EF3 A/600/3-90/068
November 1990
MERCURY LEVELS IN FISH FROM THE
UPPER PENINSULA OF MICHIGAN (ELS
SUBREGION 2B) IN RELATION TO LAKE ACIDITY
U.S. Environmental Protection Agency
. Office of Research and Development, Washington, D.C.
Environmental Research Laboratory, Cbrvallis, OR
Environmental Monitoring Systems Laboratory, Las Vegas, NV
Printed on Recycled Paper
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MERCURY LEVELS IN FISH FROM THE
UPPER PENINSULA OF MICHIGAN (ELS
SUBREGION 2B) IN RELATION TO LAKE ACIDITY
Steven P. Gloss
Wyoming Water Research Center
P.O. Box 3067
University of Wyoming
Laramie, Wyoming 82071
Thomas M. Grieb
Tetra Tech, Inc.
3746 Mt. Diablo Blvd.
Lafayette, CA 94549
Charles T. Driscoll
Dept. of Civil Engineering
Hinds Hall, Syracuse University
Syracuse, NY 13210
Carl L. Schofield
Dept. of Natural Resources
Cornell University
Ithaca, NY 14853
Joan P. Baker
Western Aquatics, Inc.
1920 Highway 54
Executive Park, Suite 220
Durham, NC 27713
Dixon Landers
U.S. EPA Environmental Research Laboratory
200 SW 35th
Corvallis, OR 97330
Donald B. Porcella
Electric Power Research Institute
3412 Hillview Avenue
Palo Alto, CA 94303
The research described in this report has been funded wholly or in part
by the U.S. Environmental Protection Agency under cooperative agreements
CR814602 (Cornell University), CR814951 (Syracuse University), and con-
tract 68-03-3439 (Kilkelly Environmental Associates). The report has
been subjected to the Agency's peer and administrative review and
approved for publication as an EPA document. Mention of trade names or
commercial products does not constitute endorsement or recommendation
for use.
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EXECUTIVE SUMMARY
The accumulation of mercury by fish and the potential human health
i
effects of eating mercury-contaminated fish have been well documented.
However, elevated mercury concentrations in fish from dilute, low-pH
I
lakes have only recently been associated with increased lake acidity.
Nevertheless, there now is ample evidence to document that elevated
levels of mercury are found in fish from lakes in remote areas with no
known point sources of mercury and that an apparent relationship exists
between lake pH and fish mercury level. j
[
The U.S. Food and Drug Administration (FDA) has set an action level
I
of 1.0 ppm methyl mercury as the limit for human consumption. Many
i
state fisheries agencies in the United States have established advi-
sories regarding consumption of fish with mercury levels that do not
exceed the standard of 1.0 ppm, usually invoking a ,standard of 0.5 ppm.
The World Health Organization (WHO) standard is 0.5 ppm.
Forty-nine drainage and seepage lakes in the Upper Michigan
Peninsula (Subregion 2B) were sampled in conjunction with Phase II of
the U.S. Environmental Protection Agency's (EPA) Eastern Lake Survey
(ELS-II) to explore the relationship between chemical and physical
characteristics of lakes and mercury concentrations in fish tissue. The
lakes were selected using a stratified random design weighted for low pH
so that acidification effects on mercury accumulation could be evalu-
ated. By coupling this study to Phase I of the EPA's Eastern Lake
Survey, (ELS-I), along with Phase II, we were able; to examine the role
of chemical and physical lake variables on the assimilation of mercury
by fish.
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Because of the concern for potential human health effects, our
focus was on "game fish" species. However, we also report mercury
levels for some nongame species as well. Mercury levels reported are
predominantly methyl mercury (99%).
Specific objectives of this study were:
1. Archive tissue samples for representative ages of fish species
collected during ELS-II.
2. Measure total mercury concentrations in selected fish samples.
3. Using statistical and deterministic approaches, identify
relationships between fish tissue mercury levels and water
quality and lake-watershed characteristics.
4. Estimate the number and percentage of lakes in the region
which have game fish with mercury levels exceeding human
health guidelines.
Although the numbers of fish analyzed for each species and each age
class were dissimilar, a general trend of increasing mean mercury
concentration as a function of age was evident for all species. This
trend was also evident in the proportion of samples that exceeded the
health criterion. For example, 7.5% of the age-4 yellow perch had
mercury concentrations greater than 0.5 ppm, while 26.2% of the age-7
yellow perch had concentrations greater than this value. Overall, a
large proportion of the yellow perch, northern pike, and largemouth bass
exceeded the Michigan state health advisory criterion. Thirty-three
percent of the northern pike and 26% of the largemouth bass exceeded 0.5
ppm. While fewer fish of all species exceeded the FDA action level of
1.0 ppm, fish with the highest mercury concentrations were well
distributed among all lakes.
iv
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It is apparent from the foregoing results that a high percentage of
i
game fish, which are the species most likely to be consumed by humans,
exceed various health guidelines for mercury. The severity and extent
of the mercury contamination problem depends upon whether the FDA action
level of 1.0 ppm methyl mercury is used or the more conservative figure
of 0.5 ppm adopted by several states, Canada, and the WHO.
Based on the probability sampling frame for the ELS-I and ELS-II
surveys, data collected on fish mercury levels for 37 of the 49 ELS-II
lakes were extrapolated to estimate fish mercury characteristics for
Subregion 2B as a whole. Regional estimates are provided for the total
number and area of lakes where fish mercury levels exceed 0.5 and 1.0
ppm. Results of these estimates show that nearly 54% of all lakes in
this subregion and nearly 82% of the surface area of all lakes have one
or more fish exceeding the 0.5 ppm Hg state health advisory. Over 18%
I
of all lakes have one or more fish exceeding 1.0 ppm Hg. However, among
i
game fish other than yellow perch (walleye, northern pike, and large-
mouth bass), at least one fish exceeds the 1.0 ppm action level in 43%
of the 457 lakes in which they occur. At least one of these same
species exceeds 0.5 ppm in 58% of the lakes in which they occur.
Numerous statistical relationships exist between;mercury levels and
water chemistry variables; however, a survey study provides no basis
from which to imply causal mechanisms. For example, steveral multiple
regression models that predict mercury levels in fish have high correla-
tion coefficients, but their variables may or may notjbe implicated in
actual causes for increased or decreased mercury levels in fish.' The
i
variables identified in multiple regression models are not always the
same as those that have the highest coefficients in simple correlation
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matrices. Overall, the most consistent variables related to the mercury
levels found in fish were those describing fish size (total length,
weight, age). Of secondary importance were variables related to lake
acidity status. Therefore, the principal benefit of this study is that
it begins to quantify the mercury problem in one subregion of the ELS
and suggests some possible lake characteristics that may warrant further
investigation as to their possible cause and effect relationships with
mercury accumulation in fish.
Additional research needed to reduce the current uncertainty about
the quantitative relationships between acidic deposition, bioaccumula-
tion of mercury in fish, and human health risks includes: (1) systema-
tic surveys designed to identify the extent and severity of mercury
bioaccumulation in fish taken from lakes in regions potentially affected
by acidic deposition, (2) studies designed to identify and quantify the
factors affecting bioaccumulation, and (3) studies designed to quantify
the consumption by humans of fish from low-ANC waters and the demography
of angler populations.
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TABLE OF CONTENTS
SECTION
TITLE
PAGE
1. INTRODUCTION AND BACKGROUND : . . . .
1.1 REVIEW OF RELATIONSHIPS BETWEEN LAKE j
ACIDITY AND MERCURY ACCUMULATION IN FISH ....
1.2 HUMAN HEALTH CONCERNS ! . . . .
1.3 OBJECTIVES OF THE SUBREGION 2B SURVEY j.
(UPPER PENINSULA OF MICHIGAN) i . . . .
1.4 REPORT FORMAT ; . . . .
2. STUDY AREA AND METHODOLOGY , . . . .
i
2.1 LAKE CHARACTERISTICS ,!....
,1
2.2 FISHERIES COLLECTIONS AND SAMPLE j
PROCESSING : . . . .
I
2.3 LABORATORY AND ANALYTICAL PROCEDURES . [ . . . .
' i
3. RESULTS
i •
3.1 OVERVIEW OF ELS I & ELS II FINDINGS .......
3.1.1 Lake Chemical Characteristics ......
i
3.1.2 Fish Species Distribution ...;....
i
3.2 MERCURY CONCENTRATIONS IN FISH MUSCLE TISSUE . .
3.2.1 Relationships Between Mercury [
Concentrations and Fish Age, j
Weight, and Length . . . .
3.2.2 Relationships Between Lake Character-
istics and Fish Mercury Concentrations . .
i
3.2.3 Interactive Effects of Fish Size and
Lake Characteristics on Mercury Levels . .
4. DISCUSSION
4.1 RELATIONSHIP OF SUBREGION 2B MERCURY
LEVELS TO OTHER GEOGRAPHIC AREAS . . .
vii
1
3
4
6
7
7"
10
12
21
21
21
29
32
37
38
47
72
72
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TABLE OF CONTENTS
(cont.)
SECTION
TITLE
PAGE
4.2 APPARENT FACTORS AFFECTING MERCURY
ACCUMULATION IN SUBREGION 2B LAKES
4.3 HUMAN HEALTH CONSIDERATIONS AND
RECOMMENDATIONS FOR ADDITIONAL RESEARCH
5. REFERENCES
APPENDIX . . .
75
79
81
85
viii
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LIST OF FIGURES
FIGURE
TITLE
PAGE
2-1
2-2
2-3
3-1
3-2
3-3
3-4
3-5
3-6
3-7
3-8
Location of ELS-II lakes sampled for
fish mercury content, summer 1987 . .
Diagram of location from which muscle tissue
was removed from fish for mercury analysis
Relationship between total mercury and j
methyl mercury in a subsample of 30 fish
from ELS-II lakes •
Comparison of mercury levels in yellow
perch from (A) drainage lakes and (B)
seepage lakes in ELS-II lakes . . .
Relationship between lake pH and total
mercury concentration for yellow perch
ages 2-4 in 27 ELS-II lakes
Plot of individual yellow perch mercury
levels (HG ppm) as a function of lake pH
(pH) and total length (TL) • •
Plot of individual mercury levels (HG ppm)
for yellow perch ages 2-4 in seepage lakes!
as a function of lake pH (pH) and total
length (TL) >
Plot of individual, mercury levels (HG ppm)
for yellow perch ages 2-4 in drainage lakes
as a function of lake pH (pH) and total ,
length (TL) '•
Plot of individual mercury levels (HG ppm)
for yellow perch ages 7 and older in j
drainage lakes as a function of lake pH 1
(pH) and total length (TL) I
Plot of individual mercury levels (HG ppm)
for large mouth bass in drainage lakes as a
function of lake pH (pH) and total length ;(TL)
|
Plot of individual mercury level's (HG ppm)
for northern pike in drainage lakes as a
function of lake pH (pH) and total length (TL)
14
18
41
46
55
56
57
58
59
60
ix
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LIST OF FIGURES
FIGURE
TITLE
PAGE
3-9 Relationship between age and total length for
all ages of yellow perch
3-10 Relationship between age and weight for all
ages of yellow perch
3-11 Relationship between age and total length for
yellow perch ages 7 and older
3-12 Relationship between age and weight for all
ages of largemouth bass
3-13 Relationship between age and total length for
all ages of largemouth bass
3-14 Relationship between age and total length for
all ages of northern pike
3-15 Relationship between age and weight for all
ages of northern pike
3-16 Plot of pH versus DOC for lakes with
mercury values for yellow perch ages 2-4 . .
64
65
66
67
68
69
70
71
x
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LIST OF TABLES
TABLE
TITLE
PAGE
2-1 Physical Characteristics of the 49
Sampled Lakes \ 9
I
3-1 Chemical Characteristics of Lakes in !
Subregion 2B, for the ELS-I Target |
Population (N = 1050), ELS-II Target j
Population (N = 597), and the 49 |
Lakes Sampled for Fish During ELS-II . . . . j 23
i
3-2 Comparison of Lake Chemistry by Lake i
Type: Seepage Lakes Versus Other I
Lake Types (Drainage, Reservoir, j
Closed) for the 49 Lakes Sampled j
During ELS-II j 25
3-3 Correlation Matrix for Water Quality j
Variables Measured in All Seepage and i
Drainage Lakes j 26
i
3-4 Correlation Matrix for Water Quality
Variables Measured in Drainage Lakes 27
3-5 Correlation Matrix for Water Quality i
Variables Measured in Seepage Lakes 28
3-6 Fish Species Caught and Frequency
of Occurrence 30
3'-7 Number of Lakes With Fish Available for '
Mercury Analysis by Species and Age .....! 33
3-8 Summary of Fish Mercury Analysis j
Results by Species and Age Class ' 34
I
3-9 Regression Equations and Correla- i
tion Coefficients (R) for Age, Total I
Length, and Weight in Yellow Perch !
(All Ages Separated by Lake Type) 39
3-10 Average Total Mercury Concentration j
(ppm) in Yellow Perch by Lake Type i
and Age Class (Number of Fish) ! 40
3-11 Correlation Coefficients (r) Between ]
Total Mercury and Age, Weight, or
Total Length for Game Fish Species
by Lake Type ;..... 42
xi
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LIST OF TABLES
TABLE
TITLE
PAGE
3-12
3-13
3-14
3-15
3-16
3-17
3-18
4-1
4-2
Mercury Levels and Percent Exceeding
Health Guidelines for Three Game Fish
Species in Samples From Subregion 2B
Correlation Matrix for Average Values
of Mercury in Yellow Perch Ages 2-4
and ELS-I Water Quality Variables
Measured in Seepage Lakes, Drainage
Lakes, and Seepage and Drainage
Lakes Combined
Correlation Matrix for Average Values
of Mercury in Age 7 and Older Yellow
Perch and ELS-I Water Quality Variables
Measured in Seepage Lakes, Drainage Lakes,
and Seepage and Drainage Lakes Combined .
Numbers of Lake Types Represented in
Mercury Analyses for Game Fish Species
Stepwise Multiple Regressions
Combining Biological and Chemical
Variables for Three Game Fish Species
and Along With Lake Type
Lakes in Which at Least One Fish of a
Given Species Exceeded Health Guidelines
Discriminant Analysis Results for All
Ages Yellow Perch > 0.5 ppm and < 0.5 ppm
Mercury
43
45
48
49
52
53
62
Population Estimates (Subregion 2B) of Lakes
With Fish Mercury Levels Exceeding 0.5 and
1.0 ppm Hg, Based on Direct Estimation From
the Sample of 49 ELS-II Lakes
Comparison of Correlations Between Water
Quality and Fish Mercury Among Several
Studies
73
77
xii
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1.1
1. INTRODUCTION AND BACKGROUND
REVIEW OF RELATIONSHIPS BETWEEN LAKE ACIDITY AND
MERCURY ACCUMULATION IN FISH
The accumulation of mercury by fish and the potential human health
i
effects of eating mercury-contaminated fish have beeni well documented
(e.g., NAS, 1978), although elevated mercury concentrations in fish from
dilute, low-pH lakes have only recently been associated with increased
lake acidity (Akielaszek and Haines, 1981; Hakanson et al., 1988; Helwig
and Heiskary, 1985; Wiener, 1983; Wren and MacCrimmon], 1983; Lathrop et
al., 1987). Nevertheless, there now is ample evidenc^ to document that
elevated levels of mercury are found in fish from lakes in remote areas
with no known point sources of mercury and that an apparent relationship
exists between lake pH and fish mercury content. j
Several important water quality and biological variables affect
mercury accumulation in fish. These factors include inercury speciation
(Huckabee et al., 1979; Bjornberg et al., 1988), rates of methylation
and demethylation (e.g., Jensen and Jernelov, 1969; Steffan et al.,
1988), age and growth patterns of fish (e.g., Scott and Armstrong,
1972), the concentration of dissolved organic carbon (DOC) (e.g., Bodaly
et al., 1984), and nutrient supply to lakes (e.g., Halkanson, 1980). The
effects of these factors may be interrelated. j
Richman et al. (1988) have discussed the following six hypotheses
on why fish in acidic or low acid neutralizing capacity (ANC) lakes
exhibit higher mercury levels than fish from circumneutral lakes, often-
in the same region.
1. Mercury may enter the watershed with acidic!deposition (i.e.,
acid rain is also polluted with Hg). i
2. The acidification of lake water may mobilize both existing
sediment-bound Hg and Hg present in the surrounding watershed.
This would increase the amount of Hg available for methylation
and bioaccumulation.
3. Lower pH may favor the production of monomethyl Hg (the more
available form to biota) over dimethyl Hg. j
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4. Acidic conditions may alter the rates of Hg methylation and/or
demethylation by microorganisms.
5. The biological conditions characteristic of an acidic lake may
be important to Hg transfer and bioaccumulation of Hg at
different ecosystem levels.
6. The biota in acidic lakes may be more efficient bioaccumulators
of Hg compared with biota from circumneutral lakes, or they may
be unable to excrete Hg at an efficient rate.
Each of these hypotheses has a reasonable scientific basis and
published work to at least partially substantiate it; however, none
taken alone or even in combination adequately verifies that acidifica-
tion of lakes or associated increases in atmospheric deposition of
mercury account for observed bioaccumulation in fish. For example, the
addition of mercury from atmospheric sources does not explain why the
fish in lakes with very similar deposition rates have very different
mercury levels. A variety of organic and inorganic chemical conditions
and physical properties of sediment may regulate mercury mobilization
from sediments. Factors associated with acidification may prevail only
under certain conditions. Conflicting evidence exists regarding the
direct effect of pH,on chemical speciation of mercury (i.e., increased
methyl mercury at lower pH) and there is undoubtedly an interaction with
microbially-mediated methylation that probably accounts for the majority
of increased methyl mercury in the water column under lower pH condi-
tions (Xun et al., 1987) as well as increased release of methyl mercury
from sediment (Ramlal et al., 1985).
Among other water quality parameters correlated with elevated
mercury in fish, dissolved organic carbon (DOC) is probably the most
frequently cited (McMurtry et al. 1989; Helwig and Heiskary 1985; Grieb
et al. 1990). However, DOC has been hypothesized to decrease following
acidification of drainage lakes (Driscoll et al. 1989). The DOC
correlations in seepage or dystrophic lakes may reflect natural, but
heretofore undetected, processes rather than association with acidic
deposition.
Biological consequences of acidification that may alter food chains
(Schindler et al. 1985) and change fish population structure (resulting
in fewer, but older and larger fish) could result in more mercury being
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"available" to accumulate in fewer fish. This relationship is unclear
at present and probably interacts with other variables (Richman et al.
1988). Indirect effects of chemical changes on biota--notably the
increased permeability (Brown, 1983) of fish gill and other membranes
may facilitate mercury uptake of fish. This has been demonstrated
experimentally (Rodgers and Beamish, 1983), albeit at calcium levels at
the high end of the range found in low ANC or acidified lakes. The
lower calcium levels typical of low ANC systems may be a factor
influencing mercury uptake. I ,
The correlative evidence that exists today between fish mercury
content and acidic deposition or acidity status of lakes is inadequate
for extrapolating any prediction beyond the data sets from which the
correlations are derived. Few, if any, causal relationships have been
verified experimentally. It is probable that if dose/response relation-
ships having predictive value can be derived, they will necessarily be
specific to geographic region, lake type, fish species, and fish size,
and perhaps dependent upon fairly detailed knowledge of other physical
and chemical characteristics in lakes. j
1.2 HUMAN HEALTH CONCERNS
There is concern about the potential indirect effects of acidic
deposition and resultant mercury accumulation in fish on human health.
The risk is to that portion of the human population that may consume
fish with elevated or unsafe levels of mercury in muscle tissue.
Mercury is an extremely toxic element that produces a variety of central
nervous system disorders and even causes death in extreme cases.
Documented cases of illness and death in humans have been associated
with consumption of fish caught near point sources pf industrial pollu-
tion (NAS, 1978; Nriagu, 1979). However, elevated levels of mercury
have been reported in the blood and hair of tourists eating fish from
lakes in Minnesota with high mercury levels, as well as in fish
consumers versus nonconsumers in the region (Phelps, et al., 1980). No
clinical symptoms of mercury poisoning existed in either case. However,
the lakes in question have no known point source of mercury pollution.
