Mercury Bioaccumulation in Fishes from Subalpine 

Lakes in the Wallowa-Whitman National Forest, 

Northeastern Oregon and Western Idaho

U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2013-1148

Mercury Bioaccumulation in Fishes from Subalpine 

Lakes in the Wallowa-Whitman National Forest, 

Northeastern Oregon and Western Idaho

By Collin A. Eagles-Smith, Garth Herring, Branden L. Johnson, U.S. Geological Survey; and Rick Graw, 

U.S. Forest Service

Open-File Report 2013–1148

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2013 

For more information on the USGS—the Federal source for science about the Earth,

its natural and living resources, natural hazards, and the environment—visit 

http://www.usgs.gov or call 1–888–ASK–USGS

For an overview of USGS information products, including maps, imagery, and publications,

visit http://www.usgs.gov/pubprod

To order this and other USGS information products, visit http://store.usgs.gov

Suggested citation:

Eagles-Smith, C.A., Herring, G., Johnson, B.L., and Graw, R., 2013, Mercury bioaccumulation in fishes 

from subalpine lakes of the Wallowa-Whitman National Forest, northeastern Oregon and western Idaho: 

U.S. Geological Survey Open-File Report 2013-1148, 38 p.

Any use of trade, firm, or product names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.

Although this information product, for the most part, is in the public domain, it also may contain

copyrighted materials as noted in the text. Permission to reproduce copyrighted items must be 

secured from the copyright owner.
iii

Contents

Executive Summary....................................................................................................................................... 1

Introduction and Objectives ........................................................................................................................... 2

Methods......................................................................................................................................................... 3

Site Description and Field Collection.......................................................................................................... 3

Fish Sample Processing and Tissue Preparation ...................................................................................... 4

Mercury Determination............................................................................................................................... 4

Stable Isotope Analysis.............................................................................................................................. 5

Otolith Preparation and Fish Age Determination........................................................................................ 5

Water Chemistry ........................................................................................................................................ 6

Geospatial and Remote Sensing Analysis ................................................................................................. 6

Statistical Analyses .................................................................................................................................... 7

Results........................................................................................................................................................... 8

Descriptive Summary Results .................................................................................................................... 8

Spatial Patterns and Size Effects............................................................................................................... 8

Landscape and Lake Effects...................................................................................................................... 9

Discussion ................................................................................................................................................... 10

Acknowledgments ....................................................................................................................................... 13

References Cited......................................................................................................................................... 14

Figures

Figure 1. Map of the Wallowa-Whitman National Forest in northeastern Oregon and western Idaho..... 18

Figure 2. Goodrich Lake and its catchment in the South Elkhorn Range. Inset: Sampled brook trout.... 19

Figure 3. Locations of study lakes and their respective catchments (defined by colored polygons) 

within each region (N. Elkhorns [red], S. Elkhorns [blue], N. Eagle Cap [ green], S. Eagles Cap [orange], 

and Seven Devils [yellow]) in the Wallowa-Whitman National Forest, northeastern Oregon and western 

Idaho ................................................................................................................................................. 20

Figure 4. Digital image of a brook trout sagittal otolith, showing annular rings ....................................... 21

Figure 5. Visual examples of gradients in catchment forest structure in the Wallowa-Whitman National 

Forest, northeastern Oregon and western Idaho ......................................................................................... 22

Figure 6. Conifer density measured as basal area (m2/hectare) of all conifers >2.5 cm dbh for 

Wallowa-Whitman National Forest lake catchments, northeastern Oregon and western Idaho................... 23

Figure 7. Linear relationships between total mercury (THg) concentrations (dw = dry weight 

concentration) in fish muscle, and fish size (standard length) from different lakes in the Wallowa-Whitman 

National Forest, northeastern Oregon and western Idaho ........................................................................... 24

Figure 8. Linear relationships between total mercury (THg) concentrations (dw = dry weight) in fish 

muscle, and fish age (estimated by counting sagittal otolith annuli) from select lakes in the WallowaWhitman National Forest, northeastern Oregon and western Idaho............................................................ 25

Figure 9. Linear relationships between total mercury (THg) concentrations (dw = dry weight) in fish 

muscle, and relative condition factor from different lakes in the Wallowa-Whitman National Forest, 

northeastern Oregon and western Idaho ..................................................................................................... 26

Figure 10. Linear relationships between total mercury (THg) concentrations (dw = dry weight) in fish 

muscle, and stable carbon isotope ratios from different lakes in the Wallowa-Whitman National Forest, 

northeastern Oregon and western Idaho ..................................................................................................... 27
iv

Figure 11. Linear relationships between total mercury (THg) concentrations (dw = dry weight) in fish 

muscle, and stable nitrogen isotope ratios from different lakes in the Wallowa-Whitman National Forest, 

northeastern Oregon and western Idaho ..................................................................................................... 28

Figure 12. Back transformed least-square mean fish muscle total mercury (THg) concentrations

for the 5 study regions in the Wallowa-Whitman National Forest................................................................ 29

Figure 13. Back transformed least-square mean fish muscle total mercury (THg) concentrations

for the 27 study lakes in the Wallowa-Whitman National Forest, northeastern Oregon and western

Idaho ............................................................................................................................................. 29

Figure 14. Partial residual plot of fish total mercury (THg) concentrations in relation to relative 

condition factor. Solid circles indicate brook trout, open circles represent rainbow trout ............................. 30

Figure 15. Fish muscle total mercury (THg) concentrations in brook trout (Salvelinus fontinalis; black 

bars) and rainbow trout (Oncorhynchus mykiss; gray bars) from five lakes where they co-occurred in the 

Wallowa-Whitman National Forest, northeastern Oregon and western Idaho. ............................................ 30

Figure 16. Relationship between conifer (>2.5 cm diameter at breast height) basal area within a lake’s 

catchment on fish THg concentrations......................................................................................................... 31

Figure 17. Influence of (A) lake surface area, (B) dissolved sulfate, and (C) dissolved organic 

carbon on fish THg concentrations in the Wallowa-Whitman National Forest, northeastern Oregon 

and western Idaho ....................................................................................................................................... 32

Figure 18. Mean total mercury (THg) concentrations in salmonid fishes from other high-elevations 

lakes in comparison to this study ................................................................................................................ 33

Tables

Table 1. Chemical and physical attributes of study lakes and their catchments in the 

Wallowa-Whitman National Forest, northeastern Oregon and western Idaho. ............................................ 34

Table 2. Fish catch data and size ranges from subalpine lakes in the Wallowa-Whitman National 

Forest, northeastern Oregon and western Idaho. ........................................................................................ 36

Table 3. Ranking of candidate models describing physical and chemical variables influencing fish THg 

concentrations in the Wallowa-Whitman National Forest, northeastern Oregon and western Idaho. .......... 37

Table 4. Ranking of candidate models describing physical variables influencing fish THg concentrations 

in the Wallowa-Whitman National Foresta, northeastern Oregon and western Idaho. ................................. 38
v 

Conversion Factors

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

Area

square meter (m2) 10.76 square foot (ft2) 

hectare (ha) 0.003861 square mile (mi2) 

square kilometer (km2) 0.3861 square mile (mi2)

Volume

liter (L) 0.264172 gallon (gal)

milliliter (mL) 0.0333814 ounce, fluid (fl. oz)

Mass

gram (g) 0.03527 ounce, avoirdupois (oz)

milligram (mg) 0.00003527 ounce, avoirdupois (oz)

microgram (μg) 0.00000003527 ounce, avoirdupois (oz)

List of Acronyms

dw dry weight

ww wet weight

Hg mercury

MeHg methylmercury

SE standard error

THg total mercury

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows: °F=(1.8×°C)+32
1 

Mercury Bioaccumulation in Fishes from Subalpine

Lakes in the Wallowa-Whitman National Forest, 

Northeastern Oregon and Western Idaho

By Collin A. Eagles-Smith, Garth Herring, Branden L. Johnson, U.S. Geological Survey, and Rick Graw, 

U.S. Forest Service

Executive Summary

Mercury (Hg) is a globally distributed pollutant that poses considerable risks to human 

and wildlife health. Over the past 150 years since the advent of the industrial revolution, 

approximately 80 percent of global emissions have come from anthropogenic sources, largely 

fossil fuel combustion. As a result, atmospheric deposition of Hg has increased by up to 4-fold

above pre-industrial times. Because of their isolation, remote high-elevation lakes represent 

unique environments for evaluating the bioaccumulation of atmospherically deposited Hg 

through freshwater food webs, as well as for evaluating the relative importance of Hg loading 

versus landscape influences on Hg bioaccumulation. The increase in Hg deposition to these 

systems over the past century, coupled with their limited exposure to direct anthropogenic 

disturbance make them useful indicators for estimating how changes in Hg emissions may 

propagate to changes in Hg bioaccumulation and ecological risk. In this study, we evaluated Hg 

concentrations in fishes of high-elevation, sub-alpine lakes in the Wallowa-Whitman National 

Forest in northeastern Oregon and western Idaho. Our goals were to (1) assess the magnitude of 

Hg contamination in small-catchment lakes to evaluate the risk of atmospheric Hg to human and 

wildlife health, (2) quantify the spatial variability in fish Hg concentrations, and (3) determine 

the ecological, limnological, and landscape factors that are best correlated with fish total mercury 

(THg) concentrations in these systems. Across the 28 study lakes, mean THg concentrations of 

resident salmonid fishes varied as much as 18-fold among lakes. Importantly, our top statistical 

model explained 87 percent of the variability in fish THg concentrations among lakes with four 

key landscape and limnological variables— catchment conifer density (basal area of conifers 

within a lake’s catchment), lake surface area, aqueous dissolved sulfate, and dissolved organic 

carbon. The basal area of conifers within a lake’s catchment was by far the most important 

variable explaining fish THg concentrations, with an increase in THg concentrations of more 

than 400 percent across the forest density spectrum. Across all study lakes, fish THg 

concentrations ranged from 0.004 to 0.438 milligrams per kilogram wet weight (mg/kg ww). 

Only a single individual fish sample exceeded the U.S. Environmental Protection Agency’s 

(USEPA) human health tissue residue criteria of 0.3 mg/kg ww. However, 54 percent of fish 

(N=177) exceeded the more stringent tissue residue criteria (0.04 mg/kg ww) adopted by the 

Oregon Department of Environmental Quality to better protect subsistence fishers. Additionally, 

2 and 10 percent of fish exceeded levels associated with reduced common loon reproduction and 

behavior, respectively. Whereas 25 and 68 percent of fish sampled exceeded concentrations 

deemed protective of mink and kingfisher, respectively. These results suggest that THg 
2 

concentrations may be present in these lakes at levels associated with ecological risk. It is 

important to note however, that accurate inference on potential impairment cannot be made 

within the context of this study design and further research is needed to better quantify these 

risks.

Introduction and Objectives

Mercury (Hg) is a globally distributed pollutant that poses considerable risks to human 

and wildlife health (Scheuhammer and others, 2007). Under appropriate biogeochemical 

conditions in aquatic ecosystems, inorganic Hg is microbially converted to the highly toxic and 

bioavailable organic form, methylmercury (MeHg), which biomagnifies through food webs 

where it can reach toxicologically relevant concentrations in top predators. As a result, the 

ecological risk of Hg is driven by a combination of both inorganic loading, as well as ecological 

parameters that facilitate MeHg production and bioaccumulation. Inland lakes are particularly 

vulnerable to Hg contamination because they are recipients of transported Hg, and their organic 

rich, anoxic sediments can promote MeHg production. 

