Next Article in Journal
Sensitivity Analysis of a Riparian Vegetation Growth Model
Previous Article in Journal
Assessment of Fecal Contamination in Oklahoma Water Systems through the Use of Sterol Fingerprints
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Environments
Volume 3
Issue 4
10.3390/environments3040029
environments-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Seasonal Variations in the Use of Profundal Habitat among Freshwater Fishes in Lake Norsjø, Southern Norway, and Subsequent Effects on Fish Mercury Concentrations

by
Tom Robin Olk
*,
Tobias Karlsson
,
Espen Lydersen
and
Asle Økelsrud
Institute for Nature, Health and Environmental Sciences, University College of Southeast Norway, Telemark 3800 Bø i, Norway
*
Author to whom correspondence should be addressed.
Environments 2016, 3(4), 29; https://doi.org/10.3390/environments3040029
Submission received: 9 September 2016 / Revised: 3 November 2016 / Accepted: 4 November 2016 / Published: 11 November 2016
Browse Figures
Versions Notes

Abstract

:
This study is based on monthly sampling of fish from grates mounted at an industrial water intake, located at a depth of 50 m in Lake Norsjø (Southern Norway) during the year 2014, to investigate seasonal variations in the use of the profundal habitat and subsequent variations in total Hg-concentrations in profundal fish. Data on various fish present in a cold and dark hypolimnion of a large, deep, dimictic lake within the upper temperate zone of the Northern Hemisphere are rare. While predominant species such as A. charr (Salvelinus alpinus) and E. smelt (Osmerus eperlanus) were continuously present in this habitat, whitefish (Coregonus lavaretus) occupied this habitat primarily during wintertime, while other common species like brown trout (Salmo trutta), perch (Perca fluviatilis) and northern pike (Esox lucius) were almost absent. Besides stomach analyses (diet) and biometry, stable isotope analyses (δ15N and δ13C) and total mercury (Tot-Hg) analyses were carried out on the caught fish. The δ13C signature and stomach analyses revealed a combined profundal-pelagic diet for all three species, A. charr with the most profundal-based diet. Length was the strongest predictor for Hg in whitefish and A. charr, while age was the strongest explanatory variable for Hg in E. smelt. A. charr was the only species exhibiting seasonal variation in Hg, highest during winter and spring.

1. Introduction

Methylated Hg is an environmental pollutant of concern in aquatic environments [1,2,3,4], as it is accumulated in biota, and concentrations rise in accordance with trophic position [5,6,7,8,9,10]. Fish and fish-eating wildlife often have toxic concentrations of total Hg (Tot-Hg) as a result [7]. In addition to trophic position, Hg-concentrations in fish are well documented to increase with increasing age [8,11] and length [8,11,12,13,14]. Contrarily, increasing weight at the same length or age results in lower Hg-concentrations, either by somatic growth dilution (SGD) [11,15,16,17,18,19,20,21], or by further concentrating Hg during starvation [22]. The combination of these two effects results in seasonal variations in Hg-concentrations in fish [23,24,25,26,27,28,29,30], however, some studies suggest that this is not the case in all populations [31,32,33].
Stable isotope ratio analyses of carbon (δ13C = C13/C12) and nitrogen (δ15N = N15/N14) are a highly valuable tool to trace the energy flow (δ13C) and trophic position (δ15N) in food webs [34,35,36], as the different isotopes have different abilities to form chemical bonds [37]. This means that molecules containing the heavier isotope are more stable, while molecules containing the lighter isotope are more readily metabolized. Therefore, δ15N increases at an average of 3.4‰ per trophic level [36,38], and δ13C can be used to trace dietary carbon sources [36], as this ratio averagely varies with habitat [39]. Habitat and depth also influence Hg, usually meaning an increase of Hg-concentrations in biota with depth [8,11,40]. Comparisons between littoral and pelagic fish at similar trophic position indicate that pelagic fish exhibit higher Hg-concentrations [41,42,43], while Chumchal and Hambright [44] document no detectable difference.
There is extensive research available on Hg-concentrations and different explanatory variables, as well as seasonal variations, however, most of the literature is limited to the littoral and pelagic zone in lakes, as seasonal data is hardly accessible in the profundal zone. This study is based on fish sampled from an industrial water intake at Fjærekilen in Lake Norsjø (Southern Norway), which provides the unique opportunity to readily sample profundal fish throughout the year. Seasonal patterns in the use of the profundal habitat, as well as seasonal variations in Hg-concentrations in fish, were identified, and the main predictors of Hg-concentrations were investigated. For A. charr (Salvelinus alpinus) and whitefish (Coregonus lavaretus), length was found to be the most important predictor of Hg-concentrations, while age was most important for E. smelt (Osmerus eperlanus). Age, length, weight and δ13C improved Hg-estimates for some of these three species. Seasonal variations in Hg-concentrations were confirmed for A. charr, with higher Hg-concentrations in spring and winter than in summer and autumn. This is likely to be a consequence of variations in the nutritional status of the fish.

2. Materials and Methods

2.1. Sampled Fish

In total, 471 fish were sampled in the water intake at a depth of ≈ 50 m in Fjærekilen, a bay south in Lake Norsjø. The most abundant species A. charr (n = 191) and E. smelt (n = 158) were present in the catch during all seasons, while whitefish (n = 117) were mainly caught between December and March (Table 1). Perch (Perca fluviatilis) (n = 4) and Northern pike (Esox lucius) (n = 1) were only sporadically present, and accordingly insufficient data was available for further analysis of these two species. Complete datasets were retrieved from 252 fish in total, 77 for A. charr, 99 for E. smelt and 76 for whitefish. These fish were used for Hg-modelling.