The U.S. Food and Drug Administration (FDA) has set an action level
of 1.0 ppm methyl mercury as the limit for human consumption (Phillips,
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et al., 1987). This limit was increased in 1978 from 0.5 ppm of total
mercury. Many state fishery agencies in the United States have estab-
lished advisories regarding consumption of fish with mercury levels that
do not exceed the action level of 1.0 ppm, usually invoking the previous
action level of 0.5 ppm. The World Health Organization (TOO) level is
0.5 ppm. In Canada, the province of Ontario has some restriction on the
harvest and/or consumption of fish in a large portion of the 1,500 lakes
(there are approximately 250,000 lakes in Ontario) where mercury data
are available (Ontario Ministry of the Environment, 1989). The
restriction may only involve a single species or size of fish in some
cases; however, elevated mercury levels have necessitated some form of
regulation in hundreds of lakes. This may, in part, reflect of the
Canadian standard of 0.5 ppm--one-half the U.S. action level. Thirty
percent of fish from low-ANC lakes in northeastern Minnesota had fish
mercury levels over 0.5 ppm while only 8% exceeded the 1.0 ppm FDA
action level. The Minnesota Health Department considers 0.5 ppm mercury
of concern for long-term consumption (Helwig and Heiskary, 1985).
In the northeastern United States, generally regarded as the area
in the United States most impacted by acidic deposition, elevated levels
of mercury in fish have been reported to be higher in acidic than non-
acidic lakes (Sloan and Schofield, 1983). Documented levels of mercury
in these lakes and others in the region (Bloomfield et al., 1980 - New
York; Akielaszek and Haines, 1981 - Maine) are of concern to health
agencies, although they typically do not exceed the 1.0 ppm action
level. None of these waters have known direct industrial sources of Hg,
deriving their mercury either from natural weathering or anthropogenic
sources via atmospheric deposition.
1.3 OBJECTIVES OF THE SUBREGION 2B SURVEY (UPPER PENINSULA
OF MICHIGAN)
Forty-nine drainage and seepage lakes in the upper Michigan
peninsula were sampled in conjunction with Phase II of the U.S.
Environmental Protection Agency's (EPA) Eastern Lake Survey (ELS-II) to
explore the relationship between chemical and physical characteristics
of lakes and mercury concentrations in fish tissue. The lakes were
selected using a stratified random design weighted for low pH so that
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acidification effects on mercury accumulation could be evaluated. Lake
selection also covered the full range of DOC values ,from Phase I of the
Eastern Lake Survey (ELS-I). By coupling this study to ELS-I (Landers
et al., 1988; Eilers et al., 1988) and ELS-II (Cusimano et al., 1988),
we were able to examine the role of chemical and physical lake variables
on the assimilation of mercury by fish. The study differs markedly from
previous work because the lakes were randomly selected from a subset of
lakes in Subregion 2B weighted as mentioned previously. This selection
procedure was designed to allow extrapolation of results to the popula-
tion of lakes in this subregion. The study included both drainage and
seepage lakes, providing the opportunity to investigate differences in
the accumulation of mercury between lake types as well as other chemical
and physical factors that affect accumulation. Because of the concern
for potential human health effects, our focus was on game fish species.
However, we also report mercury levels for some nongame species as well.
Specific objectives of this study were to: ;
1. Archive tissue samples for representative ages of fish species
collected during ELS-II. i
2. Measure total mercury concentrations in selected fish samples.
3. Identify relationships between fish tissue! mercury levels and
water quality and lake-watershed characteristics, using statis-
i
tical and deterministic approaches. |
4. Estimate the number and percentage of lakes in the region in
which game fish have mercury levels exceeding human health
action levels. |
Subregion 2B, encompassing the majority of the Upper Peninsula of
Michigan plus a small portion of northern Wisconsin, was chosen for the
ELS-II survey of fish community status and mercury levels because of (1)
the high proportion of acidic and low pH lakes in the subregion, (2) the
relative lack of existing data on fish communities in lakes in the area,
and (3) the diversity of geological and hydrological conditions in the
subregion, allowing optimal evaluation of the association between lake
characteristics and observed mercury levels in fish! from those lakes.
However, it should be emphasized that the National Surface Water Survey
(NSWS) was a survey, not a process-oriented, cause ;ancl effect research
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program. The emphasis was on developing a regional perspective on the
current status of aquatic resources with regard to the potential impacts
of acidic deposition.
1.4 REPORT FORMAT
This report is divided into five sections and an appendix:
• Section 1, Introduction and Background
• Section 2, Study Area and Methodology - describes the lake
selection criteria, fisheries sampling protocols, tissue
processing and mercury analysis, and statistical procedures.
• Section 3, Results - contains an overview of phase I and II ELS
findings including lake 'chemical characteristics and fish
species distribution as well as relationships between mercury
levels in fish and various biological and chemical factors.
• Section 4, Discussion - provides interpretation of findings in
relation to other geographical areas, potential factors
affecting mercury accumulation in fish, and human health
concerns/research needs.
• Section 5, References
Appendix - listing of individual fish mercury data by lake.
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2. STUDY AREA AND METHODOLOGY
i
This study was conducted in conjunction with the U.S. EPA's Eastern
Lake Survey - ELS-II Fish Survey. The Fish Survey involved sampling for
fish in 49 lakes located in the upper peninsula of Michigan and a small
portion of northern Wisconsin (Figure 2-1). This area is predominately
precambrian shield bedrock overlain by relatively shallow sandy soils.
The lake selection and sampling and analysis procedtires are described in
detail in Cusimano et al. (1988). The physical characteristics of the
lakes are summarized in Table 2-1, based on data collected during Phase
I of the Eastern Lake Survey (ELS-I) (Linthurst et al., 1986).
2.1 LAKE CHARACTERISTICS
A large percentage of the study lakes were acidic because the
selection procedure for the ELS-II Fish Survey lakes was weighted to
favor low-pH systems. For example, 11.3% of ELS-I lakes had acid
neutralizing capacities (ANC) < 0 ueq/L and 9% had pH values £ 5.0
(Eilers et al., 1988), whereas 41% of the ELS-II lakes had ANC < 0
ueq/L, and 25% had pH values < 5 (Cusimano et al., 1988). The pH range
for the study lakes was 4.4 to 8.2, with a mean valxie of 6.0 and a
median value of 5.8. The mean and median pH valuesjfor the seepage
lakes (mean pH = 5.6; median pH = 5.2) were 1 and l!6 units lower,
respectively, than the corresponding values for .theidrainage lakes (mean
pH = 6.6; median pH = 6.8). The ANC range was -48 to 2,726 ueq/L, with
a mean of 265 ueq/L and a median of 25 ueq/L. The ANC values were also
substantially lower in seepage lakes, with mean and;median values of
90.3 and 8.4 ueq/L as compared to corresponding values of 528 and 143
ueq/L for drainage lakes. Although the study lakesiemphasize low-pH and
I
low-ANC systems, a wide range of values was covered in the total sample.
For example, 10% of the lakes had ANC values > 1,000 ueq/L, and 18% had
ANC values > 500 ueq/L. i
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. WISCONSIN
Figure 2-1. Location of ELS-II lakes sampled for fish mercury content,
summer 1987.
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Table 2-1. Physical Characteristics of the 49 Sampled Lakes
Parameter
Mean
Median
Range
Std. dev.
Physical characteristics
Lake surface area (ha) 18 9
Site depth (m) 6.5 4.3
Secchi depth (m) 2.6 2.3
Elevation (m) 332 282
Watershed area (ha), 1,376 60
4 - 262
1.5 - 20.1
i
0.815 - 7.6
j
220 -- 546
'; ]
10 !- 54,500
37
5.3
1.3
100
7,794
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2.2 FISHERIES COLLECTIONS AND SAMPLE PROCESSING
The following species were classified as game fish for the purposes
of this study, and are listed in order of priority for analysis of fish
mercury content.
Species
Walleye (Stizostedion vitreum)
Yellow perch (Perca flavescens)
Northern pike (Esox lucius)
Lake trout (or splake) (Onchorhyncus namaycush)
Smallmouth bass (Micropterus dolomeui)
Largemouth bass (M. salmoides)
Brook trout (Salvelinus fontinalis)
Rainbow trout (0. mykiss)
Legal Size Limit (mm)
*
380
150*
500
250
300
300
250
250
No legal size limit established for yellow perch; size specified
represents minimum size likely to be consumed.
Species were ordered for analysis based on the likelihood that fish
consumed may have high levels of mercury, with a secondary criterion
based on the expected extent of distribution in lakes to be sampled. In
addition to these game species, mercury data for white suckers
(Catostomus commersoni), obtained in a companion study (Grieb et al.,
1989) funded by the Electric Power Research Institute (EPRI), are
reported here. The EPRI study focused on "index species", particularly
yellow perch and white sucker because of their more widespread distri-
bution in the study lakes. This focus on index species (those species
available in most lakes) allowed the most comprehensive examination of
the relationship between lake variables and fish mercury levels. We
discuss only the yellow perch results in this report, because white
sucker did not tend to have high mercury levels and are not classified
as game fish.
Forty-nine lakes were sampled between June 8 and August 30, 1987.
The fish were collected using variable mesh monofilament nylon gill .
10
-------
nets, trap nets, beach seines, and hook and line fishing (Landers, 1987;
Cusimano et al., 1988). The sampling effort varied jwith lake area,
ranging from three to eight net sets for each gear type. The gill and
trap nets were set for 10 to 29 hours. Each seine haul covered approxi-
mately 20 m of shoreline. Angling was conducted for 2 hours in each
lake. j
Following collection, individual fish from the target species of
interest were selected based on the established priority and measured
for total length and weight, and then sorted into several length
classes. The fish were weighed using a portable electronic balance
for the small fish (< 0.5 kg) and a spring balance for the larger fish
(> 0.5 kg). Fish that were damaged or showed signs of decomposition
were excluded from the samples. :
Fish from each lake were divided into 3 length classes by species,
and 10 to 15 fish from each length class (when available) were randomly
selected for aging. Individual whole fish were wrapped in aluminum
foil, labeled, and placed on ice as soon as practical after collection.
Specimens were subsequently frozen within 24 hours of collection. All
field collections and sample processing in the field were conducted by
personnel of Michigan State University and Lockheed Engineering and
Sciences Company, Las Vegas, NV.
Age was determined by the Wisconsin Department; of Natural Resources
using the cleithrum for northern pike, the first or! second pectoral fin
ray for white suckers, scales taken just below the ilateral line below
the insertion of the first dorsal spine for yellow Iperch, largemouth
bass, and smallmouth bass, and scales taken just posterior to the dorsal
fin and above the lateral line for trout.
After the size distributions were determined for each lake, small,
medium, and large gamefish classes were selected fo.r mercury analyses
based on their prevalence in the study lakes. The large size class
included the largest available fish for the two highest priority game
species collected. Mercury analysis in a range of jsiae classes was
intended to support the development of regression equations that would
allow some extrapolation to sizes not represented in the sample. For
index species, following preliminary aging, five fish (when available)
were randomly chosen from selected age classes, species, and lakes for
11
-------
mercury analyses. The total numbers of fish analyzed were 546 yellow
perch, 86 northern pike, 110 white sucker, 73 largemouth bass, 4
smallmouth bass, 8 walleye, and 26 brook trout. With the exception of
yellow perch, neither game fish nor index species were captured in
sufficient numbers to meet all sample design criteria.
2.3 LABORATORY AND ANALYTICAL PROCEDURES
2.3.1 Sample Preparation
The fish selected for the mercury analyses were sent frozen on dry
ice to Cornell University for tissue sample preparation. Processing
time was kept as short as possible and all samples were refrozen to
-20°C immediately after processing.
2.3.1.1 Cleaning Procedures
All equipment was cleaned before and after each use, according to
the following protocol.
1. Soak 1-oz (or 4-oz) glass vials in hot tap water with detergent for
60 minutes.
"f(
Note: Use only glass containers to wash vials. Mercury or inter-
ference contamination may result from the use of plastics.
2. Wash vial caps in hot water with detergent and then rinse them with
hot tap water and let air dry.
3. Rinse vials three times with tap water.
4, Rinse once with a nitric acid (ULTREX metal free HNO,,) solution-
:H0 -1:6 3
5. Rinse three times with distilled water.
6. Rinse with acetone (redistilled if possible).
7. Air dry on clean paper towels in a dust-free area.
8. Place a tape label with a sample number on each vial (to make
secure, be sure tape overlaps on back of bottle).
9. Cap the vials, after they are dry, by placing acetone-rinsed
aluminum foil between the vial and cap.
Note: Rinse the shiny side of the aluminum foil with acetone.
12
-------
I
10. Pre-weigh vial and cap to nearest 0.001 g; record the weight on the
vial label and in a lab notebook along with the sample identifica-
tion number.
2.3.1.2 Sample Preparation
In the preparation of samples, care was taken to avoid contamina-
tion, as small quantities of contamination are detectable at the levels
of mercury that were to be measured (ppb). The preparation was carried
out in a clean, dust-free area. Rigorous procedures were followed for
the preparation of each sample, according to the following protocol.
1 Remove the specimen from the freezer, leaving it wrapped, and let
it partially thaw, approximately 15 minutes for medium size
specimens, to make it easier to cut.
2 Remove the specimen from its wrapping and place.it on a clean glass
dissecting tray with the left side of the fish facing up and the
nose pointing toward the left. j
3 Cut vertically, with a clean stainless steel scalpel, from anterior
to the dorsal fin down to the lateral line; cut vertically from
lust posterior to the gill covering down to the lateral line, then
cut along the lateral line joining the two vertical cuts. For
consistency of comparison, the same tissue segment needs to be
removed from each fish. (Figure 2-2).
4. Scrape the muscle tissue off the bone and off the skin using a
stainless steel scalpel and forceps. ,
7.
8,
Place the muscle tissue in a pre-labeled, clean 1-oz glass vial.
Note:
If muscle tissue sample is too large to jfit in a 1-oz glass
viaT, place it in a pre-weighed clean 4-oz jar, Homogenize (using
viai, y.La.^G j-i- -1-" «• r-~ o -- - . , .
Polytron tissue grinder with teflon head) the tissue in the 4-oz
iar and then, after determining the weight or volume of the tissue,
place approximately 20 g into a pre-weighed, pre-labeled, clean
1-oz jar. The excess tissue in the 4-oz jar may be discarded.
Within the jar, homogenize the muscle tissue to a smooth, creamy
consistency. |
Cap the vial with acetone-rinsed aluminum foil-lined caps.
Weigh the muscle, vial, and cap. Record the weight in the lab
notebook with the appropriate sample identification.
1
9. Place the sample in the freezer. j
10. Clean equipment, dissecting tray and instruments after each sample,
13
-------
dm
vm
Dissection of a fish for metal analysis
s - skin
dm - dorsolateral muscle
vm - ventrolateral muscle
Figure 2-2. Diagram of location from which muscle tissue was removed
from fish for mercury analysis.
14
-------
a. Wash in hot tap water with detergent. i
b. Rinse three times with hot tap water.
c. Rinse once with 10% nitric acid, HNC>3:H20 = 1:9
d. Rinse three times with distilled water. ,
e. Rinse with acetone.
f. Let air dry. j
i
2.3.1.3 Preservation of Subsample i
It was important that the integrity of the contaminant in the
sample be maintained until analysis could be performed at Syracuse
University. Processing time was as short as possible and aliquots were
refrozen immediately. ;
The samples were shipped frozen to Syracuse University for total
mercury analyses and to Battelle Pacific Northwest Laboratory for
methylmercury analyses and an interlaboratory comparison.
I
i
2.3.2 Total Mercury Analyses ;
Samples were thawed and up to 1.2 g of wet tissue homogenate was
measured into 250 mL Pyrex boiling flasks. Twenty-five mL of concen-
trated sulfuric acid was added to each flask, which was capped with a
glass bead-filled packed air condenser tube, and placed in a 70°C water
bath for 30 minutes. Then, 3 mL of 30% hydrogen peroxide was added to
further the digestion, and the flasks were returned |to the water bath
for an additional 2 hours. ;
The flasks were removed at the end of the digestion period and
cooled in an ice bath to room temperature. Then, 100 mL of distilled
water was added to each flask through the air condenser to recover any
mercury that might have volatilized and been retained within the con-
denser during the digestion process. After the addition of the
distilled water, the flasks were further cooled to near room tempera-
ture . The condenser tubes were removed, and 4 mL of 5% potassium
I
permanganate was added to each flask to oxidize the itissue mercury to
9 +
the mercuric (Hg ) form. The flasks were stoppered until analysis.
15
-------
Mercury was analyzed with a LDC/Milton Roy Mercury Monitor
(Elemental Mercury Detector - #920404), a high-performance UV photometer
that allows for continuous and quantitative absorbance of mercury vapor
at 253.7 nm in a gaseous stream (nitrogen) referenced to a mercury-free
stream.
Twenty milliliters of hydroxylamine hydrochloride/sodium chloride
solution (15 g of each diluted to 1 L of distilled deionized water) to
reduce excess potassium permanganate was added followed by 10 mL of 10%
2+
stannous chloride to reduce Hg to elemental mercury. The samples were
immediately purged with a nitrogen stream through the mercury monitor
sequentially, and peak heights were recorded.
The detection limit, defined as three times the standard deviation
of 10 nonconsecutive reagent blank and 10 calibration blank analyses,
was 0.002 ppm (ug/g, wet weight). Each batch of 8 to 14 fish samples
analyzed was preceded by a reagent blank, a calibration blank, and four
standards. An analytical triplicate, a matrix spike, and a National
Bureau of Standards (NBS) tuna audit sample were analyzed for every 30
samples. The monitor was calibrated every day before the analysis of
tissue samples. Output peaks from sample determinations were then
compared to a calibration curve obtained from regression analysis of
standard concentrations and standard peak heights to determine tissue
concentrations. Fish samples that did not fall within the concentration
range of the standards were reanalyzed using a more appropriate amount
of tissue.
2.3.3 Quality Assurance - Precision and Accuracy
Fish collected in a pilot study from Lake Rondaxe and White Lake in
the Adirondacks, for which we had prior Hg level analyses, were used as
field blanks. Field blanks were dispersed from Cornell University and
handled the same as the ELS-II samples in the field (i.e., weighed,
measured, packaged). Values of concentration difference (initial sample
concentration minus field blank concentration) were very low (mean -
0.006 ppm; range =0.0 - 0.015 ppm; n = 12), suggesting little or no
contamination of fish samples with mercury associated with sample
collection, handling, and processing. One out of every 30 samples was
16
-------
digested and analyzed in triplicate. The coefficient: of variation
ranged from 0.0 to 17% with a mean of 3% (n = 30).
Samples of NBS albacore tuna with known concentrations of total
mercury (0.95 ± 0.1 ppm) were analyzed and showed good agreement, with
mean concentration of 0.88 ppm (SD = 0.03 ppm; rangej= 0.80 - 0.94 ppm;
n = 39). i
Ten samples of muscle tissue were split and analyzed for total
mercury at Syracuse University and Battelle Pacific Northwest
Laboratory. Results of this interlaboratory comparison showed good
2
agreement between analytical determinations (r = 0.92).
Throughout this study, analyses of standards were very consistent,
2 2
showing a strong linear relationship (mean r = 0.9992; range r =
0.9956 to 1".0; n = 99) and slope (mean slope = 205.71 abs/ppm; SD = 10.7
abs/ppm; range = 186.4 - 224.3 abs/ppm). The observed concentrations of
the calibration blanks were consistently at 0 ppm and thus were less
than or equal to twice the required detection limit.; Reagent blank
concentrations were always at 0 ppm. :
Every 30 samples, a small amount of the NBS albacore tuna was added
to a fish sample, and the percent spike recovery was calculated with
recoveries ranging from 86% to 115% with a mean of 99% (SD =5.4
•
percent; n = 30).
As part of the companion EPRI study, 30 fish were also analyzed for
methyl mercury by Battelle Pacific Northwest Laboratory to test the
assumption that most of the mercury in fish was in the methyl form.
Yellow perch were selected for most of these analyses (24 out of 30),
although 3 of each species of northern pike and whitje sucker were ana-
lyzed. A range of age classes, pH values, DOC value's, total mercury
levels in fish, and lake types (seepage and drainage;) were included in
this subsample. Methyl mercury procedures were those described in Grieb
et al. (1990). Thirteen of the 30 samples were alsp analyzed for total
mercury by Battelle and almost all of the total mercury in these fish
was methyl mercury. Neither inorganic nor dimethyljmercury was detected
(less than 0.02 ug/g, wet weight basis) in any of the samples. The
correlation between methyl mercury measured at Battelle Pacific
Northwest Laboratory and total mercury determined at Syracuse University
is shown in Figure 2-3. The methyl mercury fraction of the total
17
-------
Y = 0.003 + 0.95 X r = 0.92
Total Mercury (jug/g)
Figure 2-3. Relationship between total mercury and methyl mercury in a
subsample of 30 fish from ELS-II lakes (from Greib et al
1990).
