Effects of industrial metals on wild fish populations ...

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Ecotoxicology and Environmental Safety 61 (2005) 287–312 www.elsevier.com/locate/ecoenv

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Effects of industrial metals on wild fish populations along a metal contamination gradient Gregory G. Pylea,, James W. Rajotteb,c, Patrice Couturec,d a

Department of Biology, Nipissing University, North Bay, 100 College Drive, Box 5002, North Bay, Ont., Canada P1B 8L7 b Department of Zoology, University of Guelph, Guelph, Ont., Canada N1G 2W1 c Department of Biology, Laurentian University, Sudbury, Ont., Canada P1E 2C6 d INRS-ETE, 490 de la Covronne, Que., Canada G1K 9A9 Received 4 July 2004; received in revised form 2 September 2004; accepted 15 September 2004 Available online 24 November 2004

Abstract The purpose of this study was to examine relationships among water, sediment, and fish tissue metal concentrations as they relate to fish diversity, tissue metal accumulation, and fish morphometric and reproductive condition. Fish were captured in 12 lakes near Sudbury, Ontario, Canada, that ranged in their degree of metal contamination. In general, metal concentrations in water and sediment decreased with increasing distance from industrial operations. However, only Cu and Ni demonstrated this trend in sediments. Although 20 fish species were identified in the 12 lakes, only one species, yellow perch (Perca flavescens), was common to all 12 lakes. Fish diversity was only associated with sediment metals, suggesting that short-term processes are much less important than long-term processes for fish community recovery in metal-contaminated lakes. Multivariate characterization of water metal concentrations resulted in three lake clusters: Group 1 consisted of reference lakes; Group 2 lakes had high alkalinity, conductivity, hardness, pH, waterborne metals (especially Se), and sediment Cu and Ni concentrations; Group 3 lakes had high pH, waterborne and sediment Cu, and sediment Ni, intermediate alkalinity, conductivity, and waterborne metals (except Al and Fe), and low hardness and waterborne Al and Fe. Liver Cd, Cu, Ni, Pb, and Zn, muscle Zn, and intestinal Cd and Zn were highest, and muscle Cu and male gonadosomatic index (GSI) were lowest, in Group 3 fish. Liver, muscle, and intestinal Se concentrations, and Fulton’s condition factor (FCF), hepatosomatic index (HSI), and male GSI were highest in Group 2 fish. Group 1 fish had the highest muscle Hg concentrations and female GSI. Muscle Se appeared to have an antagonistic effect on muscle Hg accumulation as a function of distance from smelting operations. Neither Cu nor Ni, both metals of concern in the Sudbury area, was useful for predicting fish condition, probably because of homeostatic regulatory control. Liver Cd accumulation, which was negatively related to FCF (r ¼ 0:16; Po0:05), exhibited strong, nonlinear inhibition (r2 ¼ 0:99; Po0:0001) as a function of water hardness. Because Cd was not detected in water samples in this study, we suspect that branchial Ca2+ uptake may play some role in reducing dietary Cd uptake in hard water lakes. Selenium has received relatively little attention in the contaminated systems around Sudbury, yet our results demonstrated that tissue Se was related to all condition metrics studied. Moreover, evidence was provided that suggests that there is a gender-specific interaction between dietary Se and Cu uptake that may contribute to decreased female reproductive condition in wild yellow perch. r 2004 Elsevier Inc. All rights reserved. Keywords: Wild yellow perch (Perca flavescens); Metal pollution; Multivariate analysis; Metal interactions; Accumulation; Fish condition; Environmental effects; Fish diversity

1. Introduction

Corresponding author. Fax: +1 705 474 1947.

E-mail address: [email protected] (G.G. Pyle). 0147-6513/$ - see front matter r 2004 Elsevier Inc. All rights reserved. doi:10.1016/j.ecoenv.2004.09.003

Sudbury, Ontario, is home to some of the most productive nickel and copper mining operations in the world. More than a century of industrial activities in the

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G.G. Pyle et al. / Ecotoxicology and Environmental Safety 61 (2005) 287–312

region has led to widespread acidification and metal contamination in more than 7000 lakes, causing severe ecological damage (Keller et al., 1992). Before environmental remediation efforts, several aquatic species were extirpated from local water bodies (Beamish and Harvey, 1972; Yan and Welbourn, 1990). However, emission abatement and reclamation programs over the past four decades have resulted in improved water quality in area lakes and reestablishment of some aquatic communities (Keller et al., 1992, 1999). Although improvements have been observed, elevated metal concentrations in local lakes persist (Nriagu et al., 1998). Consequently, the region is thought to be in a state of ecological recovery, but not yet recovered (Keller et al., 1999). Previous studies have identified a ‘‘zone of impact’’ from industrial activities, covering some 17,000 km2 around the epicenter of mining and processing activities (Keller et al., 1992). Metal concentrations in lake water and sediments within this zone are elevated above background concentrations, and typically decrease with increasing distance from the center of ore processing operations (Nriagu et al., 1982; Bradley and Morris, 1986). Therefore, the water bodies in the Sudbury area present a unique research opportunity to exploit metal contamination gradients to study relationships among environmental metal contamination, metal accumulation in resident biota, and biological effects associated with metal contamination. Most studies that examine the effects of metals in freshwater fish use standard laboratory species, such as rainbow trout (Oncorhynchus mykiss) and fathead minnows (Pimephales promelas). However, these species rarely inhabit metal-contaminated lakes in northern Canada. Yellow perch (Perca flavescens), on the other hand, is very tolerant of metals (Taylor et al., 2003), is widely distributed throughout North America (Scott and Crossman, 1973), is abundant where it occurs, is a common sport fish among anglers, occupies important trophic positions in aquatic ecosystems (i.e., planktivore, benthivore, and piscivore, depending on age and food resource availability) (Sherwood et al., 2002b), and is known to inhabit lakes contaminated by industrial wastes. Therefore, it is important to understand how this species responds to environmental contamination in order that environmental risk assessments provide the highest degree of ecological relevance. This species has received relatively little ecotoxicological research attention in the past, although this seems to be changing (Brodeur et al., 1997; Laflamme et al., 2000; Sherwood et al., 2000, 2002a; Rajotte and Couture, 2002; Eastwood and Couture, 2002; Audet and Couture, 2003; Couture and Kumar, 2003). Recent studies by us and by others examining the effects of metals on wild yellow perch inhabiting industrially contaminated lakes in Sudbury and Rouyn

Noranda, Quebec, have generally focused on a few (usually two to four) metal-contaminated lakes, using only one or two reference lakes, and have studied only the ‘‘metals of concern’’, which are typically Cd, Cu, Ni, and Zn in these areas (Brodeur et al., 1997; Laflamme et al., 2000; Rajotte and Couture, 2002; Eastwood and Couture, 2002; Audet and Couture, 2003; Couture and Kumar, 2003). These studies have provided important insights into the biological consequences of metal contamination to wild yellow perch inhabiting these contaminated environments. However, it is still not clear if the effects described by these studies are general in nature and extend to a wider range of contamination scenarios, or how other environmental variables (including other metals) interact with the metals of concern to contribute toward observed trends in fish condition as they relate to the contamination gradient. Moreover, very little is known about how industrial metal contamination contributes to fish recolonization or diversity. The objective of this study was to exploit a metal contamination gradient of 12 Sudbury-area lakes to investigate fish species diversity, and multivariate relationships among environmental metal contamination, water quality, metal accumulation, and condition in similar-sized yellow perch. We measured 20 metals (hereafter, the word metals includes metalloids) in water, sediment, and yellow perch liver, muscle, and intestine and related those concentrations to lake water pH, hardness, alkalinity, conductivity, and total dissolved solids and to yellow perch condition factor, hepatosomatic index (HSI), and gonadosomatic index (GSI). Several results from this analysis have led to suggestions for future research into the mechanisms that may account for some rather unexpected findings.

2. Materials and methods 2.1. Study lake choice Twelve lakes in the Sudbury region (Fig. 1) were selected for water, sediment, and fish sampling based on data from previous studies (Nriagu et al., 1998; Eastwood and Couture, 2002; Rajotte and Couture, 2002). Five of these lakes formed a downstream gradient from a point source of metal input (Kelly, Mud, Simon, McCharles, and Kusk lakes); three lakes had varying contamination levels and were independent of the Kelly Lake gradient (Hannah, Whitson, and Ramsey lakes). These lakes formed a metal contamination gradient. The four furthest lakes (Barlow, Big Marsh, Birch, and Waubamac lakes) served as reference lakes.

ARTICLE IN PRESS G.G. Pyle et al. / Ecotoxicology and Environmental Safety 61 (2005) 287–312

289

Fig. 1. Map of 12 lakes in the vicinity of Sudbury, Ont., from which water, sediment, and fish samples were collected. See Table 1 for sampling dates.

2.2. Fish sampling Minnow traps were baited with Styrofoam beads and placed in littoral zones in study lakes. Traps were checked daily. A seine net was also used to catch fish in the littoral zones of every lake. All fish captured were identified to species and released. When identification was not possible in the field, fish were placed on ice and identified in the laboratory. Fish sampling dates are reported in Table 1. Approximately 20 yellow perch were sampled from each lake when possible and returned to the laboratory either on ice or in 23-L Rubbermaid containers with a portable aerator unit. On arrival, fish were dissected or else frozen at –20 1C until dissection. Fork length, total weight, and the weights of liver and gonads were recorded for each fish. Muscle, liver, and intestine flushed with saline were sampled and stored at –20 1C for metal analysis. Scales were collected from the right side of each fish, on the dorsolateral surface, immediately anterior to the insertion of the dorsal fin, and aged by M. Gauthier (Labman Aging, Cochrane, Ont., Canada). Fish condition was assessed by calculating Fulton’s condition factor (FCF), HSI, and GSI. FCF provides information about recent feeding activity, and is calculated as FCF ¼

Wt 100; L3

where Wt is the total weight of the fish (g), and L is the fork length (cm). The HSI, another metric commonly

Table 1 Sampling dates in 2001, locations, and relative distances from ore processing operations for water, sediment, and yellow perch sampling in 12 lakes in the vicinity of Sudbury, Ont. Lake

Location

Distance from smeltera (km)

Date sampled

Barlow Lake

461170 4400 N 801340 4100 W 461170 6000 N 801380 1200 W 461140 4700 N 801330 1100 W 461260 3500 N 811020 2400 W 461260 5500 N 811030 5700 W 461190 2100 N 811200 1200 W 461220 5100 N 811140 5100 W 461240 2000 N 811090 4100 W 461280 3900 N 801560 4500 W 461230 4700 N 811110 4800 W 461160 3400 N 801290 4500 W 461350 2000 N 801580 2700 W

43

May 7

39

May 7

47

June 2

Big Marsh Lake Birch Lake Hannah Lake Kelly Lake Kusk Lake McCharles Lake Mud Lake Ramsey Lake Simon Lake Waubamac Lake Whitson Lake a

4

May 23 and June 7

2

May 2 and June 6

23

May 17 and July 12

14

June 21

7

May 16 and July 13

12

May 22 and July 17

10

June 12 and July 12

50

May 8

18

May 24

Distance calculated from Inco’s Copper Cliff smelter (461260 4300 N, 801050 3900 W) to each lake using latitude/longitude coordinate data.


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used to evaluate fish condition, provides information about metabolic activity in the liver, and is calculated as HSI ¼

WL 100; Wt

where WL is the weight of the liver (g). The GSI evaluates reproductive condition based on the weight of the gonads (WG) relative to the total weight of the fish, and is calculated as GSI ¼

WG 100: Wt

2.3. Water and sediment collection Water samples were taken from each study lake at 3 depths: 1, 5, and 10 m. When the 10 m depth was not available, an alternative depth (3 m) was sampled. Water samples were collected with a Van Dorn bottle. Sampling containers were rinsed with lake water three times, filled to capacity (i.e., no head space), and placed on ice for transport back to the laboratory. Sediment samples were collected from the same locations as water samples using an Eckman grab. Grab sampling collects approximately the top 5 cm of sediment. Sediments were unloaded from the grab into a plastic tub and mixed. One sample bottle was then filled with the mixed sediment and returned to the laboratory on ice. Both water and sediment samples were refrigerated for up to 12 h on return to the laboratory. Water samples were acidified to a pH of 2 by addition of trace metal-grade nitric acid for metal determination, then stored at 4 1C until analysis (see below). 2.4. Water quality Water samples (n ¼ 3) were collected from each lake and transported back to the laboratory at Testmark Laboratories, Ltd (Sudbury, Ont., Canada) for analysis. Conductivity of each sample was measured using an Orion conductivity meter (Model 135A with Orion conductivity cell 013610). Turbidity (in nephrelometry turbidity units, NTU) was measured with a Hach ratio turbidimeter (Model 18900). Sample turbidity was recorded after a 1 min equilibration period. Water pH was measured with an Accumet pH meter (Model 915, Fisher Scientific). All samples (40 mL) were measured in clean beakers while stirred. Alkalinity was determined by titration with 0.02 N hydrochloric acid (HCl) to a final pH of 4.5. Alkalinity was calculated as the concentration of calcium carbonate present using the formula Alk ¼

V 1 N 50; 000 ; V2

where Alk=alkalinity (mg/L as CaCO3), V1=volume of HCl (mL), V2=volume of sample (40 mL), and N=normality of HCl (0.02 N). Water hardness was calculated using the formula: hardness ðmg=LÞ ¼ ð½Ca n 2:497Þ þ ð½Mg n 4:118Þ where [Ca] and [Mg] are the aqueous concentrations of Ca and Mg (in mg/L) (Clesceri et al., 1998). Total suspended and total dissolved solids (TSS and TDS, respectively) were determined by vacuum filtering a 100 mL water sample through a prebaked and weighed Whatman 934-AH glass microfiber filter. The filter paper was then placed on a prebaked and weighed dish and baked in an oven at 80 1C for 24 h. The difference between the initial and final weights of the filter paper yielded TSS. TDS were measured by adding the filtered water sample into a prebaked and weighed beaker, evaporating at 80 1C for 24 h, cooling and reweighing to determine TDS.

2.5. Sediment preparation for ICP-MS Twenty grams of sediment from each sediment sample was dried in an oven set at 100 1C for 24 h. After drying, the sample was cooled, reweighed, and then crushed with a mortar and pestle. Approximately 2 g of dry sample was digested in a 3:1 mixture of concentrated hydrochloric and nitric acids and boiled at 100 1C for 30 min. The solution was cooled and then filtered through a microfiber paper (Whatman grade 1) under vacuum into a volumetric flask. All equipment was rinsed with 2% nitric acid into the flask. The solution was brought up to 100 mL total volume with 2% nitric acid.