Lakes receive Hg from various sources, including atmospheric deposition, watershed 

runoff, and glacial meltwater (St. Louis and others, 1994; Rudd, 1995). Over the past 150–200 

years since the advent of the industrial revolution, approximately 80 percent of global emissions 

have come from anthropogenic sources, largely fossil fuel combustion (Mason and others, 1994), 

but also gold mining, and manufacturing (Pirrone and others, 2010). Mercury also is re-emitted 

to the atmosphere from forest fires and agricultural burning (Friedli and others, 2003). As a 

result, atmospheric deposition of Hg in remote environments has increased by approximately 

3-fold (Lindberg and others, 2007). Importantly, there is substantial variability in the spatial 

patterns of anthropogenic Hg releases and deposition at local, regional, and global scales, 

associated with coal-fired power plants, industrial activity, and mining (Engstrom and others, 

2007), as well as in the propensity of inorganic Hg to be methylated across the landscape. 

Therefore, it can be difficult to determine how much of the variability in Hg concentrations 

among systems can be attributed to Hg loading relative to in situ MeHg production. 

Because of their isolation, remote high elevation lakes represent unique environments for 

evaluating the bioaccumulation of atmospherically deposited Hg through freshwater food webs, 

as well as for evaluating the relative importance of Hg loading versus landscape influences on 

Hg bioaccumulation. The increase in Hg deposition to these systems over the past century

(Phillips and others, 2011), coupled with their limited exposure to direct anthropogenic 

disturbance make them useful indicators for estimating how changes in Hg emissions may 

propagate changes in Hg bioaccumulation and ecological risk. In this study, we evaluated Hg 

bioaccumulation in fishes of high elevation, sub-alpine lakes in the Wallowa-Whitman National 

Forest in northeastern Oregon and western Idaho (fig. 1). Our goals were to (1) assess the 

magnitude of Hg contamination in a collection of small catchment lakes to evaluate the risk of 

atmospherically deposited Hg to human and wildlife health, (2) quantify the spatial variability in 

fish Hg concentration across these lakes, and (3) determine the ecological, limnological, and 

landscape factors that are most strongly related to fish THg concentrations. 
3 

Methods

Site Description and Field Collection

The Wallowa-Whitman National Forest (WWNF) occupies more than 9,000 km2 of land, 

including nearly 2,500 km2

 of designated wilderness. The forest is comprised of varied 

landscapes including low-elevation grasslands, coniferous forests, and alpine meadows. Highelevation habitats are found in the Elkhorn Mountains, the Wallowa Mountains and Eagle Cap 

Wilderness, and the Hells Canyon National Recreation Area. A common feature in the 

mountains across the WWNF is small (<0.5 km2

), high-elevation (>2,000 m) lakes within 

isolated catchments that are (or were historically) stocked for recreational purposes with nonnative salmonid fishes, such as brook trout (Salvelinus fontinalis) or rainbow trout 

(Oncorhynchus mykiss) (fig. 2). These lakes are solely catchment or groundwater fed, and Hg 

sources are likely limited primarily to atmospheric deposition because they do not have 

inflowing streams. The catchments associated with these lakes generally are small (< 5 km), and 

are comprised of a combination of open, rocky terrain, and temperate, subalpine coniferous 

forest. 

Using the U.S. Forest Service limnology database, we selected 28 individual lakes within 

5 different regions of the WWNF (table 1, fig. 3). To select lakes for sampling, we identified 

those with records of self-sustaining populations of brook trout, rainbow trout, or cutthroat trout 

(Oncorhynchus clarkii), and that also occurred within a 1-day hike from a trailhead. We then 

divided these lakes into geographic regions: North Elkhorn, South Elkhorn, North Eagle Cap, 

South Eagle Cap, and Seven Devils (fig. 3) based on their geography, and randomly selected 

7 lakes per region to sample. We targeted 7 lakes per region because our goal was a total of 

25 lakes and we assumed that fish may no longer be present in some lakes with historical reports 

of fish presence. 

Between July 11 and September 1, 2011, fishes were collected in association with 

ongoing, regular U.S. Forest Service and Oregon Department of Fish and Wildlife fish 

monitoring programs from 28 lakes in the Wallowa-Whitman National Forest. At each lake, 

rainbow trout, brook trout, cutthroat trout, or lake trout (Salvelinus namaycush) were sampled 

using hook and line and 30 × 2 m variable-mesh gillnets. We wrapped collected fish in clean, 

solvent-rinsed aluminum foil and then sealed them in individually labeled polyethylene bags. 

Each fish was kept cool on ice or snow until delivery to the laboratory, where they were stored at 

-20oC. At 21 of the lakes, we also collected water samples in 125 mL acid-cleaned amber glass 

bottles. To minimize sediment disturbance and contamination of the sample with particulate 

material, we sampled from the shoreline of lakes (at the outflow if applicable), making sure we 

were standing on an emerging solid object, such as a rock or log. We uncapped each clean bottle 

underwater, and rinsed the bottle 3 times with lake water. On our final fill, we capped the bottle 

underwater and stored it on ice prior to shipment to the laboratory for filtration and chemical 

analysis. 
4 

Fish Sample Processing and Tissue Preparation

In the laboratory, we thawed each fish sample to room temperature, and then measured 

standard length to the nearest millimeter on a fish board and mass to the nearest 0.1 g on a digital 

balance. We calculated the body condition of each fish using the Relative Condition Factor (Kn), 

which accounts for potential changes in shape as fish grow (Anderson and Neumann, 1996). The 

relative condition factor was calculated as: 

Kn= W/W′, 

Where: W = fish whole body mass, in g and, 

W′ = the predicted length-specific mean mass from a predictive length-mass 

regression model calculated for each species. 

To determine W′ for each species, we used log10-transformed standard length (mm) and 

log10-transformed wet mass (g) data for all fish of each species, from all lakes in which they 

were captured (brook trout linear regression: n = 230, r2 = 0.96, intercept = -11.646, slope = 

3.099; rainbow trout linear regression: n = 85, r2

 = 0.91, intercept = -10.696, slope = 2.913; 

cutthroat trout linear regression: n = 11, r2 = 0.97, intercept = -10.520, slope = 2.854). We then 

removed sagittal otoliths from each fish, cleaned them with deionized water, and stored them in 

clean, dry vials until further processing. From each fish, we dissected 5–10 g of skinless axial 

muscle, rinsed the muscle tissue in deionized water, blotted it dry with a clean, lint-free wipe, 

and weighed it on an analytical balance to the nearest 0.0001 g. We then dried each muscle 

sample in a convection oven at 50oC for 48 h, or until a constant mass was achieved. We 

subsequently removed the samples from the drying oven and allowed them to cool to room 

temperature in a desiccator. Once cool, we measured the dry mass of each muscle tissue sample 

to the nearest 0.0001 g, and ground them to a fine powder in a tissue mill. The homogenized 

samples were then stored in a dark desiccator until chemical analyses. 

Mercury Determination 

We determined total mercury (THg) concentrations on the axial muscle of each fish 

sample because most (90–95 percent) of mercury in fish muscle tissue is in the methylmercury 

(MeHg) form (Bloom and others, 1992). Total Hg concentrations were determined following 

EPA method 7473 (U.S. Environmental Protection Agency, 2000) on a Milestone tri-cell DMA80 Direct Mercury Analyzer (Milestone Inc, Monroe, Connecticut, USA) at the USGS Forest and 

Rangeland Science Center’s Contaminant Ecology Laboratory in Corvallis, Oregon. Briefly, we 

used an integrated sequence of drying (250oC for 30s), thermal decomposition (650oC for 90s), 

catalytic conversion, and then amalgamation, followed by cold vapor atomic absorption 

spectroscopy. Unless stated otherwise, we analyzed and report all muscle tissue samples on a dry 

weight basis in order to control for variable moisture content among individuals, and because it 

is a more toxicologically relevant measure of THg concentrations. However, to facilitate 

comparison to other studies and to allow conversion to wet weight concentrations we determined 

moisture content in each sample. Moisture content in the fish muscle tissue ranged from 74.7 to 

85.8 percent with a mean (± standard error) of 79.8 ± 0.1%. 

Quality-assurance measures included analysis of two certified reference materials (either 

dogfish muscle tissue [DORM-3; National Research Council of Canada, Ottawa, Canada], or 

dogfish liver [DOLT-3; National Research Council of Canada, Ottawa, Canada], two system and 

method blanks, and two duplicates per batch of 30 samples. Recoveries (± standard error [SE]) 
5 

averaged 99.4 ± 1.8 percent (n = 60) and 97.9 ± 0.8 percent (n = 90) for certified reference 

materials and calibration checks, respectively. Absolute relative percent difference for all 

duplicates averaged 3.4 ± 0.5 percent. 

Stable Isotope Analysis 

Analysis of carbon and nitrogen stable isotope ratios (δ13C and δ15N, respectively), 

provide for a time integrated assessment of an organism’s foraging ecology (Hobson and 

Bairlein, 2003). Specifically, carbon and nitrogen stable isotope ratios can provide a quantitative 

estimate of a fish’s relative reliance on different carbon sources (for example, benthic vs. 

pelagic), as well as trophic position (Eagles-Smith and others, 2008). We analyzed stable isotope 

ratios of both elements in muscle tissue of each fish sampled. However, isotope ratios at the base 

of lake food webs often vary among lakes, making inter-lake comparisons of foraging ecology 

difficult using raw data (Post, 2002). Thus, isotope ratios in consumers need to be baseline 

adjusted for their specific lake though the analysis of isotope ratios in obligate primary 

consumers (Post, 2002). In this study, we were unable to collect adequate primary consumers 

across lakes, thus we constrain our interpretation of isotope data only to within-lake comparisons 

of individual fishes. 

Stable-isotope analysis was performed on a continuous flow isotope ratio mass 

spectrometer (IRMS; dualinlet Europa 20/20, PDZ Europa, Crewe, UK) at the University of 

California, Davis Stable Isotope Facility. Approximately 0.80–1.20 mg of tissue were weighed 

and sealed into tin capsules. Sample combustion to CO2

 and N2 occurred at 1,000oC in an inline 

elemental analyzer. A Carbosieve G column separated the gas before introduction into the IRMS. 

Standards (PeeDee Belemnite for δ13C and air for δ15N) were injected directly into the IRMS 

before and after sample peaks. Isotope ratios are expressed in per mil (‰) notation. Using δ13C 

as an example, ratios are defined by the following equation: 

δ13C = {[(13C/12C)sample/(13C/12C)standard] – 1} × 1,000. 

