Relationship between Mercury Concentration and Growth Ratesfor Walleyes, Northern Pike, and Lake Trout from Quebec Lakes

MELYSSA LAVIGNE, MARC LUCOTTE,* AND SERGE PAQUET

Collaborative Mercury Research Network, Universite du Quebec a Montreal,C.P. 8888, Succursale Centre-Ville, Montreal, Quebec H3C 3P8, Canada

Abstract.—The relationship between mercury (Hg) concentrations in fish muscle and fish growth rates was

assessed for 54 walleye Sander vitreus, 52 northern pike Esox lucius, and 35 lake trout Salvelinus namaycush

populations throughout the Province of Quebec, Canada. We used the von Bertalanffy growth model to

estimate the ages of fish specimens for a given length, and Hg concentrations in fish specimens at

standardized length were determined via a quadratic regression model. Measured values of Hg concentrations

in walleyes, northern pike, and lake trout were then correlated to the estimated age at standardized length for

each population (375, 675, and 550 mm, respectively). A model-II regression was performed to describe the

existing relationships. Growth rates were positively related to Hg concentrations in walleyes and northern pike

(when three outliers were excluded), whereas no correlation was observed for lake trout. Our findings

demonstrate that slower-growing walleyes and northern pike have higher Hg concentrations at standardized

length. For these fish species, growth rate could be used as an integrated proxy to predict Hg concentration in

fish muscle on a regional scale. Our findings support the contention that biodilution can be an important factor

regulating mercury concentrations in fish. Thus, our findings suggest that proper control of fish growth rate

through fishing pressure, lake ecology, and watershed management could be used by fisheries management

authorities to minimize the toxic risk associated with Hg exposure from fish consumption.

As for many other regions in North America,

mercury levels in freshwater fish from Quebec

(Canada) often exceed the threshold value for human

consumption, especially in the case of predatory

species (Bodaly et al. 1993; Schetagne and Verdon

1999; Evans et al. 2005; Kamman et al. 2005; Lockhart

et al. 2005; Simoneau et al. 2005). Much work has

been accomplished to explain variability in Hg

concentrations in fish and to identify key factors

enabling adequate predictions of fish Hg levels in

specific lake environments. A scientific consensus is

currently emerging about the fact that such variability

is the result of complex interactions among numerous

environmental and biological factors (Parkman and

Meili 1993; Lucotte et al. 1999; Kainz et al. 2003;

Gorski et al. 2003; Montgomery et al. 2000; Wiener et

al. 2003). However, attempts to integrate these

interactions in order to predict specific fish Hg levels

and to identify Hg ‘‘hot spots’’ remain scarce,

particularly at a wide regional scale (EPRI 2003;

Roue-Le Gall et al. 2005; Surette 2005). In addition,

environmental factors have drawn much attention from

the scientific community (Cope et al. 1990; Bodaly et

al. 1993; Driscoll et al. 1994; Wiener et al. 2003).

Despite several studies showing relationships between

fish Hg concentration and length (Scott and Armstrong

1972; Olsson 1976; Schetagne and Verdon 1999) or

trophic level (Tremblay et al. 1998), biological factors,

such as fish growth rate, are generally overlooked.

Fish growth rate, which can be defined as the

temporal change in either fish weight or length (Ricker

1980; Weatherley and Gill 1987; Hopkins 1992), has

long been assumed to play a hypothetical role in the

biodilution of contaminants (de Freitas et al. 1974;

Norstrom et al. 1976). Such influence of growth rate on

Hg accumulation and hence Hg biodilution in fish

muscle was first tested in small Finnish and Swedish

oligotrophic lakes, where intensive fishing experiments

were reported to decrease Hg concentrations in the

remaining fish (Gothberg 1983; Jernelov and Lann

1973; Verta 1990). A similar experiment was later

applied to small lakes located in northern Quebec. In

two of these experimental lakes, Hg levels in walleye

Sander vitreus populations declined by 33%, and

growth rates of these populations increased by 35%(Doire 2003; Surette 2005). Decline in Hg levels was

not explained by changes in fish diet, structural

alterations of the trophic web (Doire 2003), a reduction

in methylmercury (MeHg) levels in forage fish, or by a

reduction in the whole-lake MeHg content following

fish removal (Surette 2005; Surette et al. 2005).

Through comparisons of Hg concentration in fish

before and after intensive fishing, Surette (2005)

hypothesized that biodilution would partly explain the

observed decrease in walleye Hg concentrations.

Numerous other studies using a variety of experimental

* Corresponding author: lucotte.marc_michel@uqam.ca

Received March 28, 2008; accepted April 26, 2010Published online October 25, 2010

1221

North American Journal of Fisheries Management 30:1221–1237, 2010� Copyright by the American Fisheries Society 2010DOI: 10.1577/M08-065.1

[Article]

approaches appear to corroborate this hypothesis

because they reported lower fish Hg levels in faster

growing populations (Rask et al. 1996; Doyon et al.

1998; Harris and Bodaly 1998; Stafford and Haines

2001; Essington and Houser 2003; Stafford et al. 2004;

Simoneau et al. 2005). However, most of the

aforementioned studies are either limited in terms of

number of lakes and fish species considered or are

vague about the actual contribution of growth rate to

biodilution. Simoneau et al. (2005) were the first to

point out growth rate as a key factor to explain

interlake fish Hg level variability; studying walleye

populations from 12 Quebec (Canada) lakes, these

authors reported that Hg levels in fish muscle at a

standardized length of 350 mm were significantly

correlated to estimated ages at this length.

In this study, we further explored the relationship

between Hg concentration and growth rate in fish

populations from 118 lakes in Quebec, Canada. Three

predator fish species that typically exhibit Hg levels

exceeding the Canadian threshold value for human

consumption (0.5 mg/kg of fish body weight) were

considered: walleyes, northern pike Esox lucius, and

lake trout Salvelinus namaycush. We specifically

aimed at verifying the existence of a positive

relationship between fish muscle Hg level at standard-

ized length (Lstd

), as estimated by quadratic regression

models (Tremblay et al. 1998) and growth indexes

obtained using the von Bertalanffy model (Ricker

1980). This approach presents the advantage of using

two simple models that can be applied to a wide series

of lakes. Compared with previous studies, our study

used a markedly broader data set of lakes over an

extended spatial area covering both the boreal and

temperate domains. Such a large spatial distribution of

lakes also allowed us to examine the relationships

between fish characteristics (Hg concentration and age

at Lstd

) and geographical as well as hydrographic

characteristics. Because the species we considered are

highly valued and frequently consumed by sport fishers

(Scott and Crossman 1974), we also discuss the

implications of our findings for the management of

wild freshwater fish resources.