2.6. Fish tissue preparation for ICP-MS Samples of approximately 10–200 mg wet wt of yellow perch liver, white muscle, and intestine were dried at 60 1C to complete dryness. Dried samples (ranging from 1.3 to 20 mg) were digested in precleaned, acid-washed Teflon vials in 2 mL of analytical-grade nitric acid. Vials were heated in a 750-W microwave oven at full power for 30-, 20-, 15-, then 10-s periods, with 30-s intervals between heating events for sample shaking and mixing. Any tissues not fully digested were subjected to another 20 s of microwave heating. Samples were allowed to cool overnight, then transferred to labeled, precleaned, acidwashed, 50-mL Fisherbrand specimen containers with 18 mL of reverse osmosis water. The diluted samples were transported to Testmark Laboratories, Ltd. (Sudbury, Ont., Canada) for inductively coupled plasma mass spectrometry (ICP-MS).


2.7. Sample metal determinations via inductively coupled plasma analysis Water samples were prepared without dilution. Sediment samples were diluted one-tenth with doubledistilled water. An internal surrogate with known concentrations of rubidium and ruthenium was added to each sample (0.5 mL). At the start of each run and after every 10 samples, quality control samples were run. Quality assurance samples included blanks; 10, 100, and 1000 mg/L standards of each metal measured, and samples of known metal concentrations from the Canadian Association of Environmental Analytical Laboratories (CAEAL). Further dilutions of samples were made when necessary. All samples passed the CAEAL-accredited quality control standards of Testmark Laboratories, Ltd. Fish tissues were prepared for ICP-MS analysis by combining 10 mL of diluted sample with 0.5 mL of internal ICP-MS surrogate in labeled, sterile, plastic ICP-MS tubes. Tubes were tightly capped and stored until they were analyzed. National Research Council (NRC) standards TORT-2, DORM-2, and DOLT-2 as well as method blanks were prepared similarly to the fish tissues, and metal concentrations were determined concurrently by ICP-MS.

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demonstrated a high degree of colinearity with other metals in the data set. By these criteria, B, Cd, Ce, Co, Cr, Ga, Hg, La, Li, Mo, P, Pb, Ti, and V were removed from the water metal PCA data set, and B, Ba, Be, Bi, Ce, Cs, Dy, Er, Eu, Ga, Gd, Hf, Hg, Ho, La, Li, Lu, Mg, Mo, Nb, Nd, P, Pr, Rb, Sb, Sc, Sm, Sn, Sr, Ta, Tb, Th, Ti, Tl, Tm, U, W, Y, Yb, and Zr were removed from the sediment metal data set. Only the metals remaining after censoring were used in all subsequent analyses. Metals retained in the water metal PCA were: Al, As, Ba, Ca, Cu, Fe, Mg, Mn, Ni, Rb, Se, Sr, and Zn. Metals retained in the sediment metal PCA were: Ag, As, Ca, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Se, V, and Zn. All regressions and multiple regressions reported in this study used least-squares techniques. Pearson correlations were used to detect significant associations between pairs of variables. A partial correlation analysis was used to examine bivariate associations of five metals between compartment pairs in a five-compartment model. All statistical analyses were conducted on JMP version 5.0 statistical software.

3. Results 3.1. Water quality

2.8. Statistical treatment Before statistical analysis, variables were tested for normality using the Shapiro–Wilk test and homogeneity of variance using Levine’s test. Data not meeting parametric assumptions were log(x) or log(x+1) transformed, which usually improved distributions. Mean comparisons among variables were conducted using analysis of variance (ANOVA) followed by a TukeyKramer multiple comparison test. Statistical significance was assumed when Po0:05: Untransformed data were used to generate graphs for ease of interpretation, even in circumstances where statistical analyses were performed on log-transformed data. A principal component analysis (PCA) was conducted on water and sediment metal data. In the original analysis, 27 metals were measured in water samples and 54 metals were measured in sediment samples. Each data set was censored using a priori rules, similar to those reported in Pyle et al. (2001) to remove variables that were rarely detected in water samples, and to minimize multicolinearity among remaining variables. Metals that were not detected in a sample were not estimated, but were removed from subsequent analysis. Metals that were not detected in 450% of samples were removed. Metals that were strongly correlated with other metals in the data set were removed. However, metals that are known to be associated with mining activities in the Sudbury area, such as Cd, Cu, Ni, and Zn, were retained (if detected in 450% of samples), even if they

As PCA was used to characterize water quality in each lake in terms of metal concentration (Table 2). A scree analysis was conducted to identify those Table 2 Results of principal component analysis of water metal data from the 12 study lakes (Table 1)

Eigenvalue (L) Variance explained (%) Cumulative explained variance (%) Eigenvectors (l) Al As Ba Ca Cu Fe Mg Mn Ni Rb Se Sr Zn

PC1

PC2

PC3

2.036 61.5 61.5

0.924 27.9 89.4

0.218 6.6 96.0

0.437 0.176 0.144 0.470 0.219 0.026 0.290 0.068 0.453 0.215 0.209 0.310 0.087

0.797 0.071 0.057 0.278 0.006 0.345 0.145 0.229 0.010 0.175 0.127 0.195 0.022

0.330 0.148 0.142 0.137 0.474 0.313 0.068 0.524 0.296 0.075 0.001 0.030 0.365

Note. Eigenvalues, variance, cumulative variance explained, and eigenvectors are reported for the first three principal components (PC1–PC3). Components retained for further analysis were determined by a scree analysis. The highest and lowest eigenvector loadings for each component are highlighted in boldface.



components containing the most information, and only the first three components contained significant information associated with metal concentrations in water samples. Together, these three components accounted for 96% of the variability associated with metal concentrations in all lakes (Table 2). The PCA ordination in the plane of PC1 and PC2 yielded three distinct lake clusters (Fig. 2). Group 1 consisted of Barlow, Big Marsh, Birch, and Waubamac lakes (i.e., reference lakes). Group 2 consisted of Kelly, Mud, and Simon lakes (i.e., near-field lakes on the Kelly Lake watershed). Group 3 consisted of Hannah, Kusk, McCharles, Ramsey, and Whitson lakes, which represents two farfield lakes on the Kelly Lake chain (i.e., Kusk and McCharles lakes), and other Sudbury-area metal-contaminated lakes (i.e., Hannah, Ramsey, and Whitson lakes). Aluminum had the highest negative eigenvector (l) loading on PC1, whereas both Ca and Ni had the highest positive loading (Table 2). Aluminum loaded strongly on the positive side of PC2. Although Cu loaded negatively on PC2, a l of 0.006 suggests that it had little influence on the component. Manganese had the strongest negative loading, and Cu had the highest positive loading on PC3. Of the limnological variables measured, water pH, alkalinity, conductivity, and hardness were all strongly and positively associated with PC1 (Table 3, Fig. 2). Only water hardness was related to PC2. No limnological variables were correlated with PC3. Aluminum was the only metal negatively correlated with PC1 (Table 3). However, As, Ba, Ca, Cu, Mg, Ni, Rb, Se, and Sr were positively correlated with PC1, with Ni showing the strongest correlation (r ¼ 0:97). No metals were negatively correlated with PC2. Aluminum, Fe, and Mn all showed significant positive relationships with PC2. Manganese was negatively correlated, and Zn positively correlated, with PC3. Median water pH was lowest in Group 1 lakes, ranging from 6.0 to 6.8, compared with median pH in Group 2 and 3 lakes, which ranged from 7.0 to 8.4 (Table 4). Group 1 lakes also demonstrated the greatest degree of within-lake pH variability relative to contaminated lakes. Conductivity and TDS varied over two orders of magnitude among the study lakes (Table 4). Water conductivity in Group 1 lakes was about 30 mS/cm, representing the lowest conductivity among the lake groups. Group 3 lake conductivity was higher than that of Group 1 lakes, and ranged from 166.2 to 427.3 mS/cm. Group 2 lakes had the highest conductivity, ranging from 1098.7 to 1455.0 mS/cm. Water TDS followed the same basic trend as conductivity, with Group 1 lakes having the lowest TDS concentrations, followed by Group 3 lakes; Group 2 lakes had the highest TDS concentrations.

Fig. 2. Ordination plot from principal components analysis (PCA) of water metal concentration data in the plane of PC1 and PC2. Each point represents an optimal linear combination of 13 metals measured in water samples (n ¼ 3) from each of 12 study lakes. Lake names are provided for each point. This ordination identifies three lake clusters: Group 1, Barlow, Big Marsh, Birch, and Waubamac lakes; Group 2, Kelly, Mud, and Simon lakes; Group 3, Hannah, Kusk, McCharles, Ramsey, and Whitson lakes. Correlations between fish condition, water quality variables, and distance from smelting operations against each PC are represented by vectors. Vector lengths are proportional to correlation coefficients (r), and vector directions indicate the direction of maximum correlation in the ordination plane. FCF, Fulton’s condition factor; HSI, hepatosomatic index; GSI, gonadosomatic index, *Age-corrected.

Both alkalinity and hardness were lowest in Group 1 lakes relative to the other two lake groups (Table 4). In Group 1 lakes, alkalinity ranged from 6.3 to 8.8 mg/L as CaCO3, and hardness ranged from 8.7 to 18.0 mg/L as CaCO3. Group 2 lakes demonstrated the highest alkalinity, ranging from 32.1 to 33.7 mg/L as CaCO3, while Group 3 lakes were intermediate, ranging from 6.3 to 31.3 mg/L as CaCO3. Similarly, Group 2 lakes demonstrated the highest hardness levels (380.3–582.7 mg/L as CaCO3), while Group 3 lakes were intermediate (25.2–134.3 mg/L as CaCO3). Although three of the four lakes constituting Group 1, Waubamac, Big Marsh, and Barlow lakes, had relatively clear water (turbidity range, 1.6–2.7 NTU), a fourth Group 1 lake, Birch Lake, had some of the most turbid water (10.1 NTU) of all the study lakes (Table 4). The only lake with higher turbidity was a Group 2 lake, Mud Lake (10.4 NTU). Turbidity in the other contaminated lakes ranged from 0.4 to 3.8 NTU, with three Group 3 lakes, Hannah, Whitson, and Ramsey lakes, having an order of magnitude lower turbidity than Group 1 lakes (0.4, 0.4, and 0.6 NTU, respectively). Except for one Group 2 lake, Mud Lake (TDS, 26.7 mg/L), two of the Group 1 lakes, Birch and Barlow lakes, had the highest concentrations of TDS (21 and 22 mg/L, respectively) of any of the other study lakes. In Waubamac Lake, another Group 1 lake, TDS were not detectable. The remaining contaminated lakes ranged in


293

Table 3 Pearson correlation coefficients between principal components characterizing water and sediment samples, distance from smelting operations, water quality, and metal variables Water PC1

Sediment PC2

PC3

PC1

PC2

PC3

pH Alkalinity (mg/L as CaCO3) Conductivity (mS/cm) Hardness (mg/L as CaCO3) Distance from smelter (km)

c

0.86 0.85c 0.84c 0.74b 0.93d

n.s. n.s. n.s. 0.65a n.s.

n.s. n.s. n.s. n.s. n.s.

— — — — 0.79c

— — — — n.s.

— — — — n.s.

Ag Al As Ba Ca Cd Co Cr Cu Fe Mg Mn Ni Pb Rb Se Sr V Zn

— 0.62a 0.88c 0.90c 0.92c — — — 0.78b n.s. 0.93d n.s. 0.97d — 0.85c 0.82b 0.91d — n.s.

— 0.77b n.s. n.s. n.s. — — — n.s. 0.86c n.s. 0.60a n.s. — n.s. n.s. n.s. — n.s.

— n.s. n.s. n.s. n.s. — — — n.s. n.s. n.s. 0.67a n.s. — n.s. n.s. n.s. — 0.70a

0.84c — 0.79b — n.s. 0.78b 0.75b n.s. 0.90c n.s. — n.s. 0.97d 0.72b — n.s. — n.s. n.s.

n.s. — n.s. — 0.75b n.s. n.s. 0.91d n.s. 0.77b — 0.80b n.s. n.s. — n.s. — 0.85c 0.78b

n.s. — n.s. — n.s. n.s. n.s. n.s. n.s. n.s. — n.s. n.s. n.s. — 0.77b — n.s. n.s.

n.s., not significant (P40:05). a Po0:05: b Po0:01: c Po0:001: d Po0:0001:

TDS from 1.0 mg/L in Whitson Lake to 9.0 mg/L in Hannah Lake. Aluminum concentrations were highest in waters from Group 1 lakes (Table 5). With the exception of Kusk (Group 3) and Mud (Group 2) lakes, Al was not detected in metal contaminated lakes. Arsenic concentrations were highest in Group 2 lakes, and were below detection limits in most Group 1 lakes. Copper concentrations were lowest in Group 1 lakes, and were less than 4.5 mg/L. Although the highest water Cu concentrations were in two Group 3 lakes, Hannah (25 mg/L) and Whitson (19 mg/L) lakes, Group 2 lakes demonstrated generally higher Cu concentrations (ranging from 9.7 to 17.0 mg/L). Nickel concentrations varied over three orders of magnitude among the study lakes. Group 1 Ni concentrations were the lowest among study lakes, followed by Group 3 lakes; Group 2 lakes had the highest Ni concentrations. Ramsey (52 mg/L) and Kusk (60 mg/L) lakes were the only metal-contaminated lakes that had Ni concentrations below 100 mg/L. Selenium was not detected in any Group 1 lakes except Waubamac Lake (Se, 0.6 mg/L), but was detected in all of the metal-contaminated lakes except Whitson Lake.