Otolith Preparation and Fish Age Determination

We prepared otoliths for age determination through polishing in order to remove coarse 

material that can interfere with identifying annuli. We first mounted each otolith, with the sulcus 

facing up, to clear glass microscope slides using Loctite® cyanoacrylate adhesive. We polished 

each otolith with a series of sanding and polishing discs in order to remove overburden and 

expose the growth increments. We started with the coarse removal of external calcification and 

excess glue using 600 or 400 μm grit wet/dry sandpaper. Once the delicate internal structure of 

the otolith was revealed, we applied a sequential gradient of polishing paper (30, 15, 9, 2, 1 μm 

grit) to prepare the surface for visual assessment. Throughout the polishing step, we inspected 

the polished face of each otolith under a compound microscope (2.5× and 10× magnification) to 

ensure that sufficient polishing was occurring, and that otoliths were not overpolished, which 

results in reduction of growth increment visibility. Polishing was considered complete when 

growth increments were clearly visible from the margins to the nucleus. After polishing, we 

captured digital images of each otolith (fig. 4) at 10× magnification using a compound 

microscope equipped with a Leica® digital inline camera, connected to a desktop computer. We 

subsequently adjusted image quality as necessary using ImageJ software (Rasband, 1997) to 

facilitate identification of annual increments. We estimated the age of each fish by counting the 

sequence of transparent and opaque annuli along a plane from the nucleus to the margin, 
6 

following Secor and others (1991). Two independent, blind readings were made, each by 

different people, and the age estimates were compared. Any otoliths with age estimates that were 

not in agreement were then interpreted by a third person. If the third reading was in agreement 

with either of the other two, then that age was accepted. If all three readings were different, then 

we used the mean age for the three readings. 

Water Chemistry 

All water samples were shipped immediately to the USDA Forest Service Air Program 

Biogeochemistry Laboratory in Fort Collins, Colorado. Prior to analysis water samples were 

filtered through 0.7-μm glass fiber filters under gentle vacuum. Conductivity and pH were 

determined with a Metrohm/Brinkman Model 712 conductivity meter and Metrohm/Brinkman 

Model 809 pH meter, respectively. Acid neutralizing capacity (ANC) was determined using the 

Gran analysis technique (Gran, 1952). Anions (F-, Cl-, NO2

-

, NO3

-

, SO4

2-) and cations (Na+

, 

NH4

+

, K+, Mg2

+

, Ca2

+

) were measured with a Metrohm Model 838 Compact Ion 

Chromatograph). Dissolved organic carbon (DOC) was quantified on a Shimadzu TOC-5000 

analyzer.

Geospatial and Remote Sensing Analysis

We characterized the spatial and physical attributes of the study lakes and their 

catchments using ArcMap 10 (ESRI, Redlands, California, USA). We first combined 10-m 

resolution digital elevation models (DEM) and the National Hydrologic Dataset within the 

Spatial Analyst tool framework and then delineated areas of internal drainage and water 

accumulation in the DEM in order to assign a flow path to each grid cell. These estimates of flow 

direction and accumulation were integrated across the DEM and overlaid on the National 

Hydrological Dataset layer to determine “pour points” for delineating catchments. We used this 

approach to delineate the catchments for each lake, and then we reviewed those generated 

polygon boundaries against 1:24,000 USGS topographic maps and aerial photography to ensure 

accuracy. Within each catchment, we extracted lake elevation from the DEM and determined 

lake morphometry (lake surface area, maximum length, and perimeter) from the National 

Hydrological Dataset. Although it is well established that percent wetland area within a 

catchment can be an important driver of lake MeHg concentrations (Shanley and others, 2005), 

many of these catchments had limited wetland coverage, and geospatial wetland inventories 

lacked the resolution necessary to conduct statistically robust analyses. Therefore, we did not 

address wetland influences in this study. 

In order to assess landscape-level influences on fish THg concentrations, we estimated 

forest structure within the catchment of each lake. We used 30-m resolution grid-based forest 

composition and structure data derived from the Gradient Nearest Neighbor imputation methods 

of Ohmann and Gregory (2002). Data were acquired from the Landscape Ecology, Modeling, 

Mapping, and Analysis team (http://www.fsl.orst.edu/lemma/). The Gradient Nearest Neighbor 

models provide estimates for forest parameters, such as basal area, tree density, stand age, and

cover class (fig. 5). Using the output from these geospatial models, we characterized the total 

basal area (m2

/ha) of conifers (≥ 2.5 cm diameter at breast height [dbh]) within the catchment of 

each lake (fig. 6). Basal area is a common metric of forest structure that is related to both the 

density and size distribution of trees in an area. Basal area is generally correlated with canopy 

cover, but can be a better estimate of tree biomass because it is less sensitive to variation due to 

crown overlap. 
7 

Statistical Analyses

Our statistical approach was designed to both quantify the spatial variability of fish THg 

concentrations among lakes and regions in the WWNF, and to evaluate the relationships between 

fish THg concentrations and key ecological, limnological, and landscape variables across the 

study lakes. To do this, we employed a 2-tiered statistical approach using mixed-effects general 

linear models. This process allowed us to statistically control for individual-level factors that 

influence fish THg concentrations (such as species, fish size, and body condition) in order to 

generate comparable model estimates of lake-specific least-square mean fish THg 

concentrations. These lake-specific means could then be used to test relative importance of the 

landscape and limnological variables on those THg concentrations. 

Our primary goal in our first tier analysis was to make comparisons of fish THg 

concentrations among lakes. To do this we needed to statistically control for the effects of 

species, fish length, and fish body condition. We excluded lake trout (N=1 from a single lake) 

and cutthroat trout (N=11 from a single lake) from the analysis because they only were sampled 

from one lake and we could not make inferences across the sampling area. We used a mixedeffects, nested general linear model with region and lake (nested within region) included as fixed 

factors, and relative condition factor (Kn), standard length (SL), and δ13C as covariates. We also 

included a species x SL interaction term in the model in order to test whether the relationship 

between THg concentrations and fish size differed among species. Species was included as a 

random effect because we were limited in the number of lakes from which rainbow trout and 

brook were sampled together. Because our primary goal was to make comparisons of fish THg 

concentrations among lakes, and not necessarily to compare Hg concentrations between species, 

we simply needed to statistically control for the effect of species across lakes. Doing so using a 

fixed-effects model would have biased our lake-specific least-square mean estimates. However, 

we also were interested in testing for differences in THg concentrations between species where 

they co-occurred. Therefore, we constructed a second, fixed-effects model using a subset of the 

data from lakes where rainbow trout and brook trout co-occurred. Although we measured δ15N 

ratios in the muscle fish samples, we were unable to collect baseline invertebrate samples for 

each lake, and thus could not make estimates of δ15N-derived trophic level effects on fish THg 

concentrations among lakes (Post, 2002). Therefore, we excluded δ15N from our primary model, 

and instead evaluated the relationship between δ15N and THg concentrations on an individuallake basis using linear regression. 

In order to examine the relationships between landscape factors and fish THg 

concentrations, we used the lake-specific least-square mean fish THg concentrations from the 

primary global model described above to statistically account for the simultaneous varying 

effects of fish species, fish size, and body condition, providing standardized estimates of fish Hg 

concentrations among lakes. We used these estimates as the response variables in general linear 

models, following an information-theoretic framework (Burnham and Anderson, 2002) using 

R software (R Development Core Team, 2011), to determine the model structure and variables 

that most influenced Hg bioaccumulation in these subalpine lakes. We built and ranked separate 

competing a priori candidate models to understand how fish THg concentrations varied in 

response to select physical and chemical variables of lakes and their catchments. Physical 

variables included lake surface area (km2

), lake elevation (m), lake catchment area (km2), and the 

lake area:catchment area ratio. We also included the remotely sensed estimates of the basal area 

(m2/ha) of conifers ≥2.5cm dbh within each lake’s catchment. This commonly used silvicultural 

index integrates conifer stand density, age, and size to provide an approximation of conifer 
8 

biomass within lake catchments. Lake chemical variables included pH, acid neutralizing capacity 

(ANC; μeq/L), sulfate (SO4; mg/L), and dissolved organic carbon (DOC; mg/L). We also 

included an intercept only model and a global model. Mercury concentrations were natural log 

transformed to improve residual normality. Because we lacked water chemistry data from 

30 percent of our lakes, we first considered models from the reduced sample size where we had 

both chemical and physical data (N=18). We then conducted a similar analysis using the larger 

lake sample size for which we had only the physical parameter measurements (N=26). 

We used Akaike’s information criterion adjusted for small samples sizes (AICc) and 

considered the model with the lowest AICc value to be the most parsimonious (Burnham and 

Anderson, 2002). We included all possible variable combinations to ensure a balanced design to 

facilitate comparison of variable weights. However, to ensure that the number of parameters in 

each model did not exceed what was appropriate for our samples size, we limited the total 

number of variables simultaneously included in any given model to four. We determined the 

relative ranking of each model by subtracting each candidate model’s AICc value from the best 

model (ΔAICc). Candidate models were considered to be competitive when ΔAICc ≤ 2.0. We 

calculated Akaike weights (wi) to assess the weight of evidence that the selected model was the 

best candidate model (Burnham and Anderson, 2002). We also calculated variable weights by 

summing Akaike weights across all models that included the variable to assess the relative 

importance of each variable. We calculated model-averaged beta coefficient estimates from all 

candidate models (Burnham and Anderson, 2002). 

Unless stated otherwise, all data are presented as back-transformed least-square means 

from model output, and standard error values are estimated using the Delta method (Williams 

and others, 2002).

Results

Descriptive Summary Results

We collected and analyzed a total of 327 fish, comprised of 4 species from 28 individual 

lakes, across 5 regions in the WWNF (table 2). Across all regions, lakes, and fish lengths, the 

geometric mean (± standard error) fish THg concentrations (μg/g dw) were 0.23±0.01 in brook 

trout (N=230), 0.17±0.01 in rainbow trout (N=85), 0.24±0.03 in cutthroat trout (N=11), and 2.13 

in lake trout (N=1). We found substantial variation among lakes in the univariate relationships 

between THg concentrations and fish length (fig. 7), age (fig. 8), relative condition (Kn; fig. 9), 

δ13C (fig. 10), and δ15N (fig. 11). In general, we found positive correlations with δ15N, fish 

length, and age, but the relationships were inconsistent across lakes. Conversely, we generally 

found negative correlations with Kn. The lack of consistency in these relationships is likely due 

to the fact that each of these important factors were not simultaneously accounted for in the 

univariate analyses due to limited statistical power. 

Spatial Patterns and Size Effects 

Fish THg concentrations differed among regions (F4, 262.7 = 60.26, P < 0.001), as well as

among lakes nested within region (F22,252.3 = 15.62, P < 0.001). Fish THg concentrations were 

highest in the North Elkhorn (0.367 ± 0.032) and Seven Devils (0.320 ± 0.035) regions and 

lowest in the North Eagle Cap region (0.129 ± 0.011; fig. 12). 
9 

Lake-specific THg concentrations ranged from 0.040 ± 0.007 in Aneroid Lake (North 

Eagle Cap) to 0.718 ± 0.111 in Black Lake (North Elkhorn). Variation in fish THg 

concentrations generally was high in lakes both within, and among regions. Within regions, THg 

concentrations varied by approximately 5.6-fold in North Eagle Cap lakes, 5-fold in North

Elkhorn lakes, 2.6-fold in South Elkhorn lakes, 2.4-fold in South Eagle Cap lakes, and 1.7-fold 

in Seven Devils lakes (fig. 13). In fact, even some lakes within less than 1 km from each other 

differed by up to a factor of 2. 

After statistically controlling for the simultaneous effects of lake, region, standard length, 

and δ13C, fish THg concentrations were negatively correlated with the relative condition factor of 

individual fish (F1,282.8 = 25.96, P < 0.001; fig. 14). In fact, modeled THg concentrations 

decreased 3-fold across the range of fish condition factors. Across species, we did not find a 

relationship between THg concentrations and fish standard length (F1,272.2 = 0.64, P = 0.43). 