18
-------
mercury averaged 99%. Huckabee et al. (1979) have reported similar
findings.
2.3.4 Statistical Analyses j
Correlation and simple linear regression analyses were used to
identify factors affecting mercury concentrations in yellow perch,
northern pike, white sucker, and largemouth bass. These species were
collected from 37 of the 49 lakes sampled. The numbers of seepage and
drainage lakes with fish were 18 and 16, respectively, the remaining 3
lakes included a reservoir, a closed lake (i.e., no outlet), and a lake
classified as a seepage lake that was excluded from; the analyses due to
exceptionally high dissolved silica and ANG values (perhaps a
i
groundwater flow-through system). j
i
Pearson product-moment correlation coefficients were calculated
between fish mercury concentrations and fish age and size characteris-
tics, and between fish mercury concentrations and water quality vari-
ables. For the evaluation of the relationships between mercury concen-
trations and fish age and size, correlation coefficients were calculated
I
using fish from all 37 lakes from which yellow perch, northern pike,
white sucker, and largemouth bass were collected. The analyses of the
relationships between fish mercury concentrations and water quality
variables were then limited to a subset of 27 lakes that contained
yellow perch in age classes 2 to 4. The only species widely distributed
among both seepage and drainage lakes was yellow perch, and it was
necessary to combine samples from age classes 2 to J4 to obtain at least
3 fish from each lake. The subset of 27 lakes consisted of 15 seepage
lakes and 12 drainage lakes. i
In addition to simple correlations, multiple stepwise regressions
were performed between fish mercury concentrations and lake chemical and
physical characteristics. Variables were included in the models if they
2
had significant coefficients (p £0.05) and increased the r .
Partial correlations were performed in order to determine the
influence of lake chemistry and lake physical characteristics on fish
mercury concentrations with the effects of fish sizje and age removed.
The partial correlations removed the variance due to fish size then
19
-------
correlated the remaining variance with the physical and chemical
variable of interest.
In order to distinguish what set of variables best discriminates
between high- and low-mercury levels in yellow perch, the fish were
classified into two groups and a discriminate analysis was performed.
The groups were low-mercury fish (Hg < 0.5) and high-mercury fish
(Hg >0.5). For these two groups, a discriminate function based on
physical and chemical variables was developed using a stepwise method.
The Mann-Whitney rank-sum test was employed to determine if mercury
levels, at different ages, In drainage lakes were significantly differ-
ent from those in seepage lakes. Statistical analyses were conducted
using SPSS (Nie et al., 1975) and BMDP (Dixon, 1985) on a VAX mainframe
computer.
20
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3. RESULTS |
i
3.i OVERVIEW OF ELS-I&ELS-II FINDINGS j
The results of ELS-I and ELS-II regarding lake chemistry and fish
i
community status have been described in considerable detail by Eilers et
al. (1988), Landers et al. (1988), and Cusimano et al. (1988). Most
recently, Cusimano et al. (1988) discussed the relationship between
ELS-I and ELS-II and provided an overview of the fish community status
in ELS-II Subregion 2B lakes. The salient aspects of those findings and
discussion are repeated here because they provide important background
information for the presentation and discussion of the mercury results.
3.1.1 Lake Chemical Characteristics |
The primary objective of the ELS-I was to characterize the popula-
tion of lakes expected to have low ANC in selected areas of the eastern
United States. During the design phase of the ELS-I, researchers
recognized that the effects of both temporal and spatial variability in
lake chemistry could compromise the survey results, iThe within-lake
variability had to be minimized in order to observe differences among
lakes. An attempt was made to overcome the effects of temporal and
spatial variability through the use of the "index" concept. The index
concept rests on two major assumptions: (1) that the chemical charac-
teristics of a lake can be related to other lakes by sampling at a time
when within-lake variability is minimized and (2) that the index sample
is representative of within-lake chemistry and can be related to
chemical conditions in the lake in other locations and at other times of
the year. j
During ELS-I, a single water sample was collected at 1.5 m depth in
the deepest part of the lake during fall overturn (i.e., the fall period
when lakes were well mixed). A review of sampling seasons indicated
that the fall mixing period would provide the most appropriate index
period for sampling because of the low temporal and ispatial variability
in lake chemistry (Landers et al., 1988). It is obvious that one
sample, at one location in the lake, at one time on one day, and at a
specific season of a particular year is incapable oficharacterizing the
21
-------
complex chemical or biological dynamics of the sample lake. However,
the purpose of the survey was to characterize geographical areas, not
the dynamics of individual lakes. The chemical analysis for the fall
1984 sample provides a representative index of lake chemistry, which can
be compared with the chemistry of other lakes sampled in order to detect
regional patterns.
For the most part, the ELS-II data on fish mercury levels in
Subregion 2B reported here are interpreted relative to the ELS-I index
of lake chemistry collected in fall 1984. Relatively few measurements
of water chemistry were collected coincident with the ELS-II fish
surveys in summer. 1987, largely because of the high variability in lake
chemistry expected to occur over the 3-month sampling period (8 June to
30 August 1987). Since fish grow and live through a number of years,
fish mercury levels in 1987 were expected to reflect both past and
present-day physical and chemical conditions in the lake. Thus, the
1984 ELS-I measure of lake chemistry was selected as the best available
index of the chemical conditions to which fish populations had been
exposed. We recognize that the ELS-I data may not be direct measures of
chemical conditions during specific times and locales critical to
mercury accumulation by fish, but we assume that the ELS-I index
chemistry is at least correlated with these water quality values of
interest. We did not examine fish from a resurvey of 10 lakes conducted
in late August and early September for mercury. However, we did
correlate fish mercury levels with collection time and found no effect
on measured mercury levels.
The chemical characteristics of the ELS-I and ELS-II target popula-
tions and the 49 lakes sampled during ELS-II in Subregion 2B, based on
the ELS-I index sample, are summarized in Table 3-1. Lake-specific data
for each of the 49 lakes sampled are presented by Cusimano et al.
(1988).
Tn ELS-I, Subregion 2B was estimated to have the highest percentage
of acidic (11.3%) and low-pH lakes (9.4% with pH < 5.0) in the Upper
Midwest Region. In addition, lakes selected for sampling for the ELS-II
were specifically weighted to favor systems with low pH. Forty-one
percent (40.8%) of the lakes sampled were acidic, with ANC < 0 ueq/L,
and 24.5% had pH < 5.0. Thus, the proportion of low-ANC and low-pH
22
-------
Table 3-1. Chemical Characteristics of Lakes in Subreglon 2B, for the
ELS-I Target Population (N = 1050), ELS-1I Target Population
(N - 597), and the 49 Lakes Sampled for Fish During ELS-II
Variable
ANC
(ueq/L)
pH
Ca
(ueq/L)
Mg
(ueq/L)
Na
(ueq/L)
K (ueq/L)
Sum Base
Cations
(ueq/L)
Ext. Al
(ug/L)
DOC
(mg/L)
Color
(PCU)
SO
(ueq/L)
SiO
(rng/L)
Total P
(ug/L)
ELS-I Target
Population
Median Range
284 -49-2726
7.10 4.43-8.58
246 13-1826
148 11-984
29 3-245
13 3-30
468 54-2966
3 0-213
6.8 0.2-28.8
31 5-345
78 16-281
2.3 0.0-17.6
13 0-146
ELS-II Target
Population
Median Range
164 -48-2726
6.93 4.43-8.25
179 22-1826
95 13-984
25 3-171
14 5-30
>
282 54-2966
5 0-213
9 0.2-15.0
28 5-125
77 16-161
2.1 0.0-12.3
12 0-39
23
Lakes Sampled for
Fish in ELS-II
Median Range
25. - 48-2726
5.75 4.43-8.25
51 22-1826
32 13-984
12 3-171
12 5-30
119 54-2966
11 0-213
4.7 0.2-13.9
25 5-125
67 17-161
0.3 0.0-9.6
12 0-39
-------
lakes among the 49 ELS-II Fish Survey lakes is distinctly higher than
that for either the ELS-I target population or the ELS-II target
population. Ca and pH levels in lakes in the subregion were highly
correlated. As a result, lakes sampled in the ELS-II Fish Survey lakes
also had generally lower Ca levels than either the ELS-I or ELS-II
target populations. Calcium concentrations for the 49 ELS-II Fish
Survey lakes ranged between 22 and 1,826 ueq/L; 49% of the lakes had Ca
levels < 50 ueq/L (1.0 mg/L).
A high proportion of the lakes in Subregion 2B are seepage lakes
(37.7% of the ELS-I target population, 39.8% of the ELS-II target
population, and 59.2% of the lakes sampled in ELS-II). The chemical
characteristics of seepage and nonseepage lakes are contrasted in Table
3-2 for the 49 ELS-II Fish Survey lakes. Levels of ANC, Ca, Mg, Na, sum
of base cations, color, pH, K, DOC, SC>4 and SiO£ were significantly
lower in seepage lakes than in nonseepage lakes. Sixteen of the 20
acidic lakes sampled (80%) were seepage lakes. The high proportion of
seepage lakes in the ELS-II sample is evident in the distinctly lower
median value for Si02 in the 49 sample lakes than in either the ELS-I or
ELS-II target populations (Table 3-1).
Concentrations of extractable (total monomeric) Al measured in
ELS-I were generally quite low. An estimated 80% of the ELS-I target
population had _< 12 ug/L (Eilers et al., 1988). Values for the 49
ELS-II lakes ranged between 0 and 213 ug/L (Table 3-1), although 85.7%
of the lakes sampled had extractable Al levels < 50 ug/L.
Sulfate levels in Subregion 2B (median value 78 ueg/L for the ELS-I
target population) were slightly higher than the levels for other sub-
regions in the Upper Midwest (regional median 57 ueq/L), although lower
than the levels in lakes in the northeastern United States (regional
median 115 ueq/L) (Linthurst et al., 1986, Eilers et al., 1988). Con-
centrations for the 49 ELS-II lakes ranged between 17 and 161 ueq/L,
with a median value of 67 ueq/L. Sulfate concentrations in the 49 lakes
sampled were similar to, although slightly lower than, values for the
ELS-I and ELS-II target populations (Table 3-1).
Correlations among selected water quality variables in 27 lakes
where age 2-4 yellow perch were collected, as well as separate analyses
for drainage lakes and seepage lakes in this group, are presented in
Tables 3-3, 3-4 and 3-5, respectively. These results were generally
24
-------
Table 3-2. Comparison of Lake Chemistry by Lake Type: Seepage Lakes
Versus Other Lake Types (Drainage, Reservoir, Closed) for
the 49 Lakes Sampled During ELS -II
Seepage Lakes Other Lakes
Variable Median Range Median Range
pH 5.23 4.43-8.25 6.79 4.74-8.03
ANC -1 -46-1665 134 -20-2699
(ueq/L)
Inorg. Al 9 0-192 12 0-39
(ug/L)
Ext. Al 11 0-213 10 0-120
Ca 38 22-860 131 35-1826
(ueq/L)
Mg 26 13-766 83 16-984
(ueq/L)
Na 10 3-34 25 6-171
(ueq/L)
K (ueq/L) 10 5-21 14 5-30'
Sum Base 88 50-1680 254 68-2960
Cations
(ueq/L)
DOC 4.0 0.2-10.3 6.5 2.5-13.9
(mg/L) .
Color 21 5-80 37 10-125
(PCU)
SO, 60 17-144 85 48-161
(ueq/L)
SiO 0.1 0-3.2 2.2 0.2-9.6
(mg/L)
Total P 11 0-39 13 1-35
(ug/L)
«a
Test Statistics
Wilcox. K-S
0.0052 0.0128
0.0014* 0.0227*
>0.05 >0.05
>0.05 >0.05
0.0004* 0.0007*
0.0005* 0.0004*
0.0002* 0.0004*
0.0050 0.0074
0.0004* 0.000?'
0.0070 0.0264
0.0017* 0.0237
0.0101 >0.05
0.0001* 0.0001*
>0.05 >0.05
aCalculated p values for non-parametric comparisons of seepage lakes
versus other lake types using the Wilcoxon rank sum (Wilcox.) and
Kolmogorov-Smirnov (K-S) tests. Asterisks indicate statistical
significance at <* = 0.05 adjusted for 14 tests, i.e., p <' 0.0036.
25
-------
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consistent with the relationships expected between pH, ANC, calcium, and
DOC in drainage and seepage systems. In the drainage systems, pH and
calcium concentrations were highly correlated. Alkalinity and pH were
highly correlated in both lake types.
The relationships between other water quality variables provide
additional evidence of differences in the water chemistry between
drainage and seepage lakes. Sulfate showed a statistically significant
positive correlation with the indicators of lake acidification in the
complete set of lakes, as well as in drainage lakes. However, in
seepage lakes, a negative relationship was found between sulfate and pH.
In drainage lakes, a strong negative relationship was observed between
aluminum and pH, ANC, calcium, and conductivity. In seepage lakes,
these relationships were different. For example, tjie correlation
coefficient between conductivity and aluminum was of approximately equal
magnitude in seepage and drainage lakes, but the signs were opposite.
Additional evidence of differences between the two itypes of lakes was
the observed relationships between total phosphorus^ and aluminum
- |
concentrations. ]
i
3.1.2 Fish Species Distribution ;
Thirty-one fish species were caught in the surveys of 49 lakes in
Subregion 2B (Table 3-6). Yellow perch was the most common species,
collected in 31 lakes. Seven other species occurred in more than 10
lakes, in decreasing order of frequency: largemouth bass, bluegill
sunfish (Lepomis macrochirus), pumpkinseed sunfish( OL.. gibbosus). white
sucker, brown bullhead (Ictalurus nebulosus), golden shiner (Notemigonus
crysoleucas), and northern pike. The remaining 23jspecies were caught
in fewer than 10 lakes, although some of these species were collected in
large numbers in individual lakes. The types of fish caught in this
survey are similar to those reported for lakes in other areas of the
I
Upper Midwest (Wiener and Eilers, 1987). j
The number of fish species caught per lake varied between 0 and 13,
with a median of 3. In two lakes, no fish were caught, and in several
lakes only one species was caught. In one lake only brook stickleback
29
-------
Table 3-6. Fish Species Caught and Frequency of Occurrence ("'denotes game
fish)
Number of Lakes
in Which Species Caught
All Gill Net, Gill
Gear Trap Net Net &
Types & Angling Trap Net
Family and Species
Common name
Salmonidae
Salvelinus fontinalis
Salvelinus namaycush
•ft
brook trout
lake trout
444
111
Osmeridae
Osmerus mordax
Umbridae
Umbri limi
Esocidae
Esox lucius
Cyprinidae
Semotilus atromaculatus
Notemigonus crysoleucas
Notropis cornutus
Notropis atherinoides
Notropis emiliae
Pimephales promelas
Pimephales notatus
Hybognathus hankinsoni
Chrosomus neogaeus
Catostomidae
Catostous commersoni
rainbow smelt
central mudminnow
northern pike
creek chub
golden shiner
common shiner
emerald shiner
pugnose minnow
fathead minnow
bluntnose minnow
brassy minnow
finescale dace
white sucker
11
5
12
7
1
1
3
7
1
6
14
11
11
4
12
7
0
0
2
2
0
5
4
12
7
0
0
2
2
0
5
14
14
30
-------
Table 3-6. Fish Species Caught and Frequency of Occurrence ( denotes game
fish) (cont.)
Number of Lakes
in Which Species Caught
Family and Species
Ictaluridae
Ictalurus nebulosus
Cyprinodontidae
Fundulus diaphanus
Gasterosteidae
Culaea inconstans
Centrarchidae
Ambloplites rupestris
Micropterus dolomieui
Micropterus salmoides
Lepomis gibbosus
Lepomis macrochirus
Lepomis spp .
Pomoxis nigromaculatus
Percidae
Perca flavescens
Stizostedion vitreum
Percina caprodes
Etheostoma nigrum
Etheostoma exile
Cottidae
Co.ttus bairdi
All1 Gill Net, Gill
Gear Trap Net Net &
Common name Types & Angling Trap Net
brown bullhead 13
banded killfish 1
brook stickleback 3
rock bass 4
•k
smallmouth bass 5
*
largemouth bass 17
pumpkins eed sunfish 15
bluegill sunfish 16
sunfish hybrid 3
black crappie 3
*)V
yellow perch 31
&
walleye 2
logperch 1
johnny darter 3
Iowa darter 7
mottled sculpin 1
13 13
0 0
3 3
4 4
4 4
16 13
15 15
13 13
3 3
3 2
31 31
2 2
0 0
0 0
1 1
1 1
31
-------
(Culaea inconstans) were caught. In six lakes (12.2%) only yellow perch
were caught. Game fish were collected in 36 of the 49 lakes (73.5%).
The distribution of fish collected for mercury analysis presented
in Table 3-7 shows clearly that only yellow perch had a very represen-
tative distribution among lakes and age classes. The number of lakes
for which mercury analyses were conducted does not necessarily equal the
number of lakes in which a species was caught. Samples excluded in-
cluded young-of-the-year fish and fish too decomposed for use.
3.2 MERCURY CONCENTRATIONS IN FISH MUSCLE TISSUE
The results of the mercury analyses for each species and age class
are summarized in Table 3-8. The number and percentage of fish exceed-
ing the state and WHO health advisory criterion (0.5 ppm) and the U.S.
Food and Drug Administration (FDA) action level (1.0 ppm) are shown by
age class. The number of mercury measurements on yellow perch exceeded
the measurements for all other species combined. Yellow perch were
abundant in a wide range of age classes at most of the lakes, whereas
the other species were less abundant, particularly in seepage lakes.
The number of lakes in which fish were analyzed for mercury is less than
the total number in which fish were caught, because no game or index
species were captured in some lakes.
Although the numbers of fish analyzed in each age class were dis-
similar, a general trend of increasing mean mercury concentration as a
function of age was evident for all species. For example, although the
average mercury concentration in yellow perch was relatively constant
over age classes 1 through 6, increased concentrations were observed in
age classes 7 through 10+. This trend was also evident in the propor-
tion of samples that exceeded the health criterion. For example, 7.5%
of the age-4 yellow perch had mercury concentrations greater than 0.5
ppm, whereas 26.2% of the age-7 yellow perch had concentrations greater
than this value. Overall, a large proportion of the yellow perch,
northern pike, and largemouth bass exceeded the Michigan state health
advisory criterion. Thirty-three percent of the northern pike and 26%
of the largemouth bass exceeded 0.5 ppm. Fewer fish of all species
exceeded the FDA action level of 1 ppm, but fish with the highest
32
-------
Table 3-7. Number of Lakes with Fish Available forjMercury Analysis by
Species and Age
Age/
Species
0
1
2
3
4
5
6
7
8
9
10
11
12
Total
Yellow
Perch
1
14
24
24
25
22
18
17
17
14
3
8
1
31
White Northern Smallmouth Largemouth
Sucker Pike Bass Bass
_
3
3
8
4
4
7
7
3
1
-
-
-
13
2
1
5
7
6 2
8
3
o
3
- •
1
-
-
8 2 ]
2
1
4
6
E
0
n
2
1
i
-
-
0
Brook
Walleye Trout
-
1 2
1 3
1 1
1 1
-
1
1
1
-
-
-
- -
1 4
33
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mercury concentrations were well distributed among all lakes. Although
not shown in Table 3-8, the concentration of mercury in muscle tissue
for at least one fish of one of these species exceeded the state public
health advisory criterion in 24 of the 38 lakes. Eleven lakes contained
fish with concentrations greater than the FDA action level.
Differences in mercury concentrations among species were also
observed. For example, the mean mercury concentration in age-3 yellow
perch was 0.20 ppm, in comparison to 0.43 ppm for age-3 northern pike.
Likewise, the mercury concentrations in age-5 yellow:perch and northern
pike were 0.27 ppm and 0.47 ppm, respectively. The mercury concentra-
tions in northern pike exceeded the concentrations in the other species
i
in all age classes that had a sample size greater than four fish. On
i
the other hand, the mercury in white sucker tissue samples was rela-
i
tively low in all age classes. The average concentration was 0.16 ppm
or lower in all but age-9 fish, and the maximum concentration was 0.59
ppm in one age-9 fish. In contrast, the maximum concentrations in
yellow perch, northern pike, and largemouth bass were 2.36, 1.64, and
1.00 ppm, respectively. Walleye, which was the number one priority game
fish in the survey, was collected in only two lakes (one and eight
individuals each). The mean mercury concentration in walleye was 0.280;
no walleye exceeded health guidelines and the maximum concentration
i
observed was 0.42 ppm.