Selenium concentrations in metal-contaminated lakes ranged from 1.0 mg/L in McCharles Lake to 5.8 mg/L in Kelly Lake. All Group 2 lakes had Se concentrations greater than 4 mg/L. 3.2. Sediment quality In general, sediment metal concentrations in lakes constituting Group 2 and 3 were higher than in Group 1 lakes (Table 6). Sediment Cu and Ni exhibited the greatest variability between Group 1 and contaminated lakes. Copper concentrations in Group 1 lake sediments ranged from 58.4 to 174.7 mg/g, whereas Cu concentrations in contaminated lake sediments ranged from 349.4 to 2672 mg/g. Hannah, Kelly, McCharles, Ramsey, Simon, and Whitson lakes had sediment Cu concentrations greater than 1200 mg/g. In Group 1 lakes, sediment Ni concentrations ranged from 95.6 to 210.8 mg/g. Sediment Ni concentrations were at least four times higher in contaminated lakes, ranging from 864.5 to 4745 mg/g. Sediment Ni concentrations exceeded 1000 mg/g in all contaminated lakes except Kusk (865 mg/g). On average, sediment Co concentrations


294

Table 4 Water quality data for the 12 study lakes Lake

Group 1 Barlow Big Marsh Birch Waubamac Group 2 Kelly Mud Simon Group 3 Hannah Kusk McCharles Ramsey Whitson

pH

Conductivity (mS/cm)

TDSa (mg/L)

Alkalinity (mg/L as CaCO3)

Hardness (mg/ Turbidity (NTU) L as CaCO3)

TSS (mg/L)

Mean SEM Mean SEM Mean SEM Mean SEM

6.5 (6.0–6.5) 6.0 (5.7–6.3) 6.1 (6.0–6.3) 6.8 (6.7–7.0)

33.2 1.3 29.2 0.7 27.6 2.0 33.0 0.1

94.7 22.7 36.0 9.8 60.7 12.8 61.0 10.0

7.1 0.8 6.3 1.4 6.3 0.0 8.8 0.0

12.7 0.4 10.2 0.9 8.7 0.4 18.0 1.1

2.7 1.8 1.6 0.6 10.1 5.6 1.6 0.2

22.0 21.0 3.3 0.3 21.0 14.5 0.0 0.0

Mean SEM Mean SEM Mean SEM

8.4 (8.4–8.4) 7.5 (7.3–7.6) 7.4 (7.4–7.5)

1455.0 2.6 1266.0 4.9 1098.7 4.3

1085.0 14.0 966.7 13.2 802.7 10.7

33.7 1.4 32.1 0.8 32.1 0.8

582.7 14.3 523.5 18.8 380.3 12.8

3.0 0.1 10.4 4.3 1.1 0.1

9.0 1.0 26.7 13.7 6.0 3.0

Mean SEM Mean SEM Mean SEM Mean SEM Mean SEM

7.6 (7.6–7.6) 7.4 (7.4–7.5) 7.5 (7.5–7.5) 8.0 (7.9–8.0) 7.0 (7.0–7.0)

427.3 0.7 249.3 2.8 424.7 135.7 378.3 0.3 166.2 0.2

211.3 10.8 155.0 9.1 286.0 104.3 202.3 1.8 88.7 11.9

16.3 0.0 23.8 0.0 27.1 0.8 31.3 0.0 6.3 0.0

46.0 4.4 69.8 1.7 134.3 31.9 52.4 3.6 25.2 3.8

0.4 0.01 3.8 0.4 1.3 0.2 0.6 0.04 0.4 0.01

4.0 1.7 2.7 2.7 4.0 1.0 6.0 1.2 1.0 1.0

Note. Values represent means and SEM, except for pH, where the median and range are reported. Water samples (n ¼ 3) were taken in each lake at the same locations where sediments were collected. Highest and lowest values for each variable are indicated in boldface. a TDS, total dissolved solids; NTU, nephrelometry turbidity units; TSS, total suspended solids.

varied by approximately fourfold between reference and contaminated lakes. In Group 1 lakes, sediment Co ranged from 13.8 to 49.5 mg/g, whereas in contaminated lakes, sediment Co ranged from 47 to 205 mg/g. Other sediment metal concentrations varied from one- to three-fold between reference and contaminated lakes on average, and are given in Table 6. A PCA on sediment metal concentration yielded three principal components (from a scree analysis) that accounted for 88.3% of the variability associated with sediment metal concentrations in the 12 study lakes (Table 7). Copper and Ni had the highest positive l loadings on PC1, whereas Mn was the only metal that loaded negatively, albeit weakly, on PC1. Calcium and Cr had the strongest positive l loading on PC2, while Cu loaded negatively. On PC3, Se had the strongest positive loading, while Co and Fe loaded strongly and negatively. Silver, As, Cd, Co, Cu, Ni, and Pb were significantly and positively correlated with sediment PC1 (Table 3).

Nickel and Cu were most strongly correlated with PC1 (r ¼ 0:97 and 0.90, respectively). Calcium, Cr, Fe, Mn, V, and Zn were significantly and positively related to PC2, with Cr having the strongest relationship (r ¼ 0:91). Selenium was the only metal significantly correlated with PC3 (r ¼ 0:77). No significant negative relationships between metals and PCs were observed. The PCA ordination for sediment metals is illustrated in Fig. 3. Group 1 lakes plotted separately from the contaminated lakes, and were restricted to the negative side of PC1. Lakes from the Kelly Lake chain demonstrated an interesting ordination pattern that closely followed their downstream position in the watershed (i.e., Kelly, Simon, McCharles, Mud, and Kusk). Hannah and Whitson lakes plotted close to McCharles and Simon lakes. Ramsey Lake seemed to be distinct from all the other study lakes with respect to sediment metal concentration given its unique position on the ordination plot.


295

Table 5 Total metal concentration (mg/L) in water samples (n ¼ 3) taken from 12 study lakes Lake Group 1a

Barlow Big Marsh Birch Waubamac

Group 2

Kelly Mud Simon

Group 3

Hannah Kusk McCharles Ramsey Whitson

MDL

Al 50

As 1

Ba 1

Ca 100

Cu 1

Fe 50

Mg 100

Mn 1

Ni 1

Rb 1

Se 1

Sr 1

Zn 1


238.7 130.2 128.7 25.5 172.7 65.7 80.0 5.2

ND — 0.7 0.2 ND — ND —

12.7 1.7 10.3 0.7 14.3 2.4 10.0 1.2

2899 85 2183 160 2129 95 4203 304

4.3 0.9 3.7 1.2 2.0 0.6 2.0 0.6

389 194 421 96 714 366 412 73

1314 70 1155 131 830 30 1811 113

521 342 598 340 1726 803 344 190

9.7 0.7 9.0 1.0 7.0 1.0 8.0 1.0

2.0 0.0 1.7 0.3 2.0 0.0 1.7 0.3

ND — ND — ND — 0.6 0.2

20.0 0.6 16.3 1.5 18.3 1.3 28.3 1.3

10.3 2.4 7.3 1.7 7.0 1.0 5.7 1.8


ND — 70.7 33.9 ND —

2.4 0.3 3.3 0.3 2.3 0.3

38.4 3.8 33.3 0.9 33.3 0.3

204437 5175 186097 6537 127812 4702

15.0 1.5 17.0 7.5 9.7 0.3

477 35 653 36 422 4

17545 339 14284 632 14857 273

207 22 513 4 522 9

338.2 34.7 257.3 7.2 318.7 2.3

19.2 1.7 15.3 0.7 15.7 0.3

5.8 0.9 4.7 0.3 5.0 0.0

365.0 32.4 291.0 8.7 271.3 2.8

11.7 3.3 7.3 0.9 9.0 2.0


ND — 46.0 10.5 ND — ND — ND —

2.0 0.0 2.0 0.0 1.7 0.3 1.3 0.3 0.7 0.2

23.7 2.4 14.3 0.3 19.7 1.2 14.0 1.0 21.3 3.2

11603 1150 22071 659 43274 10589 14003 1030 6600 981

25.0 2.0 6.0 0.6 6.0 1.0 9.7 0.3 19.0 3.5

71 4 261 9 206 6 36 11 53 14

4137 377 3560 19 6363 1321 4224 240 2111 317

251 19 328 13 332 13 72 4 21 4

181.0 44.5 60.0 1.2 111.3 39.3 52.0 2.6 155.0 24.4

3.0 0.0 3.0 0.0 5.3 1.3 2.0 0.0 2.3 0.7

2.3 0.3 1.2 0.4 1.0 0.5 2.0 0.0 ND —

64.7 6.4 56.7 0.9 102.0 23.0 48.7 2.2 36.3 5.2

10.0 3.2 6.0 1.5 4.7 0.3 5.7 0.7 17.7 2.8

Note. Highest and lowest metal concentrations are represented in boldface. ND, not detected; MDL, minimum detection limit. a Lake groups are based on multivariate similarity as determined by principal component analysis. Group 1 represents reference lakes. See text for details.

3.3. Fish diversity and yellow perch samples

diversity (P ¼ 0:0005). This model took the form

Fish diversity was variable among study lakes (Table 8). Among all lakes, 20 species were identified. Six of these species, including central mudminnow, fathead minnow, johnny darter, largemouth bass, log perch, and muskellunge, occurred in one lake only. All other species occurred in at least two lakes. Yellow perch was the only species that occurred in all 12 study lakes, followed by the northern pike and pumpkinseed, which each occurred in 67% (8/12) of the lakes studied. Yellow perch was the only species caught in Birch Lake. A stepwise multiple regression analysis was conducted using the first three extracted principal components of the water and sediment metal PCAs as independent variables to account for variability associated with fish diversity. Analyses using Shannon’s diversity index (H0 ) as the dependent variable, and/or including pH, hardness, alkalinity, conductivity, and distance from smelting operations, resulted in no significant relationships (P40:05). A subsequent analysis using log-transformed water and sediment metals, in addition to pH, hardness, alkalinity, conductivity, and distance from smelting operations, yielded a significant relationship that could account for 92.4% of the variability associated with fish

H 0 ¼ 0:79ð0:44Þ þ 0:68ð0:17Þ logðsediment½Mn Þ þ 0:51ð0:10Þ logðsediment½Co Þ þ 0:41ð0:09Þ logðsediment½Cu Þ 1:37ð0:16Þ logðsediment½As Þ;

r2 ¼ 0:92;

P ¼ 0:0005;

where standard errors of estimates are given in parentheses, and square brackets represent concentrations (mg/g). Although water metal concentrations, water quality variables, and distance from smelter were included in the analysis, only sediment metal data contributed significantly to the model. Because yellow perch was the only species found in each of the 12 lakes studied, 19 or 20 yellow perch of approximately the same size were sampled from each of the study lakes (Table 9). Although we tried to sample fish of the same size in all study lakes, there was some size variability among lakes. Except for McCharles (86 mm) and Birch (242 mm), which had the smallest and largest mean yellow perch fork lengths, respectively, mean yellow perch fork length ranged from 92 mm in Mud Lake to 125 mm in Barlow Lake. Median age


296

Table 6 Total sediment metal concentrations (mg/g dry wt, n ¼ 3) from 12 Sudbury-area study lakes Lake Group 1a Barlow Big Marsh Birch Waubamac Group 2 Kelly Mud Simon Group 3 Hannah Kusk McCharles Ramsey Whitson

MDL

Ag 1

As 1

Ca 100

Cd 0.1

Co 0.1

Cr 1


ND — ND — 0.9 0.2 ND —

13.8 6.5 12.0 3.2 33.8 4.4 20.6 2.0

3644 1145 3593 751 5881 978 5098 305

0.9 0.4 0.7 0.4 1.7 0.4 2.4 0.3

22.4 8.3 49.5 27.7 13.8 2.3 28.5 0.9

44.0 10.5 52.2 9.2 32.4 2.6 53.3 0.4


7.0 3.5 1.5 0.5 2.5 1.0

46.8 17.4 18.1 1.1 51.7 16.3

6665 2772 7142 360 7047 1520

4.6 1.6 1.6 0.3 4.5 1.6

196.8 72.1 58.3 9.3 205.0 69.8


3.5 0.6 1.6 0.6 1.9 0.9 1.9 0.7 1.8 1.0

87.7 7.6 36.1 9.4 79.3 41.6 67.9 33.3 77.5 34.9

2906 781 4403 340 5528 1125 2672 971 3387 495

2.7 0.8 1.5 0.2 3.4 2.0 2.8 1.4 3.0 1.2

47.0 7.7 80.4 20.9 187.4 85.5 58.8 29.7 66.9 29.9

Cu 1

Fe 50

Mn 1

Ni 1

Pb 1

65.3 27.7 58.4 27.1 174.7 65.0 118.1 17.4

22728 4482 37691 14278 11024 2648 23715 2754

460 181 469 31 153 27 528 44

122.1 39.2 95.6 34.2 210.8 60.6 159.2 21.9

23.3 9.8 18.1 11.9 67.9 28.2 56.2 13.8

49.7 19.0 49.9 2.8 52.5 14.9

2672.0 1058.6 746.5 188.2 1658.8 584.8

37032 14488 26019 644 34855 10181

230 89 206 16 290 47

4677.5 1786.7 1194.9 187.1 4744.8 1933.6

64.8 5.8 46.3 6.3 51.6 13.9 36.3 14.6 36.2 6.5

1450.1 283.5 349.4 101.3 1287.8 671.7 1606.2 495.0 1915.5 729.5

34151 1913 29562 6352 33420 10208 26187 11777 28408 10086

264 10 380 57 328 74 292 96 307 22

1460.7 232.5 864.5 234.7 3654.3 2283.6 1889.6 1053.2 1302.6 593.6

Se 1

V 1

Zn 1

24.9 6.6 22.8 1.0 70.5 4.8 40.5 3.3

40.7 9.0 58.7 12.4 47.6 2.2 52.5 3.1

132.0 41.3 137.9 33.3 158.4 29.2 197.1 24.7

75.8 33.3 23.8 5.9 68.2 26.8

95.7 36.8 51.4 4.8 126.6 33.3

30.5 9.0 39.1 2.4 44.6 6.9

393.9 150.4 144.2 13.1 302.0 102.7

67.1 6.0 124.3 42.6 75.7 24.2 67.8 31.6 204.7 153.5

31.8 3.4 40.2 2.3 60.7 19.6 33.4 3.3 38.0 3.4

48.4 2.0 45.8 6.4 44.9 8.0 33.2 9.1 42.3 7.7

148.0 31.1 289.0 77.3 289.4 98.2 180.5 82.7 176.7 46.5

Note. Minimum and maximum metal concentrations are represented by boldface. ND, not detected; MDL, minimum detection limit; SEM, standard error of the mean. a Lake groups are the same as Table 4; see text for details.

among study lakes was 1–3 years, except in Birch Lake (4.5 years), where at least one 11-year-old fish was caught. A large number of immature fish were caught in McCharles Lake, accounting for the low fork length and weight of yellow perch from this lake.

3.4. Tissue metal concentration 3.4.1. Liver Liver metal concentrations were generally higher in fish from metal-contaminated lakes relative to reference lakes, but not always (Table 10). Pooling the data into the three groups generated by PCA (see above), liver Cd concentrations were lowest in fish from Group 1 relative to those of Group 2 or 3 (F ð2;209Þ ¼ 83:5; Po0:0001) (Fig. 4). Liver Cd concentrations in Group 3 fish were approximately eight times higher than those in fish from Group 1 or 2. Liver Cd showed a very strong nonlinear relationship with water hardness among fish inhabiting lakes in Group 2 and 3 (Fig. 5). This relationship accounted for 99% of the variability associated with

liver Cd in fish inhabiting contaminated lakes. Fish from reference lakes did not follow the relationship. Among Group 1 lakes, liver Cu concentrations ranged between 8.6 and 32.8 mg/g (Table 10). In metal-contaminated lakes, liver Cu concentrations ranged between 6.9 and 87.8 mg/g. Interestingly, liver Cu concentrations in McCharles Lake, which is downstream of Simon Lake (where liver Cu concentrations of 6.9 mg/g were lower than in fish from any other lake in the study), were 50.5 mg/g. In general, fish from Group 3 lakes had twice as much liver Cu as fish from lakes in Group 1 or 2 (F ð2;183Þ ¼ 4:3; P ¼ 0:01) (Fig. 4). Liver Ni was not detected in fish from Big Marsh Lake, and was approximately 4 mg/g in fish from Barlow and Birch lakes (Group 1 lakes; Table 10). However, liver Ni was high in fish from Waubamac Lake (20.4713.6 mg/g) relative to those from other Group 1 lakes. Liver Ni concentrations in fish from Group 1 and 2 lakes were not significantly different, and were significantly lower than those of fish from Group 3 lakes (F ð2;148Þ ¼ 8:5; P ¼ 0:0001) (Fig. 4).