2.2. Site Description

Lake Norsjø (59.29′ N, 9.36′ E) is a large (55.24 km2), deep (middle depth = 87 m, maximal depth = 171 m) and oligotrophic lake [45,46] located in Telemark county in southeast Norway. This study has been performed in Fjærekilen, which is a bay at the southern end of Lake Norsjø extending parallel to the discharge (Figure 1). The discharge to Hjellevannet in Skien is located in an adjacent bay to the north of the study site.

2.3. Sampling

The fish used in this study were acquired at an industrial water intake in Fjærekilen, which is located at a depth ≈ 50 m, 60–80 m off the shore, meaning that all fish were sampled at the same location within the profundal habitat. The fish were caught continuously at a grate (mesh size: 10 mm), which is mounted in an artificial pool inside the water intake tunnel, preventing fish being artificially transferred to the brackish fjord Frierfjorden. The grates cover the entire breadth of the water intake tunnel, and collect all fish passing through. Fish was sampled weekly between February 2014 and January 2015. The fish were frozen when collected, and the accumulated catch was stored in plastic bags every week. Additionally, fresh fish were acquired once every month during the sampling period from the grates. All fish were frozen in plastic bags sorted by sampling date and stored in a freezer (≤20 °C) at the University College of Southeast Norway until analysed.

2.4. General Analysis

The collected fish were sorted, and randomly selected subsamples of approximately 20 individuals of each species were analysed each month. Total length of each fish was determined to the closest millimetre in a measuring cone, and weight was determined to the closest gram on a scale. The otoliths were removed, and subsequently burned over a propane torch before being sectioned transversally for later age determination under a stereomicroscope at a magnification of 48 × [47] (p. 80).

2.5. Benthic Invertebrates and Stomach Content Analysis

Benthic invertebrates were caught using two traps consisting of four bundles of hemp rope each, which were placed in the sediment and emptied once a month during the study period [48]. The traps were placed on both sides of the water intake. Additional benthic invertebrates were sampled each month using an Ekman bottom grab at the sites of the traps.
Stomach samples were taken from approximately five fish of each species each month covering the entire length range. However, as a considerable number of stomachs were empty, or diet items were digested beyond recognition, approximately two stomach samples per month could be used for further analysis for each species. The stomachs were preserved in 70% ethanol in glass bottles prior to analysis. Stomach content was identified under a stereomicroscope at a magnification of 48× to the closest taxa using a taxonomic key [49], and each item’s occurrence was estimated visually in volume percent.

2.6. Preparation of Muscle Fillet Samples for SI and Hg Analysis

Approximately 2 g of muscle fillet were removed from the dorsal side of each fish under the dorsal fin. The samples were weighted on a scale at a precision to 0.1 g, before freeze-dried in a Heto Lyolab 3000 freeze-drier (Heto-Holten A/S, Allerød, Danmark) for at least 14 h at a temperature ≤30 °C. The drying process was aided by an infrared lamp. Dried samples were weighted on a scale with a precision to 0.0001 g. The dried samples were ground and homogenised using an agate pestle and a mortar. This procedure was also applied to the benthic animals, which were processed completely. Due to the animals’ low mass, the accumulated catch of each taxonomic group for the respective month was analysed as a pooled sample.

2.7. Stable Isotope Analysis

Up to 15 fish of each species each month were selected for stable isotope analysis, covering the largest possible variety in age, length and weight. In addition, the pooled benthic invertebrate samples were analysed. Between 1.0 and 1.4 mg of the selected, freeze-dried samples were weighted on a scale, and stored in tin capsules of the types Elemental Microanalysis D1006 (6 × 4 mm) and Elemental Microanalysis D1008 (8 × 5 mm). The capsules were sent to the Norwegian Institute for Energy Technology (IFE) for stable isotope analysis. Results were delivered in the delta (δ) notation, which is measured in per mil (‰) deviation from a standard material, and calculated according to the following formula:
δ13C or δ15N = (Rsample/Rstandard − 1) × 1000,
where R represents the ratio of the heavier isotope 13C or 15N to the lighter 12C or 14N [8,40]. As standard material, Pee Dee belemnite limestone was used to calculate δ13C [50], and atmospheric nitrogen for δ15N.

2.8. Hg Analysis

Freeze-dried dorsal muscle fillet samples were also used for determination of Tot-Hg-content in fish. Approximately 20 mg were used for each sample, weighted in on a Sartorius AX124 scale (precision: 0.0001 g). Total Hg was analysed by a Lumex Hg-analyser type Pyro-915 (Lumex Instruments, St. Petersburg, FL, USA) at the University College of Southeast Norway, and two replicates were analysed for each sample. Measurements were repeated if both replicates deviated by more than 10%. The calibration of the equipment was confirmed using a standard sample of tuna (European Reference Material, ERM-CE 464), which was used as control after each 20th fish. Tot-Hg-content was estimated to be the average of the two replicate samples, and concentrations were transformed to resemble wet weight (ww.) using an individual conversion factor based on the weight loss of the fillet sample of each fish. The transformation was applied, because most nations are using wet weight concentrations of Tot-Hg in fish in their monitoring programs and consumption advice guidelines. Due to insufficient mass of the freeze-dried and ground samples, benthic invertebrates could not be analysed for Tot-Hg.