Methods

Study area.—This study area covers 1,056,000 km2

within the Province of Quebec, stretching from 46–

588N and from 64–808W. The zone considered

encompasses both densely and sparsely populated

regions of Quebec, most of the hydrographic basins

of this province as well as many of its ecozones (Figure

1). A total of 118 natural lakes sustaining weak to high

fishing pressure were selected. The main characteristics

of the selected lakes are given in Lavigne (2007).

Database assemblage.—All data used in this study

were provided by five entities: the Collaborative

Mercury Research Network (COMERN); the Ministry

of Natural Resources and Wildlife of Quebec (MNRF);

the Ministry of Sustainable Development, Environ-

ment, and Parks of Quebec (MDDEP); the Energy

Corporation of James Bay (SEBJ); and the hydropower

corporation Hydro-Quebec (HQ).

Lake-based data were assembled for the three top

predator species—walleye, northern pike, and lake

trout—where fish length, age, and Hg concentration

(based on wet weight) were available for at least 10

specimens of a given species. From this data screening,

we obtained a database containing information on

5,207 walleyes (54 lakes), 2,419 northern pike (52

lakes), and 1,478 lake trout (35 lakes; see Appendix).

Fish sampling.—The fish sampling was conducted

by COMERN from 2001 to 2004, by MRNF from

1999 to 2004, by SEBJ from 1978 to 1985, and by HQ

from 1986 to 2001. Similar protocols were applied by

all for fish capture and sample collections. Experimen-

tal variable-mesh gill nets were either 46 or 61 m long

3 1.82 m high, with variable bar mesh sizes of either

2.5, 3.2, 3.8, 5.1, 6.4, 7.6, and 10.2 cm or 2.5, 3.2, 3.8,

5.1, 6.4, 7.6, 10.2, 12.7, and 15.2 cm. Total length

(mm), weight (g), sex, and maturity stage were

determined whenever possible. Aging structures such

as otoliths, scales, or bones were also collected for age

determination. A sample of fish muscle was taken from

the caudal region for Hg analysis. Bones and skin were

removed from muscle samples prior to preservation.

Samples of fish muscle were preserved frozen or

freeze-dried until being analyzed.

Age determination.—Age determination was per-

formed on aging structures using opercula, otoliths, and

(or) dorsal spines for walleyes (Campbell and Babaluk

1979; Pepin and Levesque 1985; Babaluk and Campbell

1987; Babaluk et al. 1993); cleithra or scales for

northern pike (Casselman 1974; Babaluk and Craig

1990; Laine et al. 1991); and otoliths for lake trout

(Dubois 1967; Lapointe and Clement 1985; Casselman

1987). All age estimations were performed at least

twice, typically by two different readers or until

agreement was reached on an age value. If disagreement

persisted on the age value after the third reading, the

structure was discarded and the age data were rejected.

Mercury analysis in fish muscle.—Total Hg concen-

tration in fish muscle was determined either by cold

vapor atomic fluorescence spectrometry (CVAFS;

typical detection limit of 1 g/L) or by cold vapor

atomic absorption spectrometry (CVAAS; typical

detection limit of 1 lg/L;) (Pichet et al. 1999). Details

on analytical procedures can be found elsewhere

(Tremblay et al. 1996; Pichet et al. 1999; CEAEQ

1222 LAVIGNE ET AL.

2003). The University of Quebec at Montreal, Mercury

Analytical Laboratory used CVAFS to perform anal-

yses on samples collected by COMERN, while the

Quebec Expert Center for Environmental Analyses

(Centre d’Expertise en Analyse Environnementale du

Quebec [CEAEQ]) analyzed samples collected by

MRNF using CVAAS (CEAEQ 2003). Fish caught

by SEBJ and HQ were analyzed by a series of

independent laboratories using standard CVAAS

protocol, and unidentified triplicates were introduced

at a rate of 10% to ensure quality control. All

participating laboratories followed rigorous quality

assurance–quality control procedures, including peri-

odical analytical blanks, sample replicates, certified

fish tissue standards and calibration curves. All

analytical facilities were also part of the interlaboratory

calibration program set for Hg by the Canadian Food

Inspection Agency. As a final validation procedure,

measured Hg values (mg/kg of wet weight) were

qualitatively tested using Hg level versus fish length

dispersion diagrams, and outlier samples were either

subjected to a new analysis or rejected. Sample

FIGURE 1.—Map of the Province of Quebec showing the locations of the lakes (black dots) selected to assess mercury

concentrations in fish muscle and fish growth rates and the 11 hydrological basins (00–10) in which the lakes lie; lake numbers

are those assigned by Lavigne (2007).

MERCURY AND FISH GROWTH IN QUEBEC LAKES 1223

rejection rates were 0.6% for walleyes, 1.4% for

northern pike, and 0.3% for lake trout.

Fish growth rate model.—The relationships between

fish length and age were determined using the von

Bertalanffy growth model (Ricker 1980):

LT ¼ L‘ð1� e�K½t�t0 �Þ; ð1Þ

where LT

is the total length (mm) of fish at time t(years; which represents the fish age as determined

with aging structures), L‘

is the asymptotic length

(mm), K is the growth coefficient, and t0

(years) is the

hypothetical fish age at length ¼ 0 mm. For each

combination of fish species and lake, we fitted the von

Bertalanffy growth model to age and total length data

using nonlinear regressions, for a total of 141 models.

Mercury concentrations versus fish length.—The

relationships between Hg concentration in fish muscle

and fish length were modeled using quadratic regres-

sions (Tremblay et al. 1996; 1998) of the form

½Hg� ¼ aþ bðLTCÞ þ cðLTC2Þ; ð2Þ

where [Hg] is the Hg concentration measured in fish

muscle (in mg/kg of wet body weight)and LTC is the

fish total length (mm). The intercept (a) as well as the

coefficients b and c were parameterized by applying

equation (2). The method used by Tremblay et al.

(1998) is robust enough to fit both linear and nonlinear

regressions. When the quadratic regression did not fit,

as indicated by a nonsignificant LTC2 parameter, a

simple linear regression was applied to describe the

fish population.