ARTICLE IN PRESS G.G. Pyle et al. / Ecotoxicology and Environmental Safety 61 (2005) 287–312 Table 7 Results of principal component analysis of sediment metal data from the 12 study lakes (Table 1) PC1

PC2

PC3

Eigenvalue (L) Variance explained (%) Cumulative explained variance (%)

0.913 57.1 57.1

0.387 24.2 81.3

0.113 7.1 88.3

Eigenvectors (l) Ag As Ca Cd Co Cr Cu Fe Mn Ni Pb Se V Zn

0.170 0.261 0.033 0.146 0.291 0.025 0.607 0.066 0.071 0.564 0.290 0.058 0.002 0.111

0.029 0.131 0.359 0.114 0.259 0.360 0.411 0.357 0.333 0.045 0.303 0.062 0.239 0.285

0.124 0.330 0.345 0.095 0.421 0.024 0.222 0.400 0.125 0.266 0.203 0.453 0.166 0.018

Note. Eigenvalues, variance explained, cumulative variance explained, and eigenvectors are reported for the first three principal components. Components retained for further analysis were determined by a scree analysis. The highest and lowest eigenvector loadings for each component are highlighted in boldface.

Fig. 3. Ordination plot from PCA of sediment metal concentration data in the plane of PC1 and PC2. Labeling conventions correspond to those of Fig. 2.

Liver Pb concentrations were below detection in a majority of samples from Group 1 lakes (Table 10). Liver Pb concentrations in fish from Group 2 and 3 lakes fell within the reference range, except for fish from Hannah (41.3 mg/g) and Kelly (17.9 mg/g) lakes. Fish from lakes constituting Group 2 and 3 had liver Pb concentrations that were not significantly different, but were significantly higher than those from Group 1 (F ð2;62Þ ¼ 12:5; Po0:0001) (Fig. 4).

297

Liver Se concentrations ranged from 4.8 to 90.4 mg/g among all lakes (Table 10). Selenium concentrations in liver tissues from Kelly Lake fish were at least three times higher than liver Se from any other lake. Liver Se concentrations in Group 2 fish were four times higher than in Group 1 fish, and twice the levels measured in Group 3 (F ð2;227Þ ¼ 213:6; Po0:0001) (Fig. 4). Zinc concentrations in liver tissue varied more than 15-fold, ranging from 93 to 1441 mg/g (Table 10). Fish from Group 1 lakes had liver Zn concentrations that were not significantly different from those of Group 2 fish. However, Group 3 fish had significantly higher liver Zn concentrations than either Group 1 or Group 2 fish (F ð2;236Þ ¼ 28:1; Po0:0001) (Fig. 4).

3.4.2. Muscle In general, muscle metal concentrations were lower than liver metal concentrations (Table 11). Muscle Cu was not detected in any Whitson Lake fish, and ranged between 2.9 and 14.1 mg/g among other study lakes. Muscle Cu concentrations in Group 2 fish were not significantly different from those in either Group 1 or Group 3 fish. However, muscle Cu concentrations in Group 1 fish were 47% higher than those in Group 3 fish (F ð2;132Þ ¼ 5:2; P ¼ 0:007) (Fig. 6). Muscle Hg concentrations were highest among fish from Group 1 lakes (Table 11). Among metalcontaminated lakes, muscle Hg concentrations ranged from 0.10 to 0.39. Group 1 fish had significantly higher muscle Hg concentrations than fish from Group 2 or 3 lakes, which did not significantly differ from each other (F ð2;151Þ ¼ 65:1; Po0:0001) (Fig. 6). Muscle Se concentrations were lowest among fish from Group 1 lakes (Table 11). Among the metalcontaminated lakes, fish from Simon, Hannah, and Kelly lakes had muscle Se concentrations above 10 mg/g, whereas fish from other metal-contaminated lakes had muscle Se concentrations from 2.3 to 7.7 mg/g. Group 1 lakes had fish with the lowest muscle Se concentrations. Mean muscle Se concentrations from Group 2 fish were more than double the concentrations measured in Group 3 fish, and about 14 times higher than in Group 1 fish (F ð2;182Þ ¼ 60:5; Po0:0001) (Fig. 6). Muscle Hg and Se concentrations exhibited an antagonistic effect (r ¼ 0:50; Po0:0001) and were related to the distance from major smelting operations in the area (Fig. 7). Fish caught from lakes close to metal ore smelting operations had elevated muscle Se concentrations but low muscle Hg concentrations relative to those caught from more distant lakes. Both muscle Se and Hg concentrations were significantly, but oppositely, related to distance from local smelting operations (muscle Se=1.02–0.02 distance from smelter; muscle Hg=0.92+0.14 distance from smelter).


298

Table 8 Fish species diversity in 12 study lakes around Sudbury, Ont., sampled in 2001 Speciesa

Barlowb Big Marshb Birchb Hannah Kelly Kusk McCharles Mud Ramsey Simon Waubamacb Whitson Total Proportion

Brown bullhead Central mudminnow Common shiner Creek chub Fathead minnow Five-spine stickleback Golden shiner Iowa darter Johnny darter Lake chub Largemouth bass Log perch Muskellunge Northern pike Pumpkinseed Rock bass Smallmouth bass Walleye White sucker Yellow perch Total no. of species Diversityc

+

+

+

+

+

+

+ +

+ +

+

+ + + +

+

+

+ + +

+ +

+

+ +

+ +

+

+ +

+ + + +

+

+

+ + +

+ + + 9 0.95

+ +

+

+ +

+ + + 8 0.90

+ 1 0

+ 2 0.30

+ + + + +

+

+ + +

+

+

+ + + + 8 6 7 0.90 0.78 0.85

+ + + + + 8 6 0.90 0.78

+ 11 1.04

+ + + + + + + + 13 1.11

+ +

+ + + 7 0.85

6 1 6 6 1 2 6 3 1 4 1 1 1 8 8 3 5 6 5 12

0.50 0.08 0.50 0.50 0.08 0.17 0.50 0.25 0.08 0.33 0.08 0.08 0.08 0.67 0.67 0.25 0.42 0.50 0.42 1.00

a

Brown bullhead, Ictalurus nebulosus; central mudminnow, Umbra limi; common shiner, Notropus cornutus; creek chub, Semotilus atromaculatus; fathead minnow, Pimephales promelas; five-spine stickleback, Culaea inconstans; golden shiner, Notemigonus crysoleucas; Iowa darter, Etheostoma exile; johnny darter, Etheostoma nigrum; lake chub, Couesius plumbeus; largemouth bass, Micropterus salmoides; log perch, Percina caprodes; muskellunge, Esox masquinongy; northern pike, Esox lucius; pumpkinseed, Lepomis gibbosus; rock bass, Ambloplites rupestris; smallmouth bass, Micropterus dolomieui; walleye, Stizostedion vitreum; white sucker, Catostomus commersoni; yellow perch, Perca flavescens. b Indicates reference lakes; all other lakes are known to have been affected by regional industrial activity. c Shannon’s diversity index.

Table 9 Yellow perch number, gender distribution, age, length, and weight of fish sampled in 12 study lakes in the Sudbury area in 2001 Lake

Immature

Males

Females

Total n

Median age (range)

Mean7SD fork length (mm)

Mean7SD weight (g)

Barlowa Big Marsha Bircha Hannah Kelly Kusk McCharles Mud Ramsey Simon Whitson Waubamaca

0 0 0 3 1 2 14 9 5 4 3 8

6 10 0 11 10 4 6 0 2 2 5 6

14 10 20 6 9 14 0 10 13 14 12 6

20 20 20 20 20 20 20 19 20 20 20 20

2 2 4.5 2 1 2 2 1 3 2 2 2

125749 103723 242748 95725 97714 97742 8677 92731 121730 100717 102715 106725

27725 15716 1737102 1178 1176 22751 972 1279 23725 1375 1175 16719

a

(2–4) (2–4) (3–11) (1–3) (1–3) (1–5) (2–2) (0–2) (2–5) (1–2) (2–4) (2–5)

Indicates reference lake.

Muscle Zn concentrations ranged from 23.5 to 276.9 mg/g among study lakes (Table 11). Fish from Group 1 or Group 2 lakes had relatively low muscle Zn concentrations. Fish from Group 3 lakes had muscle Zn concentrations that were approximately four times higher than those from the other two lake groups (F ð2;228Þ ¼ 4:9; P ¼ 0:008) (Fig. 6).

3.4.3. Intestine Intestinal Cd concentrations were below 2 mg/g in fish from all lakes (Table 12). Only fish from Whitson and Hannah Lakes had intestinal Cd concentrations greater than 1 mg/g (1.24 and 1.34 mg/g dry wt, respectively). Group 1 fish had the lowest intestinal Cd concentrations, followed by Group 2 fish, which had significantly


299

Table 10 Total liver metal concentrations (mg/g dry wt) in yellow perch from 12 Sudbury-area study lakes Lake

Ca

Cd

Co

Cu

Mg

Mn

Ni

Mean SEM N

3407abc 648 20

1.34de 0.18 19

3.7d 0.4 12

22.5abcd 3.5 20

633abcd 70 20

1108ab 59 20

31.3b 5.8 20

4.4bc 0.6 14

Big Marsh

Mean SEM N

1509d 532 20

1.12de 0.16 17

10.0abc 1.3 8

8.6de 5.0 7

839abc 102 20

1077abc 58 20

6.8def 0.6 20

— — 0

Birch

Mean SEM N

1908abcd 184 20

1.02e 0.13 18

7.1cd 0.9 20

32.8a 3.2 20

829d 34 20

59.0a 8.2 20

4.0c 0.4 20

Waubamac

Mean SEM N

1693bcd 236 20

0.78e 0.11 9

5.6cd 0.7 14

13.0bcde 1.8 20

919ab 80 20

1319a 80 20

13.2cd 2.6 20

Mean SEM N

3343ab 562 20

1.19de 0.11 20

7.4bcd 1.0 15

17.1bcde 5.3 11

282f 28 20

1061bc 84 20

4.7f 0.3 19

Mud

Mean SEM N

3311ab 523 19

2.14cd 0.38 13

10.8bc 2.2 14

32.1abc 4.1 13

504cdef 71 17

916bcd 35 18

Simon

Mean SEM N

1780bcd 219 20

1.83cd 0.14 20

17.4a 1.2 20

6.9e 3.1 8

356def 31 20

996bcd 44 20

5.8ef 0.9 20

Mean SEM N

3590ab 1016 20

7.49b 1.03 20

10.1bc 1.2 18

87.8a 26.4 18

539cdef 81 20

871bcd 24 19

6.2def 0.5 20

Kusk

Mean SEM N

1415cd 260 20

2.90c 0.34 16

17.4a 1.3 20

12.2bcde 2.9 16

320f 30 20

1072abc 56 20

13.0cd 2.3 20

McCharles

Mean SEM N

4207a 564 20

2.31c 0.18 20

19.5a 1.7 20

50.5a 3.8 20

351ef 62 20

995bcd 40 20

Ramsey

Mean SEM N

2745abcd 944 20

4.20c 0.55 20

6.8cd 1.1 18

15.3cde 3.2 16

413def 49 20

Whitson

Mean SEM N

1923bcd 430 20

22.37a 2.20 20

12.8ab 1.2 20

36.0ab 6.2 19

758bcde 217 20

Group 1 Barlow

Group 2 Kelly

Group 3 Hannah

Fe

1163a 125 20

6.2cdef 1.0 7

Pb

Se

Zn

2.7b 0.5 6

6.3ef 0.7 18

132.4bc 12.4 20

3.6ab — 1

6.8ef 1.1 17

99.1bc 8.9 20

2.0b 0.3 7

4.8f 0.4 20

116.0bc 5.9 20

20.4abc 13.6 13

12.0ab — 1

5.0ef 0.4 17

93.2c 9.3 20

5.4abc 1.7 6

17.9ab 5.0 4

90.4a 6.1 20

153.8bc 20.1 20

5.5abc 0.7 9

7.8b 1.4 8

28.7bc 3.2 19

133.0bc 12.7 19

3.8c 0.4 19

2.1ab — 1

19.7cd 1.3 20

136.0bc 14.0 20

8.3bc 2.8 16

41.3a 10.8 12

31.9b 2.2 20

187.1b 26.2 20

12.5def 1.1 20

231.3b 44.7 20

5.3abc 0.6 9

2.1ab 0.4 2

13.9bcde 3.9 10

22.1a 8.6 18

9.6b 1.6 13

17.9d 1.2 20

179.1b 17.5 20

1086bc 110 20

16.5bc 2.7 20

22.4ab 9.7 9

6.0b 1.4 8

15.7de 0.8 20

1441.0a 435.4 20

866cd 28 20

9.4cdef 1.4 20

8.1bc 2.3 18

4.2ab 2.6 2

22.4bcd 0.5 19

189.4b 32.7 20

Note. For any given metal, means sharing a similar superscript are not significantly different from one another (P40:05). Maximum and minimum values are indicated in boldface. Lake groups are the same as in Table 4; see text for details.

lower concentrations than Group 3 fish, which had significantly higher intestinal Cd concentrations than fish from any other group (F ð2;146Þ ¼ 14:9; Po0:0001) (Fig. 8). The lowest mean intestinal Cu concentrations were measured in fish from Simon Lake, a metal-contaminated lake (4.070.9 mg/g) (Table 12). Intestinal Cu concentrations in fish from Group 2 and 3 lakes did not significantly differ from each other. However, fish from

Group 1 lakes had significantly lower intestinal Cu concentrations than those from Group 2 lakes (F ð2;182Þ ¼ 4:2; P ¼ 0:02) (Fig. 8). Intestinal Se concentrations were less than 2 mg/g in Group 1 lakes (Table 12). Intestinal Se concentrations were lowest in fish from Group 1 lakes, followed by those in Group 3 lakes, which were 31% lower than those in Group 2 lakes (F ð2;180Þ ¼ 41:7; Po0:0001) (Fig. 8). Interestingly, intestinal Se was



Fig. 4. Comparison of liver Cd, Cu, Ni, Se, Pb, and Zn concentrations in yellow perch collected from each of three lake groups identified by water metal PCA (Fig. 2). Concentrations are reported as means7SEM (n in parentheses). Mean differences were compared by ANOVA followed by Tukey–Kramer multiple comparison test. Within each panel, bars sharing the same letter are not significantly different from one another (P40:05).

positively correlated with intestinal Cu in males (r ¼ 0:46; P ¼ 0:006; n ¼ 34), but negatively correlated in females (r ¼ 0:24; P ¼ 0:03; n ¼ 80). Intestinal Zn concentrations ranged from 28.8 to 404.9 mg/g among all study lakes (Table 12). The intestinal Zn concentrations in Ramsey Lake (Group 3) fish were at least four times higher than those in fish from any other lake. Intestinal Zn concentrations in fish from Group 1 or Group 2 lakes were not significantly different. However, Group 3 fish had mean intestinal Zn concentrations that were at least twice as high as those in other lake groups (F ð2;222Þ ¼ 7:2; P ¼ 0:0009) (Fig. 8). 3.5. Partial correlation analysis

Fig. 5. Relationship between liver Cd concentration and water hardness in 9 of the 12 study lakes. Each point represents mean7SEM (n ¼ 9 20) liver Cd concentration in yellow perch from each lake as a function of mean7SEM (n ¼ 3) water hardness. Black points represent metal-contaminated lakes (i.e., Group 2 and 3), white points represent reference lakes (i.e., Group 1).