However, the significant species x SL interaction term (F1,177.8 = 7.19, P = 0.008), indicates that 

the rate of increase in Hg per unit length of fish differed between brook trout and rainbow trout. 

Finally, we found no influence δ13C on fish THg concentrations across the study area (F1,282.9 = 

0.56, P = 0.45), indicating that bentho-pelagic coupling was relatively unimportant to fish Hg 

bioaccumulation in these lakes. 

When we applied a modified version of the model above (including species as a fixed 

effect, and adding a species x lake interaction) to a subset of lakes (Crater Lake, Fish Lake, 

LaGrande, Ruth Lake, and Van Pattern Lake) for which we had adequate sample sizes of brook 

trout and rainbow trout, we found that THg concentrations differed between species (F1,88 = 

10.69, P = 0.0016), but the significant interaction between species and lake (F4,88 = 34.05, 

P <0.0001) confounded our ability to appropriately interpret the main effect of species. Instead, 

Tukey’s pairwise tests indicate that THg concentrations were higher in brook trout than rainbow 

trout from Fish Lake and Ruth Lake, whereas they were higher in rainbow trout than brook trout 

in La Grande Reservoir. We found no statistical difference between species from Van Patten or 

Crater Lakes (fig. 15). 

Landscape and Lake Effects

The most parsimonious model explaining least-squares mean fish THg concentrations in 

lakes of the WWNF contained basal area of conifers within a lake’s catchment, SO4, lake area, 

and DOC. No other models were well supported (all ΔAICc > 2), the top model had an Akaike 

weight of 0.56 and was 1.6 times more likely than the next model, which was identical to the top 

model with the exception that it did not include DOC (table 3). 

Additionally, 87 percent of the variability in our data could be explained using the top 

model, whereas the next best model explained only 71 percent. Using variable weights to assess 

the relative importance of each variable (Burnham and Anderson, 2002), models containing basal 

area of conifers had a combined AICc weight of 0.99, followed by SO4 (0.96), and lake area 

(0.93), with moderate model support for DOC (0.57), and little evidence for effects of ANC 

(0.10), catchment area (0.06), pH (0.05), elevation (0.03), or the lake area to catchment ratio

(0.03). 

Although the variable weight for DOC was moderate, the model averaged beta 

coefficient estimates for this variable had 95-percent confidence intervals that slightly 

overlapped zero indicating that DOC (β = -0.141 ± 0.172) had a smaller effect on fish THg 

concentrations than did basal area (β = 0.075 ± 0.032), SO4 (β = -0.329 ± 0.150), or lake area 

(β = -4.855 ± 2.803). Using model-averaged estimates, the average basal area within a lake’s 
10

catchment had an overriding influence on the fish THg concentrations, resulting in a 428 percent

increase in fish THg concentrations from the catchment with the lowest to the catchment with the 

highest conifer biomass (fig. 16). Mercury concentrations also decreased by 24, 72, and 30 

percent across the observed ranges of lake area, SO4, and DOC data, respectively (fig. 17). 

Because we had a larger dataset of lakes for which only physical variables were available

(N=26 vs. N=18), we also examined the influence of physical variables alone on the larger 

dataset. In this case, the most parsimonious model explaining fish THg concentrations again 

contained catchment basal area and lake surface area (table 4). No other models were well 

supported (all ΔAICc > 2), the top model had an Akaike weight of 0.51 and was more than 4 

times more likely than the next best model, which also included the ratio of lake area to 

catchment area (table 4). Models containing catchment basal area had a combined AICc weight 

of 0.97, followed by lake area (0.94), highlighting the overriding importance of those two 

variables in controlling fish THg concentrations in the absence of lake chemistry information. 

We found little evidence for effects of catchment area (0.19), elevation (0.18), or the lake 

area:catchment ratio (0.18). Importantly, by excluding the lake chemistry data, we reduced the 

predictive power of our model to explain only 47 percent of the variability in the data. 

Discussion

Across the 28 subalpine lakes that we sampled in the WWNF, we found wide variation in 

THg concentrations of fishes, both within and among lakes. We evaluated the individual 

variation in fishes within lakes in the context of ecological and physiological drivers, such as 

foraging ecology (stable isotope-based indices) and body condition, and the among-lake 

differences within the context of limnological and catchment drivers, such as water chemistry 

and catchment forest structure. Using this hierarchical approach, we found that drivers at both 

scales are important in determining fish Hg bioaccumulation and risk to wildlife and humans. 

Additionally, these findings suggest that management options may exist at both scales that could 

be implemented to address mercury issues in lake ecosystems. However, further research is 

needed in order to better develop and test these approaches, and identify the circumstances in 

which they may be successful. Finally, fish THg concentrations were relatively low in WWNF 

lakes and comparable to other high elevation sites worldwide, but there were still common 

exceedances of some wildlife risk thresholds. 

Mercury bioaccumulation in fishes can be influenced by a broad range of ecological and 

biological factors. The propensity of MeHg to bind strongly with sulfhydryl proteins in 

biological tissue results in long-term accumulation in protein rich tissues, such as muscle, driving 

a positive relationship between Hg and fish age (Evans and others, 2005). Many fish species also 

undergo ontogenetic diet shifts as they grow (Luczkovich and others, 1995), enabling them to

target larger prey that often have higher Hg concentrations. Thus, the relationship between fish 

size and Hg concentrations generally is influenced by both age and trophic position (Wiener and 

others, 1990; Cabana and Rasmussen, 1994). However, fish Hg concentrations also are 

influenced by a fish’s growth rate (Trudel and Rasmussen, 2006), and thus their body condition.

Specifically, THg concentrations in fishes decline with faster growth because the rate of biomass 

accretion exceeds the assimilation and retention of dietary Hg (Stafford and others, 2004). This 

means that even if two fish accumulate the same total burden of Hg, the faster growing fish will 

have lower concentrations due simply to dilution with its own biomass (Stafford and others, 

2004). Similarly, fish condition (mass relative to length) also can influence Hg concentrations by 

concentrating or diluting Hg burdens. Importantly, a common complication in interpreting fish 
11

THg concentrations is the fact that fish age, body size, trophic position, and body condition are 

often correlated with one another to different degrees, meaning that they can influence the 

apparent relationships between each of these factors and fish THg concentrations. This is 

apparent here where we found inconsistent relationships between THg concentrations and age, 

length, δ15N, and relative condition factor across the WWNF lakes we sampled. However, after 

controlling for size, trophic, and site effects, we found a consistent and strong relationship 

between fish body condition and THg concentrations across the study region. Our results show 

that regardless of species, THg concentrations decreased with increasing body condition. In fact, 

across a 2.2-fold range of relative condition factors (0.64–1.42), we found nearly a 3-fold

decrease in fish THg concentrations. This highlights the potential importance of fish body 

condition in determining THg concentrations, as well as the need to use care in interpreting Hg 

concentrations in fish populations without information on the variability in body condition. This 

is a notable finding because body condition in fishes varies with many factors, such as stocking 

density, food availability, and water quality. These factors, along with body condition itself, are 

commonly targeted for fisheries management, providing a potential opportunity for management 

applications that address both fisheries and Hg risk needs. This could be particularly valuable in 

environments such as subalpine lakes where Hg loading is primarily from atmospheric sources 

that cannot be readily managed at a local or regional scale, or in other habitats where there are 

logistical impediments to managing factors related to MeHg production. 

Atmospheric deposition is the primary source of Hg to remote areas, such as the WWNF 

(Phillips and others, 2011). This is particularly true of high-elevation lakes that are largely rain 

and snow-fed, and lie within relatively small catchments with limited upland transport from 

stream networks. Mercury deposition within the region containing the WWNF has not been 

directly measured, but models suggest that it is elevated in comparison to much of the Pacific 

Northwest (U.S. Environmental Protection Agency, 2008). This is likely due to both elevated 

precipitation associated with orographic effects, as well as a mixture of the global background 

signal with more regional and localized sources. Notably, the Ash Grove cement plant in Durkee, 

Oregon lies within the region surrounding the WWNF, and is approximately 60 km from the 

closest study lake. Prior to the recent implementation of Hg controls in their processes, the plant 

was among the largest emitters of Hg in the western United States (Schmeltz and others, 2011).

However, the magnitude of current or historical Hg deposition from the plant to the subalpine 

catchments of the WWNF is unclear because no direct deposition measurements are available. 

Moreover, atmospheric deposition models such as REMSAD (U.S. Environmental Protection 

Agency, 2008), provide only a 12 km resolution, which is too coarse to incorporate lake-specific 

deposition estimates into our modeling results. Importantly, spatial variability in fish THg 

concentrations was substantial among WWNF lakes. Least-square mean fish THg concentrations 

in lakes (statistically controlling for species and size effects) varied by nearly 3-fold within 

regions, and by 18-fold across the entire study area. However, it is uncertain whether Hg 

loadings to the lakes exhibit similar variability, which would indicate very dynamic atmospheric 

vectors of Hg deposition. An important next step is to determine if sediment Hg concentrations 

mimic the trends in fish THg concentrations. Certainly source magnitude is an important driver 

of Hg bioaccumulation in aquatic food webs, but as these data suggest, only within the context of 

the ecological and biogeochemical factors driving MeHg production and the speciation of 

inorganic Hg. 
12

The physical and chemical characteristics of lakes and their catchments have long been 

linked with fish Hg concentrations because of their role in facilitating MeHg production, or in 

controlling Hg entry and transport through the food web (Snodgrass and others, 2000). Factors 

that are commonly important include wetland area, lake size, lake productivity, forest coverage, 

DOC, pH, and sulfate (Greenfield and others, 2001; St. Louis and others, 2004; Hall and others, 

2005; Driscoll and others, 2007; Shanley and others, 2012). However, aside from percentage of

wetland area and pH, conflicting data exist on the degree and direction of impact for many of the 

other parameters. Consistent with other studies, fish THg concentrations from lakes in the 

WWNF were strongly predicted by a combination of landscape and limnological factors. In fact, 

87 percent of the variability in lake-specific least-squares mean fish THg concentrations could be 

explained using a model containing (1) lake water DOC concentrations, (2) lake water sulfate 

concentrations, (3) lake surface area, and (4) average basal area of conifers within the lake’s 

catchment. Interestingly, we found negative relationships between fish THg concentrations and 

both sulfate and DOC. Because sulfate is a key component in the MeHg production process, 

dissolved SO4 concentrations are often positively correlated with MeHg concentrations in water, 

sediments, and biota (Mitchell and others, 2008). However, at high sulfate concentrations in 

reducing conditions, the rapid reduction to sulfide can subsequently inhibit MeHg production, 

resulting in a negative correlation (Benoit and others, 1999; Mehrotra and Sedlak, 2005; 

Windham-Myers and others, 2009). Interestingly, the sulfate concentrations in WWNF lakes 

ranged from 0.17 to 7.9 mg/L, with a median concentration of only 0.36 mg/L, making this an 

unlikely explanation. Thus, more intensive limnological and geochemical research across the 

study lakes would be necessary to more fully explain this relationship. 