Fish standardized length.—For each species, an

average total length was calculated for each lake. Then,

for each species, we averaged the mean total lengths

across all lakes; we referred to this grand average as the

specific mean length. The mean specific length was

rounded to the nearest 25 mm to generate the

standardized length, Lstd

.

Correlation analysis.—Pearson’s product-moment

correlation coefficient analyses were used to assess

the significance of the relations between mercury

concentration at the standardized length ([Hg]Lstd

)

and age at the standardized length (AgeLstd

) for each

species. Data to be included in the Pearson analyses of

[Hg]Lstd

as a function of AgeLstd

, were selected

according to three criteria. (1) Lakes where the

regression coefficient (r2) was not statistically different

from a zero slope (P , 0.05) for one of the two models

were removed from further analysis. (2) The model’s

fit to the observed values was assessed visually using

dispersion diagrams and the model’s validity con-

firmed if the Lstd

was not located at one of the

extremities of the length distribution and the Lstd

was

not outside of the range of the model. (3) A

homogeneous data distribution around the Lstd

was

observed. Following these criteria, we kept 54 out of

58 lakes for walleyes, 52 of 70 for northern pike, and

35 of 50 for lake trout. Data exclusion from further

analysis was mainly due to fish length values below

Lstd

or insufficient numbers of fish.

A standard major axis model-II regression was then

applied to describe the relations (Legendre and

Legendre 1998). Model-II regression was preferred to

simple linear regression because [Hg]Lstd

and AgeLstd

are both affected by independent natural variations.

The standard major axis model-II was chosen among

the different types of model-II regressions considering

that this model was best adapted to properly describe

our data set, which was characterized by the following:

(1) correlations were statistically significant, (2) the

variables compared were not of the same physical

units, (3) the variances of the x-axis and y-axis were

comparable, and (4) the normality criterion for both

axis variables was met using Shapiro–Wilk W tests

(Legendre and Legendre 1998; Legendre 2001).

Furthermore, we tested the presence of outliers using

Mahalanobis distances on the dispersion diagrams of

the Pearson analyses between [Hg]Lstd

and AgeLstd

.

When we found outliers, we then double-checked with

jackknifed distances, which reduces distortion.

Relation of fish characteristics to latitude andhydrographic region.—Multiple correlation analysis

was applied to [Hg]Lstd

values, AgeLstd

values and

latitude of the lakes for each fish species. This type of

analysis allowed calculating a Pearson’s product-

moment correlation coefficient (rPearson

) indicative of

the presence and strength of a linear relation between

variables. Bonferroni corrections were applied on

threshold values because multiple testing was involved

(Legendre and Legendre 1998).

The selected lakes were sorted into four groups

according to both their ecological (ecozones) and

hydrographical (hydrographic regions) characteristics.

As the selected lakes were distributed over a very large

territory, including four ecozones, each one with is own

pedological structure that can influence the Hg

dynamics in the watershed. Furthermore, these lakes

spread over 10 different hydrographic regions (Figure

1; Marshall and Schut 1999; CEHQ 2005) and could

host distinct fish subpopulations, each with their own

genetic characteristics that potentially modulate their

growth rate or the Hg toxicokinetics. Group 1 includes

the St. Lawrence River hydrographic region (00) and

Chaleur Bay and Perce (01), which corresponds to the

Mixed Wood Plains ecozone. Group 2 comprises the

following hydrographic regions located in the southern

Boreal Shield ecozone: St. Lawrence Southeast (02),

1224 LAVIGNE ET AL.

St. Lawrence Southwest (03), Outaouais and Montreal

(04), St. Lawrence Northwest (05), Saguenay–Lake

Saint-Jean (06), and St. Lawrence Northeast (07).

Group 3 includes the northern hydrographic region of

Hannah and Rupert bays (08). Group 4 covers the two

hydrographic regions of James Bay: Hudson Bay (09)

and Ungava Bay (10), which are located in the far north

portion of Quebec’s territory, in the northern Boreal

Shield ecozone and in the Taiga Shield ecozone.

Considering that the statistical distribution of

[Hg]Lstd

and AgeLstd

values within fish subsets did

not meet the criteria for the application of analysis of

variance (ANOVA; Shapiro–Wilk W and Levene tests

for homocedasticity of variance), nonparametric Krus-

kall–Wallis tests were applied to determine significant

differences in mean [Hg]Lstd

and mean AgeLstd

across

the four lake groups. Then, Noether nonparametric

multiple comparison tests were performed to find

which particular groups differed from the others. We

set a ¼ 0.05 for all of our statistical testing.

Statistical software.—Quadratic regressions and von

Bertalanffy models were carried out using the SPSS

v13 software (SPSS 2004). Other statistical analyses

were performed using the JMP 5.0 software (SAS

Institute 2002), with the exception of the model-II

regression analysis that was conducted using the

model-II regression and permutation test software

(Legendre 2001).

Results

Data Range

Total Hg levels measured in fish muscle were highly

variable, ranging from 0.06 to 3.71 mg/kg of muscle

(wet weight) for walleyes, from 0.07 to 4.11 mg/kg for

northern pike, and from 0.05 to 6.22 mg/kg for lake

trout. Fish total length also varied within and among

species, ranging from 110 to 780 mm for walleyes,

from 150 to 1,170 mm for northern pike, and from 120

to 1,060 mm for lake trout. Finally, age ranges (years)

were 0–33 for walleyes, 0–24 for northern pike, and 1–

41 for lake trout.

Calculation of Standardized Length

Considering all selected lakes, average total lengths

(x) for walleye, northern pike and lake trout popula-

tions ranged between 290 and 500 mm (mean specific

length ¼ 384.4 mm), 515–904 mm (mean specific

length¼ 668.3 mm), and 340–700 mm (mean specific

length ¼ 561.5 mm), respectively. Based on mean

specific lengths, we then determined Lstd

to be 375 mm

for walleye, 675 mm for northern pike and 550 mm for

lake trout.

Growth Curves and Mercury Level versus Fish Length

When considering the whole data set, mean AgeLstd

was equal to 10.81 (SD, 0.95) for slow-growing

walleyes, 7.98 (SD, 1.30) for medium-growing wall-

eyes, and 3.40 (SD, 0.72) for fast-growing walleyes.

