E PA/600/R-19/097
United States
Environmental Protection
Agency
vvEPA
A Special Report for the Great Lakes
2015 National Coastal Condition Assessment (NCCA)
and 2014 - 2018 Connecting River System Pilot
Assessments
September 2019
U.S. Environmental Protection Agency Office of Research and Development (USEPA)
Center for Computational Toxicology and Exposure
Great Lakes Toxicology and Ecology Division
1
-------
Cover images:
Top left, Round goby (Neogobius melanostomus) and dreissenid mussels from underwater video
collected in the Huron-Erie Corridor; top right, Ohio Environmental Protection Agency scientist
measuring weight of fish collected; bottom left, sediment sample collection; bottom right, Wisconsin
Department of Natural Resources scientist filtering a chlorophyll sample.
Authors of this report:
Matthew Pawlowski (Oak Ridge Institute for Science and Education), Molly Wick (Oak Ridge Institute for
Science and Education), David Bolgrien (USEPA ORD GLTED), Mari Nord (USEPA Region 5), Jon Launspach
(General Dynamics Information Technology), and Ted Angradi (USEPA ORD GLTED).
Acknowledgments:
This report supplements the 2015 National Coastal Condition Assessment report that had many authors
and contributors. We thank Elizabeth Hinchey Malloy, Todd Nettesheim, Sarah Lehmann, Hugh Sullivan,
Alexandra Bijak, John Kiddon, Will Bartsch, Meredith Brackett, Margaret Corcoran, Tim Corry, Anne
Cotter, Rose Ellison, Mary Beth Giancarlo, Paul Horvatin, Russ Kreis, Julie Lietz, Sam Miller, Megan
O'Brien, James Pauer, Mark Pearson, Jill Scharold, Nicole Singleton, Linda Harwell, Peg Pelletier, Garrett
Stillings, the captain and crew of the R/V Lake Guardian, and the captain and crew of the R/V Mudpuppy
II.
Scott Parker and Chris Filstrup served as external peer-viewers for the report.
Disclaimer:
This document has been reviewed by the U.S. Environmental Protection Agency, Office of Research and
Development, and approved for publication. Mention of trade names does not constitute endorsement
or recommendation for use.
Data availability:
National Coastal Condition Assessment data are available at https://www.epa.gov/national-aquatic-
resou rce-su rveys/ncca
Suggested citation:
Pawlowski, M., Wick, M., Bolgrien, D., Nord, M., Launspach, J., and Angradi, T. 2019. 2015 National
Coastal Condition Assessment (NCCA) and 2014-2018 Connecting River System Pilot Assessments. A
Special Report for the Great Lakes. U.S. Environmental Protection Agency, Duluth, MN, EPA/600/R-
19/097
2
-------
Table of Contents
Introduction 4
Design of the Great Lakes Component of the National Coastal Condition Assessment 5
Indicators for Assessing the Condition of the Great Lakes 9
Assessment Reporting 18
Summary of Assessment Results - Great Lakes 20
Lake Superior 23
Lake Michigan 27
Lake Huron 30
Lake Erie 34
Lake Ontario 39
The St. Marys River 43
The Huron-Erie Corridor 46
The Niagara River 50
Temporal and Spatial Comparisons Among the Great Lakes 53
Integrating the Great Lakes NCCA with Local Assessment Needs 61
Future improvements of the Great Lakes NCCA 67
Key Findings 68
References 69
Appendices 74
3
-------
Introduction
The NCCA is one of four National Aquatic Resource Surveys (NARS) administered by the US EPA's Office
of Water in partnership with states and tribes (USEPA, 2015b, 2016d). The NCCA is designed to yield
unbiased estimates of the ecological condition of the nearshore waters of the US based on a
quinquennial survey. The survey produces both area-weighted means of parameters, and categorical
condition estimates based on relevant thresholds. This report describes the methods and major findings
of the NCCA survey of the Laurentian Great Lakes' (Fig. 1) nearshore waters, connecting river systems,
and selected additional resources ("spatial enhancements") conducted by the USEPA, collaborating US
states, and other partners, during 2014-2016. This report complements the findings of the 2015 National
Coastal Condition Assessment (NCCA) survey implemented by the USEPA's Office of Water, which can be
referenced for additional background and results for the complete national assessment (USEPA, 2021a,
b). In 2010, the Great Lakes was fully incorporated into the NCCA; this report presents finding for the
second implementation (2015) of the Great Lakes component of the NCCA survey. Repeated
implementation of the survey can be used to assess change in the ecological condition of the Great
Lakes nearshore over time.
4
-------
Design of the Great Lakes component of the National Coastal
Condition Assessment
The Great Lakes NCCA design included a base design developed for the USEPA's Office of Water by the
EPA's Office of Research and Development, as well as enhanced designs to assess additional resources
(described in more detail below). The probability-based approach used for the base design and
enhancements allowed determination of statistically-valid estimates of the condition of the nearshore
waters of the Great Lakes (Diaz-Ramos et al., 1996; Stevens, 1997; Stevens et al., 1999; Stevens et al.,
2003; Stevens et al., 2004). NCCA survey sites were selected using a generalized random tessellation
stratified (GRTS) survey design. Sample locations were selected, and weights were assigned using the
spsurvey package in the statistical software environment R (Kincaid et al., 2019). Each selected site
represented a known percentage (weight) of the area in each system. Weighted means and condition
class estimates were based on site results weighted by the area each site represents.
Sites that could not be sampled due to safety or logistic issues were replaced with sites from an
overdraw sample. Overdraw sample sites were selected using the methods described above for the base
design and the weights adjusted using spsurvey. Sites were relocated but not replaced when the "X-site"
(design survey site location defined by a latitude and longitude) was not sampleable, but a nearby
location was sampleable. This relocation was permitted if the new site was within a 37 m radius of the X-
site. To improve the chances of successfully collecting benthic (lake bottom) samples, which have a high
failure rate, crews could sample anywhere within a 500 m radius of the X-site for sediment or benthic
invertebrates. To improve the chances of to successfully collecting a fish sample, crews could sample
anywhere within 1000 m of the X-site. A site was not rejected if some samples or parameters could not
be collected. For example, the failure of fish or sediment collection was not used as a determining factor
for rendering a site unsampleable for other sampling.
Base sample design
The assessed resource for the Great Lakes component of the NCCA was the nearshore US waters of the
Great Lakes defined as waters within 5 km of shore and <30 m deep. The Great Lakes nearshore sample
"frame" was first developed for the 2010 NCCA from existing standard GIS vector shoreline coverage
from NOAA (USEPA, 2014d; Kelly et al., 2015). That coverage was modified to include a coverage
extension 500 m upstream into river mouths and to add embayment areas missing from the existing
shoreline coverage.
The 2015 Great Lakes NCCA nearshore sample frame was developed by the USEPA's Office of
Research and Development (ORD) Great Lakes Toxicology and Ecology Division (GLTED). The nearshore
includes river mouths and estuaries, embayments, and open waters adjacent to the US shoreline. It
does not include the connecting river systems of the Great Lakes (water bodies between lakes plus the
upper St. Lawrence River), for which a separate frame was developed (described below).
5
-------
The base sample design assigned 45 sites to the United States portion of nearshore of each of the five
Great Lakes for a total of 225 sites (Table 1). Samples in each lake were allocated among bordering
states' waters proportionally by shoreline length.
Spatial Enhancements
Within the Great Lakes nearshore, additional sites were added in embayments and in sub-basins of Lake
Erie as spatial enhancements to increase the scope and precision of assessment estimates. Embayments
were included in the 2010 survey; the Lake Erie Basin enhancement was added in 2015.
The objective of the embayment enhancement was to increase the representation of shallower and
more sheltered embayment areas hypothesized to have a disproportionate influence on overall
nearshore condition due to their closer connections to tributaries and watershed stressors (Kelly et al.,
2015; Yurista et al., 2016). Embayments were defined as indentations of the shoreline for which the
width from a line across the opening of the indentation to the furthest inland point is greater than the
width of the opening and having an area at least as large as that of a semicircle with a diameter
equivalent to the width of the opening (Kelly et al., 2015). Some embayments have tributaries, and all
have an open water connection or channel to the adjacent Great Lake. The embayments ranged in size
from <0.01 to 93 km2 (mean, 6.6 km2) and comprised approximately 5% of the total nearshore area.
For the Great Lakes assessment summarized in this report, 361 sites (225 base sites plus 136
embayment sites) were used for the combined nearshore and embayment condition assessment. This
assessment represents 18,438 km2 of nearshore and embayment area. The nearshore subpopulation
and the embayments subpopulation of the Great Lakes were also assessed as mutually exclusive
resource areas. The nearshore-only subpopulation was represented by 218 of the 225 base sites that
were not in embayments. The embayment-only subpopulation was represented by 136 embayment
sites plus seven base sites located within embayments. Hereafter, the three Great Lakes subpopulations
are referred to as the combined nearshore and embayments, the nearshore, and embayments.
The objective of the Lake Erie basin spatial enhancement was to increase the precision of water quality
assessments in each basin. Thirty-three enhancement sites were added in Lake Erie (western, central,
and eastern basins, Fig. 1, Table 1) to bring the total to 30 sites in each basin. These extra sites were
sampled for water quality only.
6
-------
Table 1. Number of sites in the design, number of sites visited, and number successfully sampled for each subpopulation and indicator. OTI is the
Oligochaete Trophic Index. The PONAR is a type of bottom sampler. Number of OTI samples means the number of samples for which the OTI
could be calculated. Number of videos collected refers to videos of adequate quality for assessment.
Subpopulation
Number
of
design
sites
Number
of sites
visited
Water
samples
Sediment
samples
Fish tissue
samples
Fish
mercury
samples
Benthic samples
Round
goby
presence
Dreissenid
presence
Number of samples collected
Number
of OTI
samples
Number
videos
collected
Number of
samples
collected
(video or
PONAR)
Lake Superior
Nearshore
45
42
42
36
37
37
34
22
28
36
Embayments
36
36
36
36
35
35
36
33
18
31
Lake Huron
Nearshore
45
44
44
40
37
37
32
26
44
44
Embayments
23
23
23
54
47
46
20
14
53
54
Lake
Michigan
Nearshore
45
45
45
32
32
32
43
36
36
41
Embayments
55
55
54
20
22
22
54
44
19
23
Lake Erie*
Nearshore
45
44
44
34
42
41
32
31
20
40
Embayments
13
13
13
13
13
13
13
13
8
13
Lake Ontario
Nearshore
45
43
43
17
28
14
16
12
41
37
Embayments
16
16
16
12
15
14
13
11
11
16
Great Lakes
368
361
360
294
308
291
293
242
242
335
St. Marys River
100
94
94
92
35
35
92
80
80
93
Huron-Erie
Corridor
All sites
100
95
95
88
31
31
90
86
84
93
St. Clair River
19
18
18
13
7
7
18
17
15
18
Lake St. Clair
50
48
48
48
8
8
46
45
45
48
Detroit River
31
29
29
27
16
16
26
24
24
27
Niagara River
60
59
59
28
0
0
37
36
57
57
* Water-only sampling was also done at an additional 33 nearshore sites in Lake Erie such that 30 sites were sampled in each basin. This allowed for statistically
robust estimates of water-related conditions to be estimated in each basin of Lake Erie for the Lake Erie basin enhancement study (see Spatial Enhancements
section). In the Lake Erie nearshore, a total of 27, 26, and 24 sites were sampled in the western, central, and eastern basins, respectively. In Lake Erie
embayments, 3, 4, and 6 sites were sampled in the western, central, and eastern basins, respectively.
7
-------
Connecting River system Pilot Study
The Great Lakes connecting river systems are part of the Great Lakes ecosystem, but these resources
had not previously been assessed as a part of any previous NARS. Two Great Lakes connecting river
systems, the St. Marys River and the Huron-Erie Corridor (HEC), which includes the St. Clair River, Lake
St. Clair, and the Detroit River, were assessed as part of NCCA in 2014 - 2016 as a connecting channel
pilot study (Wick et al., 2019). The Niagara River was assessed in 2018. Because the existing NCCA
sample design did not include the connecting river systems, separate connecting-channel design frames
were created. The design frames included the area of each channel from the outlet of the upriver Great
Lake to the downriver Great Lake including Canadian waters. In some areas, the connecting channel
frame overlapped slightly with the Great Lake design frame, but no Great Lakes sites were in this
overlapping area. The extent of the Niagara River frame was defined to align with Buffalo River Area of
Concern boundaries to help fulfill local assessment needs.
Ninety-four probability sites were sampled in the St. Marys River, 95 probability sites were sampled in
the HEC, and 59 probability sites were sampled in the Niagara River. In addition, 16 hand-picked sites
were sampled in the St. Marys River and the HEC, and 12 hand-picked sites were sampled in the Niagara
River in coordination with state and local managers. Hand-picked sites were in areas of interest based
on Total Maximum Daily Loads, sediment remediation, or other local environmental priorities. Data
from these sites were not used in condition estimates but can provide contextual information about a
resource.
Sample Collection
Great Lakes sites were sampled from June - September in 2015. The HEC was sampled in September of
2014 and 2015, the St. Marys River was sampled in July and August of 2015 and 2016, and the Niagara
River was sampled in July of 2018. Due to logistic constraints, the connecting river systems were
sampled during a shorter interval than the lakes. Seasonal variation generally cannot be addressed in
GRTS designs (Messer et al., 1991); the sample or "index" period is assumed to be representative for
annual assessment purposes.
NCCA sampling protocols were designed so that at least one site could be sampled in a day by a 4-
person crew (USEPA, 2015c). During a site visit, crews collected water, sediment, fish, and underwater
video. Water samples were collected 0.5 m below the surface using a Kemmerer bottle. Standard
PONAR grab samples were collected for sediment toxicity, sediment contamination, and benthic
invertebrates. The top 2 cm of one PONAR sample was analyzed for concentrations of chemical
constituents, total organic carbon (TOC), grain size, and sediment toxicity. A separate intact PONAR
sample was elutriated (organisms separated from sediment) for analysis of macroinvertebrates. Water
samples were filtered with a Whatman GF/F 47-mm 0.7-micron filter for chlorophyll a and the filtered
water was used to measure dissolved nutrients. A water sample was collected in a sterile container for
enterococci analysis. Fish were collected using various gears for whole fish tissue samples. Dissolved
oxygen, pH, temperature, and conductivity profile data were collected using a multiprobe instrument.
Water clarity was measured using a weighted 20-cm black and white Secchi disk and with a
Photosynthetically Active Radiation (PAR) meter. For sites where the Secchi disk was visible on the
8
-------
bottom, Secchi depth was estimated using light attenuation determined from PAR data as described in
Appendix A.
Underwater videos were collected in the Great Lakes, St. Marys River, and HEC as described in Lietz et
al. (2015), with either a SeaViewer SeaDrop color camera or a GoPro4 camera. The camera was lowered
on a weighted line to the bottom and 1-2 minutes of video recorded. In the Niagara River, a down-
looking camera and an oblique-looking GoPro Hero5 camera (1080p resolution) and LED lights were
mounted on an open video carriage made of stainless steel The carriage was lowered to the riverbed to
collect approximately one minute of video (Wick et al., 2020).
Samples were analyzed by multiple laboratories using methods specified in the NCCA laboratory manual
(USEPA, 2016a, b) and applicable quality assurance project plan (USEPA, 2014c). Standardized field and
laboratory protocols were followed to ensure comparability of results.
Table 1 shows the number of sites visited for each lake and river system and the total number of water,
sediment, fish, benthos, and video samples collected for each Great Lake and connecting river system. A
few sites in the base design were not sampleable due to shallow water, rapids, ship traffic, or lack of
safe access, therefore the number of sites visited was smaller than the number of base design sites for
some subpopulations. The sampling methods allowed benthos, sediment, and fish samples to be
collected up to 500 m (1,000 m for fish) from the designated X-site. Even with this allowance, not all
samples could be obtained at all sites (missing data are discussed in more detail below). About 10% of
sites were revisited within a season for quality assurance purposes, but these analyses are not discussed
in this report. If too few fish were caught during the first site visit, data from the second were used for
condition estimates.
Indicators for Assessing the Condition of the Great Lakes
The NCCA indicators (Table 2) were selected to provide a broad assessment of the ecological condition
of coastal waters and the stressors impacting them. Most of the indicators were included in the original
National Coastal Assessment (NCA) of marine waters (USEPA, 2011) and could be applied to the Great
Lakes with minor modifications. Four derived indicators were used to assess the condition of the Great
Lakes: eutrophication, a benthic index, sediment quality, and ecological fish tissue contamination.
Additional indicators added to augment the assessment in the Great Lakes including cyanobacteria,
microcystin, invasive species, and fish tissue mercury.
Underwater video for assessing conditions in coastal waters was piloted in the 2010 Great Lakes
assessment and was repeated in 2015. In this report invasive species presence was based on
underwater video. Underwater video can provide other useful data about the lake bottom including
habitat characteristics including substrate type and vegetation, the presence of litter, and other
anthropogenic Impacts. Methods for video analysis are still under development (Wick et al., 2020). All
videos collected in the 2010 and 2015 Great Lakes NCCA are available to the public at
https://gispub.epa.gov/NCCA/.
9
-------
For multi-metric indicators (e.g., eutrophication, sediment quality) the assessment for that indicator was
based on the available metrics. For example, if only sediment contamination was available and sediment
toxicity was not, then the sediment quality indicator was based on sediment contamination alone.
Eutrophication Status Indicator
Water quality was assessed using a eutrophication status indicator which integrates four water quality
indicators: total phosphorus concentration, chlorophyll a concentration, near-bottom dissolved oxygen
concentration, and water clarity (as Secchi depth). Total nitrogen was also collected but was not used
for the eutrophication indicator because there are currently no published thresholds for nitrogen
impairment in the Great Lakes. The eutrophication status was derived as follows: If no component
indicators were assessed as poor and only one was assessed as fair, then the eutrophication status was
assessed as good for that lake. If one component indicator was poor and two or more were fair, then the
status was assessed as fair. If two or more components were poor, then the lake was assessed as having
poor water quality.
The International Joint Commission (IJC) Phosphorus Management Strategies Task Force (PMSTF, 1980)
developed total phosphorus, chlorophyll a, and Secchi depth thresholds for each Great Lake and each
basin of Lake Erie based on the expected trophic status for the lake or basin (Table 2a). The thresholds
were developed for "open waters", but data used to generate the thresholds included nearshore
samples (Gregor and Rast, 1979), so they were considered relevant to the nearshore and embayment
sites for the Great Lakes assessment. The PMSTF only identified a single threshold based on the trophic
status for each lake (fair to good), so the lower threshold (fair to poor) was defined for the NCCA report
as the value indicative of crossing into the next more nutrient-enriched trophic status. The NCCA
analysts and partners used IJC study results (Gregor and Rast, 1979) to identify trophic status thresholds
for selected basins (i.e., Saginaw Bay in Lake Huron and western, central, and eastern basins of Lake
Erie), that were not specified in the 1980 PMSTF report. Thresholds were not previously specified for the
Great Lakes connecting river systems. Connecting river systems were therefore assessed against the
most protective downriver thresholds after Wick et al. (2019).
Thresholds for bottom dissolved oxygen are consistent with marine water quality thresholds (Diaz and
Rosenberg, 1995; USEPA, 2011). Studies in the Great Lakes (Costantini et al., 2011; Krieger and Bur,
2009) corroborate 2 mg/L as a threshold for hypoxic condition (threshold between fair and poor
condition).
Cyanobacteria
In 2010 and 2015, phytoplankton samples were collected for taxonomic analysis. The 2010 assessment
included a pilot assessment of potential risks to human health through recreational exposure based on
cyanobacteria cell counts. Methods for using phytoplankton taxonomy for assessment of condition are
in development.
10
-------
Cyanobacteria (also known as blue-green algae) are a group of phytoplankton that have characteristics
of both algae and bacteria. Some can produce algal toxins that present a health risk to humans and
animals. Normally cyanobacteria are present in low numbers, but occasionally conditions (high nutrient
levels combined with other water quality and hydrodynamic conditions) cause blooms that form visible
green scums and can produce algal toxins that pose a human health risk. These events are called
harmful algal blooms, or HABs.
Cyanobacteria cell counts were compared to the World Health Organization (WHO, 2003a) guidelines for
potential human health risks (Table 2b). The thresholds for good conditions (<20,000 cells/mL) are based
on the WHO threshold for least concern for adverse health effects. Fair conditions are based on the
threshold for low probabilities of adverse health effects (20,000 to 100,000 cells/mL), primarily skin
irritation and short-term gastrointestinal illness. Poor conditions are based on the threshold for
moderate probabilities of adverse health effects (>100,000 cells/mL), including long-term illness in
addition to skin irritation and gastrointestinal illness).
11
-------
Table 2a. Indicators and associated thresholds used to assess condition of the Great Lakes and connecting river systems.
Stratum
Water quality (values equal to the threshold are assessed as the better condition class)
Chlorophyll a
(M-g/L)
Total phosphorus
(Hg/L)
Dissolved oxygen
(mg/L)
Secchi depth (m)
Eutrophication status
Good/
Fair
Fair/
Poor
Good/
Fair
Fair/
Poor
Good/
Fair
Fair/
Poor
Good/
Fair
Fair/
Poor
Good
Fair
Poor
Lake Superior
1.3
2.6
5
10
5
2
8
5.3
No
component
indicators
are rated
poor, and a
maximum
of one is
rated fair.
One
component
indicator is
rated poor,
or two or
more
indicators
are rated
fair.
Two or
more
component
indicators
are rated
poor.
St. Marys River
1.3
2.6
5
10
5
2
8
5.3
Lake Michigan
1.8
2.6
7
10
5
2
6.7
5.3
Lake Huron
Lake
Huron
1.3
2.6
5
10
5
2
8
5.3
Saginaw
Bay
3.6
6
15
32
5
2
3.9
2.1
Huron-Erie
Corridor
St. Clair
River
2.6
3.6
10
15
5
2
5.3
3.9
Lake St.