The role of DOC in the Hg cycle is complex (Lambertsson and Nilsson, 2006). Dissolved 

organic matter not only facilitates Hg transport through watersheds (Shanley and others, 2008), 

but also can enhance or reduce biological uptake (Wiener and others, 2006; Dutton and Fisher, 

2012), as well as serve as a carbon source for microbial activity, increasing MeHg production 

rates (Lambertsson and Nilsson, 2006). The complexity of the role played by DOC in these 

processes is driven by both its quantity and composition (Shanley and others, 2012). We found 

negative correlations between DOC and fish THg concentrations across our study lakes, which is 

consistent with studies in midwestern seepage lakes where DOC in the water column commonly 

inhibits biological uptake of MeHg (Wren and others, 1991). This result by itself is not 

surprising given the fact that the lakes we studied in WWNF lacked inflowing streams and were 

primarily fed through precipitation, catchment snowmelt, and groundwater. However, the strong

positive influence of catchment vegetation (conifer basal area) on fish THg concentrations 

suggests the potential for a greater catchment influence, one where carbon supplied to the lake by 

the catchment may drive Hg bioaccumulation. Specifically, conifer basal area within a lake’s 

catchment was overwhelmingly the most important factor that we measured in driving the 

variation in fish THg concentrations among lakes. In fact, model-averaged fish THg 

concentrations ranged from 0.11 to 0.58 μg/g dw between the lakes with the lowest and highest 

basal areas. This is a novel finding with the potential for wide-ranging implications in the 

management of Hg in forested catchments. 

Previous studies have identified forests as important components to the Hg cycling at the 

landscape scale, where Hg varies positively with percentage of area of large watersheds that are 

forested (Driscoll and others, 2007). Trees accumulate Hg from the atmosphere within their leaf 

tissue and also provide surface area for Hg deposition and adsorption to leaf surfaces (Graydon 

and others, 2009; Tebatchnick and others, 2012). Additionally, the organic and litter horizons in 
13

forest soils are major sites of Hg storage, particularly in association with soil organic matter 

(Obrist and others, 2011). To our knowledge no other study has quantified a key forest structure 

metric such as basal area at such a fine spatial resolution within a catchment in relation to 

biological Hg concentrations. The mechanism behind this relationship is not yet clear, but may 

be due to either enhanced carbon delivery associated with more organic-rich soils, elevated Hg 

scavenging from the atmosphere in tree needles, or a combination of both. It is beyond the scope 

of this work to determine these mechanisms, but clearly identifies a key need for future research

in forested watersheds. 

Organisms, such as fish from high-elevation lakes, serve as important indicators for 

understanding how background concentrations and atmospheric deposition may be influencing 

remote environments. Additionally, they provide a unique opportunity to test for the influence of 

natural landscape characteristics on Hg cycling within lakes. The THg concentrations of fishes 

from lakes in the WWNF generally are lower than those of other remote, high-elevation lakes 

worldwide (fig. 18). Interestingly, in comparison with other high-elevation lakes from National 

Parks in the western United States, average concentrations in WWNF fishes ranged from 1.4 

times higher than fish Glacier National Park, to 2.2 times lower than fish from Mount Rainier 

National Park. The causes for these differences are unclear, but given our results on the 

importance of lake chemistry and catchment characteristics, they could be due some combination 

of these factors as well as differences in Hg deposition flux. 

Further research is needed in order to better understand the relative importance of those 

other drivers, particularly interdisciplinary studies that incorporate measurements of Hg 

deposition, flux and speciation in the abiotic components of each catchment in combination with 

bioaccumulation dynamics. The THg concentrations in WWNF fishes were all below the EPA’s 

human health criteria of 0.3 μg/g ww in the fillet, except for the lone lake trout sampled from 

Lookinglass Lake. However, when assessed relative to a suite of wildlife health thresholds, 2, 10, 

25, and 68 percent of fishes sampled exceeded the whole body (ww) thresholds for common loon 

reproductive impairment (0.18 μg/g ww; Depew and others, 2012), common loon behavioral 

alterations (0.1 μg/g ww; Depew and others, 2012), and protection of mink (0.7 μg/g ww; 

Lazorchak and others, 2003), and kingfisher (0.03 μg/g ww; Lazorchak and others, 2003), 

respectively. Whether there are taxa within the forest that are being impacted, and the potential 

degree of impact is uncertain and would require further study. 

Acknowledgments

This study was funded by the U.S. Department of Agriculture Forest Service, the U.S. 

Geological Survey Forest and Rangeland Ecosystem Science Center, and the U.S. Geological 

Survey Environmental Health Mission Area. This study would not have been possible without 

many dedicated field hours from a number of people. In particular, we appreciate the support of 

David Salo, Alan Miller, Bob Rock, Leigh Woodruff, Mary Ellen Emerick, Holly Akenson, Amy 

Busch, Randi Jandt, Tim Bailey, and Jeff Yanke. We also are grateful for the efforts of Kevin 

Donner, John Pierce, Brandon Kowalski, and Nick Baker for their laboratory efforts; Jack 

Landers and Kiira Siitari for otolith preparation and reading; and Patti Haggerty for her expertise 

and guidance on geospatial analyses. We also thank David Salo, Alan Miller, Martin Fitzpatrick, 

and Leigh Woodruff for comments on previous versions of the report. 
14

References Cited

Anderson, R.O., and Neumann, R.M., 1996, Length, weight, and associated structural indices, in

Murphy, B.R. and Willis, D.W., eds., Fisheries Techniques, 2nd ed.: Bethesda, Maryland, 

American Fisheries Society, p. 447–482. 

Benoit, J.M., Gilmour, C.C., Mason, R.P., Heyes, A., 1999, Sulfide controls on mercury 

speciation and bioavailability in sediment pore waters: Environmental Science and 

Technology, v. 33, p. 951-957. 

Blais, J.M., Charpentie, S., Pick, F., Kimpe, L.E., Amand, A.S., Regnault-Roger, C., 2006, 

Mercury, polybrominated duphenyl ether, organochlorine pesticide, and polychlorinated 

biphenyl concentrations in fish from lakes along an elevation transect in the French Pyrenees: 

Ecotoxicology and Environmental Safety, v.63, p. 91-99. 

Bloom, N.S., 1992, On the chemical form of mercury in edible fish and marine invertebrate 

tissue: Canadian Journal of Fisheries and Aquatic Science, v. 49, p. 1010–1017. 

Burnham, K.P., and Anderson, D.R., 2002, Model Selection and Multimodel Inference: A 

Practical Information-Theoretic Approach, 2nd ed.: New York, Springer-Verlag. 

Cabana, G., and Rasmussen, J.B., 1994, Modeling food chain structure and contaminant 

bioaccumulation using stable nitrogen isotopes: Nature, v. 372, p. 255-257. 

Depew, D.C., Basu, N., Burgess, N.M., Campbell, L.M., Evers, D,C,, Grasman, K.A., and 

Scheuhammer, A.M., 2012, Derivation of screening benchmarks for dietary methylmercury 

exposure for the common loon (Gavia Immer): Rationale for use in Ecological Risk 

Assessment: Environmental Toxicology and Chemistry, v. 31, p. 2399-2407. 

Driscoll, C.T., Han Y., Chen, C., Evers, D., Lambert, K., Holsen, T., Kamman, N., and Munson, 

R., 2007, Mercury contamination in remote forest and aquatic ecosystems in the northeastern 

U.S.: Sources, transformations and management options: Bioscience, v. 57, p.17-28. 

Dutton, J., and Fisher, N.S., 2012, Influence of humic acids on the uptake of aqueous metals by 

the killifish Fundulus heteroclitus: Environmental Toxicology and Chemistry, v. 31, p. 2225-

2232. 

Eagles-Smith, C.A., Suchanek, T.H., Colwell, A.E., and Anderson, N.L., 2008, Mercury trophic 

transfer in a eutrophic lake: the importance of habitat-specific foraging: Ecological 

Applications, v. 18, p. A196- A212. 

Engstrom, D.R., Balogh, S.J., and Swain, E.B., 2007, History of mercury inputs to Minnesota 

lakes: Influence of watershed disturbance and localized atmospheric deposition: Limnology 

and Oceanography, v. 52, p. 2467-2483. 

Evans, M.S., Lockhart, W.L., Doetzel, L., Low, G., Muir, D., Kidd, K., Stephens, G., and 

Delaronde, J., 2005, Elevated mercury concentrations in fish in lakes in the Mackenzie River 

Basin: the role of physical, chemical, and biological factors: Science of the Total Environment, 

v. 351–352, p. 479–500. 

Friedli, H.R., Radke, L.F., Prescott, R., Hobbs, P.V., and Sinha, P., 2003, Mercury emissions 

from the August 2001 wildfires in Washington State and an agricultural waste fire in Oregon 

and atmospheric mercury budget estimates: Global Biogeochemical Cycles, v. 17, p. 1039. 

Gran, G., 1952, Determination of the equivalence point in potentiometric titrations--Part II: The 

Analyst, v. 77, p. 661-671. 

Graydon, J.A., St. Louis, V.L., Hintelmann, H.H., Lindberg, S.E., Sandilands, K.A., Rudd, 

J.W.M., Kelly, C.A., Tate, M.T., Krabbenhoft, D.P., and Lehnherr, I., 2009, Investigation of 

uptake and retention of atmospheric Hg(II) by boreal forest plants using stable Hg isotopes: 

Environmental Science and Technology, v. 43, p. 4960-4966. 
15

Greenfield, B.K., Hrabik, T.R., Harvey, C.J., and Carpenter, S.R., 2001, Predicting mercury 

levels in yellow perch: use of water chemistry, trophic ecology, and spatial traits: Canadian 

Journal of Fisheries and Aquatic Sciences , v. 58, p. 1419-1429. 

Hall, B.D., St. Louis, V.L., Rolfhus, K.R., Bodaly, R.A., Beaty, K.G., Paterson, M.L., and 

Cherewyk, K.A., 2005, Impacts of reservoir creation on the biogeochemical cycling of methyl 

mercury and total mercury in boreal upland forests: Ecosystems, v. 8, p. 248–266. 

Hobson, K.A., and Bairlein, F., 2003, Isotopic fractionation and turnover in captive Garden 

Warblers (Sylvia borin): implications for delineating dietary and migratory associations in wild 

passerines: Canadian Journal of Zoology, v. 81, p. 1630–1635. 

Jenssen, M.T.S., Borgstrom, R., Salbu, B., Rosseland, B.O., 2010, The importance of size and 

growth rate in determining mercury concentrations in European minnow (Phoxinus phoxinus) 

and brown trout (Salmo trutto) in the subalpine lake, Ovre Heimdalsvatn: Hydrobiologia, 

v. 642, p. 115-126. 

Lambertsson, L., and Nilsson, M., 2006, Organic material: The primary control on mercury 

methylation and ambient methyl mercury concentrations in estuarine sediments: 

Environmental Science and Technology, v. 40, p. 1822-1829. 

Landers, D.H., Simonich, S.L., Jaffe, D.A., Geiser, L.H., Campbell, D.H., Schwindt, A.R., 

Schreck, C.B., Kent, M.L., Hafner, W.D., Taylor, H.E., Hageman, K.J., Usenko, S., Ackerman, 

L.K., Schrlau, J.E., Rose, N.L., Blett, T.F., Erway, M.M., 2008, The Fate, Transport, and 

Ecological Impacts of Airborne Contaminants in Western National Parks (USA). EPA/600/R08/138. U.S. Environmental Protection Agency, Office of Research and Development, 

NHEERL, Western Ecology Division, Corvallis, Oregon. 