Means for northern pike were 9.74 (SD, 0.56; slow),

7.00 (SD, 0.60; medium) and 4.90 (0.79; fast). For the

lake trout, means were 22.51 (SD, 3.36; slow), 11.91

(SD, 1.82; medium), and 7.40 (SD, 1.28; fast).

The von Bertalanffy models indicated a fairly good

agreement between age and length, r2 values ranging

from 0.77 to 0.95 for the nine illustrative populations

shown in Table 1 (mean ¼ 0.81). In comparison, the

TABLE 1.—Parameters for the von Bertalanffy growth model (equation 1, where L‘¼ asymptotic length, K ¼ the growth

coefficient, and t0¼ hypothetical fish age at length ¼ 0 mm), age at standardized length (Age

Lstd), parameters for quadratic

regressions (equation 2, where a ¼ the intercept and b and c ¼ parameterized coefficients) and muscle mercury content at

standardized length ([Hg]Lstd

) for walleye, northern pike, and lake trout populations, illustrating three distinct growth patterns

(slow, medium, and fast).

Lakea N L‘

K (310�2) t0

AgeLstd

b (year) r2 a b c (310�3)[Hg]

Lstd

b

(mg 3 kg�1) r2

Walleye (375 mm)

Dana 32 889 3.98 �3.41 10.4 6 1.2 0.891 �924 5.72 9.29 1.22 6 0.26 0.751Mesgouez 27 891 5.42 �2.64 7.4 6 0.7 0.934 �318 1.82 4.48 0.36 6 0.05 0.917Saint-Francois 34 810 1.45 �1.52 2.8 6 0.5 0.876 �331 1.31 2.82 0.19 6 0.06 0.803

Northern pike (675 mm)

Pusticamica 27 2,565 2.59 �2.92 8.9 6 0.4 0.934 �734 2.78 �4.55 1.07 6 0.18 0.581Ouescapis 24 1,138 12.62 �1.09 6.0 6 0.4 0.939 �439 1.73 �1.57 0.73 6 0.09 0.759Waconichi 25 1,096 21.60 �0.86 3.6 6 0.4 0.892 �213 0.61 0.66 0.20 6 0.02 0.855

Lake trout (550 mm)

Amichinatwayach 44 1,166 3.67 �3.89 13.5 6 1.4 0.826 �1,026 2.73 5.52 0.48 6 0.10 0.786Tilly 40 1,433 3.57 �2.24 11.3 6 0.6 0.949 �733 2.03 0.007 0.39 6 0.10 0.820Chibougamau 110 844 8.33 �5.21 7.5 6 1.5 0.768 2,670 5.55 12.37 0.51 6 0.12 0.628

a Refer to Figure 1 and Table A.1 for lake identification and basic data.b Mean 6 95% confidence limits.

MERCURY AND FISH GROWTH IN QUEBEC LAKES 1225

quadratic models depicting the relationship between

fish Hg concentration ([Hg]Lstd

) and length were more

variable, r2 ranging from 0.58 to 0.92 (mean ¼ 0.70).

Table 1 clearly shows that higher [Hg]Lstd

values

correspond to slower growth rates and inversely.

Figure 2 illustrates the accumulation ([Hg]Lstd

versus

LT) and growth ([Hg]

Lstdversus Age

Lstd) curves for the

nine illustrative fish populations presented in Table 1.

Accumulation curves showed a fair segregation

between slow, medium, and fast growing populations

of walleyes and northern pike (Figure 2A, C), whereas

corresponding curves for lake trout exhibited large

regions of overlapping values of the three populations

(Figure 2E). Similarly, growth-curve segregation was

better in the case of walleyes and northern pike (Figure

2B, D) than it was for lake trout (Figure 2F). Curve

patterns also differed between walleyes and northern

pike (on the one hand) and lake trout (on the other

hand). Although the highest Hg accumulation is

associated with the slowest growth in walleyes and

northern pike, an inconsistent pattern is obtained in

lake trout, as shown for Lake Amichinatwayach, where

[Hg]Lstd

is intermediary when lake trout AgeLstd

is the

highest and there is a 95% CI overlap with the

medium- and fast-growing subsets.

Muscle Hg Levels Versus Growth

Figures 3, 4, and 5 present the dispersion diagrams

for the different estimated [Hg]Lstd

versusAgeLstd

values for all fish populations considered, as well as

model-II regressions when the linear relationship

(correlation coefficient) was statistically significant

(rPearson

6¼ 0 for P , 0.05). The correlations and

model-II regressions on the estimated values constitute

a simple way to illustrate the relationships found

between muscle Hg levels and growth rates for the

different fish populations from one lake to another.

Lakes situated on the left side of the three figures

exhibit fish with fast growth rates, whereas those to the

right side are those where fish are growing slowly.

There is a great variability in [Hg]Lstd

at a given

AgeLstd

for all three fish species considered, but on the

overall AgeLstd

variation scale, [Hg]Lstd

values are

fairly similar between species: 0.15–1.22 mg/kg for

walleyes, 0.20–1.62 mg/kg for northern pike, and

0.11–1.01 mg/kg for lake trout. Ranges for AgeLstd

also

greatly vary: 2.1–12.2 years for walleyes, 3.6–10.1

years for northern pike, and 5.5–14.2 years for lake

trout.

Standard major axis model-II regression provided a

functional equation with slope values that sensibly

differ between walleyes (0.09 mg/kg) and northern

pike (0.22 mg/kg; Figures 3, 4). This suggests higher

Hg concentrations for northern pike at any given

AgeLstd

. However, as shown on the dispersion diagram,

[Hg]Lstd

levels for northern pike were much more

variable than for walleyes. When applying the

Mahalanobis distance outlier test to exclude three lakes

exhibiting extreme values (Gaotanaga, Coutaceau, and

Dana) from the model, the regression for northern pike

was stronger. No significant relationship was found

between [Hg]Lstd

and AgeLstd

for lake trout even though

the ranges covered by these variables in that fish

population were quite similar to those of the two other

species (Figure 5).

Multiple Correlations for [Hg]Lstd

, AgeLstd

, Latitude,and Longitude

The moderate but still significant positive value of

rPearson

in Table 2 suggests that latitudinal distribution

of lakes influences the growth rate of walleyes.

Meanwhile, longitudinal distribution of lakes present

a lot more significant relationships with AgeLstd

for

walleyes and with [Hg]Lstd

and AgeLstd

for northern

pike, but the rPearson

values remained weak. On the

other hand, [Hg]Lstd

and AgeLstd

for lake trout

populations were not related to the latitudinal or

longitudinal positions.