Clair
2.6
3.6
10
15
5
2
5.3
3.9
Detroit
River
2.6
3.6
10
15
5
2
5.3
3.9
Lake Erie
Western
Basin
3.6
6
15
32
5
2
3.9
2.1
Central
Basin
2.6
3.6
10
15
5
2
5.3
3.9
Eastern
Basin
2.6
3.6
10
15
5
2
5.3
3.9
Lake Ontario
2.6
3.6
10
15
5
2
5.3
3.9
Niagara River
2.6
3.6
10
15
5
2
5.3
3.9
Sources: Total phosphorus, chlorophyll a, and Secchi depth thresholds for Great Lakes adapted from PMSTF, 1980 and IJC, 1979. Dissolved oxygen thresholds from Diaz and
Rosenberg, 1995; USEPA, 2011, Costantini et al., 2011, Krieger and Bur, 2009. See Wick et al., 2019 for detail on connecting river system thresholds.
12
-------
Table 2b. Indicators and associated thresholds used to assess condition of the Great Lakes-continued.
Strata
Cyanobacteria
All strata
Good: Least probability of adverse health
effects
Fair: Low probability of adverse health
effects
Poor: moderate probability of adverse
health effects
<20,000 cyanobacteria cells/mL
20,000 -100,000 cyanobacteria cells/mL
>100,000 cyanobacteria cells/mL
Source: World Health Organization published guidelines for potential human health risks (WHO, 2003a).
All strata
Microcystin
Good
Poor
<8 ng/L
>8 ng/L
Source: Adopted from USEPA recommendations for microcystin concentration thresholds, based on relative probability of acute health effects from recreational
exposure (USEPA, 2019b).
All strata
Enterococci
Good
Poor
<1,280 CCE/lOOmL
>1,280 CCE/lOOmL
Source: Adopted from USEPA's recreational water quality threshold for enterococci (USEPA, 2012).
All strata
Sediment quality
Sediment contamination
Sediment toxicity
Sediment quality
Good
Fair
Poor
Good
Fair
Poor
Good
Fair
Poor
Mean PECQ
is <0.1
Mean PECQ
is >0.1 and
<0.6
Mean PECQ
is >0.6
Control-
corrected
survival is
>90%
Control-
corrected
survival is
>75% and
<90%
Control-
corrected
survival is
<75%
Both
indicators
rated good
At least one
indicator
rated fair,
and none are
rated poor
At least one
indicator
rated poor
Sources: Thresholds for mean Probable Effects Concentration Quotients (PECQ) were adopted from MacDonald et al.. 2000 and Crane and Hennes, 2007. Sediment
toxicity thresholds were adopted from USEPA's National Sediment Quality Survey methods (USEPA, 2004).
13
-------
Table 2c. Indicators and associated thresholds used to assess condition of the Great Lakes-continued.
Strata
Benthic index - Oligochaete Trophic Index (OTI)
All strata
Good
Fair
Poor
OTI is <0.6
OTI is >0.6 and <1.0
OTI is >1.0
Sources: Thresholds for the OTI were adopted from methods developed for the State of the Great Lakes reporting (SOLEC, 2007, ECCC and USEPA, 2017).
All strata
Fish tissue contamination
Good
Fair
Poor
None of the measured contaminant
concentrations exceed the screening
value for any receptor group
At least one measured contaminant
concentration exceeds the screening
value for one receptor group.
At least one measured contaminant
concentration exceeds the screening
value for two or more receptor groups.
Sources: Thresholds (screening values) were calculated using toxicity reference values (TRVs) for each receptor (piscivorous fish, birds, and mammals) and
contaminant. TRVs were based on no observed adverse effect levels (NOAEL) (Sample and Arenal, 1999). See Appendix D for screening values for each contaminant
and receptor.
Strata
Fish tissue mercury
All strata
Good/Low
Poor/High
<300 ppb Hg
>300 ppb Hg
Source: Adopted from USEPA's water quality criterion for Methylmercury (USEPA, 2001).
All strata
Invasive mussels (Dreissena spp.)
Round gobies (Neogobius melanostomus)
Present
Not observed
Present
Suspected presence
Not observed
Invasive mussels
were observed in
video or collected in
PONAR sample
Invasive mussels not
observed in video or
collected in PONAR
sample
Round goby observed in
video
Possible round goby
observed (not applicable
for Niagara River)
Round goby not
observed in video
14
-------
Microcystin
Microcystins are the most common algal toxins observed in the Great Lakes (e.g., Dyble et al., 2008;
Murphy et al., 2003). The microcystin concentration was determined using the ELISA method (USEPA,
2016a) which measures the total of all microcystins without distinguishing the many individual types of
microcystins.
Thresholds for microcystin were applied based on USEPA recommendations for concentration
thresholds based on the relative probability of acute health effects from recreational exposure to
microcystins (USEPA, 2019b). The threshold of <8 ng/L was adopted for microcystin, below which the
risk of health concerns from recreational exposure to microcystin is low and condition was assessed as
good. Areas where this threshold was exceeded were assessed as being in poor condition. (Table 2b).
Most samples were below the detection limits of 1 ng/L, which is the WHO drinking water limit guideline
(WHO, 2003b).
Enterococci
Enterococcus is a large genus of lactic acid bacteria that is found in the intestines of mammals and birds.
Enterococci can be pathogenic, posing health risks of infection for humans and animals. NCCA measures
enterococci because it is an indicator of possible sewage contamination of surface waters. The USEPA
has established a recreational water quality threshold for enterococci of <1,280 calibrator cell
equivalents (CCE) per 100 mL (USEPA, 2012), below which the risk of health concerns from recreational
exposure is low and condition was assessed as good. Condition of areas where this threshold was
exceeded were assessed as poor.
Sediment Quality
Sediment quality was based on sediment contamination and sediment toxicity indicators. For the Great
Lakes, sediment contaminants were assessed using the mean Probable Effect Concentration Quotient
(PECQ, Ingersoll et al., 2001; USEPA, 2002). The PECQ a unitless value, is the measured concentration of
that contaminant divided by an established PEC, or concentration at which effects on organisms were
observed in laboratory tests. Only contaminants with reliable PECs were included: arsenic, cadmium,
chromium, copper, lead, nickel, zinc, total PCB congeners, and total PAHs (see Appendix B for included
PCB congeners and PAHs). The mean PECQ is the average of the mean PECQ for metals, the PECQ for
total PAHs and the PECQ for total PCBs. See Appendix B for more detail on how the mean PECQ was
calculated. The mean PECQ was then compared to published thresholds of <0.1 for good condition, 0.1 -
0.6 for fair condition, or >0.6 for poor condition (Table 2b, MacDonald et al., 2000; Crane and Hennes,
2007).
Sediment toxicity was assessed by measuring the survival rate of the freshwater amphipod, Hyalella
azteca after a 10-day exposure to the sediments under laboratory conditions (USEPA, 2000; USEPA,
2014c). The thresholds for sediment toxicity are based on published values. A control-corrected survival
rate of >90% was considered good condition, survival between 75% and 90% was considered fair
condition, and a survival rate of <75% was considered poor condition (USEPA, 2004).
15
-------
Sediment quality at a site was assessed as poor (i.e., high potential for exposure effects on biota) if
either one of the component indicators, contamination or toxicity, was categorized as poor; condition
was assessed as fair if either indicator was rated fair; and condition was assessed as good if both
component indicators were assessed as good.
Benthic Index
The benthic community was assessed using the Oligochaete Trophic Index (OTI), which is the method
used by the State of the Great Lakes Ecosystem Conference (SOLEC, 2007; ECCCand USEPA, 2017). The
OTI is based on the index of Howmiller and Scott (1977) with subsequent modifications by Milbrink
(1983) and Lauritsen et al. (1985). The OTI is a weighted index based on the tolerance of oligochaete
species to organic enrichment (ECCC and USEPA, 2017). The OTI ranges from 0 to 3; scores <0.6 indicate
oligotrophic/good conditions; scores between 0.6 and 1.0 indicate mesotrophic/fair conditions; and
scores >1.0 indicate eutrophic/poor conditions (Table 2c). See Appendix C for further details on how OTI
was calculated.
Ecological Fish Tissue Contamination
Fish were collected by crews using various methods (USEPA, 2015c). If fish from the target list were not
available, alternative fish species could be submitted. Whole-body fish tissues were assessed based on
USEPA guidance (USEPA, 1997). This approach evaluates whether concentrations of contaminants in fish
tissue pose a potential risk to fish and wildlife (receptors of concern) that consume fish.
Screening values (thresholds) for each receptor group (piscivorous fish, birds, and mammals) and
contaminant were calculated using established toxicity reference values (TRVs) for each receptor
(Sample and Arenal, 1999; USEPA, 2021b). The screening values for each contaminant and receptor are
in Appendix D. Condition for the fish tissue contaminant indicator was based on the number of receptor
groups with at least one screening value exceedance. Table 2c summarizes the protocol for assigning
good, fair, or poor condition to Great Lakes and connecting river systems for potential risk of
contaminant exposure to fish and wildlife.
The screening values that are used to assess fish tissue contaminant conditions differ in two important
ways compared to the those used 2010 NCCA reports. In 2010, screening values were based on the
lowest observed adverse effect levels (LOAELs) for each contaminant and receptor group. In the present
report, screening values were recalculated based on the no observed adverse effects levels (NOAEL)
which more closely align with protective endpoints used in EPA's Water Quality Criteria
recommendations (USEPA, 2016e). Using NOAEL values rather than LOAEL values results in more
protective thresholds than were used in 2010. The second change to the screening values made during
the 2015 assessment involved those for selenium. In the 2010 assessment, no sites in the Great Lakes
had good conditions for fish tissue contaminants due to screening value exceedances of selenium in the
bird receptor group at all sites (USEPA 2016d). The screening values for selenium were updated for this
report using EPA's national aquatic life criterion recommendations for selenium in freshwater which are
16
-------
less protective but more accurately reflect selenium concentrations that could cause ecologically
relevant adverse effects to wildlife populations (USEPA, 2016e). Together, these changes cause the
results in this report to appear dramatically different than those in the 2010 report. In reality, fish tissue
contaminant concentrations in the Great Lakes did not change appreciably between 2010 and 2015
(USEPA, 2021a). A full description of the methods used to calculate the screening values used in this
report is available in the NCCA 2015 Technical Support Document (USEPA, 2021b).
In the connecting river systems, fish were only collected during the 2015 sampling season, with sampling
primarily in US waters of the St. Marys River and the HEC. Fish were not collected in the Niagara River.
Population estimates for the fish tissue contamination in the connecting river systems represent only
the resource area accounted for by the sites at which fish sampling was attempted.
Fish Tissue Mercury
Mercury is among the most common toxic pollutants in fish tissue and is the basis of many fish
consumption advisories in the Great Lakes (USEPA, 2009). The USEPA has identified a human
consumption criterion of 0.3 mg methylmercury per kg (300 ppb) (USEPA, 2001). Sites with fish tissue
meeting or exceeding this value were assessed as poor; sites with fish tissue below this threshold were
in good condition (Table 2c). Like the ecological fish tissue contaminant indicator, fish tissue mercury
samples in the connecting river systems were only collected in 2015 and primarily at US sites. Population
estimates for fish tissue in the connecting river systems represent only the area represented by the sites
at which fish sampling was attempted.
Invasive Species
Round gobies (Neogobius melanostomus) and dreissenid mussels (Dreissena spp.) can cause water
quality, habitat, and food web changes that affect coastal condition. Lietz et al. (2015) showed that the
presence of these species could be determined from underwater video footage. Estimates of percent of
the resource area where round gobies were present are based on underwater video. Estimates for
dreissenids were based on video and PONAR samples together. Analysts viewed videos to identify
invasive mussels or round gobies. Analysts were trained using an EPA-supplied dataset, and a quality
assurance assessment check was completed following the analysis. Analysts identified round gobies
based on their characteristic black dorsal spot, body morphology, and demersal orientation. They
identified dreissenid mussels based on their morphology, size, and attachment to surfaces (see image on
cover page of a round goby and dreissenid mussels from video taken in the HEC). Due to cryptic
coloration, prolific algae or leafy vegetation, turbidity, or other factors that resulted in poor video
quality, mussels and gobies could be missed in videos. The estimates presented in this report are
therefore considered underestimates of invasive species presence.
Round gobies could be identified with confidence if they were near the camera or if they displayed their
black dorsal spot. However, in many cases, they were challenging to identify. Analysts reported both
confident and possible detections of round gobies. In the condition plots (described below),
presence/absence are indicated with black (confident of presence) or dark gray (suspected/possible
presence), not detected is indicated with white bars, and unassessed with light gray bars.
17
-------
Dreissenid mussels were collected in the standard PONAR samples. However, a standard PONAR sample
represents only about 0.05 m2 of bottom and may not detect dreissenid mussels if they are present at
low densities or in patches, or if the benthic substrate is too coarse for effective PONAR sampling.
Because neither PONAR sampling nor underwater video methods can always detect dreissenid mussels
at a site when they are present, using video and PONAR samples together provides a more reliable
indicator for dreissenid presence (Lietz et al., 2015). In this report, dreissenid presence is reported based
on detections in both videos and in PONAR samples. The percent of area where mussels were estimated
to be present was based on positive detection in either the PONAR sample or video (shown as black bars
in the summary assessment figures, described below). Percent area where mussels were not observed in
either PONAR samples or video was denoted by white bars in the summary assessment figures.
Unassessed area due to poor quality video and no PONAR sample was denoted by light gray bars.
Videos were collected at 534 of 550 sites in the Great Lakes and connecting river systems in 2014-2016.
Video could not be collected at 16 sites, which represent unassessed area. Due to poor video quality,
19% of the videos collected could not be analyzed, and the resource area represented by these sites was
unassessed. Video technology and analysis methods have improved since the 2016 assessments.
Improved methods were used for the Niagara River in 2018. In these higher quality videos, analysts
could confidently detect round gobies, so the category of "suspected round goby" was not included in
the assessment of the Niagara River. Positive round goby and dreissenid mussel presence at a site were
based on detection in at least one of the two videos collected at each site (down looking or oblique
video).
Assessment Reporting
Assessment results are presented in horizontal bar plots (e.g., Fig. 2). The lengths of the bars represent
the estimated percent of area within a resource (nearshore area or connecting river system area) in
each condition class. For the Great Lakes, conditions based on each indicator are reported as a percent
of the area of the nearshore and embayments combined (left column of bars), the percent area of
nearshore (center column of bars), and the percent area of embayments (right column of bars). Error
bars are 95% confidence intervals for the condition estimate.
Condition of the St. Marys River is reported as a percentage of the entire area of the St. Marys River.
Condition of the HEC is reported for the entire area of the HEC and for individual waterbodies within the
HEC: the St. Clair River, Lake St. Clair, and the Detroit River. Condition of the Niagara River is reported
for the Upper River (above the falls), the Lower River, and the entire river. The assessed Upper River
includes the Buffalo Harbor area (Lake Ontario) of the Niagara River AOC.
The connecting river systems were assessed as areas distinct from the Great Lakes (the small amount of
overlap in the design frames was ignored). Assessed resources (i.e., subpopulations) should have at least
30 sites for estimates to be statistically robust (Herlihy et al., 2000). Some subpopulations did not meet
18
-------
this criterium but are reported here for completeness. Examples include the embayment-only areas of
some lakes (e.g., Lake Erie, Lake Ontario), and individual waterbodies within the HEC (e.g., St. Clair River,
Table 1).
Field data were collected at 361 Great Lake sites (218 nearshore and 143 embayment sites), 94 sites in
the St. Marys River, 95 sites in the HEC, and 59 sites in the Niagara River (Table 1). Sites at which any
subset of the full complement of indicator data was collected (excluding fish tissue assessments) were
included in condition estimates. Sites where no data were collected for any indicators were excluded
from the determination of condition estimates. Therefore, condition estimates represent the area
assessed rather than the area identified in the original design. If an indicator was missing for any sites,
the condition based on that indicator/index is shown as "unassessed" in the bar plots for some
percentage of the original design. An individual indicator or index could be unassessed because site
conditions prevented collection of a sample or field measurement, or if the sample or data collected did
not meet quality assurance requirements.
Missing data were common for the benthic index, ecological fish tissue, and fish tissue mercury. For the
benthic index, there were two reasons for this: 1) PONAR sampling is difficult on hard substrates or
areas with dense mussel beds, and 2) the OTI calculations (see Appendix C) require that tolerance-
classified oligochaete species are present in the sample. Sites where either no PONAR sample was
collected (19% of Great Lakes nearshore samples) or classified oligochaetes were not present preventing
calculation of the OTI (14% of Great Lakes nearshore samples, Table 1) were unassessed. Lake Ontario
was the most difficult lake in which to collect benthos samples (and sediment samples) due to rocky
substrates and abundant dreissenid mussels.
The ecological fish tissue contaminant and fish tissue mercury indicators have requirements for the
species, lengths, and weights of fish used, and collecting enough fish meeting these parameters was
challenging in 2015. To increase the number of fish samples collected in 2015, the allowable fish
collection area was extended to 1000 m from the X-site, compared to 500 m in 2010. In the connecting
river systems, sampling was attempted at only a subset of sites, so the fish tissue condition estimate
represents a smaller portion of the system than other indicators (Table 1). In the Great Lakes, the fish
tissue indicator represents the same area as other indicators but does have a significant portion of
unassessed area where fish were not caught (15% of samples missing for fish tissue contamination, and
19% of samples missing for fish tissue mercury).
Several parameters were collected in the NCCA 2015 and connecting river systems that were not
included in the assessment. These include total nitrogen, chloride, conductivity, sulfate, pH, silica, total
algal toxin concentration, metals in sediment, PAHs in sediment, PCBs in sediment, diatom and non-
diatom phytoplankton taxa richness, and macroinvertebrate taxa richness. Spatial variation in measured
values for these parameters is illustrated in Appendix E.
19
-------
Summary of Assessment Results - Great Lakes
Water Quality
Based on four water quality indicators (total phosphorus, chlorophyll a, Secchi depth, and dissolved
oxygen), more of the Great Lakes combined nearshore and embayment area was in good condition than
in fair or poor condition (Fig. 2). Based on total phosphorus, condition of 44% of the combined
nearshore and embayment area was assessed as good, 29% as fair, 23% as poor, and 4% was not
assessed. Based on chlorophyll a, 73% of nearshore and embayment area was in good condition, 10%
was fair, and 17% was poor. Based on water clarity (Secchi depth), 46% of nearshore and embayment
area was in good condition, 13% was fair, 37% was poor, and 4% was not assessed. Based on dissolved
oxygen, about 98% of nearshore and embayment area was in good condition, <1% was in fair condition,
<1% was in poor condition, and <1% was not assessed. Based on the eutrophication indicator derived
from the four water quality indicators, 54% of the combined nearshore and embayment area was in
good condition, 22% was fair, 24% was poor, and less than 1% was unassessed.
Embayments in the Great Lakes had a higher percentage of area assessed as poor for total phosphorus
(50% poor), chlorophyll a (39% poor), and water clarity (53% poor) than the nearshore excluding
embayments (22%, 16%, and 36% poor, respectively). This was reflected in condition estimates based on
the eutrophication indicator: 45% of embayment area was in poor condition and only 23% of the
nearshore area was in poor condition. Based on dissolved oxygen, assessed conditions were mostly good
in both the nearshore (98% good) and in embayments (96% good).
Cyanobacteria and Microcystin
Based on cyanobacteria concentrations, 64% of the Great Lakes combined nearshore and embayment
area was in good condition, 25% was in fair condition, and 10% was in poor condition. Conditions in the
nearshore were good in 65% of the area, fair in 25%, and poor in 10%. In embayments, 57% of the area
was in good condition, 31% of area was fair, and 12% of area was poor.
Microcystin concentrations at all but one site in the Great Lakes combined nearshore and embayments
were below the threshold of 8 ng/L for low risk to recreational users. The one site where microcystin
concentrations exceeded the threshold was in Lake Erie and accounted for less than 0.1% of the
combined nearshore and embayment area. Microcystin was detected at 71 of the 361 sites in the Great
Lakes nearshore and embayments, and concentrations ranged from 0.1 - 8.4 ng/L.
Enterococci
Conditions in the combined nearshore and embayments were assessed as good for enterococci in more
than 98% of the area (Fig. 2). The rest of the area was in poor condition (<1%) or was not assessed
(<1%). Conditions in the nearshore were similar to the combined nearshore and embayments because
the nearshore comprises most of the combined area. In the embayments, 98% of the area was in good
condition with the remaining 2% in poor condition (Fig. 2).
20
-------
Ecological conditions in the Great Lake# - 2015
Nearshore 1 embayments Nearshora Emtafinents
Eutrophication indicator
Chlorophyll a
Clarity (Secchi depth)
Dissolved oxygen
Total phosphorus
Cyanobacteria
Mierocptin
Entero cocci
Sediment quaiity
Sediment contamination
Sediment toxicity
Benthos (DTI)
Fish tissue contamination
Fish tissue mercury
Dfiissenid presence
Round goby presence
'szs4
fr Of
20 40 60 ao 1000 20 40 60 80 1000 20 40 60 80
Percent of area
Figure 2. Ecological conditions of the Great Lakes based on the 2015 NCCA.
Sediment Quality
Sediment quality in the Great Lakes was in good condition in 62% of the of the combined nearshore and
embayment area with most of the remainder in fair condition (15%) or unassessed (21%) due to hard
bottom and other factors (Fig. 2). The percent of the area in good condition was higher for the
embayments (73%) than the nearshore (62%), but the nearshore had four times more unassessed area
21
-------
than embayments which tended to have more readily sampled substrate. Poor sediment quality
conditions were found in about 2% of the area in both the nearshore and embayments.