Lazorchak, J.M., McCormick, F.H., Henry, T.R., and Herlihy, A.T., 2003, Contamination of fish 

in streams of the mid-Atlantic region: an approach to regional indicator selection and wildlife 

assessment: Environmental Toxicology and Chemistry, v. 22, p. 545-553. 

Lindberg, S., Bullock, R., Ebinghaus, R., Engstrom, D., Feng, X.B., and Fitzgerald, W., 2007, A 

synthesis of progress and uncertainties in attributing the sources of mercury in deposition: 

Ambio, v. 36, p. 19–32. 

Luczkovich, J.J., Norton, S.F., and Gilmore G.R., 1995, The influence of oral anatomy on prey 

selection during the ontogeny of two percoid fishes, Lagadon rhomboids and Centropornus 

undecimalis: Environmental Biology of Fishes, v. 44, p. 79-95. 

Marusczak, N., Larose, C., Dommergue, A., Paquet, S., Beaulne, J., Maury-Brachet, R., Lucotte, 

M., Nedjai, R., Ferrari, C., 2011, Mercury and methylmercury concentrations in high altitude 

lakes and fish (Arctic charr) from the French Alps related to watershed characteristics: Science 

of the Total Environment, v. 409, p. 1909-1915. 

Mason, R.P., Fitzgerald, W.F., and Morel, F.M., 1994, The biogeochemical cycling of elemental 

mercury: Anthropogenic influences: Geochimica et Cosmochimica Acta, v. 58, p. 3191-3198. 

Mehrotra A.S., and Sedlak, D.L., 2005, Decrease in net mercury methylation rates following iron

amendment to anoxic wetland sediment slurries: Environmental Science and Technology, v. 

39, p. 2564-2570. 

Mitchell, M.J., Bailey, S.W., Shanley, J.B., and Mayer, B., 2008, Evaluating sulfur dynamics 

during storm events for three watersheds in the northeastern USA: A combined hydrological, 

chemical and isotopic approach: Hydrological Processes, v. 22, p. 4023-4034. 

Moran, P.W., Aluru, N., Black, R.W., Vijayan, M.M., 2001, Tissue contaminants and associated 

transcriptional response in trout liver from high elevation lakes of Washington: Environmental 

Science and Technology, v. 41, p. 6591-6597. 
16

Obrist, D., Johnson, D.W., Lindberg, S.E., Luo, Y., Hararuk, O., Bracho, R., Battles, J.J., Dail, 

D.B., Edmonds, R.L. Monson, R.K., Ollinger, S.V., Pallardy, S.G., Pregitzer, K.S., and Todd, 

D.E., 2011, Mercury distribution across 14 U.S. Forests. Part I: Spatial patterns of 

concentrations in biomass, litter, and soils: Environmental Science and Technology, v. 45, p. 

3974-3981. 

Ohmann, J.L., and Gregory, M.J., 2002, Predictive mapping of forest composition and structure 

with direct gradient analysis and nearest-neighbor imputation in coastal Oregon, U.S.A.: 

Canadian Journal of Forest Research, v. 32, p. 725–741. 

Phillips, J.A., Louis, V.L., Cooke, C.A., Vinebrooke, R.D., and Hobbs, W.O., 2011, Increased 

mercury loadings to western Canadian Alpine lakes over the past 150 years: Environmental 

Science and Technology, v. 45, p. 2042-2047. 

Pirrone, N., Cinnirella, S., Feng, X., Finkelman, R.B., Friedli, H.R., Leaner, J., Mason, R., 

Mukherjee, A.B., Stracher, G.B., Streets, D.G., and Telmer, K., 2010, Global mercury 

emissions to the atmosphere from anthropogenic and natural sources: Atmospheric Chemistry 

and Physics Discussions, v. 10, p. 4719–4752. 

Post, D.M., 2002, Using stable isotopes to estimate trophic position: models, methods, and 

assumptions: Ecology, v. 83, p. 703-718. 

R Development Core Team, 2011, R: A language and environment for statistical computing. R 

Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, accessed April 

15, 2013, at http://www.R-project.org/. 

Rasband, W.S., 1997, ImageJ: U.S. National Institutes of Health, Bethesda, Maryland, USA, 

accessed April 15, 2014, at http://imagej.nih.gov/ij/.

Rudd, J.W.M., 1995, Sources of methylmercury to freshwater ecosystems: A review: Water Air 

Soil Pollution, v. 80, p. 697-713. 

Scheuhammer, A.M., Meyer, M.W., Sandheinrich, M.B., and Murray, M.W., 2007, Effects of 

environmental methylmercury on the health of wild birds, mammals, and fish: Ambio, v. 36, p. 

12-18. 

Schmeltz, D., Evers, D., Driscoll, C., Artz, R., Cohen, M., Gay, D., Haeuber, R., Krabbenhoft, 

D.P., Mason, R., Morris, K., and Wiener, J.G., 2011, MercNet: a national monitoring network 

to assess responses to changing mercury emissions in the United States: Ecotoxicology, v. 20, 

p. 1713-1725. 

Secor, D.H., Dean, J.M., and Laban, E.H., 1991, Manual for otolith removal and preparation for 

microstructural examination: Belle W. Baruch Institute, University of South Carolina Press, 

Columbia, SC, p. 85. 

Shanley, J.B., and Chalmers, A.T., 2012, Streamwater fluxes of total mercury and 

methylmercury into and out of Lake Champlain: Environmental Pollution, v. 161, p. 311-320. 

Shanley, J.B., Mast, M.A., Campbell, D.H., Aiken, G.R., Krabbenhoft, D.P., Hunt, R.J., Walker, 

J.F., Schuster, P.F., Chalmers, A., Aulenbach, B.T., Peters, N.E. Marvin-DiPasquale, M,., 

Chow, D.W., and Shafer, M.M., 2008, Comparison of total mercury and methylmercury 

cycling at five sites using the small watershed approach: Environmental Pollution, v. 154, p. 

143-154. 

Shanley, J.B., Kamman, N.C., Clair, T.A., Chalmers, A., 2005, Physical controls on total and 

methylmercury concentrations in streams and lakes of Northeastern USA: Ecotoxicology, v. 

14, p. 125-134. 
17

Snodgrass, J.W., Jagoe, C.H., Bryan, A.L., Brant, H.A., and Burger, J., 2000, Effects of trophic

status and wetland morphology, hydroperiod, and water chemistry on mercury concentrations 

in fish: Canadian Journal of Fisheries and Aquatic Sciences, v. 57, p. 171-180. 

Stafford, C.P., Hanson, B., and Stanford, J.A., 2004, Mercury in fishes and their diet items from 

Flathead Lake, Montana: Transactions of the American Fisheries Society, v. 133, p. 349-357. 

St. Louis, V.L., Rudd, J.W.M., Kelly, C.A., Beaty, K.G., Bloom, N.S., and Flett, R.J., 1994, 

Importance of wetlands as sources of methyl mercury to boreal forest ecosystems: Canadian 

Journal of Fisheries and Aquatic Science, v. 51, p. 1065-1076. 

St. Louis, V.L., Rudd, J.W.M., Kelly, C.A., Bodaly, R.A., Patterson, M.J., Beaty, K., Hesslein, 

R., Hayes, A., and Majewski, A., 2004, The rise and fall of mercury methylation in an 

experimental reservoir: Environmental Science and Technology, v. 38, p. 1348-1358. 

Tebatchnick, M.D., Nogaro, G., and Hammerschmidt, C.R., 2012, Potential sources of 

methylmercury in tree foliage: Environmental Pollution, v. 160, p. 82-87. 

Trudel, M., and Rasmussen, J. B., 2006, Bioenergetics and mercury dynamics in fish: a modeling 

perspective: Canadian Journal of Fisheries and Aquatic Sciences, v. 63, p. 1890-1902. 

U.S. Environmental Protection Agency, 2000, Method 7473, Mercury in solids and solutions by 

thermal decomposition, amalgamation, and atomic absorption spectrophotometry, Test 

methods for evaluating solid waste, physical/chemical methods SW846, Update IVA. Printing 

Office (GPO), Washington, DC, USA.

U.S. Environmental Protection Agency, 2008, Model-based analysis and tracking of airborne 

mercury emissions to assist in watershed planning: accessed April 15, 2013, at 

http://www.epa.gov/owow/tmdl/pdf/final300report_10072008.pdf. 

Wiener, J.G., Knights, B.C., Sandheinrich, M.B., Jeremiason, J.D., Brigham, M.E., Engstrom, 

D.R., Woodruff, L.G., Cannon, W.F., and Balogh, S.J., 2006, Mercury in soils, lakes, and fish 

in Voyageurs National Park (Minnesota): Importance of atmospheric deposition and ecosystem 

factors: Environmental Science and Technology, v. 40, p. 6261-6268. 

Wiener, J.G., Martini, R.E., Sheffy, T.B., and Glass, G.E., 1990, Factors influencing mercury 

concentrations in walleyes in northern Wisconsin lakes: Transactions of the American 

Fisheries Society, v. 119, p. 862-870. 

Williams, B.K., Nichols, J.D., and Conroy, M.J., 2002, Analysis and management of animal 

populations—Modeling, estimation, and decision making: San Diego, California, Academic 

Press, p. 817. 

Windham-Myers, L., Marvin-Dipasquale, M., Krabbenhoft, D.P., Agee, J.L., Cox, M.H., 

Heredia-Middleton, P., Coates, C., and Kakouros, E., 2009, Experimental removal of wetland 

emergent vegetation leads to decreased methylmercury production in surface sediment: Journal 

of Geophysical Research, v. 114. 

Wren, C.D., Scheider, W.A., Wales, D.L., Muncaster, B.W., and Gray, I.M., 1991, Relation 

between mercury concentration in walleye (Stizostedion vitreum vitreum) and northern pike 

(Esox Lucius), in Ontario lakes and influence of environmental factors: Canadian Journal of 

Fisheries and Aquatic Sciences, v. 48, p. 132-139. 
18

Figure 1. Map of the Wallowa-Whitman National Forest in northeastern Oregon and western Idaho.

Colored symbols represent lakes sampled in various regions of the National Forest.
19

Figure 2. Goodrich Lake and its catchment in the South Elkhorn Range. Inset: Sampled brook trout.
20

Figure 3. Locations of study lakes and their respective catchments (defined by colored polygons) within 

each region (N. Elkhorns [red], S. Elkhorns [blue], N. Eagle Cap [ green], S. Eagles Cap [orange], and 

Seven Devils [yellow]) in the Wallowa-Whitman National Forest, northeastern Oregon and western 

Idaho. 

La Grande 

Reservoir

Van Patten

Black

Lilypad

Crawfish

Hoffer

Dutch Flat

Legore

Steamboat

Long

Granite

Aneroid

Mirror

Basin

Shelf

Ruth

Emerald

Baldy

Rock

Twin low

Twin up

Goodrich

Arrow

Heart

Culver

Lookingglass

Crater

Twin mid

Fish

OR

WA

ID

N. Elkhorns

S. Elkhorns

N. Eagle Cap

S. Eagle Cap

Seven Devils
21

Figure 4. Digital image of a brook trout sagittal otolith, showing annular rings.

