lake Huron (Table 3; Figures
2-7).
Lake Michigan
Status: Fair
Trend: Unchanging
Page 437
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STATE OF THE GREAT LAKES 201 7
Rationale: Lake Michigan's 629 watersheds were classified predominantly as FAIR (83.5%); 16.1% were classified
as POOR and less than 1% were scored as GOOD (Table 2; Figure 10). Lake Michigan was unremarkable in terms
of the distribution of each of the component stressors with one exception. Few condition transitions were noted for
Lake Michigan. Trends for Lake Michigan are based on complete data sets for the basin and therefore represent the
best available estimates. It was notable that the Lake Michigan Basin had the lowest number of watersheds in the
lowest quintile in terms of road density, suggesting that few roadless areas remain within that basin (Table 3;
Figures 2-7).
Lake Huron
Status: Fair
Trend: Unchanging
Rationale: Lake Huron's condition can best be described as FAIR as opposed to GOOD, since in addition to meeting
the criterion for GOOD condition, it almost meets the criterion for POOR condition (19.2% of watersheds), and over
50% of its watersheds fall into the FAIR category (Table 2; Figure 11). Six of the nine watershed condition
transitions represented conversion from FAIR to the POOR category, and three were the reverse. The proportion of
watersheds transition classes was minute relative to the total number of watersheds in the Lake Huron basin (1,646);
therefore, the trend is UNCHANGING (Figure 8). A portion of the Lake Huron Basin in Canada did not have 2010
era land use data; therefore, the assumption was made that there was no change in percent agricultural or developed
land. These trends are therefore to be regarded as conservative estimates of change. The Lake Huron Basin has the
highest number of watersheds in the lowest quintile for percent developed land, and the second lowest in terms of
percent agriculture, road density and population density (Table 3; Figures 2-7).
Lake Erie
Status: Poor
Trend: Unchanging
Rationale: Lake Erie's condition is rated as POOR because almost 50% of the watersheds (410 of 854) were
classified as being in POOR condition, 47.5% were in FAIR condition, and only 4.4% of watersheds were in GOOD
condition (Table 2; Figure 12). Although there were 13 condition transitions into the POOR category, this
represents a small proportion (less than 2%) of the watersheds in the Lake Erie Basin (Figure 8). Trends for Lake
Ontario are based on complete data sets for the basin and therefore represent the best available estimates. The Lake
Erie Basin had the highest number of watersheds in the upper quintile of the distribution for all four stressor
components (Table 3; Figure 2-7).
Lake Ontario
Status: Poor
Trend: Unchanging
Rationale: Most (66.2%) of Lake Ontario's watersheds fall into the FAIR category, but approximately 32% fall in
the POOR category. The operational definition based on the 20th percentile criteria puts the Lake Ontario basin into
the POOR category, but like Lake Huron, the lake could also well be described as MIXED (Figure 13). Condition
transitions in Lake Ontario included 13 watersheds moving into the POOR category (1.5% of watersheds) and 15
moving into the FAIR category from POOR (Figure 8). Trends for Lake Ontario are based on complete data sets for
the basin and therefore represent the best available estimates. The latter represents the highest number of positive
transitions in the overall Basin. The Lake Ontario Basin has the second highest number of watersheds in the upper
quintile of the distribution for all four component stressors, behind Lake Erie (Table 3; Figures 2-7).
Other Spatial Scales
The data shown are benchmarked to 2000 era AgDev scores that were derived based on the 20th and 80th percentiles
of the AgDev distribution across the entire Great Lakes Basin (Table 1). The same process was applied individually
to each lake to determine the relative condition of watersheds within each Lake. Table 3 depicts the 2000 and 2010
era distributions and transitions for individual lakes.
Page 438
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STATE OF THE GREAT LAKES 201 7
The components of the WSI (AgDev) are tabulated and scored for the land bordering each Lake rather than for the
Lakes themselves. However, there is strong evidence that the effects of land-based stress are manifested in habitats
most closely associated with each watershed. Niemi et al. (2007), Peterson et al. (2007) and Yurista and Kelly
(2009) found that the correlation between land-based stress and waterborne nutrients was highest for tributary
streams and coastal wetlands. Although the correlation becomes weaker with increasing distance from shore, the
correlation remains statistically significant in water 10 m deep or more. The greater the stress, the greater the risk of
degradation of biological features in the lakes themselves. These relationships have recently been qualitatively
scored and shown in lakewide and basinwide maps as 'threats' (or risk of degradation) by Allan et al. (2012).
Sub-Indicator Purpose
•	Assess the relative degree of stress derived from watersheds on the enviromnental quality of the Great
Lakes;
•	Infer potential risk of harm from impacts of human activities in watersheds on water quality, habitat, biota,
and natural processes.
Ecosystem Objective
The combined effects of watershed stressors should not result in the impairment of the physical, biological, or
chemical integrity of the Great Lakes as reflected in Annexes 2 (Improve quality), 4 (Manage nutrients), and 7
(protect species and their habitats) of the 2012 Water Quality Agreement.
Ecological Condition
The relative amount of stress imposed by four measures of human activity on the land within the 529,679 km2 area
of the Great Lakes Basin was assessed for each of the 5593 watersheds surrounding the Great Lakes (as generated
from an ArcHydro GIS analysis (Forsyth et al. in review). This sub-indicator will use a combined agriculture + de-
velopment stress index (AgDev) to calculate scores for individual Great Lakes watersheds using a consistent scale of
resolution among reporting periods. This stress score is adapted from a peer-reviewed methodology previously ap-
plied to the Great Lakes Basin (Host et al 2011) and revised by Johnson et al. 2015). The index is based on standard-
ized scores of data that represent key manifestations of human activity in the watersheds that are a potential risk to
the Great Lakes ecosystem health. Stressors making up the index include road density, population density, agri-
cultural land cover, and developed land cover (Host et al. 2011). These stressors together represent the majority
of the variation described by five anthropogenic stressors (agricultural/chemical loadings; land use; atmospheric
deposition; human population / development; shoreline modification) quantified by Danz et al. (2005).
This revised index differs from the State of the Great Lakes (previously known as SOLEC) 2011 version by elimi-
nating the point source data (which was found to have numerous quality issues), and revising the metric calculation
(Johnson et al., 2015). In addition, for 2016, anthropogenic stress was summarized for GLAHF (Wang et al. 2015)
watersheds on the U.S. and Canadian sides of the Great Lakes Basin (a binational effort to develop a consistent set
of drainage units for the basin; Forsyth et al. in review). An index of agriculture stress (Ag) was based on the areal
percentage of land in agricultural estimated from a cross-walked version of the 2011 National Land Cover Dataset
(NLCD, and Ontario Land Cover Compilation v.2.0 (see Land Cover sub-indicator). Development was character-
ized based on the areal percentage of urban land use, human population density (U.S. Census Bureau and Statistics
Canada; See Human Population sub-indicator) and road density (TIGER 2000 and 2010, U.S. and NRN 2nd and 7th
edition Canada). Each of these variables was scaled to range between values of 0.0 - 1.0 based on the range of data
across the Great Lakes Basin (not including the St. Lawrence River watersheds). Following the MaxRel approach
used in Host et al. (2005), the maximum of these three normalized (scaled 0-1) values for each watershed was used
as the development index (MaxRel Dev). To combine the agriculture and development values for a watershed, we
calculated a Euclidean distance from the graph origin (0,0) graph to the x, y coordinates of the Ag and MaxRel Dev
Index scores (AgDev; Figure 1). The resulting metric is called AgDev, and supersedes the former Combined Water-
shed Stress Index (State of the Great Lakes 2011 - previously knowns as SOLEC). To ensure consistency of report-
ing, we provide the AgDev index calculation based on circa 2000 data, as well as 2010 (Tables 2, 3).
Page 439
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STATE OF THE GREAT LAKES 201 7
In the absence of biological data against which to calibrate the stressor scores, we have designated the 20th percen-
tile of the distribution of stress scores for each variable and the AgDev index as the criterion for classifying a water-
shed as 'Good' vs. 'Fair'. We have designated the 80th percentile the distribution as the boundary between 'Fair'
and 'Poor'. Watersheds classified as 'Good' pose minimal risk of degradation of the biological community in Great
Lakes aquatic receiving habitats. Watersheds classified as 'Poor' are at greatest risk of having degraded Great Lakes
communities. The cutoff values representing the 20th percentile and 80th percentiles for the Great Lakes compo-
nents and AgDev scores are listed in the legend of Table 1. Status assessments for the 2010 era data are made rela-
tive to these values for 2000 era data.
Linkages
Linkages to other sub-indicators in the indicator suite include:
•	Aquatic Habitat Connectivity - the number of dams and barriers is an important factor in assessing watershed
stress
•	Coastal Wetlands: Extent and Composition
•	Water Quality in Tributaries
•	Human Population
This sub-indicator also links directly to the other indicators in the Watershed Impacts and Climate Trends indicator,
particularly Land Cover.
Comments from the Author(s)
The components and total AgDev score have been determined for every Great Lakes sub-watershed based on data
from 2000-2010. The locations at greatest risk of significant biological loss (those approaching the boundaries) and
those with greatest potential for restoration (sites with stress scores only slightly higher than the boundaries) can be
identified using biologically-based thresholds, as well as the quantile approach presented here. These are the loca-
tions where investment in protection or restoration should most likely to succeed. Johnson et al. (2015) present a
map of risk, based on biologically based thresholds derived from Kovalenko et al. 2014.
This revised version of the Watershed Stressor sub-indicator improves on the 2011 version in two ways: land use
data are derived exclusively from data derived from government sources (e.g., land use from NLCD and SOLRIS),
and the watershed framework (GLAHF) is based on a binational effort. In contrast, the 2011 version used land use
derived from a variety of sources some with known classification flaws. The new Watershed Stressor sub-indicator
(AgDev) should, therefore, be a repeatable metric that can be used for tracking trends in the future. Special note
should be made regarding assumptions of change; many transitions were found to occur in very small coastal
watersheds. These are especially susceptible to changes in area as a result of water level change, and therefore,
interpretation of condition transitions should be made with caution. Changes in road network data (e.g., TIGER in
the U.S.), for example, caused 34 small watersheds on Isle Royale in Lake Superior to appear to have changed from
GOOD to FAIR condition (these were omitted from the calculations of transitions). In addition, land use data
derived from remote sensing (e.g., NLCD, SOLRIS) are not 100% accurate, and classifications of bare ground (i.e.,
exposed bedrock, quarries, sand flats, etc.) are easily confused with spectral signatures of impervious surfaces. Thus,
areas along the coast can be subject to misclassification. The cutoff values between good/fair and fair/poor were
determined based on the count of watersheds across the entire basin, rather than on area. Because of the very large
variation in watershed sizes the cumulative distribution of area precluded the identification of reasonable cutoffs due
to large gaps into which particular targets (i.e., 20th and 80th percentiles) were likely to fall.
Page 440
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STATE OF THE GREAT LAKES 201 7
Assessing Data Quality
Data Characteristics
Strongly
Agree
Neutral or
Disagree
Strongly
Not

Agree

Unknown

Disagree
Applicable
1. Data are documented, validated, or






quality-assured by a recognized
X





agency or organization






2. Data are traceable to original






sources






3. The source of the data is a known.






reliable and respected generator of
X





data






4. Geographic coverage and scale of






data are appropriate to the Great Lakes
X





Basin






5. Data obtained from sources within






the U.S. are comparable to those from



X


Canada






6. Uncertainty and variability in the






data are documented and within






acceptable limits for this sub-indicator

X




report






Clarifying Notes:


Re: geographic coverage - the SOLRIS land cover dataset is not a complete coverage of the Canadian side of the
Great Lakes Basin. It excludes the largely forested northern regions of the Lake Superior and Lake Huron
watersheds, north of N 45.88334 and west of W83.10000.
Acknowledgments
Authors:
Terry N. Brown, Lucinda B. Johnson and George E. Host, Natural Resources Research Institute, University of Min-
nesota Duluth, 5013 Miller Trunk Highway, Duluth, MN, 55811;
Jan J.H. Ciborowski, Department of Biological Sciences, University of Windsor, 401 Sunset Avenue, Windsor, ON,
Canada. N9B 3P4.
Contributors: Members of the Great Lakes Enviromnental Indicators project - Gerald L. Niemi (Senior PI; NRRI,
University of Minnesota Duluth), Nicholas P. Danz (University of Wisconsin - Superior), and Thomas Hollenhorst
(U.S. EPA, Mid-Continent Ecology Division National Health and Enviromnental Effects Research Laboratory, Du-
luth, MN 55804) contributed to formation of the research group that identified the need for this database.
The project on which these data were based was originally funded by the U.S. Enviromnental Protection Agency
Science to Achieve Results (STAR) Estuarine and Great Lakes (EaGLe) program through funding to the Great
Lakes Enviromnental Indicators (GLEI) and Reference Condition projects (U.S. EPA Agreements EPA/R-8286750
and EPA/R-82877701, respectively). More recent work was supported by the second stage GLEI-II Indicator Test-
ing and Refinement project funded by a Great Lakes Restoration Initiative grant from the U.S. Enviromnental Pro-
tection Agency Great Lakes National Program Office to L.B. Johnson et al. (GL-00E00623-0). Although the re-
search described in this work has been partly funded by the U.S. EPA, it lias not been subjected to the agency's re-
quired peer and policy review and therefore does not necessarily reflect the views of the agency and no official en-
dorsement should be inferred. The Great Lakes Aquatic Habitat Framework project was funded by the Great Lakes
Fisheries Trust.
Page 441
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STATE OF THE GREAT LAKES 201 7
Information Sources
Allan, J.D., P.B. Mclntyre, S.D.P. Smith, B.S. Halpern, G.L. Boyerd, A. Buchsbaum, G.A. Burton, Jr. L. M. Camp-
bell, W. L. Chadderton, Jan J. H. Ciborowski, P.J. Doran, T. Eder, D. M. Infante, L. B. Johnson, C.A. Joseph, A.L.
Marino, A. Prusevich, J.G. Read, J.B. Rose, E.S. Rutherford, S.P. Sowa, and A.D. Steimnan 2012. Joint analysis of
stressors and ecosystem services to enhance restoration effectiveness. PNAS 1213841110
Ciborowski, J.J.H., G.E. Host, T.N. Brown, P. Meysembourg and L.B. Johnson. 2011. Linking land to the lakes: the
linkages between land-based stresses and conditions of the Great Lakes. Background Paper Prepared for the State of
the Lakes Enviromnent Conference 2011.
Danz NP, Regal RR, Niemi GJ, Brady VJ, Hollenhorst T, Johnson LB, Host GE, Hanowski JM, Johnston CA,
Brown T, Kingston J, Kelly JR. 2005. Environmentally stratified sampling design for the development of Great
Lakes environmental indicators. Environ. Monit. Assess. 102:41-65.
Forsyth, D K. C.M. Riseng, K.E. Welirly, L.A. Mason, J. Gaiot, T. Hollenhorst, C.M. Johnston, C. Wyrzykowski, G.
Annis, C. Castiglione, K. Todd, M. Robertson, D. M Infante, L. Wang, J.E. McKenna, G. Whelan. 2015. A con-
sistent binational watershed delineation and hydrography dataset of the Great Lakes Basin: the Great Lakes Hydro
graphy Dataset. J. Am. Water Res., In review.
Hollenhorst, T.P., Brown, T.N., Johnson, L.B., Ciborowski, J.J.H., and Host, G. E. 2007. Methods for generating
multi-scale watershed delineations for indicator development in Great Lake Coastal ecosystems. Journal of Great
Lakes Research 33 (Special Issue 3) 13-26.
Host, G.E., T. N Brown, T.P. Hollenhorst, L.B. Johnson, and J.J.H.Ciborowski. 2011. High-resolution assessment
and visualization of environmental stressors in the Lake Superior basin. Aquatic Ecosystem Health and Management
Society 14:376-385.
Host, G.E., J.A. Schuldt, J.J.H.Ciborowski, L.B. Johnson T.P. Hollenhorst, and C. Richards. 2005. Use of GIS and
remotely sensed data for a priori identification of reference areas for Great Lakes coastal ecosystems. International
Journal of Remote Sensing. 26: 5325-5342.
Johnson L.B., K.E. Kovalenko, G.E. Host, V.J. Brady, J.J.H. Ciborowski, N.P. Danz, R.W. Howe, E.D. Reavie, G.J.
Niemi. 2015. Great Lakes Enviromnental Indicators Testing and Refinement: Final Report. U.S. EPA GLNPO Pro-
ject Identifier: EPAGLNPO-2010-NS-5-1071-795. Natural Resources Research Institute Technical Report No.
NRRI/TR-2015/56.
Kovalenko, K.E., V.J. Brady, T.N. Brown, J.J.H. Ciborowski, N.P. Danz, J.P. Gathman G.E. Host, R.W. Howe,
L.B. Johnson, G.J. Niemi, and E.D. Reavie. 2014. Congruence of community thresholds in response to anthropogen-
ic stress in Great Lakes coastal wetlands. Freshwater Science 33:958-971.
Niemi, G. J., J. R. Kelly, and N. P. Danz. 2007. Enviromnental indicators for the coastal region of the North Ameri-
can Great Lakes: introduction and prospectus. Journal of Great Lakes Research 33 (Supplement 3): 1-12.
Peterson G.S., M.E. Sierszen P.M. Yurista, J.R. Kelly. 2007. Stable nitrogen isotopes of plankton and benthos re-
flect a landscape-level influence on Great Lakes coastal ecosystems. J. Great Lakes Res. 33(Spec Issue 3):27-41.
Yurista P.M., J.R. Kelly. 2009. Spatial patterns of water quality and plankton from high-resolution continuous in situ
sensing along a 537-km nearshore transect of western Lake Superior, 2004. In: Munawar M, Munawar IF, editors.
State of Lake Superior, Ecovision World Monograph Series. Burlington (Ontario): Aquatic Ecosystem Health and
Management Society, p. 439-471.
List of Tables
Table la. Summary of the status of each lake for -2010, based on the basin-wide normalized AgDev score, which is
Page 442
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STATE OF THE GREAT LAKES 201 7
applied to each lake. Shown are the number and percent of watersheds in the lowest (Good) and highest (Poor)
quintiles. Data are summarized for 5584 watersheds delineated by the Great Lakes Aquatic Habitat Framework
(GLAHF) project (Wang et al. 2015). Watersheds stress index (AgDev Cutoff values of AgDev score for the
boundary of good/fair = 0.01228; fair/poor = 0.673.
Source: T. Brown, NRRI.
Table lb. Summary of condition based on % area within each condition class for -2000 era AgDev scores and
-2010 era AgDev scores.
Source: T. Brown, NRRI.
Table 2. Number and percent of watersheds within each Great Lake basin, and assigned condition category based on
the criteria set forth in the sub-indicator description. Transitions from condition categories represent loos or gain of
the number of watersheds within a condition category from the period 2000 to 2010. Note that due to lack of SOL-
RIS land use data from a portion of western Ontario, the transition is believed to be conservative.
Source: T. Brown, NRRI. (See accompanying text and Land Cover sub-indicator for more information.)
Table 3. Summary of component stressors for 2000 and 2010 era AgDev scores, including: road density, population
density, percent development, Max-Rel Development (= relative maximum of road density, percent development,
population density), and percent agricultural land. Cutoff values for condition classes are derived based on the dis-
tribution of each stressor across the entire Great Lakes Basin. Shown below are number and percent of watersheds in
Good, Fair and Poor condition during each time period (00 = 2000; 10 = 2010). Boundary cutoffs were derived for
each variable.
Source: T. Brown, NRRI.
List of Figures
Figure 1. Condition rankings for the Great Lakes Basin circa -2010. Classes are based on lower and upper quintiles
of the Ag-Dev distribution for the entire Great Lakes Basin. Constituents of the Ag-Dev score include: percent agri-
cultural land, % developed land, population density, and road density. Data are summarized for 5,593 watersheds
across the Great Lakes Basin draining to the Great Lakes (see Forsyth et al. in review). Watersheds within the St.
Lawrence Seaway were not included in the AgDev calculations as they were extreme outliers. Condition classes,
however, were assigned to those watersheds based on the normalized scale for the rest of the basin. See text for an
explanation of the index calculation.
Source: T. Brown. NRRI.
Figure 2. AgDev combined stress score for the Great Lakes Basin based on circa -2010 era data. Color classes
based on even distribution across 7 bins.
Source: T. Brown, NRRI.
Figure 3. Percent agricultural land for the Great Lakes Basin, 2010. Color classes based on even distribution across
7 bins. Note grey area represents a data gap in the Canadian land use data set for this time period. Source:
T. Brown, NRRI. Unpublished.
Figure 4. MaxRel Development for the Great Lakes Basin, 2010. Color classes based on even distribution across 7
bins. Note grey area represents a data gap in the Canadian land use data set for this time period.
Source: T. Brown, NRRI.
Figure 5. Percent developed land for the Great Lakes Basin, 2010. Note the grey area represents a data gap in the
Canadian land use data set for this time period. Color classes based on even distribution across 7 bins.
Source: T. Brown, NRRI.
Figure 6. Population density across the Great Lakes Basin, 2010. Color classes based on even distribution across 7
bins.
Source: T. Brown, NRRI.
Figure 7. Road density across the Great Lakes Basin, 2010. Color classes based on even distribution across 7 bins.
Source: T. Brown, NRRI.
Figure 8. Change in condition from circa - 2000 to - 2010. Note that there was a data gap in the Ontario land cover
data set for the 2010 time period. Change in condition was based on the assumption of 'no change' in agriculture
Page 443
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STATE OF THE GREAT LAKES 201 7
and developed lands for those watersheds (gray), changes shown are driven by population or road density changes.
In addition, changes to 34 watersheds on Isle Royale are not shown, as they represent non-existent roads present in
the 2010 TIGER dataset, but absent in the 2000 version.
Source: T. Brown, NRRI.
Figure 9. Condition rankings for Lake Superior, circa -2010. Classes are based on lower and upper quintiles of the
Ag-Dev distribution for the entire Great Lakes Basin and then applied to Lake Superior watersheds. Cutoff values
of AgDev score for the boundary of good / fair = 0.01228; fair / poor = 0.673.
Source: T. Brown, NRRI.
Figure 10. Condition rankings for Lake Michigan watersheds, circa ~ 2010. Classes are based on lower and upper
quintiles of the Ag-Dev distribution for the entire Great Lakes Basin and then applied to Lake Superior watersheds.
Cutoff values of AgDev score for the boundary of good / fair = 0.01228; fair / poor = 0.673. Source: T. Brown,
NRRI.
Figure 11. Condition rankings for Lake Huron watersheds, circa ~ 2010. Classes are based on lower and upper quin-
tiles of the Ag-Dev distribution for the entire Great Lakes Basin and then applied to Lake Superior watersheds.
Cutoff values of AgDev score for the boundary of good / fair = 0.01228; fair / poor = 0.673.
Source: T. Brown, NRRI.
Figure 12. Condition rankings for Lake Erie watersheds, circa ~ 2010. Classes are based on lower and upper quin-
tiles of the Ag-Dev distribution for the entire Great Lakes Basin and then applied to Lake Superior watersheds.
Cutoff values of AgDev score for the boundary of good / fair = 0.01228; fair / poor = 0.673.
Source: T. Brown, NRRI.
Figure 13. Condition rankings for Lake Ontario watersheds, circa ~ 2010. Classes are based on lower and upper
quintiles of the Ag-Dev distribution for the entire Great Lakes Basin and then applied to Lake Superior watersheds.
Note that watersheds in the St. Lawrence River system were not included in the calculations of the AgDev score, but
condition classes are shown for those watersheds. Cutoff values of AgDev score for the boundary of good / fair =
0.01228; fair / poor = 0.673.
Source: T. Brown, NRRI.
Last Updated
State of the Great Lakes 2017 Technical Report
Page 444
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STATE OF THE GREAT LAKES 201 7
Lake
Number of
Number
% Watersheds
Number
% Watersheds
Number
% Watersheds
Condition

Watersheds
Watersheds
GOOD'
Watersheds
'FAIR'
Watersheds
'POOR'
Designation


GOOD'

'FAIR'

'POOR'

2010
Superior
1,534
595
38.8
917*
59.8
22
1.4
Good
Michigan
629
3
0.5
525
83.5
101
16.1
Fair
Huron
1,646
431
26.2
899
54.6
316
19.2
Fair**
Erie
854
38
4.4
406
47.5
410
48.0
Poor
Ontario
930
18
1.9
616
66.2
296
31.8
Poor
*34 Isle Royale watersheds not included.
** See Rationale for this designation in Lake-by-Lake Assessment section above.
Table la. Summary of the status of each lake for -2010, based on the basin-wide normalized AgDev score, which is applied to each lake. Shown are the number and percent of
watersheds in the lowest (Good) and highest (Poor) quintiles. Data are summarized for 5584 watersheds delineated by the Great Lakes Aquatic Habitat Framework (GLAHF)
project (Wang et al. 2015). Watersheds stress index (AgDev Cutoff values of AgDev score for the boundary of good / fair = 0.01228; fair / poor = 0.673.
Source: T. Brown, NRRI.
Lake
Number of
Watersheds
Total Area
(km2)
Watershed
area 'GOOD'
% Watershed
area 'GOOD'
Watershed
area 'FAIR'
% Watershed
area 'FAIR'
Watershed
area 'POOR'
% Watershed
area 'POOR'
Condition
Designation
2010
Superior
1,534
141,151
85,745
60.7
55,304
39.2
102
0.1
Good
Michigan
629
116,610
2
0.0
112,065
96.1
4,543
3.9
Fair
Huron
1,646
133,294
17,109
12.8
105,800
79.4
10,384
7.8
Fair
Erie
854
76,607
60
0.1
22,585
29.5
53,962
70.4
Poor
Ontario
930
80,268
2
0.0
75,765
94.4
4,501
5.6
Fair
Table lb. Summary of condition based on % area within each condition class for -2010 era AgDev scores. These data are shown for contrast only, as cutoff values derived from
watershed areas produce spurious results due to large gaps in the cumulative frequency distribution of watershed areas. See author's notes for further information.
Page 445
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STATE OF THE GREAT LAKES 201 7
Lake
Condition
Number
Watersheds
2000
% Watersheds
2000
Number
Watersheds
2010
% Watersheds
2010
Loss
2000-2010
Gain
2000-2010
All Lakes
Good
1092
19.5
1085
19.5
12
5

Fair
3368
60.2
3363
60.1
43
38

Poor
1133
20.3
1145
20.5
26
38
Lake Superior
Good
602
39.2
595
38.8
10
3

Fair
910
59.3
917
59.8
3
10

Poor
22
1.4
22
1.4
0
0
Lake Michigan
Good
3
0.5
3
0.5
0
0

Fair
528
83.9
525
83.5
6
3

Poor
98
15.6
101
16.1
3
6
Lake Huron
Good
431
26.2
431
26.2
0
0

Fair
902
54.8
899
54.6
6
3

Poor
313
19
316
19.2
3
6
Lake Erie
Good
40
4.7
38
4.4
2
0

Fair
412
48.2
406
47.5
13
7

Poor
402
47.1
410
48.0
5
13
Lake Ontario
Good
16
1.7
18
1.9
0
2

Fair
616
66.2
616
66.2
15
15

Poor
298
32
296
31.8
15
13
Table 2. Number and percent of watersheds within each Great Lake basin, and assigned condition category based on the criteria set forth in the sub-indicator description.
Transitions from condition categories represent loss or gain of the number of watersheds within a condition category from the period 2000 to 2010. Note that due to lack of
SOLRIS land use data from a portion of western Ontario, the transition is believed to be conservative.
Source: T. Brown, NRRI. (See accompanying text and Land Cover sub-indicator for more information.)
Page 446
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STATE OF THE GREAT LAKES 201 7
Lake
# Wsheds
# Wsheds
# Wsheds
# Wsheds # Wsheds # Wsheds
% Wsheds
% Wsheds
% Wsheds
% Wsheds
% Wsheds
% Wsheds
Good 00
Fair 00
Poor 00
Good 10
Fair 10
Poor 10
Good 00
Fair 00
Poor 00
Good 10
Fair 10
Poor 10
Percent Agriculture (cutoff values: good to fair =
: 0%, fair to poor
= 53.942%)







All
2054
2414
1125
2067
2422
1104
36.7
43.2
20.1
37
43.3
19.7
Superior
1126
407
1
1135
398
1
73.4
26.5
0.1
74
25.9
0.1
Michigan
137
387
105
140
389
100
21.8
61.5
16.7
22.3
61.8
15.9
Huron
595
682
369
597
682
367
36.1
41.4
22.4
36.3
41.4
22.3
Erie
140
376
338
141
380
333
16.4
44
39.6
16.5
44.5
39
Ontario
56
562
312
54
573
303
6
60.4
33.5
5.8
61.6
32.6
Road Density (cutoff values: good to fair = 0.104 km/km2, fair to poor = 7.778 km/km2)