2.9. Data Analysis

Age, length, weight and Tot-Hg-concentrations were logarithmically transformed to match normal distributions using natural logarithms. Descriptive statistics were calculated for age, length, weight, δ13C, δ15N and Tot-Hg-concentrations for each species and for the stable isotope ratios δ13C and δ15N of the pooled benthic invertebrate samples. Prior to model building, the logarithmically transformed age, length and weight and δ13C and δ15N were centered by subtracting the mean from each transformed observation in order to calculate an interpolated intercept, which represents the average specimen, based on a geometric average. In order to compare Tot-Hg-concentrations between species, all models were used to predict Tot-Hg for a set of explanatory variables, which were chosen in accordance with the maximum and minimum values in the dataset to avoid unnecessary extrapolation. The values for the explanatory variables used are 5.5 yr., 121.5 mm, 10.5 g, −29‰ and 10.14‰ for age, length, weight, δ13C and δ15N, respectively. Months were grouped in seasons, classifying January, February and March as winter, April, May and June as spring, July, August and September as summer and October, November and December as autumn. Using the centered and transformed age, length and weight, and the centered stable isotope ratios δ13C and δ15N and the factor season as explanatory variables, and the transformed Tot-Hg-concentrations as response variable, the best fitting explanatory variable was determined by creating linear models for each species and each explanatory variable in R [51]. The models using only one explanatory variable at the time were compared using Akaike information criterion (AIC), where the model with the lowest AIC was chosen for further investigation. Subsequently, multiple linear regression models were created, adding one of the other potential explanatory variables at a time. These models were compared to the original model one by one, using the log likelihood ratio statistic estimated from maximum likelihood (ML) estimates for each model. Additional explanatory variables and two-way interaction terms of two already included variables were added to the model if the more complicated model resulted in a better fit, and the log likelihood ratio statistic was significant to a significance level of α = 0.05. Once all significant explanatory variables and two-way interaction terms were added, the resulting model was refit using generalized least squares without specified variance covariates and restricted maximum likelihood estimation (REML), using the gls function from the nlme-package in R [52]. The standardised residuals of the REML-fit were plotted, and the plots were investigated for divergence from a normal distribution, heterogeneity, heteroscedasticity, and correlation to any of the potential explanatory variables. In case of divergence from the assumptions of multiple linear regressions, variance-covariates and insignificant fixed terms were added to the model according to the protocol described in [53] (pp. 90–92). All partial regressions were visualised as partial regression plots. For A. charr, the model was additionally visualised using the plot3d-function from the rgl-package [54], and the plot was extracted using the rglwidget-package [55]. For model interpretation, a significance level of α = 0.05 was used, and results with a p-value between 0.05 and 0.10 were classified near significant.
The arithmetic mean volume percentage of each diet item was calculated for each population, including all fish with at least one identified stomach content item. A. charr individuals were grouped by total length, above and below 140 mm, as fish was only found in the diet for A. charr ≥140 mm. The average diet overlap was estimated using Schoener’s similarity index [56], calculated by the following formula:
D = 100 − 0.5 Σ(|pi − qi|),
where p is the average volume percentage of one type of prey in the first group of fish, and q is the average volume percentage of the same item in the other group of fish. Diets are considered to overlap significantly if D exceeds 60% [57].

3. Results

3.1. Descriptive Statistics

A. charr (n = 77) varied in age from 3 to 19 years, with an average of 9 ± 4 years (Table 2). The individuals’ lengths varied from 74 to 283 mm, with an average of 145 ± 51 mm, while average weight was 38 ± 45 g ranging from 3 to 178 g. A. charr exhibited average δ13C and δ15N signatures of −29.64‰ ± 1.51‰ and 11.69‰ ± 1.22‰, respectively, with individual variations in δ13C ranging from −34.74‰ to −27.79‰, and from 6.89‰ to 13.51‰ for δ15N. The δ15N range of 6.62‰ indicates an individual variation in trophic position by almost two trophic levels (Λ = 1.95) within the group of A. charr analysed, assuming a δ15N enrichment by 3.4‰ per trophic level (Λ), as estimated by Minagawa and Wada and Post [36,38]. Tot-Hg-concentrations (ww.) varied between 0.07 ppm and 1.13 ppm with an average of 0.24 ± 0.21 ppm.
E. smelt (n = 99) varied in age from 1 to 8 years, while the average age was 2 ± 1 years (Table 1). The length of E. smelt varied from 87 to 113 mm, with an average of 99 ± 6 mm. Average weight was 4 ± 1 g, ranging from 2 to 8 g. The average δ13C and δ15N signatures in E. smelt were −29.14‰ ± 0.55‰ and 10.41‰ ± 0.97‰, respectively, with individual variations in δ13C from −32.38‰ to −27.60‰ and from 7.64‰ to 13.60‰ for δ15N. The range in δ15N by 5.97‰ indicates an individual variation in trophic level (Λ) by 1.76 Λ within the group of E. smelt analysed. Tot-Hg-concentrations (ww.) in E. smelt averaged at 0.22 ± 0.08 ppm, and ranged from 0.09 to 0.54 ppm.
Whitefish (n = 76) varied in age from 1 to 16 years, with an average of 5 ± 3 years (Table 1). Whitefish length varied from 130 to 310 mm, with an average of 247 ± 36 mm. The average weight was 131 ± 49 g, ranging from 13 to 265 g. Whitefish had average δ13C and δ15N signatures of −29.12‰ ± 0.49‰ and 8.60‰ ± 1.25‰, respectively. While individual δ13C signatures ranged from −30.21‰ to −27.61‰, the δ15N signatures varied between 6.39‰ and 12.63‰. The range in δ15N by 6.24‰ indicates an individual variation in trophic level (Λ) by 1.84 Λ for the whitefish analysed. Tot-Hg-concentrations (ww) ranged from 0.05 ppm to 0.49 ppm, and averaged at 0.20 ± 0.09 ppm.
Monthly pooled benthic invertebrate samples were obtained for caddisflies (Trichoptera), Chironomidae and Asellus aquaticus (Table 3). Trichoptera had an average δ13C-signature of −27.98‰ ± 0.43‰ ranging from −28.66‰ to 27.17‰, while their δ15N-signature varied between 3.28‰ and 7.96‰ and averaged at 5.46‰ ± 1.36‰. Chironomidae exhibited δ13C-signatures between −33.61‰ and −26.27‰ with an average of −30.00‰ ± 1.20‰ and δ15N-signatures between 8.21‰ and 10.69‰ with an average of 9.32‰ ± 0.42‰. The δ13C-signatures of Asellus aquaticus varied between −32.21‰ and −25.25‰ with an average of −28.92‰ ± 0.78‰, while their δ15N-signatures averaged at 6.13‰ ± 0.31‰, ranging from 4.37‰ to 7.32‰.