Sediment quality is based on sediment contamination and sediment toxicity. Sediment quality
conditions were driven primarily by sediment contamination which generally had less area assessed as
good than sediment toxicity. Conditions based on sediment contamination were good in about 65% of
the nearshore and in the combined nearshore and embayments. Sediment toxicity was in good
condition in over 70% of the nearshore and combined nearshore and embayments. Most of the rest
(>20%) of the nearshore and the combined nearshore and embayments was unassessed for sediment
toxicity due sampling problems.
Conditions based on sediment contamination were assessed as good and fair condition in 74% and 20%
of the embayments, respectively, with about 1% of the area in poor condition, and 5% unassessed.
Ninety-one percent of embayment area was in good condition for sediment toxicity, and most of the
rest in fair and unassessed conditions.
Benthic Index
In the combined nearshore and embayments resource area of the Great Lakes, the condition of 31% of
the area was assessed as good condition, 15% was in fair condition, and 21% was in poor condition
based on the Oligochaete Trophic Index (OTI). The remaining 33% of the area in the combined resource
could not be assessed with the OTI; 19% was due to PONAR samples not collected at the site, and 14%
was due to collected PONAR samples not containing the enough tolerance-classified oligochaetes
to allow calculation of the OTI.
A higher percentage of embayment area was assessed as poor (32% good, 21% fair, and 28% poor) than
the nearshore (31% good, 15% fair, and 20% poor), although a higher percentage of the nearshore was
unassessed (34%) than embayments (9%). The higher percentage of unassessed area in the nearshore
compared is because embayments were more likely to have soft sediments where
PONAR sampling attempts are more often successful.
Ecological Fish Tissue Contamination
Ecological fish tissue contaminant conditions were assessed as good in 17%, fair in 19%, and poor in 47%
of the combined nearshore and embayments area of the Great Lakes (Fig. 2). The remaining 17% of this
combined area could not be assessed due to lack of sufficient numbers of fish caught. In the Great Lakes
as a whole, fish tissue contaminant conditions did not differ greatly between the nearshore and the
embayments areas. Embayments had slightly larger areas that were in fair and poor condition than the
nearshore, but also had less unassessed area than the nearshore (Fig. 2).
For the ecological fish tissue contaminant indicator, a screening value exceedance in any single receptor
group (piscivorous mammal, fish, or bird) causes the condition to go from good to fair; a screening value
exceedance in additional receptor groups causes the index to go from fair to poor (Table 2c). The
22
-------
relatively low amounts area with good ecological fish tissue contaminant conditions in this survey was
largely due to screening value exceedances for selenium, mercury, and PCBs in piscivorous birds and
mammals.
The method for assessing ecological fish tissue contaminant conditions changed between the 2010
NCCA report and the 2015 report (see Indicator section above). In the 2010 report, none of the Great
Lakes area was assessed as good for this indicator due to very conservative (i.e., protective) screening
values for selenium. Comparisons of the 2010 results with the 2015 results using the updated method
suggested that significantly more of the Great Lakes nearshore had good and fair conditions for this
indicator in 2015 (USEPA, 2021a). However, the amount of unassessed area decreased substantially in
2015, so it is unclear whether fish tissue contamination conditions have actually improved.
Fish Tissue Mercury
Fish tissue mercury conditions were good in 73% of embayment area but only about 65% of the
nearshore and the combined nearshore and embayments areas. Only 19% of the area of the
embayments could not be assessed whereas nearly 30% of the area of the nearshore and combined
nearshore and embayments could not be assessed. The remaining area (<10%) in each subpopulation
had fish tissue mercury concentrations above the mercury threshold of 300 ppb and were assessed as
poor condition (Fig. 2). Fair condition for fish tissue mercury is not defined (Table 2c).
Invasive Species
Dreissenid mussels were estimated to be present in 56% of the Great Lakes combined nearshore and
embayments area, with 7% of the area unassessed either because a PONAR sample or a video of high
enough quality were not available. Both methods detected dreissenid mussels in 13% of the nearshore,
and PONAR samples detected an additional 24% of area where videos were poor quality or did not
detect mussels, and videos detected mussels in 18% of the area where they were not collected in
PONAR samples.
Based on underwater video, round gobies were present in 4% of the Great Lakes combined nearshore
and embayments area, with an additional 5% of area with suspected round gobies. Round gobies were
more common in the nearshore (10% of area) than in embayments (4% of area). Nineteen percent of
the area was unassessed for round goby, mostly due to poor video quality.
Lake Superior
Seventy-eight sites were sampled to assess 3,202 km2 of nearshore area, of which 42 were nearshore
sites and 36 were embayment sites. The embayments represent 211 km2 of the nearshore area. Lake
Superior sample locations are shown in Fig. 3.
23
-------
Water Quality
Conditions in the combined nearshore and embayment area of Lake Superior were assessed as mostly in
good condition based on chlorophyll a (86% of the area), water clarity (54%), and dissolved oxygen
(100%). Based on total phosphorus, more of the combined nearshore and embayment area was in fair
condition (62%) than good condition (9%). Based on the eutrophication indicator, 62% of the combined
nearshore and embayments area of Lake Superior was in good condition (Fig. 4).
A higher percent of the embayment area was in poor condition based on total phosphorus (41% poor),
chlorophyll a (23% poor), and water clarity (46% poor) than the nearshore (8%, 0%, and 23% poor,
respectively). These conditions were reflected in condition estimates based on the eutrophication
indicator: 35% of embayment area was in poor condition, and only 6% of nearshore was in poor
condition.
Sample locations
Type
o Embayments
¦ Nearshore
NCCA 2015 Frame
Type
Embayment
¦i Nearshore
N
0 30 60 120 Kilometers J / e * i\
1 i i i I i i i I ) ff r-=r\ b
Figure 3. Sample locations in Lake Superior for the 2015 NCCA.
Cyanobacteria and Microcystin
Cyanobacteria conditions in Lake Superior were assessed as good in 85% of the combined nearshore and
embayments area and fair in the remaining 15% (Fig. 4). Conditions in the nearshore were similar to the
combined nearshore and embayments. Condition based on cyanobacteria was better in the nearshore
(86% of the area in good condition) than in embayments (67% good).
Microcystin concentrations at all sites site in Lake Superior were below the threshold of 8 ng/L so 100%
of the combined nearshore and embayments area in Lake Superior was assessed as being in good
24
-------
condition. Microcystin was detected at only 7 sites Lake Superior, and each had a concentration of 0.1
Hg/L, the detection limit for the microcystin assay.
Ecological condition* In Lake Superior - 2015
iearshore & embay rnenti Nsarshore Emboymanti
Eutrophication indicator
MM
Chlorophyll a p=r-;
Clarity {Secchi depth)
Dissolved oxygen
Total phosphorus
Cyano Bacteria
Micro cystin
Enterococci
Sediment quality
Sediment contamination
Segment tonicity
Benthos fOTI;
Fish tissue contamination
Fish tissue mercury
Dreissenid presence
Round goby presence
0 20 40 SO 80 1000 20 40 60 80 100 0 20 40 80 80 100
Percent of area
Figure 4. Ecological conditions of Lake Superior based on the 2015 NCCA.
Enterococci
Enterococci cell concentrations were below the threshold value of 1,280 CCE/lOOml at nearly all the
sites in Lake Superior; 99% of the area in the combined nearshore and embayment area was assessed as
25
-------
being in good condition. All exceedances of the enterococci thresholds were in the embayments, where
7% of the area was in poor condition (Fig. 4).
Sediment Quality
Sediment quality in Lake Superior was in good condition for greater than 70% of the area of the
combined nearshore and embayments and the nearshore (Fig. 4). Within embayments, conditions were
good across 67% of the area. The lower percent of good sediment quality conditions in the embayments
was due to a slightly lower percentage of area with good conditions for sediment contamination (both
sediment contamination and sediment toxicity must be rated good for the site to be in good condition).
Benthic Index
In Lake Superior's combined nearshore and embayments, 46% of the area could not be assessed using
the OTI. Twenty-one percent of the area was unassessed because PONAR samples could not be
collected; 25% was unassessed because collected samples did not contain enough tolerance-
classified oligochaetes to calculate the OTI. In the remaining area, the condition of 40% of the area
was assessed as in good condition, 10% was fair, and 4% was poor. A higher percentage of
the embayment area was in poor condition (42% good, 36% fair, and 14% poor) than the nearshore
(40% good, 8% fair, and 3% poor).
Ecological Fish Tissue Contaminant
Of Lake Superior's combined nearshore and embayments area, 26% was in good condition for fish tissue
contaminants, 16% in fair condition, 47% in poor condition, and the remaining 11% of area not assessed
due to lack offish caught (Fig. 4). The nearshore of Lake Superior had a greater percentage of area in
poor condition or that was unassessed than the embayments area.
Fish Tissue Mercury
Fish tissue mercury conditions in Lake Superior were assessed as good in about 89% of the combined
nearshore and embayments and in both the embayments and nearshore individually (Fig. 4). In the
combined nearshore and embayments, conditions were poor in less than one percent of the area. All
sites where poor fish tissue mercury conditions were detected were in embayments, where about 6% of
the area was in poor condition. The unassessed area was due to lack of fish caught.
Invasive Species
Based on a single site in the nearshore near Port Wing, Wisconsin, dreissenid mussels were estimated to
be present across 3% of Lake Superior's combined nearshore and embayments area. Dreissenids are
common in the Duluth-Superior Harbor, which is about 40 km southwest from this site, but dreissenids
are otherwise been rare in the open waters of Lake Superior (Trebitz et al., 2019). It has been
hypothesized that dreissenids are uncommon in Lake Superior because water temperatures, food
availability, and the concentrations of Ca2+ (or some combination of these factors) may be limiting
mussel abundance (Ramcharan et al., 1992; Trebitz et al., 2019). However, the observation of
26
-------
dreissenids in this survey and other recent surveys (Trebitz et al., 2019) in Lake Superior suggest that
Lake Superior may be (or may be becoming) more suitable habitat for dreissenids. Video and PONAR
data from future NCCA surveys will help to determine whether dreissenids are becoming more
widespread in Lake Superior's nearshore.
Based on underwater video, round gobies were suspected for an estimated 4% of the combined
nearshore and embayments area in Lake Superior, with no positive goby observations and with 34% of
the area unassessed due to poor video quality.
Lake Michigan
One hundred sites were sampled to assess 7,869 km2 of Lake Michigan's nearshore area, of which 45
were nearshore base sites and 55 were embayment sites. The embayments represent 402 km2 of the
nearshore area. Lake Michigan sample locations are shown in Fig. 5.
Water Quality
Most of the combined nearshore and embayment area of Lake Michigan was assessed as being in good
condition based on the four components of the eutrophication indicator (Fig. 6). Based on the
eutrophication indicator, 62% of the combined nearshore and embayment area of Lake Michigan was in
good condition, 15% was in fair condition, 23% was in poor condition, and less than one percent was
unassessed.
More of the area of embayments was in poor condition for total phosphorus (46%), chlorophyll a (30%),
and water clarity (45% poor), than the nearshore (24%, 10%, and 34% poor, respectively). Based on the
eutrophication indicator, 35% of embayment area was in poor condition, and 22% of nearshore area
was in poor condition.
Cyanobacteria and Microcystin
Condition based on cyanobacteria cell counts was good in 78% of the Lake Michigan combined
nearshore and embayment area, was fair in 16%, and poor in 6% of the area (Fig. 6). In the embayments,
71% of the area was in good condition and 22% and 7% of the area was in fair and poor condition,
respectively (Fig. 6).
Microcystin concentrations at all sites in Lake Michigan were below the threshold of 8 ng/L for low risk
to recreational users so 100% of the combined nearshore and embayment area was in good condition.
Microcystin was detected at only 16 of the 100 sites in Lake Michigan and concentrations ranged from
of 0.1-1.16 ng/L.
Enterococci
In Lake Michigan, only one embayment site exceeded the USEPA recreational threshold of 1,280
CCE/100 mLfor enterococci. Condition based on enterococci was assessed as good in 99% of the
embayment area and 100% of the nearshore.
27
-------
Sample locations
Type
o Embayments
¦ Nearshore
NCCA 2015 Frame
Type
I 1 Embayment
Nearshore
M
• s
o
¦ Lake Michigan
W
60
I
120 Kilometers
I
%, ...
k
Figure 5. Sample locations in Lake Michigan for the 2015 NCCA.
Sediment Quality
Sediment quality conditions in Lake Michigan were assessed as good in more than 70% of the nearshore
and in the combined nearshore and embayments. In the embayments, more than 90% of the area was
in good condition (Fig. 6). Though sediment contaminant conditions were similar in the nearshore and
embayments, the nearshore had a small amount of area in poor condition based on sediment toxicity,
whereas none of the area in the embayments was assessed as poor based on sediment toxicity.
Benthic Index
Benthos conditions could not be assessed in 19% of the combined Lake Michigan nearshore and
embayment area. Of the unassessed area, 5% was due to PONARs not being collected and 14% was due
to PONAR samples not containing enough the tolerance-classified oligochaetes to calculate the OTI (see
Appendix C). A slightly higher percentage of the Lake Michigan embayments was assessed as
28
-------
poor condition (41% good, 15% fair, and 25% poor) than the nearshore (45% good, 17% fair, and 19%
poor).
Ecological conditions in Lake Michigan - 2015
Nearshore & embay merits Nearshore Embay menls
Eutrophication indicator
Chlorophyll a
Clarity (Secchi depth)
Dissolved oiygen
Total phosphorus
Cyanobacteria
Micro cystin
Enterocoeei
Sediment quality
Sediment contamination
Sediment toxicity
Benthos (OTi)
Fish tissue contamination
Fish tissue mercury
Dreissenict presence
Round goby presence
o 20 m m so 1000 20 «o w so 1000 20 *0 w m
Percent of area
Figure 6. Ecological conditions of Lake Michigan based on the 2015 NCCA.
Ecological Fish Tissue Contamination
Fish tissue contaminant conditions in Lake Michigan were good in 11%, fair in 21%, and poor in 52% of
the combined nearshore and embayment area with the remaining area unassessed due to lack of
29
-------
sufficient fish caught (Fig. 6). The percent of area in good, fair, and poor condition did not differ greatly
between the nearshore and embayments.
Fish Tissue Mercury
Fish tissue mercury conditions in Lake Michigan were assessed as good in 67% of the embayment area
and about 60% of the nearshore and the combined nearshore and embayment area (Fig. 6). Conditions
were poor in about 8% of the nearshore and the combined nearshore and embayment, with the
remaining 32% unassessed. In embayments, conditions were poor in 5% and unassessed in 28% of the
area.
Invasive Species
Dreissenid mussels were estimated to be present in 78% of Lake Michigan's combined nearshore and
embayment area, with just 2% of area unassessed. Dreissenids were detected using both video and
PONAR at 20% of the combined nearshore and embayment area. PONAR samples alone detected
dreissenids at an additional 40% of area where videos were of poor quality or did not record mussels.
Videos detected mussels in 18% of the area where PONAR samples were not collected. Mussels were
observed across 52% of the embayment area and 80% of the nearshore area.
Round gobies were estimated to be present across 8% of the combined Lake Michigan nearshore and
embayment area, with an additional 3% of the area suspected of having round gobies. Three percent of
the area was unassessed for round goby due to poor quality video. Round gobies were less common in
embayments (7% of area) than in the nearshore (11% of area).
Lake Huron
Sixty-seven sites were sampled to assess 3,289 km2 of Lake Huron's nearshore, including 44 base sites
and 23 embayment sites. The embayments represent 119 km2 of the area. Lake Huron sample locations
are shown in Fig. 7.
Water Quality
Most of the combined nearshore and embayment area of Lake Huron was in good conditions based on
chlorophyll a (59% good), water clarity (43% good), and dissolved oxygen (100% good; Fig. 8). Based on
total phosphorus, 43% of combined nearshore and embayment area was in good condition, 43% was in
fair condition, and 14% was in poor condition. Based on the eutrophication indicator, 47% of the
combined nearshore and embayment area was in good condition, with 36% fair, and 17% poor.
Based on total phosphorus, 30% of embayment area and 43% of the nearshore area was in good
condition. Based on chlorophyll a, 61% of embayment area and 59% of the nearshore area was in good
conditions. For water clarity, 30% of embayment area had good conditions; 43% of nearshore area had
good conditions. All assessed area in both the nearshore and embayments had good conditions based
30
-------
on dissolved oxygen. Based on the eutrophication indicator, more of the embayment area was in poor
condition (35% poor) than the nearshore (16% poor).
Figure 7. Sample locations in Lake Huron for the 2015 NCCA.
St Marys
River
Sample locations
Type
o Embayments
¦ Nearshore
NCCA 2015 Frame
Type
I I Embayment
¦¦ Nearshore
I' 1 Saginaw Bay
Cyanobacteria arid Microcystin
Conditions based on cyanobacteria cell counts were assessed as good in 34%, fair in 43%, and poor in
23% of the Lake Huron combined nearshore and embayment area (Fig. 8). Conditions in the nearshore
were the same as the combined nearshore and embayments. In the embayments, 43% of the area had
good conditions, 39% of the area was in fair condition, and 17% of the area was in poor condition based
on cyanobacteria.
Microcystin concentrations at all sites in Lake Huron were below the threshold of 8 |ig/L for low risk to
recreational users so 100% of the combined nearshore and embayment area was assessed as good.
Microcystin was detected at only 18 of the 67 sites in Lake Huron and concentrations ranged from of
0.11-4.23 ng/L.
31
-------
Enterococci
No sites in the Lake Huron exceeded the recreational threshold of 1,280 CCE/lOOmLfor enterococci, so
conditions were assessed as good in 100% of the Lake Huron combined nearshore and embayment area.
Detected concentrations of enterococci ranged from 130 -1,159 CCE/lOOmL.
Sediment Quality
Sediment quality conditions in Lake Huron were good in 64% of the nearshore area and in the combined
nearshore and embayments. Sediment quality conditions were in good condition in 87% of embayment
area (Fig. 8). The smaller amount of area in the nearshore in good condition was because of the slightly
smaller percentage of area in the nearshore with good sediment contamination and sediment toxicity
conditions. There were no poor sediment quality conditions in either the embayments or nearshore, but
sediment conditions could not be assessed because of hard bottom at about 27% of the area in the
nearshore and 13% of the area in the embayments (Fig. 8).
Benthic Index
Benthos conditions could not be assessed in 41% of the combined nearshore and embayment area in
Lake Huron. Of this, 27% was unassessed because PONAR samples could not be collected and 14%
was unassessed because PONAR samples did not contain enough tolerance-classified oligochaetes to
calculate the OTI. In the assessed area, 13% of the combined nearshore and embayment
area was in good condition, 19% was fair, and 27% was poor. For Lake Huron embayments, 9% of the
area was in good condition, 35% was in fair condition, and 17% was in poor condition. For
the nearshore, 14% of the area was in good condition, 18% was in fair condition, and 27% was in poor
condition.
Ecological Fish Tissue Contamination
In the combined nearshore and embayments area of Lake Huron, 7% was in good condition, 14% was
fair, 53% was poor, and the remaining area could not be assessed due to lack of fish caught. More
embayment area was in poor condition (73%) than the nearshore (52%).
Fish Tissue Mercury
Fish tissue mercury conditions in Lake Huron were assessed as good in 69% of the area of the
embayments, 52% of the nearshore, and 53% of the combined nearshore and embayments (Fig. 8).
Condition based on fish tissue mercury conditions could not be assessed in 41% of the nearshore and
22% of the area of embayments. Conditions were assessed as poor in about 7% of the area of the
nearshore and the combined nearshore and embayments and were poor in about 9% of the embayment
area.
32
-------
Ecological conditions in Lake Huron 2015
Nearthora 1 embayment* iearshore Embafments
Eutrophication indicator
Chlorophyll a
Clarity (Secchi depth)
Dissolved oxygen
Total phosphorus
Sediment quality
Sediment contamination
Sediment toxicity
Benthos (Oil)
Fish tissue contamination
Fish tissue mercury
Dreissenid presew:;
Round goby presence
20 40 80 80 1C0Q 20 40 00 ftO 1050 20 40 80 BO 1MJ
Percent of area
Figure 8. Ecological conditions of Lake Huron based on the 2015 NCCA.
Invasive Species
Dreissenid mussels were estimated to be present across 46% of Lake Huron's combined nearshore and
embayment area, with 7% of the area unassessed because a PONAR sample was not collected or a good
quality video was not available. Dreissenid mussels were detected using both PONAR and video in 9% of
Lake Huron's combined area, and PONAR samples alone detected dreissenids in an additional 17% of
area where videos were of poor quality or did not detect mussels. Videos detected mussels in 20% of
33
-------
area where they were not observed in PONAR samples. Dreissenid mussels were similarly frequent in
the nearshore (45% of area) and embayments (48% of area).
Round gobies were estimated to be present in 4% of Lake Huron combined nearshore and embayment
area, with an additional 2% of the area having suspected round gobies. Eighteen percent of the area was
unassessed for round goby because of poor quality videos. Round gobies were not observed at any
embayment sites in Lake Huron.
Lake Erie
Ninety sites were sampled in Lake Erie, of which 44 were base sites and 13 were embayments (Fig. 9).
An additional 33 sites were sampled for water quality parameters, microcystin, and cyanobacteria to
increase the precision of condition estimates for each basin of the lake (western, central, and eastern).
These sites represent an area of 2,700 km2 of the US nearshore coastal area. The embayments represent
80 km2 of the area. Condition assessment results for Lake Erie, which are based on base sites and the
embayment enhancement (57 sites). Condition assessment results for Lake Erie basins are based on all
90 sites (see Table 1 footnote).
Figure 9. Sample locations in Lake Erie for the 2015 NCCA.