A five-compartment partial correlation analysis was conducted using water, sediment, intestine, muscle, and liver concentrations of Cu, Cd, Ni, Se, and Zn. This analysis evaluates relationships between pairs of variables while holding common variance among remaining variables constant. Copper and Ni were highly correlated between water and sediment compartments


301

Table 11 Total muscle metal concentrations (mg/g dry wt) in yellow perch from 12 Sudbury-area study lakes Lake Group 1 Barlow

Mean SEM N

Ca

Cr

Cu

3913a 819 20

38.5a 25.0 20

7.5ab 0.4 20

Fe

Hg

Mg

Mn

Ni

Se

Zn

93.1a 7.9 18

0.50ab 0.05 20

610abc 29 20

16.1ab 4.2 17

2.2a 0.2 13

1.5f 0.1 6

34.1bc 3.9 20

7.4bc 4.2 8

144.5a 143.3 2

1.6ef 0.4 4

23.5c 7.0 19

Big Marsh

Mean SEM N

742cd 139 20

10.1ab 6.6 19

3.6cd 0.6 11

441.0a 296.7 8

0.74a 0.06 19

455c 29 20

Birch

Mean SEM N

1423abc 253 20

2.7ab 0.2 20

13.3a 2.4 20

87.0a 12.9 16

0.75a 0.11 20

789a 22 20

19.0a 2.6 20

3.2a 0.7 19

1.4f 0.1 12

39.6bc 2.9 20

Waubamac

Mean SEM N

1803bc 545 20

2.6ab 0.3 20

6.2bc 0.7 19

64.9a 6.0 15

0.52ab 0.06 19

472bc 22 20

3.2c 0.9 16

2.5a 0.8 9

1.5f 0.1 7

24.8bc 2.7 14

Mean SEM N

1293abc 155 20

52.9a 37.0 20

14.1ab 7.0 3

494.9a 304.5 9

0.18d 0.02 15

742a 33 20

8.9bc 5.5 8

40.5a 30.3 12

24.0a 1.7 19

44.2b 5.2 20

Mud

Mean SEM N

798bcd 108 19

3.6ab 0.1 19

5.1bcd 0.5 10

93.4a — 1

0.10d 0.00 4

506bc 54 19

1.7bc 0.7 2

1.5a 0.2 8

7.7c 1.2 19

26.5bc 2.9 19

Simon

Mean SEM N

1168bc 212 20

4.9ab 0.9 20

11.0bcd 8.0 7

99.8a 15.5 3

0.26abcd — 1

712a 21 20

1.7c 0.3 9

5.4a 3.7 5

10.3b 0.6 20

29.5bc 2.1 20

Mean SEM N

1654ab 255 20

9.4ab 5.8 20

11.2ab 3.7 5

198.4a 108.7 5

0.16d 0.01 14

735a 36 19

2.4c 1.1 10

6.5a 3.7 11

11.8b 0.6 19

64.2b 24.1 20

Kusk

Mean SEM N

1752abc 514 20

3.1ab 0.4 20

2.9d 0.1 11

122.4a 22.3 12

0.39bc 0.04 18

756a 42 20

2.8c 0.5 15

1.4a 0.1 4

3.8d 0.4 20

276.9b 169.6 19

McCharles

Mean SEM N

513d 85 20

3.1ab 0.3 20

6.8b 0.5 20

96.7a 31.2 5

0.10bcd — 1

228d 15 20

1.0abc — 1

1.4a 0.1 10

2.3def 0.1 20

Ramsey

Mean SEM N

996cd 428 20

1.8b 0.2 18

3.1d 0.7 9

76.2a 15.6 8

0.25cd 0.05 14

622ab 26 20

3.5bc 0.9 11

6.4a 2.8 4

3.3de 0.3 19

Whitson

Mean SEM N

1461bc 301 20

1.9b 0.1 20

85.1a 17.1 4

0.15d 0.02 9

843a 99 20

3.2bc 0.6 11

1.0a 0.0 2

5.6c 0.4 20

Group 2 Kelly

Group 3 Hannah

ND — —

24.9bc 3.3 20 266.5a 64.5 20 33.5bc 4.8 20


(Fig. 9). Copper displayed no clear relationships between environmental compartments and tissues. However, there was a weak positive relationship between intestinal and muscle Cu. Partial correlations using water or muscle Cd could not be evaluated owing to the low concentrations in these compartments. However, intestinal Cd was strongly related to liver Cd. There were opposite relationships between Ni in yellow perch tissues and Ni in water or sediment. Liver Ni was negatively correlated with water Ni, but muscle Ni was positively correlated with water

Ni. However, liver Ni was positively correlated with sediment Ni, but muscle Ni was negatively correlated with sediment Ni. All relationships involving sediment Se were weak, whereas all those involving water Se were strong and positive. Conversely, all relationships involving sediment Zn were strong and negative, whereas those involving water Zn were weak. Intestinal Se and Zn were strongly and positively related to muscle and liver Se and Zn, which were strongly correlated with each other.



Fig. 6. Comparison of muscle Cu, Hg, Se, and Zn concentrations in yellow perch collected from each of three lake groups identified by water metal PCA (Fig. 2). Labeling conventions follow Fig. 4.

Fig. 7. Relationship of yellow perch muscle Se and Hg concentrations as a function of distance from major industrial activities in Sudbury, Ont. Points represent mean7SEM (sample sizes provided in Table 11) log(x)-transformed data for Hg and Se.

3.6. Fish condition Fish condition was analyzed on the basis of the water metal PCA groupings using age-corrected metrics (Fig. 10). Fish from Group 1 lakes demonstrated significantly lower FCFs than fish from either Group 2 or Group 3 lakes (F ð2;228Þ ¼ 96:0; Po0:0001). Fish from Group 2 lakes had 2.1 times higher FCFs than fish from Group 1 lakes, and 1.7 times higher than those from Group 3 lakes. Age-corrected HSI demonstrated trends similar to that for FCF, in that fish from Group 1 and 3 lakes had the lowest HSI, while fish from Group 2 lakes had the

highest HSI (F ð2;228Þ ¼ 28:9; Po0:0001) (Fig. 9). The HSI in fish from Group 2 lakes was approximately double that of fish from either Group 1 or Group 3 lakes. GSI was assessed only for mature male and female fish (Fig. 10). In males, fish from Group 1 fish had an intermediate GSI relative to fish from Group 2 and Group 3 (F ð2;59Þ ¼ 24:7; Po0:0001). Fish from Group 2 lakes had the highest mean GSI, which was 1.8 times higher than that in fish from Group 1 lakes and 4.6 times higher than that in Group 3 lakes. Female GSI was highest in Group 1 fish relative to those from Group 2 and 3 lakes (F ð2;125Þ ¼ 10:0; Po0:0001). Mean female GSI of fish from Group 1 lakes was 1.4 to 2 times higher than those of Group 2 and 3 lakes, respectively. GSI did not significantly differ among females of Group 2 and 3 lakes. 3.7. Correlation analysis A Pearson correlation analysis was conducted to determine significant relationships among fish condition and tissue metal concentration with water quality characteristics (pH, alkalinity, hardness, and conductivity), distance from ore smelting activities, and sediment (Cd, Co, Cr, Cu, Ni, Se, and Zn) and water (Ca, Cu, Ni, Se, and Zn) metals (Table 13). Uncorrected FCF and HSI, male GSI, Shannon’s diversity index, liver metals (including Cd, Co, Cu, Ni, Se, and Zn), muscle Cr and Cu, and intestinal Co, Cu, and Ni were not significantly associated with any of the correlates (P40:05). All the nonmetal water quality variables (pH, alkalinity, hardness, conductivity), sediment Cu and Ni, and water Ca, Cu, Ni, and Se were negatively related to distance from


303

Table 12 Total intestine metal concentrations (mg/g dry wt) in yellow perch from 12 Sudbury-area study lakes Lake Group 1 Barlow

Mean SEM N

Ca

Cd

10781a 5346 17

0.56cd 0.21 11

Co

Cr

Cu

Fe

0.34abc 0.14 8

31.9a 20.6 16

18.1bc 3.0 17

359ab 109 17

0.67abc 0.25 9

5.3ab 1.3 13

12.2bcd 2.6 14

Hg

Mg

Mn

Ni

Se

Zn

0.27b 0.04 16

440ab 89 17

42.4ab 15.4 17

2.7ab 0.6 11

1.4de 0.2 8

73.9b 18.3 17

601a 219 14

0.29b 0.03 14

333abc 74 13

32.5ab 8.3 14

5.3ab 1.6 5

1.3cde 0.1 4

55.7bcd 16.3 14

Big Marsh

Mean SEM N

8418ab 3976 14

0.24cd 0.06 5

Birch

Mean SEM N

1826ab 292 20

0.16d 0.03 6

0.28bc 0.03 17

6.9ab 3.0 20

15.0bc 0.8 20

179bc 20 20

0.64a 0.09 19

386a 21 20

29.1a 3.1 20

5.9ab 1.8 20

Waubamac

Mean SEM N

0.15cd 0.04 2

0.21c 0.05 8

3.2bc 0.8 19

8.7cd 1.0 20

124cd 18 19

0.29b 0.02 19

261bc 14 20

12.7bcd 3.4 20

1.7b 0.3 11

0.44bcd 0.09 14

0.34abc 0.08 13

2.8bc 0.2 18

29.0ab 6.4 13

140cd 42 15

0.26b 0.02 15

352abc 38 16

2.8f 0.4 15

4.4ab 1.1 16

8.7a 1.4 18

50.7bc 6.0 18

0.28cd 0.04 12

0.25c 0.05 15

3.4abc 0.5 19

11.0de 5.4 15

127bcd 24 10

0.21b 0.03 5

260cd 46 18

2.5f 0.3 19

2.9ab 0.4 14

3.2bc 0.5 19

62.3b 7.6 19

382ab 17 20

5.1def 0.7 20

6.1a 1.2 19

5.9a 0.4 20

76.8b 7.5 20

Group 2 Kelly

Mean SEM N

983bcd 307 20 1572bc 455 18 924bcd 182 19

1.5e 0.1 15 1.4cde 0.1 6

73.0b 10.4 20 31.8d 5.8 20

Mud

Mean SEM N

Simon

Mean SEM N

1296bc 190 20

0.88ab 0.14 20

0.68a 0.10 20

2.6bc 0.1 20

4.0e 0.9 13

78d 12 14

Mean SEM N

1225bc 102 18

1.34a 0.12 18

0.51abc 0.22 15

5.2ab 0.5 18

39.5a 5.0 18

101cd 10 17

0.37b 0.06 18

354ab 19 18

8.5cde 2.3 18

7.6a 2.8 18

6.7a 0.6 18

71.9b 3.3 18

Kusk

Mean SEM N

830cd 161 20

0.38cd 0.05 13

0.67ab 0.10 19

5.8bc 3.7 18

8.0de 2.2 15

107cd 16 19

0.32b 0.04 14

376ab 27 20

17.5abc 3.7 20

4.4ab 1.0 14

2.9bcd 0.3 20

97.8b 18.9 20

McCharles

Mean SEM N

445d 85 20

0.36cd 0.04 20

0.52abc 0.13 17

11.8bc 9.6 20

7.3de 1.0 20

332abcd 166 4

0.10b — 1

148d 19 12

3.6ef 0.9 14

10.3ab 6.7 18

1.5e 0.1 16

28.8cd 4.5 20

Ramsey

Mean SEM N

419d 60 20

0.49bc 0.07 10

0.25bc 0.03 11

1.5c 0.1 14

5.7de 0.7 17

90d 18 12

0.33b 0.05 14

346ab 25 20

14.7abc 1.6 20

6.1ab 2.3 10

3.0bc 0.2 20

404.9a 78.6 20

Whitson

Mean SEM N

973bcd 197 19

1.24a 0.16 18

0.23c 0.05 19

2.5bc 0.3 19

66cd 9 4

0.18b 0.02 11

357ab 22 19

11.4bcd 1.6 19

2.7ab 0.5 8

3.5b 0.3 19

60.4b 4.9 19

Group 3 Hannah

5.0cde 1.9 3

ND — —


smelting operations. Age-corrected FCF was significantly related to water quality characteristics, distance from smelter, sediment Co and Ni, and waterborne Ca, Ni, and Se. Age-corrected HSI was related to hardness and conductivity. Female GSI was significantly and negatively related to sediment metals (Cd, Co, Ni, and Se) and waterborne Se. Muscle Hg concentrations were negatively related to water quality characteristics (except hardness), and positively related to distance from smelter. Interestingly, muscle Ni concentrations were

not associated with sediment or water Ni concentrations or water quality characteristics. Muscle Se concentrations were positively related to water quality characteristics, sediment Ni and Cu concentrations, and waterborne metals, including Se. Muscle Se was also negatively related to distance from smelter. Muscle Zn concentrations were negatively related to sediment Zn concentrations, and unrelated to waterborne Zn concentrations. Intestinal Cd concentrations were negatively related to distance from smelter, and positively



Fig. 8. Comparison of intestinal Cd, Cu, Se, and Zn concentrations in yellow perch collected from each of three lake groups identified by water metal PCA (Fig. 2). Labeling conventions follow Fig. 4.

related to sediment and water Cu and Ni concentrations, but not to sediment Cd concentrations. Intestinal Cr and Hg exhibited only negative relationships in individual sediment metals (Cu and Co, respectively). Intestinal Se was positively related to pH, conductivity, sediment Cu and Ni, and all the waterborne metals tested, and negatively related to distance from smelter. Intestinal Zn had only negative associations with some sediment metals (i.e., Co, Cr), including sediment Zn. A second Pearson correlation analysis was conducted to examine relationships between tissue metal concentration and fish condition, including age-corrected FCF and HSI and male and female GSI (Table 14). None of the condition metrics were associated with liver, muscle, or intestinal Cu or Ni (P40:05). FCF was negatively correlated (Po0:05) with liver Cd, and muscle and intestinal Hg, but positively related to liver Pb and liver, muscle, and intestinal Se. Liver Se had the strongest relationship with FCF. The HSI was negatively related (Po0:05) to muscle Hg and intestinal Cd and Hg, but positively related to liver and muscle Se. Male GSI exhibited significant negative relationships with liver and intestinal Cd and positive relationships with liver and muscle Se. Female GSI was negatively associated with liver and intestinal Se and intestinal Hg, and positively related to intestinal Cu and Zn.