3.2. Use of the Profundal Habitat

A. charr were present in the profundal habitat the whole year, with the highest occurrence in September and December (Table 1). E. smelt was also present all year, except for June, and most were caught in December. Whitefish were primarily caught during winter between December and March.

3.3. Stomach Content and Diet

3.3.1. Benthic Invertebrates

Chironomidae sp. were found in the stomachs of all species, and contributed to the diet with 44, 25 and 26 vol % for A. charr (n = 41), E. smelt (n = 31) and whitefish (n = 22), respectively. In E. smelt, Chironomidae sp. were only found between August and December (Figure 2a). Pisidium sp. were found in A. charr (2 vol %) and whitefish (16 vol %), but not in E. smelt. Ostracods were found in A. charr restricted to the period between August and February (1 vol %) (Figure 2b), and they were continuously present in E. smelt (9 vol %) and whitefish (4 vol %) (Figure 2a,c). Phryganea grandis were only found in A. charr, exclusively from March to June (5 vol %). Caddisflies (Trichoptera) were only found in E. smelt (2 vol %), while Asellus aquaticus was only found in whitefish, contributing to 2 vol %.

3.3.2. Pelagic Invertebrates

Copepods were found in all investigated fish species, and constituted 12, 48 and 8 vol % in A. charr, E. smelt and whitefish, respectively. In A. charr, copepods were a seasonal item, only found from August to February. Cladocerans, i.e., Daphnia sp. were only found in E. smelt (5 vol %).

3.3.3. Fish and Other Items

Fish occurred in the stomach samples of A. charr (20 vol %) and whitefish (21 vol %). Regarding whitefish, fish were only found between January and May. Fish roe were seasonally present in all three fish species, primarily in September and February in A. charr (6 vol %), and in December and January in E. smelt (8 vol %) and whitefish (9 vol %). In whitefish, an ant (Formicidae spp.) was found (2 vol %), while unidentified remains constituted 11, 4 and 13 vol % in A. charr, E. smelt and whitefish, respectively.
The largest A. charr, individuals >140 mm (n = 20), consumed less Chironomidae sp., Pisidium sp. and copepods (34, 0 and 2 vol % compared to 54, 4 and 21 vol %), but more roe (9 vol % compared to 3 vol %) than smaller individuals, <140 mm (n = 21) (Figure 3). Additionally, the largest individuals consumed fish (≈40 vol %). Approximately 10 vol % of the stomach content of both groups remained unidentified. Schoener’s similarity index [57,58], indicated no significant overlap in the diets of A. charr above and below 140 mm of length (D = 51%).

3.4. Hg-Models

3.4.1. Model Intercepts and Residual Standard Error

Model intercepts were significant for all species, with A. charr exhibiting the lowest intercept for autumn data of −1.825 {degrees of freedom (df) = 29, standard error (SE) = 0.070, t = −26.23, p < 0.001}, followed by a general intercept for whitefish of −1.703 (df = 72, SE = 0.031, t = −55.10, p < 0.001), and the highest general intercept for E. smelt of −1.553 (df = 94, SE = 0.027, t = −58.56, p < 0.001). This corresponds to estimated Tot-Hg-concentrations (ww.) of 0.16 ppm for A. charr in autumn with a length of 137 mm and a δ15N of 11.69‰. Average E. smelt, which were 2 years of age, 99 mm in length, 4 g in weight, and had a δ13C-signature of −29.14‰, exhibited an estimated Tot-Hg-concentration of 0.21 ppm. The average whitefish, measuring 244 mm, weighing 118 g, and being 4 years old, had an estimated Tot-Hg-concentration of 0.18 ppm. Residual standard errors for the Hg-models are estimated to 0.499, 0.264 and 0.269 for A. charr, E. smelt and whitefish, respectively. The predicted Tot-Hg-concentrations for the dataset for comparison exhibit values of 0.09 and 0.22 ppm ww. for whitefish and E. smelt, respectively. A. charr varies in predicted Tot-Hg (ppm ww.) between 0.12 and 0.13 in summer and autumn, respectively, and 0.17 and 0.18 in winter and spring, respectively.

3.4.2. Length

The partial linear regression between the centered and transformed length and logarithmically transformed Tot-Hg was significant and positive for all species (Figure 4), meaning that Tot-Hg-concentrations increase with increasing length. The slopes were estimated to 1.592 (df = 71, SE = 0.131, t = 12.20, p < 0.001), 1.927 (df = 94, SE = 0.622, t = 3.10, p = 0.003) and 4.944 (df = 72, SE = 0.851, t = 5.81, p < 0.001) for A. charr, E. smelt and whitefish, respectively.

3.4.3. Age

The partial linear regressions between the centered and transformed age and logarithmically transformed Tot-Hg were positive and significant for E. smelt and whitefish, with slopes of 0.186 (df = 94, SE = 0.060, t = 3.09, p = 0.003) and 0.238 (df = 72, SE = 0.058, t = 4.09, p < 0.001), respectively (Figure 5). For A. charr, however, including a partial regression with age as an explanatory variable did not improve the model.

3.4.4. Weight

Despite the general tendency of fish with higher weight having higher Tot-Hg-concentrations, partial linear regressions between centered and transformed weight and logarithmically transformed Tot-Hg were negatively significant with slopes of −0.532 (df = 94, SE = 0.116, t = −4.57, p < 0.001) and −1.081 (df = 72, SE = 0.255, t = −4.24, p < 0.001) for E. smelt and whitefish, respectively (Figure 6). This effect is caused by the high correlation between length and weight of 0.645 and 0.962 for E. smelt and whitefish, respectively. However, as the partial regression using weight as explanatory variable is significant for E. smelt and whitefish, weight provides additional information for the estimation of Tot-Hg-concentrations in muscle fillet tissue of these species. Adding a partial linear regression with weight as an explanatory variable did not improve the Hg-model for A. charr.