3.2.1 Relationships Between Mercury Concentrations and Fish Age,
Weight, and Length j
All game fish species for which sufficient samples were obtained
showed increased mercury concentrations with increased age, length, and
weight. Among these variables, there were species differences as to
|
which showed the strongest correlation with mercury concentrations. In
addition, the strength of these correlations varied among seepage and
drainage systems and among all lakes considered together.
The abundance and wide distribution of yellow perch provided an
excellent opportunity to evaluate the relationships between mercury
concentration and age, weight, and length of fish. The results indi-
cated differences in mercury accumulation between drainage and seepage
lakes (Table 3-9). Although the mercury concentrations were very
37
-------
similar in both types of lakes for yellow perch ages 2 through 5, higher
concentrations were found in older fish from drainage lakes. However,
statistically significant differences in total mercury concentrations in
fish from seepage and from drainage lakes were found only in age-1 and
age-7 yellow perch (Table 3-10). Generally, there were more large
yellow perch with higher mercury concentrations in drainage lakes, and
in seepage lakes there was greater variability in mercury concentrations
in all sizes of yellow perch (Figure 3-1). The slopes of the regression
lines in Figure 3-1 are significantly different (p < .05), with drainage
lakes showing a much greater effect of length on mercury concentration
than seepage lakes. Similar differences existed between seepage and
drainage lakes for age and weight of yellow perch, indicating the impor-
tance of age and size on increasing mercury levels in fish from drainage
lakes. Other game fish showed similar results, with total length
usually providing a stronger relationship to mercury level than weight
(Table 3-11). Age, although often highly correlated with mercury, is an
impractical parameter from a health advisory standpoint.
An examination of mercury levels by fish size for major game fish
species indicates that a high proportion of fish of legal size exceeded
state health guidelines (Table 3-12). For example, from 35% to 100% of
legal size northern pike exceeded 0.5 ppm, and 12% to 85% of yellow
perch over 150 mm exceeded the guideline. These results, if the assump-
tion of representative sampling is accepted, mean that at least from one
in eight to one in three game fish exceed standards.
3.2.2 Relationships Between Lake Characteristics and Fish Mercury
Concentrations
Among game fish species collected for mercury analysis, yellow
perch provided the largest numbers overall and were the most widely
distributed among lakes. Therefore, in terms of relating mercury
concentrations to lake characteristics, perch allow the most comprehen-
sive comparisons. Only yellow perch in age classes 1 and 7 had statis-
tically different mercury levels between seepage and drainage lakes.
Mercury levels in young perch, ages 2 through 4, showed little effect of
age. This group was represented in 27 lakes and was used for initial
comparisons to lake characteristics.
38
-------
Table 3-9. Regression Equations and Correlation Coefficients (R) for
Age, Total Length, and Weight in Yellow Perch (All Ages
Separated by Lake Type)
Lake Type
Seepage
Drainage
All Lakes
Seepage
Drainage
All Lakes
Seepage
Drainage
All Lakes
•&
-= significant
Equation
.021 Age +
.089 Age -
.052 Age +
.0015 TL +
.0049 TL -
.0032 TL -
.0009 WT +
.0037 WT +
.0021 WT +
correlation,
R #
.177 .24
.061 .60
.073 .45
.023 .32
.454 .69
.215 .54
.212 .29
.105 .77
.167 .54
p < .05.
of Fish
312 *
203 *
547 *
312 *
203 *
547 *
312 '*
203 *
547 *
39
-------
Table 3-10. Average Total Mercury Concentration (ppm) in Yellow Perch by
Lake Type and Age Class (Number of Fish)
Age Class
Lake Type
All lakes
Seepage
Drainage
1*
.19
(76)
.20
(63)
.13
(13)
2
0.22
(87)
0.23
(47)
0.22
(39)
3
0.20
(107)
0.22
(59)
0.20
(36)
4
0.25
(67)
0.27
(39)
0.23
(23)
5
0.27
(56)
0.30
(32)
0.25
(20)
7*
0.38
(42)
0.26
(23)
0.54
(17)
8
0.49
(27)
0.45
(11)
0.59
(13)
9
0.56
(22)
0.44
(8)
0.70
(12)
10
.90
(9)
.190
(1)
.989
(8)
11
.94
(9)
.36
(2)
1.51
(4)
rt
Statistically significant difference in total mercury concentrations between
seepage and drainage lakes; Mann-Whitney U Test: age class 1, p - 0.02; age
class 7, p - 0.03.
40
-------
400
400
Total Length (mm)
Figure 3-1.
Comparison of mercury levels in yellow perch from (A)
drainage lakes and (B) seepage lakes in ELS-II lakes.
41
-------
Table 3-11. Correlation Coefficients (r) Between Total Mercury and Age,
Weight, or Total Length for Game Fish Species by Lake Type.
(N «= Number of Fish in Parentheses)
Species
Drainage
Seepage
All Lakes
Yellow Perch
Age
Weight
Length
.60 (202)
.77 (202)
.69 (202)
.24 (310)
.29 (310)
.32 (310)
.45 (545)
.54 (545)
,54 (545)
Largemouth Bass
Age
Weight
Length
.72 (26)
.66 (26)
.82 (26)
.52 (44)
.49 (45)
.52 (45)
.58 (72)
.55 (72)
.63 (72)
Northern Pike
Age
Weight
Length
.44 (66)
.51 (66)
.61 (66)
.94 (8)
.94 (8)
.90 (8)
.48 (85)
.55 (85)
.62 (85)
Walleye
Age
Weight
Length
.93 (8)
.85 (8)
.87 (8)
.93 (8)
.85 (8)
.87 (8)
Brook Trout
Age
Weight
Length
.42 (18)
.65 (18)
.71 (18)
.67 (7)
.78 (7)
.85 (7)
,67 (25)
,77 (25)
,82 (25)
42
-------
Table 3-12. Mercury Levels and Percent Exceeding Health Guidelines for
Three Game Fish Species in Samples From Subregion 2B
Size Class
(mm)
Total
X Hg # Fish
Hg
Min
Hg
Max #>.05 %>.05
#>.!
%>.!
Northern Pike
200-300
301-400
401-500
501-600
601-700
701-800
801-900
901-1000
All
50-100
101-150
151-200
201-250
251-300
301-350
All
.210
.193
.390
.470
.628
.548
1.12
1.00
.471
.237
.205
.269
.446
.890
1.45
.300
2
8
22
23
16
4
1
2
78
23
250
165
49
25
7
519
.13
.10
.20
.17
.30
.40
1.12
.93
.10
Yellow
.00
.00
.00
.00
.05
.29
.00
.29
.33
1.07
1.64
.99
.68
1.12
1.07
1.64
Perch
.44
.97
1.01
1.07
1.89
2.36
2.36
0 0.0
0 0.0
3 13.6
8 34.8
11 68.8
3 75.0
1 100 . 0
2 100 . 0
28 35.9
0
18
20
17
17
6
78
Largemouth Bass
50-100
101-150
151-200
201-250
251-300
301-350
351-400
401-450
All
.132
.201
.170
.261
.576
.553
.628
.595
.375
11
9
2
12
11
13
4
2
64
.06
.12
.16
.15
.20
.21
.36
.55
.06
.20
.35
.18
.44
.81
.88
1.00
.64
1.00
0
0
0
0
8
7
2
2
0.0
7.2
12.1
34.7
68.0
85.7
15.0
0.0
0.0
0.0
0.0
72.7
53.8
50.0
100 . 0
19 29.7
0
0
2
1
0
0
1
1
5
0
0
1
2
13
5
21
0
0
0
0
0
0
1
0
1
0.0
0.0
9.1
4 '•
C
0
100.0
50.0
6.4
0.0
0.0
0.4
4.1
52.0
71.4
4.1
0.0
0.0
0.0
0.0
0.0
0.0
50.0
0.0
1.6
43
-------
The
, 2
Pearson product-moment correlation coefficients were calculated
between the average mercury concentrations in yellow perch ages 2-4 and
water quality variables obtained from the ELS-I (Linthurst et al.,
1986). The variables presented (Table 3-13) are those that have shown
significant relationships with fish mercury in this or other published
studies. Each element of the correlation matrix gives the correlation
coefficient (r), the number of observations (lakes), and the statistical
significance of the coefficient. Correlation coefficients were derived
using regression weighted by the inverse of the variance about the mean
for mercury levels in each lake. All statistically significant (p <
0.05) r values are denoted.
The results for the complete set of 27 lakes indicate consistent,
negative relationships between pH and mercury in muscle tissue.
strongest relationship was that observed between pH and mercury
0.48) in drainage lakes (Figure 3-2). Statistically significant corre-
lations were also observed between fish mercury and total aluminum.
There was no relationship apparent, however, between fish mercury and
DOC, total phosphorus, color, or sulfate.
The results of the correlation analyses between fish mercury and
water quality variables in seepage lakes are different from those
obtained for the drainage lakes, and the influence of the seepage lakes
on the relationships observed in the complete set of 27 lakes is easily
seen. The relationships between fish mercury and pH were similar in all
three sets of lakes, but a negative correlation was also observed
between mercury and ANC in the seepage lakes. Seepage lakes showed no
correlation between aluminum and mercury values.
Seepage lakes also exhibited a negative correlation between fish
mercury and DOC, whereas drainage lakes exhibited no relationships with
DOC. This strong negative correlation between DOC and fish mercury in
seepage lakes was not anticipated based on results of previous studies,
which showed either no relationship (Helwig and Heiskary, 1985; Lathrop
et al., 1987) or a positive relationship between mercury and DOC
(McMurtry et al. 1989). Another distinct difference among these three
sets of lakes was the statistically significant positive correlation
that was observed between fish mercury and sulfate in seepage lakes.
44
-------
Table 3-13. Correlation Matrix for Average Values of Mercury in Yellow
Perch Ages 2-4 and ELS-I Water Quality !Variables Measured
in Seepage Lakes, Drainage Lakes, and Seepage and Drainage
Lakes Combined I
All Lakes Seepage Lake!
Variable (n _ 2?)1 (n _ 15)
* *
pH -0.41 -0.56
•&
ANC (ueq/L) -0.15 -0.55
Calcium -0.12 -0.42
(ueq/L)
Conductivity -0.09 0.18
(u MHOS/cm)
Aluminum 0.38 0.32
(ueq/L)
Total phosphorus -0.27 --0.29
(ug/L)
DOC (mg/L) -0.37 -0.69*
Color (pcu) -0.05 -0.52
ifc
Sulfate (ueq/L) 0.36 0.74
Silica -0.01 -0.56
(mg/L Si02)
Watershed Area/ -0.13 -0.10
Lake Area
Watershed Area -0.19 -0.40
(ha)
Elevation (m) -0.21 -0.28
Lake Size (ha) -0.02 -0.32
•^
n = number of lakes
5 Drainage Lakes
(n - 12)
-0.69*
-0.49
-0.46
-0.47
0.41
-0.11
-0.04
-0.24
-0.44
-0.28
*
-0.58
-0.57*
-0.10
-0.10
*denotes statistically significant correlation coefficient (p •<. 0.05)
45
-------
D>
0)
c
.2
43
CO
•*-<
c
o
o
c
o
o
o
CO
•*-•
o
" Seepage Lakes, DOC <4.2
• Seepage Lakes, DOC >4.2
A Drainage Lakes
Lake pH
Figure 3-2. Relationship between lake pH and total mercury concentra-
tion for yellow perch ages 2-4 in 27 ELS-II lakes. (DOC
value of 4.2 mg/L is mean value for all seepage lakes.)
46
-------
Among the physical characteristics of the lakes examined for
correlation with average mercury levels (Table 3-13), only watershed
area and the ratio of watershed area to lake area showed significant (p £
.05) correlations. In both cases, these correlations were for drainage
lakes and were negative. These results suggest that smaller drainage
lakes with lower flushing rates tend to have higher mercury levels in
I
yellow perch ages 2-4. j
Older yellow perch, which tended to have higher 'overall mercury
levels, did not show the same correlations with acidity indicator vari-
ables as the younger perch in seepage lakes (Table 3-14). Although
sample sizes (lakes) varied somewhat and were smaller than sample sizes
for the age 2-4 group, none showed significant correlations with pH, Ca,
or ANC. However, in drainage lakes the relationship with pH was similar
for older (>7) perch. Mercury levels in older fish from drainage lakes
showed a negative (-0.69) correlation for pH (p < 0.001) based on
average values for mercury. '
Both aluminum and total phosphorus were correlated with mercury
levels in all lakes and in seepage lakes. The negative correlation for
DOC and silica in seepage lakes was stronger for older perch than for
the 2-4 age groups, as was the positive relationship;with sulfate. In
addition, older perch showed positive relationships to watershed area,
elevation, and lake size in seepage lakes. j
Other game fish species were collected in smaller numbers than
yellow perch. Furthermore, when these samples were broken down by lake
type, it was unusual to find more than three (maximum of five) different
lakes represented for any species/age class combination (Table 3-15).
Therefore, meaningful statistical analyses were precluded for lake
chemical and physical variables in these groups. i
I
3.2.3 Interactive Effects of Fish Size and Lake Characteristics on
Mercury Levels
The previous analyses demonstrate that in many tases both biologi-
cal (age, length, weight) factors and lake chemical and physical
variables may be correlated with fish mercury levels. We conducted a
series of stepwise forward multiple regression analyses to ascertain the
47
-------
Table 3-14. Correlation Matrix for Average Values of Mercury in Age 7
and Older Yellow Perch and ELS-I Water Quality Variables
Measured in Seepage Lakes, Drainage Lakes, and Seepage and
Drainage Lakes Combined
Variable
N
PH
ANC (ueq/L)
Calcium
(ueq/L)
Conductivity
(u MHOS/cm)
Aluminum
(ueq/L)
Total phosphorus
(ug/L)
DOC (mg/L)
Color (pcu)
Sulfate (ueq/L)
Silica
(mg/1 Si02)
Watershed Area/
Lake Area
Watershed Area (ha)
Elevation (m)
Lake Size (ha)
All Lakes
(n = 27)1
17
.40
.29
.30
.30
.58*
-.84*
-.43
.02
•if
.53
.27
.12
.07
&
.60
.17
Seepage Lakes
(n = 15)
8
.58
.11
.26
.62
.79*
, -.88*
-.84*
-.37
.78*
•Se
-.85
.66
.92*
.84*
.77
Drainage Lakes
(n = 12)
9
-.69*
-.25
-.19
-.20
.52
-.15
-.03
.08
.02
-.18
-.41
-.44
-.26
-.10
.n - number of lakes
denotes statistically significant correlation coefficient (p < 0.05)
48
-------
Table 3-15< Numbers of Lake Types Represented in Mercury Analyses for
Game Fish Species !
Species name Common name Age
Salvelinus fontinalis brook trout 1
2
3
4
6
7
All
Esox lucius northern pike 2
3
4
5
6
7
8
10
Micropterus dolomieui smallmouth bass 4
Micropterus salmoides largemouth bass 0
1
2
3
4
5
6
7
8
9
10
# Seepage
i Lakes
0
0
1
1
1
1
1
1
1
1
2
0
! 0
1
0
o
1
1
1
1
3
3
1
2
2
0
0
#
Drainage
1
3
0
0
0
0
3
4
5
4
5
2
1
2
1
1
1
1
0
3
2
1
2
0
0
1
1
49
-------
Table 3-15. Numbers of Lake Types Represented in Mercury Analyses for
Game Fish Species (cont.)
Species name Common name Age
Perca f lave sc ens yellow perch 0
1
2
3
4
5
6
7
8
9
10
11
12
Stizostedion vitreum walleye 1
2
3
4
8
# Seepage
Lakes
1
8
12
11
13
9
10
6
7
6
1
2
0
0
0
0
0
0
#
Drainage
0
5
11
10
10
11
8
9
8
7
2
4
1
1
1
1
1
1
50
-------
relative importance of these parameters in determining observed mercury
levels. In addition, we included lake type as an indicator variable to
see if drainage or seepage lake was a significant factor. Seepage lakes
were assigned a value of zero and drainage lakes a value of one. The
combination of all variables considered together generally provided the
highest r values, although in several instances the variable, lake type,
was not selected in the regression equation (Table 3.'16). For example,
five variables together comprise a multiple r value of 0.71 for all ages
!
of yellow perch. Multiple regression for largemouth ibass in all lakes
identified nine variables (Table 3-16) with a multiple r = 0.93, with
total length alone having an r = 0.74. Northern pike had a multiple r =
0.91 for seven variables. ,
In all multiple regression equations, the first !variable chosen was
either related to a biological characteristic or a meastire of lake
acidity status (e.g., pH, AIT). However, numerous other parameters that
were not significantly correlated by themselves with 'mercury levels were
selected as less important variables. Lake type was iselected only for
yellow perch ages 2-4 and for largemouth bass. j
Despite the fairly robust relationships observed in the stepwise
multiple regressions, there is considerable variation in the places
where individual fish that exceed health guidelines may be found (Table
3.17). Chemical variables associated with lake acidity status appear
important in many analyses, but the strong relationships between mercury
and biological variables tend to confound the situation. The chemical
data suggest that larger numbers of fish with high mercury levels may
occur in more acidic lakes. However it is obvious from Table 3-17 that
some individual fish with high mercury levels may be found in a variety
j
of lake types. Figures 3-3 to 3-8 depict the mercury levels of individ-
ual fish for yellow perch, largemouth bass, and northern pike as a
function of lake pH and total length. Again, the occurrence of individ-
ual fish with high mercury levels is somewhat sporadic.
The complexity of variables apparently related to mercury levels in
fish led us to examine these relationships in additional ways. First,
we conducted partial correlation analyses in order to better understand
i
how much interaction or influence existed among various parameters and
I
their correlation with mercury levels. We did theseianalyses on yellow
51
-------
Table 3-16. Stepwise Multiple Regressions Combining Biological,
Chemical, and Physical Variables for Three Game
Fish Species, Along With Lake Type
Yellow Perch (all ages)
Hg (ppm) = 0.174 + .002 (wt) + <003 (AIT) - .003 (WA/LA) +0.26 (age)
- .0005 (elevation), r = 0.71
Yellow Perch (age 2-4)
Hg (ppm) = .660 - .103 (pH) + .0001 (Ca) + .001 (TL) + .076 (lake type)
- .002 (WA/LA), r = 0.60
Yellow Perch (age 7)
Hg (ppm) = -0.279 + .005 (TL) + .007 (AIT) - .031 (DOC)
- .001 (elevation), r = .80
Largemouth Bass (all ages)
Hg (ppm) - 2.06 + .002 (TL) - .309 (pH) - .002 (ANC) - .003 (AIT)
+ .965 (lake type) - .0004 (wt) + .066 (age)
- .002 (elevation) + .007 (lake size), r - .93
Northern Pike (all ages)
Hg (ppm) - 1.774 + .016 (AIT) - .008 (Ca) - .022 (WA/LA) + .006 (ANC)
+ .046 (age) + .00009 (wt) - .175 (pH), r = .91
VTA/LA - Watershed Area/Lake Area
52
-------
Table 3-17.
Lakes in Which at Least One Fish of a Given Species Exceeded
Health Guidelines
HYDRO
SPECIES LAKE NAME TYPE
BROOK TROUT GOPHER LAKE
ISLAND LAKE
(NO NAME) 2B3-055
TWIN LAKES
LARGE MOUTH BASS GOPHER LAKE
ISLAND LAKE
TWIN LAKES (EASTERN)
RICHARDSON LAKE
OSTRANDER LAKE
CASEY LAKE
(NO NAME) 2B2-061
DEEP LAKE
RUMBLE LAKE
TWIN LAKES
NORTHERN PIKE TWIN LAKES (EASTERN)
OSTRANDER LAKE
ROUND LAKE
KLONDIKE LAKE
BONE LAKE
GRAND SABLE LAKE
RUMBLE LAKE
SMALL MOUTH BASS GRAND SABLE LAKE
CATARACT BASIN
WALLEYE BONE LAKE
WHITE SUCKER (NO NAME) 2B2-024
WRIGHT LAKE
CATARACT BASIN
(NO NAME) 2B2-082
BUTO LAKE
TWIN LAKE
(NO NAME) 2B3-055
BONE LAKE
GRAND SABLE LAKE
RUMBLE LAKE
TWIN LAKES
S
D
D
D
S
S
S
S
S
S
D
D
D
D
S
S
D
D
D
D
D
D
R
D
S
S
R
D
D
D
D
D
D
D
D
-1-
PH HG>0.5
5.05
6.56
7.41
8.03
5.05
5.34
5.90
5.91
7.05
8.25
5.53
5.85
8.00
8.03
5.90
7.05
6.93
7 . 62
7.65
7.86
8.00'
7.86
7.42
I
7.65
5.75
6.14
7.42
5.60
6.10
6.83
7.41
7.65
7.86
8.00
8.03^
-
-
-
.