4. Discussion Industrial activities have been taking place in the Sudbury region for more than century, resulting in some of the most heavily metal-contaminated environments in the world (Keller and Gunn, 1995). Consequently, there

Fig. 9. Partial correlation analysis (‘‘path analysis’’) of five metals (Cu, Cd, Ni, Se, and Zn) in a five-compartment model (water, sediment, intestine, muscle, and liver). Arrows represent most likely direction of metal flow. Values are partial correlation coefficients for individual metals between any two compartments (while holding common variance with other variables constant). N/A, not available.

are several studies that report on metal concentrations in the aquatic ecosystems of the Sudbury region (Nriagu et al., 1982, 1998; Nriagu and Wong, 1983; Nriagu, 1983; Carignan and Nriagu, 1985; Bradley and Morris, 1986). However, most of these studies focus on the primary metals of concern, such as Cu, Ni, and Zn. Few studies have looked at the range of metals that are reported in this study in water, sediments, and fish


tissues in an attempt to relate environmental metal contamination to fish diversity, tissue metal accumulation, and fish condition. Multivariate characterization of water metal concentrations led to three distinct clusters of study lakes: Group 1 included reference lakes, Group 2 consisted of three proximal lakes in the Kelly Lake chain (including Kelly Lake itself), and Group 3 included distal lakes on the Kelly Lake chain and other metal-contaminated Sudbury-area lakes. Group 1 lakes were characterized by having low alkalinity, low conductivity, low hard-

Fig. 10. Comparison of age-corrected FCF, age-corrected HSI, male GSI, and female GSI among the three lake groups identified by PCA (Fig. 2). Labeling conventions follow Fig. 4.

305

ness, low water pH, low waterborne metal (except Al) concentrations, low sediment Cu and Ni concentrations, but high waterborne Al concentrations. Although Group 1 lakes did not violate the Ontario Provincial Water Quality Objectives (PWQOs) for As (PWQO, 5 mg/L), Cu (5 mg/L), Ni (25 mg/L), Se (100 mg/L), or Zn (30 mg/L), they exceeded PWQOs for Al (PWQO, 75 mg/ L) and Fe (300 mg/L) (OMEE, 1994). Group 2 lakes were characterized by high alkalinity, high conductivity, high hardness, high pH, high waterborne metal (except Al, which was intermediate between Group 1 and 3 lakes) concentrations, and high sediment Cu and Ni concentrations. All Group 2 lakes exceeded the PWQOs for Cu, Fe, and Ni. In fact, Ni concentrations in Group 2 lakes exceeded the Ni PWQO by at least an order of magnitude. Group 3 lakes were characterized by high pH, waterborne Cu, and sediment Cu and Ni, intermediate alkalinity, conductivity, and waterborne metals (except Al, Cu, and Fe), and low hardness and waterborne Al and Fe. All Group 3 lakes exceeded the PWQOs for Cu and Ni. Sediment metal concentrations were consistently higher than waterborne metal concentrations, which is in good agreement with other studies on Sudbury-area lakes (Nriagu et al., 1982, 1998; Audet and Couture, 2003). Although metal levels were elevated in sediments relative to water, only sediment Cu and Ni were sufficiently elevated to discriminate between reference and metal-contaminated lakes, and were negatively correlated with distance from smelting operations,

Table 13 Pearson correlation coefficients for significant relationships between yellow perch condition and tissue metal accumulation and water quality characteristics (pH, alkalinity, hardness, conductivity), distance from ore smelting operations, and sediment and water metal concentrations (n ¼ 12) pH

Dist. FCFe HSIe GSIfemale Mus. Hgf Mus. Ni Mus. Se Mus Zn Int. Cd Int. Cr Int. Hg Int. Se Int. Zn

0.86a 0.62c n.s. n.s. 0.73b n.s. 0.77b n.s. n.s. n.s. n.s. 0.73b n.s.

Alk.

0.78b 0.75b n.s. n.s. 0.60a n.s. 0.62a n.s. n.s. n.s. n.s. n.s. n.s.

Hard.

0.63c 0.91d 0.79b n.s. n.s. n.s. 0.71b n.s. n.s. n.s. n.s. n.s. n.s.

Cond. Dist.

0.76b 0.90d 0.74b n.s. 0.58c n.s. 0.81e n.s. n.s. n.s. n.s. 0.69c n.s.

— 0.67c n.s. n.s. 0.84a n.s. 0.86a n.s. 0.67c n.s. n.s. 0.81e n.s.

Sediment metals (mg/g)

Water metals (mg/L)

Cd

Co

Cr

Cu

Ni

Se

Zn

Ca

Cu

Ni

Se

Zn

n.s. n.s. n.s. 0.62f n.s. 0.61f n.s. n.s. n.s. n.s. n.s. n.s. n.s.

n.s. 0.66c n.s. 0.63c n.s. n.s. n.s. n.s. n.s. n.s. 0.60c n.s. 0.61c

n.s. n.s. n.s. n.s. n.s. n.s. n.s. 0.66f n.s. n.s. n.s. n.s. 0.60f

0.87a n.s. n.s. n.s. 0.81e n.s. 0.76e n.s. 0.67f 0.62f n.s. 0.76e n.s.

0.86a 0.62c n.s. 0.69c 0.85a n.s. 0.83d n.s. 0.60c n.s. n.s. 0.76b n.s.

n.s. n.s. n.s. 0.63f n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s.

n.s. n.s. n.s. n.s. n.s. n.s. n.s. 0.70c n.s. n.s. n.s. n.s. 0.81b

0.79b 0.89d 0.69c n.s. 0.68c n.s. 0.76b n.s. n.s. n.s. n.s. 0.64c n.s.

0.87a n.s. n.s. n.s. 0.82e n.s. 0.81e n.s. 0.70c n.s. n.s. 0.77b n.s.

0.94d 0.73e n.s. n.s. 0.81e n.s. 0.90d n.s. 0.64c n.s. n.s. 0.84a n.s.

0.76b 0.78b n.s. 0.60c n.s. n.s. 0.83a n.s. n.s. n.s. n.s. 0.80b n.s.

n.s. n.s. n.s. n.s. n.s. n.s. 0.62c n.s. n.s. n.s. n.s. 0.58c n.s.

Note. Correlations among tissue, sediment, and water metal concentrations were conducted using log-transformed data. n.s., not significant (P40:05). Alk., alkalinity (mg/L as CaCO3); Hard., hardness (mg/L as CaCO3); Cond., conductivity (ms/cm); Dist., distance from smelter (km). a Po0:05: b Po0:01: c Po0:001: d Po0:0001: e Corrected for age. f Mus.=muscle; Int.=intestinal.


306

Table 14 Pearson correlation coefficients describing relationships between tissue metal concentrations and wild yellow perch condition of fish caught from 12 lakes along a metal contamination gradient Tissue

Metal

FCF

HSI a

Male GSI b

Female GSI

Liver

Cd Se Pb

0.16 0.57d 0.26a

n.s. 0.43d n.s.

0.38 0.42c n.s.

n.s. 0.29c n.s.

Muscle

Hg Se

0.39d 0.42d

0.30c 0.25c

n.s. 0.52c

n.s. n.s.

Intestine

Cd Cu Hg Se Zn

n.s. n.s. 0.22c 0.27c n.s.

0.20a n.s. 0.27c n.s. n.s.

0.42b n.s. n.s. n.s. n.s.

n.s. 0.20a 0.27b 0.20a 0.23b

Note. Only significant relationships are shown. FCF, age-corrected Fulton’s condition factor; HSI, age-corrected hepatosomatic index; GSI, gonadosomatic index. a Po0:05: b Po0:01: c Po0:001: d Po0:0001:

which agrees with other studies (Nriagu et al., 1982; Bradley and Morris, 1986). Sediments provide a longterm record of industrial contamination in metalpolluted systems. Consequently, elevated Cu and Ni concentrations in lake sediments in Sudbury-area lakes probably reflect the long-term deposition of these metals through atmospheric deposition and surface water runoff into these lakes, whereas water metals probably reflect short-term conditions. Nriagu et al. (1998) have demonstrated that industrial activities since the late 1880s in the region have resulted in saturation of Cu and Ni in local watersheds. Although emission abatement programs have been operating for more than 30 years, previously deposited Cu and Ni can serve as a source of contamination for many years to come. Lake sediments are the ultimate receptors of Cu and Ni inputs, which is reflected in our data. Chemical and physical processes at the sediment–water interface can potentially recycle Cu and Ni back into the water column, thereby serving as a source of metal contamination in contaminated lakes (Belzile and Morris, 1995). The high positive partial correlation of Cu and Ni between water and sediment compartments observed in this study (Fig. 9) supports this argument. The multivariate analysis of water metal contamination could not account for the significant variability associated with fish diversity among the 12 study lakes. However, a combination of four sediment metals (As, Co, Cu, and Mn) could account for 92% of this variability. Regression estimates for Co, Cu, and Mn had a positive sign, whereas As yielded a negative sign in the relationship with diversity. Both Co and Cu can be removed from the water column through the formation

of Mn-oxyhydroxide precipitates (Belzile and Morris, 1995). Therefore, removal of Co and Cu from the water column through the formation of Mn-oxyhydroxide precipitates, coupled with a concomitant decrease in available As, may provide conditions suitable for the reestablishment of fish communities in recovering lakes. The fact that the water metal characterization could not account for fish diversity in these lakes, but sediment metal concentrations could, suggests that long-term processes are more important for the reestablishment of fish species to metal-contaminated lakes relative to short-term processes. Although this might seem intuitive, this conclusion requires caution because our analysis did not account for the myriad of biotic (e.g., predator–prey relationships) or abiotic (temperature, dissolved oxygen, etc.) processes typically associated with the establishment and maintenance of stable fish communities (Jackson et al., 2001). Yellow perch was the only species present in all 12 study lakes (Table 8). Industrial activities in the Sudbury area resulted in severe acidification of local lakes, resulting in widespread loss of aquatic species from contaminated systems (Keller et al., 1992). Fish community structure in many acidified systems suffered the loss of many acid-sensitive species, which allowed acidtolerant species, such as yellow perch (Eaton et al., 1992), to establish and dominate ecological niches left behind by extirpated species. Consequently, the presence of yellow perch in all 12 study lakes not only reflects its relative ubiquity in northern Ontario lakes (Scott and Crossman, 1973), but also reflects the effects of historical acidification of local watersheds. Liver Cd, Cu, Ni, and Zn were highest in yellow perch from Group 3 lakes relative to those from any other lake group (Fig. 4). Group 3 lakes were characterized as having intermediate water hardness relative to the other lake groups. Hardness cations, such as Ca2+ and Mg2+, are well known to decrease aqueous metal uptake by out competing metal cations at uptake sites (Pagenkopf, 1983; Taylor et al., 2000; Pyle et al., 2002). However, water pH can also influence metal bioavailability and subsequent uptake by fish. At low pH, free ionic metal species tend to dominate total metal concentration, and at high pH, metals tend to form inorganic complexes with hydroxides and carbonates (Stumm and Morgan, 1981). Free ionic metal ions that dominate under low pH conditions are more bioavailable, and thus more toxic, than inorganic metal complexes (Kushner, 1993). The high pH associated with Group 3 lakes would imply that waterborne metals should have been less bioavailable than if pH were lower. However, our data suggest that the intermediate water hardness probably had a more important role than water pH in terms of mediating liver metal uptake in Group 3 lakes. The role of water hardness was particularly evident for Cd accumulation in livers of yellow perch inhabiting


contaminated lakes (Fig. 5). The contribution of aqueous Cd concentration or speciation to liver Cd concentration could not be estimated in our study, even though aqueous Cd was not detected in most lake waters in this study and was therefore not reported, its concentration increased in lakes near contamination point sources in other studies of the same area (P. Couture and G. Pyle, unpublished results). Therefore, aqueous Cd likely also influenced liver Cd concentration. Nevertheless, the relationship reported here (Fig. 5) does indicate that hardness is an important influence on liver Cd concentration in wild yellow perch. Similarly, Cd concentrations in Sudbury lake sediments have been reported to be elevated above background concentrations (Nriagu et al., 1998). Yellow perch probably take up Cd through a dietary route by consuming Cd-contaminated benthic macroinvertebrates that inhabit Cd-contaminated sediments. Audet and Couture (2003) demonstrated that yellow perch liver Cd concentrations were related to diet in fish inhabiting Whitson Lake, which is a Group 3 lake in this study. Moreover, elevated liver Cd concentrations have been associated with physiological dysfunction and impaired swimming performance in wild yellow perch from Sudbury-area lakes (Rajotte and Couture, 2002; Couture and Kumar, 2003; Audet and Couture, 2003). Zohouri et al. (2001) and Baldisserotto et al. (2004) demonstrated that rainbow trout fed a Ca-supplemented diet took up less waterborne Cd (to gills, liver, and kidney) than fish fed a normal diet. They reasoned that dietary Ca may be transported to the gills where it blocks waterborne Cd uptake. We speculate that the influence of water hardness on liver Cd accumulation in yellow perch from contaminated lakes in this study may be due to an effect related to but opposite that reported by the two recent studies (Zohouri et al., 2001; Baldisserotto et al., 2004). Because waterborne Cd concentrations were so low in this study, it is not likely that they contributed significantly to the elevated liver Cd concentrations observed in fish from contaminated lakes; dietary Cd is a more likely source. This was corroborated by the high intestinal Cd concentration observed in fish from metal-contaminated lakes (Groups 2 and 3, Fig. 8) and a high partial correlation coefficient between intestinal and liver Cd, suggesting that liver Cd likely originated from the diet (Fig. 9). It may be that much more waterborne Ca is taken up through the gills in fish inhabiting hard-water lakes, thus interfering with intestinal Cd uptake processes, analogous to the dietary Ca interference of branchial Cd uptake reported by Zohouri et al. (2001) and Baldisserotto et al. (2004). In soft-water lakes, less Ca is available to accommodate this putative mechanism, leading to higher Cd uptake and subsequent accumulation in liver. Controlled laboratory studies are required to verify this speculation.