3.4.5. Stable Isotope Ratio δ13C

The centered stable isotope ratio of carbon, δ13C, was significantly, negatively correlated to the logarithmically transformed Tot-Hg-concentration in E. smelt (Figure 7) with a slope of −0.157 (df = 94, SE = 0.049, t = −3.18, p = 0.002). A partial linear regression between δ13C and Tot-Hg, however, neither improved the model for A. charr or whitefish.

3.4.6. Stable Isotope Ratio δ15N

The stable isotope ratio of nitrogen, δ15N, as an explanatory variable did not improve the models for whitefish and E. smelt, and was thus omitted. However, a non-significant partial linear regression with the slope of 0.016 (df = 71, SE = 0.030, t = 0.55, p = 0.586) was included in the model for A. charr (Figure 8) due to heteroscedastic residuals in relation to δ15N. In addition, δ15N was incorporated in the A. charr model as a variance-covariate, estimating the variance at the centered cδ15Ni by the following formula:
v a r ( ε i ) = σ 2 × e 2 ∗ 0.1543 ∗ c d 15 N i

3.4.7. Season

Including the factor season improved the model for A. charr significantly, resulting in different intercepts per season. The lowest intercept (−1.849) was estimated for summer, which was not significantly different from the autumn intercept of −1.825 (df = 20, SE = 0.109, t = −0.22, p = 0.826). The winter intercept (−1.528) was near significantly higher (df = 12, SE = 0.167, t = 1.78, p = 0.079) than the autumn intercept, and the highest intercept in spring (−1.470) was significantly higher (df = 12, SE = 0.100, t = 3.55, p < 0.001) than the autumn intercept. This indicates that the average A. charr with a length of 137 mm and a δ15N of 11.69‰ exhibits average Tot-Hg-concentrations (ww.) of 0.23, 0.16 and 0.22 ppm in spring, summer, and autumn and winter, respectively. Interaction terms involving season and length or δ15N were not significant, thus only the intercept of the partial regressions depends on season (Figure 9). However, as variances, thus standard deviations, differed with season, it was also used as a variance-covariate (varIdent structure) in the model for A. charr, with the largest standard deviation in winter (4.37). The second largest standard deviation (3.85) occurred in summer, followed by a standard deviation of 3.32 in autumn, and the smallest standard deviation (1.84) in spring. The factor season was not significant for E. smelt and whitefish.

4. Discussion

4.1. Age, Size and Weight Distributions, Stable Isotope Ratios and Tot-Hg

The average δ13C ratios of all three species caught in the profundal zone are similar, i.e., between −30‰ and −29‰ (Table 1). According to Vander Zanden and Rasmussen [39], profundal diet has the most depleted carbon signature, on average −30.5‰, followed by the pelagic, on average −28.4‰. The pooled benthic invertebrate samples from the area around the water intake exhibit similar average δ13C-signatures as found in the fish species (Table 3), i.e., between ca. −28‰ and −30‰. The average δ15N of Trichoptera (5.46‰), the only primary consumer sampled in this study, additionally resembles the profundal average δ15N of 5.2‰ estimated by Vander Zanden and Rasmussen [39]. Consequently, all three species investigated in this study feed on a mixture of pelagic and profundal diet, also confirmed by the stomach analyses. A. charr appeared to consume most profundal prey, as it was previously found to be the weaker competitor against whitefish [58,59], thus forced to occupy the less energetically favourable profundal niche [60,61,62]. This was also reflected in the highest δ15N ratios measured for A. charr (Table 1), as profundal primary consumers produce higher baseline δ15N than pelagic zooplankton [39]. The largest range in δ15N, exhibited by A. charr, however, was a result of the combination of benthivorous small individuals and rather piscivorous individuals. E. smelt primarily feeds on zooplankton, mainly pelagic copepods (primary consumers). However, some omnivorous, benthic organisms, such as Chironomidae sp., were found in the diet, and may cause the δ13C signatures to resemble more profundal levels, as well as increased span in trophic position. However, as E. smelt primarily feeds on small, short lived organisms, temporal variations in dietary stable isotope ratios are to be expected [35,63,64,65], and δ13C ratios in zooplankton may reach values resembling profundal organisms [66]. Whitefish exhibit the lowest values of δ15N, however, the signatures are fairly similar to those of E. smelt. A combination of profundal and pelagic prey was found in the stomach samples of whitefish, and the range in trophic position by 1.84 Λ is most likely caused by different feeding habitats and some piscivory.
The distributions of Tot-Hg for the sampled fish species appeared to be influenced by habitat [8,11,40], trophic position [5,6,7,8,10] and age distributions [8,11]. Due to the similar, profundal habitat, the average Tot-Hg-concentrations (ww.) for A. charr (0.24 ± 0.21 ppm), E. smelt (0.22 ± 0.08 ppm) and whitefish (0.20 ± 0.09 ppm) did not differ substantially. However, A. charr exhibited the largest range, highest values and lowest median Tot-Hg-concentrations (Table 1), which was likely caused by a catch of mainly small and young fish from a species with the highest potential to accumulate Tot-Hg due to high maximum age [8,11,67], a profundal diet consumed all year [8,11,40,60], and the highest average δ15N [5,6,7,8,10]. For the standard dataset of explanatory variables, A. charr exhibited intermediate predicted Tot-Hg-concentrations (0.12–0.18 ppm ww.), likely due to their more profundal diet compared to whitefish, and their larger size compared to E. smelt. The Tot-Hg-concentrations, measured in E. smelt and whitefish, were similar. Whitefish spawn in the profundal zone [60], but they have access to pelagic, perhaps even littoral prey, as they do not occupy the profundal zone all year [60]. Consequently, the Tot-Hg-concentration in the diet of whitefish is decreased when they do not consume profundal prey [8,11,40]. Contrarily, E. smelt was not shown to ingest any littoral prey, which may be one reason for the higher average Tot-Hg-concentrations measured in E. smelt. Additionally, E. smelt matures at an age of 2–4 years [68], which often leads to stagnating growth [69]. Consequently, Tot-Hg will not further be diluted by increasing tissue mass in mature E. smelt [15,16,20,21]. The early stagnation in growth and the pelagic to profundal diet of E. smelt likely leads to E. smelt having the highest concentrations of Tot-Hg (0.22 ppm ww.) corrected for a standard set of explanatory variables. As whitefish only occupy the profundal zone for spawning during winter [60], and they exhibit higher growth rates than E. smelt, their predicted Tot-Hg-concentration (0.09 ppm ww.) for the standard dataset was the lowest in this study.