X
X
X
X
-
X
-
X
X
X
.
X
-
X
X
X
.
-
-
_
-
X
X
-
.
-
-
-
-
-
HG>1 . 0
_
-
-
.
.
-
-
-
-
-
-
.
-
X
-
X
.
X
-
-
.
-
-
_
_
_
-
-
-
-
-
-
-
-
53
-------
Table 3-17. Lakes in Which at Least One Fish of a Given Species Exceeded
Health Guidelines
HYDRO
SPECIES LAKE NAME TYPE
YELLOW PERCH JOHNSON LAKE
HERBERT LAKE
PECK AND RYE LAKE
LAKE. NITA
MALLARD LAKE
CRANBERRY LAKE
ELEVENMILE LAKE
TRIANGLE LAKE
QUINLAN LAKE
ISLAND LAKE
TOIVOLA LAKES (WEST)
RICHARDSON LAKE
PINE LAKE
DELENE LAKE
OSTRANDER LAKE
CATARACT BASIN
WEST BRANCH LAKES (SE)
(NO NAME) 2B2-061
(NO NAME) 2B2-082
BUTO LAKE
OTTER LAKE
TWIN LAKE
ROUND LAKE
(NO NAME) 2B3-055
KLONDIKE LAKE
BONE LAKE
GRAND SABLE LAKE
RUMBLE LAKE
TWIN LAKES
FOX LAKE
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
R
D
D
D
D
D
D
D
D
D
D
D
D
D
C
PH HG>0 . 5
4.55
4.83
4.95
4.96
5.06
5.10
5.13
5.13
5.24
5.34
5.43
5.91
6.07
6.90
7.05
7.42
4.74
5.53
5.60
6.10
6.81
6.83
6.93
7.41
7.62
7.65
7.86
8.00
8.03
4.94
X
_
X
X
X
-
_
X
X
X
X
X
X
-
_
_
X
X
-
X
X
_
_
_
-
.
X
_
X
X
HG>1 . 0
_
_
_
_
_
_
X
«
_
_
_
_
_
_
_
X
X
_
X
X
.
_
_
_
_
_
„
_
X
54
-------
YELLOW PERCH - ALL AGES AND
HGPPM
2.36 -
1.57 -
0.79
0.00
348.00
ALL LAKES
258.67
TL
169.33
8.03
80.00
4.55
Figure 3-3.
Plot of individual yellow perch mer<
a function of lake pH (pH) and total
55
;:ury levels (HG ppm) as
length (TL).
-------
YELLOW PERCH
AGES 2 TO 4 - SEEPAGE LAKES
HCPPM
0.76
0.51
0.25 •
0.00
2S6.00
TL
162
7.05
96.00
Figure 3-4. Plot of individual mercury levels (HG Ppra) for yellow perch
ages 2-4 in seepage lakes as a function of lake pH (pH) and
total length (TL). » *
56
-------
YELLOW PERCH
AGES 2 TO 4 - DRAINAGE LAKES
HG-PPM
0.72 -
0.48 -
0.25 -
0.01
237.00
8.03
TL
Figure 3-5.
Plot of individual mercury levels (HG;ppm) for yellow perch
ages 2-4 in drainage lakes as a function of lake pH (pH)
and total length (TL).
57
-------
YELLOW PERCH
AGES 7 AND GREATER
DRAINAGE
HCPPH
2.36 •
8.03
93
PH
TL
110.00
Figure 3-6.
aer,! levels
-------
LARGE MOUTH BASS
ALLAGES
DRAINAGE
H&PPM
1.00 -
0.69 -
0.37
0.06
446.00
TL
8.03
Figure 3-7.
Plot of individual mercury levels (HG ppm) for largemouth
bass in drainage lakes as a function of lake pH (pH) and
total length (TL). !
59
-------
NORTHERN PIKE
ALL AGES
DRAINAGE
HGPPM
1.64 •
1.13
0.61
0.10
o
8.0
TL
487
267
5.9
Figure 3-8. Plot of individual mercury levels (HG ppm) for northern
pike in drainage lakes as a function of lake pH (pH) and
total length (TL).
60
-------
perch ages 2-4 and all ages of yellow perch combined. Generally speak-
ing, few meaningful effects were observed, although some exceptions
exist. For example, in seepage lakes for yellow perch ages 2-4, signi-
ficant (p < .05) portions of the variation in mercury levels due to
length and weight are explained by pH. However, the correlations with
length and weight are still significant. In addition, the effect of DOC
on length and weight correlation was minimal. I
Drainage lake results, on the other hand, indicate that correla-
tions of mercury levels with length and weight are largely affected by
the variables DOC, lake size, watershed area/lake area, and watershed
size. When partial correlation coefficients were computed controlling
•For these variables, either singly or in combination for yellow perch
ages 2-4, the relationships between mercury levels and fish size
(length, age, weight) were not significant. However, these correlation
coefficients, while significant to begin with, are typically low (r < .4)
for v-nlow perch ages 2-4 because mercury levels dojnot vary greatly in
this 0^oup. j
When all ages of yellow perch were considered for all lakes, as
well as for seepage and drainage lakes separately, partial correlations
indicated that across this large broader range of sizes, the correlation
due to size was affected very little by any physical or chemical
variable. Therefore, size variables were truly and not spuriously
j
correlated with mercury levels.
Further verification of these results for all ages of yellow perch
1
was obtained by performing a discriminant analysis,iwhich included the
same biological, chemical, physical, and lake type variables employed in
the stepwise multiple regressions, to differentiate! between fish with
high (> 0.5) or low (<0.5) mercury levels. This analysis determined the
classification variables most useful in discriminating between fish with
high and low levels of mercury (Table 3-18). The final classification
variables selected were dominated by fish size and lake acidity status
variables. This discriminant function successfully classified 82% of
all yellow perch in high or low mercury groups. Interestingly, although
total length was not among the final classification variables in the
function, it was the first variable chosen in the analysis and had the
strongest individual correlation. j
61
-------
Table 3-18. Discriminant Analysis Results for All Ages Yellow Perch
:> 0.5 ppm and < 0.5 ppm Mercury
Chi-Square Standardized Canonical
Discriminant Function
Coefficients
Correlation Between Discrimin-
ating Variables and Canonical
Discriminant Functions
179.91
(p <.001)
Age 0.61
Wt 0.42
pH -0.21
Alt -0.57
Elevation -0.44
TL 0.74
Wt 0.72
Age 0.70
Alt 0.36
pH -0.25
(all other variables <0.17)
62
-------
We examined possible differences in growth rates among lake types
(seepage versus drainage) because of the importance of size variables in
determining mercury levels. Although yellow perch and largemouth bass
generally appear to grow faster in drainage lakes (Figures 3-9 to 3-13),
the opposite is true for northern pike (Figures 3-14 and 3-15) .
Furthermore, if we examine the distribution of individual fish with high
mercury levels in Figures 3-3 to 3-8, it is apparent that, with the
exception of yellow perch age 7 and older, large older fish in seepage
and drainage lakes have similar mercury levels. '
The apparent and somewhat unanticipated negative relationship
between fish mercury and DOC in seepage lakes (Tables. 3-10, and 3-11)
led us to examine relationships between pH and DOG in these lakes
(Figure 3-16). Although there is a significant positive correlation
between DOC and pH for all seepage lakes, if DeLene Lake (pH 6.9, DOC =
10.3) is dropped from the analysis, the relationship for the other
seepage lakes is insignificant. Therefore, the DOC effect on mercury
levels does not appear to be related to a pH effect.
63
-------
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r~
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p.
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4. DISCUSSION
It Is apparent from the foregoing results that a high percentage of
game fish, which are the species most likely to be consumed by humans,
exceed various health guidelines for mercury. The severity and extent
of the mercury contamination problem depends upon whether the FDA action
level of 1.0 ppm methyl mercury is used or the more conservative figure
of 0.5 ppm adopted by several states, Canada, and the WHO. Furthermore,
an additional perspective for Subreglon 2B may be obtained by comparing
the mercury levels in fish, and the number of lakes affected, to these
same factors in other geographical areas. Finally, some examination of
the relationship between fish mercury levels and lake acidity factors Is
possible for this subregion.
4.1 RELATIONSHIP OF SUBREGION 2B MERCURY LEVELS TO OTHER
GEOGRAPHIC AREAS
Subregion 2B lakes sampled for ELS-II had fish that exceeded 0.5
ppm Hg In 24 of 38 lakes (38 = number of lakes with fish analyzed for
mercury). This represents 65% of the lakes in the survey with game
fish. However the percentage of all game fish analyzed for mercury that
exceeded 0.5 and 1.0 ppm was lower at 17% and 3%, respectively. While
these percentages are relatively low, they largely reflect samples of
fish in size classes that may not be heavily sought by anglers (e.g.,
Table 3-8). However, it is reasonable to assume that a fairly high
percentage of larger and older fish have levels exceeding the guidelines
(see Table 3-12).
Based on the probability sampling frame for the ELS-I and ELS-II
surveys, data collected on fish mercury levels for the 49 ELS-II lakes
can be extrapolated to estimate fish mercury characteristics for
Subregion 2B as a whole. Regional estimates are provided for the total
number and area of lakes in which fish mercury levels exceed 0.5 and 1.0
ppm (Table 4-1). Similar estimates are provided for yellow perch and
other game fish combined. A more detailed explanation of the techniques
employed in these regional expansions is provided by Cusimano et al
(1988). Results of these estimates show that nearly 54% of all lakes in
this subregion and nearly 82% of the surface area of all lakes have one
72
-------
Table 4-1. Population Estimates .(Subregion 2B) of Lakes with Fish
Mercury Levels Exceeding 0.5 and 1.0 ppm Hg, Based on Direct
Estimation From the Sample of "49" ELS-II Lakes
! Percentage
' of Lakes
SamplePopulation (±95% CL) Number Area
Number of Lakes
Regional Assessment of Fish Mercury Content
i
ELS-II target population 49 639.5 ' j
38 547.2 (32.8) 85.6 96.1
Lakes with measurements
of fish mercury
> 1 fish with > 0.5 ppm
24
344.9
> 1 fish with > 1.0 ppm 10 116.8
(36.8)
(32.4)
i
Yellow Perch Mercury Content +
: - !
Yellow perch caught 31 446.3 (33.6)'
18 192.7 (33.9)
>_ 1 yellow perch with
> 0.5 ppm
>_ 1 yellow perch with
> 1.0 ppm
59.2
(25.0)
53.9 81.7
18.3 13.7
43.2 70.7
13.3 2.5
Other Game Fish Mercury Content
Other fish caught
Measurements of fish
mercury for other
game fish species
> 1 fish with
> 0.5 ppm
>_ 1 fish with
> 1.0 ppm
22
17
10
456.8
413.6
241.4
178.4
(44.6)
(51.8)
(58.9)
(113jl)
58.4 84.1
43.1 38.8
73
-------
or more fish exceeding the 0.5 ppm Hg state health advisory. Over 18%
of all lakes are estimated to have one or more fish exceeding 1.0 ppm
Hg. However, game fish other than yellow perch (walleye, northern pike,
and largemouth bass) are estimated to have at least one fish over the
1.0 ppm action level in 43% of the 457 lakes in which they occur.
A report on mercury in fish (pike) from Swedish lakes (Hakanson et
al., 1988) indicated that 250 to 300 lakes in Sweden were "blacklisted"
because they were known to contain fish that exceeded the 1.0 ppm Hg
Swedish guidelines. However, using statistical techniques to extra-
polate to all Swedish lakes over 0.01 km2 (10 ha), the authors estimated
that 11.3% of the approximately 83,200 lakes exceeded the 1.0 ppm
guidelines. Moreover, if the WHO action level of 0.5 ppm was invoked,
over 50% of all Swedish lakes (>10 hectares) would exceed the standard.
In North America, although the muscle tissue of some fish has been
found to contain more than 1.0 ppm of mercury, widespread bioaccumula-
tion of mercury in fish in excess of the FDA action level has not been
reported. The sparse data regarding the mercury content of fish taken
from waters with low acid neutralizing capacity (ANC) indicate that:
1. 8% of all fish collected from 23 low-ANC lakes in northeastern
Minnesota were reported by Helwig and Heiskary (1985) to have
mercury levels in excess of the 1.0 ppm FDA action level,
2. approximately 20-30 low-ANC lakes in Wisconsin have consumption
advisories due to fish mercury levels (J. Weiner, U.S. Fish and
Wildlife Service, La Crosse, WI - personal communication to
S.P. Gloss), and
3. although Sloan and Schofield (1983) reported levels of mercury
in fish to be higher in low-ANC lakes than in higher ANC lakes
in the northeastern United States, reported mercury levels in
fish sampled there typically do not exceed the 1.0 ppm action
level (Bloomfield et al., 1980; Akielaszek and Haines, 1981).
There are no published data on mercury levels in fish taken from low-ANC
waters in the southeast, including Florida, or in fish taken from
low-ANC waters in the western United States. The most extensive data on
mercury concentrations in fish come from analysis of 70,000 to 80,000
fish taken from approximately 1,500 predominantly low-ANC lakes, out of
a total of 250,000 lakes, in Ontario, Canada. Approximately 7% of the
74
-------
fish analyzed contained more than 0.5 ppm mercury--the standard set by
the Canadian government as the limit for human consumption. A smaller
percentage of piscivorous (fish-eating) fish taken from low-ANC lakes
contained more than 1.0 ppm mercury. These data have prompted the
Province of Ontario to place restrictions on the harvesting and/or
consumption of some sizes and species of fish in three-quarters of the
approximately 1,500 lakes sampled. |
Although the data from different geographic areas do not lend
themselves to strict quantitative comparisons, it appears that Subregion
2B lakes are demonstrating fish mercury levels similar to those of other
areas with low-ANC (acid-sensitive) waters in North America. In addi-
tion, the proportion of lakes affected is high, especially for large
game fish. However, based upon projections by Hakanson et al. (1980),
the contamination in Subregion 2B may not be as severe as that in
Sweden. I
4.2 APPARENT FACTORS AFFECTING MERCURY ACCUMULATION IN
SUBREGION 2B LAKES
Results obtained in this study concerning the influence of fish
size and age on mercury concentration are consistent with those of other
recent studies (Lathrop et al., 1985; Helwig and Heiskary, 1985;
Akielaszek and Haines, 1981). A positive correlation was observed
between mercury concentration and fish length, weight, and age in all
game species. Simple linear regressions describing the relationships
among these variables were also statistically significant. However,
there were large differences in the correlation coefficients obtained
for these relationships between seepage and drainage pLakes in yellow
perch. Greater variability in the mercury concentrations at all lengths
was observed in the seepage lakes, and the maximum observed concentra-
tions were greatest in the drainage lakes. Additionally, the largest
yellow perch in the older age classes were obtained from drainage lakes.
However, the highest mercury levels' in yellow perch ages 2-4 were in
seepage lakes, where younger perch apparently are larger than perch of a
similar age in drainage lakes. The observed differences in mercury
concentrations between lake types may therefore be related to factors
affecting growth as well as differences in water quality factors that
affect the availability of mercury.
75
-------
This study also indicated differences in mercury concentrations
among species. In particular, mercury concentrations in white sucker in
each age class were lower than those measured in all other species.
These differences do not appear to be related to lake differences. The
mean concentration of mercury in age-3, -6, and -7 yellow perch, for
example, was statistically greater than in white sucker in the 12 lakes
in which they were both captured. Since mercury concentrations were
lower in all age classes, these differences cannot be explained by
differences in duration of exposure. Moreover, large differences in
mercury concentrations were not observed among different age classes of
white sucker. Differences in growth rate also do not appear to explain
the differences in mercury concentrations between white sucker and other
species. The indirect evidence available indicates that the observed
differences between white sucker and the other species could be due to
differences in food, chemical characteristics of benthic and pelagic
environments, or uptake and elimination rates.
The correlations between fish mercury and water quality variables
found in our study are compared with the results of similar studies
(Helwig and Heiskary, 1985; Lathrop et al., 1987; McMurtry et al., 1989)
in Table 4-2. Each study involved the statistical analysis of water
quality and fish mercury data from a large number of lakes. The geo-
graphical regions, fish species, and statistical approaches varied
between studies.
Most of these studies indicate a fairly consistent negative corre-
lation between fish mercury and pH, as well as between mercury and other
indicators of lake acidification such as ANC, calcium, conductivity, and
aluminum. The pH and ANC correlations were most consistent, the ANC,
calcium, conductivity and aluminum correlations were less consistent.
Our results were mixed, with significant negative correlations between
mercury and pH in both seepage and drainage lakes, but with a signifi-
cant negative correlation between mercury and ANC only for seepage
lakes. Calcium, aluminum, and conductivity were not significantly
correlated with mercury in either lake type, but aluminum had a signifi-
cant, albeit weak, positive correlation when all lakes were grouped
together (Table 3-13).
76
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Several studies have indicated a positive correlation between fish
mercury and aluminum concentrations in water. In our study, a positive
correlation was found only when all lakes were grouped together. Helwig
and Heiskary (1985) also found good correlations for northern pike and
walleye in drainage lakes. McMurtry et al. (1989) found a positive
correlation between mercury and aluminum for lake trout, but found no
significant correlation for smallmouth bass.
Negative correlations have been noted between fish mercury and
total phosphorus or other measures of lake productivity such as
chlorophyll a (Lathrop et al., 1989; Helwig and Heiskary, 1985). Our
results also showed significant negative correlations for total
phosphorus in seepage lakes and all lakes together.
One of the most interesting results of this study was the relation-
ship between DOC and mercury. Dissolved organic carbon and mercury
concentrations were not correlated in drainage lakes, but a consistent
and statistically significant negative correlation was observed between
mercury and DOC in seepage lakes. The role that DOC may have in influ-
encing observed mercury concentrations is indicated in Figure 3-2, which
shows the relationship between pH and mercury in yellow perch ages 2-4.
The wide variability in the relationship at the lower pH range can be
explained by DOC concentrations in the lakes. At low DOC concentra-
tions, mercury concentrations in fish were relatively high, and the
lower mercury concentrations occurred in lakes with higher DOC concen-
trations. The observed negative relationship between DOC and mercury
may be due to mercury complexation with organics that reduces either the
bioavailability of mercury or its uptake across gill membranes.
Several other studies noted the opposite effect--an increase in
fish mercury with increasing DOC or color (Helwig and Heiskary, 1985;
Suns et al., 1987; McMurtry et al., 1989). The proposed mechanisms have
included increased methylation rates with increasing DOC (particularly
at low DOC ranges) (McMurtry et al., 1989), increased mercury release
from the sediments due to humic substances (Saar and Weber, 1982), and
concentration of mercury in precipitation by terrestrial organic matter
in the watershed (Gorham et al., 1984). The above studies were not
78
-------
restricted to seepage lakes, and in one case, drainage lakes were
emphasized (Helwig and Heiskary, 1985).
Among other significant chemical variables, the
most obvious were
the positive and negative correlations between mercury levels and
sulfate and silica, respectively, in seepage lakes. Physical variables
showed correlations only in drainage lakes for yellow perch ages 2-4 and
only in seepage lakes for yellow perch age 7 and older.