Distribution of [Hg]Lstd

and AgeLstd

by HydrographicRegions

Values of [Hg]Lstd

and AgeLstd

values were signif-

icantly lower for the walleyes living in the southern

hydrographic regions (Figure 6A, B). The highest

[Hg]Lstd

values for walleyes were found in the middle

eastern part of Quebec, and the highest AgeLstd

were

found from the middle to the northern parts of the

provincial territory. There was no statistical difference

for the distributions of [Hg]Lstd

and AgeLstd

between

hydrographic regions for northern pike (Figure 6C, D)

and lake trout (Figure 6E, F). These findings are in

agreement with the analysis of latitudinal correlations.

DiscussionMercury Variability in Muscle

Our results showed a high variability of Hg levels in

three piscivorous fish species, with concentrations

ranging over two orders of magnitude. Among the

9,104 fish specimens analyzed in this study, 50% were

above the 0.5 mg Hg/kg threshold value for human

consumption in Canada, the highest concentration was

6.00 mg Hg/kg of wet body weight. The [Hg]Lstd

values for the 118 lakes studied varied by a factor of up

to 10, and more than 60% of these lakes were hosting

individuals with [Hg]Lstd

over 0.5 mg Hg/kg. The

distribution of [Hg]Lstd

values from one lake group to

another also illustrates the extent of this variability,

considering that the only statistically significant

1226 LAVIGNE ET AL.

FIGURE 2.—Quadratic regressions (Hg levels versus total length; left panels) and von Bertalanffy growth curves (total length

versus age; right panels) for representative (A)–(B) walleyes, (C)–(D) northern pike, and (E)–(F) lake trout populations in Quebec.

For each combination of the three fish species and three growth rates (slow [diamonds], medium [times signs], and fast [circles]), one

representative population (lake; see Table 1) was chosen, namely, Dana (slow), Mesgouez (medium), and Saint-Francois (fast) in (A)

and (B); Pusticamica, Ouescapis, and Waconichi in (C) and (D); and Chibougamau, Tilly, and Amichinatwayach in (E) and (F).

MERCURY AND FISH GROWTH IN QUEBEC LAKES 1227

difference in [Hg]Lstd

regional distributions was found

between group 1 (St. Lawrence River – 00) and group

3 (Hannah Bay and Rupert Bay – 09), and only in the

case of walleye populations. It is rather surprising that

differences in [Hg]Lstd

cannot be further related to

geographic or hydrographic repartition of the lakes,

considering the large area covered by this study (800

km E-W by 1320 km S-N) and the fact that most fish

populations in a given hydrographic region have been

isolated from each other (Scott and Crossman 1974).

Variability in Growth Rates

High variability in the growth rate of different fish

populations of a given species has often been reported

in the literature (Pauly 1978; Weatherley and Gill

1987; Froese and Pauly 2000). Such variability is also

reflected in this study; that is, the observed distribution

of AgeLstd

varied between 7 and 10 years for each

species considered. The large variability in AgeLstd

observed for walleye populations is similar to that

reported by Simoneau et al. (2005). These authors also

reported fast growth rates for walleye populations, even

in northern locations, as well as high intraregional

growth rate variability. Despite the high variability

observed in walleye growth rates, there is a positive

correlation between AgeLstd

and latitude for the 54

lakes for which data are available (rPearson

¼ 0.5307, P, 0.0001; Table 2). This observation is in agreement

with the general trend in walleye growth rates, reported

to usually be slower in northern regions (Colby and

Nepszy 1981; Galarowicz and Wahl 2003). This trend

could also explain the significant difference observed

between the distributions of AgeL375

for group 1 (St

Lawrence River hydrographic region) and groups 3 and

4(northern hydrographic regions; Figure 6). Similarly,

the same trend could explain differences in the

distribution of [Hg]L375

between groups 1 and 3

(Figure 6), considering the strong and highly signifi-

FIGURE 3.—Estimated Hg levels at 375 mm standardized length ([Hg]L375

) versus estimated ages at that length (AgeL375

)

for walleyes.

1228 LAVIGNE ET AL.

cant correlation existing between [Hg]L375

and AgeL375

for walleyes (rPearson

¼ 0.7410, P , 0.0001; Table 2).

On the other hand, the absence of such a trend between

AgeLstd

and latitude or hydrographic regions for

northern pike and lake trout populations (Table 2;

Figure 6) probably reflects the high interlake variability

of growth rates we encountered. The latter observation

contradicts the generally accepted positive relations

reported between fish growth rates and the water

temperature (or the number of degree-days) and the

inverse relation between growth and latitude (Cassel-

man 1978; Pauly 1978; Colby and Nepszy 1981;

O’Connor et al. 1981; Weatherley and Gill 1987; Clapp

and Wahl 1996; Claramunt and Wahl 2000; Galar-

owicz and Wahl 2003). Both lower [Hg]L675

and lower

AgeL675

in more temperate climate could also be

explained by the influence of denser human popula-

tions and thus enhanced anthropogenic nutrient

loadings to lakes, which in turn enhances ecosystems

productivity and fish growth rates (Fortin et al. 1996).

Muscle Hg Levels and Growth Rates

The strong positive relationship we observed

between [Hg]L375

and AgeL375

for the 54 walleye

populations (r2¼ 0.549, P , 0.0001; Figure 3), which

suggest an influence of growth rate on Hg concentra-

tion in this fish species, is similar to that obtained by

Simoneau et al. (2005) between [Hg]L350

and AgeL350

for 12 walleye populations from Quebec (rPearson

¼0.9244, P , 0.0001). Likewise, using a bioenergetics

model, Harris and Bodaly (1998) estimated that 19% of

the variability in walleye muscle Hg levels from two

Ontario lakes could be related to growth rates.

We found a weaker relationship between Hg]L675

and AgeL675

for northern pike, even after removing

three outliers from the model. Elsewhere, Wren and

MacCrimmon (1986) reported higher levels of Hg in

FIGURE 4.— Estimated Hg levels at 675 mm standardized length ([Hg]L675

) versus estimated ages at that length (AgeL675

) for

northern pike. Open circles denote outliers identified with Mahalanobis distances on the correlation.