All
1102
3351
1140
1045
3256
1292
19.7
59.9
20.4
18.7
58.2
23.1
Superior
512
887
135
471
907
156
33.4
57.8
OO
00
30.7
59.1
10.2
Michigan
6
431
192
5
400
224
1
68.5
30.5
0.8
63.6
35.6
Huron
441
984
221
437
962
247
26.8
59.8
13.4
26.5
58.4
15
Erie
84
487
283
74
447
333
9.8
57
33.1
8.7
52.3
39
Ontario
59
562
309
58
540
332
6.3
60.4
33.2
6.2
58.1
35.7
Population Density (cutoff values: good to fair =
1.557 people/km2, fair to poor
= 62.104 people/km2)





All
1083
3399
1111
1252
3235
1106
19.4
60.8
19.9
22.4
57.8
19.8
Superior
747
705
82
810
640
84
48.7
46
5.3
52.8
41.7
5.5
Michigan
13
456
160
23
442
164
2.1
72.5
25.4
3.7
70.3
26.1
Huron
311
1221
114
356
1184
106
18.9
74.2
6.9
21.6
71.9
6.4
Erie
4
460
390
53
411
390
0.5
53.9
45.7
6.2
48.1
45.7
Ontario
8
557
365
10
558
362
0.9
59.9
39.2
1.1
60
38.9
Percent Developed (cutoff values: good to fair = 0%, fair to poor =
17.284%)







All
1395
3065
1133
1391
3045
1157
24.9
54.8
20.3
24.9
54.4
20.7
Superior
564
841
129
564
841
129
36.8
54.8
8.4
36.8
54.8
8.4
Michigan
5
479
145
5
474
150
0.8
76.2
23.1
0.8
75.4
23.8
Huron
698
750
198
697
750
199
42.4
45.6
12
42.3
45.6
12.1
Erie
74
411
369
70
410
374
8.7
48.1
43.2
8.2
48
43.8
Ontario
54
584
292
55
570
305
5.8
62.8
31.4
5.9
61.3
32.8
MaxRel(roads, population, %developed) (cutoff values: good to fair = 0.006, fair to poor = 0.212)
Page 447
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STATE OF THE GREAT LAKES 201 7
Lake
# Wsheds
# Wsheds
# Wsheds
# Wsheds
# Wsheds
# Wsheds
% Wsheds
% Wsheds
% Wsheds
% Wsheds
% Wsheds
% Wsheds
Good 00
Fair 00
Poor 00
Good 10
Fair 10
Poor 10
Good 00
Fair 00
Poor 00
Good 10
Fair 10
Poor 10
All
1095
3359
1139
1046
3337
1210
19.6
60.1
20.4
18.7
59.7
21.6
Superior
555
846
133
511
886
137
36.2
55.1
8.7
33.3
57.8
8.9
Michigan
4
470
155
4
454
171
0.6
74.7
24.6
0.6
72.2
27.2
Huron
447
990
209
448
976
222
27.2
60.1
12.7
27.2
59.3
13.5
Erie
53
467
334
46
461
347
6.2
54.7
39.1
5.4
54
40.6
Ontario
36
586
308
37
560
333
3.9
63
33.1
4
60.2
35.8
AgDev (cutoff values: good to fair =
0.012, fair to poor = 0.673)








All
1091
3367
1135
1050
3398
1145
19.5
60.2
20.3
18.8
60.8
20.5
Superior
601
911
22
560
952
22
39.2
59.4
1.4
36.5
62.1
1.4
Michigan
3
526
100
3
525
101
0.5
83.6
15.9
0.5
83.5
16.1
Huron
431
902
313
431
899
316
26.2
54.8
19
26.2
54.6
19.2
Erie
40
412
402
38
406
410
4.7
48.2
47.1
4.4
47.5
48
Ontario
16
616
298
18
616
296
1.7
66.2
32
1.9
66.2
31.8
Table 3. Summary of component stressors for 2000 and 2010 era AgDev scores, including: road density, population density, percent development, Max-Rel Development
(= relative maximum of road density, percent development, population density), and percent agricultural land. Cutoff values for condition classes are derived based on the
distribution of each stressor across the entire Great Lakes Basin. Shown below are number and percent of watersheds in Good, Fair and Poor condition during each time
period (00 = 2000; 10 = 2010). Boundary cutoffs were derived for each variable. Note: very low or zero 'good to fair' transition values reflect the large percentages (some-
times in excess of 20%) of the watersheds in the basin with very low or zero development and / or agriculture levels.
Page 448
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STATE OF THE GREAT LAKES 201 7
NAluf.il Qftiaure*! A
Research Initiluttt ZA
—^	 " N
v'fJiW By NWi
nccQKgthd: l-*tyrrwn->n
L*De»iI£r f fiSH
varaifl, v>.rrM nilirtrvj
U*K LAk*t Hbiibi l-it.'.i?
F..<.~»-v»yi 4*1*
100 200	300 40D 500km	_ .	. B „
— ¦— —	¦Good Fair ¦Poor
jaifcWN
Figure 1. Condition rankings for the Great Lakes Basin circa -2010. Classes are based on lower and upper quintiles
of the Ag-Dev distribution for the entire Great Lakes Basin. Constituents of the Ag-Dev score include: percent agri-
cultural land. % developed land, population density, and road density. Data are summarized for 5,593 watersheds
across the Great Lakes Basin draining to the Great Lakes (see Forsyth et al. in review). Watersheds w ithin the St.
Lawrence Seaway were not included in the AgDev calculations as they were extreme outliers. Condition classes,
however, were assigned to those watersheds based on the normalized scale for the rest of the basin. See text for an
explanation of the index calculation.
Source: T. Brown. NRRI.
Page 449
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STATE OF THE GREAT LAKES 201 7
-2010 Combined Ag.-Dev. stress
100 200 300 400 500km	.
— — ——	Low B	^^J'High
" . • ~-
Figure 2. AgDev combined stress score for the Great Lakes Basin based on circa ~2010 era data. Color classes
based on even distribution across 7 bins.
Source: T. Brown, NRRI.
Nntar.il ResQurcvft
iOesearc h Ifliiitut*
t*Ckl
V.'IMt	Ilih-'jfl
rwwiMnc d«r.«
Page 450
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STATE OF THE GREAT LAKES 201 7
-2010 Agriculture
100 200 300 400 500km	_ .	l.
— — ——	Low B	^^J'High
Figure 3. Percent agricultural land for the Great Lakes Basin 2010. Color classes based on even distribution across
7 bins. Note grey area represents a data gap in the Canadian land use data set for this time period.
Source: T. Brown, NRRI. Unpublished.
Nntar.il Resource*
iOesearc h Ifliiitut*
t*Ckl
V.'IMt	Ilih-'jfl
rwwiMnc d«r.4
Page 451
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STATE OF THE GREAT LAKES 201 7
-2010 Max. dev. stress
100 200 300 400 500km	_ ¦	l.
— — ——	Low B	^^J'High
Figure 4. MaxRel Development for the Great Lakes Basin, 2010. Color classes based on even distribution across 7
bins. Note grey area represents a data gap in the Canadian land use data set for this time period.
Source: T. Brown, NRRI.
Nntar.il ResQurcvft
iOesearc h Ifliiitut*
t*Ckl
GduI	'lih-'jfl
if in* I***. d«r.4
Page 452
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STATE OF THE GREAT LAKES 201 7
-2010 Developed land
100 200 300 400 500km	_	l.
— — ——	Low H	J^BHigh
Figure 5. Percent developed land for the Great Lakes Basin, 2010. Note the grey area represents a data gap in the
Canadian land use data set for this time period. Color classes based on even distribution across 7 bins.
Source: T. Brown, NRRI.
Nntar.il ResQurcvft
iOesearc h Ifliiitut*
t*Ckl
GduI	'lih-'jfl
rwwiMnc d«r.4
Page 453
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STATE OF THE GREAT LAKES 201 7
-2010 Population
100 200 300 400 500km	_	l.
— — ——	Low B	^^J'High
	ESBBsmsnnr
Figure 6. Population density across the Great Lakes Basin, 2010. Color classes based on even distribution across 7
bins.
Source: T. Brown, NRRI.
Nntar.il ResQurcvft
iOesearc h Ifliiitut*
»WIH. t*Ckl JM
V.'IMt	Ilih-'jfl
rmwirtifc d«r.«
Page 454
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STATE OF THE GREAT LAKES 201 7
-2010 Roads
100 200 300 400 500km	HI I	l.
— — —-	Low^M ^^jHigh
	SnMBSSRfiirn
Figure 7. Road density across the Great Lakes Basin, 2010. Color classes based on even distribution across 7 bins.
Source: T. Brown, NRRI.
Nntar.il ResQurcvft
iOesearc h Ifliiitut*
VjlifrtTrt Lltbuur
wtn-v
G'Ml Imtoi fefortk- 'iJh-'Jfl
dttt
Page 455
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STATE OF THE GREAT LAKES 201 7
Changed watersheds
• Good-* Fair
¦ fair -# Poor
a fair -> Good
p Poor-»Fair
Figure 8. Change in condition from circa ~ 2000 to ~ 2010. Note that there was a data gap in the Ontario land cover
data set for the 2010 time period. Change in condition was based on the assumption of 'no change' in agriculture
and developed lands for those watersheds (gray), changes shown are driven by population or road density changes.
In addition, changes to 34 watersheds on Isle Royale are not shown, as they represent non-existent roads present in
the 2010 TIGER dataset, but absent in the 2000 version.
Source: T. Brown, NRRI.
Page 456
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STATE OF THE GREAT LAKES 201 7
50 lOO^SO Igjgton	_Good Fair _poor
Figure 9. Condition rankings for Lake Superior, circa ~2010. Classes are based on lower and upper quintiles of the
Ag-Dev distribution for the entire Great Lakes Basin and then applied to Lake Superior watersheds. Cutoff values
of AgDev score for the boundary of good / fair =0.01228; fair / poor = 0.673.
Source: T. Brown, NRR1.
N*tur*l ftoMurett
Retearch institute
GwviQfH: _._
Syiibrm Labsi«iirv linn
»««
U*i£ ijtM tusuiZi Ks&iaE
FttfranwrV data
Page 457
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STATE OF THE GREAT LAKES 201 7
JP 100	150 200_250km	- ^ ^ -poor
Figure 10. Condition rankings for Lake Michigan watersheds, circa ~ 2010. Classes are based on lower and upper
quintiles of the Ag-Dev distribution for the entire Great Lakes Basin and then applied to Lake Superior watersheds.
Cutoff values of AgDev score for the boundary of good / fair = 0.01228; fair / poor = 0.673.
Source: T. Brown, NRRI.
N*tur*l ftoMurett
Retearch institute
GwviQfH: _._
!kyilnm Labsi«iirv linn
»««
U*i£ ijtM tusuiZi Ks&iaE
FttfrawwrV data
Page 458
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STATE OF THE GREAT LAKES 201 7
I	50 100	150 20O_2SOkm	g Goo(J ^ -poor
Figure 11. Condition rankings for Lake Huron watersheds, circa ~ 2010. Classes are based on lower and upper quin-
tiles of the Ag-Dev distribution for the entire Great Lakes Basin and then applied to Lake Superior watersheds.
Cutoff values of AgDev score for the boundary of good / fair = 0.01228; fair / poor = 0.673.
Source: T. Brown, NRRI.
N«tur«4 Pwurcfi A
R«i*arch Initiluto iji
" N
Page 459
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STATE OF THE GREAT LAKES 201 7
jp 100_J50 200_230kn.	- ^ ^ - poQr
Figure 12. Condition rankings for Lake Erie watersheds, circa ~ 2010. Classes are based on lower and upper quin-
tiles of the Ag-Dev distribution for the entire Great Lakes Basin and then applied to Lake Superior watersheds.
Cutoff values of AgDev score for the boundary of good / fair = 0.01228; fair / poor = 0.673.
Source: T. Brown, NRRI.
N*tur*l ftoMurett
Retearch institute
_. _
Labsi«iirv linn
»««
U*i£ ijtM tusuiZi Ks&iaE
FttfranwrV data
Page 460
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STATE OF THE GREAT LAKES 201 7
100	150 700^50^	_Good Fair _poor
Figure 13. Condition rankings for Lake Ontario watersheds, circa ~ 2010. Classes are based on lower and upper
quintiles of the Ag-Dev distribution for the entire Great Lakes Basin and then applied to Lake Superior watersheds.
Note that watersheds in the St. Lawrence River system were not included in the calculations of the AgDev score, but
condition classes are shown for those watersheds. Cutoff values of AgDev score for the boundary of good / fair =
0.01228; fair / poor = 0.673.
Source: T. Brown NRRI.
N*tur*l ftoMurett
Retearch institute
_._
!kyilnm Labsi«iirv linn
»««
U*i£ 1-sfcM tusuiZi Ks&iaE
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STATE OF THE GREAT LAKES 201 7
Sub-Indicator: Hardened Shorelines
Overall Assessment
Status: Undetermined
Trend: Undetermined
Rationale: An overall assessment is not possible as information allowing a direct comparison to previous
hardened shoreline indicator status is only available for the Lake Ontario shoreline.
Lake-by-Lake Assessment
Lake Superior
Status: Undetermined
Trend: Undetermined
Rationale: Available information does not allow a direct comparison to previous hardened shoreline indicator
status.
Lake Michigan
Status: Undetermined
Trend: Undetermined
Rationale: Available information does not allow a direct comparison to previous hardened shoreline indicator
status.
Lake Huron
Status: Undetermined
Trend: Undetermined
Rationale: Available information does not allow a direct comparison to previous hardened shoreline indicator
status.
Lake Erie
Status: Undetermined
Trend: Undetermined
Rationale: Available information does not allow a direct comparison to previous hardened shoreline indicator
status.
Lake Ontario
Status: Poor
Trend: Deteriorating
Rationale: Updated (2015) shoreline classification datasets for Lake Ontario, not including connecting channels,
indicate that approximately 68.5% of the shoreline reaches are in the minor protection or no protection category
which is below the poor threshold of 70%. In other words. Lake Ontario has approximately 30% of its shoreline in a
heavily or moderately hardened state/condition. The long term trend of Lake Ontario appears to be deteriorating,
however, while the short term trend also appears to be deteriorating, there is some uncertainty in the data which
could make it more likely that the short term trend is unchanging. While the percent of shoreline in the no protection
category was comparable to the previous State of the Great Lakes report update (2001-2002), reductions in the
unclassified category were offset by increases in the minor protection moderate protection, and heavy protection
categories suggesting a potential trend towards increased overall shoreline hardening in some areas. However, the
redistribution of the proportions of classified shoreline types may be attributed to the increased availability of higher
resolution aerial photographs than what were available during the last review of this indicator. This allows for a
more detailed delineation of the shoreline to be performed. There is uncertainty in the trend analysis due to
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STATE OF THE GREAT LAKES 201 7
variations in input datasets as discussed further below.
Sub-Indicator Purpose
•	To assess the amount of shoreline altered by the construction of shore protections, such as sheet piling,
rip rap, and other erosion control shore protection structures.
•	To infer the potential harm to aquatic-dependent life, water quality, and natural processes from
conditions created by shore protections.
Ecosystem Objective
Shoreline conditions should be healthy to support aquatic and terrestrial plant and animal life, including the rarest
species.
This sub-indicator best supports work towards General Objective #9 of the 2012 Great Lakes Water Quality
Agreement which states that the Waters of the Great Lakes should "be free from other substances, materials, or
conditions that may negatively impact the chemical, physical, or biological integrity of the Waters of the Great
Lakes.
Ecological Condition
Measure
The amount (kilometres) of shoreline that has been hardened (or "protected") through construction of sheet piling,
rip rap and other erosion control shore protection structures. Shoreline reaches are categorized using descriptions
from the 1997 baseline shoreline classification dataset and include highly protected (>70-100% hardened),
moderately protected (>40-<70% hardened), minor protection (>15-<40% hardened), no protection (< 15%
hardened), non-structural protection, and unclassified.
Note: measure does not include artificial coastal structures that extend out into the waters, such as jetties, groins,
breakwaters, piers, etc.
Status Assessment
The reference values for basin wide and lake wide scales are as follows.
Good: >80% of the shoreline reaches have minor to no protection
Fair: >70 - < 80% of the shoreline reaches have minor to no protection
Poor: < 70% of the shoreline reaches have minor to no protection
Trend Assessment
Improving: Net decrease or no net increase in the percentage of hardened shorelines in the highly protected or
moderately protected categories
Unchanging: No change in the amount percentage of hardened shorelines in the highly protected or moderately
protected categories
Deteriorating: Net increase in the percentage of hardened shorelines in the highly protected or moderately protected
categories
Trend determination will be based on no net increase in the percent of shoreline in the highly protected and
moderately protected categories. The defined parameters are intended to support an assessment of relative change
over time and represents an initial suggestion for establishing preferred conditions. However, further discussion and
refinement of the categories is required to reflect improved understanding of shoreline hardening and ecosystem
impacts. The Status Justification section below outlines some of the challenges with attempting to define reference
conditions for hardened shorelines.
Status Justification
There is limited documentation on specific shoreline hardening objectives, particularly at the basin wide and lake
wide scales. The proposed endpoint values for a hardened shoreline status assessment provide a descriptive point of
reference using the baseline Great Lakes (previously known as SOLEC) estimates of the extent and intensity of
shoreline hardening. Various enviromnental services can be impacted by shoreline hardening including changes or
reductions in aquatic habitat, alterations in sediment transport, and changes in nearshore groundwater-lake
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STATE OF THE GREAT LAKES 201 7
interactions (see Province of Ontario, 2001). There are a variety of challenges in defining appropriate target values
regarding shoreline hardening. In particular, a refined assessment should reflect the differing quality and quantity of
environmental services being provided (or not provided) by differing shoreline locations (e.g. pollution filtration,
fish habitat, etc.) and weight the necessity and amount of the shoreline services required to achieve established
ecosystem goals relative to the extent and impact of various shoreline hardening activities. However, the ecological
services provided by natural shorelines and the impacts of hardened shorelines are difficult to measure as they often
relate to many complex, long-term, and interdependent ecological processes (such as pollution filtration and
sediment transport), in addition to more immediate and observable effects such as habitat and habitat loss. There are
also variations in the extent to which certain types of shoreline hardening activities actually impact various
ecological services based on the age, quality, and design characteristics of the shoreline structures. The current
assessment categories only provide a general estimate of the extent and intensity of shoreline hardening and do not
reflect an assessment of the relative sensitivity to shoreline hardening on each lake. The selected category ranges
account for the fact that some shoreline hardening already exists on the Great Lakes and is likely to be maintained
into the future. The trend assessment captures the relative change in the percent of shoreline with >40% hardening.
For the purpose of this report, an overall undetermined reference value has been selected for the basin wide
assessment due to the lack of a standardized dataset on many of the lakes that can be directly compared to the
baseline conditions established for the State of the Great Lakes (previously known as SOLEC) hardened shoreline
sub-indicator. Where updated datasets do exist, they tend to be limited in geographic scope (i.e. they do not cover a
full lake basin) or there are issues in matching the existing hardened shoreline assessment categories. The baseline
conditions, as represented in the 2009 and 2011 Great Lakes/SOLEC hardened shoreline indicator reports, are
provided in Table 1 for reference.
Lake Ontario does have a full dataset that was compared with the baseline conditions identified in previous State of
the Great Lakes reporting based on NOAA 1997 data. This dataset was developed in 2001 and 2002 to support the
International Joint Commission's (IJC's) International Lake Ontario - St. Lawrence River Regulation Study. A
similar methodology was utilized to classify the full U.S. and Canadian Lake Ontario shoreline based on the type
and extent of shoreline hardening (see Stewart, 2002) with the results summarized in the Flood and Erosion
Prediction System (FEPS) database (see Baird, 2005). The dataset was used to model water level impacts on
shoreline structure lifespan and as a result, there are small gaps where direct comparisons to the baseline data set are
difficult. In particular, there were some instances where the percent of very low quality shoreline structures was not
identified as they were not included in the water level impact modeling. In the case of the Great Lakes comparison,
these areas were identified within the unclassified category, even though there was likely some shoreline hardening
occurring. It should also be noted that the updated Lake Ontario classification dataset utilized a higher resolution
shoreline delineation than was used in the baseline conditions identified in previous State of the Great Lakes report-
ing. As a result, the classified shoreline extent is greater for the updated dataset. Finally, the updated dataset
estimates the percent hardened shoreline using standard 1 km reaches along the full shoreline whereas the baseline
dataset categorized reaches of variable (and generally greater) length.
To assess potential changes in the Lake Ontario shoreline since the 2011 State of the Great Lakes report, the U.S.
Army Corps of Engineers (US ACE) reviewed all existing geospatial data for the Lake Ontario shoreline and
determined that while a delineation of the New York State side of Lake Ontario was performed in 2012, there has
been no recent delineation of the Canadian side of Lake Ontario since the 2001 and 2005 analysis. Therefore, the
US ACE developed an updated shoreline dataset of the entire Lake Ontario Shoreline using data from two sources.
The United States shoreline was delineated and classified in August 2012 by AECOM in association with the New
York State Office of General Services (NYSOGS) and the New York State Department of Enviromnental
Conservation (NYSDEC) for the IJC's Lake Ontario - St. Lawrence River water level study. Each feature within the
data layer produced by AECOM represents a reach of shoreline of differing classification. Classification reaches
were not defined according to a set unit of measure. The existing NYSOGS/NYSDEC shoreline polyline was
modified to most accurately represent the actual shoreline boundary based on 2010-2012 Bing Maps aerial imagery.
The shoreline was then split into classifications according to the type of shoreline based on review of 2012 oblique
imagery produced by the US ACE. The Canadian shoreline was digitized and classified by the USACE-Buffalo
District in 2015 using the AECOM classification scheme. Specifically, each feature within the data layer was
created to represent a reach of shoreline based on predefined categories of shoreline types. Similar to the AECOM
data format, the shoreline reaches defined by the US ACE were not initially defined by the 1 km reach standard used
in the 2002/2005 dataset. Following the AECOM methodology, the US ACE delineated the Canadian shoreline of
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STATE OF THE GREAT LAKES 201 7
Lake Ontario to most accurately represent the shoreline based on a review of 2010-2012 imagery depicted on Bing
Maps and ESRI world imagery aerial basemaps. Both the Bing Maps and ESRI imagery use photographs from
various sources including federal, state and local entities using satellites and aerial photography. The classifications
were assigned by shoreline type based on an additional review of imagery that was accessed via Google Earth Pro
and oblique imagery from Bing Maps (Pictometry). The imagery found in Google Earth Pro included high resolution
aerial photos from multiple sources taken between May 2015 and September 2015, and the Bing Maps oblique
imagery was taken from 2007-2015. To create the final contiguous 2015 shoreline dataset, the AECOM delineation
of the New York State shoreline was merged with the dataset created by the US ACE. The merged dataset was then
copied and divided into 1 km reaches. In order to determine the percent of each shoreline type within the 1 km
reaches, a statistical analysis (tabulate intersection) was performed using ArcGIS. The analysis compared the
predefined 1 km reaches with the classifications that were determined from the AECOM/USACE delineation of the
shoreline. The resulting output included shoreline classification, length and percentage of each type in each of the
1,988 reaches that were included in the 1 km shoreline reach dataset.
Table 2 provides the length of shoreline in the baseline, 2001-2002 datasets, and 2015 datasets, along with the
percent of shoreline within the various percent hardening categories for Lake Ontario. The percent of shoreline
within the moderately (40 to 70% hardened) and major (>70% hardened) categories increased by 0.9 and 1.0 %,
respectively while the percent of the shoreline within the minor (15 to 40% hardened) increased 6.8% and no
protection category (<15% hardened) was reduced by 1.3%. The extent of shoreline in the minor and low protection
categories is still below the poor threshold established and resulted in the poor status classification. The results
suggest that there has been a slight increase in the amount of shoreline hardening since the 2001-2002 dataset was
established and a deteriorating trend was identified. However, since the overall length of categorized shoreline
decreased due to the refined shoreline delineation, there is uncertainty as to whether the identified change represents
a true increase or a difference in dataset methodologies. Figure 1 provides maps of the baseline Lake Ontario
shoreline hardening categorization and the 2001-2002 Lake Ontario data, and Figure 2 shows the updated 2015 Lake
Ontario data.
The reason we did not include the data for the connecting channels in this assessment is due to a lack of data to
compare to on the short term. In the baseline data the connecting channels or rivers were included as separate
entities for comparison. In the 2011 classification they were not given a classification nor were they compared.
When we classified Lake Ontario we, simply put, started where the 2012 AECOM dataset left off in New York
State, which did not include the Niagara River or the St. Lawrence.
Linkages
The hardening of shorelines can result in the loss of habitat, further erosion of unprotected properties adjacent to the
structure, water quality degradation and the interruption of natural shoreline processes including reduced sediment
transport. The hardened shoreline can be directly linked to other sub-indicators currently used to assess the Great
Lakes Basin. Those sub-indicators/indicators are:
Coastal Wetlands sub-indicators- Fish spawning and feeding habitat associated with coastal wetlands can both be
accentuated or diminished based on the physical modification to the shoreline and the effects it may have on coastal
and nearshore processes, as well as effects on habitat structure along the Great Lakes shoreline. These data will help
to assess where both beneficial and unfavorable impacts occur.
Watersheds Impacts- this is directly related to changes in land cover climactic dynamism in areas with increased or
increasing amounts of anthropogenic shoreline modification that diminish littoral drift and impact regional sediment
management.
Comments from the Author(s)
There is uncertainty when trying to make a direct comparison between the different datasets for Lake Ontario. The
shoreline reach categorizations are defined differently in all three datasets. However, the closest comparison can be
made between the 2001-2002 data and the 2015 data since these data use the fixed 1 km shoreline reaches. The
large increase in the minor protection and decrease in unclassified categories could be the result of the availability of
higher resolution aerial imagery. In addition, it is possible that the difference in shoreline length could be due to
variation in lake water levels during the period that aerial images are taken and the increased availability of higher
resolution aerial photographs than those used in the 2001-2002 data. The most recent Lake Ontario dataset used the
2001-2002 shoreline delineation as a guide marker, but followed the shoreline present in the aerial imagery in order
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STATE OF THE GREAT LAKES 201 7
to obtain an accurate depiction of shoreline for comparison. Since the sub-indicator is based on a relative difference
in the percent of shoreline within various categories, it is still possible to make some comparisons. However, it
should be recognized that direct comparisons between datasets will be highly uncertain without using a common
baseline shoreline delineation and comparable reach lengths. Finally, as stated in the 2011 State of the Great Lakes
report, the baseline dataset is not clear on the transition between percent protected categories. For example, a
shoreline reach that is 70% hardened could fall within either the 40% to 70% category or the 70% to 100% category.
More explicit transitions were used for the categorization of the updated datasets.
There are opportunities for future updates to the hardened shorelines sub-indicator. Updated high resolution aerial
imagery exists for much of the Great Lakes shoreline and oblique imagery was collected in 2012 for the U.S.
shoreline of the Great Lakes. This information will make it possible to duplicate the Lake Ontario effort across the
other Great Lakes to create new datasets of the shoreline and update any existing reach delineations, shoreline
classifications, and the percent of shoreline hardening. Any efforts to create new or update existing datasets should
ensure that classification methodologies are similar to past efforts (e.g. as used for the updated Lake Ontario
shoreline classification) and standardized reach delineations are utilized. Consideration should be given to including
all anthropogenic features that are not currently included in the dataset in an updated basin wide dataset. If a basin
wide dataset is completed in the future following the basic procedures used for the 2015 Lake Ontario dataset, then
this new dataset should be used as the baseline moving forward. This would allow for the use of a measure that
would compare the ratio of human modified shoreline to the total length of shoreline in the Great Lakes Basin. This
would allow for comparison on a lake-by-lake basis, as well as provide an easy to understand overview for the entire
basin.
Assessing Data Quality
Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not
Applicable
1. Data are documented, validated, or
quality-assured by a recognized agency
or organization