Numerous statistical relationships exist between mercury levels and
water chemistry variables; however, a survey study provides no basis
from which to imply causal mechanisms. For example, several multiple
regression models that predict mercury levels in fish have high
correlation coefficients, but their variables may or may not be
implicated in actual causes for increased or decreased mercury levels in
fish. The variables identified in multiple regression models are not
always the same as those that have the highest coefficients in simple
correlation matrices. Overall, the most consistent variables related to
the mercury levels found in fish were those describing fish size (total
length, weight, age). Therefore, the principal benefit of this study is
that it begins to quantify the mercury problem in one;subregion of the
ELS and suggests some possible lake characteristics that may warrant
further investigation as to their possible cause and effect relation-
ships with mercury accumulation in fish. '
i
COMMENDATIONS
Given our current knowledge about the processes controlling mercury
uptake and accumulation in low ANC lakes, as well as our lack of infor-
mation about the extent and severity of the problem, it is not possible
to quantitatively infer the importan6e of acidic deposition to observed
mercury levels in fish from low ANC lakes. It is possible that acidifi-
cation makes some contribution to increased mobilization, methylation,
and bioaccumulation of mercury in fish from certain lakes. However, it
seems apparent that many natural processes or other anthropogenic
activities may also cause bioaccumulation of mercury tp levels that may
represent a human health hazard. i
79
-------
The major uncertainty in defining the extent and severity of the
mercury problem in the United States is the lack of systematic survey
data that would allow some type of extrapolation to the entire lake
resource. Although some individual states are gradually increasing
their databases on mercury levels in fish, there is not a coordinated
effort to develop the necessary information on a regional or national
scale. Similarly, the data on harvest and consumption rates of fish
species and sizes are probably not sufficient to assess current health
risks. However, risk to an individual consumer could be estimated (and
has been; NAS, 1978) for various consumption scenarios, even without
comprehensive mercury contamination data.
Additional research needed to reduce the current uncertainty about
the quantitative relationships between acidic deposition, bioaccumula-
tion of mercury in fish, and human health risks includes: (1) systema-
tic surveys designed to identify the extent and severity of mercury
bioaccumulation in fish taken from lakes in regions potentially affected
by acidic deposition, (2) studies designed to identify and quantify the
factors affecting bioaccumulation, and (3) studies designed to quantify
the consumption by humans of fish from low-ANC waters and the demography
of angler populations.
80
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fish from three northern Main lakes. Bull. Environ. Contam.
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Bjornberg, A., L. Hakanson and K. Jundbergh. 1988. A theory on the
mechanisms regulating the bioavailability of mercury in natural
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i
Bloomfield, J.A., S.O. Quinn, R.J; Scrudato, D. Long/ A. Richards and F.
Ryan. 1980. Atmospheric and watershed inputs of mercury to
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Brown, D.J.A. 1983. Effect of calcium and aluminum Concentration on
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!. :
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Dixon, W.J. 1985. BMDP statistical software.
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Phelps, R.W., T.W. Clarkson, T.B. Kershaw and B. Wheatley. 1980
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i
Phillips, G.R., P.A. Medvick, D.R. Skaar and D.E. Knight. 1987.
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i
Ramlal, P.S., J.W.M. Rudd, A. Turutani and L. Xun. 1985. The effect of
pH on methylmercury production and decomposition in lake sediments
Canadian Journal of Fisheries and Aquatic Sciences 42:685-692.
Richman, L.A., C.D. Wren and P.M. Stokes. 1988. Facts and fallacies
concerning mercury uptake by fish in acid stressed lakes Water
Air Soil Pollution 37:465-473. i
•
Rodgers, D.W. and F.W.H. Beamish. 1983. Water quality modifies uptake
of waterborne methylmercury by rainbow trout, Salmo gairdneri.
Canadian Journal of Fisheries and Aquatic Sciences 40:824-828.
Saar, R.A. and J.H. Weber. 1982. Fulvic acid: modifier of metal-ion
chemistry. Environ. Sci. Technol. 16(9):510-517.
Schindler, D.W., K.H. Mills, D.F. Malley, D.C. Findlay, J.A. Shearer
I.J. Davies, M.A. Turner, G.A. Lindsey and D.R. Cruickshank 1985
Long-term ecosystem stress: The effects of years of experimental
acidification on a small lake. Science 228:1395-1401.
'
i
Scott, D.P. and F.A.J. Armstrong. 1972. Mercury concentration in
relation to size in several species of freshwater fishes from
Manitoba and northwestern Ontario. J. Fish. Res. Bd Canada
29:1685-1690. ;
j
Sloan, R. and C.L. Schofield. 1983. Mercury levels in brook trout
(Salvelinus fontinalis) from selected acid and limed Adirondack
lakes. Northeastern Environ. Sci. 2:(2-4):165-170.
Steffan, R.J., E.T. Korthals and M.R. Winfrey. 1988. iEffects of
acidification on mercury methylation, demethylation, and violatili-
zation in sediments from an acid-susceptible lake. Appl Environ
Microbiol. 54(8):2003-9.
I
Suns, K., G. Hitchin, B. Loescher, E. Pastorek and R. Pearce. 1987.
Metal accumulations in fishes from Muskoka-Haliburton lakes in
Ontario (1978-1984). Ontario Ministry of the Environment Tech
Rep. 38 pp. i
83
-------
Wiener, J.G. 1988. Personal communication. U.S. Fish and Wildlife
Service, La Crosse, WI.
Wiener, J.G. 1983. Comparative analyses of fish populations in
naturally acidic and circumneutral lakes in northern Wisconsin.
U.S. Fish and Wildlife Service Report FWS/OBS - 80/40.16. 107 pp.
Wiener, J.G. and J.M. Eilers. 1987. Chemical and biological status of
lakes and streams in the Upper Midwest: Assessment of acidic
deposition effects. Lake and Reservoir Management 3:365-378.
Wren, C.D. and H.R. MacCrimmon. 1983. Mercury levels in the sunfish,
Lepomis gibbosus, relative to pH and other environmental variables
of Precambrian Shield lakes. Can. J. Fish. Aquat. Sci.
40:1737-1744.
Xun, L., N.E.R. Campbell and J.W.M. Rudd. 1987. Measurements of
specific rates of net methyl mercury production in the water column
and surface sediments of acidified and circumneutral lakes.
Canadian Journal of Fisheries and Aquatic Sciences 44:750-757.
84
-------
6. APPENDIX
85
-------
Appendix. Individual data for fish mercury analysis subregion 2B.
SPECIES
BROOK TROUT
BROOK TROUT
BROOK TROUT
BROOK TROUT
BROOK TROUT
BROOK TROUT
BROOK TROUT
BROOK TROUT
BROOK TROUT
BROOK TROUT
BROOK TROUT
BROOK TROUT
BROOK TROUT
BROOK TROUT
BRCOK TROUT
I"""' TROUT
BROOK TROUT
BROOK TROUT
BROCK TROUT
BROOK TROUT
BROOK TROUT
BROOK TROUT
BROOK TROUT
BROOK TROUT
BROOK TROUT
BROOK TROUT
BROOK TROUT
BROOK TROUT
LARGEMOUTH
LARGEMOUTH
LARGEMOUTH
LARGEMOUTH
LARGEMOUTH
LARGEMOUTH
LARGEMOUTH
LARGEMOUTH
LARGEMOUTH
LARGEMOUTH
LARGEMOUTH
LARGEMOUTH
LARGEMOUTH
LARGEMOUTH
LARGEMOUTH
LARGEMOUTH
LARGEMOUTH
LARGEMOUTH
LARGEMOUTH
LARGEMOUTH
" "
BASS
BASS
BASS
BASS
BASS
BASS
BASS
BASS
BASS
BASS
BASS
BASS
BASS
BASS
BASS
BASS
BASS
BASS
BASS
BASS
LAKE ID
2B1-061
2B1-061
2B1-061
2B1-061
2B1-061
2B1-061
2B1-061
2B3-007
2B3-007
2B3-007
2B3-007
2B3-007
2B3-007
2B3-007
2B3-007
2B3-007
2B3-031
2B3-031
2B3-031
2B3-031
2B3-031
2B3-031
2B3-031
2B3-031
2B3-031
2B3-055
2B3-055
2B3-055
2B1-016
2B1-016
2B1-016
2B1-016
2B1-016
2B1-022
2B1-022
2B1-022
2B1-022
2B1-022
2B1-061
2B1-061
2B1-061
2B1-061
2B1-061
2B1-061
2B1-061
2B1-061
2B1-061
2B2-061
LAKE NAME
GOPHER LAKE
GOPHER LAKE
GOPHER LAKE
GOPHER LAKE
GOPHER LAKE
GOPHER LAKE
GOPHER LAKE
ISLAND LAKE
ISLAND LAKE
ISLAND LAKE
ISLAND LAKE
ISLAND LAKE
ISLAND LAKE
ISLAND LAKE
ISLAND LAKE
ISLAND LAKE
TWIN LAKES
TWIN LAKES
TWIN LAKES
TWIN LAKES
TWIN LAKES
TWIN LAKES
TWIN LAKES
TWIN LAKES
TWIN LAKES
(NO NAME)
(NO NAME) .
(NO NAME)
DEEP LAKE
DEEP LAKE
DEEP LAKE
DEEP LAKE
DEEP LAKE
TWIN LAKES (EASTERN)
TWIN LAKES (EASTERN)
TWIN LAKES (EASTERN)
TWIN LAKES (EASTERN)
TWIN LAKES (EASTERN)
GOPHER LAKE
GOPHER LAKE
GOPHER LAKE
GOPHER LAKE
GOPHER LAKE
GOPHER LAKE
GOPHER LAKE
GOPHER LAKE
GOPHER LAKE
(NO NAME)
HYDRC
TYPE
S
S
S
S
S
S
S
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
S
S
S
S
S
S
S
S
S
S
S
S
S
S
D
) AGE
P-IJ^ -
3
3
4
4
6
6
7
1
1
1
1
1
1
2
2
2
1
1
1
1
1
1
1
2
2
*
*
. 2
3
3
3
3
6
4
4
4
5
5
0
0
0
0
0
0
0
0
0
3
TL
(MM)
— i-— — =
361
245
397
358
451
457
508
215
190
188
195
211
272
276
274
270
246
233
221
240
217
209
211
367
383
211
179
349
220
217
234
222
307
207
220
211
240
255
117
118
92
90
108
90
90
92
116
276
WT
(CMS)
800
200
760
650
1251
1400
1775
100
85
60
80
110
240
245
235
210
144
106
87
129
92
72
73
470
550
109
61
560
127
132
169
135
380
113
129
120
183
203
23
21
10
10
17
10
11
11
23
300
(
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0,
0,
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
HS
PPM)
2300
0200
3400
0600
3700
2700
3600
0300
0200
0200
0300
0200
0200
0400
,1200
,0400
.1400
.1500
.1500
.1300
.1500
.1400
.1100
.2900
.2700
.1800
.2600
.2000
.2200
.2700
.1500
.2600
.2900
.2500
.3500
.4400
.2700
.5300
.1600
.2100
.1900
. 2000
.2500
. 1700
.1800
.1900
.3200
.7300
86
-------
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
LARGEMOUTH BASS
NORTHERN PIKE
NORTHERN PIKE
2B2-061 (NO NAME)
2B2-075 RICHARDSON LAKE
2B2-075 RICHARDSON LAKE
2B2-075 RICHARDSON LAKE
2B2-075 RICHARDSON LAKE
2B2-075 RICHARDSON LAKE
2B2-075 RICHARDSON LAKE
2B2-075 RICHARDSON LAKE
2B2-075 RICHARDSON LAKE
2B2-075 RICHARDSON LAKE
2B2-075 RICHARDSON LAKE
2B2-075 RICHARDSON LAKE
2B3-027 CASEY LAKE
2B3-027 CASEY LAKE
2B3-027 CASEY LAKE
2B3-027 CASEY LAKE
2B3-027 CASEY LAKE
2B3-027 CASEY LAKE
2B3-027 CASEY LAKE
2B3-027 CASEY LAKE
2B3-027 CASEY LAKE
2B3-030 ISLAND LAKE
2B3-030 ISLAND LAKE
2B3-030 ISLAND LAKE
2B3-030 ISLAND LAKE
2B3-030 ISLAND LAKE
2B3-030 ISLAND LAKE
2B3-030 ISLAND LAKE
2B3-030 ISLAND LAKE
2B3-030 ISLAND LAKE
2B3-031 TWIN LAKES
2B3-031 TWIN LAKES
2B3-031 TWIN LAKES
2B3-031 TWIN LAKES
2B3-031 TWIN LAKES
2B3-031 TWIN LAKES
2B3-031 TWIN LAKES
2B3-031 TWIN LAKES
2B3-037 RUMBLE LAKE
2B3-037 RUMBLE LAKE
2B3-037 RUMBLE LAKE
2B3-037 RUMBLE LAKE
2B3-037 RUMBLE LAKE
2B3-037 RUMBLE LAKE
2B3-037 RUMBLE LAKE
2B3-037 RUMBLE LAKE
2B3-037 RUMBLE LAKE
2B3-037 RUMBLE LAKE
2B3-037 RUMBLE LAKE
2B3-071 OSTRANDER LAKE
2B3-071 OSTRANDER LAKE
2B3-071 OSTRANDER LAKE
2B1-022 TWIN LAKES (EASTERN)
2B1-022 TWIN LAKES (EASTERN)
D
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
S
S
S
S
S
'[
1
' 4
! 1
! 1
2
3
3
3
4
5
5
1 5
8
' 4
4
4
! 4
1 4
! 4
4
5
: 5
! 4
1 4
1 4
4
4
6
6
1 7
8
3
4
' 4
' 4
1 4
4
1 5
6
0
i 0
0
1 0
1 0
1
1
1
1
1
9
! 5
i 7
10
*
5
320
88
78
205
203
180
183
314
303
310
261
379
261
263
267
297
275
271
297
318
304
273
269
275
26S
257
330
351
370
334
241
292
333
296
296
304
302
319
108
81
83
94
81
114
216
102
150
102
446
330
356
431
620
634
550
7
6
109
104
67
84
370
410
410
240
800
200
232
240
'350
278
257
350
400
373
270
225
270
230
210
430
530
640
480
200
395
520
350
380
400
370
440
14
6
8
10
6
18
139
13
42
13
1620
320
550
1060
1515
1650
0.6900
0 . 1100
0.0800
0.1600
0.2400
0.1600
0.1800
0.5300
0.2800
0.4300
0.2000
0.4200
0.1300
0.1400
0 . 1400
. 0.1900'
0.1100
0.1600
0.1100
0.3400
0.1300
0.8100
0.7700
0.7200
0.7300
0.5900
0.8800
0.7300
1.0000
0.8300
0.2300
0.3900
0.4400
0 . 5400
0.3300
0.4800
0.6000
0.7300
0.1300
0.0700
0.0900
0.0600
0.1100
0.1200
0.3000
0.1300
0.3500
0.1400
0.6400
0.2100
0.3600.
0 . 5500
0.4100
0.5900
87
-------
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
231-022
2B3-009
2B3-009
2B3-009
2B3-009
2B3-009
2B3-009
2B3-009
2B3-009
2B3-009
2B3-009
2B3-012
2B3-012
2B3-012
2B3-012
2B3-012
2B3-012
2*3-012
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-028
2B3-028
2B3-028
2B3-028
2B3-028
2B3-028
. 2B3-028
2B3-028
2B3-028
2B3-028
2B3-034
2B3-034
2B3-034
2B3-034
2B3-034
2B3-034
TWIN LAKES (EASTERN)
GRAND SABLE LAKE
GRAND SABLE LAKE
GRAND SABLE LAKE
GRAND SABLE LAKE
GRAND SABLE LAKE
GRAND SABLE LAKE
GRAND SABLE LAKE
GRAND SABLE LAKE
GRAND SABLE LAKE
GRAND SABLE LAKE
ROUND LAKE
ROUND LAKE
ROUND LAKE
ROUND LAKE
ROUND LAKE
ROUND LAKE
ROUND LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
KLONDIKE LAKE
KLONDIKE LAKE
KLONDIKE LAKE
KLONDIKE LAKE
KLONDIKE LAKE
KLONDIKE LAKE
S
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
;D
D
D
D
D
D
D
D
D
D
R
R
R
R
R
R
R
R
R
R
D
D
D
D
D
D
8
2
2
3
3
3
4
4
4
5
6
3
3
3
3
4
5
5
2
2
2
2
2
3
3
3
3
4
5
5 .
5
5
5
5
7
8
8
10
*
1
2
2
3
4
4
5
5
6
2
2
2
3
3
3
850
496
598
587
656
556
655
705
608
591
578
576
466
485
424
520
578
533
267
296
367
396
369
446
410
451
356
566
464
540
620
437
469
504
625
525
610
924
389
289
349
325
369
467
415
398
430
552
384
370
393
403
473
425
4300
920
900
1280
1800
1140
1950
2110
1620
1480
1220
980
515 •
610
450
760
840
830
117
135
250
320
260
460
340
480
260
1080
500
900
1340
400
540
660
1390
800
1100
5440
304
118
260
191
285
590
350
380
409
878
285
245
324
324
420
450
1.1200
0.4100
0.3700
0.5900
0.4700
0.5500
0.6300
0.6100
0.8600
0.7900
0.4400
0.8800
1.0300
1.0700
0.7400
0.8800'
1.6400
0.7400
0.2900
0.1300
0.1100
0.2000
0.1600
0.2700
0.2000
0.2000
0.1000
0.2800
0.2900
0.4700
0.3000
0.3300
0.2600
0.3100
0.4000
0.3500
0.4300
1.0700
0.2300
0.0800
0.1700
0.0700
0.2400
0 . 1100
0.1200
0.3000
0.3500
0.1500
0.2500
0.1100
0.3300
0.2300
0.4400
0.3400
88
-------
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
NORTHERN PIKE
SMALL MOUTH BASS
SMALL MOUTH BASS
SMALL MOUTH BASS
SMALL MOUTH BASS
WALLEYE
WALLEYE
WALLEYE
WALLEYE .
WALLEYE
WALLEYE
WALLEYE
WALLEYE
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
2B3-034
2B3-034
2B3-034
2B3-034
2B3-034
2B3-034
2B3-034
2B3-034
2B3-034
2B3-037
2B3-037
2B3-037
2B3-037
2B3-037
2B3-037
2B3-037
2B3-037
2B3-037
2B3-037
2B3-037
2B3-037
2B3-037
2B3-037
2B3-037
2B3-071
2B3-071
2B3-071
2B3-071
2B3-071
2B3-071
2B3-071
2B3-071
2B3-009
2B3-009
2B3-028
2B3-028
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B2-004
2B2-004
2B2-004
2B2-004
2B2-004
2B2-004
2B2-004
2B2-004
2B2-004
2B2-004
KLONDIKE LAKE
KLONDIKE LAKE
KLONDIKE LAKE
KLONDIKE LAKE
KLONDIKE LAKE
KLONDIKE LAKE
KLONDIKE LAKE
KLONDIKE LAKE
KLONDIKE LAKE
RUMBLE LAKE
RUMBLE LAKE
RUMBLE LAKE
RUMBLE LAKE
RUMBLE LAKE
RUMBLE LAKE
RUMBLE LAKE
RUMBLE LAKE
RUMBLE LAKE
RUMBLE LAKE
RUMBLE LAKE ,
RUMBLE LAKE
RUMBLE LAKE
RUMBLE LAKE
RUMBLE LAKE
OSTRANDER LAKE
OSTRANDER LAKE
OSTRANDER LAKE
OSTRANDER LAKE
OSTRANDER LAKE
OSTRANDER LAKE
OSTRANDER LAKE
OSTRANDER LAKE
GRAND SABLE LAKE
GRAND SABLE LAKE
CATARACT BASIN
CATARACT BASIN
BONE LAKE '.
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
WRIGHT LAKE
WRIGHT LAKE
WRIGHT LAKE
WRIGHT LAKE
WRIGHT LAKE
WRIGHT LAKE '
WRIGHT LAKE
WRIGHT LAKE
WRIGHT LAKE
WRIGHT LAKE
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
S
S
S
S
S
S
S
R
D
D
R
R
D
D
D
D
D
D
D
D
S
S
S
S
S
S
S
S
S
S
3
' 4
4 '
4
! 4
! 5
1 5
5
5
2
2
2
3
i 3
5
6
1 6
: 7
7
7
' 7
7
1 8 .
1 8
2
1 3
3
3
3
" 4
! 5 '
1 7
' 4
1 4
, 4
4
i 1
! 2
3
' 4
' 4
4
: 8
8
1 3
1 3 .