Sample locations
Type
O Embayments
¦ Nearshore
A Enhancements
NCCA 2015 Frame
Type
I Embayment
Erie Central Basin
M Erie Eastern Basin
I Erie Western Basin
0 30 60 120 Kilometers
1 i i i I i i i I
34
-------
Water Quality
Based on water quality, Lake Erie had the highest percentage of combined nearshore and embayment
area assessed in poor condition (Fig. 10). Forty-three percent of the combined nearshore and
embayment area of Lake Erie was in poor condition based on total phosphorus; 54% was in poor
condition based on chlorophyll a, and 70% was in poor condition based on water clarity. Based on
dissolved oxygen, 92% of Lake Erie combined nearshore and embayment area was in good condition.
Based on the eutrophication indicator, 60% of Lake Erie's combined nearshore and embayment area
was in poor condition, 17% was in fair condition, and 23% was in good condition.
Embayments in Lake Erie had a higher percentage of area in poor condition for total phosphorus (81%
poor), chlorophyll a (92% poor), and water clarity (88% poor) than the nearshore alone (47%, 53%, and
70% poor, respectively). Based on the eutrophication indicator, 92% of Lake Erie embayment area was in
poor condition, compared to 60% of Lake Erie nearshore area. It should be noted that embayments are
fewer in Lake Erie than in other the Great Lakes, and the embayment-only assessment was based on just
13 sites (Table 1).
Water quality in the combined nearshore and embayments in Lake Erie basins improved from west to
east (Fig. 11). Based on total phosphorus, the western basin had no area in good condition, the central
basin had 27% in good condition, and the eastern basin had 83% in good condition. For chlorophyll a, 9%
of the combined nearshore and embayment area was in good condition in the western basin, 23% in the
central basin, and 40% in the eastern basin. Based on water clarity, the western basin had no nearshore
area in good condition, the central basin had 7% in good condition, and the eastern basin had 33% of
combined nearshore and embayment area in good condition. For the eutrophication indicator, 82% of
the combined area was in poor condition for the western basin, 77% was in poor condition for the
central basin, and 21% was in poor condition for the eastern basin.
35
-------
Eutrophication indicator
Chlorophyll a
Clarity (Secchi depth)
Dissolved oxygen
Total phosphorus
Cyanobacteria
Microcystin
Enterococci
Sediment quality
Sediment contamination
Sediment toxicity
Benthos (OTI)
Fish tissue contamination
Fisti tissue mercury
Dreissenfd present;
Round goby presence
Ecological conditions in Lake Erie - 2015
Neerthore 1 embayments iearshore Embayment*
==2
0 20 40 60 80 1000 20 40 60 60 1000 20 40 60 60 100
Percent of area
Figure 10. Ecological conditions of Lake Erie based on the 2015 NCCA.
36
-------
Western Basin
Central Basin
Eutrophication indicator
Chlorophyll a
Clarity (Secchi depth)
Dissolved oxygen
Total phosphorus
Microcystin
Cyanobacteria
=_
i r
...J... L
i i
J L
J 1
—M-g"
i
1 1
1 1
20 40 60
Eastern Basin
80
Eutrophication indicator
Chlorophyll a
Clarity (Secchi depth)
Dissolved oxygen
Total phosphorus
Microcystin
Cyanobacteria
100 0
F
100 0
20
nr
40
T
60
Lake Erie (all)
80
100
20
~r
40
60
80
100
Percent of area
¦ Good n Fair Q Poor ~ Unassessed
Figure 11. Water quality conditions summarized by basin from the Lake Erie enhancement of the 2015
NCCA. The lower right panel combines data from each of the three basins.
Cyanobacteria and Microcystin
Cyanobacteria conditions were assessed as good in 36% of the combined nearshore and embayment
area of Lake Erie, fair in 43% of the area, and poor in 21% of the area (Fig. 10). Conditions in the
nearshore were the same as the combined nearshore and embayments. In the embayments,
cyanobacteria conditions were good in 26% of the area, fair in 38%, and poor in 37%.
Microcystin concentrations at all but one embayment site in Lake Erie were below the threshold of 8
|j.g/L for low risk to recreational users. This site accounted for less than 1% of the combined nearshore
and embayment area assessed in Lake Erie, so more than 99% of the nearshore area of Lake Erie was in
good condition based on microcystin. Because of the low number of embayment sites in Lake Erie, this
37
-------
one site accounted for 10% of the area of Lake Erie embayments. Microcystin was detected at 21 of the
57 sites in Lake Erie and concentrations ranged from of 0.11-8.37 ng/L.
Cyanobacteria and microcystin were both collected at the additional 33 Lake Erie basin enhancement
sites. The western basin had the highest percentage of the combined nearshore and embayment area in
poor condition based on cyanobacteria (47%, Fig. 11). Forty percent of the central basin was in poor
condition, and 17% of the eastern basin was in poor condition. None 33 basin enhancement sites had
microcystin concentrations that exceeded the microcystin threshold of 8 ng/L. One embayments site in
the central basin exceeded the threshold.
Enterococci
No sites in Lake Erie exceeded the recreational threshold of 1,280 CCE/lOOmL. One site was unassessed.
Sediment Quality
Sediment quality conditions in the combined nearshore and embayment areas of Lake Erie were
assessed as good and fair in 34% and 38%, respectively. Sediment quality conditions could not be
assessed in 25% of this combined area. Because a high percent of the nearshore was unassessed, more
of the embayments were assessed as being in good, fair, and poor condition than the nearshore. Poor
sediment quality conditions in the embayments were due to both sediment contamination and toxicity,
whereas poor sediment quality conditions in the nearshore resulted from poor sediment toxicity
conditions alone (Fig. 10).
Benthic Index
Based on the OTI, 13% of the combined nearshore and embayment area was in good conditions; 14% of
the area was in fair conditions, and 42% was in poor condition. Benthic conditions could not be assessed
in 31% of the area in the combined nearshore and embayment in Lake Erie. Of that, 29% of the area
was unassessed because PONARs could not be collected, and 2% was unassessed because OTI could not
be calculated for the PONAR samples that were collected. All the area that could not be assessed was in
the nearshore. In the Lake Erie nearshore, 13% of the area was in good condition, 14% was in fair
condition, and 41% was in poor condition. In Lake Erie embayments, 7% of the area was in good
condition, 10% was in fair condition, and 83% was in poor condition.
Ecological Fish Tissue Contamination
Fish tissue contaminant conditions were good in 38%, fair in 30%, and poor in 28% of the combined
nearshore and embayment area of Lake Erie. Only 4% of this area could not be assessed (Fig. 10). All of
the sites that were unassessed were in the nearshore, and fish tissue contaminant conditions in the
nearshore of Lake Erie were very similar to the combined nearshore and embayment area (Fig. 10).
Conditions were good in 14%, fair in 21%, and poor in 65% of the embayments area.
Fish Tissue Mercury
38
-------
Fish tissue mercury conditions in Lake Erie were assessed as good in 93% of the area in the embayments
and could not be assessed in the remaining 7% (Fig. 10). Nearshore conditions were good in about 86%
of the area and were poor in 4% of the area. Fish tissue mercury conditions could not be assessed in the
remaining 10% of the nearshore. Conditions in the combined nearshore and embayment area were
nearly equivalent to the conditions in the nearshore because embayments represent a small area of
Lake Erie.
Invasive Species
Dreissenid mussels were estimated to be present in 60% of Lake Erie's combined nearshore and
embayment area, with 11% of area unassessed because a PONAR sample could not be collected or
because of poor video quality. Dreissenid mussels were detected in 13% of the combined nearshore and
embayment area using both PONAR sampling and underwater video. Dreissenid mussels were collected
in PONAR samples at an additional 30% of area where videos did not detect them. Mussels were
detected by videos but not collected in PONAR samples for 17% of combined nearshore and embayment
area. Dreissenid mussels were similarly frequent in the embayments (65% of area) and the nearshore
(59% of area).
Round gobies were suspected to be present in 9% of Lake Erie's combined nearshore and embayment
area, but there were no confident detections of gobies in Lake Erie. However, over half of the combined
area (53%) was unassessed for round goby, mostly because of poor video quality.
Lake Ontario
In Lake Ontario, 59 total sites that were sampled to assess 1,378 km2 of nearshore area. There were 43
NCCA base sites and 16 embayment sites sampled (Fig. 12). The embayments represent a 96 km2 of the
area.
Water Quality
Based on the four water quality indicators, most of the combined nearshore and embayment area of
Lake Ontario was in good condition (Fig. 13). Based on the eutrophication indicator, 61% of the
combined nearshore and embayment area was in good condition, with 24% in fair condition, and 15% in
poor condition.
Lake Ontario embayments had high percentages of area in poor condition based on total phosphorus
(87% poor), chlorophyll a (87% poor), and water clarity (81% poor) compared to the nearshore (9%, 7%,
and 28% poor, respectively). Based on the eutrophication indicator, 87% of embayment area was in
poor condition, but only 9% of nearshore area was in poor condition. Like Lake Erie, embayments are
few in the US waters of Lake Ontario. There were just 16 sites sampled in embayments, and 43 sites
sampled in the nearshore for water quality.
39
-------
Sample locations
Type
o Embayments
¦ Nearshore
IMCCA 2015 Frame
Type
I ! Embayment
Nearshore
rw
Niagara Rivar
Lake Ontario
\
30 60 120 Kilometers
_l I I I I I I I
K
Figure 12. Sample locations in Lake Ontario for the 2015 NCCA,
Cyanobacteria and Microcystin
Conditions based on cyanobacteria were assessed as good in 66% of the combined nearshore and
embayment area of Lake Ontario, fair in 27% of the area, and poor in 7% of the area (Fig. 13). Conditions
were good in 70% of nearshore area, with 26% in fair condition, and 5% in poor condition. In the
embayments, 19% of area was in good condition, 50% was in fair condition, and 31% was in poor
condition.
Microcystin concentrations at ail sites in Lake Ontario were below the threshold of 8 (ig/L for low risk to
recreational so that 100% of the combined nearshore and embayment area assessed in Lake Ontario
had good microcystin conditions. Microcystin was detected at only 9 of the 59 sites in Lake Ontario and
concentrations ranged from of 0.108-1.625 ng/L.
40
-------
Ecological conditions in Lake Ontario - 2015
Nearshore 1 embayments Nearshora Emtafinents
Fish tissue contamination
Fish tissue mercury
Dreissenict presence
Round goby present
0 20 40 60 ao 1000 20 40 60 80 1000 20 40 60 80
Percent of area
Figure 13. Ecological conditions of Lake Ontario based on the 2015 NCCA.
Sediment Quality
Sediment quality conditions were assessed as 21% good and 21% fair in the combined nearshore and
embayment areas with the remaining 58% of area unassessed for sediment quality because of hard
substrates (Fig. 13). The amount of area with good sediment quality condition was similar in the
nearshore and embayments, which reflected similar amounts of area in each with good conditions for
the sediment contamination component. However, the percent of area in the nearshore that could not
41
-------
be assessed was more than double that of embayments. Condition based on sediment toxicity was
assessed as good in 75% of the area of the embayments compared to only 23% of the nearshore, much
of which was unassessed.
Benthic Index
Sixty-nine percent of the Lake Ontario combined nearshore and embayment area could not
be assessed with the OTI. Of the assessed area, 10% was in good condition, 9% was fair, and 12% was
poor (Fig. 13). Fifty-nine percent of the area was unassessed because PONAR samples could not be
collected and 10% was unassessed because the OTI could not be calculated. Of the nearshore area that
could be assessed using the OTI, 9% was in good condition, 9% was fair, and 9% was poor. For
the embayments, 25% of area was in good condition, 6% was fair, and 38% was poor.
Ecological Fish Tissue Contamination
Based on fish tissue contaminants, 7% of the combined nearshore and embayment area of Lake Ontario
was in good condition, 13% was fair, 35% was poor, and the remaining 45% of area could not be
assessed due to lack of fish caught (Fig. 13). Conditions in the nearshore area were similar to the
combined nearshore and embayment area while conditions in the embayments alone were in poor
condition in more than 80% of the area.
Fish Tissue Mercury
Fish tissue mercury conditions in Lake Ontario embayments were assessed as good in 50% of the area
and poor in 31% of the area. Conditions were assessed as good and poor in 26% and 7%, respectively, of
the area of the nearshore (Fig. 13). The remaining area in each could not be assessed due to lack of
fishing success.
Invasive Species
Dreissenid mussels were estimated to be present in 71% of Lake Ontario's combined nearshore and
embayment area, with 13% of the area not assessed because a PONAR sample could not be collected or
due to poor video quality. Dreissenid mussels were detected in 19% of Ontario's combined nearshore
and embayment area using both PONAR and video. They were observed in an additional 6% of area
based on PONAR samples. Mussels were detected from videos for an additional 46% of area where they
were not collected in PONAR samples. Videos were especially helpful in Lake Ontario because much of
the bottom was hard and PONAR sampling was often not successful. Dreissenid mussels were more
common in the nearshore, where they were estimated to be present in 74% of area, compared to 31%
of embayment area.
Round gobies were estimated to be present in 4% of the Lake Ontario combined nearshore and
embayment area, with an additional 17% of area with possible round gobies. Six percent of the area
could not be assessed for round goby because of poor video quality. Round gobies were observed only
in the nearshore.
42
-------
The St. Marys River
In 2015, 50 sites were sampled; and in 2016, 44 sites were sampled in the St. Marys River. The two years
of data (94 sites) were pooled to assess the 470 km2 of the St. Marys River (Fig. 14).
Water Quality
Conditions in the St. Marys River (Fig. 15) were mostly assessed as good based on chlorophyll a and
dissolved oxygen (61% and 93% good, respectively), mostly fair based on total phosphorus (55% of area
in fair condition), and mostly poor based on water clarity (90% poor). Based on the eutrophication
indicator, 58% of the system was assessed as fair, 37% was poor, and only 5% was good. St. Marys River
was assessed with Lake Huron water quality thresholds, which are protective of Lake Huron's
oligotrophic status (see Wick et a!., 2019, for a discussion of threshold selection).
Figure 14. St. Marys River sample locations for the 2015-2016 NCCA.
Cyanobacteria and Microcystin
Based on cyanobacteria concentration, 60% of the area in the St. Marys River was in good condition and
40% of the river was in fair condition. No microcystins were detected in any St. Marys River water
samples, so condition of 100% of the area in the St. Marys River was assessed as good for microcystins
(Fig. 15).
43
-------
Enterococci
No sites in the St. Marys River exceeded the recreational threshold of 1,280 CCE/100 mLfor enterococci.
Sediment Quality
Sediment quality conditions were assessed as good in 57% of the area in the St. Marys River (Fig. 15).
About 40% of the area was in fair condition and about 1% in poor condition. Conditions for sediment
contamination and sediment toxicity were both mostly good (63% and 86%, respectively). Poor
conditions for sediment contamination were detected at only one site in the St. Marys River, accounting
for about 1% of the area.
Benthic Index
Based on the OTI, 51% of area of the St. Marys River was in good condition, 21% was fair, and 13% was
in poor condition. The remaining 15% could not be assessed because no PONAR sample could be
collected (2%) or the OTI could not be calculated (13%).
Ecological Fish Tissue Contamination
Fish were sampled for fish tissue contaminants at 40 sites in the St. Marys River in 2015 (primarily in US
waters) and no sites were sampled in 2016. Enough fish of target species were caught at 35 sites.
Condition estimates represent conditions in the sample areas, not the entire resource. Contaminant
conditions were estimated to be good in 10%, fair in 30%, and poor in 48% of the sampled area with the
remaining area unassessed. The same contaminants (selenium and mercury) that contributed to the
mostly fair and poor fish tissue conditions in the Great Lakes contributed to the mostly fair and poor
conditions in the St. Marys River.
44
-------
Ecological conditions In the St. Marys River - 2015 & 2016
Eutrophication indicator
Chlorophyll a
Clarity (Secchi depth)
Dissolved oxygen
Total phosphorus
Cyan o bacteria
fvlicrocystin
Enterococci
Sediment Quality
Sediment contamination
Sediment toxicity
Benthos (OH)
Fish tissue contamination
Fish tissue mercury
Dreissenid oresenee
Round goby presence
L
¦r
: .
:
| Good
G Fair
D po"
[] Un assesses!
G Absent
I Suspected soby
| - 'esent
G Unassessed
0 20 40 60 80 100
Percent of area
Figure 15. Ecological conditions of the St. Marys River based on the 2015-2016 NCCA.
Fish Tissue Mercury
Fish tissue mercury conditions were good in 70% of the sampled area in the St. Marys River, and poor in
10% of the sampled area. The remaining area was unassessed due to lack of fish caught.
45
-------
Invasive Species
Dreissenid mussels were estimated to be present in 21% of the area of the St. Marys River. Dreissenids
were detected in both PONAR samples and videos in 4% of the area. PONAR samples alone detected
dreissenids in 5% of the area. Videos detected mussels in an additional 12% of area where they were not
collected in PONAR samples.
Round gobies were suspected to be present in 2% of the St. Marys River. Twenty-three percent of the
area was unassessed for round goby mostly because of poor video quality.
Huron-Erie Corridor (HEC)
Fifty-seven sites were sampled in 2014 and 48 sites were sampled in 2015 to assess the condition of the
1,287 km2 of the HEC (Fig. 16). For the assessment, ninety-five first site visits were combined from the
two years of sampling. In 2014, two of the sites were revisited in the same year. In 2015, seven different
sites sampled in 2014 were revisited. Revisited sites were not used in the condition estimates.
Subpopulation assessments of the St. Clair River, Lake St. Clair, and the Detroit River were based on 18,
48, and 29 sites, respectively.
Water Quality
Condition of most of the area of the HEC was assessed as good based on total phosphorus, chlorophyll
a, and dissolved oxygen (81%, 86%, and 100%, respectively). Only 24% of HEC area was in good
condition based on water clarity, with 21% fair and 54% in poor condition. Based on the eutrophication
indicator, 45% of the area of the HEC was in good condition, 39% was fair, and 15% was poor. The HEC
was assessed using water quality thresholds for the central/eastern basin of Lake Erie which are more
protective of water quality than the thresholds for western Lake Erie (see Wick et al., 2019, for a
discussion of threshold selection).
Water quality decreased down-river within the HEC, with more of the area of the Detroit River in poor
condition than the St. Clair River or Lake St. Clair. For example, based on total phosphorus, 95% of the
St. Clair River was area in good condition, 84% of Lake St. Clair was area in good condition, and 47% of
the Detroit River was in good condition. Likewise, based on chlorophyll a, 100% of area in the St. Clair
River, 86% of Lake St. Clair, and 76% of the Detroit River was in good condition. Lake St. Clair had the
highest percentage area (27%) in good condition based on water clarity compared to the St. Clair River
(10%) and the Detroit River (4%).
46
-------
Figure 16. Huron-Erie Corridor sample locations for the 2014-2015 NCCA.
Cyanobacteria and Microcystin
Cyanobacteria conditions in the HEC were good in 28% of the area, fair in 60%, and poor in 11% of the
area (Fig. 17). Condition based on cyanobacteria declined downriver from the St. Clair River (37% good)
to Lake St. Clair (28% good) and the Detroit River (23% good).
Microcystin concentrations were below the threshold of 8 |ig/L for low risk to recreational users at all
sites in the HEC so 100% of the area was assessed as good. Microcystin was not detected in the St. Clair
River. Microcystin was detected at 9 of 49 sites in Lake St. Clair with concentrations ranging from 0.12 -
1.66 ng/L. In the Detroit River, microcystin was detected at 8 of 31 sites, with concentrations ranging
from of 0.16 - 0.99 |J.g/L.
Enterococci
Enterococci cell concentrations were below the threshold of 1,280 CCE/lOOmL for low risk to
recreational users at ail but one site in the Detroit River. Conditions based on enterococci were good in
100% of the St. Clair River and Lake St. Clair.
47
-------
Sediment Quality
Sediment quality conditions were good and fair in 83% and 14% of the area, respectively (Fig. 17). Lake
St. Clair had the highest percent of area in good condition (90%) followed by the St. Clair River (44%) and
the Detroit River (30%). Poor sediment quality conditions were detected in 12% of the Detroit River area
resulting from both sediment contamination and sediment toxicity. No poor sediment quality conditions
were detected in the St. Clair River or Lake St. Clair.
Ecological conditions in the HEC - 2«14 & 3015
Eutroph cater
All HEC sites
St. Clair River
,
Lake St. Clair
•
Detroit River
,
j , 1
Chlorophyll a
I
:
3- ; ; : feg- ;
Clarity (Secchi depth)
—i • ;
=1 i : : -
- ; ;
-
3— ; ;—r~ ¦
i ; ^
1— ; ;
Dissolved omfgeri
i
; :
........... .
Total phosphorus
Cyano bacteria
: :
¦
3-
Microcyatin
;
;
:
•
D F«"'
Errterococci
;
•
Sediment quality
:
[] Unssseased
' ;
;
' f : ; ¦
_=K ¦.
Sediment contamination
13 : : :
!¦¦¦',
: :
1 ;
; ;
,—1 , ,
: : :
i
: : :
T
Sediment toxicity
zzj-"r" i "¦ ¦
'—l '
~ : :
Benthos (OTI)
HHHHHHHB : :
k
=i- :
j- . . . ' .
=t— . . .
Fish tissue contamination 1
m=- : :, :
: :
=d
liiiiiiiiiiiiiii
j " : '
•
Fish tissue mercury
....".".".'.p'.'.'J
| J
"
'
! - [] Absent
.
3-
Dreissenid presence
, 1 ,
¦¦¦¦¦¦¦¦-— - § S .jjpecteo goby
, . | Present
Round goby presence -
t,
1= ~
Pt- n Unassessed
1 >—•
I 1 1 1 1
13 40 60 80
i i i i i i i i i i i
20 CD 80 0 20 40 OD SO
Percent of area
1 1 ! i !