MERCURY AND FISH GROWTH IN QUEBEC LAKES 1229

FIGURE 5.—Estimated Hg levels at 550 mm standardized length ([Hg]L550

) versus estimated ages at that length (AgeL550

) for

lake trout.

TABLE 2.—Matrix of Pearson’s product-moment correlation coefficients for walleyes, northern pike, and lake trout from

selected lakes, where AgeLstd

is age at standardized length and [Hg]Lstd

is muscle mercury content at standardized length. Values

in bold italics are statistically different from 0; P-values are in parentheses. Asterisks denote statistical significance following

Bonferroni correction with the adjusted a ¼ 0.0167 (i.e., 0.05/3).

[Hg]Lstd

AgeLstd

Latitude Longitude

Walleye (N ¼ 54; std ¼ 375)

[Hg]Lstd

1.0000Age

Lstd0.7410 (,0.0001)* 1.0000

Latitude 0.2097 (0.1280) 0.5307 (,0.0001)* 1.0000Longitude �0.3877 (0.0038)* �0.3333 (0.0138)* �0.2095 (0.1285) 1.0000

Northern pike (N ¼ 52; std ¼ 675)

[Hg]Lstd

1.0000Age

Lstd0.5204 (0.0001)* 1.0000

Latitude �0.1603 (0.2562) 0.0806 (0.5702) 1.0000Longitude �0.4792 (0.0003)* �0.4815 (0.0003)* �0.0495 (0.7273) 1.0000

Lake trout (N ¼ 35; std ¼ 550)

[Hg]Lstd

1.0000Age

Lstd0.1837 (0.2907) 1.0000

Latitude 0.3284 (0.0541) 0.2094 (0.2273) 1.0000Longitude 0.3289 (0.0537) �0.1657 (0.3414) 0.0598 (0.7330) 1.0000

1230 LAVIGNE ET AL.

FIGURE 6.—Relationships between (1) estimated Hg levels and ages at standardized lengths for (A)–(B) walleyes, (C)–(D)northern pike, and (E)–(F) lake trout and (2) groupings according to watershed. Group 1 corresponds to watershed 01 in Figure

1; group 2 corresponds to watersheds 02–07; group 3 corresponds to watershed 08; and group 4 corresponds to watersheds 09

and 10; the number of fish populations in each group is given in parentheses.; outliers (circles) are identified in Figure 4. Plots

with the same letter are not significantly different.

MERCURY AND FISH GROWTH IN QUEBEC LAKES 1231

the muscle of northern pike from Lake Tadenac

compared with Tadenac Bay in Ontario, whereas

growth rates were higher in the lake. However, many

fish species known as prey for northern pike were

reported to have higher Hg levels in Lake Tadenac than

in Tadenac Bay, and higher Hg levels in the lake

sediments were measured. Other intensive fishing

experiments held in small oligotrophic lakes of Sweden

and Finland led to a decrease muscle Hg levels in

northern pike and a concurrent increase in their growth

rates (Gothberg 1983; Verta 1990). However, even

though the Quebec intensive fishing experiment

successfully lowered northern pike Hg levels in two

of the five lakes under study, no concurrent change in

fish growth rates in the lakes was observed (Doire

2003; Surette 2005). This would indicate that in this

case biodilution was not responsible for the observed

decrease in northern pike Hg concentrations.

We determined that lake trout [Hg]L550

cannot be

explained by variations in AgeL550

. Another study

reported a similar lack of consistency between Hg

levels and growth rates for lake trout (Stafford and

Haines 2001). Inversely, Stafford et al. (2004) found a

significant negative relationship between the two

variables in Lake Flathead, Montana. However, these

two studies differed completely from ours in both the

experimental design and statistical treatment of the

data, and thus, results are hardly comparable. Evans et

al. (2005) reported that lake trout and walleyes from 20

lakes located in the Mackenzie River basin (Canada)

have higher mean age than those of the northern

Alberta, whereas measured mean Hg concentrations

were higher in the Mackenzie River basin (north)

compared with northern Alberta (north–south gradi-

ent). The authors propose that slower growth rates in

the north might be responsible for these observations.

Although biodilution applies to all fish species

(Jackson and Schindler 1996), this effect regarding

Hg concentrations in lake trout sampled in this study

appears masked by other factors affecting the accumu-

lation of the contaminant in the fish flesh.

Factors Affecting Growth Rate and Hg Accumulation

The strong relationships observed between [Hg]Lstd

and AgeLstd

for walleyes and northern pike seem to be

related to a biodilution process (Figures 3, 4). Data

presented here were gathered in situ. Thus, the

influences of numerous biotic and abiotic factors

affecting Hg behavior in aquatic environments and fish

growth rate were not segregated. One of (or a

combination of) these factors may have an impact on

Hg bioavailability, biomagnification, and bioaccumula-

tion, on the one hand, and on fish growth on the other

hand. A study by Roue-Le Gall et al. (2005) can be used

to illustrate the latter. Using a model based on simple

environmental parameters, these authors were able to

qualitatively predict the Hg accumulation patterns for

yellow perch Perca flavescens, walleyes, and northern

pike, and successfully ranked a set of 45 lakes from six

regions of the boreal forest (Quebec) from the highest to

the lowest Hg levels found in fish. The variables used by

the authors were (1) the ratio between primary watershed

area and lake area, (2) the ratio between drainage area

and lake area, (3) riparian wetland coverage, (4) land use

and vegetation coverage of the primary watershed, (5)

water quality variables (dissolved organic carbon

concentration, pH, chlorophyll a), and (6) sportfishing

intensity. The selected criteria thus excluded fish growth

rates, even if many of these factors may covary with or

against this parameter.

Given the complexity of the interactions between all

environmental factors having an impact on either the

Hg cycle in aquatic environments or its accumulation

in fish flesh, and given the variability exhibited by

these parameters from one lake to another, it becomes

clear that the modeling of Hg pathways from sources to

fish muscle will remain an intricate task. Our results

demonstrate that fish growth rate represents an

integrated parameter with the potential to help model

Hg accumulation in walleyes and northern pike on an

intraregional to interregional scale.