X




2. Data are traceable to original sources

X




3. The source of the data is a known,
reliable and respected generator of data

X




4. Geographic coverage and scale of
data are appropriate to the Great Lakes
Basin


X



5. Data obtained from sources within
the U.S. are comparable to those from
Canada

X




6. Uncertainty and variability in the
data are documented and within
acceptable limits for this sub-
indicator report


X



Clarifying Notes:
1.	There is documentation prepared as part of the IJCs International Lake Ontario - St. Lawrence River
Study (see Stewart, 2002). The classification itself was undertaken by private contractors with considerable
experience in shoreline classification procedures. However, there is no formal validation methodology for
undertaking this type of shoreline classification. The 2015 data documentation was prepared by the
US ACE and includes documentation provided by AECOM
2.	The data can be traced to original sources
3.	The classification itself was undertaken by private contractors and US ACE employees with considerable
experience in shoreline classification and aerial photography interpretation procedures
4.	The geographic scale for the updated information only covers Lake Ontario and cannot be used for Great
Lakes Basin wide assessments
5.	The procedure for identifying hardened shorelines was applied consistently on both the Canadian and
U.S. shorelines of Lake Ontario. However, the identification and interpretation of hardened shorelines was
influenced by the imagery availability and resolution which varied greatly along certain areas of the
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STATE OF THE GREAT LAKES 201 7
Canadian shoreline. The specific age and quality of input imagery used for individual shoreline reaches are
not identified.
6. The variation in reach length and detail of shoreline delineation between the baseline dataset .the 2001 -
2002 Lake Ontario data, and the 2015 Lake Ontario data result in uncertainty in the overall status and
trends analysis regarding hardened shorelines
Acknowledgments
Authors: Anthony Friona, U.S. Army Corps of Engineers, ERDC (2015)
E. Pirschel and T. Crockett, U.S. Army Corps of Engineers, Buffalo District (2015)
Information Sources
AECOM. 2012. Shoreline Structural Classification of the New York State Portion of Lake Ontario. (GIS Dataset)
Prepared in association with the New York State Office of General Services (NYSOGS) and New York State
Department of Enviromnental Conservation (NYSDEC) for the International Joint Commission's (IJC) Lake
Ontario- St. Lawrence River water level study.
Baird. 2005. Final Flood and Erosion Prediction System Database (MS Access Database). Prepared for the
Coastal Zone Technical Working Group of the International Joint Commissions International Lake Ontario
- St. Lawrence River Study.
National Oceanic and Atmospheric Administration (NOAA). 1997. Great Lakes and St. Lawrence River Medium
Resolution Vector Shoreline Data. (GIS dataset)
Province of Ontario. 2001. Understanding Natural Hazards. Ministry of Natural Resources. Queen's Printer for
Ontario.
Shantz, M. 2011. State of the Great Lakes 2011: Hardened Shorelines. Environment Canada, Burlington, ON.
Stewart, C.J. 2002. Task Summary Report: A Revised Geomorphic, Shore Protection, and Nearshore
Classification of the Canadian and United States Shoreline of Lake Ontario and the St. Lawrence River.
Prepared for the Coastal Zone Technical Working Group of the International Joint Commissions
International Lake Ontario - St. Lawrence River Study.
List of Tables
Table 1. Baseline Great Lakes hardened shoreline classification used in the 2009 and 2011 State of the Great Lakes
Hardened Shoreline indicator report assessments. Original data is from NOAA, 1997.
Source: National Oceanic and Atmospheric Administration (1997)
Table 2. Comparison of baseline Great Lakes hardened shoreline classification (using 1997 data), 2011 hardened
shoreline classification (using 2002-2005 data), and updated hardened shoreline classification for Lake Ontario
using 2015 data.
Source: Baseline data from National Oceanic and Atmospheric Administration (1997), 2001-2002 Lake Ontario
data from Stewart (2002) and Baird (2005), and the 2015 updated data from AECOM (2012) and the United States
Army Corps of Engineers - Buffalo District (2015)
List of Figures
Figure 1. Maps of baseline Great Lakes/SOLEC hardened shoreline classification (top figure) and updated (2001-
2002) hardened shoreline classification for Lake Ontario (bottom figure).
Source: Baseline Great Lakes/SOLEC data from National Oceanic and Atmospheric Administration (1997) and
updated Lake Ontario Data from Stewart (2002) and Baird (2005)
Figure 2. Map of the 2015 Lake Ontario Hardened shoreline classification update.
Source: New York shoreline data from AECOM (2012) and Canadian shoreline data from the United States Army
Corps of Engineers - Buffalo District (2015)
Last Updated
State of the Great Lakes 2017 Technical Report
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STATE OF THE GREAT LAKES 201 7
Baseline Great Lakes hardened shoreline classification
Lake/
Connecting
Channel
Heavily
Protected
(%)
(>70%
Moderately
Protected
(%) (40-
70%
Minor
Protection
(%) (15-
40%
No
Protection
(%)
(<15%
Non-
structural
Protection
(%)
Unclassified
(%)
Total
Shoreline
(km)
Lake Superior
3.1
1.1
3
89.4
0.03
3.4
5080
St. Marys River
2.9
1.6
7.5
81.3
1.6
5.1
707
Lake Michigan
8.6
2.9
30.3
57.5
0.1
0.5
2713
Lake Huron
1.5
1.0
4.5
91.6
1.1
0.3
6366
St. Clair River
69.3
24.9
2.1
3.6
0.0
0.0
100
Lake St. Clair
11.3
25.8
11.8
50.7
0.2
0.1
629
Detroit River
47.2
22.6
8.0
22.2
0.0
0.0
244
Lake Erie
20.4
11.3
16.9
49.1
1.9
0.4
1608
Niagara River
44.3
8.8
16.7
29.3
0.0
0.9
184
Lake Ontario
10.2
6.3
18.6
57.2
0.0
6.2
1772
St.
Lawrence
12.6
9.3
17.2
54.7
0.0
6.2
2571
Table 1. Baseline Great Lakes hardened shoreline classification used in the 2009 and 2011 State of the
Great Lakes Hardened Shoreline indicator report assessments. Original data is from NOAA, 1997.
Source: National Oceanic and Atmospheric Administration (1997)
Comparison of baseline Great Lakes hardened shoreline classification and updated classification

Baseline
2011 Lake Ontario
2015 Lake Ontario

Classification
Classification
Classification
Length of Shoreline Categorized (km)
1772.0
2444.3
1988.0
1. Heavily Protected (%)(>70% hardened)
10.2
20.0
21.0
2. Moderately Protected (%)(40-70%
6.3
8.0
8.9
3. Minor Protection (%) (15-40% protected
18.6
5.7
12.5
4. No Protection (%) (<15% protected)
57.2
57.3
56.0
5. Non-structural Protection (%)
0.0
0.1
0.0
6. Unclassified (%)
6.2
8.8
1.6
Table 2. Comparison of baseline Great Lakes hardened shoreline classification (using 1997 data), 2011 hardened
shoreline classification (using 2002-2005 data), and updated hardened shoreline classification for Lake Ontario using
2015 data.
Source: Baseline data fromNational Oceanic and Atmospheric Administration (1997), updated Lake Ontario data from
Stewart (2002) and Baird (2005), and updated Lake Ontario data from U.S. Army Corps of Engineers (2015).
Page 468
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STATE OF THE GREAT LAKES 2017
Lake Ontario Hardened
Shorelines - Baseline Conditions
Legend
Great lakes Shoreline
Hardened Shoreline (by reach)
	 >70% to 100%
	 >40% lo 70%
>15% 10 40%
	 <15%
	 Non-Structural Protection
Unclassified
Source:
Shoreline Classification: National
Oceanic and Atmospheric
Administration (1997)
Base Imagery: Esri. i cubed, US DA
FSA. USGS. AEX. GeoEye.
AeroGRID. Getmapping, IGP
Lake Ontario Hardened
Shorelines - Updated Conditions


Legend
Great Lakes Shoreline
Hardened Shoreline (by reach)
	>»70% to 100%
	 >=40% to <70%
»«15% to <40%
	 <15%
	Non-Structural Protection
Unclassified
Figure 1. Maps of Baseline Great Lakes hardened shoreline classification (top figure) and updated (2001-2002)
hardened shoreline classification for Lake Ontario (bottom figure).
Source: Baseline Great Lakes data from National Oceanic and Atmospheric Administration (1997) and updated Lake
Ontario data from Stewart (2002) and Baird (2005)
Source:
Shoreline Classification: Baird (2005)
and Stewart (2002)
Base Imagery: Esri, i-cubed, USOA
FSA. USGS. AEX. GeoEye,
AeroGRID. Getmapping. IGP
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STATE OF THE GREAT LAKES 2017
Figure 2. Map of the 2015 Lake Ontario Hardened shoreline classification update.
Source: New York Shoreline data from AECOM (2012) and Canadian shoreline data from the United States Army
Corps of Engineers - Buffalo District (2015)
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STATE OF THE GREAT LAKES 201 7
*
Sub-Indicator: Tributary Flashiness
Overall Assessment
Trend: Unchanging
Rationale: The R-B Index (RBI) for the 27 rivers included in this report was either unchanging or decreasing
in most rivers, and had improved or remained unchanged in the short-term. Attention should focus on 3 riv-
ers, in particular, that had deteriorating trends (as defined on page 5) and Lake Ontario which had the high-
est proportion of rivers with increasing RBI.
Lake-by-Lake Assessment
Individual lake basin assessments were not prepared for this report.
River-bv-River Assessment - Lake Superior
Pic River (CAN)
Trend: Unchanging
Rationale: The RBI exhibits no significant trend (p=0.65) and the average RBI increased only slightly from 1995-
2004 (0.099) to 2005-2014 (0.102).
Pigeon River (US)
Trend: Unchanging
Rationale: There is no significant trend in RBI (p=0.35) nor has the average RBI changed in the past two decades
(both are 0.110).
River-bv-River Assessment - Lake Michigan
Fox River (US)
Trend: Unchanging
Rationale: There is no significant long-term trend since 1989 (p=0.22) and the past two decades have an almost
identical RBI (0.143 in 1995-2004 vs 0.145 in 2005-2014).
Muskegon River (US)
Trend: Increasing
Rationale: Although the long-term trend is significantly downward (i=-0.64; p<0.001), there is a significant increase
since 1996 (r=0.76; p<0.001) and the average RBI increased substantially from 1996-2004 (0.061) to 2005-2014
(0.075).
Manistee River (US)
Trend: Increasing
Rationale: The long-term trend is upward (r=0.52; p<0.001), and the largest increases in average RBI have occurred
in the past two decades (0.037 in 1995-2004 and 0.043 in 2005-2014).
Pere Marquette River (US)
Trend: Increasing
Rationale: There is a significant upward long-term trend in RBI (r=0.60; p<0.001), and much of that increase has
occurred in the past decade (0.049 in both 1985-1994 andl995-2004 to 0.057 in 2005-2014).
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STATE OF THE GREAT LAKES 2017
White River (US)
Trend: Increasing
Rationale: The RBI has a significant upward trend (r=0.29; p=0.029), and the average RBI has increased primarily
in the past decade from 0.066 in 1995-2004 to 0.075 in 2005-2014.
Escanaba River (US)
Trend: Decreasing
Rationale: The long-term trend is significantly downward (r=-0.75; p<0.001), though the decreases were largely in
the late 1960s and the average RBI is similar between 1995-2004 (0.100) and 2005-2014 (0.106).
Grand River (US)
Trend: Decreasing
Rationale: There is a significant downward long-term trend in RBI (r=-0.26; p=0.035), and although there was a
slight increase in the mid-1990s, the average RBI has decreased from 1995-2004 (0.077) to 2005-2014 (0.074).
River-bv-River Assessment - Lake Huron
French River (CAN)
Trend: Unchanging
Rationale: Neither the long-term trend (p=0.10) nor the average RBI have change substantially from 1995-2004
(0.025) to 2005-2014 (0.026).
Au Sable River (US)
Trend: Unchanging
Rationale: There is no significant trend in RBI starting in 1997 (p=0.15), and though the average RBI increased
slightly from 0.046 in 1997-2004 to 0.051 in 2005-2014, much of the decade trends downward.
Magnetawan River (CAN)
Trend: Unchanging
Rationale: There is not a significant long-term trend since 1973 (p=0.63) and the average RBI has been similar
across all 4 decades (0.049-0.052).
Maitland River (CAN)
Trend: Unchanging
Rationale: There is no significant long-term trend in RBI (p=0.66), and the average RBI from 1985-1996 was
identical to 2003-2014 (0.291). Data were missing for 1997-2002.
Thunder Bay River (US)
Trend: Unchanging
Rationale: The RBI has no significant downward trend (p=0.10). Although there were substantial decreases in the
earlier record, since measurements restarted in 2002 the average RBI from 2002-2008 (0.081) was very similar to
past 6 years (0.087).
Wanapitei River (CAN)
Trend: Unchanging
Rationale: The long-term data exhibit no significant trend (p=0.59), yet the recent average RBI (2005-2014) was
much lower (0.066) than the past decade (0.081 from 1995-2004).
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STATE OF THE GREAT LAKES 2017
Saginaw River (US)
Trend: Decreasing
Rationale: There is a significant downward long-term trend since 1997 (r=-0.63; p=0.002) and the average RBI has
dropped from 0.222 in 1997-2004 to 0.157 in 2005-2014.
Nottawasaga River (CAN)
Trend: Decreasing
Rationale: Although there is no significant long-term trend since 1993 (p=0.10), and average RBI has decreased
substantially from 0.074 in 1995-2004 to 0.065 in 2005-2014.
River-bv-River Assessment - Lake Erie
River Raisin (US)
Trend: Unchanging
Rationale: There is no significant long-term trend in RBI (p=0.75), and the average RBI was very similar between
1995-2004 (0.162) and 2005-2014 (0.161).
Grand River (OH-US)
Trend: Unchanging
Rationale: There is no significant long-term trend (p=0.10), and the average RBI is similar between 1995-2004
(0.362) and 2005-2014 (0.363).
Maumee River (US)
Trend: Increasing
Rationale: Over the long-term record, there lias been a significant increase in RBI (r=0.52; p=0.007), yet the past ten
years have trended downward with a higher RBI from 1995-2004 (0.294) compared to 2005-2014 (0.280).
Sandusky River (US)
Trend: Increasing
Rationale: Although the long-term trend is significantly upward (r=0.34; p=0.007), the average RBI decreased from
1995-2004 (0.395) to 2005-2014 (0.375).
Thames River (CAN)
Trend: Increasing
Rationale: There is no significant long-term trend in RBI since 1956 (p=0.11), but there is since 1985 (r=0.59;
p<0.001). The average RBI increased over the past two decades from 0.204 in 1994-2004 to 0.221 in 2005-2014.
Cattaraugus River (US)
Trend: Increasing
Rationale: Although the long-term trend is not significant (p=0.16), the RBI lias been increasing since the mid-2000s
and the average RBI increased from 0.369 in 1995-2004 to 0.396 in 2005-2014.
Portage River (US)
Trend: Increasing
Rationale: There is a significant upward long-term trend in RBI (r=0.26; p=0.038), and the average RBI has
increased consistently from 1975-1984 (0.476) to the past decade (0.538).
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STATE OF THE GREAT LAKES 2017
River-bv-River Assessment - Lake Ontario
Humber River (CAN)
Trend: Unchanging
Rationale: The RBI exhibits no significant trend (p=0.57) and though the average RBI decreased from 1995-2003
(0.261) to 2008-2014 (0.241), the recent average is very similar to the long-term average (0.243).
Don River (CAN)
Trend: Increasing
Rationale: The long-term trend is upward (r=0.37; p=0.007), though much of that increase was from 1955 to 1975,
there has also been a slight increase in the average RBI from 0.520 in 1985-1994 to 0.530 in 1995-2004 and 0.534 in
2005-2014.
Seneca River (US)
Trend: Increasing
Rationale: The trend in RBI since 1997 is upward, though not significantly so (r=0.46; p=0.056). The average RBI
has increased from 0.079 in 1997-2004 to 0.090 in 2005-2014.
Sub-Indicator Purpose
•	The purpose of this sub-indicator is to quantify the nebulous concept of flashiness, which is an important
aspect of the hydrologic regime as it reflects the frequency and rapidity of short term changes in river flow
to which aquatic ecosystems are adapted.
•	Increasing or decreasing trends in flashiness may result in increased stress at lake areas that are influenced
by river flows and may influence aquatic organisms that use rivers for all or part of their lives.
•	The Hydrologic Alteration (R-B Flashiness Index - RBI) is used to quantify the hydrologic responsiveness
(i.e. flashiness) of a Great Lakes tributary to temporal changes in precipitation and runoff.
Ecosystem Objective
The ecosystem objective is to avoid hydrologic alteration. Periodic changes in flow rate are characteristic of streams
and rivers, and the organisms that live in them are adapted to those changes. Spring floods may be important in
opening up spawning areas or nurseries. Higher energies associated with storm runoff flush finer sediment from
gravel beds, improving them as habitats for invertebrates and as spawning sites for salmonids. However, changes in
the hydrologic regime, either by reduced flashiness such as occurs when a dam is constructed, or by increased flash-
iness such as occurs with urbanization, require adaptation by the resident organisms; if the changes are great enough,
they can lead to the displacement of the native community and its replacement by another, often less desirable com-
munity.
This sub-indicator best supports work towards General Objective #9 of the 2012 Great Lakes Water Qualify Agree-
ment which states that the Waters of the Great Lakes should "be free from other substances, materials, or conditions
that may negatively impact the chemical, physical, or biological integrity of the Waters of the Great Lakes."
Ecological Condition
Tributary flashiness is a measure that reflects the frequency of short-term changes in streamflow; the flow of a
flashy stream increases and decreases dramatically in hours or a few days in response to rainfall.
Measure
This sub-indicator measures the flashiness of hydrological response of a stream or river to precipitation (rainfall)
and runoff (snowmelt) events. The Richards-Baker Flashiness Index (RBI for short) is calculated from mean daily
flows from the U.S. Geological Survey or Enviromnent and Climate Change Canada, usually on an annual basis, by
dividing the sum of the absolute values of day-to-day changes in mean daily flow by the total discharge over that
time interval (Baker et al. 2004).
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STATE OF THE GREAT LAKES 2017
365
Zk
n=1
365
W = 1
Streams assessed for this sub-indicator are listed in Table 1. Most of these streams cover a range of flashiness and
land use, have long flow records, have a large watershed area, and stations are of a great distance (multiple
miles/kilometres) from a dam and sufficiently upstream of the Great Lake it feeds.
Endpoints
Desirable outcomes are lack of trend in flashiness, or in most cases of altered ecosystems, reductions in flashiness.
To assess endpoints, we used two approaches. For long-term trends, we used a Spearman's rank correlation with
statistical significance determined at the ~=0.05 level. This non-parametric test does not assume normality or equal
variance in the data, nor does it assume a linear trend. It does however, test for monotonic trends. For short-term
trends, we examined the 10 year running average in the data over the past two decades (see Figures 1-5) and com-
pared the average RBI from 1995-2004 with 2005-2014, where possible.
Status
Overall, the long-term and short-term trends in RBI varied by river. Over the short-term (past two decades), 9 of the
27 rivers had increasing RBI, 6 had decreasing trends, and 12 had unchanging trends (Table 1, Figures 1-5). To-
gether, this suggests that 18 of the rivers, or 67%, were at or approaching desirable outcomes for flashiness. Fur-
thermore, of the 7 rivers in the previous Great Lakes Indicator (previously known as SOLEC) report that are also
included in this update, 5 have the exact same or similar status and 2 of the rivers had trends that have improved
(changed from increasing to unchanging or decreasing). This data implies that flashiness has been improving over
time as well.
However, it is also important to compare the short-term and long-term trends to see if there have been declines in the
RBIs (Table 1). Most of the rivers exhibited similar trends in the long- (since 1950) and short- (since 1995) term-
18 of the 24 rivers with long-term data either didn't change or went from decreasing to unchanging. Three rivers
(Maumee, Sandusky, Wanapitei) improved in the recent decade over the long-term trends (i.e., changed from un-
changing to decreasing, or increasing to decreasing). Yet, 9 rivers had increasing trends with 3 of the 9 in particular,
showing a deteriorating trend, i.e. RBI changed from decreasing or unchanging to increasing. These included the
Muskegon (Michigan), Thames (Erie), and Cattaraugus (Erie) Rivers. In summary, most rivers are showing either
stable or improving long-term trends, however; three rivers should be closely monitored for continued deteriorating
trends.
Collated by lake. Lake Superior and Lake Huron were the only lakes with no rivers with increasing flashiness. Lake
Erie and Ontario, in contrast, has the most number of rivers with increasing flashiness. However, in Lake Erie the
two largest rivers that together made up 25% of the watershed area were exhibiting decreasing trends. Lake Michi-
gan had 4 rivers with increasing flashiness, but the rivers with decreasing or unchanging flashiness made up a great-
er proportion of the watershed (27% vs 9%). Yet, any of the lakes with a substantial number of smaller rivers exhib-
iting increasing trends (i.e., Michigan and Erie) should be closely monitored in case these smaller rivers are showing
changes that would take longer to detect in larger watersheds. In Lake Ontario two of the three rivers assessed ex-
hibited increasing flashiness, which may suggest this lake should be monitored more closely for affects such as in-
creased erosion and fine sediment export, decreased habitat, and displacement of native biological communities.
Some of the rivers assessed (i.e., the Maumee and Sandusky Rivers) are known to have increasing discharge. It is
important to note that the RBI may not reflect the influence of increasing discharge if storm events have changed in
character to become more dispersed and cover more days. The influence of higher discharges may be quite similar
to higher flashiness, hence some rivers may have negative impacts, such as high fine sediment export and erosion,
even with declining flashiness trends.
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STATE OF THE GREAT LAKES 2017
Linkages
Linkages to other sub- indicators in the indicator suite include:
•	Precipitation Amounts in the Great Lakes Basin
•	Water Quality in Tributaries
•	Baseflow due to Groundwater
This sub- indicator also links directly to the other sub-indicators in the Watershed Impacts category, particularly
Land Cover.
Comments from the Author(s)
This index offers an integrated perspective on changing hydrology in selected, and hopefully representative, major
Great Lakes tributaries. It can be used to track the effects of, and guide decisions about, land use changes as they
affect hydrology and its impact on riverine ecosystems. It utilizes basic flow data from the U.S. Geological Survey
and Enviromnent and Climate Change Canada, however, a number of rivers have patchy or incomplete data sets.
The R-B Index is easy to calculate from widely available data, and has come into widespread use. Possible range of
values is from 0 to 2. Typical values are from 0.05 (very stable) to about 1.2 (very flashy). The RBI integrates all
flow data, rather than picking a given percentile. It is believed to be the only flashiness index or index of hydrologic
alteration which incorporates the temporal sequence of flows, a very important part of the concept of flashiness. The
RBI is relatively stable from year-to-year (i.e. insensitive to weather effects), consequently it is relatively sensitive
to longer-term trends.
For small streams, the hydrologic response is too rapid to be adequately resolved by daily flow data. For such sys-
tems, a version of the R-B Index based on hourly flow data can be used. However, index values derived from hour-
ly data cannot be directly compared with those derived from daily data. Since the best use of the RBI is to track the
hydrologic response of a stream through time, the index based on daily data is still useful for small streams, even if
it under-represents the true flashiness. Most of the watersheds selected for this sub-indicator are large, and flows
change relatively slowly, so daily data are adequate for calculating the RBI. Most of these streams cover a range of
flashiness and land use, have long flow records, have a large watershed area, and stations are of a great distance
(over 3 miles) from a dam and sufficiently upstream of the Great Lake it feeds. More information about the RBI, and
some applications in the Midwestern United States, can be found in the paper cited below.
Given the observed increases in discharge in some of the rivers, this metric may be improved by further examining
potential increases in long-term and short-term trends in discharge along with RBI across all rivers. This would
further flush out potential reasons for trends in RBI as well as serve as a linkage to the effects of other sub-indicators
such as precipitation and the watershed stressor index.
Assessing Data Quality
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not
Applicable
1. Data are documented, validated, or
quality-assured by a recognized agency or
organization
X





2. Data are traceable to original sources
X





3. The source of the data is a known,
reliable and respected generator of data
X





4. Geographic coverage and scale of data
are appropriate to the Great Lakes basin
X





5. Data obtained from sources within the
U.S. are comparable to those from
Canada
X





Page 476
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STATE OF THE GREAT LAKES 2017
6. Uncertainty and variability in the data
are documented and within acceptable
limits for this sub-indicator report
X