4.2. Use of the Profundal Habitat

All fish species sampled occurred in the profundal zone in similar patterns as reported by Borgstrøm and Saltveit [60]. A. charr was caught all year, with the highest presence in autumn, as they are likely forced to occupy the profundal niche by competition with whitefish [58,59,61,62]. E. smelt was also caught in the profundal zone all year, but fewer individuals were caught in summer. E. smelt is an important prey species for larger fish, primarily brown trout (Salmo trutta), and E. smelt is reported to undergo diurnal vertical migrations feeding in the epilimneon at night and staying close to the bottom at daytime [70,71]. However, as predator avoidance should be most pronounced in the growth season, when there were few E. smelt caught in the profundal zone, it is more plausible, that E. smelt feed on benthic invertebrates in the profundal zone, when zooplankton is scarce. The use of the profundal habitat of E. smelt may be size-dependent, as no E. smelt with a length exceeding 113 mm were caught in this study. Cannibalistic individuals of E. smelt with lengths up to 135 mm are observed in many Norwegian populations of E. smelt [72] (pp. 68–69), including the population in Lake Norsjø [73]. Whitefish was the only species caught, which was completely absent during summer, and the largest numbers were caught in January through March. Analogously, Borgstrøm and Saltveit [60] reported most whitefish were caught (200–300 per week) in January and February, with decreasing numbers in spring, and no whitefish caught in summer. This seasonal occurrence is caused by the different behaviour of three distinct whitefish morphs in Lake Norsjø, littoral whitefish, stream whitefish, and winter whitefish, the latter spawning at 15–70 m depth in January and February [74]. Borgstrøm [75], who sampled whitefish with gill nets, only caught whitefish at 25–50 m depth during spawning. Conclusively, all whitefish caught in this study belong to the winter whitefish population, which utilises the profundal habitat for spawning and subsequent feeding on roe during winter. Therefore, most of the whitefish caught are spawning, adult individuals, however, also few immature individuals were caught.

4.3. Ontogenetic Diet Shift in A. Charr

An ontogenetic diet shift can be observed in the stomach samples of A. charr at a length of 140 mm. The diet shifts form predominantly Chironomidae sp., some pelagic prey such as copepods, and other items like Phryganea grandis and roe to a diet mainly based on fish, Chironomidae sp., Phryganea grandis and roe. Subsequent to the diet shift, Tot-Hg-concentrations and length continued to increase, while the increase in δ15N, thus trophic position, stagnated. The ontogenetic diet shift in A. charr, which have invertebrate consumption and cannibalism as different stages in the same life history strategy, has been proposed by e.g. Finstad et al. [76]. Another explanation for the differences in the two groups is a dimorphism with invertebrate eating dwarfs and cannibalistic giants [77], which could persist permanently [78]. Parker and Johnson [79], for example, have observed phenological differences between A. charr morphs such as different numbers of gill rakers. However, molecular techniques have only revealed slight genetic differences at first [80,81,82,83], and different phenotypes were rather thought to be a result of genetic and environmental components in combination [84,85]. More recently, evidence for larger genetic differences in A. charr was found, especially if different populations inhabit different niches [86,87,88,89,90,91]. Further investigations in Lake Norsjø are necessary in order to determine, whether A. charr undergoes an ontogenetic diet shift, or if there are two different life history strategies. For this purpose, differences in gill raker counts could be examined.

4.4. Factors Determining Tot-Hg-Concentrations (Model Results)

4.4.1. Length, Age and Weight

Length exhibited significant, positive partial regressions to Tot-Hg in all fish species, and length is frequently used as proxy for Hg-concentrations [8,11,12,13,14]. Age was significantly, positively correlated to Tot-Hg in E. smelt and whitefish, as older fish have accumulated more Tot-Hg throughout their longer lives [8,11]. The partial linear regressions between weight and Tot-Hg were significant and negative for E. smelt and whitefish, and it is important to note that they are corrected for effects also explained by age or length. Hg is diluted by organic matter, either through algal bloom dilution (ABD) [17,18], or through SGD in fish [15,16,20,21], two effects that cannot be separated without laboratory procedures [92]. SGD occurs at higher rates in fish with high growth rates, but also in fish gaining weight, thus it is likely the cause of lower Tot-Hg-concentrations at higher weight corrected for length. The opposite effect has also been reported in starving fish, which exhibit relatively high concentrations of Tot-Hg [22] and low weight corrected for length.

4.4.2. Habitat Effect and δ13C

The only species investigated with a significant partial regression between δ13C and Tot-Hg is E. smelt, which exhibits higher Tot-Hg-concentrations with more depleted δ13C. Consequently, E. smelt vary in diet and possibly habitat [8,11,40], and Tot-Hg-concentrations are influenced by that variance. There are several studies reporting that littoral fish accumulate less Hg than pelagic fish at the same trophic level [41,42,43], and as Hg is influenced by depth [11], a profundal diet likely leads to higher Tot-Hg-concentrations than a pelagic diet.