Conclusions

We developed an extensive database integrating

information from several sources on fish age, length,

and Hg concentrations for thousands of fish specimens

from three species commonly captured by sport fishers

in Quebec. More than 50% of the specimens had Hg

levels in their muscular tissues exceeding the threshold

mercury value of 0.5 mg/kg of fish wet body weight for

human consumption in Canada. More than 60% of the

lakes had fish at standardized mean length exceeding

this value. The use of a growth indicator—the

estimated age at standardized length (AgeLstd

)—

enabled us to explain more than 50% of the fish

muscle Hg concentration at standardized length for

walleyes, and between 25% and 50% of the variation

for northern pike populations living in more than 50

Quebec lakes. AgeLstd

values were also correlated with

latitude for walleye populations and with longitude for

northern pike. Lakes located in the northern part of

Quebec are consequently more likely to contain

walleyes with elevated muscle Hg levels. Contrary to

what was previously reported or suggested, the

variations in fish muscle Hg levels for lake trout

populations do not seem to be correlated to growth rate.

For this species, no correlation between AgeLstd

and

[Hg]Lstd

was observed. We hypothesize that Hg

1232 LAVIGNE ET AL.

biodilution following faster growth rate coupled to

multiple covarying factors affecting both Hg accumu-

lation in the fish and growth rate may explain lower Hg

levels for walleyes and northern pike. In this context,

growth rate represents a valid integrative indicator of

multiple environmental factors and varies in the

significance of its effect on Hg concentrations in

muscle tissues of theses species.

Quebec has one of the world’s greatest densities of

lakes (Downing et al. 2006). Considering the extensive

use of Quebec’s fish resources for commercial,

subsistence, and leisure, the management of human

health risks related to Hg in fish remains a critical

issue. Considering the high variability of Hg levels in

fish muscle reported here, proper Hg risk management

suggests extensive environmental monitoring efforts or

development of an integrated indicator enabling intra-

regional to interregional modeling of the Hg situation.

We consider that the simple approach applied in this

study allows for the achievement of such a task on

large geographic scales that would minimize sampling

efforts; however, this would involve accepting loss of

prediction preciseness compared with other sophisti-

cated modeling tools, such as the Onefish (Harris and

Bodaly 1998) or the Dynamic Mercury Cycling model

(EPRI 2002) models. Fish growth rates are often used

as indicators of the performance of fish population or

of the health of lake ecosystems. This information is

thus frequently available for numerous lakes (Munkit-

trick and Dixon 1989) and could be used for the

elaboration of regional recommendations on safe fish

consumption limits that would reflect the local Hg

situation better than the current national guidelines.

Growth rate may also be used as a preliminary

indicator to target hot spots where the regular

consumption of walleyes and northern pike may be

unsafe. Moreover, decision makers in the field of

fisheries management, being aware that fish growth

rates may be accelerated through a certain degree of

fishing pressure and control of lake ecology and

watershed management, may use the findings reported

in this paper to properly control predatory fish growth

rates in order to enhance Hg biodilution in fish of

edible interest for sport or subsistence fishers.

Acknowledgments

This research project was financially supported by

the Natural Sciences and Engineering Research

Council of Canada through a Research Network grant

administered by the Collaborative Mercury Research

Network. Our work would have been impossible

without the support from Hydro-Quebec, the Quebec

Ministry for Sustainable Development, Environment

and Parks, as well as from the Quebec Ministry for

Natural Resources and Fauna, all of whom coopera-

tively shared fish information, analysis, and occasion-

ally field operations with us. The authors would like to

thank the anonymous reviewers of this manuscript for

their helpful comments and constructive suggestions.

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Appendix: Basic Data

TABLE A.1.—Basic data and sources for lakes selected to examine the relationship between mercury concentrations in fish

muscle and fish growth rates, including 54 walleye (Wal; 5,207 individuals), 52 northern pike (NP; 2,419), and 35 lake trout (LT;

1,478) populations (lakes).

Lake

RegionaNumber of

angling events Year

Number of fish Coordinates

Data sourcebName Number Wal NP LT Latitude (N) Longitude (W)

Achikunipis 1 09 1 1990 15 56844 007 00 73840 015 00 1Achiyaskunapiskuch 2 09 1 1990 24 19 52831 000 00 75812 000 00 1Alegrain 3 09 1 1990 16 56821 000 00 73817 000 00 1Amichinatwayach 4 09 1 1989 44 54817 000 00 73805 000 00 1Anonyme A 5 09 1 1988–1990 24 13 54844 050 00 76802 003 00 1Archipel Saint-Pierre 6 00 1 2003 175 46805 001 00 73801 015 00 3Au Brochet 7 09 1 1992 23 49836 046 00 69835 033 00 1Au Goeland 8 08 2 1988–1990 79 53 49847 000 00 76848 000 00 1Aux Cedres 9 07 1 1992 30 48850 000 00 69807 000 00 1Aux Dores 10 08 1 2001 66 23 58 49852 000 00 74820 000 00 3Aux Sangsues 11 04 1 2001 202 46828 015 00 77856 000 00 3–4Bienville 12 09 1 1989 46 55805 000 00 72840 000 00 1Bob-Grant 13 05 1 1990 20 47845 000 00 73831 000 00 1Brehrad 14 05 1 1991 42 47852 000 00 73849 000 00 1Bruce 15 09 1 1988 22 53811 000 00 77855 000 00 1Cecile 16 05 1 1990 24 49807 005 00 74802 022 00 1Chaumont 17 09 1 1987 10 53823 000 00 70835 000 00 1Chibougamau 18 08 4 1999–2002 145 53 110 49850 000 00 74815 000 00 1Chicobi 19 08 1 2004 114 48851 043 00 78831 000 00 3Clarkie 20 09 1 1990 30 18 52814 000 00 75830 000 00 1Conn 21 09 1 1988 30 52835 000 00 77836 000 00 1Corvette 22 09 1 1989 16 53825 000 00 74803 000 00 1Cosnier 23 08 1 2004 251 50854 000 00 72843 000 00 3Coutaceau 24 09 1 1978 25 26 53833 000 00 76835 000 00 2Craven 25 09 1 1988 23 54820 000 00 76856 000 00 1Dana 26 08 3 1979–1991 32 43 50853 000 00 77820 000 00 1David 27 04 1 2004 10 46815 030 00 74852 039 00 3De Ganne 28 09 1 1990 15 55838 000 00 76812 000 00 1De l’Est 29 02 1 2004 17 47811 011 00 69833 041 00 3Des Vœux 31 09 7 1987–1999 53 268 53856 015 00 72837 035 00 1Deschamps 32 08 1 1990 25 13 48835 012 00 75831 018 00 1Desjardins 33 04 1 2001 223 47817 000 00 78814 000 00 3–4Dissimieux 34 07 1 1992 23 49852 000 00 60848 000 00 1Du Tast 35 08 1 1979 12 51800 000 00 77822 000 00 1Duparquet 36 08 2 2002–2003 258 48828 009 00 79816 019 00 3–4Duxbury 37 09 1 1988 29 52827 000 00 77828 000 00 1Earhart 38 04 1 2004 16 46805 000 00 75826 000 00 3Eau Claire 39 08 2 1989–1990 63 50810 000 00 75810 000 00 1En Cœur 40 05 1 2004 32 45858 005 00 74800 041 00 3Evans 41 08 3 1979–1991 73 95 50855 000 00 77800 000 00 1–2Faguy 42 05 1 1990 28 22 48833 000 00 74805 000 00 1Fregate 43 09 1 1989 16 53812 000 00 74845 000 00 1Fressel 44 09 1 1989 24 55827 000 00 75812 000 00 1Gaillarbois 45 07 1 1997 29 52800 000 00 67827 000 00 1Gaotanaga 46 04 1 1990 14 11 47838 000 00 77835 022 00 1Giffard 47 08 3 1979–1991 20 50 51808 000 00 76855 000 00 1–2Grasset 48 08 1 1990 29 28 49856 033 00 78809 043 00 1Hazeur 49 10 7 1987–1999 202 54858 000 00 69812 000 00 1Herve 50 09 1 1987 10 54827 000 00 71814 000 00 1