Acknowledgments
Authors: Laura Johnson, National Center for Water Quality Research, Heidelberg University, Tiffin, OH, USA.
Information Sources
Literature Citation:
Baker, D.B., R.P. Richards, T.T. Loftus, and J.K. Kramer. 2004. A New Flashiness Index: Characteristics and Ap-
plications to Midwestern Rivers and Streams. Journal of the American Water Resources Association 40(2): 503-522.
Data sources: http://waterdata.usgs.gov/nwis/rt or http://wateroffice.ec.gc.ca/ See Table 1 for station identification
codes.
List of Tables
Table 1. Rivers used for the Tributary Flashiness sub-indicator. When a stream includes several HUC8s but does
not comprise a complete HUC6, the HUC8 is listed that includes the gaging station from which the flow data are
derived.
List of Figures
Figure 1. The R-B flashiness index for tributaries to Lake Superior. Note differences in y-axis scales. Solid lines
indicate the 10 year running average.
Data sources: http://waterdata.usgs.gov/nwis/rt or http://wateroffice.ec.gc.ca/
Figure 2. The R-B flashiness index for tributaries to Lake Michigan. Note differences in y-axis scales. Solid lines
indicate the 10 year running average.
Data sources: http://waterdata.usgs.gov/nwis/rt or http://wateroffice.ec.gc.ca/
Figure 3. The R-B flashiness index for tributaries to Lake Huron. Note differences in y-axis scales. Solid lines indi-
cate the 10 year running average.
Data sources: http://waterdata.usgs.gov/nwis/rt or http://wateroffice.ec.gc.ca/
Figure 4. The R-B flashiness index for tributaries to Lake Erie. Note differences in y-axis scales. Solid lines indi-
cate the 10 year running average.
Data sources: http://waterdata.usgs.gov/nwis/rt or http://wateroffice.ec.gc.ca/
Figure 5. The R-B flashiness index for tributaries to Lake Ontario. Note differences in y-axis scales. Solid lines
indicate the 10 year running average. Data sources: http://waterdata.usgs.gov/nwis/rt or http://wateroffice.ec.gc.ca/
Last Updated
State of the Great Lakes 2017 Technical Report
Page 477
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STATE OF THE GREAT LAKES 2017
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indicate the 10 year running average.
Data sources: http://waterdata.usgs.gov/nwis/rt or ttp://wateroffice.ec.gc.ca/
Page 479
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STATE OF THE GREAT LAKES 2017
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indicate the 10 year running average.
Data sources: http://waterdata.usgs.gov/nwis/rt or ttp://wateroffice.ec.gc.ca/
Page 480
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STATE OF THE GREAT LAKES 2017
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Data sources: http://waterdata.usgs.gov/nwis/rt or ttp://wateroffice.ec.gc.ca/
Page 481
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STATE OF THE GREAT LAKES 2017
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cate the 10 year running average.
Data sources: http://waterdata.usgs.gov/nwis/rt or ittp://wateroffice.ec.gc.ca/
Page 482
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STATE OF THE GREAT LAKES 2017
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Data sources: http://waterdata.usgs.gov/nwis/rt or ..ftp://wateroffice.ec.gc.ca/
Page 483
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STATE OF THE GREAT LAKES 201 7
Sub-Indicator: Human Population
Overall Assessment
Trend: Increasing
Rationale: The long-term trend of the total population in the Great Lakes is increasing. The region has expe-
rienced 19.3% growth in population from 1971 to 2011. In the short term, there has been a 1.5% increase in
total population from 2006 to 2011.
Lake-by-Lake Assessment
Lake Superior
Trend: Decreasing
Rationale: Over the long term, the Lake Superior basin has experienced a 3.7% decrease in population. The short-
term trend indicates a 0.1% decrease in population from 2006 to 2011.
Lake Michigan
Trend: Increasing
Rationale: Population in the Lake Michigan basin has increased by 15.9% over the long term. In the short term,
there has been a 0.9% increase in population.
Lake Huron
Trend: Increasing
Rationale: From 1971 to 2011, the population has increased by 34.1% in the Lake Huron basin with the Canadian
side of the basin having over a 60% increase in population over the same time period. Short term growth has been
significantly lower with the population increasing by 0.2% from 2006 to 2011.
Lake Erie
Trend: Increasing
Rationale: The long-term trend indicates a 2.7% increase in population from 1971 to 2011. Over the short term,
there has been a 0.7% decrease in population.
Lake Ontario
Trend: Increasing
Rationale: From 1971 to 2011, the population has increased by 51.7% in the Lake Ontario basin. Over the short
term, the population has increased by 5.7%. The population of the province of Ontario has also grown the most over
the long term; the population increased 70% over the same 40-year period.
Sub-Indicator Purpose
• To assess the current human population trends in the Great Lakes region.
Ecosystem Objective
Humans are a key driving force in the overall impact on the enviromnent in the Great Lakes Basin, and emphasis
should be placed on ensuring humans are working, playing and living sustainably.
This sub-indicator best supports work towards General Objective #9 of the 2012 Great Lakes Water Quality Agree-
ment which states that the Waters of the Great Lakes should "be from other substances, materials, or conditions that
may negatively impact the chemical physical or biological integrity of the Waters of the Great Lakes." However, the
human population has an impact on achieving all General Objectives of the Great Lakes Water Quality Agreement.
Page 484
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STATE OF THE GREAT LAKES 201 7
Ecological Condition
In this report, the Great Lakes Basin is defined as the watershed of the Great Lakes.
Measures
There are numerous approaches to determining population distribution in the Great Lakes Basin (Table 1). In the
United States, population figures are not available by watershed as they are in Canada. The U.S. County Adjusted
Ratio Approach was developed, which uses two levels of adjustment to U.S. county census data to determine the
population in the Great Lakes Basin. A geographic information system was used to calculate the proportion of a
county within the Great Lakes Basin. The first level of adjustment multiplies the county population by the propor-
tion of the county within the Great Lakes Basin. This step assumes that population distribution across counties is
uniform, which is not necessarily the case. To ensure a higher level of accuracy, a second level of adjustment is
completed for counties with populations close to 40 000 people in the Lake Superior basin, and counties of 100 000
people in the four other lake basins. This second level adjustment involves examining the county to ensure that ma-
jor population centres have been accurately represented in the population calculations. Adjustments were also neces-
sary in the Chicago Metropolitan Area (Cook, DuPage, Lake and Will counties) to reflect the 6.4 million that draw
drinking water from Lake Michigan. Only 4.2% of Cook County is located within the Lake Michigan basin, which
would result in a first level adjusted population of approximately 220 000 people. However, the second level adjust-
ed population of 4.9 million people in Cook County more accurately reflects the county's impact on the basin. In
total, 21 counties of 653 had ratios adjusted to more accurately reflect their population calculations (Table 2).
Adjustments since 2011 Reporting Cycle
A few adjustments have been made to the methodology since the 2011 reporting cycle. In previous reports, U.S.
intercensal data was used as it would align with Canadian census years. In this report, U.S. census data lias been
used to improve accuracy of population figures. However, this results in an imperfect aligmnent of census data since
U.S. census data is collected a year before Canadian census data. The lengths of the census cycles are also different;
U.S. census data is collected every 10 years, while Canadian census data is collected every 5 years. This report fol-
lows the shorter Canadian census cycle; data from 1971 represents 1970 U.S. census data and 1971 Canadian census
data (Table 3). Also, a larger number of counties were reviewed in preparation for this report to determine if a sec-
ond level of adjustment was warranted to ensure greater accuracy of population values. Previously, only counties
that reached a population threshold of 100 000 people or greater in the Lake Erie, Huron, Michigan and Ontario ba-
sins, or 40 000 or greater in the Lake Superior basin in 1971 would be reviewed for a second level of adjustment. In
this report, any county that reached a population of 100 000 people or greater from 1971 to 2011, or 40 000 or great-
er in the Lake Superior basin was reviewed for a second level of adjustment. As a result, 34 more counties were
reviewed in this report than in the previous indicator report. Of these 34, nine counties had ratios adjusted to better
reflect the amount of their population residing in the Great Lakes Basin.
Total Populations in the Great Lakes Region (Ontario and Eight Great Lakes States)
In 2011, the Great Lakes region was home to about 39 385 438 people (Table 4); 25.8% reside in Canada while
74.2% reside in the United States. The Lake Ontario basin continued to experience the largest growth. Population in
the Lake Michigan and Lake Huron basins stayed relatively static in the short term (2006-2011) while population in
Lake Superior and Lake Erie experienced a slight decline. Of the Great Lakes region, the Province of Ontario con-
tinued to exhibit the largest amounts of growth with a 6.0% increase in population from 2006 to 2011. From 1971 to
2011, the population has increased by 51.7% in the Lake Ontario basin. The population of the Province of Ontario
has also grown the most over the long term; the population increased 70.1% from 1971 to 2011. Furthermore, 87%
of Ontario's population resides in the Great Lakes Basin (Figure 1).
Lake Basins
The total population in the Great Lakes Basin has increased since 1971 (Figure 2), though population growth rates
have varied across the years (Figure 3).
The population in the Lake Superior basin is significantly smaller than in the other basins; 1.5% of the total popula-
tion in the Great Lakes Region live in the Lake Superior basin. Furthermore, it is the only basin in the Great Lakes
where both the short and long term trends indicate a decline in population (Figure 3).
The Lake Michigan basin is home to 33.8% of the total Great Lakes population, the most populated basin of all the
Great Lakes. The population in this basin reflects population adjustments as well as the 6.4 million people in Cook,
DuPage, Lake and Will counties that draw drinking water from Lake Michigan. This basin is completely contained
within the United States (Figure 4); more specifically, the states of Illinois, Indiana, Michigan and Wisconsin are
located in the Lake Michigan basin.
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STATE OF THE GREAT LAKES 201 7
Lake Huron's population has remained relatively static as population growth from 2006 was 0.2%. On average, the
population in the Lake Huron basin has made up roughly 7.7% of the total population in the Great Lakes Basin (Fig-
ure 4). Over time, the proportion of the population living on the Canadian side of the basin has increased. A 40-year
span yielded an 8.1% increase in the proportion of Canadians living in the Lake Huron basin.
In the Lake Erie basin, population has remained fairly stable and is the second most populated of the Great Lakes
basins. The greatest population change occurred from 1991 to 1996 with a population increase of 3.0%. Nearly
30.9% of the Great Lakes population resides in the basin (Figure 4) - 25.3% of Lake Erie's population lived in the
U.S. basin and 5.6% lived in the Canadian basin (Figure 5).
The Lake Ontario basin has consistently experienced the greatest amount of growth in the Great Lakes region. The
population change in the Lake Ontario basin since 1971 has exhibited an average growth of 5.3% (per 5-year period)
This lake basin has been home to the greatest proportion of Canadians living in the Great Lakes region from 1971-
2011; 18.8% of the total population in the Great Lakes region are Canadians that live in the Lake Ontario basin
(Figure 4).
Linkages
Humans are a key driving force in the overall impact on the enviromnent. Emphasis should be placed on ensuring
humans are working, playing and living sustainably. Further analysis in population trends, urban sprawl and con-
sumption rates can help understand and calculate the different impacts humans can have on the enviromnent. As
human population continues to grow, cities will expand outwards, resulting in the loss of agricultural and natural
lands. Associated impacts of urban growth include an increase in air pollution through gridlock, fragmentation of
habitats and greater strains on water systems for drinking water. This sub-indicator is essential in identifying areas
within the Great Lakes Basin that may be facing increased enviromnental pressures as a result of large amounts of
population growth. In the Greater Toronto Area, located on the northwestern shores of Lake Ontario, urban areas
have grown roughly 20% from 1985 to 1995 and roughly 15% from 1995 to 2005 (Furberg & Ban, 2012). Areas of
high population can be identified on the northwestern shores of Lake Ontario, the southwestern tip of Lake Michi-
gan and along the northern and western shores of Lake Erie/St. Clair-Detroit River Ecosystem (Figure 6). This is
contrasted by the Lake Huron and Lake Superior basins that have very few large population centres. Rapid popula-
tion growth can result in a great amount of stress exerted on the enviromnent due to inadequate transportation sys-
tems and demands for infrastructure (Mikovits, Rauch & Kleidorfer, 2014; Addie, 2013).
Assessing Data Quality
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not
Applicable
1. Data are documented, validated, or
quality-assured by a recognized agency or
organization
X





2. Data are traceable to original sources
X





3. The source of the data is a known,
reliable and respected generator of data
X





4. Geographic coverage and scale of data
are appropriate to the Great Lakes Basin
X





5. Data obtained from sources within the
U.S. are comparable to those from
Canada

X




6. Uncertainty and variability in the data
are documented and within acceptable
limits for this sub-indicator report
X





Acknowledgments
Authors: Lindsay Wong, Enviromnent and Climate Change Canada
Contributors: Stacey Cherwaty-Pergentile, Enviromnent and Climate Change Canada
Page 486
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STATE OF THE GREAT LAKES 201 7
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Population Division, U.S. Census Bureau (2002). CO-EST2001-12-39 - Time Series
Estimates by County: April 1, 1990 to April 1, 2000. Retrieved June 15, 2015 from
https://www.census.gov/popest/data/intercensal/st-co/files/CO-EST2001-12-39.pdf
of Michigan Intercensal Popu-
from
of Minnesota Intercensal Popu-
from
of New York Intercensal Popu-
from
of Ohio Intercensal Population
Page 487
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STATE OF THE GREAT LAKES 201 7
Population Division, U.S. Census Bureau (2002). CO-EST2001-12-42 - Time Series of Pennsylvania Intercensal
Population Estimates by County: April 1, 1990 to April 1, 2000. Retrieved June 15, 2015 from
https://www.census.gov/popest/data/intercensal/st-co/files/CO-EST2001-12-42.pdf
Population Division, U.S. Census Bureau (2002). CO-EST2001-12-55 - Time Series of Wisconsin Intercensal Popu-
lation Estimates by County: April 1, 1990 to April 1, 2000. Retrieved June 15, 2015 from
https://www.census.gov/popest/data/intercensal/st-co/files/CO-EST2001-12-55.pdf
Ren, L., E. Cui, and H. Sun, 2014. Temporal and spatial variations in the relationship between urbanization and wa-
ter quality. Enviromnental Science and Pollution Research 21(23), 13646-13655.
Statistics Canada. (2009). Table 153-0036 - Selected population characteristics, Canada, major drainage areas and
sub-drainage areas, every 5 years, CANSIM (database). Retrieved June 15. 2015 from
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U.S. Bureau of the Census, Population Estimates and Population Distribution Branches. (1992). Intercensal Esti-
mates of the Resident Population of States and Counties 1970-1979. Retrieved June 15, 2015 from
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U.S. Bureau of the Census, Population Estimates and Population Distribution Branches. (1992). Intercensal Esti-
mates of the Resident Population of States and Counties 1980-1989. Retrieved June 15, 2015 from
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U.S. Census Bureau, Population Division. (2015). Annua! Estimates of the Resident Population in Illinois: April 1,
2010 to July 1, 2014. Retrieved June 15, 2015 from http://quickfacts.census.gov/qfd/states/170001k.html
U.S. Census Bureau, Population Division. (2015). Annual Estimates of the Resident Population in Indiana: April 1,
2010 to July 1, 2014. Retrieved June 15, 2015 from http://quickfacts.census.gov/qfd/states/180001k.html
U.S. Census Bureau, Population Division. (2015). Annual Estimates of the Resident Population in Michigan: April
1, 2010 to July 1, 2014. Retrieved June 15, 2015 from http://quickfacts.census.gov/qfd/states/260001k.html
U.S. Census Bureau, Population Division. (2015). Annual Estimates of the Resident Population in Minnesota: April
1, 2010 to July 1, 2014. Retrieved June 15, 2015 from http://quickfacts.census.gov/qfd/states/270001k.html
U.S. Census Bureau, Population Division. (2015). Annual Estimates of the Resident Population in New York: April
1, 2010 to July 1, 2014. Retrieved June 15, 2015 from http://quickfacts.census.gov/qfd/states/360001k.html
U.S. Census Bureau, Population Division. (2015). Annual Estimates of the Resident Population in Ohio: April 1,
2010 to July 1, 2014. Retrieved June 15, 2015 from http://quickfacts.census.gov/qfd/states/390001k.html
U.S. Census Bureau, Population Division. (2015). Annual Estimates of the Resident Population in Pennsylvania:
April 1, 2010 to July 1, 2014. Retrieved June 15, 2015 from http://quickfacts.census.gov/qfd/states/420001k.html
U.S. Census Bureau, Population Division. (2015). Annual Estimates of the Resident Population in Wisconsin: April
1, 2010 to July 1, 2014. Retrieved June 15, 2015 from http://quickfacts.census.gov/qfd/states/550001k.html
U.S. Census Bureau, Population Division. (2011). Intercensal Estimates of the Resident Population for Counties of
Illinois: April 1, 2000 to July 1, 2010. Retrieved June 15, 2015 from
https://www.census.gOv/popest/data/intercensal/county/CO-EST00INT-01/CO-EST00INT-01-17.xls
Page 488
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STATE OF THE GREAT LAKES 201 7
U.S. Census Bureau, Population Division. (2011). Intercensal Estimates of the Resident Population for Counties
of Indiana: April 1, 2000 to July 1, 2010. Retrieved June 15, 2015 from
https://www.census.gOv/popest/data/intercensal/county/CO-EST00INT-01/CO-EST00INT-01-18.xls
U.S. Census Bureau, Population Division. (2011). Intercensal Estimates of the Resident Population for Counties of
Michigan: April 1, 2000 to July 1, 2010. Retrieved June 15, 2015 from
https://www.census.gOv/popest/data/intercensal/county/CO-EST00INT-01/CO-EST00INT-01-26.xls
U.S. Census Bureau, Population Division. (2011). Intercensal Estimates of the Resident Population for Counties of
Minnesota: April 1, 2000 to July 1, 2010. Retrieved June 15, 2015 from
https://www.census.gOv/popest/data/intercensal/county/CO-EST00INT-01/CO-EST00INT-01-27.xls
U.S. Census Bureau, Population Division. (2011). Intercensal Estimates of the Resident Population for Counties of
New York: April 1, 2000 to July 1, 2010. Retrieved June 15, 2015 from
https://www.census.gOv/popest/data/intercensal/county/CO-EST00INT-01/CO-EST00INT-01-36.xls
U.S. Census Bureau, Population Division. (2011). Intercensal Estimates of the Resident Population for Counties of
Ohio: April 1, 2000 to July 1, 2010. Retrieved June 15, 2015 from
https://www.census.gOv/popest/data/intercensal/county/CO-EST00INT-01/CO-EST00INT-01-39.xls
U.S. Census Bureau, Population Division. (2011). Intercensal Estimates of the Resident Population for Counties of
Pennsylvania: April 1, 2000 to July 1, 2010. Retrieved June 15, 2015 from
https://www.census.gOv/popest/data/intercensal/county/CO-EST00INT-01/CO-EST00INT-01-42.xls
U.S. Census Bureau, Population Division. (2011). Intercensal Estimates of the Resident Population for Counties of
Wisconsin: April 1, 2000 to July 1, 2010. Retrieved June 15, 2015 from
https://www.census.gOv/popest/data/intercensal/county/CO-EST00INT-01/CO-EST00INT-01-55.xls
List of Tables
Table 1. Population Estimate Approaches (2011)
Source: Statistics Canada, U.S. Census Bureau
Table 2. Counties requiring second level ratio adjustment to better reflect population numbers in basin
Source: Government of Ontario, U.S. Census Bureau
Table 3. Years of Intercensal and Census Data
Source: Statistics Canada, U.S. Census Bureau
Table 4. Total Population in each Great Lakes Basin
Source: Statistics Canada, U.S. Census Bureau
List of Figures
Figure 1. Population in Ontario by Census Subdivision (2011)
Source: Government of Ontario, Statistics Canada
Figure 2. Total population in the Great Lakes Region from 1971-2011
Source: Statistics Canada, U.S. Census Bureau
Figure 3. Population change (%) in each Great Lakes Basin from 1971-2011
Source: Statistics Canada, U.S. Census Bureau
Figure 4. Proportion of Total Great Lakes Population by Canadian and U.S. Lake Basin
Source: Statistics Canada, U.S. Census Bureau
Figure 5. Proportion of Canadian and U.S. Population located in each Great Lakes Basin
Source: Statistics Canada, U.S. Census Bureau
Figure 6. Population by County or Census Subdivision in the Great Lakes Basin (2010/2011)
Page 489
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STATE OF THE GREAT LAKES 201 7
Source: Government of Ontario, Statistics Canada, U.S. Census Bureau
Last Updated
State of the Great Lakes 2017 Technical Report
Page 490
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STATE OF THE GREAT LAKES 201 7
Approach
Population Estimates (2011)

Ontario
Quebec
Eight Great Lakes
States
Total
Great Lakes and St.




Lawrence River Region




- All of Ontario, Quebec
12,851,821
7,903,001
83,805,970
104,560,792
and Eight Great Lakes




States




Great Lakes Region -
All of Ontario and All
12,851,821

83,805,970
96,657,791
Eight Great Lakes States




Great Lakes Basin -




Canadian population in
Great Lakes Basin and
11,234,177

37,681,537
48,915,714
population in U.S. Coun-
ties partially or wholly
contained within basin

Great Lakes Basin -




Canadian population in
Great Lakes Basin and




population in U.S. using
County Adjusted Ratio
Approach (without
Chicago Metropolitan
Area)
11,234,177

21,785,962
33,020,139
Great Lakes Basin -




Canadian population in
Great Lakes Basin and




population in U.S.
using County Adjusted
Ratio Approach (with
Chicago Metropolitan
Area), used in this
11,234,177
-
28,151,261
39,385,438
report




Table 1. Population Estimate Approaches (2011)
Source: Statistics Canada, U.S. Census Bureau
Page 491
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STATE OF THE GREAT LAKES 201 7
County
Basin - Original Ratio
Basin - Adjusted Ratio
Rationale
Cook, IL
Lake Michigan - 4.24%
Lake Michigan - 95%
Population extracts drinking water from
DuPage, IL
Lake Michigan - 0%
Lake Michigan - 50%
Lake Michigan
Lake, IL
Lake Michigan -11.51%
Lake Michigan - 90%

Will, IL
Lake Michigan - 0%
Lake Michigan - 50%

LaPorte, IN
Lake Michigan 24.09%
Lake Michigan - 35%
Accounting for Michigan City
St. Joseph, IN
Lake Michigan - 39.68%
Lake Michigan - 70%
Accounting for South Bend and
Mishawaka
Jackson, MI
Lake Erie - 16.24%
Lake Erie - 7%
Larger population centres in Michigan

Lake Michigan - 83.76%
Lake Michigan - 93%
basin than Erie
Marquette, MI
Lake Michigan - 55.97%
Lake Michigan - 45%
Larger population centres in Superior

Lake Superior - 44.03%
Lake Superior - 55%
basin than Michigan
Oakland, MI
Lake Erie - 79.65%
Lake Erie - 90%
Larger population centres in Erie basin

Lake Huron - 20.35%
Lake Huron - 10%
than Huron
Saginaw, MI
Lake Huron - 97.81%
Lake Huron - 99%
Larger population centres in Huron basin

Lake Michigan - 2.19%
Lake Michigan - 1%
than Michigan
St. Louis, MN
Lake Superior - 49.27%
Lake Superior - 90%
Accounting for Duluth
Chemung, NY
Lake Ontario - 9.10%
Lake Ontario - 3%
Few population centres in basin
Jefferson, NY
Lake Ontario - 57.8%
Lake Ontario - 75%
Numerous population centres in basin
Onondaga, NY
Lake Ontario - 93.07%
Lake Ontario - 98%
Numerous population centres in basin
Steuben, NY
Lake Ontario - 12.75%
Lake Ontario - 5%
Few population centres in basin
Tompkins, NY
Lake Ontario - 81.70%
Lake Ontario - 92%
Numerous population centres in basin
Erie, PA
Lake Erie - 52.89%
Lake Erie - 80%
Accounting for Erie
Douglas, WI
Lake Superior - 57%
Lake Superior - 80%
Accounting for Superior
Kenosha, WI
Lake Michigan - 22.8%
Lake Michigan - 75%
Accounting for Kenosha
Racine, WI
Lake Michigan - 47.77%
Lake Michigan - 75%
Accounting for Racine
Waukesha, WI
Lake Michigan - 7.72%
Lake Michigan - 15%
Accounting for Menomonee Falls
Table 2. Counties requiring second level ratio adjustment to better reflect population numbers in basin
Source: Government of Ontario, U.S. Census Bureau
Population Data (1970/1971 - 2010/2011)
Canada
1971
1976
1981
1986
1991
1996
2001
2006
2011
U.S.
1970
1976*
1980
1986*
1990
1996*
2000
2006*
2010
*Intercensal population estimates
Table 3. Years of Intercensal and Census Data
Source: Statistics Canada, U.S Census Bureau
Page 492
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STATE OF THE GREAT LAKES 201 7
Lake
Basin
1971
1976
1981
1986
1991
1996
2001
2006
2011
Superior
603,507
621,886
618,958
585,591
586,389
584,622
585,630
581,722
581,093
Michigan
11,497,315
11,718,373
11/796,578
11,857,716
12,042,678
12,561,047
13,101,190
13,211,346
13,325,057
Huron
2,332,911
2,519,332
2,612,775
2,617,271
2,776,130
2,917,142
3,018,271
3,123,897
3,129,153
Erie
11,863,069
11,829,173
11,784,483
11,559,464
11,741,188
12,098,730
12,258,143
12,266,000
12,180,736
Ontario
6,703,266
7,036,916
7,241,352
7,570,122
8,213,685
8,653,646
9,121,919
9,621,761
10,169,399
Total
33,000,068
33,725,680
34,054,145
34,190,163
35,360,070
36,815,187
38,085,152
38,804,726
39,385,438
Table 4. Total Population in each Great Lakes Basin
Source: Statistics Canada, U.S. Census Bureau
N
A
Figure 1. Population in Ontario by Census Subdivision (2011)
Source: Government of Ontario, Statistics Canada
Lege ml
r '1 Qrnm
Population
(^~1 ChM = $0Q
Gfilni
rititoEne
a
I
I 1 Sup*-**
Page 493
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STATE OF THE GREAT LAKES 201 7
40,000,000
38,000,000
36,000,000
c
0
1	34,000,000
fr
a.
32,000,000
30,000,000
28,000,000
1971 1976 1981 1986 1991 1996 2001 2006 2011
Year
Figure 2. Total Population in the Great Lakes Region (1971-2011)
Source: Statistics Canada. U.S. Census Bureau
Superior
Michigan
Huron
Ontario
Figure 3. Population Change (%) in each Great Lakes Basin from 1971-2011
Source: Statistics Canada, U.S. Census Bureau
Page 494
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STATE OF THE GREAT LAKES 201 7
4094
35%
30%
25%
41
¦"=
| 20%
V
tf
CL
i.5%
10%
5%
0%
	1
H
p|



11


I US
¦ Canada
Superior	Huron	Erie	Ontario Michigan
Lake Basin
Figure 4. Proportion of Total Great Lakes Population by Canadian and U.S. Lake Basin
(2011)
Source: Statistics Canada, U.S. Census Bureau
¦ Canada
Michigan
Superior
Lake Basin
Ontario
Huron
Figure 5. Proportion of Canadian and U.S. Population Located in each Great Lakes Basin (2011)
Source: Statistics Canada. U.S. Census Bureau
Page 495
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STATE OF THE GREAT LAKES 201 7
Lrgond
QntfUW
Population
2010(2011
Figure 6. Population by County or Census Subdivision in the Great Lakes Basin (2010/2011)
Source: Government of Ontario, Statistics Canada, U.S. Census Bureau
Page 496
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STATE OF THE GREAT LAKES 201 7
Sub-Indicator: Precipitation Amounts in the Great Lakes Basin
Overall Assessment
Trend: Increasing
Rationale: The annual precipitation anomaly (the departure from the 1961-1990 base-period average) for the
period of study (1948-2015) for Canadian stations within the Great Lakes Basin displays a statistically signif-
icant (at the 0.05 level) increasing trend of 10.9% over the study period. While the 2015 anomaly is only
0.13% (above the 1961-1990 base-period average), a better measurement of the "current status" of the sub-
indicator (due to the interannual variability of precipitation amounts is the average of the 5 previous years
(2011-2015) which is 6.5% above the base-period average. In summary: the annual amount of precipitation
in the Great Lakes Basin is above the long-term average and displays an increasing trend.
Lake-by-Lake Assessment
Individual lake basin assessments were not prepared for this report.
Sub-Indicator Purpose
The purpose of this sub-indicator is to assess the amount of precipitation falling within the Great Lakes Basin, both
annually and seasonally, and to infer the potential impact on ecological components of the Great Lakes Basin that
varying precipitation amounts due to climate change will have.
Ecosystem Objective
• To maintain the ecosystem of the Great Lakes and surrounding region by allowing the hydrologic system of
the Great Lakes Basin to continue to follow historic patterns. Changes to the frequency, seasonal distribu-
tion, or magnitude of precipitation will impact the hydrological system of the entire Great Lakes Basin,
having effects such as altering water levels or changing the rates and patterns of storm runoff - directly in-
fluencing the distribution of pollutants, nutrients that support algae/bacteria growth, and invasive species.
This sub-indicator best supports work towards General Objective #9 of the 2012 Great Lakes Water Quality Agree-
ment which states that the Waters of the Great Lakes should "be free from other substances, materials, or conditions
that may negatively impact the chemical, physical, or biological integrity of the Waters of the Great Lakes." This
sub-indicator also relates to all of the General Objectives in the Agreement as this sub-indicator directly applies to
them all.
Ecological Condition
The annual precipitation anomaly for 1948-2015 is shown in Figure 1. It displays a statistically significant (at the
0.05 level) increasing trend of 10.9% over the period. An increasing trend of 6.2% was found for the 30-year period
from 1986-2015; however, this trend was found to be statistically insignificant which means the trend cannot be
confidently discerned from the variability in the data signal over this time period. While no conclusions can be made
over the 30-year period alone, this sub-indicator shows an increasing trend over the longer period (1948-2015).
The precipitation anomaly can also be analyzed on a seasonal basis with seasons being defined as winter (December,
January, February); spring (March, April, May); summer (June, July, August); and autumn (September, October,
November). The 9-year running means of the seasonal precipitation anomalies for the Great Lakes Basin over the
period of record (1948-2015) are shown in Figure 2. Autumn was found to have a statistically significant increasing
trend of 15.2% over the study period while winter, spring, and summer each displayed statistically insignificant but
increasing trends of 7.8%, 11.7%, and 8.9%, respectively.
While 2015 showed only a slight positive anomaly, the number and magnitude of positive anomalies is generally
larger towards the end of the period (1948-2015). Five of the 10 years showing the most total annual precipitation
during the entire study period, shown in Table 1, have occurred since 2000 with all 10 occurring in the later-half of
Page 497
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STATE OF THE GREAT LAKES 201 7
the period. The largest positive precipitation anomaly (21.5%) was recorded in 2008 and the largest negative (-
18.3%) in 1963.
In the next century, annual precipitation is expected to increase by up to 20% across the Great Lakes Basin with
greater annual precipitation projected for Lake Superior (Logfren et al. 2002; McKenney et al. 2011). Lake-effect
precipitation continues to be observed in future projections and is expected to increase due to decreasing ice cover
on lakes (Burnett et al. 2003; Notaro et al. 2014). The form of precipitation is also expected to change, with more
precipitation falling as rain and freezing rain and less as snow. Shifts in the timing of precipitation are expected,
where rainfall will increase in the spring but decrease in the summer (Kling et al. 2003; Hayhoe et al. 2010).
Linkages
An increase in global temperature will enhance the ability of the atmosphere to store and transport water vapour
which will affect storm evolution and geographical distribution. In any one region, this may result in both the
amount and type of precipitation varying at rates greater than those predicted by local climate change alone. This
could result in significant changes in both precipitation event frequency and magnitude in the Great Lakes Basin,
which will affect the hydrological system of the entire basin. The impacts of such changes would be numerous;
some examples specific to an increase in precipitation event magnitude include crop loss due to storm-damage, ero-
sion and flooding.
Precipitation Amounts in the Great Lakes Basin links directly to almost all other sub-indicators in the suite as pre-
cipitation events are a driving force in hydrology, nutrient and toxin distribution, and shoreline and wetland health.
Some specific examples include:
•	Beach Advisories—Runoff following precipitation events and related bacteria loading is a major concern to
beach safety
•	Coastal Wetland Sub-indicators (Coastal Wetland Amphibians, Coastal Wetland Birds, Coastal Wetland
Fish, Coastal Wetland Invertebrates, Coastal Wetland Plants, Coastal Wetland: Extent and Composition)—
Change in precipitation event frequency or intensity will have a direct impact on coastal wetlands
•	Tributary Flashiness—Precipitation events, especially extreme ones, lead to tributary flashiness
This sub-indicator also links directly to the other sub-indicators in the Watershed Impacts and Climate Trends cate-
goiy.
•	Precipitation Amounts in the Great Lakes Basin directly contributes to all 14 Beneficial Use Impairments
laid out in Annex 1 of the 2012 Great Lakes Water Quality Agreement:
•	Restrictions on fish and wildlife consumption
•	Tainting of fish and wildlife flavour
•	Degradation of fish and wildlife populations
•	Fish tumours or other deformities
•	Bird or animal deformities or reproduction problems
•	Degradation of benthos
•	Restrictions on dredging activities
•	Eutrophication or undesirable algae
•	Restrictions on drinking water consumption, or taste and odour problems
•	Beach closings
•	Degradation of aesthetics
•	Added costs to agriculture or industry
•	Degradation of phytoplankton and zooplankton populations
•	Loss of fish and wildlife habitat
Comments from the Author(s)
Analysis was performed using the 147 grid-points of the Canadian Gridded Temperature and Precipitation Anoma-
lies Dataset (CANGRD) that fell within the definition of the Great Lakes Basin. The CANGRD is based on the Ad-
justed and Homogenized Canadian Climate Data (AHCCD) dataset. More information about the precipitation data
used in CANGRD can be found inMekis and Vincent (2011). Also, see the Canadian Enviromnental Sustainability
Indicators (CESI) - Precipitation Change in Canada indicator.
Page 498
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STATE OF THE GREAT LAKES 201 7
Trend values were estimated using the method of Sen (1968). For a computed trend to be deemed statistically signif-
icant it must be large enough to stand out from the variability of the data. Statistical significance was computed us-
ing Kendall's test (Kendall 1955) at the 0.05 level. While trends deemed statistically insignificant may still be true,
there is also a tangible chance that they instead represent cyclic variations in the data.
Assessing Data Quality
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not
Applicable
1. Data are documented, validated, or
quality-assured by a recognized agency or
organization
X