13 40 eo so
Figure 17. Ecological conditions of the HEC based on the 2014-2015 NCCA.
48
-------
Benthic Index
Benthos conditions could not be assessed 6%of the St. Clair River, 6% of Lake St. Clair, and 16% of the
Detroit River. In the Detroit River and Lake St. Clair, unassessed areas were more often the result
of PONAR failures (hard bottom) than from sites where the OTI could not be calculated. In the St. Clair
River, all the sites that could not be assessed were because the OTI could not be calculated.
Based on the OTI, conditions were good in 10% of the entire HEC area, in 60% of the St. Clair River, 8% of
Lake St. Clair, and 6% of the Detroit River. In the St. Clair River, none of the area was in fair
condition and 34% was in poor condition. Fair and poor conditions were present in 21% and 65% of Lake
St. Clair, respectively, and in 6% and 72% of the Detroit River, respectively.
Ecological Fish Tissue Contamination
Fish tissue was sampled at 33 sites in the HEC in 2015; no sites were sampled in 2014. Most of the
sampling occurred in US waters. Of the sampled sites, enough fish were collected at 31 sites.
Conditions reported are for the area where sampling was attempted. Conditions based on fish
tissue contaminants were good in 8%, fair in 12%, and poor in 65% of the HEC area with the
remaining 15% of area not assessed due to lack of fish caught (Fig. 17).
Of the HEC subpopulations, fish tissue contaminant conditions were good in 10% of Lake St. Clair
and 5% of the Detroit River; no good conditions were found in the St. Clair River. Conditions were
fair in 19%, 10%, and 17% of the sampled area in the St. Clair River, Lake St. Clair, and Detroit
River, respectively (Fig. 17). The remaining areas in the St. Clair River and Detroit River were in
poor condition. In Lake St. Clair, 20% of area could not be assessed and the remaining area was in
poor condition. Sample sizes for waterbody-specific (i.e., subpopulation) condition estimates for
fish tissue contaminants in the HEC were below the recommended sample size of 30 for reliable
condition estimates.
Fish Tissue Mercury
Of the sampled HEC area, 58% was in good condition for fish tissue mercury, 25% was in poor condition,
and 17% could not be assessed. Conditions were good in 91% of the area in the St. Clair River and 80% of
the Detroit River, but only 50% of Lake St. Clair was in good condition (Fig. 17).
Invasive Species
Dreissenid mussels were estimated to be present in 93% of the HEC, with less than a percent of area
unassessed. Dreissenids were detected using both PONAR and video in 28% of the area of the HEC.
Mussels were collected in PONAR samples in an additional 60% of the area, and in videos alone in an
additional 5% of the area. Dreissenid mussels were estimated to be present in 98% of Lake St. Clair.
Round gobies were estimated to be present in 7% of the HEC and were suspected in an additional 2% of
area. Seven percent of the area was unassessed for round gobies due to videos not being collected or
being poor video quality.
49
-------
The Niagara River
In 2018, 59 sites were sampled (29 sites in the upper river and 30 sites in the lower river) to assess 67.3
km2 of the Niagara River (Fig. 18).
Water Quality
Conditions in the Niagara River (Fig. 19) were mostly good for total phosphorus, chlorophyll a, and DO
(>99%, 97%, and 97% of area in good condition, respectively). Fifty-four percent of the area was in good
condition for clarity while 19% of the area was unassessed for clarity due to swift currents which
prevented accurate Secchi depth or PAR readings. Based on the eutrophication indicator, 84% of the
system was in good condition, 16% was in fair condition, and none of the area was in poor condition.
The Niagara River was assessed with Lake Ontario water quality thresholds, which are protective of Lake
Ontario's oligo-mesotrophic status.
Water quality was similar in the upper and lower Niagara Rivers. Total phosphorus, chlorophyll a, and
DO were nearly all in good condition in both the upper and lower river. Based on water clarity, 55% of
area was in good condition in the upper river, and 47% of area was in good condition in the lower river.
However, the upper river had a larger area with poor (17%) and unassessed (21%) conditions for water
clarity than the lower river (7% and 3%, respectively). Based on the eutrophication indicator, 83% of the
upper river area was in good condition and 93% of the lower river was in good condition.
Cyanobacteria and Microcystin
Cyanobacteria conditions in the Niagara River were assessed as good in 87% of the area and fair in 13%
of the area, with no area in poor condition (Fig. 19). Conditions were slightly better in the lower river,
where 97% of area was in good condition, than in the upper river where 86% of area was in good
condition.
Microcystin concentrations were below the threshold of 8 ng/L for low risk to recreational users at all
sites in the Niagara River, however about 15% of the area of the Upper and Lower River were
unassessed due to missing samples (Fig. 19). All microcystin concentrations at sampled locations were
below method detection limits and it is likely that microcystin conditions were good throughout the
river (including the unassessed sites).
Enterococci
Enterococci cell concentrations were below the threshold of 1,280 CCE/lOOmL for low risk to
recreational users at all sites in the Niagara River (Fig. 19).
Sediment Quality
Sediment quality conditions in the Niagara River were 11% good, 14% fair, and 7% poor, with 68% of
area unassessed. The large unassessed area was due to the prevalence of rocky substrates where
sediment samples could not be collected. Rock substrates were less common in the lower Niagara River,
50
-------
leading to more assessed area there (Fig. 19). The lower Niagara also had larger percents of area with
good sediment contaminant and sediment toxicity conditions than the upper Niagara River. As a result,
the lower Niagara River had a higher proportion of area with good and fair sediment quality conditions
(40% and 20%, respectively) and had less unassessed area (33%) than the Upper River, which had 7% of
area with good conditions, 14% of area with fair conditions, and 72% of area unassessed. Both the upper
and lower river had about 7% of the area assessed as poor for sediment quality.
Niagara River
o Probability sites
| | Resource area
Depth
0 - 4 m
>4 m
Figure 18. Niagara River sample locations during 2018. The section directly above and below Niagara
Falls could not be sampled due to fast currents and rapids.
Benthic Index
Benthic conditions in the Niagara River were assessed as good in 21% of the area, fair in 5%, and poor in
23%; the remaining 51% couid not be assessed due to hard bottom or a lack of tolerance-classified
oligochaetes (Fig. 19). Benthic conditions were similar in the Upper Niagara River because it accounts for
51
-------
more than half of the assessed area in the whole river. However, more of the Lower Niagara River was
assessed as fair (17%) or poor (40%), and less was unassessed (23%) than the upper River (3%, 21%, and
54%, respectively).
Ecological conditions in the Niagara River - 2018
All sites Upper River Lower River
Percent of area
Figure 19. Ecological conditions of Niagara River, 2018.
Invasive Species
Based on PONAR samples and video combined, mussels were present in 67% of the total river area.
Dreissenid mussels were present in 80% of the lower river and 66% of the upper river area. Based on
PONAR alone, mussels were estimated to be present in 35% of the area, and based on video alone, the
estimate was 50% of area. This demonstrates the importance of using both methods together for the
best estimate of mussel presence. Due to fast currents, 3% of the area was unassessed for dreissenid
mussels because neither video nor PONAR could be collected (percent PONAR failure was much higher
than video due to current and hard substrate, Fig. 19).
52
-------
Round gobies were estimated to be present in 20% of the Niagara River area based on underwater
video. Videography was often difficult because of the swift currents. Seventy-four percent of videos had
less than 50 seconds at the bottom. Round gobies were present in 43% of area in the lower river, and
17% of the upper river. Three percent of the area was unassessed for round gobies because video could
not be collected due to currents.
Temporal and Spatial Comparisons Among Great Lakes
Comparisons Between 2010 and 2015 Findings
The weighted means of measured continuous variables used for assessing the ecological condition in
2010 and 2015 were compared using Z-tests (a=0.05). With only two surveys completed so far, trend
analysis is not reliable, and it is unknown if differences observed between 2010 and 2015 are due to
normal interannual variability, a temporal trend, or some combination of the two.
In Lake Superior, average concentrations of total phosphorus, total nitrogen, and cyanobacteria cell
counts were significantly higher in 2015 than in 2010 (Fig. 18a, b). In Lake Huron, total phosphorus,
chlorophyll a, cyanobacteria, and PAH PECQ (for sediment) were all significantly higher in 2015 than in
2010 (Fig. 18a, b). Bottom dissolved oxygen concentration was lower and Secchi depth was shallower in
Lake Huron in 2015 than in 2010. In Lake Erie, total phosphorus, total nitrogen, bottom dissolved
oxygen, cyanobacteria, and PAH PECQ were all significantly higher in 2015 than in 2010 while sediment
toxicity was significantly lower in 2015 (Fig. 18a-c). None of the variables differed between 2015 and
2010 in Lake Michigan, except for weighted mean site depth due to random variation in site locations
(Fig. 18d). In Lake Ontario, total nitrogen, cyanobacteria, and OTI were all significantly higher in 2015
than in 2010, and sediment toxicity was significantly lower in 2015 than in 2010 (Fig. 18a, b, d).
53
-------
CD
E
m
D
i_
o
.c
CL
CO
O
-C
Q.
ro
o
CD
E
c
o
CD
O
¦*—*
'c
"co
0.06
0.05
0.04
0.03
0.02
0.01
0.00
O)
3
CO
">>
.c
CL
2
o
O
_G_
*
-r-
*
—T—
J
• 2010
o 2015
Q.
0)
"O
o
o
0
CO
"i 1 r
Lake Superior Lake Huron Lake Michigan
Lake Erie
Lake Ontario
Figure 18a. Comparison of conditions among lakes and between 2010 to 2015. Plots show weighted
mean concentrations of continuous assessment variables for 2010 and 2015 surveys and ±95%
confidence intervals. Stars indicate where there was a significant difference between 2010 and 2015
based on aZ-test (a=0.05). For reference, NCCA thresholds are shown with blue (good/fair) and red
(fair/poor) lines. For water quality, thresholds are lake specific. There is no applicable Great Lakes
threshold for total nitrogen.
54
-------
O)
E
O
o
E
o
o
CO
E
a3
ro
£
o
CD
¦Q
O
c
03
>
O
o
o
LU
CL
V)
ro
0)
14
12
10
8
6
4
2
0
250000
200000
150000
100000
50000
0
0.8
0.6
0.4
0.2
0.0
* o
*G
m a
i5
• 2010
o 2015
M 5
*
*
XT
*
—r~
*
-r-
*
~!—
*
"I—
s n
I n
* 5
• O
m n
o
o
LU
CL
en
X
<
CL
0.8
0.6
0.4
0.2
o.o H
o
o
1 1 1 1
Lake Superior Lake Huron Lake Michigan Lake Erie
o
Lake Ontario
Figure 18b. Comparison of conditions among lakes and between 2010 to 2015. Plots show weighted
mean concentrations of continuous assessment variables for 2010 and 2015 surveys and ±95%
confidence intervals. Stars indicate where there was a significant difference between 2010 and 2015
based on aZ-test (a=0.05). For reference, NCCA thresholds are shown with blue (good/fair) and red
(fair/poor) lines. For water quality, thresholds are lake specific.
55
-------
o
u
UJ
CL
i2
o
0.5
0.4 -
0.3 -
0.2 -
0.1 -
0.0
li
1$
m 2
$5
O
O
UJ
Q-
c
CO
0
1.0
0.8 -
0.6 -
0.4 -
0.2 -
0.0 -
•
• o
* o
• o
• U
Lake Superior Lake Huron Lake Michigan Lake Erie Lake Ontario
Figure 18c. Comparison of conditions among lakes and between 2010 to 2015. Plots show weighted
mean concentrations of continuous assessment variables for 2010 and 2015 surveys and ±95%
confidence intervals. Stars indicate where there was a significant difference between 2010 and 2015
based on aZ-test (a=0.05). For reference, NCCA thresholds are shown with blue (good/fair) and red
(fair/poor) lines. For water quality, thresholds are lake specific.
56
-------
1.8 -
1.6 -
1.4 -
1.2 -
1—
1.0 -
o
0.8 -
0.6 -
0.4 -
0.2 -
0.0 -¦
20 1
18 -
16 -
t-
—'
14 -
-C
-4—'
Q.
12 -
(1)
O
10 -
8 -
6 -I
5? :
2010
2015
<
h
f1
m
bOH
b
L
*
"i r
Lake Superior Lake Huron Lake Michigan
Lake Erie
Lake Ontario
Figure 18d. Comparison of conditions among lakes and between 2010 to 2015. Plots show weighted
mean concentrations of continuous assessment variables for 2010 and 2015 surveys and ±95%
confidence intervals. Stars indicate where there was a significant difference between 2010 and 2015
based on aZ-test (a=0.05). For reference, NCCA thresholds are shown with blue (good/fair) and red
(fair/poor) lines. For water quality, thresholds are lake specific.
To compare invasive species presence between 2010 and 2015, condition estimates (i.e., percent area
with the species present) were compared. However, differences in the percent of area unassessed in the
two survey years makes it challenging to directly compare invasive species results. In 2010, more of the
Great Lakes nearshore and embayment area was unassessed for round gobies and dreissenid mussels
than in 2015 due to poor video quality. For round goby in the combined nearshore and embayments,
54% of area was unassessed in 2010, and 19% was unassessed in 2015. In both years, Lake Erie had the
highest percentage of area unassessed for round gobies (73% in 2010 and 54% in 2015). For dreissenids,
16% of the Great Lakes nearshore and embayment area was unassessed in 2010 and 8% was unassessed
in 2015. In both years, Lake Erie had the highest percentage of area unassessed for mussels, 14% in 2015
and 28% in 2010.
To compare the presence of invasive species between 2010 and 2015, estimates were also normalized
to the area assessed in each year. In the Great Lakes, round gobies were observed in a smaller portion of
the assessed area of the combined nearshore and embayments in 2015 (6%) than in 2010 (18%). Lake
Huron, Lake Erie, and Lake Ontario all had a greater percent assessed area with round goby in 2010
(35%, 7%, and 37%, respectively) than in 2015 (5%, 0%, and 5%, respectively). Lake Michigan had similar
57
-------
percent area with round goby in 2010 (12%) and 2015 (8%). Lake Superior had no assessed area with
confident round goby detections in 2010 or 2015. In the entire Great Lakes combined nearshore and
embayments, similar percentages of the assessed area had mussels in 2010 (57%) and 2015 (60%).
Comparisons Among Great Lakes in 2015
The weighted means of measured continuous variables used to assess ecological condition were
compared among lakes using Z-tests (a=0.05). Comparisons of continuous data gives a spatial
perspective on difference in indicator values among lakes. Because condition thresholds for water
quality parameters vary among lakes, comparisons among lakes were not attempted.
The comparison of weighted means for continuous parameters showed that nutrient concentrations
(i.e., total phosphorus and total nitrogen) were significantly higher in Lake Erie than in the other four
lakes (Table 3). Average chlorophyll a concentration was significantly higher in Lake Erie than in any
other lake. Dissolved oxygen and Secchi depth were significantly lower in Lake Erie than in any other
lake. Some of the other lakes also differed from one another based on these water quality parameters,
but the magnitude of the differences between the other lakes was not as great as the magnitude of the
difference between Lake Erie and other Great Lakes (Fig. 18).
The parameters associated with the sediment PECQs show that Lake Erie had more contaminated
sediments than other lakes (Table 3). The weighted averages for PECQs of metals, PAHs, and PCBs in
Lake Erie sediments were all higher than the other lakes (Fig. 18b-c, Table 3). As a result, mean and total
PECQs were also higher in Lake Erie than in the other lakes (Fig. 18c, Table 3).
Among-lake variation for most of the other ecological parameters was more complicated than for water
and sediment quality parameters. For example, Lakes Erie and Huron both had much greater average
cyanobacteria cell concentrations than the other lakes. But, in many other cases, sediment toxicity (%
survival of test organisms), average OTI values, fish tissue mercury concentration, and microcystin
concentrations were not significantly different between Lake Erie and the other lakes (Table 3). All the
parameters that were significantly different between Lake Superior and Lake Erie also significantly
differed between Lake Superior and Lake Ontario. But, most of the differences between parameters in
Lakes Superior and Ontario were smaller than the differences between Lakes Superior and Erie.
Among lakes, Lake Erie was the most unlike the other lakes with 50 significant differences in lake-to-lake
comparisons of parameters (from Table 3), followed by Lake Superior (47 differences). Lake Huron was
least unlike the other lakes with 39 significant differences in lake-to-lake comparisons followed by Lake
Michigan with 42 differences.
58
-------
Table 3. Results from Z-tests for lake-to-lake comparisons of continuous variables for the combined nearshore and embayments in 2015. Upward
arrows indicate that the lake listed before the "/" has a significantly larger (p < 0.05) area-weighted average for the given parameter and
downward arrows indicate that lake had significantly smaller averages for the parameter. Cells without arrows indicate that the parameter did
not differ significantly between lakes.
Indicator
LE /
LE/
LE/
LE/
LH/
LH/
LH/
LM/
LM/
LO/
LH
LM
LO
LS
LM
LO
LS
LO
LS
LS
Total phosphorus
t
t
t
t
t
t
t
Total nitrogen
t
t
t
t
4^
4'
t
Clarity (Secchi depth)
4-
4'
4^
4^
4^
t
4^
Dissolved oxygen
4-
4'
4^
4^
4^
4^
t
4^
4^
Chlorophyll a
t
t
t
t
t
t
4^
t
t
Metals PECQ
t
t
t
t
4^
4^
4^
t
PAHs PECQ
t
t
t
t
4'
t
PCBsPECQ
t
t
t
t
4^
4^
t
Total PECQ
t
t
t
t
4^
4^
4^
t
Mean PECQ
t
t
t
t
4^
4^
4'
t
Sediment toxicity (% survival)
4-
t
t
Cyanobacteria cell concentration
t
t
t
t
t
t
t
t
Enterococci cell concentration
Microcystin concentration
t
t
t
t
4^
t
t
OTI value
t
t
4^
t
t
Fish tissue mercury concentration
4^
t
4^
t
4^
t
t
59
-------
Comparisons Between the Nearshore and Embayments in 2015
The Great Lakes embayment enhancement was included in both 2010 and 2015 assessments. Based on
the 2010 assessment, total phosphorus, dissolved oxygen, Secchi depth, and site depth differed
significantly between sites in embayments and at non-embayment nearshore sites (Kelly et al., 2015). In
general, nearshore water quality (e.g., temperature, specific conductivity, total phosphorus, and
chlorophyll a) became more oligotrophic with increased depth, and total phosphorus concentrations
were positively correlated with an indicator of watershed agriculture land-use. Assessments of
nearshore conditions (percent of area in good, fair, or poor condition) inclusive of embayments differed
from nearshore conditions in the original base design (excluding embayments) by less than 2% (Kelly et
al., 2015). These results showed that that although some parameters differed between embayment and
non-embayment nearshore, the influence of embayments on overall nearshore conditions at the Lake
scale was small.
For 2015, differences in weighted mean indicator values were compared between embayments and the
nearshore using Z-tests (a=0.05). For each indicator, the estimated percent area in good condition and
amount of overlap in 95% confidence intervals were compared between the combined nearshore and
embayments design (using all 361 sites) and the original base design (225 sites). The combined
nearshore and embayments design completely overlaps with the base nearshore design and these
comparisons were intended to determine how condition estimates change when the embayments
enhancement sites are added to the base design, which only has seven sites in embayments. Z-tests
were not used for these comparisons because this test assumes that the resources do not overlap (Kelly
et al., 2015).
The differences in mean water quality parameters between the embayments and nearshore in 2015 for
all the lakes combined were mostly consistent with results from 2010 reported by Kelly et al. (2015,
Table 4). For all lakes combined, neither chlorophyll a nor total nitrogen differed between the
embayments and the rest of the nearshore in 2015. Secchi depth and site depth were significantly
shallower in embayments than the rest of the nearshore in both years (Table 4; Kelly et al., 2015).
However, dissolved oxygen was not significantly lower in embayments than the rest of the nearshore in
2015, unlike in 2010. Total phosphorus concentrations were significantly higher in embayments than in
the rest of the nearshore in most lakes (Table 4). Water and sediment quality parameters in Lake
Superior suggested that embayments were less oligotrophic (e.g., lower bottom dissolved oxygen and
higher total phosphorus, chlorophyll a, and cyanobacteria cells concentration) than the rest of the
nearshore. Lake Erie had fewer significant differences between embayments and the rest of the
nearshore than the other Great Lakes.
Among the Great Lakes, the percent nearshore area in good condition estimated from the base design
or the combined nearshore and embayments design were similar (i.e., confidence intervals substantially
overlapping) for all the indicators. This is unsurprising because the inclusion of results from a small area
of the nearshore (embayments comprise about 5%), no matter how different, would not be expected to
have much influence on overall condition estimates. These findings show that although the embayment
60
-------
enhancement revealed differences in conditions between embayments and the rest of the nearshore, it
did not strongly or consistently influence the estimates of overall nearshore condition.
Table 4. Results from Z-tests comparing weighted means of continuous assessment variables in
embayments and the rest of the nearshore in 2015. If means for embayment sites were significantly
higher or lower than means for the rest of the nearshore based on two-tailed Z-test (a=0.05), it is
denoted with an up arrow or down arrow, respectively. If no arrow is present, the difference was not
significant at a=0.05.