! 3
5
5
' 5
; 5
1 5
] 6
6
390
410
493
526
414
490
436
478
470
551
486
535
620
565
723
610
610
659
686
650
721
675
927
659
506
555
581
549
536
577
734
466
256
297
268
335
196
321
368
409
404
415
431
560
330
308
295
341
312
342
298
331
287
362
296
357
550
740
365
680
460
524
500
1000'
620
850
1450
1150
2200
1200
1280
1300
1800
1477
1150
1740
5000
1620
790
1130
1260
950
950
1250
2400
545
260
380
250
520
65
280
420
680
540
600
660
1380
440
320
300
460
320
480
280
380
250
560
0.2800
0.2300
0.4600
0.4200
0.2300
0.2900
0.2900
0.3900
0.2400
0.2700
0.3500
0.2200
0.5300
0.5400
0.5000
0.7800'
0.6400
0.7500
0.9900
0.9000
0.6800
0.7900
0.9300
0.5900
0.2000
0.1700
0.1700
0.1900
0.1700
0.1800
0.4000
0.2900
0.3900
0.3800
0.2300
0.1900
0.1100
0.2000
0.1900
0.3600
0.2600
0.2800
0.4200
0.4200
0 . 0400
0.0022-
0.0022
0 . 0400
0.0100
0.0400
0.0600
0.0300
0.0600
0.0400
89
-------
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITS SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
2B2-004
2B2-004
2B2-024
2B2-024
2B2-024
2B2-024
2B2-024
2B2-024
2B2-024
2B2-024
2B2-024
2B2-024
2B2-024
2B2-024
2B2-061
2B2-061
2B2-061
2B2-061
2B2-061
2B2-061
2B2-061
2B2-061
2B2-061
2B2-061
2B2-061
2B2-061
2B2-082
2B2-082
2B2-082
2B2-082
2B2-082
2B2-082
2B3-009
2B3-009
2B3-009
2B3-009
2B3-009
2B3-012
2B3-012
2B3-020
2B3-020
2B3-020
2B3-020
2B3-020
2B3-020
2B3-020
2B3-020
2B3-020
2B3-020
2B3-023
2B3-023
2B3-023
2B3-023
WRIGHT LAKE
WRIGHT LAKE
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)'
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
GRAND SABLE LAKE
GRAND SABLE LAKE
GRAND SABLE LAKE
GRAND SABLE LAKE
GRAND SABLE LAKE
ROUND LAKE
ROUND LAKE
BUTO LAKE
BUTO LAKE
BUTO LAKE
BUTO LAKE
BUTO LAKE
BUTO LAKE
BUTO LAKE
BUTO LAKE
BUTO LAKE
BUTO LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
s
s
s
s
s
s
s
s
s
s
s
s
s
s
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
6
7
2
2
2
2
3
6
6
6
7
7
7
8
3
3
4
4
4
6
6
6
7
7
9
9
5
7
7.
8
8
8
1
2
3
3
3
1
1
5
5
6
6
6
6
7
7
8
8
3
3
3
3
340
393
174
169
174
173
172
359
410
255
370
317
322
344
240
210
279
285
196
. 426
350
360
287
349
388
385
326
333
398
361
343
352
146
228
315
232
234
101
107
351
335
316
375
365
362
356
356
366
359
336
322
395
311
420
660
39
44
40
44
43
420
725
130
450
255
240
360
138
82
175
200
77
650
400
475
204
300
485
527
360
400
620
460
440
480
31
131
321
121
128
10
12
390
400
320
460
460
440
440
440
460
450
490
340
690
310
0.0800
0.0400
0.2400
0.1400
0.2300
0.1000
0.2500
0.0500
0.0300
0.1100
0.0100
0.0400
0.0800
0.0300
0.1400
0.1200'
0.4900
0.2800
0.1900
0 . 1500
0.3900
0.2700
0.1000
0.3800
0.4900
0.5900
O'.IOOO
0.1200
0.3200
0.2700
0.2000
0.1000
0.0800
0.0600
0.0700
0.0500
0.0400
0.0800
0.0900
0.0500
0.0600
0.0700
0.0900
0.0600
0.0900
0.0700
0.0400
0.0600
0.0900
0.1000
0.0300
0.0500
0.0500
90
-------
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
•WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
WHITE SUCKER
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-028
2B3-028
2B3-028
'2B3-028
2B3-028
2B3-028
2B3-028
2B3-028
2B3-028
2B3-028
2B3-028
2B3-028
233-028
2B3-031
2B3-031
2B3-031
2B3-031
2B3-031
2B3-031
2B3-031
2B3-031
2B3-031
2B3-031
2B3-031
2B3-031
2B3-031
2B3-031
2B3-031
2B3-037
2B3-037
2B3-037
2B3-037
2B3-037
2B3-037
2B3-037
2B3-055
2B3-055
2B3-055
2B3-055
2B3-055
233-055
2B3-057
2B3-057
2B1-035
2B1-035
2B1-035
2B1-035
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
TWIN LAKES
TWIN LAKES
TWIN LAKES
TWIN LAKES
TWIN LAKES
TWIN LAKES
TWIN LAKES
TWIN LAKES
TWIN LAKES
TWIN LAKES
TWIN LAKES
TWIN LAKES
TWIN LAKES
TWIN LAKES
TWIN LAKES
RUMBLE LAKE
RUMBLE LAKE
RUMBLE LAKE
RUMBLE LAKE
RUMBLE LAKE
RUMBLE LAKE
RUMBLE LAKE
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(MO NAME)
TWIN LAKE
TWIN LAKE
LAKE NITA
LAKE NITA
LAKE NITA
LAKE NITA
91
D
D
D
D
D
D
D
R
R
R
R
R
R
R
R
R
R
R
R
R
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
S
S
S
S
3
6
6
6
6
6
6
*
1 3"
3
1 3
3
4
5
6
6
6
6
; 7
' 7
3
3
3
3
i 3
3
1 3
1 3
3
3
1 3
6
6
1 6
7
; 2
3
: 3
1 3
3
' 4
) 4
*
1
1 3
! 3
1 3
! 3
4
! 7
1 1
!*1
,1 -
I 1
1
i
311
496
516
349
380
451
419
344
410
342
306
502
390
320
419
573
503
505
484
429
247
374
234
367
257
338
223
246
326
220
320
362
351
259
380
261
371
255
332
320
320
345
243
120
250
243
237
182
337
371
139.
138
142
143
310
1100
1300
440
540
1000
720
660
' 943
600
348
1261
824
372
800
1840
1380
1360
1100
820
170
570
150
530
190
400
120
170
420
120
380
515
520
200
620
213
560
160
440
378
360
460
132
15
152
142
141
58
290
570
37
35
41
36
0.0500
6.4000
0.3600
0.0500
0.0900
0.1500
0.0500
0.0500
0.0700
0.0600
0.0200
0.3900
0.0800
0.0400
0.1600
0.4400'
0.3100
0.4500
0.5600
0.1800
0.0300
0.1500
0.0600
0.0700
0.0500
0.0700
0.0500
0.0200
0.0500
0.0100
0.0600
0.0900
0.0700
0.0010
0.2100
0.0300
0.0400
0.0200
0.0400
0.0100
0.0400
0.0300
0.0400
0.0300
0.0900
0.0200
0.0500
0.0100
0.0200 .
0.0800
0.0002
0.1600
0.0002
0.0004
-------
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
2B1-035
2B1-035
2B1-035
2B1-035
2B1-035
2B1-035
2B1-035
2B1-035
2B1-035
2B1-035
2B1-035
2B1-035
2B1-035
2B1-035
2B1-035
2B1-035
2B1-035
2B1-035
2B1-035
2B1-039
2B1-039
2B1-039
2B1-039
2B1-039
2B1-039
2B1-039
2B1-039
2B1-039
2B1-039
2B1-039
2B1-039
2B1-039
2B1-039
2B1-039
231-039
2B1-039
2B1-040
2B1-040
2B1-040
2B1-040
2B1-040
2B1-040
2B1-040
2B1-040
2B1-040
2B1-040
2B 1-040
2B1-040
2B1-040
2B1-040
2B1-040
2B1-040
2B1-040
2B1-040
LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
WEST
WEST
WEST
WEST
WEST
WEST
WEST
WEST
WEST
•WEST
WEST
WEST
WEST
WEST
WEST
WEST
WEST
WEST
WEST
WEST
WEST
WEST
WEST
WEST
WEST
WEST
WEST
WEST
WEST
WEST
WEST
WEST
WEST
WEST
WEST
NITA
NITA
NITA
NITA
NITA
NITA
NITA
NITA
NITA
NITA
NITA
NITA
NITA
NITA
NITA
NITA
NITA
NITA
NITA
BRANCH
BRANCH
BRANCH
BRANCH
BRANCH
BRANCH
BRANCH
BRANCH
BRANCH
BRANCH
BRANCH
BRANCH
BRANCH
BRANCH
BRANCH
BRANCH
BRANCH
BRANCH
BRANCH
BRANCH
BRANCH
BRANCH
BRANCH
BRANCH
BRANCH
BRANCH
BRANCH
BRANCH
BRANCH
BRANCH
BRANCH
BRANCH
BRANCH
BRANCH
BRANCH
LAKES
LAKES
LAKES
LAKES
LAKES
LAKES
LAKES
LAKES
LAKES
LAKES
LAKES
LAKES
LAKES
LAKES
LAKES
LAKES
LAKES
LAKES
LAKES
LAKES
LAKES
LAKES
LAKES
LAKES
LAKES
LAKES
LAKES
LAKES
LAKES
LAKES
LAKES
LAKES
LAKES
LAKES
LAKES
.
(SW)
(SW)
(SW)
(SW)
(SW)
(SW)
(SW)
(SW)
(SW)
(SW)
(SW)
(SW)
(SW)
(SW)
(SW)
(SW)
(SW)
(SE)
(SE)
(SE)
(SE)
(SE)
(SE)
(SE)
(SE)
(SE)
(SE)
(SE)
(SE)
(SE)
(SE)
(SE)
(SE)
(SE)
(SE)
S
s
'S' '
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
1
1
' 1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
3
4
2
2
2
2
2
2
4
4
5
7
7
7
8
8
9
9
11
1
1
2
2
2
2
3
3
3
3
3
3
4
4
5
6
7
7
126
144
140'
144
146
141
135
145
201
195
191
229
191
240
242
231
240
286
296
135
115
117
118
120
116
116
112
182
312
180
203
241
292
335
275
278
88
85
92
, 90
115
91
117
111
115
118
115
109
113
111
166
238
166 •
165
24
38
32
38
38
35
29
37
124
97
108
160
88
200
220
134
160
380
460
22
16
16
15
14
13
16
14
53
409
62
98
151
331
434
249
216
6
6
8
7
13
8
18
14
12
14
13
11
13
14
37
173
33
46
0.1500
0.1200
0.2000
0.0018
0.1000
0.0900
0.2000
0.1100
0.0000
0.0012
0.1530
0.0600
0.0001
0.4900
0.4200
0.6000'
0.3200
0.6300
0.0500
0.5900
0.5600
0.2500
0.5800
0.5400
0.7200
0.4500
0.4200
0.6600
2.3600
1.0100
0.7500
1.0000
1.1900
2.2500
1.4200
1.8900
0.3300
0.1900
0.1800
, 0.2000
0.2500
0.2000
0.3100
0.3900
0.2000
0.2200
0.3200
0.0900
0.2100
0.3400
0.3400
0.9400
0.0700
0.0800
92
-------
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
2B1-040
2B1-041
2B1-041
2B1-041
2B1-041
2B1-041
2B1-041
2B1-041
2B1-041
2B1-041
2B1-041
2B1-041
2B1-041
2B1-041
2B1-041
2B1-041
2B1-041
2B1-041
2B1-047
2B1-047
2B1-047
2B1-047
2B1-047
2B1-047
2B1-047
2B1-047
2B1-047
2B1-047
2B1-047
2B1-047
2B1-047
2B1-047
2B1-047
2B1-047
2B1-047
2B1-047
2B1-047
2B1-047
2B1-047
2B1-047
2B1-052
2B1-052
2B1-052
2B1-052
2B1-052
2B1-052
2B1-052
2B1-052
2B1-052
2B1-052
2B1-052
2B1-052
2B1-052
2B1-052
WEST BRANCH LAKES
TRIANGLE LAKE
TRIANGLE LAKE
TRIANGLE LAKE
TRIANGLE LAKE
TRIANGLE LAKE
TRIANGLE LAKE
TRIANGLE LAKE
TRIANGLE LAKE
TRIANGLE LAKE
TRIANGLE LAKE
TRIANGLE LAKE
TRIANGLE LAKE
TRIANGLE LAKE
TRIANGLE LAKE
TRIANGLE LAKE
TRIANGLE LAKE
TRIANGLE LAKE
JOHNSON LAKE
JOHNSON LAKE
JOHNSON LAKE
JOHNSON LAKE
JOHNSON LAKE
JOHNSON LAKE
JOHNSON LAKE
JOHNSON LAKE
JOHNSON LAKE
JOHNSON LAKE
JOHNSON LAKE
JOHNSON LAKE
JOHNSON LAKE
JOHNSON LAKE
JOHNSON LAKE
JOHNSON LAKE
JOHNSON LAKE
JOHNSON LAKE
JOHNSON LAKE
JOHNSON LAKE
JOHNSON LAKE
JOHNSON LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
(SE)
1
D
S ;
s1
s1
s
s
s
s
s '
s :
S i
s
s j
S '
s!
s1
s i
s '
s !
s
s ]
s
s !
s |
s !
.s '
s
s
s
s
s
s :
s ]
s
s
s
s '
s
s !
s
S ;
S '
si
s 1
s ''<
s
S i
s !
s
s
s
s
s
s
'11
" 2
2
3
3
4
5
5
6
6
6
6
6
7
7
7
8
9
1
1
1
1
1
1
1
1
1
2
2
2
2
2
3
3
3
3
3
4
4
5
1
1
1
1
1
1
1
1
1
1
1
1
1
1
348
111
107
110
111
105
180
105
133
130
119
103
110
168
166
110
257
290
123
123
83
82
85
80
124
84
123
170
171
175
125
170
241
208
176
175
224
200
246
263
123
131
122
121
121
122
119
130
131
130
118
130
123
129
.470
13 •
. "ll
13
11
10
54
10
20
18
13
9
11 .
41
41
12
174
285
17
18
6
5
5
5
19
5
.. 18
54
49
56
18
52
174
94
57
57
129
82
181
224
20
25
22
21
22
22
21
24
25
25
19
25
24
22
1.9400
0.3800
0.4500
0.5000
0.6700
0.5800
0.1200
0.8400
0.1600
0.1300
0.5000
0.7100
0.9700
0.6300
0.5500
0.2400'
1.0800
1.2800
0.2900
0.1200
0.2900
0.4200
0.3300
0.3400
0.2500
0.3900
0.2000
0.3100
0.3100
0.4100
0.2300
0.4000
0.5400
0.3200
0.3800
0.4900
0.4100
0.5400
0.7600
0.6600
0.2800
0.2800
0.3800
0.3200
0.2900
0.2800
0.3200
0.2900
0.2300
0.2600
0.2700
0.2600
0.3100
0.2900
93
-------
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
2B1-052
2B1-052
2B1-052
2B1-052
2B1-052
2B1-052
2B1-052
2B1-052
2B1-052
2B1-052
2B1-052
2B1-052
2B1-052
2B1-052
2B1-052
2B1-052
2B1-052
2B1-052
2B1-052
2B1-052
2B1-052
2B1-052
2B1-052
2B1-052
2B1-052
2B1-064
2B1-064
2B1-064
2B1-064
2B1-064
2B1-064
2B1-064
2B1-064
2B1-064
2B1-064
2B1-064
2B1-064
2B1-064
2B1-064
2B1-064
2B1-064
2B1-064
2B1-064
2B1-064
2B1-064
2B2-007
2B2-007
2B2-007
2B2-007
2B2-007
2B2-007
2B2-007
2B2-007
2B2-007
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
PECK AND RYE LAKE
MALLARD LAKE
MALLARD LAKE
MALLARD LAKE
MALLARD LAKE
MALLARD LAKE
MALLARD LAKE
MALLARD LAKE
MALLARD LAKE
MALLARD LAKE
MALLARD LAKE
MALLARD LAKE
MALLARD LAKE
MALLARD LAKE
MALLARD LAKE
MALLARD LAKE
MALLARD LAKE
MALLARD LAKE
MALLARD LAKE
MALLARD LAKE
MALLARD LAKE
TOIVOLA LAKES (WEST)
TOIVOLA LAKES (WEST)
TOIVOLA LAKES (WEST)
TOIVOLA LAKES (WEST)
TOIVOLA LAKES (WEST)
TOIVOLA LAKES (WEST)
TOIVOLA LAKES (WEST)
TOIVOLA LAKES (WEST)
TOIVOLA LAKES (WEST)
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
1
1
1
1
1
1
1
3
3
3
3
3
4
4
5
5
5
5
5
5
5
5
5
6
8
0
1
1
1
2
2
2
2
2
2
2
3
3
3
3
4
4
5
6
6
1
I
1
1
2
2
3
3
3
123
123
123
122
130
130
116
176
176
149
150
150
188
172
151
185
149
129
150
171
196
151
150
173
180
89
114
123
125
123
132
120
156
130
164
150
191
148
172
150
195
187
150
185
185
95
. 98
100
100
115
116
117
119
155
22
21
21
23
26
25
17
47
53
34
33
33
56
42
33
57
31
24
35
40
75
31
34
58
64
7
12
19
19
20
23
18
39
'22
48
34
55
32
54 "
35
84
65
36
56
58
8
10
10
10
15
15
14
16
38
0.2800
0.0600
0.0500
0.2700
0.3600
0.2000
0.2800
0.2600
0.4000
0.0700
0.4200
0.1000
0.3800
0.3700
0.5000
0.1300'
0.5200
0.3100
0.5100
0.2400
0.1700
0.5100
0.4100
0.0900
0.2900
0.2500
0.1900
0.3000
0.4000
0.4600
0.3200
0.5700
0.4000
0.0800
0.1100
0.0400
0.4500
0.2330
0.5300
0.2100
0.3400
0.4400
0.2600
0.5200
0.7700
0.1900
0.0700
0.1500
0.1700
0.4000
0.1100
0.2700
0.4000
0.2000
94
-------
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
2B2-007
2B2-007
2B2-007
2B2-007
2B2-007
2B2-007
2B2-007
2B2-007
2B2-007
2B2-007
2B2-007
2B2-007
2B2-007
2B2-038
2B2-038
2B2-038
2B2-038
2B2-038
2B2-038
2B2-038
2B2-038
2B2-038
2B2-038
2B2-038
2B2-038
2B2-038
2B2-038
2B2-038
2B2-038
2B2-038
2B2-038
2B2-038
2B2-044
2B2-044
2B2-044
2B2-044
2B2-044
2B2-044
2B2-044
2B2-044
2B2-044
2B2-044
2B2-044
2B2-044
2B2-044
2B2-044
2B2-044
2B2-044
2B2-044
2B2-044
2B2-044
2B2-044
2B2-044
2B2-044
TOIVOLA LAKES
TOIVOLA LAKES
TOIVOLA LAKES
TOIVOLA LAKES
TOIVOLA LAKES
TOIVOLA LAKES
TOIVOLA LAKES
TOIVOLA LAKES
TOIVOLA LAKES
TOIVOLA LAKES
TOIVOLA LAKES
TOIVOLA LAKES
TOIVOLA LAKES
OTTER LAKE
OTTER LAKE
OTTER LAKE
OTTER LAKE
OTTER LAKE
OTTER LAKE
OTTER LAKE
OTTER LAKE
OTTER LAKE
OTTER LAKE
OTTER LAKE
OTTER LAKE
OTTER LAKE
OTTER LAKE
OTTER LAKE
OTTER LAKE-
OTTER LAKE
OTTER LAKE
OTTER LAKE
QUINLAN LAKE
QUINLAN LAKE
QUINLAN LAKE
QUINLAN LAKE
QUINLAN LAKE
QUINLAN LAKE
QUINLAN LAKE
QUINLAN LAKE
QUINLAN LAKE
QUINLAN LAKE
QUINLAN LAKE
QUINLAN LAKE
QUINLAN LAKE
QUINLAN LAKE
QUINLAN LAKE
QUINLAN LAKE
QUINLAN LAKE
QUINLAN LAKE
QUINLAN LAKE
QUINLAN LAKE
QUINLAN LAKE
QUINLAN LAKE
(WEST)
(WEST)
(WEST)
(WEST)
(WEST)
(WEST)
(WEST)
(WEST)
(WEST)
(WEST)
(WEST)
(WEST)
(WEST)
s
s
s
s
s I
s .'
s
s
s
s :
s
s
1
S !
1
D '
D i
D I
D
D :
D :
D
D
D
D
D
D
D
D !
D
D
D
D
D
S
S i
S
s !
s •
s
S !