4.4.3. Biomagnification and δ15N

Hg is reported to bioaccumulate and biomagnify, and predators may have concentrations million times higher than the surrounding water [9], which can reach toxic levels in fish and fish eating wildlife [7]. This effect is usually linked to an increase in Hg by trophic position measured in δ15N [5,6,7,8,10], however, no partial correlation between δ15N and Tot-Hg has been significant in this study. Conclusively, δ15N did not contribute additional information crucial to estimating Tot-Hg-concentration in fish, it may, however, function as a proxy for Tot-Hg-concentrations, as it may be correlated to length or age, which it is for E. smelt and whitefish. These two species also appeared to feed on homogenous diets throughout all length classes, resulting in a reduced effect of δ15N on Tot-Hg-concentrations. The δ15N signatures were only included in the model for A. charr, and showed an insignificant positive trend, indicating a slight increase in Tot-Hg-concentrations with increasing δ15N. A. charr appeared to increase in δ15N up to the ontogenetic diet shift to piscivory, then only length and Tot-Hg continued to increase (Figure S1). Tot-Hg-concentrations increase substantially, once A. charr being piscivorous, however, δ15N did not seem to increase further at that point. Thus, the residual variance of Tot-Hg-concentrations increased with increasing δ15N, as high values of δ15N covered the increase in Tot-Hg subsequent to reaching the maximum trophic position.

4.4.4. Seasonal Variation

Seasonal variations in Tot-Hg-concentrations were significant for A. charr, which exhibit significantly higher concentrations in spring and near significantly higher concentrations in winter than in autumn. This seasonal pattern is likely caused by ABD and SGD [11,15,16,17,18,19,20,21], as dilution lowers Tot-Hg-concentration during the growth season (summer). A. charr may then be starving during winter, which leads to near significantly higher Tot-Hg-concentrations [22], and significantly higher Tot-Hg-concentrations in spring before the onset of the growth season. Similar seasonal variations related to growth rates and condition have been reported in littoral and pelagic habitat and streams [23,24,25,26,27,28,29,30]. The different residual variances per season are likely caused by different sample sizes, however, the highest variance in winter may also be supported by different reactions to starvation. The individual resource demand is dependent on size, and large animals need more food in order to sustain themselves [93,94,95,96], meaning that their habitat must provide a higher resource density to avoid starvation [96,97]. Byström et al. [98] found that small A. charr could even be able to sustain close to optimal growth rates in ice-covered lakes during winter, which indicates that small A. charr should not be subject to starvation in Lake Norsjø, while larger individuals probably are. However, even small A. charr may be subject to starvation or reduced growth during winter in Lake Norsjø as A. charr only compete with whitefish for profundal resources from late autumn to spring, when whitefish occurs in the profundal zone [60].

5. Conclusions

Tot-Hg-concentrations in fish increased with length and age in the profundal zone, while a less depleted δ13C signature, and lower weight, corrected for length, resulted in higher Tot-Hg-concentrations. A slight Increase in Tot-Hg with increasing δ15N or trophic position was found in A. charr. Both the use of the profundal habitat and Tot-Hg-concentrations may vary seasonally. Winter whitefish in Lake Norsjø were only found in the profundal habitat during their spawning period in winter. Tot-Hg-concentrations varied with season for A. charr, and were highest in spring and lowest in summer, likely as an effect of nutritional status.

Supplementary Materials

The following are available online at file:///E:/1-manuscripts/environments/environments-152406/environments-152406-suppl.-original/SupplementS1.html, Figure S1: RGL Model for A. charr; The following are available online at www.mdpi.com/2076-3298/3/4/29/s1, Supplement S2: R-code and model construction.

Acknowledgments

We would like to thank the University College of Southeast-Norway for funding this study. We also thank Johan Romnes Ellingsen (INOVYN Norge AS, principal engineer) to grant us access to the fish caught at the industrial water intake in Lake Norsjø. We thank Eirik Fjeld (Norwegian Institute for Water Research, senior researcher) for reviewing our statistics and draft.

Author Contributions

Espen Lydersen conceived and designed the experiments; Tom Robin Olk, Tobias Karlsson and Asle Økelsrud performed the experiments; Tom Robin Olk analysed the data; Tom Robin Olk, Tobias Karlsson, Asle Økelsrud and Espen Lydersen wrote the paper.

Conflicts of Interest

The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

Abbreviations

The following abbreviations are used in this manuscript:
ABDAlgal bloom dilution
AICAkaike information criterion
DfDegrees of freedom
GlsGeneralised least squares
HgMercury
MLMaximum likelihood
ppmParts per million
REMLRestricted maximum likelihood
SDStandard deviation
SEStandard error
SGDSomatic growth dilution
Tot-HgTotal Mercury
ww.Wet weight