Indicator
Great
Lake
Lake
Lake
Lake
Lake
Lakes all
Superior
Michigan
Huron
Erie
Ontario
Clarity (Secchi depth)
4-
4-
4'
4'
4'
Site depth
4-
4'
4'
4'
4'
4'
Dissolved oxygen
4'
4'
4'
Total nitrogen
t
Total phosphorus
t
t
t
t
Chlorophyll a
t
t
Cyanobacteria cell concentration
t
t
Enterococci cell concentration
Microcystin concentration
4'
Mean metals PECQ
t
Total PAHs PECQ
t
t
Total PCBs PECQ
t
Mean PECQ
t
t
Sediment toxicity (% survival)
OTI value
Comparisons between Great Lakes and connecting river systems
Graphical comparisons of condition (e.g., the percent of the area in good or poor condition) between
lakes and connecting river systems are possible for non-water-quality indicators from the bar plots for
each lake or channel. For a comparison of water quality indicators between lakes and connecting river
systems based on weighted mean values, see Wick et al. (2019).
Integrating the Great Lakes NCCA with Local Assessment Needs
The main objective of the NCCA is to assess system-wide conditions. However, both the site data and in
some cases the resource assessments (e.g., connecting river systems) can address local needs. The NCCA
probabilistic survey design can help managers understand the range of conditions for a given system for
multiple indicators. Great Lakes Area of Concern (AOC, USEPA, 2019a) managers, for example, can
compare hand-picked sites of interest to conditions in the larger system based on probabilistic surveys
(e.g., Bellinger et al., 2016) to help define achievable remediation and restoration goals for removal of
beneficial use impairments. Site-based findings can help identify areas that may need further study, or
61
-------
supplement existing studies. The following two case studies demonstrate how NCCA (or related
connecting channel) data can supplement and provide context for local studies and projects. However,
data collected during Great Lakes NCCA sampling are not intended as a replacement for AOC monitoring
or assessment.
Detroit River sediment contamination
The Detroit River is the downriver-most waterbody in the Huron-Erie Corridor. This 51-km river connects
Lake St. Clair to western Lake Erie. The river is bordered on the west by the Detroit, Michigan metro
area, and to the east by Windsor, Ontario. The Detroit River is listed as a binational AOC under the Great
Lakes Water Quality Agreement due to contaminants discharged to the river by industry, bacteria from
municipal discharges, combined sewer overflows, habitat loss, and nonpoint source pollution (Esman,
2008).
USEPA, in partnership with state and local stakeholders, have done extensive sampling of targeted sites
to identify areas needing remediation of sediments contaminated with high levels of PCBs, PAHs, and
metals. Figure 19 shows sites sampled for sediment contamination for AOC assessment (USEPA, 2014a,
2014b, 2015a, 2016b, 2016c). The sites shown include available AOC assessment data sampled in 2013-
2016 excluding one major remediation area, the north Trenton Channel. Sites sampled were along the
Detroit shoreline in water <3 m deep at locations of historical contaminant sources. The total area
characterized by the points shown was about 11 km2. Based on these data, the area identified by AOC
managers for remediation was about 3.5 km2.
The sampling and analysis methods for sediment characterization in the AOC were different from NCCA
methods used to sample sediment in the Huron-Erie corridor in 2014-2015. The AOC samples were full
sediment cores. For comparing AOC results with NCCA data from PONAR surface samples, only the
result from the top layer of the core was used. The AOC sites were analyzed for metals, PAHs, and PCB
aroclors, and the connecting river system sites were analyzed for metals, PAHs, and PCB congeners.
Congeners are individual PCBs, and aroclors are defined groups of PCB congeners that are commonly
found together because of their use in industrial products. Because the sediment contamination
indicator is calculated as total PCBs measured (aroclors or congeners) divided by a PEC, the resulting
indicator is unitless and can be compared among data sets, with the caveat that the PCBs were
measured differently. PECQs for the AOC sites were classified using the same thresholds as connecting
river system sites (Fig. 19, Table 2).
At a site level, the range of conditions in these hotspots can be compared to conditions across the
Detroit River. Sediment contamination conditions at connecting river system sites outside of the shallow
area along the Detroit shoreline were generally classified as good or fair, whereas the sites along the
Detroit shoreline were classified as fair or poor. Clearly these areas are degraded compared to the
Detroit River generally.
Based on the 2014-2015 connecting river system assessment, 45% of the Detroit River was classified in
good condition for sediment contamination, 45% was classified in fair condition, and only 4% was
62
-------
classified in poor condition (Fig. 17), despite the known contaminant issues along Detroit's waterfront.
Six percent of the system was unassessed. Based on the targeted AOC sites, approximately 3.5 km2
(including the north Trenton Channel) were identified as in poor enough condition to warrant
remediation. This area represents 3.4% of the total area in the Detroit River system of 104 km2, which is
similar to the estimate of 4% poor conditions from the 2014-2015 NCCA assessment. The perception of
the Detroit River is that contamination is widespread. However, as a percentage of the entire river area,
it is quite localized. The probabilistic connecting river system survey indicated that the Detroit River
system is in fair or good condition. However, areas that are contaminated were in (and represent a large
proportion of) shallow shoreline areas that may be accessible to people. Supplementing a survey design
with hand-picked sites at contaminated or reference sites can help ensure the entire range of conditions
are identified and help cross-reference the probabilistic assessment with long-term or project-based
datasets.
63
-------
s
/.
Trenton o
Channel
»«
*>
X
* *
-------
needed. Several indicators used in NCCA relate to HABs, including total phosphorus, chlorophyll a,
Secchi depth, dissolved oxygen, cyanobacteria, and microcystins.
HABs are transient seasonal events, and sampling of connecting river systems was conducted during a
short period (about a week) within an index period from May through September. Seasonal variation
cannot be addressed in the NCCA GRTS designs; the index period is assumed to be representative for
annual assessment purposes (Messer et al., 1991). However, in both 2014 and 2015, Lake St. Clair, which
has frequent HABs (Davis et al., 2014), was sampled in September, which is a peak time for algal blooms.
At a site level, results based on connecting river system sampling can be compared with nearly
coincidental satellite imagery for the lake during sampling (Fig. 20). Across several indicators, including
total phosphorus, chlorophyll a, cyanobacteria concentration, and microcystin concentration, poor
conditions were detected along the south shore of Lake St. Clair. For microcystin, there were very few
detections in the HEC, but all detections were in this region of Lake St. Clair. Blooms are visible in
satellite photos along that shore, corroborating the physical sampling. Michigan Tech Research
Institute's HAB model (publicly available at https://greatlakesremotesensing.org/. Shuchman et al.,
2013) utilizes satellite imagery to map harmful algal blooms. Their model also mapped blooms along the
south shore of Lake St. Clair during the time of sampling. NCCA site-scale data can be useful for
understanding spatial variability within a system and can help provide the basis for further studies.
65
-------
Figure 20. Satellite photos (https://earthexplorer.usgs.gov/) and maps of water quality indicators in Lake
St. Clair. Sites in Lake St. Clair were sampled between 9/26 - 10/2/2014 and 9/23 -26/2015. Algal blooms
are visible in satellite imagery from both 2014 and 2015, and are reflected in total phosphorus, chlorophyll
a, cyanobacteria, and microcystin results. Squares indicate the site was sampled in 2014 and circles
indicate the site was sampled in 2015. For total phosphorus, chlorophyll a, and cyanobacteria, blue
indicates good conditions, yellow indicates fair condition, and red indicates poor condition. For
microcystin, all sites were below the threshold of 8 |ig/L (Table 2b). Sites shown in orange were above 1
|ig/L, which is the WHO threshold for drinking water (WHO, 2003b; note that NCCA thresholds are based
on EPA guidelines for recreational use, USEPA 2017a). Sites in blue for microcystin were below 1 |ig/L.
Three of the sites on the south shore of the lake were hand-picked based on anecdotal evidence of
nutrient enrichment associated with the input of the Thames River.
66
-------
Future Improvements of the Great Lakes NCCA
The probabilistic design and standardized methods of data collection make the NCCA a useful tool to
assess the conditions of the Great Lakes nearshore waters and connecting river systems over time.
Through the collection and analyses of the 2010 and 2015 NCCA data, some gaps and methodological
shortcomings were identified. As these are addressed, future iterations of this assessment will be able to
better detect changes in conditions through time.
Microcystins
More than 99% of the combined nearshore and embayment area in the Great Lakes was in good
condition based on microcystin concentrations despite known issues with Microcystis blooms in portions
of the Great Lakes. Microcystin concentrations are highly dependent on many poorly understood factors
that change quickly in space and time. Additional research may reveal indicators more sensitive to the
risks posed by microcystins. Because it is based on a short period of sampling, the current NCCA design
and approach may not be ideal for assessing Great Lakes conditions based on microcystin. Additional
research is also needed to assess conditions based on other types of algal toxins present in the Great
Lakes.
Benthic Index
Because of missing benthic indicator information that resulted from the absence of tolerance-classified
oligochaetes in samples and coarse substrate that prevent PONAR sampling, there has been an ongoing
collaboration among GLTED, the Great Lakes National Program Office (GLNPO), and others towards
developing alternative approaches to assessing the benthic community. Possible future approaches
might include application of a modified oligochaete trophic index (Burlakova et al., 2018) or a benthic
index that is not limited to oligochaetes, like the one used in the marine NCCA assessment (Gillett et al.,
2015).
Underwater Video
Underwater video collection and analysis methods for use in NCCA is an area of active research at
GLTED. Video quality is a concern because poor quality videos can result in unassessed area, which is
already a problem for PONAR sampling. Improvements in video technology and collection protocol
between 2010 and 2015 resulted in a reduction in proportions of area unassessed, and new video
collection methods tested in the Niagara River further reduced proportions of area unassessed and
reduced the uncertainty of invasive species detection. This improved method addresses previous issues
with camera movement, lighting, and focal distance, and was implemented throughout the Great Lakes
in NCCA 2020-21.
Underwater video classification and analysis methods are also in development. In this report, the
presence of round gobies was based on underwater video, and the presence of dreissenid mussels was
based on video and PONAR samples. Underwater video also contains additional information about
habitat characteristics (bottom type, vegetation information) and anthropogenic impacts like litter and
67
-------
human-made features. Methods are being developed to extract these data from the videos and to
standardize underwater video analysis methods. To improve the reliability and efficiency of underwater
video analysis, USEPA ORD GLTED, GLNPO, USEPA Region 5, USEPA Office of Water, and state and
federal partners piloted a crowdsourcing application, Deep Lake Explorer, to facilitate classification and
interpretation of underwater video (https://www.zooniverse.org/proiects/usepa/deep-lake-explorer).
Results showed that crowdsourcing applications like Deep Lake Explorer may be helpful for large video
imagery datasets resulting from lake wide assessments (Wick et al., 2020).
Key Findings
• Among the Great Lakes, water quality, including nutrients, water clarity, chlorophyll a, and
dissolved oxygen, was poorest in Lake Erie basins. Among Lake Erie basins, conditions were
poorest in the western basin.
• Based on water quality indicators, more of the Great Lakes combined nearshore and
embayment area was in good condition than in fair or poor condition. Embayments in the Great
Lakes had a higher percentage of area with poor conditions than the nearshore.
• Based on weighted mean values, water clarity was generally lower and total phosphorus
concentration was generally higher in embayments than the nearshore.
• Weighted mean total phosphorus concentrations were higher in 2015 than in 2010 for Lakes
Superior, Huron, and Erie. Weighted mean total nitrogen concentrations were higher in 2015
than in 2010 for Lakes Erie and Ontario.
• Based on water quality, more of the lower Niagara River was in good condition than the upper
Niagara River.
• In the Great Lakes combined nearshore and embayment area, condition based on cyanobacteria
concentrations was good in 64% of the area.
• Weighted mean cyanobacteria cell concentrations were over five times higher in 2015 than in
2010 for Lakes Huron and Erie. It is important to note that year to year variability in
cyanobacteria blooms can be large, and the sampling design is not suited to capture peak
conditions or seasonal averages.
• For most indictors, water quality was poorer in the western basin of Lake Erie than anywhere
else in the survey.
• Greater than 99% of the combined nearshore and embayment area in the Great Lakes was in
good condition based on microcystin concentrations relative to a recreational contact threshold.
• Ninety-eight percent of the combined nearshore and embayment area was in good condition
based on enterococci concentration.
• Sediment quality in the Great Lakes was in good condition in 62% of the combined nearshore
and embayments. Sediment quality conditions were driven primarily by sediment contamination
rather than sediment toxicity.
• Assessed condition based on sediment quality was generally poorer in embayments than the
nearshore.
• Weighted mean sediment toxicity was lower in 2015 than in 2010 for Lakes Erie and Ontario.
68
-------
• Benthic condition could not be assessed at many sites either because no PONAR sample could
be collected (75 of 550 sampled Great Lakes and connecting river systems sites) or because the
OTI could not be calculated (67 of 550 sampled Great Lakes and connecting river systems sites).
• Because of the rocky substrate and swift currents of the Niagara River, NCCA methods for
collecting benthic samples and underwater video performed poorly there than in the other
systems.
• Except for Lake Superior, Lake Michigan, and the St. Marys River, more of the assessed area was
in poor benthic condition than was in either good or fair condition.
• Invasive species (dreissenid mussels and round gobies) were detected over a greater percent of
the nearshore area than embayment area.
• Combining data from underwater video and PONAR sampling increased the number of sites at
which dreissenids were detected.
• Because embayments comprise a small percentage of the total nearshore area, conditions in
embayments had a small effect on estimates of nearshore conditions at lake scales.
• Data collected during Great Lakes NCCA sampling, including in connecting river systems, can
directly or indirectly support local assessment needs, including monitoring and assessment
within Great Lakes Areas of Concern, but NCCA designs, methods, and findings are not intended
as replacements for locally-managed AOC monitoring or assessment.
References
Bellinger, B.J., Hoffman, J.C., Angradi, T.R., Bolgrien, D.W., Starry, M., Elonen, C., Jicha, T.M., Lehto, L.P.,
Seifert-Monson, L.R., Pearson, M.S., Anderson, L., 2016. Water quality in the St. Louis River Area of
Concern, Lake Superior: Historical and current conditions and delisting implications. Journal of Great
Lakes Research, 42, 28-38.
Brezonik, P.L., Bouchard Jr, R.W., Finlay, J.C., Griffin, C.G., Olmanson, L.G., Anderson, J.P., Arnold, W.A.,
Hozalski, R. 2019. Color, chlorophyll a and suspended solids effects on Secchi depth in lakes:
implications for trophic state assessment. Ecological Applications, e01871.
Burlakova, L.E., Kovalenko, K.E., Schmude, K.L., Barbiero, R.P., Karatayev, A.Y., Lesht, B.M., 2018.
Development of new indices of Great Lakes water quality based on profundal benthic communities.
Journal of Great Lakes Research, 44, 618-628.
CCME (Canadian Council of Ministers of the Environment). 1999. Canadian Water Quality Guidelines for
the Protection of Aquatic Life: Polycyclic Aromatic Hydrocarbons (PAHs). Winnipeg, Manitoba.
http://ceag-rcqe.ccme.ca/download/en/2017rei 7511685
Carmichael, W.W., Boyer, G.L. 2016. Health impacts from cyanobacteria harmful algae blooms:
Implications for the North American Great Lakes. Harmful algae, 54, 194-212.
Costantini, M., Kolesar, S., Ludsin, S.A., Mason, D.M., Rae, C.M., Zhang, H. 2011. Does hypoxia reduce
habitat quality for Lake Erie walleye? A bioenergetics perspective. Canadian Journal of Fisheries and
Aquatic Sciences, 68, 857-879.
Crane, J.L., MacDonald, D.D., Ingersoll, C.G., Smorong, D.E., Lindskoog, R.A., Severn, C.G.,
Berger, T.A., Field, L.J. 2002. Evaluation of numerical sediment quality targets for the St. Louis River
Area of Concern. Archives of Environmental Contamination and Toxicology, 43, 1-10.
69
-------
Crane, J., Hennes, S. 2007. Guidance for the use and application of sediment quality targets for the
protection of sediment-dwelling organisms in Minnesota. Minnesota Pollution Control Agency.
MPCA Document: tdr-gl-04. https://www.pca.state.mn.us/sites/default/files/tdr-gl-04.pdf
Davis, T.W., Watson, S.B., Rozmarynowycz, M.J., Ciborowski, J.J., McKay, R.M., Bullerjahn, G.S. 2014.
Phylogenies of microcystin-producing cyanobacteria in the lower Laurentian Great Lakes suggest
extensive genetic connectivity. PloS One, 9, el06093.
Diaz, R.J., Rosenberg, R. 1995. Marine benthic hypoxia: A review of its ecological effects and the
behavioral responses of benthic macrofauna. Oceanography and Marine Biology Annual Review, 33,
245-303.
Diaz-Ramos, S., Stevens, D.L., Olsen, A.R. 1996. EMAP Statistical Methods Manual. EPA/620/R-96/002,
U.S. Environmental Protection Agency, Office of Research and Development, NHEERL-Western
Ecology Division, Corvallis, Oregon.
https://nepis.epa.gov/Exe/ZvPDF.cgi/9100UUQQ.PDF?Dockev=9100UUQQ.PDF
Dyble, J., Fahnenstiel, G.L., Litaker, R. W., Millie, D.F., Tester, P.A. 2008. Microcystin concentrations and
genetic diversity of Microcystis in the lower Great Lakes. Environmental Toxicology: An International
Journal, 23, 507-516.
Environment Canada and the U.S. Environmental Protection Agency (EC and USEPA). 2014. State of the
Great Lakes 2011. Cat No. Enl61-3/l-2011E-PDF. EPA 950-R-13-002. htt p s: // b i n at i o n a I. n et / w p -
content/uploads/2014/ll/sogl-2011-technical-report-en.pdf
ECCC and USEPA (Environment and Climate Change Canada and the U.S. Environmental
Protection Agency). 2017. State of the Great Lakes 2017 Technical Report Cat No. Enl61-
3/1E-PDF. EPA 905-R-17-001. htt ps://lb ii n at iio n a I. n et/w p-
content/uploads/2017/09/SQGL 20: iniical Report-EN.pdf
Esman, L.A. 2008. The Michigan Department of Environmental Quality Biennial Remedial Action Plan
Update for the Detroit River Area of Concern. Michigan Department of Environmental Quality.
https://www.niichigan.gov/docyirnents/deq/deq-water-ogl-Detiroit-Riveir-RAP-
01022001 F
Fairey, R., Long, E.R., Roberts, C.A., Anderson, B.S., Phillips, B.M., Hunt, J.W., Puckett, H.R., Wilson, C.J.
2001. An evaluation of methods for calculating mean sediment quality guideline quotients as
indicators of contamination and acute toxicity to amphipods by chemical mixtures. Environmental
Toxicology and Chemistry: An International Journal, 20, 2276-2286.
Field, L.J., Norton, S.B. 2014. Regional models for sediment toxicity assessment. Environmental
Toxicology and Chemistry, 33, 708-717.
GLWQA (Great Lakes Water Quality Agreement). 2012. Protocol amending the agreement between
Canada and the United States of America on Great Lakes water quality, 1978, as amended on
October 16, 1983 and on November 18, 1987.
https://legacvfiles.iic.org/tinvmce/uploaded/GLWQA%202012.pdf
Gillett, D.J., Weisberg, S.B., Grayson, T., Hamilton, A., Hansen, V., Leppo, E.W., Pelletier, M.C., Borja, A.,
Cadien, D., Dauer, D., Diaz, R. 2015. Effect of ecological group classification schemes on performance
of the AMBI benthic index in US coastal waters. Ecological Indicators, 50, 99-107.
Gregor, D.J., Rast, W. 1979. Trophic characterization of the U.S. and Canadian nearshore zones of the
Great Lakes. Submitted to the Pollution from Land Use Activities Reference Group of the
International Joint Commission.
https://scholar.uwindsor.ca/cgi/viewcontent.cgi?article=1237andcontext=iicarchive
Herlihy, A.T., Larsen, D.P., Paulsen, S.G., Urquhart, N.S., Rosenbaum, B.J. 2000. Designing a spatially
balanced, randomized site selection process for regional stream surveys: the EMAP Mid-Atlantic
pilot study. Environmental Monitoring and Assessment, 63, 95-113.
70
-------
Ho, J.C., Michalak, A.M. 2015. Challenges in tracking harmful algal blooms: A synthesis of evidence from
Lake Erie. Journal of Great Lakes Research, 41, 317-325.
Howmiller, R.P., Scott, M.A. 1977. An environmental index based on relative abundance of oligochaete
species. Journal of Water Pollution Control Federation, 49, 809-815.
Ingersoll, C.G., MacDonald, D.D., Wang, N., Crane, J.L., Field, L.J., Haverland, P.S., Smorong, D.E. 2001.
Predictions of sediment toxicity using consensus-based freshwater sediment quality guidelines.
Archives of Environmental Contamination and Toxicology, 41, 8-21.
Kelly, J.R., Yurista, P.M., Starry, M., Scharold, J., Bartsch, W., Cotter, A. 2015. Exploration of spatial
variability in nearshore water quality using the first Great Lakes National Coastal Condition
Assessment survey. Journal of Great Lakes Research, 41, 1060-1074.
Kincaid, T., Olsen, T., Stevens, D., Piatt, C., White, D., Remington, R., Kincaid, M.T. 2019. Package
'spsurvey'. https://cran.r-proiect.org/web/packages/spsurvev/index.html
Krieger, K.A. 1984. Benthic macroinvertebrates as indicators of environmental degradation in the
southern nearshore zone of the central basin of Lake Erie. Journal of Great Lakes Research, 10, 197-
209.
Krieger, K.A., Bur, M.T. 2009. Nearshore hypoxia as a new Lake Erie metric. Lake Erie Protection Fund
Project SG334-07. http://lakeerie.ohio.gOv/Portals/0/Closed%20Grants/small%20grants/SG%20334"
07%20Final%20Report.pdf
Lauritsen, D.D., Mozley, S.C., White, D.S. 1985. Distribution of oligochaetes in Lake Michigan and
comments on their use as indices of pollution. Journal of Great Lakes Research, 11, 67-76.