2. Data are traceable to original sources
X





3. The source of the data is a known,
reliable and respected generator of data
X





4. Geographic coverage and scale of data
are appropriate to the Great Lakes Basin

X




5. Data obtained from sources within the
U.S. are comparable to those from
Canada





X
6. Uncertainty and variability in the data
are documented and within acceptable
limits for this sub-indicator report
X





Clarifying Notes:
No US data were available for this reporting cycle.
Acknowledgments
Authors:
Kevin Anderson, Enviromnent and Climate Change Canada
Victor Isaac, Enviromnent and Climate Change Canada
Contributors:
Lucie Vincent, Environment and Climate Change Canada
Information Sources
The Canadian Gridded Temperature and Precipitation Anomalies Dataset (CANGRD):
http://open.canada.ca/data/en/dataset/3d4b68a5-13bc-48bb-adl0-801128aa6604
The Adjusted and Homogenized Canadian Climate Data (AHCCD) dataset:
http://open.canada.ca/data/en/dataset/9c4ebc00-3ea4-4fe0-8bf2-66cfelcdddld
The Canadian Enviromnental Sustainability Indicators (CESI) - Precipitation Change in Canada:
https://www.ec.gc.ca/indicateurs-indicators/default.asp?lang=En&n=ACD78526-l
Mekis, E and L. A. Vincent, 2011: An overview of the second generation adjusted daily precipitation dataset for
trend analysis in Canada. Atmosphere-Ocean 49 (2), 163-177.
Lofgren B. M„ F. H. Quinn, A. H. Clites, R. A. Assel, A. J. Eberhardt, and C. L. Luukkonen, 2002: Evaluation of
potential impacts on Great Lakes water resources based on climate scenarios of two GCMs. Journal of Great Lakes
Research, 28 (4), 537-554.
McKenney, D.W., J. H. Pedlar, E. B. Rood, andD. Price, 2011: Revisiting projected shifts in the climate envelopes
of North American trees using updated general circulation models. Global Change Biology, 17 (8), 2720-2730.
Page 499
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STATE OF THE GREAT LAKES 201 7
Burnett, A. W., M. E. Kirby, H. T. Mullins, and W, P. Patterson, 2003: Increasing Great Lake-effect snowfall during
the twentieth century: a regional response to global wanning?. Journal of Climate, 16 (21), 3535-3542.
Notaro, M„ D. Lorenz, C. Hoving, and M. Schuimner, 2014: Twenty-First-Century Projections of Snowfall and
Winter Severity across Central-Eastern North America. Journal of Climate, 27 (17), 6526-6550.
Kling, G.W., K. Hayhow, L. B. Johnson. J. J. Magnuson, S. Polasky, S. K. Robinson, B. J. Shuter, M. M. Wander,
D. J. Wuebbles, and D. R. Zak, 2003: Confronting Climate Change in the Great Lakes Region: Impacts on Our
Communities and Ecosystems. Union of Concerned Scientists and the Ecological Society of America.
Hayhoe, K„ J. VanDorn, T. Croley, N. Schlegal, andD. Wuebbles, 2010: Regional Climate Change Projections for
Chicago and the US Great Lakes. Journal of Great Lakes Research, 36 (Suppl. 2), 7-21.
Sen, P., K„ 1968: Estimates of the regression coefficient based on Kendall's tau. Journal of the American Statistical
Association, 63, 1379-1389.
Kendall, M„ G., 1955: Rank Correlation Methods. 2nd Ed. Charles Griffin and Company, 196pp.
List of Tables
Table 1. The 10 years showing the most annual precipitation (in % anomaly above the 1961-1990 mean) in the
Great Lakes Basin.
Source: Enviromnent and Climate Change Canada
List of Figures
Figure 1. Annual precipitation anomaly (from the 1961-1990 mean) for the Great Lakes Basin over the period
1948-2015. Note that the mean for a particular 9-year interval is centred on the middle year, meaning the first year
for which the running mean can be defined is 1952 and the last is 2011.
Source: Enviromnent and Climate Change Canada
Figure 2. 9-year running means of seasonal precipitation anomalies (from the 1961-1990 seasonal means) for the
Great Lakes Basin over the period of record (1948-2015). Note that the mean for a particular 9-year interval is cen-
tred on the middle year, meaning the first year for which the running mean can be defined is 1952 and the last is
2011.
Source: Enviromnent and Climate Change Canada
Last Updated
State of the Great Lakes 2017 Technical Report
Rank
Year
% Anomaly
1
2008
21.5
2
1985
18.1
3
2013
17.9
4
2014
16.0
5
1996
15.3
6
1977
11.2
7
2004
10.2
8
1995
10.0
9
1988
9.9
10
2001
9.3
Table 1. The 10 years showing the most annual precipitation (in % anomaly above the 1961-1990 mean) in the
Great Lakes Basin.
Source: Enviromnent and Climate Change Canada
Page 500
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STATE OF THE GREAT LAKES 201 7
30
T
_T
T
T
1*1
Environment and	Environnement et
Climate Change Canada Changement climatique Canada
20 -
M
-10
-20 -
-30
Departures from 1961 -1990 average / Ecarts par rapport a la moyenne 1961 -1990
with 9-year running mean (black) / avec moyenne mobile sur 9 ans (noir)
j	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	L
1950	1960
1970
1980
Year/Annee
1990	2000
2010
Figure 1. Annual precipitation anomaly (departure from the 1961-1990 mean) for the Great Lakes Basin over the
period 1948-2015. Note that the mean for a particular 9-year interval is centred on the middle year, meaning the
first year for which the running mean can be defined is 1952 and the last is 2011.
Source: Environment and Climate Change Canada
Page 501
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STATE OF THE GREAT LAKES 201 7
o
CM
O
i
o
CM
1950 1960 1970 1980 1990 2000 2010
•	Winter
Spring
Summer
•	Autumn
Environnement et
Changement climattque Canada
Environment and
Climate Change Canada
Year/Annee
Figure 2. 9-year nmning means of seasonal precipitation anomalies (from the 1961 -1990 seasonal means) for the
Great Lakes Basin over the period of record (1948-2015). Note that the mean for a particular 9-year interval is cen-
tred on the middle year, meaning the first year for w hich the running mean can be defined is 1952 and the last is
2011.
Source: Environment and Climate Change Canada
Page 502
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STATE OF THE GREAT LAKES 201 7
Sub-Indicator: Surface Water Temperature
Overall Assessment
Trend: Increasing
Rationale: Based on open-lake surface water temperature measurements, summer (July-September)
water temperatures are increasing at statistically significant rates in most of the Great Lakes. The
rate on Lake Erie is increasing but not at a statistically significant rate. Insufficient data exists to
make a determination regarding Lake Ontario. Additionally, based on the date of the onset of
summer stratification from NDBC data, all of the upper lakes (Superior, Michigan, and Huron) are
stratifying earlier and are thus classified as increasing. There is insufficient data from US and
Canadian sources to evaluate onset of stratification dates in Lake Erie because buoys are deployed
later than the onset of stratification in most years. Data from the National Data Buoy Center
(NDBC), which is part of the National Oceanic and Atmospheric Administration, and Environment
and Climate Change Canada (ECCC) is used in this report (Figure 1). These data are available for
most lakes since 1980 (excepting Lake Ontario) and no other reliable, consistent datasets are
available for evaluating these trends over a longer period. Verified data from 2015 were not available
at the time of preparation of this report. All of the data are from open-water buoys, so trends noted
here do not necessarily reflect trends in coastal waters or interconnecting channels.
Lake-by-Lake Assessment
Lake Superior
Trend: Increasing
Rationale: Linear regression of data from 1979-2014 suggest that the summer surface water temperature on
Lake Superior has increased at a rate of approximately 0.8±0.4C/decade over the period of interest.
Wanning rates measured at three separate buoys are statistically consistent with each other, though the
rates appear slightly elevated in the eastern part of the lake. Linear regression of data from 1979-2014
suggest that the onset of summer stratification in Lake Superior has become earlier over the period of
interest at a rate of approximately 4±2 days per decade. The moorings in the western and central parts of
the lake show somewhat reduced rates.
Lake Michigan
Trend: Increasing
Rationale: Linear regression of data from 1980-2014 suggest that the summer surface water temperature on
Lake Michigan has increased at a rate of approximately 0.5±0.2C/decade over the period of interest.
Wanning rates measured at two separate buoys are statistically consistent with each other.
Linear regression of data from 1980-2014 suggest that the onset of summer stratification in Lake Michigan
has become earlier over the period of interest at a rate of approximately 5±2 days per decade. The rate is
consistent between two NOAA NDBC buoys.
Lake Huron
Trend: Increasing
Rationale: Linear regression of data from 1980-2014 suggest that the summer surface water temperature on
Lake Huron has increased at a rate of approximately 0.7±0.3C/decade over the period of interest. Wanning
rates measured at two separate buoys are statistically consistent with each other. Linear regression of data
from 1980-2014 suggest that the onset of summer stratification in Lake Huron has become earlier over the
period of interest at a rate of approximately 5±2 days per decade. The moorings in the southern portion of
the lake shows a somewhat reduced rate.
Lake Erie
Trend: Undetennined
Rationale: Linear regression of data from a single NDBC buoy (45005) from 1980-2014 suggest that the
summer surface water temperature on Lake Erie has increased at a rate of approximately 0.1±0. lC/decade
over the period of interest, so that the wanning trend there is not statistically significant. Data from ECCC
Page 503
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STATE OF THE GREAT LAKES 2017
buoys also show a positive trend, but over a shorter time span. This trend is also not statistically significant.
Temperatures at the ECCC buoys are lower than at the NDBC buoy, which is located in the shallow
western basin of the lake. Due to its shallow depth. Lake Erie stratifies significantly earlier than the other
Great Lakes. There are few years where the buoys are deployed before the onset of stratification.
Lake Ontario
Trend: Undetermined
Rationale: While the interannual variability at different buoys in Lake Ontario is correlated, insufficient
temperature data exists to make reliable estimates of trends. NDBC data collection in Lake Ontario started
in 2002, too recently to make a determination of significant trends. Not enough ECCC data exists early
enough in the year to identify the date of onset of stratification.
Sub-Indicator Purpose
• The purpose of this sub-indicator is to assess trends in surface water temperature for each of the five
Great Lakes by measuring changes in duration and spatial extent of water temperature using long-term
data, and to infer the impact of climate change on the Great Lakes Region. This sub- indicator
measures the thermal properties of the Great Lakes that affects the ecosystems' function and influences
water evaporation from the lakes that affects lake's water level (if higher surface water temperatures
persists, this may lead to reduced winter ice cover and increased water evaporation from the lakes
resulting in lower water levels).
Ecosystem Objective
There should be no change in temperature that would adversely affect any local or general use of the
waters.
This sub-indicator best supports work towards General Objective #9 of the 2012 Great Lakes Water Quality
Agreement which states that the Waters of the Great Lakes should "be free from other substances,
materials, or conditions that may negatively impact the chemical, physical, or biological integrity of the
Waters of the Great Lakes."
Ecological Condition
Surface water temperature is directly dependent on regional air temperatures and hence regional climate.
Upward trends in surface water temperatures have been documented on the Laurentian Great Lakes (e.g.
Austin and Colman 2007, Huang et al. 2012) as well as on lakes around the world (O'Reilly et al. 2015).
Water temperature is a primary ecosystem driver, affecting a wide range of processes including nutrient
uptake, metabolism rates, and defines fish habitat. Surface heat and moisture fluxes (evaporation) are also a
strong function of surface water temperature. Summer surface water temperatures are a reflection of not
only summer air temperatures, but ice conditions the previous winter (Austin and Colman 2007). In
addition, the onset of summer stratification (the date on which the lake stays above the temperature of
maximum density, or about 4°C) provides a robust, integrated measure of winter conditions, in which
higher-ice winters tend to result in a later onset of stratification and low-ice winters result in earlier onset of
stratification. In lakes without significant ice formation (e.g. Michigan, Ontario), the onset of stratification
is a reflection more of the winter thermal storage of the lake, again with colder years resulting in a later
onset of stratification. The date of the onset of stratification is a strong predictor of the summer surface
water temperature, and the results in this report are consistent with each other: the date of the onset of
stratification is getting earlier, and summer surface water temperatures are increasing. While the date of the
onset of winter conditions would also be a useful metric, it is a more difficult date to determine, and most
buoys are not left in late enough into the season to make consistent observations of it.
There is a great deal of natural inter-annual variability that sits on top of the wanning trend. Several
features are consistent across the lakes. First and perhaps most importantly, a significant jump occurs
between 1997 and 1998, a strong El Nino year. It has been pointed out (van Cleave et al. 2014) that taken
separately, summer water temperature prior to 1998 and from 1998 to the present have no significant
Page 504
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STATE OF THE GREAT LAKES 2017
trends, but a strong discontinuity between the average water temperature between these two time periods.
The offset between these two time periods for the upper lakes is on the order of 2C.
In addition, there have been two "extreme" years since the last report. 2012 was an anomalously warm
year, following a very warm winter of 2011-2012 (Bai et al. 2014), in which very little ice formed on the
lakes (including, remarkably. Lake Erie), resulting in an (in many cases) record-setting early onset of
stratification and consequent warm summer. Conversely, in 2014, the "polar vortex" resulted in extreme
cold conditions across the Great Lakes region (Clites et al. 2014, Gronewold 2015), during which ice cover
was extremely heavy across the lakes, resulting in very late overturn dates and relatively low summer water
temperatures. The effect of these two extreme events to a certain extent offset each other, so that the trends
observed in this report are to first order consistent with those of the 2011 report.
Linkages
There is a clear link between the onset of summer stratification and average summer water temperatures.
Further, the onset of summer stratification is closely tied to ice cover in lakes that form ice cover (Austin
and Colman 2007). Taking this a step further, recent (unpublished) work has shown a strong link between
average winter air temperatures and the amount of ice cover, suggesting a series of statistically significant
linkages (winter air temperatures -> ice cover -> onset of stratification -> summer water temperatures)
which may prove useful to resource managers.
Trends towards earlier stratification onset (and later breakdown) imply that the period of stratification is
increasing. Separate research (Austin and Colman 2008) suggest that over the period 1906-2006, the length
of the period of summer stratification has increased from roughly 145 days to 170 days, an increase of
about 20%. This is going to have significant implications for primary productivity in the lakes, as well as
oxygen depletion in shallower, more productive parts of the Great Lakes.
Comments from the Author(s)
The observed trends towards earlier stratification and higher summer water temperatures are driven by
long-term changes in atmospheric conditions, primarily air temperature, which is widely acknowledged to
be a consequence of changing atmospheric chemistry, specifically the addition of carbon dioxide (and to a
lesser extent methane) from the burning of fossil fuels. It is important, however, that the Great Lakes
community continue to develop a better understanding of the impacts of wanning on lake ecosystems, as
well as to continue to carefully document these trends.
While there are some groups that periodically deploy equipment over the winter, there are no structures in
place to guarantee funding for systematic, year-round measurements of temperature in the Great Lakes
during the winter months. As these systems have strong seasonal connectivity, developing a long-term
program for year-round measurements should be a priority. Likewise, there are very few long-term
measurements of thermal structure (temperature throughout the water column) so little if anything is known
about trends in features like thennocline depth.
Assessing Data Quality
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not Applicable
Data are documented,
validated, or quality-assured
by a recognized agency or
organization
X





Data are traceable to original
sources
X





The source of data is a
known, reliable, and
respectable generator of data
X





Geographic coverage and

X




Page 505
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STATE OF THE GREAT LAKES 2017
scale of data are appropriate
to the Great Lakes Basin






Data obtained from sources
within the US are comparable
to those from Canada

X




Uncertainty and variability in
the data are documented and
within acceptable limits for
this sub-indicator report
X





Acknowledgments
Authors:
Jay Austin, University of Minnesota Duluth
Ram Yerubandi, Environment and Climate Change Canada
Information Sources
Austin, J. A., and S. M. Colman. 2007. Lake Superior summer water temperatures are increasing more
rapidly than regional air temperatures: A positive ice-albedo feedback. Geophysical Research Letters 34:
L06604, doi: 10.1029/2006GL029021
Austin, J. A. and S. Colman (2008), A Century of Directly Measured Temperature in Lake Superior.
Limnology and Oceanography, 53(6) 2274-2730.
Bai, X, J. Wang, J. Austin, and 12 others. 2014. A record-breaking low ice cover over the Great Lakes
during winter 2011/2012: combined effects of a strong positive NAO and La Nina. Climate Dynamics. DOI
10.1007/s00382-014-2225-2.
Clites, A. H„ J. Wang, K.B. Campbell, A. D. Gronewold, R. A. Assel, X. Bai, and G. A. Leshkevich. 2014.
Cold Water and High Ice Cover on Great Lakes in Spring 2014. Eos Transactions AGU 95: 305-306.
Gronewold, D. 2015. Impacts of extreme 2013-2014 winter conditions on Lake Michigan's fall heat
content, surface temperature, and evaporation. Geophys.Res. Lett. 42. 3364-3370. DOI:
10.1002/2015GL063799
Huang, A., Y.R. Rao, and W. Zhang (2012). On recent trends in atmospheric and limnological variables in
Lake Ontario. J. Climate. DOI: 10.1175/JCLI-D-l 1-00495.1
O'Reilly, C. and 60 other authors, including J. Austin (2015). Rapid and highly variable wanning of lake
surface waters around the globe. Geophvs. Res. Lett., doi:10.1002/2015GL066235.
Van Cleave, K„ J. D. Lenters, J. Wang, E. M. Verhamme. 2014. A regime shift in Lake Superior ice cover,
evaporation, and water temperature following the warm El Nino winter of 1997-1998. Limnology and
Oceanography 59: 1889-1898, doi:10.4319/lo.2014.59.6.1889
List of Figures
Figure 1. Location of surface buoys used in this report.
Source: National Data Buoy Center (NDBC) and Environment and Climate Change Canada (ECCC)
Figure 2. Dates of onset of stratification on the three upper Great Lakes. The onset of stratification date is
the first date after which the surface water temperature stays above the temperature of maximum density.
Trends are in units of days per decade and negative trends indicate the date of onset is becoming earlier.
Note that the ranges on the y-axes differ.
Source: National Data Buoy Center (NDBC) and Environment and Climate Change Canada (ECCC)
Figure 3. Summer (July-September) surface water temperature trends for the upper Great Lakes.
Source: National Data Buoy Center (NDBC) and Environment and Climate Change Canada (ECCC)
Page 506
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STATE OF THE GREAT LAKES 201 7
Figure 4. Summer surface water temperature trends for the lower Great Lakes
Source: National Data Buoy Center (NDBC) and Environment and Climate Change Canada (ECCC)
Last Updated
State of the Great Lakes 2017 Technical Report
Page 50/
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STATE OF THE GREAT LAKES 201 7
3! O ^
Figure X, Location of surface buoys used in this report.
Source: National Data Buoy Center (NDBC) and Environment and Climate Change Canada (ECCC)
Page 508
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STATE OF THE GREAT LAKES 201 7
Lake Superior
AUG
JUL
JUN
45001
45004
45006
-2+2 d/de
cade
r =§*§ Messrs
1980 1985 1990 1995 2000 2005 2010 2015
Lake Michigan
JUL
JUN
MAY-
APR
45002
45007
-5+3 d/de
-6+3 d/de
1980 1985 1990 1995 2000 2005 2010 2015
Lake Huron
AUG
JUL
JUN
MAY
45003
45008
• -7+3 d/d
3±2 d/de
1980 1985 1990 1995 2000 2005 2010 2015
Figure 2. Dates of onset of stratification on the tliree upper Great Lakes. The onset of stratification date is
the first date after which the surface water temperature stays above the temperature of maximum density.
Trends are in units of days per decade and negative trends indicate the date of onset is becoming earlier.
Note that the ranges on the y-axes differ.
Source: National Data Buoy Center (NDBC) and Environment and Climate Change Canada (ECCC)
Page 509
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STATE OF THE GREAT LAKES 201 7
Lake Superior
20
~	45001
~	45004
~	45006
o
i-
§
0,6+0.4 C
1 ±0.5 C/c
0.8+0.4 C
/decade
ecade
/decade
1980 1985 1990 1995 2000 2005 2010 2015
Lake Michigan
24
~	45002
~	45007
0.4+0.2 C
/decade
- 20
0.6+0.2 C
/decade
| 18
1980 1985 1990 1995 2000 2005 2010 2015
Lake Huron
~	45003
~	45008
20
0.7+0.2 C
/decade
t 18
0.6+0.3 C
/decade
1980 1985 1990 1995 2000 2005 2010 2015
Figure 3. Summer (July-September) surface water temperature trends for the upper Great Lakes.
Source: National Data Buoy Center (NDBC) and Environment and Climate Change Canada (ECCC)
Page 510
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STATE OF THE GREAT LAKES 201 7
Lake Ontario
o
24
22
20
E
E
18
16
. . •: •
t
45012
45159
1980 1985 1990 1995 2000 2005 2010 2015 2020
Lake Erie
25
24
o
f 23
| 22
3
CO
21
20
• . ••
•	45005
•	45132
•	45142

0.1 ±0.1 C/det:ade
~ ~

1980 1985 1990 1995 2000 2005 2010 2015 2020
Figure 4. Summer surface water temperature trends for the lower Great Lakes.
Source: National Data Buoy Center (NDBC) and Environment and Climate Change Canada (ECCC)
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STATE OF THE GREAT LAKES 201 7
*
Sub-Indicator: Ice Cover
Overall Assessment
Trend: Decreasing
Rationale: The basin-wide loss of ice cover from 1973 to 2015 is 26%, i.e., the annual trend is -0.6% per year.
The annual trend for the period 1973-2013 is -0.79% per year. The interannual (year-to-year) change of An-
nual Maximum Ice Coverage (AMIC) is due to the interannual variability of North Atlantic Oscillation
(NAO) and El Nino and Southern Oscillation (ENSO). Lake ice cover is a sensitive indicator of regional cli-
mate and climate change. Seasonal ice cover repeats each year with large interannual variability. For exam-
ple, the maximum ice coverage over all of the Great Lakes was 95% in 1979 and only 11% in 2002. Possible
contributors include interannual and interdecadal climate variability, and long-term trends, possibly related
to global climate warming. Even in response to the same climate forcing, Great Lakes ice cover may experi-
ence different spatial and temporal variability due to an individual lake's orientation, depth (i.e., water heat
storage), and turbidity (i.e., albedo due to sedimentation). Since the last update to the Great Lakes ice cover
database (Wang et al. 2012a), there has been significant change in ice cover on the Great Lakes, in particular
in the last two winters (93% and 89% coverage for 2013/14 and 2014/15, respectively), which was considera-
bly above the long-term average of 53.2%.
Lake-by-Lake Assessment
Lake Superior
Trend: Decreasing
Rationale: Lake Superior ice cover is highly controlled by the atmospheric teleconnection patterns such as NAO,
ENSO, AMO (Atlantic Multi-decadal Oscillation), and PDO (Pacific Decadal Oscillation). Lake Superior has the
highest total loss of ice area among the lakes at -39% since 1973 (see Tables 1 and 2; Figure 2a). The annual (deca-
dal) trend was estimated to be -0.9% (-9%).In addition to that the ice cover is controlled by the atmospheric telecon-
nection patterns such as NAO, ENSO, AMO, and PDO. The other mechanism is partially due to the wanning caused
by the ice/water albedo feedback (Wang et al. 2005; Austin and Colman 2007), because Lake Superior has the deep-
est depth and largest volume of water of all five lakes. AMIC was above Long-Term Average during winters of
2013/14, 2014/15.
Lake Michigan
Trend: Decreasing
Rationale: Lake Michigan ice cover is controlled by the atmospheric teleconnection patterns such as NAO, ENSO,
AMO, and PDO. Lake Michigan has the second lowest ice loss at -17% since 1973. The annual (decadal) trend was
estimated to be -0.39% (-3.9%) (see Tables 1 and 2; Figure 2b). AMIC was above Long-Term Average during win-
ters of 2013/14, 2014/15.
Lake Huron
Trend: Decreasing
Rationale: Lake Huron has the second highest total loss among the lakes at -22% since 1973. The annual (decadal)
trend was estimated to be -0.51% (-5.1%). Lake Huron ice cover is controlled by the atmospheric teleconnection
patterns such as NAO, ENSO, AMO, and PDO. AMIC was above Long-Term Average during winters of 2013/14,
2014/15.
Lake Erie
Trend: Decreasing
Rationale: Lake Erie has the second lowest ice loss at -17% since 1973. The annual (decadal) trend was estimated to
be -0.48% (-4.8%). Lake Erie ice cover is controlled by the atmospheric teleconnection patterns such as NAO, EN-
SO, AMO, and PDO. Another important factor is that Lake Erie is the shallowest lake and its heat content is the
smallest. Although located in the southern part of the basin with wanner air and water temperatures, on average,
most winters. Lake Erie is almost completely ice covered. AMIC was above Long-Tenn Average during winters of
2013/14, 2014/15
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STATE OF THE GREAT LAKES 2017
Lake Ontario
Trend: Decreasing
Rationale: The annual (decadal) trend was estimated to be -0.36% (-3.6%). Lake Ontario ice cover is controlled by
the atmospheric teleconnection patterns such as NAO, ENSO, AMO, and PDO. Lake Ontario has the smallest mean
ice cover of all lakes. AMIC was above Long-Term Average during winters of 2013/14, 2014/15.
Sub-Indicator Purpose
The overall purpose of this sub-indicator is to assess winter ice cover and its impacts on seasonal and interannual
lake temperature and accompanying physical changes to each lake over time by measuring the thermal properties of
the Great Lakes that affect the ecosystems' function and influence water evaporation from the lakes that affects wa-
ter levels. This sub-indicator tracks the extent of winter ice cover for each of the five Great Lakes by measuring
changes in duration and spatial extent of water temperature and ice cover using long term data. This sub-indicator is
also used to infer potential impact of climate change on wetlands since ice cover affects water levels and protects the
shorelines including wetlands from erosion by waves and storms.
Ecosystem Objective
Change in lake ice cover during the winter due to climate change will affect water temperature on the Lakes in the
following spring and summer and, in turn, affect lake ecosystems. Awareness of occurrence will encourage human
response to reduce the stressor towards minimizing biological disruption.
This sub-indicator best supports work towards General Objective #9 of the 2012 Great Lakes Water Quality Agree-
ment which states that the Waters of the Great Lakes should "be free from other substances, materials, or conditions
that may negatively impact the chemical, physical, or biological integrity of the Waters of the Great Lakes."
Ecological Condition
This sub-indicator is used as a potential assessment of climate change, particularly within the Great Lakes Basin.
Changes in water and air temperatures will influence ice development on the lakes and, in turn, affect coastal wet-
lands, nearshore aquatic enviromnents, and inland enviromnents. More importantly, ice cover directly controls the
lake water temperature change, duration of stratification, and fish behaviours. Based on the observations (Figure 1),
the highest maximum ice cover for each Great Lake occurred in 1977-1979, 1994, and 2014-2015. For Lakes Mich-
igan, Erie and Ontario, the highest maximum ice over took place in 1977, 1978 and 1979 respectively.
This sub-indicator will measure annual maximum and average ice concentrations of the Great Lakes area. According
to Assel (2005), the daily spatial average ice cover for each of the Great Lakes was calculated from daily grids. Dai-
ly grids were generated by linear interpolation of observed ice cover grids between adjacent dates for a given winter
season from the date of first ice chart to date of last ice chart (Assel 2005). Lake-averaged ice cover prior to date of
first ice chart and after date of last ice chart was assumed to be zero. The daily lake-averaged ice cover on each of
the Great Lakes is used to calculate the seasonal average ice cover. The seasonal average ice cover is the sum of the
daily lake-averaged ice cover over a winter divided by 182 (the number of days between 1 December to the follow-
ing 31 May). The seasonal average ice cover is calculated for days when the lake-averaged ice cover was greater
than or equal to 5%.
The seasonal average ice cover is an index of the severity of an annual ice cycle. Ancillary ice cycle variables calcu-
lated for each winter are the Julian dates that the first and last observed lake- averaged ice cover was greater than or
equal to 5% and the duration of the ice cover, that is, the difference between dates of last and first ice.
Annual maximum ice cover (AMIC) is defined as a maximum percentage of ice cover in one day during an ice sea-
son (winter). This snapshot of the ice season is a realization that can be measured, which reflects the overall atmos-
pheric cumulative effects on lake ice. Furthermore, its seasonal and interannual variability can be accurately record-
ed and analyzed (Bai et al. 2012). The trend of AMIC for a specified period can be calculated (Wang et al. 2012a,b).
However, the trend varies with different length of the time series included, because there is multidecadal variability
in lake ice that is caused by multidecadal atmospheric (Wang et al. 2016, submitted) and water thermal forcings.
There is spatial variability in AMIC trend. The highest trend is located along heavy ice-covered coasts in Lake Supe-
rior, Georgian Bay, northern Lake Huron, and northern Lake Michigan. Offshore ice cover has smaller trend, since
Page 513
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STATE OF THE GREAT LAKES 2017
ice cover is not continuous. Research is needed to map the grid points of a lake using GIS to calculate each grid
point trend. This on-going research will reveal spatial trend distribution over the Great Lakes (Mason et al. 2016).
During the 2015/16 winter. Great Lakes annual maximum ice coverage (AMIC) was observed to be 33%, signifi-
cantly below the long-term average, mainly due to the simultaneous occurrence of a strong El Nino, positive North
Atlantic Oscillation (NAO), warm phase of Atlantic Multi-decadal Oscillation (AMO), and warm phase of Pacific
Decadal Oscillation (PDO).
Air temperatures over a lake are one of the few factors that control the formation of ice on that surface. Colder win-
ter temperatures increase the rate of heat released by the lake, thereby increasing the freezing rate of the water.
Milder winter temperatures have a similar controlling effect, only the rate of heat released is slowed and the ice
forms more slowly. Globally, some inland lakes appear to be freezing up at later dates, and breaking-up earlier, than
the historical average, based on a study of 150 years of data (Magnuson et al. 2000). These trends add to the evi-
dence that the earth has been in a period of global wanning for at least the last 150 years.
The freezing and thawing of lakes is a very important aspect to many aquatic and terrestrial ecosystems. Many fish
species rely on the ice to give their eggs protection against predators during the late part of the ice season. Nearshore
ice has the ability to change the shoreline as it can encroach upon the land during winter freeze-up times. Even in-
land systems are affected by the amount of ice that forms, especially within the Great Lakes basin. Less ice on the
Great Lakes allows for more water to evaporate and be spread across the basin in the form of snow. This can have an
effect on the foraging animals (such as deer) that need to dig through snow during the winter in order to obtain food.
Linkages
Linkages to other sub-indicators in the indicator suite include:
•	Coastal Wetlands: Extent and Composition
•	Hardened Shorelines - less ice cover exposes the shoreline to waves generated by winter storms that accel-
erates erosion
•	Dreissenid Mussels
•	Harmful Algal Blooms - higher water temperatures and less ice cover may be related to more and earlier
algal blooms
•	Toxic Chemicals in Great Lakes Herring Gull Eggs - a link has been shown between contaminant levels in
Herring Gull eggs and Ice Cover
This sub-indicator also links directly to the other sub-indicators in the Watershed Impacts and Climate Trends indi-
cator. It is indirectly linked to other sub-indicators that track trends in wetland area and habitat change.
Comments from the Author(s)
This report is based on previous and on-going research. This indicates that research is the base to accurate lake ice
analysis, prediction, projection, and application. Therefore, long-term investment in research is the key for our better
understanding the lake ice and related climate changes, as well as its implications to ecosystem in the Great Lakes
(Bai et al. 2015). Research can guarantee to deliver physical sound knowledge to the forecast and application to
serve the Great Lakes community.
Assessing Data Quality
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not
Applicable
1. Data are documented, validated, or
quality-assured by a recognized agency or
organization
X