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Couture and Rajotte (2003) argued that Cu metabolism in fish is under homeostatic regulatory control because Cu is an essential element. In metal-contaminated lakes, liver Cu concentrations are usually regulated below 50 mg/g dry wt. However, once this threshold is exceeded, Cu homeostatic control mechanisms become overloaded and liver Cu concentrations can increase. Data reported here support this idea. The only cases of yellow perch liver Cu concentrations exceeding the 50 mg/g threshold occurred in Group 3 lakes (i.e., Hannah and McCharles lakes; Table 10). Liver Cu concentrations in fish from Hannah Lake exceeded liver Cu concentrations in fish from every other lake by at least 42%, demonstrating the loss of regulatory control of liver Cu in these fish. Moreover, the weak partial correlations between environmental compartments and fish tissues, and between liver and muscle Cu (Fig. 9), in addition to the lack of significant Pearson correlations between water or sediment Cu concentrations and concentrations measured in tissues (Table 13), also suggest that internalized Cu is tightly regulated. Hepatic Se concentrations were highest in yellow perch from Group 2 lakes, which included Kelly, Mud, and Simon lakes. Selenium enrichment has been identified in Kelly Lake, among other Sudbury-area lakes, in previous studies (Nriagu and Wong, 1983; Chen et al., 2001). Sediment Se concentrations did not vary among the three lake groups; however, waterborne Se concentrations exceeded 4 mg/L in Group 2 lakes, and were the highest compared with other lake groups. Probably several different factors contributed to the high liver Se concentrations in yellow perch from Group 2 lakes. Kelly Lake has received raw sewage in the past, and currently receives treated sewage effluents (Gunn and Keller, 1995). Microbiological processes in lake sediments can convert Se into organic forms, such as methylated selenides and selenoamino acids, for example, selenocystein and selenomethionein (Chau et al., 1976; Maier and Knight, 1994). Although organic Se compounds are generally volatile, they are readily absorbed by primary producers and accumulate in primary consumers (Besser et al., 1993). Fish feeding on primary consumers can efficiently accumulate organic Se (Bertram and Brooks, 1986), which could contribute to the high hepatic Se concentrations observed in this study. Inorganic Se, primarily in the form of selenate and selenite, can be taken up by fish directly from the water. However, once taken up by fish, this Se is associated with unbound intracellular pools and is rapidly excreted (Bertram and Brooks, 1986). Dietary Se is associated with bound intracellular pools in the fish and is excreted much more slowly, resulting in greater bioaccumulation from dietary sources than from waterborne sources (Hodson and Hilton, 1983; Bertram and Brooks, 1986).



Accumulation of waterborne Se concentrations of 2–5 mg/L in food organisms is sufficient to cause toxicity in some fish (Maier and Knight, 1994). Waterborne Se concentrations in all Group 2 lakes of this study were within this potentially toxic threshold range. The elevated intestinal Se concentrations in fish from Group 2 lakes (Fig. 4), the strong Pearson correlation between fish muscle and intestinal Se concentrations and water Se (Table 13), and the high partial correlation coefficients between water and intestine, between intestine and liver and muscle, and between liver and muscle (Fig. 9) suggest that the diet is an important source of Se in these fish. Moreover, liver Se concentrations above 10 mg/g dry wt can be associated with toxic effects (Maier and Knight, 1994), and a tissue-based Se criterion of 4 mg/g has been proposed to protect aquatic life (Hamilton, 2002). Liver Se concentrations in yellow perch from Group 2 lakes ranged between 19.7 and 90.4 mg/g, and those from Group 3 lakes ranged between 12.5 and 31.9 mg/g, exceeding the 10 mg/g threshold by as much as nine times. Liver Se concentrations in Group 1 lakes were all below the 10 mg/g threshold. This suggests that fish from Group 2 and 3 lakes could demonstrate Se-related toxicity. However, our understanding of Se toxicity to fish remains incomplete, and caution is warranted in interpreting these results (e.g., Kennedy et al., 2000). In general, metal concentrations in muscle tissue were considerably lower than those in liver tissue, which is consistent with other studies (Bradley and Morris, 1986; Rajotte and Couture, 2002). Muscle Cu concentrations were significantly lower in Group 3 fish relative to Group 1 fish (Fig. 6). It is therefore possible that lower muscle Cu may be related to the increased deposition of Cu in the liver of Group 3 fish. That is, increased Cu ligands in liver could result in decreased muscle Cu concentrations. Muscle Hg concentrations were highest in Group 1 lakes and lowest in the metal-contaminated Groups 2 and 3 (Fig. 6). Conversely, muscle Se concentrations were lowest in Group 1 lakes and significantly elevated in Group 2 and 3 lakes (particularly Group 2 lakes). Selenium has a well-known antagonistic influence on Hg accumulation (reviewed in Goyer, 1997). Although a few studies have demonstrated the effectiveness of decreasing Hg uptake into fish by raising waterborne Se concentrations experimentally (Turner and Swick, 1983; Paulsson and Lundbergh, 1991), few studies have demonstrated that this effect occurs under natural conditions. Results from the current study demonstrate muscle Se concentration is negatively correlated, and muscle Hg concentration is positively correlated, with increasing distance from smelting operations (Fig. 7). This result demonstrates the antagonistic influence of Se on Hg accumulation, and corroborates another recent study demonstrating the same effect in wild yellow perch

and walleye populations from Sudbury-area lakes (Chen et al., 2001). In contrast to muscle Cu concentrations, muscle Zn concentrations were highest in Group 3 lakes (Fig. 6)— the same lakes demonstrating the highest liver Zn concentrations. Neither water nor sediment Zn concentrations showed appreciable variation among the three lake groups (Tables 5 and 6). However, Group 3 lakes demonstrated intermediate hardness relative to other lake groups, which may have contributed to the pattern of muscle Zn concentrations observed. Zinc uptake is dependent on waterborne Ca2+ concentrations (Spry and Wood, 1989). Once taken up, Zn typically accumulates in gill and muscle tissues (McGeer et al., 2000). Zinc is an essential micronutrient, like Cu, and is under tight homeostatic control (Watanabe et al., 1997). Although Zn can be taken up by fish through both waterborne and dietary routes, branchial uptake is probably more important (Spry et al., 1988). Homeostatic control of Zn uptake is regulated primarily through excretory mechanisms and by controlling gastrointestinal uptake (Handy, 1996). This is reflected in the strong negative correlations between muscle and intestinal Zn concentrations and industrial metal concentrations (including Zn) in sediments, and the strong negative partial correlation between sediment and intestinal Zn (Table 13), suggesting that fish in contaminated lakes decrease dietary Zn uptake (Fig. 9). Although dietary Zn uptake is reduced in fish from contaminated lakes, it is not eliminated, as demonstrated by the elevated intestinal Zn concentrations in Group 3 fish (Fig. 8). Zinc that is absorbed via the intestine is strongly related to liver and muscle Zn (Fig. 9). Similarly, Zn that accumulates in the liver is strongly related to muscle Zn, suggesting a possible transport pathway between liver and muscle tissue (Fig. 9). The weak partial correlation coefficients between aqueous Zn and intestinal, muscle, and liver Zn are possibly a reflection of the homeostatic regulation of internalized Zn. However, the low water hardness observed in Group 3 lakes may have facilitated Zn uptake from the water and subsequent accumulation in muscle (and liver) without competitive interference from hardness cations at gill uptake sites. Both sediment Cu and Ni concentrations were elevated in Group 2 and 3 lakes (Table 6). However, intestinal Ni concentrations did not vary in yellow perch among lake groups, and only Group 2 fish had significantly higher intestinal Cu relative to Group 1 fish (Fig. 8). The elevated intestinal Cu in Group 2 fish probably reflects elevated Cu concentrations in benthic food items in Group 2 lakes. That both Ni and Cu are elevated in sediments of metal-contaminated Sudbury-area lakes and Ni is not elevated in yellow perch intestines in Group 2 and 3


lakes suggest that dietary Ni uptake and distribution mechanisms may be distinct from those controlling dietary Cu uptake. In a laboratory study, lake whitefish fed Ni-supplemented diets demonstrated an initial increase in intestinal Ni concentration during the first 10 days of exposure, followed by a significant decrease in intestinal Ni that persisted for the remainder of the 104-day study (Ptashynski and Klaverkamp, 2002). This suggests that physiological protective mechanisms in the intestine may act to regulate dietary Ni uptake. Partial correlation analysis of our data demonstrated that Ni accumulation in muscle was positively related to water Ni and negatively related to sediment Ni. However, Ni accumulation in liver tissue was negatively related to water Ni and positively related to sediment Ni (Fig. 9). At present, the physiological mechanisms that drive Ni uptake, accumulation, and metabolism are unknown. Our data, however, suggest that there may be two separate mechanisms driving Ni accumulation in muscle and liver tissues; that, is, sediment (presumably dietary) Ni is preferentially transported (by an unknown pathway) to liver, whereas waterborne Ni is preferentially transported to muscle (by another unknown pathway). Fish condition was assessed using three different metrics: FCF, HSI, and GSI for mature male and female fish, respectively. FCF is a measure of somatic energy reserves associated with recent feeding activity, HSI is associated with liver energetic reserves and metabolic activity, and GSI is a measure of reproductive potential. Condition factors can be influenced by age, in part because young fish have different feeding rates and metabolic activity associated with rapid growth relative to older fish (Farkas et al., 2003). In addition, because the condition factor calculated in this study used an exponent of 3.0 (see Materials and Methods), and the scaling coefficient of yellow perch is naturally above 3, smaller fish will normally have lower condition factors compared with larger fish of the same populations (see Eastwood and Couture (2002), and Couture and Rajotte (2003), for a discussion of the scaling coefficient and of the condition factor). Although we attempted to sample fish of the same size in any given lake (i.e., similar age) during this study, there was some size and age variability in our samples, which prevented the detection of significant differences in yellow perch FCF or HSI among the three lake groups. However, by correcting both FCF and HSI for age, significant effects were evident. Neither male nor female GSI was corrected for age because only mature, breeding fish were included in the analysis. Fish from Group 2 lakes had significantly higher agecorrected FCF and HSI values than fish from the other two lake groups. Other studies report lower FCF and other metrics of morphometric condition in yellow perch from metal-contaminated lakes relative to fish from reference lakes (Brodeur et al., 1997; Laflamme

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et al., 2000; Rajotte and Couture, 2002; Eastwood and Couture, 2002). However, these studies examined fish of a narrower and more consistent size range than those reported here, and condition calculations were not agecorrected. Moreover, the lakes included in these studies had a much narrower range of limnological productivity than the range of lakes considered here. There are probably a couple of factors contributing to the elevated FCF in fish from Group 2 lakes. For example, Kelly, Mud, and Simon lakes, which constitute Group 2, are very productive lakes owing to the domestic wastewater discharge into Kelly Lake. The elevated FCF probably reflects greater feeding activity in fish from Group 2 lakes relative to feeding activity in fish from other lakes. This is further supported by elevated HSI (Fig. 10) in the same fish, reflecting storage of energetic reserves, presumably because of the increased feeding rates. Fish can compensate for metal-induced ionoregulatory dysfunction or increased energetic costs of detoxification if enough food is available (D’Cruz et al., 1998). Nonetheless, liver Cd concentrations and muscle and intestinal Hg concentrations were inversely related to age-corrected FCF (Table 14), suggesting that these metals may still lead to a decrease in growth-related condition in wild yellow perch. The positive relationship between tissue Se concentrations and FCF (Table 14) is probably a reflection of the elevated feeding status of fish from Group 2 lakes relative to other lakes, leading to the high tissue Se concentrations observed. Fish from Group 3 lakes had significantly higher FCFs relative to fish from Group 1 lakes. This difference probably reflects seasonal effects on fish condition. Audet and Couture (2003) demonstrated that yellow perch living in a metal contaminated lake during the summer had increased condition relative to yellow perch inhabiting a reference lake. Fish from Group 3 lakes were collected somewhat later in the season than Group 1 fish (Table 1). The effect of metal contamination is best reflected in yellow perch GSI. Males from Group 2 lakes demonstrated higher GSIs than males from any of the other two lake groups. This elevated GSI may be a reflection of feeding status (as discussed above) resulting in more energetic resources being diverted to gonad development in postspawn animals. As with the positive correlations between FCF and tissue Se, positive correlations between male GSI and liver and muscle Se concentrations probably reflect a higher dietary Se uptake in Group 2 fish associated with greater feeding activity. However, males from Group 3 lakes showed significantly decreased GSIs relative to males from the other two lake groups. This may be a reflection of metalinduced impairment of reproductive potential in these animals. Liver and intestinal Cd concentrations were negatively related to male GSI, suggesting that dietary



Cd may contribute to decreased reproductive potential in male yellow perch. Female GSI was significantly lower in fish from metal-contaminated lakes (Groups 2 and 3) than in fish from reference lakes (Group 1) (Fig. 10). Female GSI was negatively associated with liver and intestinal Se and intestinal Hg (Table 14). Also, female GSI displayed a positive relationship with intestinal Cu (Table 14). Interestingly, intestinal Se concentrations were positively correlated with intestinal Cu in males, but negatively correlated in females. This suggests that dietary Cu and Se are antagonistic to intestinal uptake in females but not in males. This implies that the interaction between Cu and Se in intestinal uptake pathways varies between males and females. Although Se is known to be antagonistic to Cu toxicity in mammals (reviewed in Gaetke and Chow, 2003), less is known about Cu–Se interactions in fish. Furthermore, there is no evidence suggesting that the mechanism varies between males and females, as suggested by our data. Further study into the interaction between dietary Se and Cu and its effects on yellow perch reproduction is warranted. Liver Se concentrations above 10 mg/g have been linked to toxic effects in fish (Maier and Knight, 1994), and waterborne Se concentrations above 10 mg/L have been associated with reproductive impairment in fish (Hermanutz et al., 1992). Waterborne Se concentrations never exceeded 10 mg/L in this study; however, fish from every metal-contaminated lake had liver Se concentrations above 10 mg/g, suggesting that hepatic Se accumulation may be linked to potential reproductive impairment in fish from contaminated lakes. Despite the antagonistic effect of Se on Hg bioaccumulation in muscle tissue (Fig. 7), dietary Hg was associated with decreased reproductive potential in female yellow perch. Consequently, environmental contamination, particularly by Se, may result in decreased reproductive potential in female yellow perch in contaminated lakes relative to reference lakes.

contamination gradient is probably from branchial and dietary sources, depending on the specific metal, its availability, and the presence of competing factors such as water hardness. Essential metals, such as Cu and Zn, are tightly regulated in wild yellow perch, and therefore, tissue Cu or Zn accumulation is not particularly useful for predicting fish condition in metalcontaminated lakes. Selenium, on the other hand, which is also an essential micronutrient, was strongly associated with fish condition. However, caution must be used in interpreting the relationship between tissue Se concentration and the condition metric of interest. Because Se is primarily a dietary contaminant, lake trophic status probably interacts with Se accumulation such that fish having high tissue Se concentrations also have high growth condition factors (FCF and HSI). However, tissue Se was negatively associated with female GSI, suggesting that reproductive impairment may be experienced by fish that have accumulated high Se concentrations. The relationship among intestinal Se and Cu concentrations and male and female GSI suggests that Cu and Se uptake dynamics may be different in male and female fish, thereby affecting reproductive condition differently. Furthermore, muscle Se has an antagonistic relationship with muscle Hg as a function of distance from industrial activities. Selenium has received relatively little research attention in aquatic ecosystems in the Sudbury area, and more research is required. Liver Cd was negatively associated with growth condition (FCF) and reproductive condition in males (GSI). Our data demonstrated that water hardness was an important factor mediating hepatic Cd accumulation, and that liver Cd accumulation is dominated by dietary sources. Consequently, we speculate that branchial Ca2+ uptake in hard water lakes probably contributes to a decrease in dietary Cd uptake in wild yellow perch. Again, controlled laboratory experiments are warranted to verify this hypothesis.