Lietz, J.E., Kelly, J.R., Scharold, J.V., Yurista, P.M. 2015. Can a rapid underwater video approach enhance
the benthic assessment capability of the National Coastal Condition Assessment in the Great Lakes?
Journal of Environmental Management, 55, 1446-1456.
Long, E.R. 2006. Calculation and uses of mean sediment quality guideline quotients: A critical
review. Environmental Science & Technology, 40, 1726-1736.
MacDonald, D.D., Ingersoll, C.G., Berger, T.A. 2000. Development and evaluation of consensus-based
sediment quality guidelines for freshwater ecosystems. Archives of Environmental Contamination
and Toxicology, 39, 20-31.
Messer, J.J., Linthurst, R.A., Overton, W.S. 1991. An EPA program for monitoring ecological status and
trends. Environmental Monitoring and Assessment, 17, 67-78.
Milbrink, G. 1983. An improved environmental index based on the relative abundance of oligochaete
species. Hydrobiologia, 102, 89-97.
Murphy, T., Irvine, K., Guo, J., Davies, J., Murkin, H., Charlton, M., Watson, S. 2003. New microcystin
concerns in the lower Great Lakes. Water Quality Research Journal, 38, 127-140.
Paerl, H.W., Hall, N.S., Calandrino, E.S. 2011. Controlling harmful cyanobacterial blooms in a world
experiencing anthropogenic and climatic-induced change. Science of the total environment, 409,
1739-1745.
Phosphorus Management Strategies Task Force (PMSTF). July 1980. Phosphorus management for the
Great Lakes: Final report of the Phosphorus Management Strategies Task Force to the International
Joint Commission's Great Lakes Water Quality Board and Great Lakes Science Advisory Board.
Windsor, Ontario. 129 pp. http://agrienvarchive.ca/download/P-management G lakes.pelf
Ramcharan, C.W., Padilla, D.K., Dodson, S.I. 1992. Models to predict potential occurrence and density of
the zebra mussel, Dreissena polymorpha. Canadian Journal of Fisheries and Aquatic Sciences, 49,
2611-2620.
Sample, B.E., Arenal, C.A. 1999. Allometric models for interspecies extrapolation of wildlife toxicity data.
Bulletin of Environmental Contamination and Toxicology, 62, 653-663.
71
-------
Shuchman, R.A., Leshkevich, G., Sayers, M.J., Johengen, T.H., Brooks, C.N., Pozdnyakov, D. 2013. An
algorithm to retrieve chlorophyll, dissolved organic carbon, and suspended minerals from Great
Lakes satellite data. Journal of Great Lakes Research, 39, 14-33.
SOLEC (State of the Lakes Ecosystem Conference). 2007. State of the Great Lakes Draft, Indicator #104.
https://nepis.epa.gov/IExe/ZvPIDIF.cgi/P1004lllET. PIP IF?IDoclkev=P 100411IET. PIP IF
Stevens, D.L., Jr. 1997. Variable density grid-based sampling designs for continuous spatial populations.
Environmetrics, 8, 167-95.
Stevens, D.L., Jr., Olsen, A.R. 1999. Spatially restricted surveys over time for aquatic resources. Journal of
Agricultural, Biological, and Environmental Statistics, 4, 415-428.
Stevens, D.L., Jr., Olsen, A.R. 2003. Variance estimation for spatially balanced samples of environmental
resources. Environmetrics, 14, 593-610.
Stevens, D.L., Jr., Olsen, A.R. 2004. Spatially-balanced sampling of natural resources in the presence of
frame imperfections. Journal of the American Statistical Association, 99, 262-278.
Trebitz, A.S., Hatzenbuhler, C.L., Hoffman, J.C., Meredith, C.S., Peterson, G.S., Pilgrim, E.M., Barge, J.T.,
Cotter, A.M., Wick, M.J. 2019. Dreissena veligers in western Lake Superior - Inference from new
low-density detection. Journal of Great Lakes Research, 45, 691-699.
Tyler, J.E. 1968. The Secchi disc. Limnology and Oceanography, 13, 1-6.
USEPA. 1993. Wildlife Exposure Factors Handbook. Volume I of II. (EPA/600/R-93/187a). U.S.
Environmental Protection Agency. Washington, DC.
https://cfpulb.ep3.gov/nee3/irislk/ireeoirdispll3v.cfrn7deicb27gg
USEPA. 1997. Ecological risk assessment guidance for Superfund: Process for designing and conducting
ecological risk assessments - Interim final. EPA/540/R-97/006. U.S. Environmental Protection
Agency, Environmental Response Team. Edison, NJ. https://seirnspulb.ep3.gov/woirlk/IHQ/157g41.pdf
USEPA. 2000. Methods for measuring the toxicity and bioaccumulation of sediment-associated
contaminants with freshwater invertebrates. 2nd ed., EPA/600/R-99/064. U.S. Environmental
Protection Agency, Office of Water and Office of Science and Technology. Washington, DC.
https://nepis.ep3.gov/Exe/ZvPDIF.egi/30003.SIBA. PIPF?Dockey=30003SBA. PIP IF
USEPA. 2001. Water quality criterion for the protection of human health: Methylmercury. EPA/823/R-
01/001. U.S. Environmental Protection Agency, Office of Water and Office of Science and
Technology. Washington, DC.
https://nepis.ep3.gov/IExe/ZvPIPIF.egi/20003UU4. PIP F?Dockey=20003UU4. PIP IF
USEPA. 2002. Interpretation of the results of sediment quality investigations. In A Guidance Manual to
Support the Assessment of Contaminated Sediments in Freshwater Ecosystems. Vol. Ill, EPA-905-
B02-001-C. U.S. Environmental Protection Agency, Great Lakes National Program Office. Chicago, IL.
https://nepis.ep3.gov/IExe/ZvPIP F.cgi/2000CNDI. PIP F?Dockev=2000CNDI. PIP IF
USEPA. 2003. Technical summary of information available on the bioaccumulation of arsenic in aquatic
organisms. EPA-822-R-03-032. U.S. Environmental Protection Agency, Office of Science and
Technology Office of Water. Washington, DC. 2003.
https://nepis.ep3.gov/IExe/ZvPIP F.cgi?Dockev=P1002YTX. PIP IF
USEPA. 2004. The incidence and severity of sediment contamination in surface waters of the United
States, National Sediment Quality Survey. 2nd ed., EPA-823-R-04-007. U.S. Environmental Protection
Agency, Office of Science and Technology and Standards and Health Protection Division.
Washington, DC. https://seirnspulb.ep3.gov/woirlk/10/500015268.pdf
USEPA. 2009. Guidance for implementing the January 2001 Methylmercury Water Quality Criterion.
EPA-823-R-09-002. U.S. Environmental Protection Agency, Office of Water. Washington, DC.
https://nepis.ep3.gov/Exe/ZyPDF.cgi/P1003 RPA.PDF?Dockey=P1003RPA. PDF
72
-------
USEPA. 2011. National coastal condition report IV. EPA-842-R-10-003. U.S. Environmental Protection
Agency, Office of Research and Development and Office of Water. Washington, DC.
https://cfpulb.epa.gov/si/si public record report.cfm?Lab=NHEERLanddirEntrvlD=232403
USEPA. 2012. Recreational water quality criteria. EPA-820-F-12-058. U.S. Environmental Protection
Agency, Office of Water Regulation and Standards, Criteria and Standards Division. Washington, DC.
https://www.epa.gov/sites/production/files/2015-10/documents/rwqc2012.pdf
USEPA. 2014a. Final Assessment of Contaminated Sediments River Rouge/Ecorse Shoreline Site
Characterization Report, Detroit River Area of Concern, Wayne County, Michigan. Great Lakes
Architect-Engineer Services. Contract: EP-R5-11-10. EA Project No. 6256115.10
USEPA. 2014b. Final assessment of contaminated sediments Celeron Island Area site characterization
report, Detroit River Area of Concern, Grosse lie, Michigan. Great Lakes Architect-Engineer Services
Contract: EP-R5-11-10. EA Project No. 6256114.10.
USEPA. 2014c. National Coastal Condition Assessment: Quality Assurance Project Plan. EPA-841-R-14-
005. U.S. Environmental Protection Agency, Office of Water, Office of Wetlands, Oceans and
Watersheds. Washington, DC. https://www.epa.gov/sites/production/files/2013-
11/documents/ncca-qapp.pdf
USEPA. 2014d. National Coastal Condition Assessment: Site Evaluation Guidelines. EPA-841-R-14-006.
U.S. Environmental Protection Agency, Office of Water. Washington, DC.
https://www.epa.gov/sites/production/files/2013-ll/documents/ncca-siteeval.pdf
USEPA. 2015a. Final Assessment of Contaminated Sediments Mid/Lower Trenton Channel Area Site
Characterization Report, Detroit River Area of Concern, Grosse lie, Michigan. Great Lakes Architect-
Engineer Services Contract: EP-R5-11-10. EA Project No. 62561.18.
USEPA. 2015b. National Coastal Condition Assessment 2010. EPA-841-R-15-006. U.S. Environmental
Protection Agency, Office of Water. Washington, DC.
https://www.epa.gov/sites/piroduction/filles/2016-01/docuirnents/ncca 2010 irepoirt.pdf
USEPA. 2015c. National Coastal Condition Assessment: Field Operations Manual. EPA-841-R-14-007. U.S.
Environmental Protection Agency, Office of Water. Washington, DC. https://www.epa.gov/national-
aquatic-resource-survevs/national-coastal-condition-assessment-2015-field-operations-manual
USEPA. 2016a. National Coastal Condition Assessment 2015: Laboratory Operations Manual. EPA-841-R-
14-008. U.S. Environmental Protection Agency, Office of Water. Washington, DC.
https://www.ep3.gov/sites/piraduction/filles/201G~
03/documents/ncca 2015 lorn version 2.0 jjuly 2015.pdf
USEPA. 2016b. Final Assessment of Contaminated Sediments Riverbend Area Site Characterization
Report, Detroit River Area of Concern, Detroit, Michigan. Great Lakes Architect-Engineer Services
Contract: EP-R5-11-10. EA Project No. 62561.25.
USEPA. 2016c. Final Assessment of Contaminated Sediments Harbortown Area Site Characterization
Report, Detroit River Area of Concern, Detroit, Michigan. Great Lakes Architect-Engineer Services.
Contract: EP-R5-11-10. EA Project No. 62561.28.
USEPA. 2016d. National Coastal Condition Assessment Great Lakes 2010 Technical Memo.
https://www.epa.gov/sites/production/filles/2016-
acuments/ncca great lakes 2010 tech memo.pdf
USEPA. 2016e. Aquatic Life Ambient Water Quality Criterion for Selenium - Freshwater. EPA-822-R-16-
006. Office of Science and Technology Office of Water. Washington, D.C.
USEPA. 2019a. "Great Lakes Areas of Concern." EPA. June 20, 2019. Accessed June 25, 2019:
https://www.epa.gov/gireat-llalkes-aocs
USEPA. 2019b. Recommended Human Health Recreational Ambient Water Quality Criteria or Swimming
Advisories for Microcystins and Cylindrospermopsin. EPA-HQ-OW-2016-0715. U.S. Environmental
73
-------
Protection Agency, Office of Water, Washington, DC.
https://www.epa.gov/sites/prodyction/files/2019-05/docynients/hh-rec-criteria-habs-pirepulb-
2019.pdf
USEPA. 2021a. National Coastal Condition Assessment: A Collaborative Survey of the Nation's Estuaries
and Great Lake Nearshore Waters. EPA 841-R-21-001. U.S. Environmental Protection Agency, Office
of Water. Washington, DC. https://www.epa.gov/system/files/documents/2021-
09/nccareport_final_2021-09-01.pdf
USEPA. 2021b. National Coastal Condition Assessment 2015 Technical Support Document. U.S.
Environmental Protection Agency, Office of Water. Washington, DC.
https://www.epa.gov/system/files/documents/2021-09/ncca-2015-tsd-final.20210901.pdf
Wick, M.J., Angradi, T.R., Pawlowski, M., Bolgrien, D.W., Launspach, J.J., Kiddon, J., Nord, M. 2019. An
assessment of water quality in two Great Lakes connecting channels. Journal of Great Lakes
Research, 45, 901-911. https://doi.org/10.1016/ii.iigllr.2019.08.001
Wick, M. J., Angradi, T. R., Pawlowski, M.B., Bolgrien, D., Debbout R., Launspach, J., Nord, M. 2020. Deep
Lake Explorer: A web application for crowdsourcing the classification of benthic underwater video
from the Laurentian Great Lakes. Journal of Great Lakes Research, 46, 1469-1478.
WHO. 2003a. Guidelines for safe recreational water environments Volume 1: Coastal and fresh waters.
World Health Organization. Geneva, Switzerland.
https://apps.who. int/iris/bitstream/handle/10665/42591/9241545801.pdf;isessionid=3395C8313A
6A2DF8468A64FlA9DA6D70?seauence=l
WHO. 2003b. Cyanobacterial toxins: Microcystin-LR in Drinking-water. Background document for
development of WHO Guidelines for Drinking-water Quality. WHO/SDE/WSH/03.04/57. World
Health Organization. Geneva, Switzerland.
https://www.who.iint/wateir sanitation health/dwq/chemicals/cvanobactoxins.pdf
Appendices
Appendix references included in main references above.
Appendix A - Water Clarity
For the eutrophication indicator, water clarity is characterized by Secchi depth (m) and light extinction
coefficient (Kd; m-1). Secchi depth can be assigned a condition class (e.g., good, fair, poor) according to
established thresholds (Table 2a from main text). In 2015, approximately 30% of sites (includes all NCCA
and enhancements) did not have Secchi depth measurements. This was because the Secchi disk was
visible resting on the bottom (designated as "clear-to-bottom" sites) or Secchi depth could not be
measured for safety or logistical reasons. Crews were able to collect profiles of photosynthetic active
radiation (PAR), from which Kd is calculated, at most sites.
To assign a Secchi depth condition class to a clear-to-bottom site, site depth was considered first. If site
depth was greater than the good/fair Secchi depth threshold for that waterbody (Table 2a from main
text), then the Secchi depth condition was classified "good". If site depth was less than or equal to the
good/fair threshold then a condition class could not be unambiguously assigned as fair or poor. At
those sites, the missing Secchi depth could be estimated using the site's Kd. If neither PAR data nor
74
-------
Secchi depth were available or data did not meet QA criteria (next section) and site depth was less than
the good/fair threshold, then Secchi depth and its condition class, were considered unassessed.
Methods for measuring water clarity
According to NCCA sampling protocols, Secchi depth was measured three times with a weighted 20-cm
diameter black and white Secchi disk (USEPA, 2015c). The mean of three Secchi depth measurements
was used as the indicator.
PAR profiles were measured with a LI-COR LI-1500 PAR meter equipped with two PAR (LI-COR LI-192)
sensors. An underwater sensor was lowered (downcast) and then raised (upcast) through the water
column. Light intensity (lz; |JE m"2s_1) was recorded at prescribed depths (z; meters) during both casts
(USEPA 2014c). An ambient (air) sensor reported varying surface light intensity (l0) arising, for instance,
from passing clouds. The normalized PAR attenuation (lz/lo) is assumed to follow Beer's law where light
intensity decreases exponentially with distance (equation 1). Applied here, the negative slope of the
linearized relationship, Kd (rrf1), is the reduction of light per meter of water depth (equation A2). Light
extinction (Kd) was calculated for each site by fitting a linear regression to the combined downcast and
upcast PAR profile data. For quality assurance, regressions required at least four PAR data points and an
R2 >0.7 to yield a valid Kd 1 value. Smaller values of Kd indicate more clear water.
(Al) Iz/Io = exp(~Kd *z)
(A2) ln(Iz/Io) = -Kd *z
Predicting Secchi Depth from Extinction Coefficient
A power function was used to model the relationship between Secchi depth and Kd for 2015 data (Tyler,
1968). A Secchi depth - Kd model is derived for each NCCA cycle based on that year's dataset. The
relationship between Secchi depth and Kd depends on a combination of site-specific factors like
chlorophyll o, suspended solids, colored dissolved organic matter (e.g., Brezonik et al., 2019). If these
factors change across the Great Lakes over time, this relationship may also change. By basing estimated
Secchi depth on a model derived based on that years' data, each NCCA cycle is an independent
assessment. States wishing to estimate Secchi depth for a given lake or region can either apply the Great
Lakes or Great Lakes/connecting river systems models reported here, or use the methods described
here and a subset of the data to define the Secchi depth - Kd regression for their region of interest.
Two different models were derived and applied for this report. For sites located in the Great Lakes, the
model was based on all sites located in the lakes including multiple site visits and enhancement sites
(embayments, Lake Erie enhancement; equation A3 and Fig. Al). A total of 298 sites with both qualified
PAR profile data and Secchi depths were used.
(A3) Secchi depthest = 1.3891 * Kd0983 r2 = 0.90
For sites located in the connecting river systems, the model was based on all sites located in the lakes
(including multiple site visits and enhancement sites) as well as sites located in the connecting river
75
-------
systems and collected 2014 - 2016 (equation A4 and Fig. Al). A total of 454 sites with both qualified PAR
profile data and Secchi depths were used.
(A4) Secchi depthest = 1.3035 * Kd 0,9'7 r2 = 0.86
These models were similar to the model derived for the Great Lakes NCCA in 2010 (equation A5; EPA
2016d).
(A5) Secchi depthest = 1.31 * Kc
-0.91
r2 = 0.79
Based on the site location, either equation A3 or A4 was used to estimate Secchi depth based on a site's
Kd at clear-to-bottom sites where site depth was less than the good/fair threshold for that waterbody,
and to assign those sites a condition class.
Q.
0J
Q
IE
u
u
JV
y
<
$
•
t *
•
•
L — i/ t^Tm M.
r*~J
9^ /
•
w\
•
m
L*j
P*
•
0 2 4 6 8 10 12 14 16 18
Measured Secchi Depth (m)
76
-------
c 100
- 10
u
u
OJ
1/1
¦a
aj
in
ro
aj
0.1
V = 1-3
R2 =
D35x"0-977
0.8594
\
*•
d 18
16
— 14
Q. 12
w
$
I
r
I
Q
•
•A
*
ll(
F
t_\
W\
•
•
y
-------
The toxicity indicator is based on survival tests in which laboratory organisms were exposed to the
collected sediments, capturing responses to a broader range of sediment properties that might
contribute to overall toxicity. The sediment toxicity indicator compares control-corrected survival of the
amphipod Hyalella azteca to preestablished thresholds. To assess sediment toxicity, acute 10-day
sediment toxicity tests were used to measure the survival of Hyalella azteca in sediments.
Field collection
For both sediment contamination and toxicity sample collection, field crews collected the top 2 cm of
surface sediments at the predetermined probabilistic site or a proximal location as prescribed in the
Field Operations Manual (USEPA, 2015c). Great Lakes crews used a stainless-steel standard PONARgrab
sample. Crews composited samples to reach the total sediment volume required for analysis.
Laboratory Analysis
Samples were analyzed for contaminant concentrations of metals (including mercury), polycyclic
aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and organochlorine pesticides using a
variety of spectrometry methods (USEPA, 2016a).
Sediment toxicity tests were performed to determine the percent survival of laboratory amphipods
(Hyalella azteca) following 10 days of exposure to sample sediments (USEPA, 2015c). Control tests using
reference sediments were run in parallel with the sample tests. Sample and control tests used a flow-
through approach with 4 replicate chambers with 10 organisms in each chamber; a minimum of 80%
survival of control organisms was required to meet test acceptability criteria (USEPA, 2000; USEPA,
2015c).
Sediment Contamination Calculations
For any given contaminant, results were excluded from the sediment contamination indicator
calculations if the associated laboratory method detection limits (MDLs) exceeded the threshold effect
concentration (TEC) values for freshwater sediments (Table Bl; Field and Norton, 2014). Sample results
reported as non-detects (values less than the MDL) were substituted with one-half of the MDL.
Total PAH and total PCB concentrations (in units of ng/g) were computed before estimating PECQs for
these two chemical classes. The specific PAHs and PCB congeners included in the total PAH and PCB
concentrations are listed in Table Bl. PECQs were calculated (equation Bl) for total PAHs, total PCBs,
and individually for each of the seven metals with consensus-based probable effects concentration
values (arsenic, cadmium, chromium, copper, lead, nickel, and zinc concentrations in ng/g; Table Bl) as
the contaminant concentration result divided by the PEC for that chemical class (i.e., total PAH, total
PCB) or metal (Table Bl):
, . num — concentrati°n
\ i V - PEC
78
-------
Once PECQs for the seven individual metals were calculated, a mean PECQ for metals was calculated
(equation B2) as the sum of the PECQs for the seven individual metals, divided by seven, which is the
number of metals included in the analysis:
Ylndividal metal PECQs
(B2) Mean PECQmetals = * *-
The mean PECQ was calculated (equation B3) as the average of the above PECQs:
(B3) Mean PECQ = ^mean PECQ™etals+PECQT0tal PAHs+PECQT0tal PCBs)
where n is the number of contaminant classes (i.e., metals, PAHs, PCBs) for which site data were
available. Data were considered not available only if the lab did not attempt to measure the
concentrations of chemicals in a chemical class. Labs measured concentrations of all three contaminant
classes for Great Lakes and connecting river systems samples, so n was always 3 in this report.
Sediment Toxicity Calculations
Control-corrected survival of H. azteca was calculated (equation B4) for each sample by dividing sample
mean percent survival, or average survival across sample replicates, by control mean percent survival, or
average survival across corresponding control replicates, as follows:
_ , , , , sample mean percent survival
(B4) Control-corrected survival = :—
control mean percent survival
Thresholds
Sediment contamination thresholds were based on literature review and best professional judgment.