2. Data are traceable to original sources
X





3. The source of the data is a known,
reliable and respected generator of data
X





Page 514
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STATE OF THE GREAT LAKES 2017
4. Geographic coverage and scale of data
are appropriate to the Great Lakes Basin
X





5. Data obtained from sources within the
U.S. are comparable to those from
Canada
X





6. Uncertainty and variability in the data
are documented and within acceptable
limits for this sub-indicator report

X




Acknowledgments
Authors:
Jia Wang, Ph.D.. Ice Climatologist, NOAA Great Lakes Environmental Research Laboratory, Ann Arbor, Michigan
Darlene Langlois, Environment and Climate Change Canada
Anne Clites, NOAA Great Lakes Enviromnental Research Laboratory, Ann Arbor, Michigan
All data analyzed and charts created by the authors.
Information Sources
Austin, J. A., and S. Colman, 2007. Lake Superior summer water temperatures are increasing more rapidly than re-
gional air temperatures: a positive ice-albedo feedback. Geophys. Res. Lett., 34, L06604,
doi:10.1029/2006GL029021.
Assel, R. 2005. Classification of Annual Great Lakes Ice Cycles: Winters of 1973-2002. Journal Of Climate 18:
4895-4905.
Bai, X., J. Wang, C. Sellinger, A. Clites, and R. Assel, 2012. Interannual variability of Great Lakes ice cover and its
relationship to NAO and ENSO. J. Geophys. Res., doi: 10.1029/2010JC006932
Bai, X., J. Wang, R. Assel, D.J. Schwab, A. Clites, J. Bratton, M. Colton, J. Lenters, B. Lofgren, T. Wohlleben, S.
Helfrich, J. Austin, 2015. A record breaking low ice cover over the Great Lakes during winter 2011/2012, Clim.
Dyn. 44,5: 1187-1213.
Magnuson, J.J., Robertson, D.M., Benson, B.J., Wynne, R.H., Livingston D.M., Arai, T„ Assel, R.A., Barry, R.G.,
Carad, V., Kuusisto, E„ Granin, N.G., Prowse, T.D., Stewart, K.M., and Vuglinski, V.S. 2000. Historical trends in
lake and river ice covering the Northern Hemisphere. Science 289(9): 1743-1746.
Mason, L.A., C.M. Riseng, A.D. Gronewold, E.S. Rutherford, J. Wang, A. Clites, S.D. P. Smith, andP.B. Mclntyre,
2016. Fine-scale spatial variation in ice cover and surface temperature trends across the surface of the Laurentian
Great Lakes. Climate Change, 1-13.
Wang, J., M. Ikeda, S. Zhang, and R. Gerdes, 2005. Linking the northern hemisphere sea ice reduction trend and the
quasi-decadal Arctic Sea Ice Oscillation Climate Dyn., 24: 115-130, DOI: 10.1007/s00382-004-0454-5.
Wang, J., R.A. Assel, S. Walterscheid, A. Clites, and X. Bai, 2012a. Great lakes ice climatology update: winter 2006
- 2011 description of the digital ice cover data set, NOAA Technical Memorandum GLERL-155, 37 pp. 2012.
Wang, J., X. Bai, H. Hu, A. Clites, M. Colton, and B. Lofgren, 2012b. Temporal and spatial variability of Great
Lakes ice cover, 1973-2010. J. Climate, DOI: 10.1175/2011JCL14066.1
Wang, J. X. Bai, Z. Yang, A. Clites, A. Manome, B. Lofgren, J. Bratton, H. Hu, P. Chu, and G. Leshkevich, 2016.
Interannual and decadal variability of Great Lakes ice cover, 1973-2015: Hindcast of lake ice using multi-variable
regression models (submitted to JGLR)
Page 515
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STATE OF THE GREAT LAKES 2017
List of Tables
Table 1. Regression linear trend of AMIC, x=a+bt in percentage (I in years starting with 0 at 1973, a is constant,
and b is the trend/slope).
Table 2. The same as Table 1, except excluding 2014 and 2015 ice seasons.
Table 3. Statistic parameters (mean and standard deviation) of AMIC five individual lakes using data from 1973-
2015.
List of Figures
Figure 1. Time series of five Great Lake AMIC for the period 1973-2015, which is based on the binational NIC and
CIS dataset.
Source: NOAA Great Lakes Enviromnental Research Laboratory (GLERL) and National Ice Service (NIC)
Figures 2a-e. AMIC (in %) of all five individual lakes for the period 1973-2015. The linear lines are the trend in
AMIC calculated from the least square fit method. Units for the vertical axes are in %.
Source: Enviromnent and Climate Change Canada Ice Service
Last Updated
State of the Great Lakes 2017 Technical Report

a
b/annual
Decadal
Total loss up to


trend
trend
2015
Superior
77.00
-0.90
-9.0
-38.70
Michigan
39.04
-0.39
-3.9
-16.77
Huron
66.87
-0.51
-5.1
-21.93
Erie
90.86
-0.48
-4.8
-20.64
Ontario
26.00
-0.36
-3.6
-15.48
Basin
62.00
-0.60
-6.0
-25.80
Table 1. Regression linear trend of AMIC, x=a+bt in percentage (t in years starting with 0 at 1973, a is constant,
and b is the trend/slope).

a
b/annual
Decadal
Total loss up to


trend
trend
2013
Superior
79.73
-1.09
-10.9
-44.69
Michigan
50.57
-0.61
-6.1
-25.01
Huron
80.92
-0.66
-6.6
-27.06
Erie
97.82
-0.56
-5.6
-22.96
Ontario
43.80
-0.50
-5.0
-20.50
Basin
62.43
-0.79
-7.9
-32.39
Table 2. The same as Table 1, except excluding 2014 and 2015 ice seasons.
From 1973 to 2015
Superior
Michigan
Huron
Erie
Ontario
Basin
mean
63.05
40.69
64.74
83.92
29.03
53.20
standard deviation
30.55
22.62
24.08
26.13
21.50
24.03
Table 3. Statistic parameters (mean and standard deviation) of AMIC five individual lakes using data from 1973-
2015.
Page 516
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STATE OF THE GREAT LAKES 2017
Great Lakes Annual Maximum Ice Coverage 1973-2015
too
90.7 (1994)
Long-term average
(53 2%)
I 74 6 (2003)
92.5
(2014)
•88 8
(2015)
™ 70

o
o
12.9
(2012)
115(1958) 9.3(2002)
1975
1990
1995
2000
2005
2010
2015
Figure 1. Time series of five Great Lakes' AMIC for the period 1979-2015, which is based on the biiiational NIC
and CIS dataset.
Source: NOAA Great Lakes Environmental Research Laboratory (GLERL) and National Ice Service (NIC)
¦ E-wr<.rvTi#M &iu
-------
STATE OF THE GREAT LAKES 2017
Canada
	V
rt
Area/Aire : 60,426 km2
Enw.K>nm«n«	Maximum Ice Coverage for the weeks 0101-0528, seasons:
¦ m. on*.	1972/73-2014/15
La couverture maximale de cjlace pour les semaines
0101-0528, saisons: 1972/73-2014/15
Lake Michigan /
Lac Michigan
120
HO
100
90
80
70
60
50
40
30
20
10
0
14.95 % of data is interpolated / 14.95 % des donnees sont interpolees
Canadian Ice Service - Environment Canada / Service canadien des glaces - Environnement Canada
(2015-05-05 12:59 IceCraph - Canadian Ice Service/Graphe des glaces - Service canadien des glaces 2.0.6 2013/07/24)
¦in
usaiciin oiwoKNfNfNinMoaiLnwiJuriciairKNmoirimQHOirKNNOKNfNinirKNmctmw
(NHMOHHMHNHHOHHHOrlHHMNOHOHOHMOHOHNHHHOONOHON
(N (N rl fN N(NfNrHNN(NfflrjfN(NfOrHHHNW(N(N(N(NHHHfflW(NrH(NMf5f5(NHfONfflfN
Trendline Details:
= -0.53x + 49.58
Slope = -5.21 (+/-G.67) % per decade
For 1972/73: x=0 y=49.58%
For 2014/15: x=42 y=27.71%
I Ice Coverage / couverture des glaces
I Interpolated Data / Interpolee
Median / medians 1980/61-2009/10 Trend / tendance
I Lowest / Le plus bas: 2002 0319 - 11.83%
I Highest / Le plus haut: 1977 0212 - 93.04%
Details sur la tendance:
y = -0.53x + 49.58
Pente = -5.21 (,+1-6.67) % par decennie
Pour 1972/73: x=0 y=49.58%
For 2014/15: x=42 y=27.71%
# Earliest and latest weeks the maximum occurred in the period /
aines le plus tot et le plus tard dans la periode lorsqu'on a atteint le maximu
Figure 2b.	
¦	& ¦ Environment €nvir0nncm«int Maximum Ice Coverage for the weeks 0101-0528, seasons:
¦	¦ cmm* c*n*d*	1972/73-2014/15
La couverture maximale de glace pour les semaines
0101-0528, saisons: 1972/73-2014/15
Area / Aire : 61,887 km2
Lake Huron /
Lac Huron
Canada
i
rn rf LT>
ID t*. 00
o5 9i oi
ci o
r-. co
Ol Cl
H fSI CO
CO 00 00
>NiowNincimuia«o(NCioimmaiwiri(Niniriio
(MMHOHONHNMrlriHOrlHHOHOOM
>00000000000000000000000
1iin«scoaiort(Nmtin«s»cioH(Nfn'tiflior
cooo»»cccocioiaioicicicncioiciooooooo(
WClClClClClQClClCl CiClGlGlCiOOOOOOOOOOOOOOOO
HHrlHHHrtHHHHrtHHrlrl(NMN(N(N(N(NNN(N(N(N(N(N(NN
Date
fN ® In
oi in vo
H(NO
15.16 % of data is interpolated / 15.16 % des donnees sont interpolees
Canadian Ice Service - Environment Canada / Service canadien des glaces - Environnement Canada
(2015-05-05 12:56 IceGraph - Canadian Ice Service/Graphe des glaces - Service canadien des glaces 2.0.6 2013/07/24)
Trendline Details:
y = -O.Slx + 77.32
Slope = -6.05 (+/-7.71) % per decade
For 1972/73: x=0 y=77.32%
For 2014/15: x=42 y=51.95%
¦	Ice Coverage / couverture des glaces
¦	Interpolated Data / Interpolee
^—Median / mediane 1980/81-2009/10 Trend / tendance
¦	Lowest / Le plus bas: 2012 0305 - 24.32%
¦	Highest / Le plus haut: 1994 0226 - 97.88%
# Earliest and latest weeks the maximum occurred in the period /
Semaines le plus tot et le plus tard dans la periode lorsqu'on a atteint le maximum
Details sur la tendance:
y = -0.61 x + 77.32
Pente = -6.05 (+/-7.71) % par decennie
Pour 1972/73: x=0 y=77.32%
For 2014/15: x=42 y=51.95%
Figure 2c.
Page 518
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STATE OF THE GREAT LAKES 2017
¦	• ¦ Environment £nviiom*m«Mt Maximum Ice Coverage for the weeks 0101-0528, seasons:
¦	I cm cm	1972/73-2014/15

130
120
110
100
, 90
80
70
60
50
40
30
20
10
0
CanadS
La couverture maximale de glace pour les semaines
0101-0528, saisons: 1972/73-2014/15
Lake Erie /
Lac Erie
Area / Aire : 28,578 km

Mr-SB
I
ll
wc^NinciofNiniriOi'Ninr
NH H OIN HOOHHHt
f) (N(N fN Hf
NfN N (N t
in f in
ci & oi
id r-.
r>. r>-
Ch Ol
» mo
g. »
h (n m tt
00 00 00 00
Cl Q Cl ffi
in to
00 00
ai a-1
r*», oo
00	oo
01	o\
)iomo\cioimfNinffiNm(N(N(NOimmmoi(Ninmoif>ioi(Nm
l(NOriHHOHOMNHHH(NNOOOHHOON(NHHO
>000000000000000000000000000
ciOHNmtmifisoooiOHfNffltmiosoooioHNffltm
MCiCtClffiOlClQOtClClOOOOOOOOOOrlHHrlrlri
CtClClOiQClQaiOtCfiClOOOOOOOOOOOOOOOO
HrlHHHHHHHHHrNfN(N(NNN(NrNN(NN(N(N(N(N(N
Date
15.27 % of data is interpolated / 15.27 % des donnees sont interpolees
Canadian Ice Service - Environment Canada / Service canadien des glaces - Environnement Canada
(2015-05-05 12:55 IceCraph - Canadian Ice Service/Graphe des glaces - Service canadien des glaces 2.0.6 2013/07/24)
Trendline Details:
y = -0.48* + 92.86
Slope = -4.77 (+/-7.S) % per decade
For 1972/73: x=0 y=92.86%
For 2014/15: x=42 y=72.87%
I Ice Coverage / couverture des glaces ¦ Interpolated Data / Interpolee
— Median / mediane 1980/81-2009/10 Trend / tendance
I Lowest / Le plus bas: 1998 0122 - 8.36%
I Highest / Le plus haut: 1978 0226 - 99.63%
# Earliest and latest weeks the maximum occurred in the period /
Sem aines le plus tot et le plus tard dans la periode lorsqu'on a attaint le m axin
Details sur la tendance:
y = -G.48X + 92.86
Pente = -4.77 (+/-7.S) % par dec
Pour 1972/73: x = 0 y=92.86%
For 2014/15: x=42 y=72.87%
Figure 2d.	
¦ • ¦ Environment Enviiciweraent Maximum Ice Coverage for the weeks 1204-0514, seasons:
¦ cm CM	1972/73-2014/15
La couverture maximale de glace pour les semaines
1204-0514, saisons: 1972/73-2014/15
Lake Ontario /
Lac Ontario
CanadS
Area / Aire : 20,154 km2
OlOO(Nin(NMCl(NIOtNQm(NWr>JOOMlOClO\»(N®mi5lClinOl(Ni5lClClfftiaQ(N(NmOl(NOimiO
HOHOHHHHO(N(NrlHnrlOrlNHH(NrtHOrt(NHHNrlHM(N(NHH(NO(N(NrlO(N
IMrirNCNtNCN (NHHHH(N(N(NrlfO(N(N(NM(N(NN(NHH(NrliMMrlH(NNfOrl(NHH(Nff)N
OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
mtmioNMoiOH(NmtmioNoomoHNmtm»NwciOH(Nmtm®soociOH(Nfnrrm
NNSSSNNOOBOOOSJOOOBCeMMOtGtClOIClClOClOlClOOOOOOOOOOHrtHHHH
ffi Cl O Cl Ci C\ QQ9iClQOlOi9iQi7{ffiffiQCl9iQOl?i0lQOOOOOOOOOOOOOOOO
HrlHHrlHHHHHHHHHHHHrlHrlrtHHrlHHH(N(N(N(N(N(NN(N(N(N(N(N(NN(N(N
Date
12.71 % of data is interpolated / 12.71 % des donnees sont interpolees
Canadian Ice Service - Environment Canada / Service canadien des glaces - Environnement Canada
(2015-05-05 12:59 IceCraph - Canadian Ice Service/Graphe des glaces - Service canadien des glaces 2.0.6 2013/07/24)
Trendline Details:
y = -0.58x +41.77
Slope = -5.77 (+/-6.0) % per decade
For 1972/73: x = 0 y=41.77%
For 2014/15: x=42 y=17.58%
¦	Ice Coverage / couverture des glaces ¦ Interpolated Data / Interpolee
¦^"Median / mediane 1980/81-2009/10 Trend / tendance
¦	Lowest / Le plus bas: 2002 0219 - 3.02%
¦	Highest / Le plus haut: 1979 0219 - 86.91%
# Earliest and latest weeks the maximum occurred in the period /
Semaines le plus tot et le plus tard dans la periode lorsqu'on a atteint le maximum
Details sur la tendance:
y = -0.58x + 41.77
Pente = -5.77 (+/-6.0) % par decennie
Pour 1972/73: x=0 y=41.77%
For 2014/15: x=42 y=17.58%
Figure 2e
Figures 2a-e. A MIC (in %) of all five individual lakes for the period 1973-2015. The linear lines are the trend in
A MIC calculated from the least square fit method. Units for the vertical axes are in %. Other parameters are given in
each lake.
Source: ECCC Ice Service
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STATE OF THE GREAT LAKES 2017
/
Sub-Indicator: Water Levels
Overall Assessment
Trend: Decreasing (30-year trend, 1985-2015)
Rationale: Water level conditions have historically varied, and continue to vary considerably across each of
the Great Lakes (Quinn and Edstrom 2000) (Lenters 2004). For all the Great Lakes, it is difficult to say
whether there is a discernable trend over the past 100 years. However, notable basin-scale water level dynam-
ics over the past 30 years include a significant decline in the late 1990s (Assel, Quinn, and Sellinger 2004),
persistent and record-low monthly mean levels (Gronewold and Stow 2014) on Superior (August and Sep-
tember 2007) and Michigan-Huron (December 2012 and January 2013), and a record setting rise on both Su-
perior and Michigan-Huron between January 2013 and December 2014 (Gronewold, et al. 2015). At the end
of calendar year 2015, monthly and annual mean water levels on all of the Great Lakes were at or above their
long-term averages. It is informative to note that water level data on the Great Lakes dates back to the 1800s
and any assessment of temporal trends in water levels depends on the period selected from this historical rec-
ord. For the purposes of this report, trends have been identified in annual water levels across the most recent
5,30, and 100 year periods. It is understood and recognized that additional periods could be used in the as-
sessment as well and that the trends could be different than what is presented in this assessment report.
Lake-by-Lake Assessment
Lake Superior
Trend: 5-year: Increasing
30-year: Decreasing
100-year: Decreasing
Rationale: Lake Superior annual mean and monthly mean water levels were predominantly below long-term
average values between the late 1990s and 2013. In 2013 and 2014, however. Lake Superior water levels rose at a
very high rate and are currently above long-term average values. Although water levels on Lake Superior are higher
than they were at the time of the previous State of the Great Lakes (SOGL) report, the potentially high-rate of
interannual variability in climate conditions and the regional hydrologic cycles makes it difficult to determine
whether these changes are expected to persist in the future.
Lake Michigan and Lake Huron
Trend: 5-year: No significant trend
30-year: Decreasing
100-year: No significant change
Rationale: Note: Lake Michigan and Lake Huron are commonly considered one lake system from a long-term
hydrological perspective and therefore are referenced collectively as Lake Michigan-Huron.
Lake Michigan-Huron annual mean and monthly mean water levels were predominantly below long-term average
values between the late 1990s and late 2014. In late 2014 and 2015, however (as part of a rise that began in early
2013) water levels rose above long-term average values. Although water levels on Lake Michigan-Huron are higher
than they were at the time of the previous State of the Great Lakes (SOGL) report, the potentially high-rate of
interannual variability in climate conditions and the regional hydrologic cycles makes it difficult to determine
whether these changes will persist.
Lake Erie
Trend: 5-year: No significant change
30-year: Decreasing
100-year: Increasing
Rationale: Monthly mean and annual mean water levels on Lake Erie have oscillated above and below long-term
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STATE OF THE GREAT LAKES 2017
average values for the past decade, including from 2011 through 2015. However, water level conditions on Lake
Erie (unlike conditions on Lakes Superior and Michigan-Huron) did not approach record annual and monthly lows
during this period. It is informative to note, however, that water levels on Lake Erie rose dramatically in 2011 (in
response to widespread increased annual precipitation) and then declined (starting in November 2011) for 10 straight
months - the longest continuous period of monthly mean water level decline in Lake Erie's history (Gronewold and
Stow 2013).
Lake Ontario
Trend: 5-year: No significant change
30-year: No significant trend
100-year: Increasing
Rationale: Water levels have been oscillating above and below long-term annual and monthly average values for
the past 30 years. Water level variability on Lake Ontario is impacted by regulation of its outflows and, in general,
has been reduced for the past 50 years (relative to the pre-regulation period). The International Joint Commission
(IJC) periodically investigates alternative approaches to managing Lake Ontario outflows to help restore Lake
Ontario's coastal wetlands while balancing other water resource management planning considerations. Most
recently, the IJC announced the adoption of "Plan 2014".
Other Spatial Scales
Water level fluctuations at finer spatial scales (including the fluctuations on Lake St. Clair) are not considered in this
report.
Sub-Indicator Purpose
•	The purpose of this sub-indicator is to track seasonal, inter-annual, and long-term (i.e. decadal) trends in
lakewide-average water levels across each of the Great Lakes.
•	The water levels sub-indicator is used in support of the climate change category, as well as general
objective #9 of the Great Lakes Water Quality Agreement (GLWQA) and the climate change, lakewide
management, and habitat and species annexes of the GLWQA.
Ecosystem Objective
Water level fluctuations have strong influences on Great Lakes habitats and the biological communities associated
with them. Impacts of alterations in water level fluctuations on shoreline ecosystems (particularly coastal wetlands)
are widely-documented, and underscore important additional (but less apparent) relationships between ecosystem
response, human intervention, and climate change.
Ecological Condition
Changes in Great Lakes water levels take place over a variety of time scales ranging from hourly fluctuations to
those taking place over hundreds and even thousands of years. Most of the monthly and interannual changes docu-
mented over the past 150 years are the result of changes in the Great Lakes hydrologic cycle. These are influenced
by natural and anthropogenic factors, and long-term climate trends (Baedke and Thompson 2000; Booth and Jack-
son 2003). Fluctuating water levels and changing connecting channel flows on the Great Lakes pose significant risks
to the economic, social, and enviromnental well-being of the Great Lakes region. High water levels can cause sig-
nificant damage due to flooding, erosion, overtopping of shore protection structures, loss of beaches and recreational
lands and their economic and social benefits, and loss of wetlands. High channel flows can impede navigation, and
there can be a greater susceptibility to storm damage from wind and waves. Low water can lead to increased dredg-
ing, encroachment of development in the nearshore, exposure of mudflats, undercutting of shore protection, loss of
marina services and access to boat launch facilities, risks to water supply infrastructure, nearshore water quality is-
sues, and ecosystem effects (e.g. isolating fish from their spawning habitats, or stranding wetlands). From an ecolog-
ical perspective, short and long-term lake level fluctuations are critical to maintain healthy coastal habitats, especial-
ly coastal wetlands. However, dramatic or sustained long-term changes can degrade coastal habitats.
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STATE OF THE GREAT LAKES 2017
The summary below is an account of the overall factors affecting the Great Lakes water balance (Neff and Killian
2003), and ultimately the water levels, and a limited discussion on water level history and variability.
The natural factors associated with long-term water level changes in the Great Lakes include environmental process-
es that contribute to inflow to, outflow from, and storage in the system. Within broad scales, water inflow and out-
flow are dictated by climatically-induced changes that affect the components of the hydrologic cycle. These compo-
nents include over-lake precipitation, runoff, over-lake evaporation, and connecting channel flows (i.e. the flow of
water into and out of each lake through the upstream and downstream connecting channel). Groundwater flows,
while an important contribution to the water cycle, do not represent a significant contribution to water level variabil-
ity relative to over-lake precipitation, runoff, and evaporation (Piggott et al. 2007).
An additional natural factor that affects water levels is glacial isostatic adjustment (GIA), which is the response of
the earth's crust to removal of the weight of the last glacial ice sheets that crossed the area (Wilcox et al.,2007; IU-
GLS 2009). Unlike hydrologic factors, GIA impacts on water levels vary from one location to another around a
lake. At some locations, water levels appear to be rising as a result of GIA, while levels appear to be falling at other
locations on the same lake. This has an implication for the analysis of historic water level data at a specific location.
Regulation of the outflows from Lake Superior and Lake Ontario seeks to lessen high and low levels (Wilcox et al.
2007). Lake Superior water levels have been regulated since 1916. In its 1914 Order of Approval, the International
Joint Commission (IJC) established the International Lake Superior Board of Control and delegated to it responsibil-
ity for setting Lake Superior outflows. The Board of Control established a regulation plan that has undergone several
revisions. The regulation plan currently in place incorporates the concept of balancing Lake Superior and Lake
Michigan-Huron levels.
With the approval by the IJC of the hydropower project at Cornwall, Ontario and Massena, New York under the
Order of Approval of 1952, Lake Ontario's outflow became subject to regulation. The first regulation plan became
operational in 1960. The subsequent reduction of the variability in Lake Ontario water levels has been shown to
diminish wetland plant diversity and the habitats they support (LOSLR Study Board, 2006).
Water levels are measured at several locations along the shore of the Great Lakes and their connecting channels by
the National Oceanic and Atmospheric Administration (NOAA) in the United States and by the Canadian Hydro-
graphic Service (CHS) in Canada (CHS, 2008). Several gauges in the current network of multiple gauges have been
in operation only since 1918, while others have gauge records (some less reliable) extending back to the 1840s.
Status and Trends in Lake Levels
Hydrographs in Figures 1 of recorded lake levels show some similarities of interest (Wilcox et al.2007). Generally,
periods of higher levels occurred in the late 1920s, the mid-1950s, and from the early 1970s to mid-1990s. Pro-
nounced low lake level periods occurred in the mid-1920s, the mid-1930s and the mid-1960s (Wilcox et al. 2007).
Though, less well documented, low levels also occurred in the late 1890s, following a long period of high lake lev-
els.
Based on the historical record as shown in Figure 1, there appears to be a range within which the lake levels remain,
but paleo records indicate a range that may have been greater fBrown et al. 2012).
There is considerable uncertainty in how climate change, particularly changes in precipitation and evaporation, may
impact net basin water supplies and water levels and flows in the Great Lakes-St. Lawrence River region. The cur-
rent state-of-the-art in climate models indicates that water levels are likely to be above and below their long-term
averages in the future, but that there is not strong evidence for a pronounced shift in the long-term mean.
Linkages
•	Coastal Wetland sub-indicators (Coastal Wetland Invertebrates, Coastal Wetland Fish, Coastal Wetland
Birds, Coastal Wetland Amphibians, Coastal Wetland Plants, and Coastal Wetlands: Extent and Composi-
tion) - water levels have a major influence on undiked coastal wetlands and are basic to any analysis of
wetland change trends
•	Phytoplankton, Zooplankton, Benthos, Diporeia, Preyfish, Lake Trout, Walleye, Lake Sturgeon and Fish
Eating and Colonial Nesting Waterbirds
•	Ice Cover
•	Surface Water Temperature
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STATE OF THE GREAT LAKES 2017
Comments from the Author(s)
The authors concur with previous State of the Great Lakes (previously known as SOLEC) reports that additional
future reporting cycles may want to focus on explicit connections between water level variability and ecosystem
response. Given the water level patterns over the past 10 years, it seems that there might be a significant opportuni-
ty to improving that understanding if and when ecological data becomes available.
Assessing Data Quality
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not
Applicable
1. Data are documented, validated, or
quality-assured by a recognized agency or
organization
X