S ,
s !
s '
s
s
s
s
s
s '
s
s
s
s
s
s- '
3
3
3
3
3
7
7
7
7
8
8
8
9
1
1
2
3
3
4
4
4
4
4
5
5
5
6
7
7
8
9
9
4
4
4
4
5
5
5
6
6
6
6
6
7
7
7
7
7
7
8
8
9
9
127
98
115
127
125
146
141
146
161
158
171
201
176
105
117
123
127
123
117
142
182
159
125
180
153
197
150
179
233
191
219
262
141
113
143
151
155
162
161
175
180
160
162
175
175
170
185
180
239
189
155
182
240
295
20
9
14
20
20
29
33
35
43
42
52
73
53.
12
14
19
21
18
18
25
66
35
21
56
29
74
31
57
122
72
111
205
26
13
27
30
42
48
46
53
61
44
43
51
55
52
57
66
146
63
39
60
160
300
0.1300
0.2900
0.1100
0.0700
0.1900
0.6900
0.2700
0.5100
0.1800
0.2800
0.4100
0.8100
0.3600
0.0800
0.1000
0.1300'
0.1700
0.2900
0.0300
0.2500
0.0200
0.2400
0.1500
0.2900
0.4800
0.7500
0.3600
0.3100
0.6200
0.5400
0.8300
1.1600
0.6000
0.4200
0.0600
0 . 1100
0.6300
0.1800
0.2200
0.3600
0.3100
0.1300
0.4200
0.1500
0.0500
0.0400
0.2800
0.0900
0.8000
0.0300
0.2000
0.0200
0.8600
0.0900
95
-------
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
2B2-044
2B2-049
2B2-049
2B2-049
2B2-049
2B2-049
2B2-049
2B2-049
2B2-049
2B2-049
2B2-049
2B2-049
2B2-049
2B2-049
2B2-049
2B2-049
2B2-049
2B2-049
2B2-049
2B2-049
2B2-049
2B2-049
2B2-049
2B2-049
2B2-049
2B2-049
2B2-061
2B2-061
2B2-061
2B2-061
2B2-061
2B2-061
2B2-061
2B2-061
2B2-061
2B2-061
2B2-061
2B2-061
2B2-061
2B2-061
2B2-061
2B2-061
2B2-061
2B2-061
2B2-061
2B2-061
2B2-061
2B2-061
2B2-061
2B2-061
2B2-061
2B2-061
2B2-075
2B2-075
QUINLAN LAKE
CRANBERRY LAKE
CRANBERRY LAKE
CRANBERRY LAKE
CRANBERRY LAKE
CRANBERRY LAKE
CRANBERRY LAKE
CRANBERRY LAKE
CRANBERRY LAKE
CRANBERRY LAKE
CRANBERRY LAKE
CRANBERRY LAKE
CRANBERRY LAKE
CRANBERRY LAKE
CRANBERRY LAKE
CRANBERRY LAKE
CRANBERRY LAKE
CRANBERRY LAKE
CRANBERRY LAKE
CRANBERRY LAKE
CRANBERRY LAKE
CRANBERRY LAKE
CRANBERRY LAKE
CRANBERRY LAKE
CRANBERRY LAKE
CRANBERRY LAKE
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
RICHARDSON LAKE
RICHARDSON LAKE
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
S
S
9
1
3
3
3
4
4
5
5
5
6
6
6
6
6
6
6
7
7
7
7
7"
7
8
9
11
*
*
2
2
2
3
3
4
4
5
6
6
7
8
8
8
9
9
10
10
10
10
10
10
10
11
2
2
291
114
108
112
109
114
110
153
170
184
186
156
157
153
158
165
166
153
152
176
164
173
180
171
172
170
253
285
83
87
97
122
113
126
180
102
147
166
237
201
257
234
220
222
302
259
284
296
280
256
195
273
108
114
300
15
13
13
14
14
14
39
54
61
68
41
47
40
40
44
47
37
36
61
49
49
66
59
53
53
183
262
5
6
8
17
13
18
49
10
28
39
126
71
138
120
99
103
366
194
225
268
262
148
70
232
10
13
0.0900
0.0008
0.0400
0.0700
0.0900
0.0400
0.1100
0.0400
0.0500
0.1700
0.0200
0.0200
0.0200
0.0300
0.0600
0.0600
0.0001
0.0400
0.0900
0.0300
0.0300
0.0400
0.0100
0.0200
0.0500
0.0400
1.5800
1.1900
0.3100
0.3800
0.4400
0.2000
0.1900
0.3000
0.2300
0.0500
0.2700
0.1700
1.0700
0.6800
1.0200
0.8600
0.2900
0.8300
1.2600
0.6300
1.3100
1.5000
0.7900
1.3600
0.7900
1.7900
0.1600
0.1700
96
-------
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH .
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH -
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-075
2B2-079
2B2-079
2B2-079
2B2-079
2B2-079
2B2-079
2B2-079
2B2-079
2B2-079
2B2-079
2B2-079
2B2-079
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
RICHARDSON LAKE
PINE LAKE
PINE LAKE
PINE LAKE
PINE LAKE
PINE LAKE
PINE LAKE
PINE LAKE
PINE LAKE
PINE LAKE
PINE LAKE
PINE LAKE
PINE LAKE
97
S
S
S
S
S '
S
S
S
S
S
S '
S
S
S
S '"
s :
S
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s ;
s
s
s
s
s
s i
s
s
s
s
s
s
s !
s
s
s
S :
s
s
s
s '
i
2
2
2
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
4
4
4
4
4
4
4
4
4
4
4
4
5
5
5
6
6
7
7
7
8
*
1
1
1
1
2
4
4
4
5
5
5
107
111
112
113
115
115
129
122
110
121
115
112
111
120
128
121
122
111
115
125
121
121
118
131
120
106
121
130
118
125
135
127
171
162
129
187
194
215
175
190
201
240
124
121
120
112
122
127
150
172
158
165
161
167
10
10
12
12
13
15
22
15
13
17
14
14
12
16
20
15
19
13
15
19
17
17
16
21
17
12
16
21
14
19
24
18
44
39
18
63
80
103
72
82
76
133
21
19
19
15
20
22
34
54
35
47
38
51
0.1600
0.1700
0.1500
0.2100
0.1100
0.1600
0.1500
0.1900
0.1900
0.1000
0.1300
0.1600
0.0700
0.1000
0.1100
0.1300
0.0700
0.1600
0.1300
0.0900
0.1600
0.1200
0.0900
0.1300
0.1600
0.0800
0.1500
0.1700
0.1600
0.1400
0.0900
0.1500
0.2000
0.1800
0.0900
0.4100
0.3400
0'. 5200
0.3000
0.3200
0.3800
0.5300
0.0800
0.0900
0.0800
0.1100
0.0600
0.1200
0.1600
0.1400
b . 3700
/0 . 1900
0.1700
0.3500
-------
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PZRCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
2B2-079
2B2-079
2B2-079
2B2-079
2B2-079
2B2-079
2B2-079
2B2-082
2B2-082
2B2-082
2B2-082
2B2-082
2B2-082
2B2-082
2B2-082
2B2-090
2B2-090
2B2-090
2B2-090
2B2-090
2B2-090
2B2-090
2B2-090
2B2-090
2B2-090
2B2-090
2B2-090
2B2-090
2B2-090
2B2-090
2B2-098
2B2-098
2B2-098
2B2-098
2B2-098
2B2-100
2B2-100
2B2-100
2B2-100
2B2-100
2B2-100
2B2-100
2B2-100
2B2-100
2B2-100
2B2-100
2B2-100
2B2-100
2B2-100
2B2-100
2B2-100
2B2-100
2B2-100
2B3-009
PINE LAKE
PINE LAKE
PINE LAKE
PINE LAKE
PINE LAKE
PINE LAKE
PINE LAKE
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
ELEVENMILE LAKE
ELEVENMILE LAKE
ELEVENMILE LAKE
ELEVENMILE LAKE
ELEVENMILE LAKE
ELEVENMILE LAKE
ELEVENMILE LAKE
ELEVENMILE LAKE
ELEVENMILE LAKE
ELEVENMILE LAKE
ELEVENMILE LAKE
ELEVENMILE LAKE
ELEVENMILE LAKE
ELEVENMILE LAKE
ELEVENMILE LAKE
DELENE LAKE
DELENE LAKE
DELENE LAKE
DELENE LAKE
DELENE LAKE
HERBERT LAKE
HERBERT LAKE
HERBERT LAKE
HERBERT LAKE
HERBERT LAKE
HERBERT LAKE
HERBERT LAKE
HERBERT LAKE
HERBERT LAKE
HERBERT LAKE
HERBERT LAKE
HERBERT LAKE
HERBERT LAKE
HERBERT LAKE
HERBERT LAKE
HERBERT LAKE
HERBERT LAKE
HERBERT LAKE
GRAND SABLE LAKE
s
s
s
s
s
s
s
D
D
D
D
D
D
D
D
S
S
S
S
S
S
S
S
S
S
S
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
D
5
5
5
5
6
9
11
4
4
4
4
5
5
6
8
1
1
1
1
1
2
2
4
4
4
4
5
6
7
9
2
2
2
2
2
1
1
1
1
2
2
2
2
2
2
3
3
3
4
4
5
5
6
1
168
158
163
167
170
211
324
147
135
168
168
174
183
185
167
150
113
116
110
112
154
150
155
181
152
180
187
181
214
314
103
96
109
112
115
130
110
120
117
135
135
165
165
140
128
168
183
188
196
198
205
190
207
116
42
35
40
45
56
86
520
29
23
45
40
46
55
56
47
30
13
14
13
13
34
29
33
75
32
61
62
40
109
380
11
•8
10
17
11
21
15
17
19
26
27
45
44
28
24
46
65
70
75
88
88
73
99
16
0.2100
0.3400
0.1400
0.1700
0.2400
0.4900
0.6700
0.1500
0.1000
0.2300
0.2700
0.3300
0.2300
0.2600
0.4900
0.3000'
0.1900
0.1700
0.2400
0.1000
0.2500
0.3100
0.3500
0.4600
0.3300
0.4100
0.3300
0.4300
0.3400
0.2900
0.0500
0.0900
0.0600
0.0300
0.0400
0.1900
0.2000
0.1500
0.1400
0.2000
0.1500
0.2800
0.3000
0.2300
0.1800
0.1730
0.3400
0.3200
0.2300
0.3600
0.4100
0.1300
0:0200
0.0900
98
-------
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
2B3-009
2B3-009
2B3-009
2B3-009
2B3-009
2B3-00.9
2B3-009
2B3-009
2B3-009
2B3-009
2B3-009
2B3-009
2B3-009
2B3-009
2B3-009
2B3-009
2B3-009
2B3-009
2B3-009
2B3-009
2B3-009
2B3-012
2B3-012
2B3-012
2B3-012
2B3-012
2B3-012
2B3-012
2B3-012
2B3-012
2B3-012
2B3-012
2B3-012
2B3-013
2B3-013
2B3-013
2B3-013
2B3-013
2B3-013
2B3-013
2B3-013
2B3-013
2B3-013
2B3-013
2B3-013
2B3-013
2B3-013
2B3-020
2B3-020
2B3-020
2B3-020
2B3-020
2B3-020
2B3-020
GRAND SABLE
GRAND SABLE
GRAND SABLE
GRAND SABLE
GRAND SABLE
GRAND SABLE
GRAND SABLE
GRAND SABLE
GRAND SABLE
GRAND SABLE
GRAND SABLE
GRAND SABLE
GRAND SABLE
GRAND SABLE
GRAND SABLE
GRAND SABLE
GRAND SABLE
GRAND SABLE
GRAND SABLE
GRAND SABLE
GRAND SABLE
ROUND LAKE
ROUND LAKE
ROUND LAKE
ROUND LAKE
ROUND LAKE
ROUND LAKE
ROUND LAKE
ROUND LAKE
ROUND LAKE
ROUND LAKE
ROUND LAKE
ROUND LAKE
FOX LAKE
FOX LAKE
FOX LAKE
FOX LAKE
FOX LAKE
FOX LAKE
FOX LAKE
FOX LAKE
FOX LAKE
FOX LAKE
FOX LAKE
FOX LAKE
FOX LAKE
FOX LAKE
BUTO LAKE
BUTO LAKE
BUTO LAKE
BUTO LAKE
BUTO LAKE
BUTO LAKE
BUTO LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
C
C
C
C
C
C
C
C
C
C
C
C
C
C
D
D
D
D
D
D
D
! 1
1
! 1
2
! 2
! 2 -
1 2
i 2
1 3
"1 5
5
•i 5
1 5
i 6
i 6
\ 6
| 6
; 7
7
8
i 9
' 1
i 1
•!i
i 2
2
1 2
2
2
!3
!3
5 •
! 7
12
i 3
13
!3
!3
3
4
4
4
4
7
8
8
11
2
;3 -
3
'3
;3
13 '
'13
126
118
114
161
112
119
163
122
161
166
166
168
165
168
200
189
180
207
196
241
181
99
104
109
126
140
144
134
127
120
116
131
252
111
113
110
115
110
120
137
135
143
135
192
173
217
309
120
116
122
113
172
121
113
17
17
15
48
14
17
42
17
48
53
47
55
44
45
87
71
58
88
84
124
63
8
10
12
20
27
30
23
20
17
15
22
190
12
14
13
12
14
15
22
20
26
22
73
46
105
410
14
16
19
14
49
15
14
0.1000
0.1000
0.1400
0.2400
0.1000
0.1300
0.2000
0.1500
0.1600
0.2600
0.2900
0.1900
0.0900
0.5300
0.1400
0.1000
0.3900
0.4600
0.4300
0.0600
0.4300
0.2500
0.1600
0.1100
0.1800
0.1600
0.1900
0.1100
0.1800
0.1200
0.1600
0.0300
0.4600
0.1700
0.2100
0.2000
0.1500
0.250C
0.470C
0.240C
0.410C
0.250C
0.330(
0.6801
O.OlOi
0.210
1.220
0.220
0.230
0.110
0.29C
0.26C
0.26C
0.21C
99
-------
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
2B3-020
2B3-020
2B3-020
2B3-020
2B3-020
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-023
2B3-028
2B3-028
2B3-028
2B3-028
2B3-028
2B3-028
2B3-028
2B3-028
2B3-028
2B3-028
2B3-028
2B3-028
2B3-028
2B3-028
233-028
2B3-028
2B3-028
2B3-028
2B3-030
2B3-030
2B3-030
2B3-030
2B3-030
2B3-030
2B3-030
2B3-030
2B3-030
2B3-030
2B3-031
BUTO LAKE
BUTO LAKE
BUTO LAKE
BUTO LAKE
BUTO LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
BONE LAKE
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
CATARACT BASIN
ISLAND LAKE
ISLAND LAKE
ISLAND LAKE
ISLAND LAKE
ISLAND LAKE
ISLAND LAKE
ISLAND LAKE
ISLAND LAKE
ISLAND LAKE
ISLAND LAKE
TWIN LAKES
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
R
S
S
S
S
S
S
S
S
S
S
D
3
3
5
5
12
1
1
2
2
2
2
3
4
5
5
5
6
6
6
7
7
7
9
9
9
3
3
3
3
3
3
. 3
4
5
5
5
5
7
8
9
9
11
11
2
3
3
3
4
4
4
6
8
8
2
132
126
156
168
326
92
91
109
106
111
114
127
197
172
174
216
175
188
171
220
215
242
211
267
260
146
145
165
155
153
145
142
146
220
176
219
175
195
261
285
281
272
276
106
107
112
129
110
120
124
158
182
176
106
20
19
' 40
42
400
10
10
16
13
15
18
25
100
57
50
140
58
81
69
120
105
160
93
250
200
40
40
66
54
39
41
38
41
163
86
173
81
126
299
327
335
350
340
9
13
13
18
12
18
23
38
65
59
10
0.2900
0.1700
0.1800
0 . 2400
1.4100
0.0100
0.0001
0.0100
0.0200
0.0200
0.0100
0.0200
0.1400
0.0800
0 . 0800
0.1400
0.2000
0,0500
0.0600
0.3300
0.0600
0.1600
0.1000
0.1900
0.3000
0.0500
0.0500
0.0300
0.0600
0.0500
0.0500
0.0400
0.0670
0.1300
0.1000
0.1300
0.0800
0.0600
0.2500
0.2200
0.1800
0.2400
0.2300
0.4000
0.2500
0.3500
0.3100
0.2200
0.3400
0.4700
0.4700
0.8500
0.5100
0.1600
100
-------
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
YELLOW PERCH
2B3-031
2B3-031
2B3-031
2B3-031
2B3-031
2B3-031
2B3-031
2B3-031
2B3-031
2B3-031
2B3-031
2B3-031
2B3-031
2B3-034
2B3-034
2B3-034
2B3-034
2B3-034
2B3-037
2B3-037
2B3-037
2B3-037
2B3-037
2B3-037
2B3-037
2B3-037
2B3-037
2B3-037
2B3-037
2B3-037
2B3-037
2B3-037
2B3-037
2B3-037
2B3-037
2B3-055
2B3-055
2B3-055
2B3-055
2B3-055
2B3-055
2B3-055
2B3-055
2B3-055
2B3-055
2B3-055
2B3-055
2B3-055
2B3-055
2B3-057
2B3-071
2B3-071
2B3-071
2B3-071
TWIN LAKES
TWIN LAKES
TWIN LAKES
TWIN LAKES
TWIN LAKES
TWIN LAKES
TWIN LAKES
TWIN LAKES
TWIN LAKES
TWIN LAKES
TWIN LAKES
TWIN LAKES
TWIN LAKES
KLONDIKE LAKE
KLONDIKE LAKE
KLONDIKE LAKE
KLONDIKE LAKE
KLONDIKE LAKE
RUMBLE LAKE
RUMBLE LAKE
RUMBLE LAKE
RUMBLE LAKE
RUMBLE LAKE
RUMBLE LAKE
RUMBLE LAKE
RUMBLE LAKE
RUMBLE LAKE
RUMBLE LAKE
RUMBLE LAKE
RUMBLE LAKE
RUMBLE LAKE
RUMBLE LAKE
RUMBLE LAKE
RUMBLE LAKE
RUMBLE LAKE
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
(NO NAME)
TWIN LAKE
OSTRANDER LAKE
OSTRANDER LAKE
OSTRANDER LAKE
OSTRANDER LAKE
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
S
S
S
S
' 2
2
3
3
4
5
1 6
6
6
7
] 7
8
i 9
I 4
i 7
8
1 9
' 11
*
!. 2
2
3
1 3
3
1 3
3
3
4
5
6
: 6
8
8
8
10
i 2
' 2
' 2
2
2
3 •
1 3
3
1 3
3
: 4
4
1 4
4
! 3
2
1 2
1 3
1 3
120
120
105
112
177
146
157
192
165
166
216
180
230
165
251
169
267
220
110
106
101
121
115
109
117
119
113
125
110
228
125
198
161
232
217
149
150
146
145
155
237
206
178
204
195
211
209
202
195
170
115
129
118
115
14
15
10
11
51
25
49
57
45
47
103
58
132
46
203
46
221
108
13
12
11
16
15
14
16
16
12
19
13
134
18
77
43
121
108
35
35
39
34
43
184
103
60
118
96
112
117
111
99
50
11
18
11
13
0.2000
0.1700
0 . 2400
0.2300
0.5100
0.1600
0.2500
0.5200
0.3100
0.4400
0.3300
0.6600
0.2300
0.1000
0.2600
0.1000
0.3300
0.4000
0.0100
0.0600
0.0700
0.0800
0.0600
0.0100
0.2000
0.0800
0.0300
0.0200
0.1000
0.1500
0.0700
0.3300
0.2600
0.4400
0.2700
0.1500
0.1500
0.0500
0.1800
0.1300
0.2600
0.3300
0.3100
0.2900
0.2200
0.3500
0.3200
0.2500
0.2800
0.0600
0.0900
0.0100
0.2900
0.0200
101
-------
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
YELLOW
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
PERCH
2B3-071
2B3-071
2B3-071
2B3-071
2B3-071
2B3-071
2B3-071
OSTRANDER
OSTRANDER
OSTRANDER
OSTRANDER
OSTRANDER
OSTRANDER
OSTRANDER
LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
LAKE
S
S
S
S
S
S
S
3
3
3
3
3
4
10
118
114
116
116
109
125
196
12
• 13
10
11
9
13
74
0.
0.
0.
0.
0.
0.
0.
1000
0500
0200
0900
0200
1200
1900
* Denotes a missing value.
TL - total length
WT - weight
S - seepage
D - drainage
R - reservoir
*US. GOVERNMENT PRINTING OFFICE: 1991-518-187/25603
102
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