Mean PECQ thresholds were developed from prior studies relating guideline exceedances and observed
toxicity levels (Ingersoll et al., 2001; Crane et al., 2002; Crane and Hennes, 2007). Good sediment
contamination conditions were assigned to sites with mean PECQs < 0.1, fair conditions were assigned
to sites with mean PECQs >0.1 and <0.6, and poor conditions were assigned to sites with mean PECQs
>0.6 (Table 2b from main text).
Thresholds for control-corrected survival of H. azteca (USEPA, 2004) were used to determine the
sediment toxicity indicator condition for freshwater samples (Table 2b from main text). Good conditions
were assigned to sites with >90% control-corrected survival, fair conditions were assigned to sites where
control-corrected survival was >75% and <90%, and poor conditions were assigned when control-
corrected survival was <75% (Table 2b from main text).
Sediment Quality Indicator
The sediment contamination and sediment toxicity indicators contribute equally to the sediment quality
indicator (Table 2b from main text). A site was assessed as good if both component indicators were
classified as good. A site was assessed as fair if either or both indicators were assessed as fair. A site was
79
-------
assessed as poor if either or both component indicators were rated poor. If either sediment
contamination or sediment toxicity data were missing for an individual site, good, fair, or poor condition
was assigned based on the indicator that was available.
Table Bl: Chemicals with reliable, published threshold and probable effects concentrations. TEC values
are used to screen results for exclusion from analyses and PEC values are used to estimate PECQs for
each metal individually and for total PAHs and PCBs. The PAHs and PCBs included in these totals are
summarized in the footnotes. PECQ calculations for metals should use metal concentrations in ng/g;
PECQ calculations for PAHs and PCBs should use concentrations in ng/g.
Consensus-based threshold Consensus-based probable effects
Contaminant effects concentration (TEC) values concentration (PEC) values
Arsenic
9.7
33.0
Cadmium
0.99
4.98
Chromium
43.4
111
Copper
31.6
149
Lead
35.8
128
Nickel
22.7
48.6
Zinc
121
459
Total PCB congeners1
60
676
Total PAHs2
1610
22800
^otal PCBs included the following congeners: 8,18, 28, 44, 52, 66, 77,101,105,110,118,126,128,138,
153,170,180, 187, 195, 206, 209
2Total PAHs included sum of low molecular weight (LMW) PAHs (Acenaphthene, Acenaphthylene,
Anthracene, Fluorene, 2-methylnaphthalene, Naphthalene, and Phenanthrene) and high molecular weight
(HMW) PAHs (Benz(a)anthracene, Benzo(a)pyrene, Chrysene, Dibenz(a,h)anthracene, Fluoranthene, and
Pyrene)
Sources: MacDonald et al., 2000; Crane and Hennes, 2007; CCME, 1999; Crane et al., 2002
Appendix C - Benthic Index
Of the 512 sites in the Great Lakes and connecting river systems from 2014-2018 where benthos
samples could be collected, only 444 samples were assessed for benthic condition (Table 1 from main
text). This is because the oligochaete trophic index (OTI) used for classifying benthic condition requires a
sample to have oligochaetes that are listed in Table CI. Benthos samples from 68 of the 512 sites where
benthos samples could be collected either did not contain any oligochaete species or did not contain the
oligochaetes listed in Table CI and the benthic condition for these sites was classified as unassessed.
Benthic condition was also classified as unassessed at sites where benthos samples could not be
collected due to PONAR failures (due to hard substrate, mussel shells, etc.). OTI values were calculated
(equation CI) for the 444 Great Lakes and connecting river systems samples that did contain the proper
oligochaetes according to the methods described below. Once calculated, OTI values were compared to
the thresholds in Table 2c from main text to assign condition classes.
The OTI value is calculated for each site as:
n„ + E n, #2 E »- + 3 E n«
X'»0+ E%.+ E ma + El
80
-------
Where Yj^o, Yjlu Yjl2, and Yjl3 refer to the sum of the densities (number per m2) of all species in
tolerance groups 0, 1, 2, and 3, respectively (Table CI). The constant c adjusts the ratio to the
total density (nan, in units of number per m2) of all mature and immature tubificid and lumbriculid
oligochaetes in the sample. The possible values for c are:
c= 1 when naii >3600
c= 0.75 when 1200 < naii <3600
c= 0.5 when 400 < naii <1200
c= 0.25 when 130 < naii <400
c= 0 when nau <130
Table CI. Oligochaete Trophic Index categories used for Benthic Indictor. Classifications are from State
of the Great Lakes 2011 (EC and USEPA, 2014) - Benthic Diversity and Abundance Table 1, which was
based on classifications from Howmiller and Scott (1977), Milbrink (1983), Krieger (1984), and Lauritsen
et al. (1985). Only species in the families, Naididae (formerly Tubificidae) and Lumbriculidae were
included. Taxa in bold were not observed in the Great Lakes or connecting river systems in 2014-2016.
Group 0
Group 1
Group 2
Group 3
Unassigned4
Limnodrilus profundicola
Arcteonais lomondi2
Aulodrilus pluriseta
Limnodrilus hoffmeisteri
Branchiura sowerbyi (2)
Rhyacodrilus coccineus
Aulodrilus americanus
Limnodrilus angustipenis
Tubifex tubifex1
Chaetogaster diaphanus (2)
Rhyacodrilus montana
Aulodrilus limnobius
Limnodrilus cervix
Dero sp. (2)
Rhyacodrilus sp.
Aulodrilus pigueti
Limnodrilus claparedianus
llyodrilus frantzi
Spirosperma nikolskyi
Dero digitata2
Limnodrilus maumeensis
Naidinae
Stylodrilus heringianus
llyodrilus templetoni
Limnodrilus udekemianus
Nais sp.
Lumbriculidae3
Isochaetides freyi
Potamothrix bedoti
Nais bretscheri
Trasserkidrilus superiorensis
Slavina appendiculata2
Potamothrix moldaviensis
Ophidonais serpentina (2)
Trasserkidrilus americanus
Spirosperma ferox
Potamothrix vejdovskyi
Paranais grandis
Tubifex tubifex1
Uncinais uncinata2
Quistadrilus multisetosus
Paranais litoralis
Piguetiella sp.
Piguetiella blanci (2)
Specaria
Stylaria lacustris (2)
Tubificinae
Varichaetadrilus
Vejdovskyella intermedia (1)
37"ubifex tubifex is assigned to Group 0 or Group 3 according to the following rules (ECCC and USEPA, 2017):
if the ratio of abundances of no oligochaetes to m oligochaetes (L hoffmeisteri) < 1 then T. tubifex is classified as a 3; if the ratio is >1 then T.
tubifex is classified as a 0; however, if the ratio is close to one (0.75 to 1.25) then T. tubifex is a 3 if c > 0.5 and a 0 if c < 0.5;
- if n0: n3 < 0.75 then Group 3;
- if n0: n3 > 1.25 then Group 0;
-if n0:n3 = 0.75 - 1.25 then Group 0 if c < 0.5 or Group 3 if c > 0.5;
- ifn3 = 0then GroupO if n0
is relatively high and/or c is low (i.e., c = 0 or 0.25); otherwise Group 3
2These species were not included in State of the Great Lakes 2011 (EC and USEPA, 2014) list presumably because they were thought to be in
the family Naididae, not Tubificidae, although they were included in group 2 in earlier publications. However, recent taxonomy changes have
reclassified Tubificidae to Naididae which has several subfamilies including Naidinae and Tubificinae, so they were included in Group 1.
3 SOLEC (ECCC and USEPA, 2017) classified all immature Lumbriculidae as Stylodrilus heringianus. Therefore, taxa in NCCA 2015 GL samples that
were identified as Lumbriculidae are assigned Group
= 0.
"Unassigned oligochaete taxa with numbers in parentheses are group assignments recommended by Kurt Schmude, Univ. of Wisconsin -
Superior (Burlakova et al., 2018). These assignments were not applied in these calculations of OTI but are included in case future changes are
made to the OTI.
81
-------
Appendix D - Ecological Fish Contamination
An ecologically based method was used to assess contaminant burdens in whole fish using an approach
based on USEPA's Ecological Risk Assessment Guidance for Superfund: Process for Designing and
Conducting Ecological Risk Assessments (USEPA, 1997). Field crews targeted fish species based on a
recommended species list (USEPA, 2015c). Whole fish tissue samples of mostly forage-sized fish were
then analyzed for measurable concentrations of 13 contaminants of concern (Table Dl; USEPA, 2016a).
Fish tissue concentrations of contaminants were compared to screening values, which consider receptor
body weight, food ingestion rate, and contaminant-specific toxicity reference values (TRVs). TRVs are
concentrations above which ecologically relevant adverse effects might occur in wildlife after long-term
dietary exposure to the contaminant. The methods for estimating screening values for each receptor
group and contaminant are described in detail in the 2015 NCCA technical support document (USEPA,
2021b). These methods differed from the methods used in the 2010 NCCA report in two key ways. The
first change involved using TRVs based on no observed adverse effect levels (NOAELs) to estimate
screening values rather than using lowest observed adverse effect levels (LOAELs) themselves as
screening values (which was done in 2010). The second change involved using an updated approach for
estimating the selenium screening values which is based on new guidance for selenium in freshwater
(USEPA 2021b).
Some of the contaminants of concern include multiple, related compounds whose tissue concentrations
must be summed before being compared to screening values (Table Dl). In addition, screening values for
arsenic were derived for inorganic arsenic. Because the labs reported the total concentration of arsenic
(i.e., the sum of its organic and inorganic forms), the concentration of inorganic arsenic was estimated by
taking 10% of the total arsenic concentration reported by the lab (USEPA, 2003). Finally, the screening
values represent estimated effects concentrations in dried fish tissue. However, the lab results are
reported as contaminant concentrations in fresh (frozen) fish. To address this, the final step before
comparing fish tissue contaminant concentrations to screening values was to convert each concentration
from a wet weight concentration to a dry weight concentration. This was done by dividing the wet weight
concentrations of each contaminant or contaminant group by 0.28, which adjusts for the 72% of fish
tissue made up of water (USEPA, 1993).
To derive the overall fish tissue contaminant indicator at each site, the number of receptor groups with
screening value exceedances are counted and good, fair, and poor conditions are assigned according to
the rules in Table 2c.
82
-------
Table Dl. Summary of estimated fish tissue screening values in mg/kg (dry weight) for each freshwater
receptor group.
Ecological contaminant of
concern
Freshwater
fish
Freshwater
mammal
Piscivorous
birds
Cadmium
1125.9217
11.6807
7.9001
Dieldrin
0.9658
0.4361
0.5216
Hexachloro benzene
0.026
12.736
0.9012
Inorganic Arsenic3
0.4039
1.3849
28.5892
Lindane
221.0475
101.892
4.5878
Mercury
2.1081
0.4076
0.1682
Mi rex
5.8274
0.836
0.0561
Selenium
6.05
2.5473
2.2423
Total Chlordaneb
NA
50.3383
4.4846
Total DDTC
4.1878
10.1892
1.2394
Total Endosulfand
0.004
15.5742
67.3318
Total Endrin6
2.3035
1.978
0.158
Total PCBsf
1.1463
0.714
1.009
a Thresholds for arsenic represent inorganic arsenic only. Inorganic arsenic
concentrations in fish tissue are estimated from total arsenic concentrations by
multiplying total arsenic by 0.10 (USEPA, 2003)
b Total Chlordane includes the sum of alpha-chlordane, cis-nonachlor, gamma-
chlordane, heptachlor, heptachlor epoxide, oxychlordane, and trans-nonachlor.
There is no established NOAEL value for total chlordanes for freshwater fish (NA =
Not Applicable).
cTotal DDT includes the sum of concentrations of OPDDD, OPDDE, OPDDT, PPDDD,
PPDDE, PPDDT
d Total Endosulfan is the sum of the concentrations of Endosulfan Sulfate,
Endosulfan I, and Endosulfan II
e Total Endrin is the sum of the concentrations of Endrin, Endrin Ketone, and Endrin
Aldehyde
'Total PCBs includes the sum of the concentrations of congeners 8,12,18, 28, 44,
52, 66, 77, 101, 105, 110, 118, 128, 138, 153, 170, 180, 187, 195, 206, 209
83
-------
Appendix E - Summary of additional Great Lakes NCCA and connecting river system data not included
in assessments
This appendix summarizes spatial patterns in parameters sampled in the Great Lakes in the 2015 NCCA
survey that were not included in the main report. These include water quality parameters, algal toxin
data, sediment chemistry data, phytoplankton taxa richness, and macroinvertebrate taxa richness. The
water quality and algal toxin parameters included here do not have established condition thresholds and
are not included in the population estimates presented in the body of the report. For sediment
chemistry, constituent parameters (mean metals PECQ, PAH PECQ, PAH PECQ) not shown in the main
report are included here. These data are available to the public on the NCCA website and may be useful
for state, regional, or local management needs. Plotted data is from first visits to each site; second visit
data are available on the NCCA website. Sites with no data are a result of a sample not being collected
or analyzed for that parameter.
Water Quality
Included here are results for total nitrogen, chloride, conductivity, sulfate, pH, and silica. Silica was only
sampled at 23 sites in Wisconsin waters in Lake Michigan and Lake Superior. Additional parameters
determined for some or all sites but not reported here include temperature, and soluble reactive
phosphorus (SRP), dissolved organic carbon (DOC), and turbidity.
Total nitrogen concentrations were consistently relatively low in the nearshore of Lake Huron, northern
Lake Michigan, and the east basin of Lake Erie (Fig. El) and was relatively high in the St. Marys River,
western Lake Erie, and Lake Ontario. Chloride concentrations were lowest in the nearshore of Lake
Superior and the St. Marys River (Fig. E2) and was highest in the nearshore of Lake Ontario and Lake
Erie. As for chloride, conductivity was highest in the nearshore of Lake Erie and Lake Ontario (Fig. E3),
and lowest in the nearshore of Lake Superior and the St. Marys River. Sulfate concentrations were
highest in the nearshore of Lake Ontario and eastern Lake Michigan (Fig. E4) and was lowest in the
nearshore of Lake Superior and the St. Marys River. Sites in Lake Superior consistently had a lower pH
than the other Great Lakes (Fig. E5), Silica was only measured at a few sites; concentrations ranged from
the detection limit of 0.1 mg/L to 2.55 mg/L (Fig. E6).
Algal Toxins
In addition to microcystin analysis using the ELISA method reported in the main report, algal toxins
analyzed by liquid chromatography-mass spectrometry (LCTX method) were also included as a research
indicator in the 2015 NCCA. Toxins determined included Anatoxin-a, Azaspiracid-1, Cylindrospermopsin,
Domoic acid, Dinophysistoxin-2, Gymnodimine, Microcystin-HiLR, Microcystin-HtYR, Microcystin-LA,
Microcystin-LF, Microcystin-LR, Microcystin-LW, Microcystin-LY, Microcystin-RR, Microcystin-WR,
Microcystin-YR, Nodularin-R, Okadaicacid, Pectinotoxin-2, and 13-desmethyl spirolide-C. Reported here
are the sums of individual algal toxin concentrations measured (Figure E7). Most locations for which at
least one toxin was above the detection limit were in Lake Erie or the Huron-Erie Corridor, although
detections occurred in Green Bay, Lake Michigan, Saginaw Bay, Lake Huron, and eastern Lake Ontario.
84
-------
Sediment Chemistry
Included here are the three components of the sediment contamination indicator reported in the main
report: Mean probable effects concentration quotient (PECQ) for metals, total PECQfor PAHs, and total
PECQfor PCBs. Each PECQ is a unitless measure of the concentration of the contaminant in a site's
sediment compared to concentrations that may adversely affect benthic organisms. For more detail
about how these indicators are calculated, see Appendix B.
Sites with high mean PECQfor metals, high total PECQfor PAHs and high total PECQfor PCBs occurred in
all the Great Lakes (Figs E8, E9, E10). The St. Marys River, Lake Erie, and Lake Ontario has consistently
had the highest PECQs for metals and PAHs. Lake Erie, Lake Ontario, and Green Bay, Lake Michigan had
the most sites with high PECQ for total PCBs
Phytoplankton
Diatom richness, or the number of diatom taxa present in the sample, was highly variable across the
Great Lakes. Diatom richness was high in Grand Traverse Bay, Lake Michigan and at sites scattered
throughout the Huron-Erie Corridor, Green Bay (Lake Michigan), Thunder Bay and Saginaw Bay (Lake
Huron), and Western Lake Erie (Fig. Ell). Richness of non-diatom phytoplankton taxa was also highly
variable, but generally high in Saginaw Bay and Green Bay (Fig. E12). A small number of more isolated
sites in other areas of the Great Lakes also had high non-diatom phytoplankton taxa richness.
Macroinvertebrates
Macroinvertebrate richness, or the number of macroinvertebrate taxa present in the benthos sample,
was highest in the Huron-Erie Corridor, Chequamegon Bay and Keweenaw Bay (Lake Superior), and in
around the Straights of Mackinaw between Lake Michigan and Lake Huron (Fig. E13). Macroinvertebrate
richness was low in the St. Mary River, western and eastern Lake Erie, and along the south shore of Lake
Ontario. Many sites (especially in eastern Lake Erie and Lake Ontario) had no data because PONAR
samples could not be collected due to rocky substrates.
85
-------
TN (mg/L)
Figure El, Total nitrogen concentration at sites sampled in the Great Lakes and connecting river systems for the 2015 NCCA. Figure symbology is
based on value thresholds that represent quartiles of the measured results for sites in the Great Lakes, excluding the connecting river systems.
-------
Figure E2, Chloride concentration at sites sampled in the Great Lakes and connecting river systems for the 2015 NCCA. Figure symbology is based
on value thresholds that represent quartiles of the measured results for sites in the Great Lakes, excluding the connecting river systems.
87
-------
Conductivity (jjS/m)
o 88-210
o 220 - 280
• 290 - 300
• 310-940
o No data
Sampling area
Figure E3, Conductivity at sites sampled in the Great Lakes and connecting river systems for the 2015 NCCA. Figure symbology is based on value
thresholds that represent quartiles of the measured results for sites in the Great Lakes, excluding the connecting river systems.
88
-------
Sulfate (ug/L)
o 2.8-15
Figure E4. Sulfate concentration at sites sampled in the Great Lakes and connecting river systems for the 2015 NCCA. Figure symbology is based
on value thresholds that represent quartiles of the measured results for sites in the Great Lakes, excluding the connecting river systems.
89
-------
Figure E5. pH measured at sites sampled in the Great Lakes and connecting river systems for the 2015 NCCA. Figure symbology is based on value
thresholds that represent quartiles of the measured results for sites in the Great Lakes, excluding the connecting river systems.
-------
Figure E6. Silica concentration measured at sites sampled in the Great Lakes and connecting river systems. Silica was only measured at the sites
shown. Figure symbology is based on value thresholds that represent quartiles of the measured results.
91
-------
o
°
o°^
6 q.
O CDOCQd **?
°o0qp 8
o S
o
p
T
o
©
°o °o
"I
%
o
o
o
©oo
Sum of algal toxins (ug/L)
o 0 - 0.38
© 0.38-0.44
• 0.44-0.51
Q 0.51 - 14.63
o No data
Sampling area
o<$D o at)
Figure E7. Sum of algal toxin concentration determined with the LCTX method at sites sampled in the Great Lakes and connecting river systems
for the 2015 NCCA. Figure symbology is based on value thresholds that represent quartiles of the measured results for sites in the Great Lakes,
including the connecting river systems. The lowest threshold of 0.38 |ig/L(turquoise) represents a total for which all sites where all algal toxins
measured were below their respective detection limits.
92
-------
Total Metals PECQ
o 0.0099 - 0.056
© 0.057-0.12
® 0.13-0.24
o 0.25-1.3
o No data
Sampling area
Figure E8. Mean PECQ for metals at sites sampled in the Great Lakes and connecting river systems for the 2015 NCCA. Figure symbology is based
on value thresholds that represent quartiles of the measured results for sites in the Great Lakes, including the connecting river systems.
93
-------
Figure E9. Total PECQfor sediment PAHs at sites sampled in the Great Lakes and connecting river systems for the 2015 NCCA. Figure symbology
is based on value thresholds that represent quartiles of the measured results for sites in the Great Lakes, including the connecting river systems.
-------
o*
•••V
/ .3
it
.v
f
0
1
I
o
Q®«
.o°'
>%> ® ^
Total PCBs PECQ
o 0.0015-0.015
o 0.016
• 0.017-0.016
o 0.017-0.27
o No data
Sampling area
T
O 3D |
Figure E10. Total PECQ for sediment PCBs at sites sampled in the Great Lakes and connecting river systems for the 2015 NCCA. Figure symbology
is based on value thresholds that represent quartiles of the measured results for sites in the Great Lakes, including the connecting river systems.
95
-------
Diatom richness
Fig. Ell. Diatom taxa richness at sites sampled in the Great Lakes and connecting river systems for the 2015 NCCA. Figure symbology is based on
value thresholds that represent quartiles of the measured results for sites in the Great Lakes, including the connecting river systems.
96
-------
Non-diatom algae richness
o 7-25
© 26-30
Fig. E12. Non-diatom phytopiankton taxa richness at sites sampled in the Great Lakes and connecting river systems for the 2015 NCCA. Figure
symbology is based on value thresholds that represent quartiles of the measured results for sites in the Great Lakes, including the connecting
river systems.
97
-------
• O I
O 0»OOCti
•0»Qp
CD
©
0 $
o
o
4
©
«0| # S*>
• °5
%
o
o
o
©•o
Benthos taxa richness
o
1 - 14
®
15-22
•
23-33
0
34 - 68
o
No data
Sampling area
0
Fig. E13. Benthic macroinvertebrate taxa richness at sites sampled in the Great Lakes and connecting river systems for the 2015 NCCA. Figure
symbology is based on value thresholds that represent quartiles of the measured results for sites in the Great Lakes, including the connecting
river systems.
98
------- |