2. Data are traceable to original sources
X





3. The source of the data is a known,
reliable and respected generator of data
X





4. Geographic coverage and scale of data
are appropriate to the Great Lakes Basin
X





5. Data obtained from sources within the
U.S. are comparable to those from
Canada
X





6. Uncertainty and variability in the data
are documented and within acceptable
limits for this sub-indicator report
X





Acknowledgments
Authors:
Andrew Gronewold, National Oceanic and Atmospheric Administration Great Lakes Enviromnental Research
Laboratory, Ann Arbor, Michigan
Contributors:
Wendy Leger, Enviromnent and Climate Change Canada
Jacob Bruxer, Enviromnent and Climate Change Canada
John Allis, U.S. Army Corps of Engineers
Keith Kompoltowicz, U.S. Army Corps of Engineers
Tim Hunter, National Oceanic and Atmospheric Administration Great Lakes Enviromnental Research Laboratory
Joeseph Smith, Cooperative Institute for Limnology and Ecosystems Research, National Oceanic and Atmospheric
Administration,
Anne Clites, National Oceanic and Atmospheric Administration, Great Lakes Enviromnental Research Laboratory
Derrick Beach, Enviromnent and Climate Change Canada
Information Sources
Assel, R. A., Quinn, F. H., and Sellinger, C. E. (2004). Hydroclimatic factors of the recent record drop inLaurentian
Great Lakes water levels. BAMS, 85(8), 1143-1151.
Baedke. S. J., and Thompson, T. A. (2000). A 4,700-Year Record of Lake Level and Isostasy for Lake Michigan.
Journal of Great Lake Research, 26 (4), 416-426.
Booth, R. K„ and Jackson S. T. (2003). A high-resolution record of late-Holocene moisture variability from a
Michigan raised bog, USA. Holocene, 13 (6), 863-876.
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STATE OF THE GREAT LAKES 2017
Brown, D. (2011). International Analysis of Implementing a new Water Level Regulation Plan or Building New
Structures: White Paper #2 What are the Key International Institutional Requirements for Building New Regulatory
or Compensatory Structures? Prepared for the International Upper Great Lakes Study Adaptive Management
Technical Work Group. March 31, 2011.
Donahue, M. (2011). An Institutional/Governance Analysis for Implementing a Non-regulation Adaptive Response
to Water Level Related Impacts. Prepared for the International Upper Great Lakes Study Adaptive Management
Technical Work Group.
Gronewold, A. D., and Stow, C. A. (2013). Unprecedented seasonal water level dynamics on one of the Earth's
largest lakes. Bulletin of the American Meteorological Society, 95(1).
Gronewold, A. D., and Stow, C. A. (2014). Water loss from the Great Lakes. Science, 343(6175), 1084-1085.
Gronewold, A. D., Clites, A., Bruxer, J., Kompoltowicz, K„ Smith, J., Hunter, T„ et al. (2015). Water levels surge
on Great Lakes. EOS,, 96(6), 14-17.
International Upper Great Lakes Study (2009). Impacts on Upper Great Lakes Water Levels: St. Clair River. Final
Report to the International Joint Commission, December 2009.
International Upper Great Lakes Study (2012). Lake Superior Regulation: Addressing Uncertainty in Upper Great
Lakes Water Levels. Final Report to the International Joint Commission, March, 2012.
International Lake Ontario - St. Lawrence River (LOSLR) Study Board (2006). Options for Managing Lake Ontario
and St. Lawrence River Water Levels and Flows. Final Report to the International Joint Commission, March, 2006.
Lenters, J. (2004). Trends in the Lake Superior water budget since 1948: a weakening seasonal cycle. Journal of
Great Lakes Research, 20-40.
Neff, B.P., and Killian, J.R., 2003, The Great Lakes water balance—Data availability and annotated bibliography of
selected references: U.S. Geological Survey Water-Resources Investigations Report 02-4296, 37 p.
Piggott et al. 2007
Quinn, F., and Edstrom, J. (2000). Great Lakes diversions and other removals. Canadian Water Resources Journal,
25(2), 125-151.
Wilcox, D.A, Thompson, T.A., Booth, R.K., and Nicholas, J.R., 2007, Lake-level variability and water availability
in the Great Lakes: U.S. Geological Survey Circular 1311, 25 p.
List of Figures
Figure 1. Water Levels in the Great Lakes. Red represents a 5-year trend, green represents a 30-year trend and blue
represents a 100-year trend.
Source: NOAA-GLERL
Last Updated
State of the Great Lakes 2017 Technical Report
Page 524
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STATE OF THE GREAT LAKES 2017
184 0
1B3 5
183 0
152 5 -
162 0
177.5
177 0
176 5
in
Q 176.0
_l
Q
yj 175-5
lh_
m
¦§- 1750
at
>
a>
— 174-5
5
I
> 174 0
173.5
1710 -
75 5
75.0
74 5 -
74 0
73 5 -
I" ' I'l" I'' I
Mtchigan-Huron
i	r™i	r—i
Ontario
f iyn/ Vu
£
o
o
p
01
¦H
Ju
B
1860	1880	1900	1920	1940	1960	1980 2000 2020
Figure 1. Water Levels in the Great Lakes. Red represents a 5-year trend, green represents a 30-year trend and blue
represents a 100-year trend.
Source: NOAA-GLERL
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STATE OF THE GREAT LAKES 201 7
Sub-Indicator: Base Flow Due to Groundwater
Overall Assessment
Trend: Undetermined
Rationale: For stream gages in the U.S. portion of the Great Lakes Basin, the Base Flow Index is near the
long-term average. Although base flow appears to be increasing in the western part of the basin and decreas-
ing in the central and eastern part of the Great Lakes Basin, the changes in Base Flow Index (ratio of base
flow to overall streamflow) are small, indicating that climatic variation during the time periods analyzed may
be driving the overall base flow values. Specific subwatershed trends have not been determined for each
stream gage in the Great Lakes Basin, and examination of local trends may indicate impacts on base flow not
apparent at the lake-basin scale. The stream gage data used in this analysis were only from the U.S. portion of
the basin and the time period from 2005-2013 was compared to records prior to 2005. Other analysis of
trends such as annual evaluations may reveal more subtle patterns.
Lake-by-Lake Assessment
Individual lake basin assessments were not prepared for this report.
Sub-Indicator Purpose
•	This sub-indicator estimates the contribution of base flow to total stream flow by subwatershed (lake-scale)
•	To detect changes in the source of water (base flow versus direct runoff) to total streamflow. Large changes in
base flow or base flow-index would indicate potential changes in the groundwater contribution to streamflow
that could affect the ecological function of the stream or riparian users.
Ecosystem Objective
The capacity of base flow to maintain in-stream flow conditions and aquatic habitat at, or near potential is not com-
promised by anthropogenic factors.
This sub-indicator best supports work towards General Objective #9 of the 2012 Great Lakes Water Quality Agree-
ment which states that the Waters of the Great Lakes should "be free from other substances, materials, or conditions
that may negatively impact the chemical, physical, or biological integrity of the Waters of the Great Lakes."
Ecological Condition
Measure
Aquatic ecosystems in the streams of the Great Lakes Basin have developed in response to natural variations in flow
including low-flow conditions. Because base flow maintains streamflow volume and helps regulate stream tempera-
ture, it is considered important in maintenance of aquatic ecosystems. Long term average base flow relative to
streamflow is referred to as a Base Flow Index. The Base Flow Index is a dimensionless value between 0 and 1
where increasing values of the index indicate increasing groundwater discharge and base flow. Significant extents
of sand and gravel within a watershed often result in relatively large values of base flow index while significant ex-
tents of clay often result in relatively small values. Human impacts on base flow can potentially be detected using
trend analysis of base flow, however, climatic factors can also result in Base Flow Index changes over time.
Endpoint
Anthropogenic factors are not responsible for deviations in the base flow characteristics of subwatersheds. No quan-
tifiable endpoint or reference value is available at this time.
Background
Base flow provides a relatively stable supply of usually high quality water with stable temperatures. The Base Flow
Index indicates how much of a stream's flow comes from base flow compared to total streamflow. A high Base
Flow Index indicates that direct runoff contributes little to overall streamflow. Changes in the Base Flow Index
through time indicate variability in the relative contribution of base flow and runoff to streamflow, and these chang-
Page 526
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STATE OF THE GREAT LAKES 2017
es may have implications for the health of stream ecosystems. Changes in the Base Flow Index through time may
result from climate variability and (or) anthropogenic influences.
A significant portion of precipitation over the inland areas of the Great Lakes Basin returns to the atmosphere by
evapotranspiration. Water that does not return to the atmosphere either flows across the ground surface or infiltrates
into the subsurface and recharges groundwater. Water that flows across the ground surface discharges into surface
water features (rivers, lakes, and wetlands) and then flows toward and eventually into the Great Lakes. Water that
infiltrates into the subsurface and recharges groundwater also results in flow toward the Great Lakes. Groundwater
contributes more than 50 percent of the flow in streams that discharge into the Great Lakes (Grannemann et al.
2000). Accordingly, groundwater is a critical factor in the establishment and maintenance of aquatic habitat
(Mortsch et al. 2003) because groundwater discharge often provides thermal refuges for fish in summer when tem-
peratures are high. Most recharged groundwater flows at relatively shallow depths at local scales and discharges into
adjacent surface water features as base flow. However, groundwater also flows at greater depths at regional scales
and discharges either directly into the Great Lakes or into distant surface water features. The quantities of ground-
water flowing at these greater depths can be significant locally but are generally believed to be modest relative to the
quantities flowing at shallower depths.
The component of stream flow due to runoff from the ground surface is rapidly varying and transient, and results in
the peak discharges of a stream. Groundwater discharge to surface water features in response to precipitation is
greatly delayed relative to surface runoff. The stream flow resulting from groundwater discharge is, therefore, more
uniform. In the Great Lakes region, groundwater discharge is often the dominant component of base flow. Base flow
is the less variable and more persistent component of total streamflow (example hydrograph in figure 1).
For this report, base flow represents the consistent part of streamflow that is not surface-runoff. In the Great Lakes
region groundwater discharge is often the dominant component of base flow. However, anthropogenic activities
such as flow regulation, the storage and delayed release of water using dams and reservoirs, creates a steady stream-
flow signature that is similar to that of groundwater discharge. This analysis does not distinguish between base flow
from groundwater discharge and base flow-type streamflow patterns resulting from anthropogenic activities.
Status of Base Flow
Base flow is frequently determined using hydrograph separation. This process uses streamflow monitoring infor-
mation as input and partitions the observed flow into rapidly and slowly varying components, i.e., surface runoff and
base flow, respectively. Figure 1 illustrates the daily streamflow monitoring information and the results of hydro-
graph separation for the Nith River at New Hamburg, Ontario, for January 1 to December 31, 1993. The rapidly
varying response of streamflow to precipitation and snow melt are in contrast to the more slowly varying base flow.
Application of hydrograph separation to daily streamflow monitoring information results in lengthy time series of
output. Various measures are used to summarize this output. For example, the Base Flow Index is a simplified sum-
mary of the relative contribution of base flow to streamflow that is appropriate for use in regional scale studies (Neff
et al., 2005). Base Flow Index is defined as the average rate of base flow relative to the average rate of total stream-
flow, has no units, and varies from zero to one where increasing values indicate an increasing contribution of base
flow to streamflow. The value of Base Flow Index for the data shown in Figure 1 is 0.28, which implies that 28% of
the observed flow is estimated to be base flow
For this sub-indicator, five base flow separation techniques were applied to daily mean streamflow data from 227
gages operated by the U.S. Geological Survey (USGS) in the U.S. portion of the Great Lakes Basin. The five tech-
niques were: BFI (Base Flow Index method or the UKIH method, Wahl and Wahl, 2003), HYSEP local minimum,
HYSEP fixed interval, and HYSEP sliding interval (Sloto and Crouse, 1996), and PART (Rutledge, 1998). The 227
gages were selected according to the following criteria; the gages: (1) had data available in the USGS NWIS data-
base, (2) were included in the Neff et al. (2005) report, and (3) had at least 3 continuous years of complete daily
streamflow data for each time period (pre-2005 and 2005-2013). The Base Flow Index in each time period for each
basin is the mean of five base flow separation techniques. The gages are a subset of the 959 gages that Neff et al.
(2005) used to develop a regression model to predict base flow in ungagged basins (Figure 2). The pre-2005 values
presented in this report are not taken directly from Neff et al. (2005), but were re-calculated using the same 5 base
flow separation methods as applied to the 2005-2013 data. The base flow indices in this report may therefore be
slightly different from the Neff et al (2005) report, but are well-suited for a direct comparison between pre-2005 and
2005-2013 (Figure 3 and Table 1).
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STATE OF THE GREAT LAKES 2017
When the base flow indices for all 227 gages are aggregated together, there is very little change in the overall mean
Base Flow Index, suggesting there was not a consistent shift in Base Flow Index for the entire U.S. portion of the
Great Lakes Basin (table 1). The change in the mean and maximum Base Flow Index values using any of the analy-
sis methods are generally less than one percent. The estimated minimum Base Flow Index value is sensitive to
small changes and has a higher percent change. Overall, this simple comparison does not indicate significant trend
in the sub-indicator. However, many individual gages show differences in the Base Flow Index between the two
time periods (Table 2). Of the 227 gages, 142 had increases or decreases in all 5 base flow separate techniques.
This change was considered a "strong" signal and these basins are shown as red or blue points in Figure 3 and are
summarized in Table 2. The largest within-basin negative change was -0.15 and the largest positive change was
0.15. This value represents a large change in Base Flow Index (recalling that the scale goes from 0 to 1). The posi-
tive and negative responses show some spatial clustering (Figure 3) suggesting that regional climatic patterns and
(or) regional land use changes are driving at least part of the change in Base Flow Index. However, this entire anal-
ysis is very coarse and a more rigorous analysis of individual streamflow records at a more refined time interval may
be necessary to identify trends.
Additional information is also required to determine the extent to which human activities have impaired groundwa-
ter discharge. Change in base flow over time may be subtle and difficult to quantify (e.g., variations in the relation
of base flow to climate) and may be continuous (e.g., a uniform increase in base flow due to aging water supply in-
frastructure and increasing conveyance losses) or discrete (e.g., an abrupt reduction in base flow due to a new con-
sumptive water use). Change may also be the result of cumulative impacts due to a range of historical and ongoing
human activities, and may be more pronounced and readily detected at local scales than at the scales that are typical
of continuous stream flow monitoring.
The discharge of groundwater to surface water features is the end-point of the process of groundwater recharge,
flow, and discharge. Human activities impact groundwater discharge by modifying the components of this process
where the time, scale, and to some extent the severity, of these impacts is a function of hydrogeological factors and
the proximity of surface water features. Increasing the extent of impervious surfaces during residential and commer-
cial development and installation of drainage to increase agricultural productivity are examples of activities that may
reduce groundwater recharge and ultimately groundwater discharge.
Withdrawals of groundwater as a water supply and during dewatering (pumping groundwater to lower the water
table during construction, mining, etc.) remove groundwater from the flow regime and may also reduce groundwater
discharge. Groundwater discharge may be impacted by activities such as the channelization of water courses that
restrict the motion of water across the groundwater and surface water interface. Human activities also have the ca-
pacity to intentionally, or unintentionally, increase groundwater discharge. Induced storm water infiltration, convey-
ance losses within municipal water and wastewater systems, and closure of local water supplies derived from
groundwater are examples of factors that may increase groundwater discharge. Climate variability and change may
compound the implications of human activities relative to groundwater recharge, flow, and discharge.
Linkages
Base flow due to the discharge of groundwater to the rivers, inland lakes and wetlands of the Great Lakes Basin is a
significant and often major component of stream flow, particularly during low-flow periods. Base flow frequently
satisfies flow, level, quality and temperature requirements for aquatic species and habitat. Water supplies and the
capacity of surface water to assimilate wastewater discharge are also dependent on base flow. Base flow due to
groundwater discharge is therefore critical to the maintenance of water quantity, quality, and integrity of aquatic
species and habitat. Natural factors such as climate variability modify both average rates of base flow and the annual
distribution of flow. Pressures such as urban development and water use, in combination with the potential for cli-
mate change impacts, may alter base flow. Reductions in base flow may compromise the assimilative capacity of
surface water for wastewater discharge during periods of otherwise low flow and result in reduced water quality.
The higher the Base Flow Index value, the more sensitive the watershed is to climate change based on the Sensitivi-
ty Mapping and Local Watershed Assessments for Climate Change Detection and Adaptation Monitoring report."
Comments from the Author(s)
Groundwater has important societal and ecological functions across the Great Lakes Basin. Groundwater is typically
a high quality water supply that is used by a significant portion of the population, particularly in rural areas where it
is often the only available source of water. Groundwater discharge to rivers, lakes, and wetlands is also critical to
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STATE OF THE GREAT LAKES 2017
aquatic species and habitat and to in-stream water quantity and quality. These functions are concurrent and occa-
sionally conflicting.
Pressures such as urban development and water use, in combination with the potential for climate impacts and fur-
ther contamination of the resource, may increase the frequency and severity of these conflicts. In the absence of sys-
tematic accounting of groundwater supplies, use, and dependencies, it is the ecological function of groundwater that
is most likely to be compromised.
Managing the water quality of the Great Lakes requires an understanding of water quantity and quality within the
inland portion of the basin, and this understanding requires recognition of the relative contributions of surface runoff
and groundwater discharge to stream flow. The results described in this report indicate the significant contribution of
groundwater discharge to flow within the tributaries of the Great Lakes.
The dynamics of groundwater flow and transport are different than those of surface-water flow. Groundwater dis-
charge responds more slowly to climate and maintains stream flow during periods of reduced water availability, but
this capacity is known to be both variable and finite. Contaminants that are transported by groundwater may be in
contact with geologic materials for years, decades, and perhaps even centuries or millennia. As a result, there may
be considerable opportunity for attenuation of contamination prior to discharge. However, the lengthy residence
times of groundwater flow also limit opportunities for the removal of contaminants, in general, and non-point source
contaminants, in particular.
Further research and analysis is required to determine if the changes in base flow noted are due to human causes and
to determine conditions on a lake-by-lake basis.
Recent investigations on trends in streamflow characteristics (Hodgkins et al. 2007) could be expanded to the Cana-
dian part of the basin. Similarly, analyses of trends in groundwater recharge (Rivard et al. 2009) could be completed
in greater detail across both the Canadian and U.S. portions of the basin.
Assessing Data Quality
Data Characteristics
Strongly
Agree
Agree
Neutral or
Unknown
Disagree
Strongly
Disagree
Not
Applicable
1. Data are documented, validated, or
quality-assured by a recognized agency or
organization
X





2. Data are traceable to original sources
X





3. The source of the data is a known,
reliable and respected generator of data
X





4. Geographic coverage and scale of data
are appropriate to the Great Lakes Basin


X



5. Data obtained from sources within the
U.S. are comparable to those from
Canada


X



6. Uncertainty and variability in the data
are documented and within acceptable
limits for this sub-indicator report

X




Acknowledgments
Authors:
Norman Grannemann, U.S. Geological Survey, nggranne@usgs. gov
Howard Reeves, U.S. Geological Survey, hwreeves@usgs.gov
Jared Trost, U.S. Geological Survey, itrost@usgs.gov
Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the
Page 529
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STATE OF THE GREAT LAKES 2017
U.S. Government.
Information Sources
Hodgkins, G.A, Dudley, R.W., and Aichele, S.S., 2007, Historical changes in precipitation and streamflow in the
U.S. Great Lakes Basin, 1915-2004: U.S. Geological Survey Scientific Investigations Report 2007-5118, 31 p.
Neff, B.P., Day, S.M., Piggott, A.R., and Fuller, L.M. 2005. Base Flow in the Great Lakes Basin: U.S. Geological
Survey Scientific Investigations Report 2005-5217, pp. 23.
Piggott, A.R., Moin, S., and Southam, C. 2005. A revised approach to the UKIH method for the calculation of base
flow: Hydrol. Sci. J., 50:911-920.
Rivard, C., Vigneault, H., Piggott, A.R., Larocque, M., and Anctil, F. 2009. Groundwater recharge trends in Canada:
Can. J. Earth Sci., 46:841-854.
Rutledge, A.T., 1998, Computer programs for describing the recession of ground-water discharge and for estimat-
ing mean ground-water recharge and discharge form streamflow data - update: U.S. Geological Survey Water-
Resources Investigations Report 98-4148, 43 p.
Sloto, R.A., and Crouse, M.Y., 1996, HYSEP: A computer program for streamflow hydrograph separation and
analysis: U.S. Geological Survey Water-Resources Investigations Report 96-4040, 46 p.
Wahl, T. L., and K. L. Wahl. 2003. "BFI: A Computer Program for Determining an Index to Base Flow." US Bureau
of Reclamation. http:/7www. usbr.gov/pmts/hydraulicsjab/hvahl/bfi/ (sept 2010).
List of Tables
Table 1. Comparison of Base Flow Index for 227 gages with at least 3 continuous years of daily streamflow data in
two time periods: pre-2005 and 2005 to 2013. The gages are all in the U.S. portion of the Great Lakes Basin and
were included in the Neff et al. (2005) analysis of base flow.
Source: unpublished data from J. Trost, U.S. Geological Survey
Table 2. Number of gages (grouped by the magnitude of the change) in which all 5 base flow separation methods
indicated either an increase or decrease in Base Flow Index from pre-2005 to 2005-2013. These correspond to the
red and blue points in figure 3.
Source: unpublished data from J. Trost, U.S. Geological Survey
List of Figures
Figure 1. Hydrograph of observed total stream flow (black) and calculated base flow (red) for the Nith River at New
Hamburg during 1993.
Source: Environment Canada and the U.S. Geological Survey.
Figure 2. Location of gages with streamflow records used in Neff et al. (2005) to predict base flow in ungagged
basins.
Source: Environment Canada and the U.S. Geological Survey (Neff et al., 2005).
Figure 3. Difference in Base Flow Index for selected stream gages on the U.S. part of the Great Lakes Basin for the
period 2005-2013 and data prior to 2005.
Source: unpublished analysis from J. Trost, U.S. Geological Survey
Last Updated
State of the Great Lakes 2017 Technical Report
Page 530
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STATE OF THE GREAT LAKES 2017
Method
Pre-2005
2005-2013







minimum
mean
maximum
minimum
mean
maximum
BFI
0.071
0.575
0.955
0.120
0.578
0.958
HYSEP fixed interval
0.257
0.708
0.957
0.241
0.702
0.960
HYSEP local minimum
0.226
0.642
0.955
0.196
0.633
0.954
HYSEP sliding
0.259
0.708
0.957
0.231
0.702
0.959
PART
0.064
0.711
0.965
0.058
0.710
0.966
Table 1. Comparison of Base Flow Index for 227 gages with at least 3 continuous years of daily streainflow data in
two time periods: pre-2005 and 2005-2013. The gages are all in the U.S. portion of the Great Lakes Basin and were
included in the Neff et al. (2005) analysis of base flow.
Source: Neff et al. (2005) and U.S. Geological Survey
Magnitude of change
between time periods
0 to 0.05 change
0.05 to 0.10 change
more than 0.10 change
Total
Increase Decrease
Number of gages
42
7
4
74
14
1
53
89
Table 2. Number of gages (grouped by the magnitude of the change) in which all 5 base flow separation methods
indicated either an increase or decrease in Base Flow Index from pre-2005 to 2005-2013. These correspond to the
red and blue points in figure 3.
Source: U.S. Geological Survey
Page 531
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STATE OF THE GREAT LAKES 2017
1000
100
10
1993/10/01
Figure 1. Hydrograph of observed total stream flow (black) and calculated base flow (red) for the Nith River at New
Hamburg during 1993.
Source: Environment Canada and the U.S. Geological Survey.
Page 532
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STATE OF THE GREAT LAKES 2017
LAKE SUPERIOR'^1
, m •• '
pPiAK^,
fr-GNmRIO
LAte j£l£
•M ~ J HL/fltyV /«•
Figure 2. Location of gages with streamflow records used in Neff et al. (2005) to predict base flow in ungagged
basins.
Source: Environment Canada and the U.S. Geological Survey (Neff et al., 2005).
Page 533
-------
STATE OF THE GREAT LAKES 2017
W
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-------
STATE OF THE GREAT LAKES 201 7
6. Conclusion & Overall Assessment Summary Table
The overall assessment for ecosystem conditions in the Great Lakes is Fair and the trend is Unchanging since the
last assessment in 2011. Significant progress has been made to reduce toxic chemicals in the Great Lakes over the
past 40 years. However, challenges exist including, excessive nutrient loadings to Lake Erie and other nearshore
areas of the Great Lakes which are contributing to harmful and nuisance algae. And while the number of new
invasive species entering the Great Lakes has been dramatically reduced, the impact and spread of existing invaders
in the basin are posing significant consequences for the ecosystem. This overall assessment is based on the nine
science-based indicators that assess water quality and ecosystem health. The assessment also takes into
consideration climate trends (see table below). There are 44 sub-indicators that support the nine high-level
indicators used for state of the Great Lakes reporting and are used to measure progress against the nine General
Objectives of the Agreement.
Indicator
Sub-Indicator
Lake Superior | Lake Michigan
Lake Huron
Lake Erie
Lake Ontario
Overall Assessment
Drinking Water
Beaches



SI
Good and Unchanging
Fair to Good and Unchanging
Fish Consumption
Contaminants in Edible Fish
Toxic Chemical Concentrations
Unchanging Improving
Improving Unchanging
Unchanging
Unchanging
Deteriorating
Unchanging
Improving
Unchanging
Fair and Unchanging

Toxic Chemicals in Sediments
Unchanging Unchanging Unchanging
Improving
Improving


Toxic Chemicals In Great Lakes Whole
Fish
Unchanging
Improving
Unchanging
Unchanging
Improving
Fair and Unchanging to
Improving

Toxic Chemicals in Great Lakes
Herring Gull Eggs
Improving
Improving
Improving
Unchanging
Unchanging
Atmospheric Deposition of Toxic
Chemicals
No lake was assessed separately
Great Lakes Basin assessment is Fair and Improving

Coastal Wetland Amphibians
Unchanging
Unhanging
Unchanging



Coastal Wetland Birds
Coastal Wetland Fish
Unchanging
Unchanging Unchanging Deteriorating
No lake was assessed separately
Great Lakes Basin assessment is Fair and Improving
Improving

Coastal Wetland Invertebrates
No lake was assessed separately
Great Lakes Basin assessment is Fair and Deteriorating

Coastal Wetland Plants
Coastal Wetlands: Extent and
Composition
Undetermined
Undetermined Deteriorating Unchanging
No lake was assessed separately
Great Lakes Basin assessment is Undetermined

Aquatic Habitat Connectivity
Phytoplankton
Zooplankton
Benthos
Dipareia
Prey Fish
Improving
Deteriorating
Unchanging
Unchanging
Deteriorating
Unchanging
Unchanging
Hot p r i n ntin-
Improving
Improving
Unchanging
Unchanging
Unchanging
Fair and Unchanging
Nutrients and Algae
Unchanging
Unchanging
Unchanging
Unchanging
Deteriorating
Deteriorating
Detenorating
Undetermined
Improving