1

2

3
22

Figure 5. Visual examples of gradients in catchment forest structure in the Wallowa-Whitman National 

Forest, northeastern Oregon and western Idaho.
23

Figure 6. Conifer density measured as basal area (m2/hectare) of all conifers >2.5 cm dbh for WallowaWhitman National Forest lake catchments, northeastern Oregon and western Idaho. Regions in bold: 

NE=North Elkhorn, SE=South Elkhorn, NEC=North Eagle Cap, SEC=South Eagle Cap, SD=Seven 

Devils. Note, incomplete data for Ruth Lake catchment.

Figure 7. Conifer density measured as basal 

area (m2/hectare) of all conifers >2.5 cm 

dbh for Wallowa Whitman National Forest 

lake catchments. Regions in bold: 

NE=North Elkhorn; SE=South Elkhorn; 

NEC=North Eagle Cap; SEC=South Eagle 

Cap; SD=Seven Devils. Note, incomplete 

data for Ruth Lake catchment.

Crawfish Dutch Flat

Van Patten Baldy Rock Twin up

Twin low 

(includes Twin up)

Goodrich Legore Long

Steamboat

Granite

Mirror Aneroid

Heart

Arrow

Culver

Lookingglass

Crater Fish Twin mid Emerald

Basin

Shelf

La Grande 

Reservoir

Lilypad

Hoffer

Black

NE NE NE NE

NE SE SE SE

SE NEC NEC NEC

NEC NEC SEC SEC

SEC SEC SEC SD

SD Avg Basal Area of Conifers 

( >2.5 cm dbh) (m2/hectare)
24

Figure 7. Linear relationships between total mercury (THg) concentrations (dw = dry weight 

concentration) in fish muscle, and fish size (standard length) from different lakes in the WallowaWhitman National Forest, northeastern Oregon and western Idaho. Closed circles represent brook trout 

(Salvelinus fontinalis), and open circles represent rainbow trout (Oncorhynchus mykiss).
25

Figure 8. Linear relationships between total mercury (THg) concentrations (dw = dry weight) in fish 

muscle, and fish age (estimated by counting sagittal otolith annuli) from select lakes in the WallowaWhitman National Forest, northeastern Oregon and western Idaho. Results are provided for 21 lakes 

because some lakes did not contain brook trout.
26

Figure 9. Linear relationships between total mercury (THg) concentrations (dw = dry weight) in fish 

muscle, and relative condition factor from different lakes in the Wallowa-Whitman National Forest, 

northeastern Oregon and western Idaho. Closed circles represent brook trout (Salvelinus fontinalis), 

and open circles represent rainbow trout (Oncorhynchus mykiss).
27

Figure 10. Linear relationships between total mercury (THg) concentrations (dw = dry weight) in fish 

muscle, and stable carbon isotope ratios from different lakes in the Wallowa-Whitman National Forest, 

northeastern Oregon and western Idaho. Closed circles represent brook trout (Salvelinus fontinalis), 

and open circles represent rainbow trout (Oncorhynchus mykiss).
28

Figure 11. Linear relationships between total mercury (THg) concentrations (dw = dry weight) in fish 

muscle, and stable nitrogen isotope ratios from different lakes in the Wallowa-Whitman National Forest, 

northeastern Oregon and western Idaho. Closed circles represent brook trout (Salvelinus fontinalis), 

and open circles represent rainbow trout (Oncorhynchus mykiss).
29

Figure 12. Back transformed least-square mean fish muscle total mercury (THg) concentrations for the 5 

study regions in the Wallowa-Whitman National Forest. Differing letters among bars indicate 

statistically significance differences in THg concentrations at alpha = 0.05 

Figure 13. Back transformed least-square mean fish muscle total mercury (THg) concentrations for the 27 

study lakes in the Wallowa-Whitman National Forest, northeastern Oregon and western Idaho. 

Fish THg (μg/g dw)

0.0

0.1

0.2

0.3

0.4

0.5

A

A

B B

C

Region

0.0

0.2

0.4

0.6

0.8

1.0

Fish THg (μg/g dw)

North 

Elkhorn

North 

Eagle Cap

South 

Eagle Cap

South 

Devils

South

Elkhorn

Lake Name
30

Figure 14. Partial residual plot of fish total mercury (THg) concentrations in relation to relative condition 

factor. Solid circles indicate brook trout, open circles represent rainbow trout. Partial residuals are 

derived from the global mixed-effects general linear model that includes lake, region, fish length, and 

δ13C. 

Figure 15. Fish muscle total mercury (THg) concentrations in brook trout (Salvelinus fontinalis; black bars) 

and rainbow trout (Oncorhynchus mykiss; gray bars) from five lakes where they co-occurred in the 

Wallowa-Whitman National Forest, northeastern Oregon and western Idaho. 

Relative Condition Factor

0.50 0.75 1.00 1.25 1.50

Partial residual of fish 

muscle THg concentration

-3

-2

-1

0

1
31

Figure 16. Relationship between conifer (>2.5 cm diameter at breast height) basal area within a lake’s 

catchment on fish THg concentrations. Data are plotted as partial residuals from the best model to 

adjust for the effects of other independent factors on fishTHg concentrations. Each data point 

represents a single lake.

Conifer Basal Area (m2

/ha)

5 10 15 20 25 30

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Partial residual of 

fish [THg]
32

Figure 17. Influence of (A) lake surface area, (B) dissolved sulfate, and (C) dissolved organic carbon on 

fish THg concentrations in the Wallowa-Whitman National Forest, northeastern Oregon and western 

Idaho. Data are plotted as partial residuals from the best model to adjust for the effect of other 

independent factors on fish THg concentrations.

Lake surface area (km2)

0.00 0.04 0.08 0.12 0.16

-1.6

-1.2

-0.8

-0.4

0.0

0.4

0.8

Dissolved sulfate (mg/L)

0.1 1 10

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

DOC (mg/L)

0.0 0.5 1.0 1.5 2.0 2.5 3.0

-1.25

-1.00

-0.75

-0.50

-0.25

0.00

0.25

A

B

C

Partial residual of fish muscle [THg]
33

Figure 18. Mean total mercury (THg) concentrations in salmonid fishes from other high-elevations lakes in 

comparison to this study. 1Landers and others, 2008; 2Marusczak and others, 2011; 3Jenssen and 

others, 2010; 4Moran and others, 2007; 5Blais and others, 2006. 
34

Table 1. Chemical and physical attributes of study lakes and their catchments in the Wallowa-Whitman National Forest, 

northeastern Oregon and western Idaho. 

[

a

% of catchment area; bbm2/ha, diameter at breast height >2.5cm; BRTR = Brook trout, RATR = Rainbow trout, LATR = Lake trout, 

CUTR = Cutthroat trout] 

Region Lake Name Latitude Longitude

Elevation 

(m)

Lake Surface 

Area (km2)

Catchment 

Area (km2)

Canopy 

Covera

Basal 

Areab Fish Species

N. Eagle Cap Aneroid Lake 45.208873 -117.203488 2286 0.1531 5.425 44.73 15.59 BRTR, RATR

N. Eagle Cap Granite/Hawk Lake 45.231943 -117.330740 2436 0.0131 0.263 31.88 9.75 RATR

N. Eagle Cap Legore Lake 45.310301 -117.346113 2725 0.0116 0.130 27.12 8.63 RATR

N. Eagle Cap Long Lake 45.233449 -117.439989 2169 0.1479 4.763 45.39 16.22 BRTR

N. Eagle Cap Mirror Lake 45.178258 -117.309000 2315 0.1007 1.876 38.42 12.85 BRTR

N. Eagle Cap Steamboat Lake 45.229967 -117.417914 2244 0.1602 2.132 47.86 17.05 BRTR

N. Elkhorns Black Lake 44.954079 -118.220636 2240 0.0169 0.499 55.08 21.68 BRTR

N. Elkhorns Crawfish Lake 44.935206 -118.265268 2101 0.0559 0.368 59.19 28.61 BRTR

N. Elkhorns Dutch Flat Lake 44.930837 -118.220282 2238 0.0152 0.769 45.91 20.35 BRTR

N. Elkhorns Hoffer Lake 44.949454 -118.236193 2278 0.0096 0.864 53.03 18.36 BRTR

N. Elkhorns LaGrande Reservoir 45.137065 -118.201446 1558 0.1189 31.891 52.86 25.01 BRTR, RATR

N. Elkhorns Lilypad Lake 44.957612 -118.226323 2179 0.0084 0.071 60.97 28.96 BRTR

N. Elkhorns Van Patten Lake 44.955503 -118.185464 2254 0.0593 0.971 51.67 20.58 BRTR, RATR

S. Eagle Cap Arrow Lake 45.109944 -117.393089 2386 0.0051 0.099 30.72 9.94 RATR

S. Eagle Cap Crater Lake 45.058066 -117.275198 2301 0.0458 0.209 45.37 18.73 BRTR, RATR

S. Eagle Cap Culver Lake 45.093022 -117.348249 2146 0.0272 0.527 51.98 18.21 BRTR

S. Eagle Cap Fish Lake 45.046502 -117.092215 2031 0.2564 2.991 62.16 30.70 BRTR, RATR

S. Eagle Cap Heart Lake 45.105766 -117.387880 2228 0.0116 0.195 45.11 15.52 BRTR

S. Eagle Cap Lookinglass Lake 45.080028 -117.363020 2226 0.0989 0.747 52.91 19.71 BRTR, LATR

S. Eagle Cap Twin Lakes 45.080853 -117.054626 1962 0.0237 0.071 44.08 22.31 RATR

S. Elkhorns Baldy Lake 44.845230 -118.313904 2171 0.0672 0.601 58.71 28.74 BRTR

S. Elkhorns Goodrich Lake 44.809660 -118.060431 2094 0.0661 1.266 41.65 16.47 BRTR

S. Elkhorns Rock Creek 44.822190 -118.109395 2338 0.0968 0.924 40.7 14.79 BRTR

S. Elkhorns Twin Lakes_Lower 44.808119 -118.086737 2335 0.0286 0.631 33.95 12.94 BRTR

Seven Devils Basin Lake 45.344960 -116.555504 2250 0.0257 0.670 57.62 23.62 RATR

Seven Devils Emerald Lake 45.212100 -116.570336 2072 0.0808 4.456 52.1 22.12 BRTR

Seven Devils Ruth Lake 45.242100 -116.556800 2215 0.0326 0.476 NA NA BRTR, RATR

Seven Devils Shelf Lake 45.342280 -116.550757 2275 0.0327 1.511 52.67 21.47 CUTR
35

Table 1. Chemical and physical attributes of study lakes and their catchments in the Wallowa-Whitman National Forest, northeastern 

Oregon and western Idaho - Continued.