1236 LAVIGNE ET AL.

TABLE A.1.—Continued.

Lake

RegionaNumber of

angling events Year

Number of fish Coordinates

Data sourcebName Number Wal NP LT Latitude (N) Longitude (W)

Jolliet 51 08 2 1979–1990 27 44 51833000 00 76854 000 00 1Julian 52 09 1 1988 26 54826000 00 77855 000 00 1Kawayapiskach 53 09 1 1989 28 52837000 00 78830 000 00 1Kowskatehkakmow 54 09 6 1990–2000 179 110 53827000 00 77827 000 00 1Largarde 55 04 1 2001 211 46816038 00 77826 053 00 3, 4Lamain 56 09 1 1990 17 55854000 00 75806 000 00 1Laraire 57 09 1 1989 15 55827000 00 72857 000 00 1Le Royer 58 08 1 2004 30 50 49835030 00 74827 030 00 4Lessard 59 08 1 1988 31 49832000 00 75839 000 00 1Letemplier 60 07 1 1988 17 49827022 00 68847 001 00 1Loups Marins 61 10 2 1988–1989 18 56830030 00 65803 030 00 1Maicasagi 62 08 1 1979 30 49857020 00 76833 012 00 2Malartic 63 08 2 2002–2003 320 48815052 00 78806 018 00 3, 4Matagami 64 08 3 1979–1991 62 121 49853000 00 77830 000 00 1, 2Maulnier 65 09 1 1990 19 56836052 00 73859 025 00 1McNab 66 09 1 1988 15 52853000 00 77827 000 00 1Megiscane 67 08 1 2004 228 48835007 00 75851 057 00 3, 4Mesgouez 68 08 2 1979–1990 27 26 50825039 00 75805 028 00 1, 2Midway 69 07 1 1992 30 52828000 00 67802 000 00 1Morpain 70 09 1 1989 30 55800000 00 74818 000 00 1Mureau 71 05 2 1989–1990 22 54855000 00 73813 000 00 1Nemenjiche 72 08 1 2004 26 34 49822000 00 74826 000 00 4Nemiscau amont 73 08 1 1979 16 51826000 00 76843 000 00 2Nemiscau 74 08 1 1990–1991 44 20 51826000 00 76843 000 00 1Obatogamau 75 08 2 2001–2002 81 62 49835037 00 74827 020 00 3Ouescapis 76 08 1 1990 27 24 50814000 00 77800 000 00 1Pamigamachi 77 09 1 1988 19 54810000 00 77828 000 00 1Petit Loups Marins 78 09 2 1989–1990 44 56807000 00 73815 000 00 1Pikutamaw 79 09 9 1984–2000 281 245 52834000 00 77805 000 00 1, 2Poncheville 80 08 2 1979–1991 43 58 50810000 00 76855 000 00 1, 2Preissac 81 04 1 2002 634 48820000 00 78820 000 00 3, 4Pusticamica 82 08 2 1988–1989 45 27 49821000 00 76823 000 00 1Quenonisca 83 08 3 1979–1991 29 53 50836000 00 76833 000 00 1, 2Rinfret 84 05 1 1990 25 48824000 00 73840 000 00 1Rodayer 85 08 1 1990 19 20 50852000 00 77842 000 00 1Roggan 86 09 1 1988 22 54808000 00 77849 000 00 1Saint-Francois 87 03 1 2004 34 114 45809025 00 74822 022 00 3Saint-Pierre 88 00 1 2002 495 320 46816010 00 72837 048 00 3Sans nom 89 07 1 1996 11 51808000 00 67800 000 00 1Serigny 90 10 4 1993–1999 17 138 55822000 00 69838 000 00 1Six-Milles 91 04 1 2004 125 46826037 00 78801 050 00 3, 4Soscumica 92 08 1 1990 24 53 50815000 00 77827 000 00 2Theodat 93 08 1 1990 24 12 50855000 00 76810 000 00 1Tilly 94 09 2 1987–1989 39 53855000 00 73858 000 00 1Vaulezar 95 09 1 1993 30 19 54833000 00 71850 000 00 1Vieux-Comptoir 96 09 1 1988 14 52847000 00 77833 000 00 1Village nord 97 09 1 1990 21 52812042 00 75817 050 00 1Waconichi 98 08 2 2000–2001 21 25 30 50808000 00 74800 000 00 3Waswanipi 99 08 4 1979–1990 111 91 49834000 00 76829 000 00 1, 2Yasinski 100 09 1 1988 24 53816041 00 77834 059 00 1

a Hydrographic region as defined by the Ministry of Sustainable Development, Environment, and Parks (Figure 1).b (1) Hydro Quebec, (2) Bay James Energy Society, (3) Ministry of Sustainable Development, Environment, and Parks, and (4) Collaborative

Mercury Research Network.

MERCURY AND FISH GROWTH IN QUEBEC LAKES 1237