Lake Sturgeon
IW9
improving
Improving
improving

Walleye
Unchanging
Unchanging
Unchanging
Improving
Unchanging
Lake Trout
Unchanging
Improving
Improving
Improving
Improving
Fish Eating and Colonial Nesting
Waterbirds
Unchanging
Unchanging
Unchanging
Unchanging
Unchanging
Nutrients in Lakes
Unchanging
Unchanging
Deteriorating
Deteriorating
Undetermined
Deteriorating
Fair and Unchanging to
Deteriorating
Ciadophora
Undetermined
Undetermined Undetermined
Harmful Algal Blooms
Undetermined
Undetermined
Undetermined
Deteriorating
Deteriorating

Water Quality in Tributaries
Unchanging
Undetermined
Unchanging
wnmm
Unchanging
Invasive Species
Impacts of Aquatic Invasive Species
Dreissenid Mussels
Deteriorating Deteriorating Deteriorating
Improving


Unchanging
Deteriorating
Mtttlontting
Sea Lamprey
Improving
Deterioratine
Improving
Deteriorating
Improving
Improving
Unchanging

Groundwater






Fair and Undetermined
Watershed Impacts and
Climate Trends
Forest Cover
Unchanging
Unchanging
Unchanging
Unchanging
Unchanging
improving
Deteriorating
Fair and Unchanging
Land Cover
Unchanging
Unchanging
Unchanging
Watershed Stressors
Hardened Shorelines
Unchanging
Undetermined
Unchanging
Undetermined
Unchanging
Undetermined
Unchanging Unchanging
Undetermined Deteriorating
Tributary Ftashiness
No lake was assessed separately
Great Lakes Basin trend is Unchanging

Human Population
Decreasing
Increasing Increasing increasing Increasing


Precipitation Amounts
(1948-2015)
No lake was assessed separately
Great Lakes Basin trend is ®


Surface Water Temperature
(1979/1980-2014)
~
~
~
Undetermined
Undetermined

Climate Trends
Ice Cover
(1973-2015)
*

*
*
4


Water Levels
(1985-2015)
*
*
~
*
No significant
change


Baseflow Due to Groundwater
No lake was assessed separately
Great Lakes Basin trend is Undetermined

| s.„,u5:| soon"!
Page 535
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STATE OF THE GREAT LAKES 201 7
7. Acknowledgements
Acknowledgements included are for the State of the Great Lakes 2017 reports.
The State of the Great Lakes 2017 preparation team included:
Environment and Climate Change Canada
Nancy Stadler-Salt, lead
Stacey Cherwaty-Pergentile
Jennifer Collingswood
Lucas Van Landschoot
United States Environmental Protection Agency
Jacqueline Adams, lead
Todd Nettesheim
Karen Rodriguez
Paul Horvatin
Kristin TePas (Sea Grant)
Meg Corcoran (ORISE)
Kathryn Meyer (ORISE)
This report contains contributions from hundreds of authors, contributors and reviewers to the sub-indicator reports
and the indicator assessments, and their work is sincerely appreciated. Their voluntary time and effort to collect,
assess and report on conditions of the Great Lakes ecosystem components reflects their dedication and professional
cooperation.
Individual authors and contributors are recognized at the end of their respective report component.
We recognize the participation of the following organizations as authors and members of the Ecosystem Indicators
and Reporting Task Team who have contributed invaluable advice and support in the development of the State of
the Great Lakes 2017 products.
0 LAKE SUPERIOR 1^1 Environment and Environnement et
STATE UNIVERSITY B B
TOLEDO
Climate Change Canada Changement climauque Canada
Ontario ££*<
Ontario i
IUSGS
Fisheries and Oceans
Canada
P£ches et Oceans
Canada
Ministry of the
Environment
and Climate Change
Mrafcyof
nJFomty
1*1 Canada	Canada
Natural Resources Ressources nalureftes
University of Minnesota
L MVERSITYtf WISCONSIN


UMMMM
ONTARIO
US Army Corp#
University ofEnginwrs.
of Windsor
uv\m y>j
MILWAUKEE {JMD
"l BFKX:KK)RT
Uwmrr a Mmnoot* Duuhh
Driv«i» It Dittt'lf
MAgncutlure and Agriculture et
Agn-Food Canada Agroalimentaire Canada
LimnoTechO- CMU CILERtV
CENTRAL MICHIGAN	Z ~ ~	..
university	Cooperative Institute for unnologt
iixl Ecomtcim Roonh
CHIDE school of natural
L,i((>I I KfiL***, |V	NUHh RESOURCES &ENVIRONMEI
f	U1111 L university or michigan-
UNIVERSITY OF WISCONSIN	—^	n
River Falls	U Buffalo State
USDA U.S. Department of
Agriculture
UMD
Natural Rtiourcn
D»mvcK Inttitiii*
osu
Oregon State
OHEIDELBE
UNIVERSI
S«ia liranl
t* 'Ontario
ii .I*Agriculture.
Food and Rural Affairs
t
~
GREEN BAY
360' OF LEARNING
¦vnwt
C
nature i• naturh K.SCANADA
CANADA
UNIVERSITY OF
TORONTO
M ISSISSAUG A
Page 536
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STATE OF THE GREAT LAKES 201 7
Appendix 1 - Data Quality
Environment and Climate Change Canada and U.S. Environmental Protection Agency staff seek to disseminate the highest
quality infonnation available to a wide variety of environmental managers, policy officials, scientists and other interested
public. The importance of the availability of reliable and useful data is implicit in the State of the Great Lakes reporting
process.
To ensure that data and infonnation made available to the public by federal agencies adhere to a basic standard of objectivity,
utility, and integrity, the U. S. Office of Management and Budget issued a set of Guidelines in 2002 (OMB 2002).
Subsequently, other U.S. federal agencies have issued their own guidelines for implementing the OMB policies. According to
the Guidelines issued by the U.S. Environmental Protection Agency (U.S. EPA 2002), infonnation must be accurate, reliable,
unbiased useful and uncompromised though corruption or falsification.
Other assessment factors (U.S. EPA 2003) that are typically taken into account when evaluating the quality and relevance of
scientific and technical infonnation include:
•	Soundness - the extent to which the scientific and technical procedures, measures, methods or models employed to generate
the infonnation are reasonable for, and consistent with, the intended application
•	Applicability and Utility - the extent to which the infonnation is relevant for the intended use
•	Clarity and Completeness - the degree of clarity and completeness with which the data, assumptions, methods, quality
assurance, sponsoring organizations and analyses employed to generate the infonnation are documented
•	Uncertainty and Variability - the extent to which the variability and uncertainty (quantitative and qualitative) in the
infonnation or in the procedures, measures, methods or models are evaluated and characterized
•	Evaluation and Review - the extent of independent verification validation and peer review of the infonnation or of the
procedures, measures, methods or models
Recognizing the need to more fonnally integrate concerns about data quality into the State of the Great Lakes reporting
process, organizers developed a Quality Assurance Project Plan (QAPP). The QAPP recognizes that, as an entity, does not
directly measure any environmental or socioeconomic parameters.
Existing data are contributed by cooperating federal, state and provincial environmental and natural resource agencies, non-
governmental environmental agencies or other organizations engaged in Great Lakes monitoring. Additional data sources
may include local governments, planning agencies, and the published scientific literature. Therefore, State of the Great Lakes
reporting relies on the quality of datasets reported by others.
Characteristics of datasets that would be acceptable for indicator reporting include:
•	Data are documented, validated, or quality-assured by a recognized agency or organization.
•	Data are traceable to original sources.
•	The source of the data is a known reliable and respected generator of data.
•	Geographic coverage and scale of data are appropriate to the Great Lakes Basin
•	Data obtained from sources within the United States are comparable with those from Canada.
Additional considerations include:
•	Gaps in data availability should be identified if datasets are unavailable for certain geographic regions and/or contain a level
of detail insufficient to be useful in the evaluation of a particular sub-indicator.
•	Data should be evaluated for feasibility of being incorporated into sub-indicator reports. Attention should be given to
budgetary constraints in acquiring data, type and fonnat of data time required to convert data to usable fonn, and the
collection frequency for particular types of data.
State of the Great Lakes reporting relies on a distributed system of infonnation in which the data reside with the original
providers. Although data reported through this process are not centralized, clear links for accessibility of the data and/or the
sub- indicator authors are provided. The authors hold the primary responsibility for ensuring that the data used are adequate
for sub-indicator reporting. Users of the sub-indicator information, howe\>er, are obliged to e\>aluate the usefulness and
appropriateness of the data for their own application, and they are encouraged to contact the authors with any concerns or
questions.
Page 537
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STATE OF THE GREAT LAKES 201 7
Appendix 2 - Status and Trend Definitions for State of the Great Lake
Reporting
Status and Trend Definitions for State of the Great Lakes 2017 Reports
Below are generic definitions for the terms used to assess the sub-indicators and indicators in the suite. Specific
language for these terms is included in each complete sub-indicator report included in Chapter 5 of this document.
STATUS Terms

GOOD
Most or all ecosystem components are in acceptable condition.
FAIR
Some ecosystem components are in acceptable condition.
POOR
Very few or no ecosystem components are in acceptable condition.
UNDETERMINED
Data are not available or are insufficient to assess condition of the
ecosystem components.

TREND Terms

IMPROVING
Metrics show a change toward more acceptable condition.
UNCHANGING
Metrics generally show no overall change in condition.
DETERIORATING
Metrics show a change away from acceptable condition.
UNDETERMINED
Metrics do not indicate a clear overall trend, or data are not available
to report on a trend.
Climate information is not assessed in the same manner as other indicators in this report. For example, the
ecosystem has adapted to and needs both high and low water levels and neither condition can be assessed as Good or
Poor. However, prolonged periods of high or low water levels may cause stress to the ecosystem. Therefore, climate
trends are simply assessed as Increasing, Unchanging or Decreasing over a defined period of time. In addition to the
climate trends sub-indicators, there are two additional sub-indicators that are assessed using the trend analysis noted
here, i.e. Tributary Flashiness and Human Population, for a similar explanation as noted above, that increasing or
decreasing flashiness or population cannot be assessed as Good or Poor.
INCREASING
Indicator metric is increasing.
DECREASING
Indicator metric is decreasing.
UNCHANGING
Indicator metric has not changed over time.
UNDETERMINED
Data are not available to report on a trend.
Page 538
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STATE OF THE GREAT LAKES 201 7
Appendix 3 - Tracking Progress: Another Perspective
The overall assessment for the Great Lakes is Fair and Unchanging, however, the assessments of the
sub-indicators can also be plotted individually to show that most of the results fall within the shaded zone
of Fair and Improving, Fair and Unchanging, and Good and Unchanging. Summarizing the information
in this manner shows an alternate way to track progress. Hie goal is to see more sub-indicators assessed
in the upper right zone of the chart. Note that the figure includes 37 sub-indicator assessments as the
remaining 7 sub-indicators are not assessed in this same manner.
Improving
•

OO
OO
OO
o
0
Unchanging


o
OO
OO
OO
/rs
A
OO
OO
OO
Undetermined
•
00
00
00

Deteriorating
• •
• •

000


Poor
Undetermined
Fair

Good
STATUS
Page 539
-------
STATE OF THE GREAT LAKES 201 7
Appendix 4 - Acronyms and Abbreviations
Agencies and Organizations
ATSDR - Agency for Toxic Substances and Disease Registry
CAMNet - Canadian Atmospheric Mercury Network
CCFM - Canadian Council of Forest Ministers
CCME - Canadian Council of Ministers of the Environment
CDC - Center for Disease Control (U.S.)
CHS - Canadian Hydrographic Service
CIS - Canadian Ice Service
CMI-CWF - Clean Michigan Initiative-Clean Water Fund
CORA - Chippewa Ottawa Resource Authority
CWS - Canadian Wildlife Service
DFO - Department of Fisheries and Oceans Canada
EC - Environment Canada
ECCC - Enviromnent and Climate Change Canada
ECO - Enviromnental Careers Organization
EDDMapS - Early Detection & Distribution Mapping System
EIA - Energy Information Administration (U.S.)
EMAN - Ecological Monitoring and Assessment Network
GLBET - Great Lakes Basin Ecosystem Team (USFWS)
GLC - Great Lakes Commission
GLCWC - Great Lakes Coastal Wetlands Consortium
GLFC - Great Lakes Fishery Commission
GLNPO - Great Lakes National Program Office (U.S. EPA)
IJC - International Joint Commission
IUCN - International Union for the Conservation of Nature
LEC - Great Lakes Fishery Commission's Lake Erie Committee
LEPMAG - Lake Erie Percid Management Advisory Group
LSSU - Lake Superior State University
MDEQ - Michigan Department of Enviromnental Quality
MDNR - Michigan Department of Natural Resources
NAPS - National Air Pollution Surveillance (EC)
NDBC - National Data Buoy Centre
NHEERL - National Health & Enviromnental Effects Research Laboratory (U.S. EPA)
NISC - National Invasive Species Council
NOAA - National Oceanic and Atmospheric Administration
NOS - National Ocean Service
NRCan - Natural Resources Canada
NRCS - Natural Resources Conservation Service (USDA)
NRRI - Natural Resources Research Institute (University of Minnesota - Duluth)
NYSDEC - New York State Department of Enviromnental Conservation
NYSOGS - New York State Office of General services
ODNR - Ohio Department of Natural Resources
ODW - Ohio Division of Wildlife
OGS - Ontario Geological Survey
OIPIS - Ontario Invasive Plant Information System
OMAF - Ontario Ministry of Agriculture and Food (now OMAFRA, see below)
OMAFRA - Ontario Ministry of Agriculture, Food and Rural Affairs
Page 540
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STATE OF THE GREAT LAKES 201 7
OMOE - Ontario Ministry of Environment
OMOECC - Ontario Ministry of the Environment and Climate Change
OMNRF - Ontario Ministry of Natural Resources and Forestry
OSCIA - Ontario Soil and Crop Improvement Association
ORISE - Oak Ridge Institute for Science and Education
PDEP - Pennsylvania Department of Enviromnental Protection
REMAP - Regional Enviromnental Monitoring and Assessment Program (U.S.)
STC - Standing Technical Committee
TNC - The Nature Conservancy
UKIH - United Kingdom Institute of Hydrology
US ACE - United States Army Corp of Engineers
USDA - U.S. Department of Agriculture
USEPA - U.S. Enviromnental Protection Agency
USFDA - U.S. Food and Drug Administration
USFWS - U.S. Fish and Wildlife Service
USFS - U.S. Forest Service
USGS - U.S. Geological Survey
WBCSD - World Business Council for Sustainable Development
WDNR - Wisconsin Department of Natural Resources
WiDPH - Wisconsin Department of Public Health
WWF - World Wildlife Fund (Canada)
Units of Measure
C - Celsius
Cfu - colony forming units
cm - centimeter (centimetre), 10"2 meters (metres)
Chi a - chlorophyll a, a measure of phytoplankton biomass
F - Fahrenheit
Fg - femptogram, 10"15 gram
ft - feet (British system)
g Dw m-2 - grams dry weight per meter (metre) squared
g m-2 - grams per meter (metre) squared (areal biomass)
ha - hectare, 10,000 square meters (metres)
lbs - pounds (British system)
kg - kilogram, 1000 grams
km - kilometer (kilometre)
kt - British kiloton: 2xl06 pounds; metric kilotonne: 106 kg or 2.2xl06 pounds
kWh - kilowatt-hour
m - meter (metre)
mg - milligram, 10~3 gram
MGD - Million Gallons per Day (3785.4 m3 per day)
mg m"3 - milligram per cubic meter (metre), volumetric biomass
mg/kg - milligram per kilogram, part per million
mg/1 - milligram per liter (litre)
ml - milliliter (millilitre), 10"3 liter (litre)
MLD - Million Liters (Litres) per Day (1000 m3 per day)
mm - millimeter (millimetre), 10"3 meter (metre)
MWh - megawatt-hour
ng - nanogram, 10"9 gram
Page 541
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STATE OF THE GREAT LAKES 201 7
ng/g - nanogram per gram, part per billion
ng /g dw - nanogram per gram, dry weight
ng/1 - nanogram per liter (litre)
pg - picogram, 10"12 gram
pg/m3 - picogram per cubic meter (metre)
pH - per Hydrogen (a unit of acidity)
ppb - part per billion
ppm - part per million
ton - British ton, 2000 lb
tonne - metric tonne, 1000 kg
|ig - microgram, 10"6 gram
(ig/g - microgram per gram, part per million
(ig/1 - microgram per liter (litre)
|ig/m3 - microgram per cubic meter (metre)
|im - micrometer (micrometre), micron, 10"6 meter (metre)
ww - wet weight
Imperial to Metric conversion chart
Imperial -> Metric
1 inch = 2.54 centimeters (centimetres)
1 gallon =3.8 liters (litres)
1 ounce = 28.35 grams
1 fluid ounce = 29.57 milliliters
(millilitres)
1 mile =1.6 kilometers (kilometres)
1 pound = 0.45 kilogram (450 grams)
1 Fahrenheit= -17.21 Celsius
Chemicals, Nutrients & Bacteria
2,4-D - 2,4-dichlorophenoxyacetic acid
2,4,5-T - 2,4,5-trichlorophenoxyacetic acid
AFFF - Aqueous film forming foam
ALA - a-linolenic acid
ATE—ally -2,4,6 -tribromopheny 1 ether
BaP - Benzo[a]pyrene
BDE - Brominated diphenyl ethers
BFR - Brominated flame retardants
cHAB - cyanobacteria-based harmful algal blooms
CO - Carbon monoxide
DBDPE - decabromodiphenylethane
DDC - syn-Dechlorane Plus
DDT - l,l,l-trichloro-2,2-bis(p-chlorophenyl) ethane ordichlorodiphenyl-trichloroethane
DDD - l,l-dichloro-2,2-bis(p-chlorophenyl) ethane
DDE - l,l-dichloro-2,2-bis(chlorophenyl) ethylene ordichlorodiphenyl-dichloroethene
DHA - Docosahexaenoic acid
DOC - Dissolved organic carbon
anti-DP - anti-Dechlorane Plus
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syn-DP - syn-Dechlorane Plus
EPA - Eicosapentaenoic acid
HABs - Harmful Algal Blooms
HBB -Hexabromobenzene
HBCDD - Hexabromocyclododecane
HCB - Hexachlorobenzene
a-HCH - Hexachlorocyclohexane
y-HCH - Lindane
HE - Heptachlor epoxide
Hg - Mercury
HxBDE - Sum-hexabrominated diphenyl ethers
NAB - Nuisence Algal Blooms
MCs - Microcystins
MCPCAs - Medium chain polychlorinated alkanes
MeHg - Methylmercury
MIB - 2-methylisoborneol
NAPH - Naphthalene
N02 - Nitrogen dioxide
NO ,:TP - Nitrogen trioxide
N0X - Nitrogen oxides
NPE - Polyclilorinated alkanes
03- Ozone
OC - Organochlorine
OCS - Octaclilorostyrene
OCPs - Organochlorine pesticides
PAH - Polynuclear aromatic hydrocarbons
PBDE - Polybrominated diphenyl ether
PBEB - [pentabromoethyl benzene
PCA - Polyclilorinated alkanes
PCB - Polyclilorinated biphenyls
PCDD - Polyclilorinated dibenzo-p-dioxin
PCDDF - Polyclilorinated dibenzo furan
PCN - Polyclilorinated naphthalenes
PeBDE - Sum-pentabrominated diphenyl ethers
PFAA - Perfluoroalkyl acids
PFBA - Perfluorobutanoic acid
PFBS - Perfluorobutane sulfonate
PFC - Perfluoroalkyl Compounds
PFDA - Polyclilorinated alkanes
PFDoA - Polyclilorinated alkanes
PFDS - Polyclilorinated alkanes
PFOA - Perfluorooctanoic acid
PFOS - Perfluorooctanyl sulfonate
PFSA - Perfluoroalkyl sulfonate acids
PFTrA - Perfluorotridecanoic acid
PFUnA - Perfluoroundecanoic acid
PHC - Polyhalogenated carbazoles
POP- Persistent organic pollutants
PM10 - Atmospheric particulate matter of diameter 10 microns or smaller
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PM2 5 - Atmospheric particulate matter of diameter 2.5 microns or smaller
PPCP - Pharmaceuticals and personal care products
PUFA - Polyunsaturated fatty acid
SCPCA - Short chain polychlorinated alkanes
S02 - Sulfur dioxide
SPCB - Suite of PCB congeners that include most of PCB mass in the enviromnent
SRP:TP - Soluble Reactive Phosphorus: Total Phosphorous
TBB - 2-ethylhexyl-2,3,4,5-tetrabromobenzoate
TBE - l,2-bis(2,4,6-tribromophenoxy)ethane
TBPH - bis(2-ethylhexyl)-tetrabromophthalate
TCDD - Tetrachlorodibenzo-p-dioxin
TCE - Trichloroethylene
TDS - Total dissolved solids
TDP -Total Dissolved Phosphorous
TeBDE - Sum-tetrabrominated diphenyl ethers
TFN - 3-trifluoromethyl-4'-nitrophenol
TGM - Total gaseous mercury
TOC - Total organic carbon
TON - Total Oxidized Nitrogen
TRS - Total reduced sulfur
VOC - Volatile organic compound
Reports, Programs, Policies and Guidelines
APF - Agricultural Policy Framework (Canada)
ARET - Accelerated Reduction/Elimination of Toxics program (Canada)
BOB - Ballast On Board (also Upbound Transoceanic Ballasted vessels)
BEACH - Beaches Enviromnental Assessment and Coastal Health (U.S. Act of 2000)
BEACON - Beach Advisory and Closing On-line Notification
CEP A - Canadian Enviromnental Protection Act, 1999
CHT - Contaminants in Human Tissue program (part of EAGLE)
CSMI - Cooperative Science Monitoring Initiative
CWM - Coastal Wetland Monitoring Program
DWSP - Drinking Water Surveillance Program (Canada)
EFP - Enviromnental Farm Plan (Ontario)
FEQG - Federal Enviromnental Quality Guidelines (Canada)
GAP - Gap Analysis Program (land cover assessment)
GLAHF - Great Lakes Aquatic Habitat Framework
GLANSIS - Great Lakes Aquatic Nonindigenous Species Information
GLHGMP - Great Lakes Herring Gull Monitoring Program
GLRI - Great Lakes Restoration Initiative (USEPA)
GLMMP - Great Lakes Marsh Monitoring Program
GLWQA - Great Lakes Water Quality Agreement
GRDI - Genomic Research and Development Initiative
HGEMP - Herring Gull Egg Monitoring Program
IADN - Integrated Atmospheric Deposition Network
LAMP - Lakewide Action and Management Plan
LHBP - Lake Huron Binational Program
MMP - Marsh Monitoring Program
NAFTA - North America Free Trade Agreement
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NATA - National Air Toxics Assessment (U.S.)
NEEAR - National Epidemiological and Enviromnental Assessment of Recreational [Water Study]
NMAN - Nutrient Management Planning software (Ontario)
NISA - National Invasive Species Act (U.S.)
NMP - Nutrient Management Plan (Ontario)
NPRI - National Pollutant Release Inventory (Canada)
NRVIS - Natural Resources and Values Information System (OMNRF)
ODWQS - Ontario Drinking Water Quality Standard
PGMN - Provincial Groundwater-Monitoring Network (Ontario)
PWQO - Provincial Water Quality Objectives
RAP - Remedial Action Plan
SIP - State Implementation Plan
SOGL - State of the Great Lakes
SOLEC - State of the Lakes Ecosystem Conference
SOLRIS - Southern Ontario Land Resource Information System
SWMRS - Seasonal Water Monitoring and Reporting System (Canada)
WISCLAND - Wisconsin Initiative for Statewide Cooperation on Landscape Analysis and Data
WIC - Women Infant and Child (Wisconsin health clinics)
Other
AAQC - Ambient Air Quality Criterion (Ontario)
AFO - Animal Feeding Operation
AHCCD - Adjusted and Homogenized Canadian Climate Data
AIS - Aquatic Invasive Species
ALB - Asian Longhorned Beetle
AMIC - Annual Maximum Ice Coverage
AMO - Atlantic Multi-decadal Oscillation
AOC - Area of Concern
AOU - Area of the Undertaking
AQI - Air Quality Index
BA - Abnormal Barbels
B AV - Beach Action Value
BKD - Bacterial Kidney Disease
BMP - Best Management Practices
BOD - Biochemical Oxygen Demand
BR - Biotic Response
BUI - Beneficial Use Impairments
CAFO - Concentrated Animal Feeding Operations
CANGRD - Canadian Gridded Temperature and Precipitation Anomalies Dataset
CBT - Caffeine Breath Test
CCAVQR - Consumer Confidence/Water Quality Report
CCR - Consumer Confidence Report
CESI - Canadian Enviromnental Sustainability Indicators
CEC - Chemicals of Emerging Concern
CEP A - Canadian Enviromnental Protection Act, 1999
CMA - Census Metropolitan Area (Canada)
CMC - Chemicals of Mutual Concern
CSO - Combined Sewer Overflow
CSSC - Chicago Sanitary Ship Canal
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CUE - Catch per Unit of Effort
DJF - December, January, February - meteorological Winter
DNA - Deoxyribonucleic acid
eDNA - Enviromnental Deoxyribonucleic acid
DRP - Dissolved Reactive Phosphorus
DWS - Drinking Water System (Canada)
E AB - Emerald ash borer
EAGLE - Effects on Aboriginals of the Great Lakes program (Canada)
EAPI - External Anomaly Prevalence Index
ENSO - El Nino Southern Oscillation
EMS - Early Mortality Syndrome
EO - Element Occurrence
EPR - Extended Producer Responsibility
ESV - Early Successional Vegetation
FCGO - Fish Community Goals and Objectives
FCO - Fish Community Objectives
FD - Focal Discoloration
FIADB - Forest Inventory and Analysis Database (USDA Forest Service)
FEPS - Flood Erosion Prediction System
FQI - Floristic Quality Index
FRs - Flame Retardants
FTU - Fonnazin Turbidity Unit
GBBG - Great Black-backed Gull
GCMs - Global Circulation Models
GHG - Greenhouse Gases
GIA - Glacial Isostatic Adjustment
GIS - Geographic Information System
GLATOS - Great Lakes Acoustic Telemetry Observation System
GLB - Great Lakes Basin
GLEI - Great Lakes Enviromnental Indicators
GMO - Genetically Modified Organisms
HABs - Harmful Algal Blooms
HUC - Hydrologic Unit Code
IBI - Index of Biotic Integrity
IEC - Index of Ecological Condition
IGLD - International Great Lakes Datum (water level)
IMAC - Interim Maximum Acceptable Concentration
IPM - Integrated Pest Management
ISA - Impervious Surface Area
ISSA - Invasive Species System Approach
IUGLS - International Upper Great Lakes Study
IWM - Integrated Watershed Management
JJA - June, July, August - meteorological Summer
LE - Lesion
LEL - Lowest Effect Level
LOSL - Lake Ontario and upper St. Lawrence River
LU/LC - Land use/Land cover
MAC - Maximum Acceptable Concentration
MACT - Maximum Available Control Technology
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MAM - March, April, May - meteorological Spring
MCL - Maximum Contaminant Level
MEI - Modified Enviromnental Index
MDR - Mean Deviation Ratio
MRDL - Maximum Residual Disinfectant Level
MSA - Metropolitan Statistical Area (U.S.)
MSWG - Municipal Solid Waste Generation
NAO - North Atlantic Oscillation
NCCA - National Coastal Condition Assessment
NLCD - National Land Cover Dataset
NOAEC - No Observable Adverse Effect Concentrations
NOAEL - No Observable Adverse Effect Level
NOBOB - No Ballast On Board (also Cargo Laden vessels)
NTU - Nephelometric Turbidity Units
OIT - Organisms in Trade
OTI - Oligochaete Trophic Index
PBT - Persistent Bioaccumulative Toxic (chemical)
PCA - Principal Components Analysis
PDO - Pacific Decadal Oscillation
PEL - Probable Effect Level
PICA - Priority Island Conservation Areas
PNP - Permit Nutrient Plans (U.S.)
RBI - Richards-Baker Flashiness Index RG - Raised Growths
RWQC - Recreational Water Quality Criteria
SDWIS - Safe Drinking Water Information System (U.S.)
SON - September, October, November - meteorological Autumn
SPP. or spp. - Species
SQI - Sediment Quality Index
SSO - Sanitary Sewer Overflow
STAR - Science to Achieve Results
SUV - Sport Utility Vehicle
TAC - Total Allowable Catch
TCC - Total Category Change
TCR - Total Colifonn Rule
TDI - Tolerable Daily Intake
TEQ - Toxic Equivalent
TIGER - Topological Integrated Geographic Encoding and Reference (U.S. Census Bureau)
TIS - Terrestrial Invasive Species
TM - Thematic Mapper
T&O - Taste and odour
TRI - Toxics Release Inventory (U.S.)
TT - Treatment Technique
UNECE - United Nations Economic Commission for Europe
VGI - Volunteer Geographic Information
VKT - Vehicle Kilometers Traveled
WQI - Water Quality Index
WTP - Water Treatment Plant
WWTP - Waste Water Treatment Plant
YOY - Young-of-year
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