[

a

ANC = acid neutralizing capacity, meq/L ; bCond = conductivity, mS/cm ; cAnions and cations, mg/L; DOC = dissolved organic carbon, mg/L; bdl 

= below detection limit, NA = not analyzed] 

Region Lake Name pH ANCa Condb Nac NH4c Kc Mgc Cac Fc Clc SO4c DOCc

N. Eagle Cap Aneroid Lake 7.819 842.4 92.6 0.776 0.016 0.816 1.146 14.074 0.020 0.073 7.906 1.05

N. Eagle Cap Granite/Hawk Lake 6.371 51 6.8 0.333 0.017 0.248 0.174 0.935 bdl 0.284 0.199 1.53

N. Eagle Cap Legore Lake 7.16 191.1 20.4 0.598 bdl 0.326 0.285 3.225 0.015 0.070 0.848 0.222

N. Eagle Cap Long Lake 7.094 110.9 11.2 0.426 bdl 0.275 0.193 1.822 bdl 0.051 0.201 0.601

N. Eagle Cap Mirror Lake 7.083 100.2 10.6 0.459 bdl 0.336 0.12 1.414 bdl 0.052 0.359 0.403

N. Eagle Cap Steamboat Lake 6.843 93 9.9 0.493 bdl 0.288 0.202 1.24 bdl 0.062 0.207 0.497

N. Elkhorns Black Lake 6.751 132.5 13.4 0.629 bdl 0.255 0.682 1.587 bdl 0.069 0.209 1.5

N. Elkhorns Crawfish Lake 7.104 205.6 20.3 1.061 bdl 0.42 0.359 2.87 0.015 0.175 0.307 2.47

N. Elkhorns Dutch Flat Lake 7.264 220.4 21.2 0.966 bdl 0.284 0.412 2.801 0.013 0.052 0.173 1.58

N. Elkhorns Hoffer Lake 6.598 95.5 9.5 0.491 bdl 0.198 0.277 1.288 bdl 0.058 0.216 0.76

N. Elkhorns LaGrande Reservoir NA NA NA NA NA NA NA NA NA NA NA NA

N. Elkhorns Lilypad Lake NA NA NA NA NA NA NA NA NA NA NA NA

N. Elkhorns Van Patten Lake NA NA NA NA NA NA NA NA NA NA NA NA

S. Eagle Cap Arrow Lake 7.081 157.9 14.5 0.607 bdl 0.525 0.23 1.982 bdl 0.067 0.285 0.557

S. Eagle Cap Crater Lake 6.757 88.5 9.8 0.489 0.014 0.221 0.084 0.579 bdl 0.095 0.598 0.775

S. Eagle Cap Culver Lake 6.712 110.8 16.1 0.65 bdl 0.52 0.45 1.374 bdl 0.062 1.875 0.266

S. Eagle Cap Fish Lake 7.437 185.6 20.0 1.072 bdl 0.202 0.263 2.657 0.012 0.123 0.524 NA

S. Eagle Cap Heart Lake 6.988 163.4 14.5 0.618 bdl 0.436 0.163 2.184 bdl 0.050 0.256 0.927

S. Eagle Cap Lookinglass Lake 6.548 64.5 7.1 0.37 bdl 0.283 0.085 1.129 bdl 0.057 0.552 0.616

S. Eagle Cap Twin Lakes NA NA NA NA NA NA NA NA NA NA NA NA

S. Elkhorns Baldy Lake NA NA NA NA NA NA NA NA NA NA NA NA

S. Elkhorns Goodrich Lake 7.686 490.3 53.6 0.695 bdl 0.352 0.797 7.454 0.022 0.077 2.248 0.664

S. Elkhorns Rock Creek NA NA NA NA NA NA NA NA NA NA NA NA

S. Elkhorns Twin Lakes_Lower 7.61 644.4 58.2 1.149 bdl 0.214 1.031 8.209 0.020 0.072 0.331 2.77

Seven Devils Basin Lake 7.546 429.8 45.9 0.684 0.031 0.175 0.306 6.976 bdl 0.131 0.450 1.54

Seven Devils Emerald Lake 7.397 244.3 29.2 1.007 bdl 0.156 0.484 1.812 0.014 0.082 2.248 NA

Seven Devils Ruth Lake 7.074 221.6 31.4 0.608 0.011 0.158 0.286 3.531 bdl 0.113 0.416 NA

Seven Devils Shelf Lake NA NA NA 0.586 0.068 0.386 0.366 2.436 bdl 0.318 0.343 NA
36

Table 2. Fish catch data and size ranges from subalpine lakes in the Wallowa-Whitman National Forest, northeastern Oregon and western Idaho.

Brook trout Rainbow trout Cutthroat trout Lake trout

Lake Name

SL Range 

(mm)

Mean SL 

(mm) N

SL Range 

(mm)

Mean SL 

(mm) N

SL Range 

(mm)

Mean SL 

(mm) N

SL Range 

(mm)

Mean SL 

(mm) N

Aneroid Lake 145 - 265 212.5 10 NA 290 1 -- -- -- -- -- --

Arrow Lake -- -- -- 165 - 320 238.4 10 -- -- -- -- -- --

Baldy Lake 204 - 257 230.5 2 -- -- -- -- -- -- -- -- --

Basin Lake -- -- -- 218 - 320 286.3 6 -- -- -- -- -- --

Black Lake 161 - 248 210.9 10 -- -- -- -- -- -- -- -- --

Crater Lake 245 - 276 263.6 5 250 - 290 268.2 9 -- -- -- -- -- --

Crawfish Lake 152 - 220 188.3 10 -- -- -- -- -- -- -- -- --

Culver Lake 129 - 175 154.9 11 -- -- -- -- -- -- -- -- --

Dutch Flat Lake 170 - 245 210.2 10 -- -- -- -- -- -- -- -- --

Emerald Lake 150 - 196 167.9 15 -- -- -- -- -- -- -- -- --

Fish Lake 182 - 215 201.1 8 200 - 300 244.4 9 -- -- -- -- -- --

Goodrich Lake 147 - 206 184.4 15 -- -- -- -- -- -- -- -- --

Granite/Hawk Lake -- -- -- 215 - 238 225.5 6 -- -- -- -- -- --

Heart Lake 162 - 220 198.8 8 -- -- -- -- -- -- -- -- --

Hoffer Lake 133 - 190 167.9 10 -- -- -- -- -- -- -- -- --

LaGrande Reservoir 150 - 239 199.7 15 154 - 250 202.4 12 -- -- -- -- -- --

Legore Lake -- -- -- 146 - 247 204.7 9 -- -- -- -- -- --

Lilypad Lake 160 - 220 177.1 10 -- -- -- -- -- -- -- -- --

Long Lake 205 - 220 215.8 5 -- -- -- -- -- -- -- -- --

Lookinglass Lake 105 - 178 150.1 10 -- -- -- -- -- -- 659 -- 1

Mirror Lake 98 - 174 138.1 12 -- -- -- -- -- -- -- -- --

Rock Creek 115 - 204 168.1 15 -- -- -- -- -- -- -- -- --

Ruth Lake 174 - 218 196.6 8 290 - 330 303.3 3 -- -- -- -- -- --

Shelf Lake -- -- -- -- -- -- 133 - 265 191.2 11 -- -- --

Steamboat Lake 125 - 209 183.9 11 -- -- -- -- -- -- -- -- --

Twin Lakes_Eagle Cap -- -- -- 210 - 285 233.2 10 -- -- -- -- -- --

Upper Twin Lakes_Elkhorns 153 - 231 175.2 20 -- -- -- -- -- -- -- -- --

Van Patten Lake 145 - 215 177.8 10 161 - 200 183.1 10 -- -- -- -- -- --
37

Table 3. Ranking of candidate models describing physical and chemical variables influencing fish THg concentrations in the Wallowa-Whitman 

National Forest, northeastern Oregon and western Idaho. [Models presented include only those that were within 8 AICC units of the top model (ΔAICC = 0) and the null (intercept) models;

 aVariables considered 

included basal area, lake area, lake elevation, lake catchment area, ratio of lake area to lake catchment area, lake pH, acid neutralizing capacity (ANC), lake 

sulfate (SO4), and dissolved organic carbon (DOC); b

Number of estimated parameters in the model including the intercept and variance; cSecond-order Akaike's 

Information Criterium (AICc); d

The difference in the value between the AICc of the current model and the value of the most parsimonious model; e Likelihood of 

the model given the data, relative to candidate models; f

The weight of the evidence that the top model is better than the selected model, given the candidate 

model set ]. 

Model Structure n Kb -2LogL AICCc ΔAICCd wi

e

Evidence 

ratiof r2

Basal area + lake area + SO4 + DOC 18 6 -1.673 17.963 0.000 0.56 1.00 0.87

Basal area + lake area + SO4 18 5 4.986 19.986 2.023 0.20 1.58 0.71

Basal area + lake area + SO4 + ANC 18 6 3.096 22.733 4.770 0.05 2.10 0.75

Basal area + lake area + SO4 + catchment area 18 6 4.716 24.353 6.390 0.02 2.60 0.76

Basal area + lake area + SO4 + elevation 18 6 4.887 24.524 6.561 0.02 2.61 0.72

Basal area + lake area + SO4 + lake area to catchment ratio 18 6 4.960 24.596 6.634 0.02 2.89 0.72

Basal area + lake area + SO4 + pH 18 6 4.979 24.615 6.653 0.02 2.95 0.71

Basal area + lake area + ANC + pH 18 6 5.039 24.676 6.713 0.02 2.98 0.81

Basal area + SO4 + catchment area 18 5 10.012 25.013 7.050 0.02 3.07 0.53

Basal area + SO4 18 4 14.518 25.596 7.633 0.01 3.40 0.50

Intercept 18 2 34.997 39.798 21.835 0.00 55130.92 -
38

Table 4. Ranking of candidate models describing physical variables influencing fish THg concentrations in the Wallowa-Whitman National Foresta, 

northeastern Oregon and western Idaho. 

Model Structure n Kb -2LogL AICCc ΔAICCd wi

e

Evidence 

ratiof r2

Basal area + lake area 26 4 30.375 40.279 0.000 0.51 1.00 0.46

Basal area + lake area + lake area to catchment ratio 26 5 30.226 43.226 2.947 0.12 4.36 0.46

Basal area + lake area + catchment area 26 5 30.309 43.309 3.029 0.11 4.55 0.46

Basal area + lake area + elevation 26 5 30.365 43.365 3.085 0.11 4.68 0.46

Basal area + lake area + catchment area + lake area to catchment ratio 26 6 30.016 46.437 6.158 0.02 21.73 0.47

Basal area + lake area + catchment area + elevation 26 6 30.047 46.468 6.189 0.02 22.07 0.47

Basal area 26 3 39.503 46.594 6.315 0.02 23.51 0.23

Basal area + lake area + lake area to catchment ratio + elevation 26 6 30.218 46.639 6.360 0.02 24.05 0.46

Intercept 26 2 46.336 50.858 10.579 0.00 198.20 -

[Model presented include only those that were within 8 AICC units of the top model (ΔAICC = 0) and the null (intercept) models; a

Variables considered included basal area, lake area, lake elevation, lake catchment area, and ratio of lake area to lake catchment area; bNumber of estimated 

parameters in the model including the intercept and variance; c

Second-order Akaike's Information Criterium (AICc); dThe difference in the value between the 

AICc of the current model and the value of the most parsimonious model; e

Likelihood of the model given the data, relative to candidate models; fThe weight of 

the evidence that the top model is better than the selected model, given the candidate model set].
Publishing support provided by the U.S. Geological Survey 

Publishing Network, Tacoma Publishing Service Center 

For additional information contact:

Director, Forest and Rangeland Ecosystem Science Center

U.S. Geological Survey

777 NW 9th St., Suite 400

Corvallis, Oregon 97330

http://fresc.usgs.gov/ 
Printed on recycled paper

Eagles-Smith and others— Mercury Bioaccumulation in Fishes from Subalpine Lakes in the Wallowa-Whiman National Forest, Northeastern Oregon and Western Idaho—Open-File Report 2013-1148