5. Conclusions Acknowledgments By exploiting a wide metal contamination gradient, our data provide insights into some of the complex relationships among metals and other environmental variables that mediate tissue metal accumulation and condition in wild yellow perch. Fish diversity along the contamination gradient was associated with sediment metals only, suggesting that long-term processes are involved in reestablishing fish communities. However, our analysis did not examine other controlling factors, such as predator–prey relationships and habitat quality, which surely contribute to community structure and stability. Metal accumulation in yellow perch along the

The authors acknowledge G. Watson and K. Hunt of INCO, Ltd., and M. Charbonneau and staff at Testmark Laboratories, Ltd., for their support and financial assistance. The authors also acknowledge the following for their assistance: J. Coulas, C. Vernescu, K. Farquar, T. Champagne-Rajotte, D. Champagne, and R. Champagne. P.C. was supported by an NSERC research grant, P.C. and G.P. by funding from INCO, Ltd., and J.R. by an Industrial NSERC scholarship with INCO, Ltd., as a supporting partner.


References Audet, D., Couture, P., 2003. Seasonal variations in tissue metabolic capacities of yellow perch (Perca flavescens) from clean and metalcontaminated environments. Can. J. Fish. Aquat. Sci. 60, 269–278. Baldisserotto, B., Kamunde, C., Matsuo, A., Wood, C.M., 2004. A protective effect of dietary calcium against acute waterborne cadmium uptake in rainbow trout. Aquat. Toxicol. 67, 57–73. Beamish, R.J., Harvey, H.H., 1972. Acidification of the La Cloche Mountain Lakes, Ontario, and resulting fish mortalities. J. Fish. Res. Bd. Can. 29, 1131–1143. Belzile, N., Morris, J.R., 1995. Lake sediments: sources or sinks of industrially mobilized elements? In: Gunn, J.M. (Ed.), Restoration and Recovery of an Industrial Region. Springer, New York, pp. 183–193. Bertram, P.E., Brooks, A.S., 1986. Kinetics of accumulation of selenium from food and water by fathead minnow. Water Res. 20, 877–884. Besser, J.M., Canfield, T.J., Lapoint, T.W., 1993. Bioaccumulation of organic and inorganic selenium in a laboratory food chain. Environ. Toxicol. Chem. 12, 57–72. Bradley, R.W., Morris, J.R., 1986. Heavy metals in fish from a series of metal contaminated lakes near Sudbury, Ontario. Water Air Soil Pollut. 27, 341–354. Brodeur, J.C., Sherwood, G., Rasmussen, J.B., Hontela, A., 1997. Impaired cortisol secretion in yellow perch (Perca flavescens) from lakes contaminated by heavy metals: in vivo and in vitro assessment. Can. J. Fish. Aquat. Sci. 54, 2752–2758. Carignan, R., Nriagu, J.O., 1985. Trace metal deposition and mobility in the sediments of two lakes near Sudbury, ON. Geochim. Cosmochim. Acta 49, 1753–1764. Chau, Y.K., Wong, P.T.S., Silverberg, B.A., Luxon, P.L., Bengert, G.A., 1976. Methylation of selenium in the aquatic environment. Science 192, 1130–1131. Chen, Y.W., Belzile, N., Gunn, J.M., 2001. Antagonistic effect of selenium on mercury assimilation by fish populations near Sudbury metal smelters? Limnol. Oceanogr. 46, 1814–1818. Clesceri, L.S., Greenberg, A.E., Eaton, A.D. (Eds.), 1998, Standard Methods for the Examination of Water and Wastewater, 20th ed. Am. Public Health Assoc., Washington, DC. Couture, P., Kumar, P.R., 2003. Impairment of metabolic capacities in copper and cadmium contaminated wild yellow perch (Perca flavescens). Aquat. Toxicol. 64, 107–120. Couture, P., Rajotte, J.W., 2003. Morphometric and metabolic indicators of metal stress in wild yellow perch (Perca flavescens) from Sudbury, Ontario: a review. J. Environ. Monit. 5, 216–221. D’Cruz, L.M., Dockray, J.J., Morgan, I.J., Wood, C.M., 1998. Physiological effects of sublethal acid exposure in juvenile rainbow trout on a limited or unlimited ration during a simulated global warming scenario. Physiol. Zool. 71, 359–376. Eastwood, S., Couture, P., 2002. Seasonal variations in condition and liver metal concentrations of yellow perch (Perca flavescens) from a metal-contaminated environment. Aquat. Toxicol. 58, 43–56. Eaton, J.G., Swenson, W.A., McCormick, J.H., Simonson, T.D., Jensen, K.M., 1992. A field and laboratory investigation of acid effects on largemouth bass, rock bass, black crappie and yellow perch. Trans. Am. Fish. Soc. 121, 644–658. Farkas, A., Salanki, J., Specziar, A., 2003. Age- and size-specific patterns of heavy metals in the organs of freshwater fish Abramis brama L. populating a low-contaminated site. Water Res. 37, 959–964. Gaetke, L.M., Chow, C.K., 2003. Copper toxicity, oxidative stress, and antioxidant nutrients. Toxicology 189, 147–163. Goyer, R.A., 1997. Toxic and essential metal interactions. Annu. Rev. Nutr. 17, 37–50.

311

Gunn, J.M., Keller, W., 1995. Urban lakes: integrators of environmental damage and recovery. In: Gunn, J.M. (Ed.), Restoration and Recovery of an Industrial Region. Springer, New York, pp. 257–269. Hamilton, S.J., 2002. Rationale for a tissue-based selenium criterion for aquatic life. Aquat. Toxicol. 57, 85–100. Handy, R.D., 1996. Dietary exposure to toxic metals in fish. In: Taylor, E.W. (Ed.), Toxicology of Aquatic Pollution: Physiological, Cellular, and Molecular Approaches. Society of Experimental Biology Seminar Series. Cambridge University Press, Cambridge, UK, pp. 29–60. Hermanutz, R.O., Allen, K.N., Roush, T.H., Hedtke, S.F., 1992. Effects of elevated selenium concentrations on bluegills (Lepomis macrochirus) in outdoor experimental streams. Environ. Toxicol. Chem. 11, 217–224. Hodson, P.V., Hilton, J.W., 1983. The nutritional requirements and toxicity to fish of dietary and waterborne selenium. Environ. Biogeochem. 35, 335–340. Jackson, D.A., Peres-Neto, P.R., Olden, J.D., 2001. What controls who is where in freshwater fish communities—the roles of biotic, abiotic, and spatial factors. Can. J. Fish. Aquat. Sci. 58, 157–170. Keller, W., Gunn, J.M., 1995. Lake water quality improvements and recovering aquatic communities. In: Gunn, J.M. (Ed.), Restoration and Recovery of an Industrial Region. Springer, New York, pp. 67–80. Keller, W., Gunn, J.M., Yan, N.D., 1992. Evidence of biological recovery in acid-stressed lakes near Sudbury, Canada. Environ. Pollut. 78, 79–85. Keller, W., Heneberry, J.H., Gunn, J., 1999. Effects of emission reductions from the Sudbury smelters on the recovery of acid-and metal-damaged lakes. J. Aquat. Ecosyst. Stress Rec. 6, 189–198. Kennedy, C.J., McDonald, L.E., Loveridge, R., Strosher, M.M., 2000. The effect of bioaccumulated selenium on mortalities and deformities in the eggs, larvae, and fry of a wild population of cutthroat trout (Oncorhynchus clarki lewisi). Arch. Environ. Contam. Toxicol. 39, 46–52. Kushner, D.J., 1993. Effects of speciation of toxic metals on their biological activity. Water Pollut. Res. J. Can. 28, 111–128. Laflamme, J.-S., Couillard, Y., Campbell, P.G.C., Hontela, A., 2000. Interrenal metallothionein and cortisol secretion in relation to Cd, Cu, and Zn exposure in yellow perch, Perca flavescens, from Abitibi lakes. Can. J. Fish. Aquat. Sci. 57, 1692–1700. Maier, K.J., Knight, A.W., 1994. Ecotoxicology of selenium in freshwater systems. Rev. Environ. Contam. Toxicol. 134, 31–48. McGeer, J.C., Szebedinszky, C., McDonald, D.G., Wood, C.M., 2000. Effects of chronic sublethal exposure to waterborne Cu, Cd or Zn in rainbow trout: 2: tissue specific metal accumulation. Aquat. Toxicol. 50, 245–256. Nriagu, J.O., 1983. Arsenic enrichment in lakes near the smelters at Sudbury, Ontario. Geochim. Cosmochim. Acta 47, 1523–1526. Nriagu, J.O., Wong, H.K., 1983. Selenium pollution of lakes near the smelters at Sudbury, Ontario. Nature 301, 55–57. Nriagu, J.O., Wong, H.K.T., Coker, R.D., 1982. Deposition and chemistry of pollutant metals in lakes around the smelters at Sudbury, Ontario. Environ. Sci. Technol. 16, 551–560. Nriagu, J.O., Wong, H.K., Lawson, G., Daniel, P., 1998. Saturation of ecosystems with toxic metals in Sudbury basin, Ontario, Canada. Sci. Tot. Environ. 223, 99–117. Ontario Ministry of Environment and Energy (OMEE), 1994. Provincial Water Quality Objectives. Queens Printer of Ontario, Toronto. Pagenkopf, G.K., 1983. Gill surface interaction model for trace-metal toxicity to fishes: role of complexation, pH, and water hardness. Environ. Sci. Technol. 17, 342–347. Paulsson, K., Lundbergh, K., 1991. Treatment of mercury contaminated fish by selenium addition. Water Air Soil Pollut. 56, 833–841.



Ptashynski, M.D., Klaverkamp, J.F., 2002. Accumulation and distribution of dietary nickel in lake whitefish (Coregonus clupeaformis). Aquat. Toxicol. 58, 249–264. Pyle, G.G., Swanson, S.M., Lehmkuhl, D.M., 2001. Toxicity of uranium mine-receiving waters to caged fathead minnows, Pimephales promelas. Ecotoxicol. Environ. Saf. 48, 202–214. Pyle, G.G., Swanson, S.M., Lehmkuhl, D.M., 2002. The influence of water hardness, pH, and suspended solids on nickel toxicity to larval fathead minnows (Pimephales promelas). Water Air Soil Pollut. 133, 215–226. Rajotte, J.W., Couture, P., 2002. Effects of environmental metal contamination on the condition, swimming performance, and tissue metabolic capacities of wild yellow perch (Perca flavescens). Can. J. Fish. Aquat. Sci. 59, 1296–1304. Scott, W.B., Crossman, E.J., 1973. Freshwater Fishes of Canada. Fisheries Research Board of Canada, Ottawa. Sherwood, G.D., Rasmussen, D.J., Rowan, D.J., Brodeur, J., Hontela, A., 2000. Bioenergetic costs of heavy metal exposure in yellow perch (Perca flavescens): in situ estimates with a radiotracer (137Cs) technique. Can. J. Fish. Aquat. Sci. 57, 441–450. Sherwood, G.D., Kovecses, J., Hontela, A., Rasmussen, J.B., 2002a. Simplified food webs lead to energetic bottlenecks in polluted lakes. Can. J. Fish. Aquat. Sci. 59, 1–5. Sherwood, G.D., Pazzia, I., Moeser, A., Hontela, A., Rasmussen, J.B., 2002b. Shifting gears: enzymatic evidence for the energetic advantage of switching diet in wild-living fish. Can. J. Fish. Aquat. Sci. 59, 229–241.

Spry, D.J., Wood, C.M., 1989. A kinetic method for the measurement of zinc influx in vivo in the rainbow trout, and the effects of waterborne calcium on flux rates. J. Exp. Biol. 142, 425–446. Spry, D.J., Hodson, P.V., Wood, C.M., 1988. Relative contributions of dietary and waterborne zinc in the rainbow trout, Salmo gairdneri. Can. J. Fish. Aquat. Sci. 45, 31–41. Stumm, W., Morgan, J.J., 1981. Aquatic Chemistry. Wiley, New York. Taylor, L.N., McGeer, J.C., Wood, C.M., McDonald, D.G., 2000. Physiological effects of chronic copper exposure to rainbow trout (Oncorhynchus mykiss) in hard and soft water: evaluation of chronic indicators. Environ. Toxicol. Chem. 19, 2298–2308. Taylor, L.M., Wood, C.M., McDonald, D.G., 2003. An evaluation of sodium loss and gill metal binding properties in rainbow trout and yellow perch to explain species differences in copper tolerance. Environ. Toxicol. Chem. 22, 2159–2166. Turner, M.A., Swick, A.L., 1983. The English–Wabigoon river system: IV. Interaction between mercury and selenium accumulated from waterborne and dietary sources by northern pike (Esox lucius). Can. J. Fish. Aquat. Sci. 40, 2241–2250. Watanabe, T., Kiron, V., Satoh, S., 1997. Trace minerals in fish nutrition. Aquaculture 151, 185–207. Yan, N.D., Welbourn, P.M., 1990. The impoverishment of aquatic communities by smelter activities near Sudbury, Canada. In: Woodwell, G.M. (Ed.), Patterns and Processes of Biotic Impoverishment. Cambridge University Press, New York, pp. 477–494. Zohouri, M.A., Pyle, G.G., Wood, C.M., 2001. Dietary Ca inhibits waterborne Cd uptake in Cd-exposed rainbow trout, Oncorhynchus mykiss. Comp. Biochem. Physiol. C 130, 347–356.