References

  1. Gilmour, C.C.; Riedel, C.S. Measurement of Hg methylation in sediments using high specific-activity Hg-203 and ambient incubator. Water Air Soil Pollut. 1995, 80, 747–756. [Google Scholar] [CrossRef]
  2. Macalady, J.L.; Mack, E.E.; Nelson, D.C.; Scow, K.M. Sediment microbial community structure and mercury methylation in mercury-polluted clear lake, California. Appl. Environ. Microbiol. 2000, 66, 1479–1488. [Google Scholar] [CrossRef] [PubMed]
  3. Hollweg, T.A.; Gilmour, C.C.; Mason, R.P. Mercury and methylmercury cycling in sediments of the mid-Atlantic continental shelf and slope. Limnol. Oceanogr. 2010, 55, 2703–2722. [Google Scholar] [CrossRef]
  4. Lehnherr, I.; St. Louis, V.L.; Kirk, J.L. Methylmercury cycling in high arctic wetland ponds: Controls on sedimentary production. Environ. Sci. Technol. 2012, 46, 10523–10531. [Google Scholar] [CrossRef] [PubMed]
  5. Cabana, G.; Rasmussen, J.B. Modeling food-chain structure and contaminant bioaccumulation using stable nitrogen isotopes. Nature 1994, 372, 255–257. [Google Scholar] [CrossRef]
  6. Atwell, L.; Hobson, K.A.; Welch, H.E. Biomagnification and bioaccumulation of mercury in an arctic marine food web: Insights from stable nitrogen isotope analysis. Can. J. Fish. Aquat. Sci. 1998, 55, 1114–1121. [Google Scholar] [CrossRef]
  7. Watras, C.J.; Back, R.C.; Halvorsen, S.; Hudson, R.J.M.; Morrison, K.A.; Wente, S.P. Bioaccumulation of mercury in pelagic freshwater food webs. Sci. Total Environ. 1998, 219, 183–208. [Google Scholar] [CrossRef]
  8. Eagles-Smith, C.A.; Suchanek, T.H.; Colwell, A.E.; Anderson, N.L. Mercury trophic transfer in a eutrophic lake: The importance of habitat-specific foraging. Ecol. Appl. 2008, 18, A196–A212. [Google Scholar] [CrossRef] [PubMed]
  9. Kidd, K.; Clayden, M.; Jardine, T. Bioaccumulation and biomagnification of mercury through food webs. In Environmental Chemistry and Toxicology of Mercury; Liu, G., Cai, Y., O’Driscoll, N., Eds.; Wiley: Hoboken, NJ, USA, 2012; pp. 455–499. [Google Scholar]
  10. Lavoie, R.A.; Jardine, T.D.; Chumchal, M.M.; Kidd, K.A.; Campbell, L.M. Biomagnification of mercury in aquatic food webs: A worldwide meta-analysis. Environ. Sci. Technol. 2013, 47, 13385–13394. [Google Scholar] [CrossRef] [PubMed]
  11. Stafford, C.P.; Hansen, B.; Stanford, J.A. Mercury in fishes and their diet items from Flathead Lake, Montana. Trans. Am. Fish. Soc. 2004, 133, 349–357. [Google Scholar] [CrossRef]
  12. Huckabee, J.W.; Elwood, J.W.; Hildebrand, S.G. Accumulation of mercury in freshwater biota. In Biogeochemistry of Mercury in the Environment; Nriago, J.O., Ed.; Elsevier/North-Holland Biomedical Press: New York, NY, USA, 1979; pp. 277–302. [Google Scholar]
  13. Driscoll, C.T.; Yan, C.; Schofield, C.L.; Munson, R.; Holsapple, J. The mercury cycle and fish in the Adirondack lakes. Environ. Sci. Technol. 1994, 28, 136–143. [Google Scholar] [CrossRef]
  14. Cidzdziel, J.V.; Hinners, T.A.; Pollard, J.E.; Heithmar, E.M.; Cross, C.L. Mercury concentrations in fish from Lake Mead, USA, related to fish size, condition, trophic level, location and consumption risk. Arch. Environ. Contam. Toxicol. 2002, 43, 309–317. [Google Scholar] [CrossRef] [PubMed]
  15. Thomann, R.V. Bioaccumulation model of organic chemical distribution in aquatic food chains. Environ. Sci. Technol. 1989, 23, 699–707. [Google Scholar] [CrossRef]
  16. Verta, M. Changes in fish mercury concentrations in an intensively fished lake. Can. J. Fish. Aquat. Sci. 1990, 47, 1888–1897. [Google Scholar] [CrossRef]
  17. Pickhardt, P.C.; Folt, C.L.; Chen, C.Y.; Klaue, B.; Blum, J.D. Algal blooms reduce the uptake of toxic methylmercury in freshwater food webs. Proc. Natl. Acad. Sci. USA 2002, 99, 4419–4423. [Google Scholar] [CrossRef] [PubMed]
  18. Pickhardt, P.C.; Folt, C.L.; Chen, C.Y.; Klaue, B.; Blum, J.D. Impacts of zooplankton composition and algal enrichment on the accumulation of mercury in an experimental freshwater food web. Sci. Total Environ. 2005, 339, 89–101. [Google Scholar] [CrossRef] [PubMed]
  19. Karimi, R.; Chen, C.Y.; Pickhardt, P.C.; Fisher, N.S.; Folt, C.L. Stoichiometric controls of mercury dilution by growth. Proc. Natl. Acad. Sci. USA 2007, 104, 7477–7482. [Google Scholar] [CrossRef] [PubMed]
  20. Ward, D.M.; Nislow, K.H.; Folt, C.L. Bioaccumulation syndrome: Identifying factors that make some stream food webs prone to elevated mercury bioaccumulation. In Year in Ecology and Conservation Biology; Ostfeld, R.S., Schlesinger, W.H., Eds.; The New York Academy of Sciences: New York, NY, USA, 2010; Volume 1195, pp. 62–83. [Google Scholar]
  21. Lepak, J.M.; Kinzli, K.-D.; Fetherman, E.R.; Pate, W.M.; Hansen, A.G.; Gardunio, E.I.; Cathcart, C.N.; Stacy, W.L.; Underwood, Z.E.; Brandt, M.M.; et al. Manipulation of growth to reduce mercury concentrations in sport fish on a whole-system scale. Can. J. Fish. Aquat. Sci. 2012, 69, 122–135. [Google Scholar] [CrossRef]
  22. Hobson, K.A.; Alisauskas, R.T.; Clark, R.G. Stable-nitrogen isotope enrichment in avian tissues due to fasting and nutritional stress: Implications for isotopic analysis of diet. Condor 1993, 95, 388–394. [Google Scholar] [CrossRef]
  23. Meili, M.; Wills, D. Seasonal concentration changes of Hg, Cd, Cu and Al in a population of roach (Rutilus rutilus). In Heavy Metals in the Environment; Lekkas, T.D., Ed.; Commission of the European Communities Consultants: Athens, Greece, 1985; pp. 709–711. [Google Scholar]
  24. Staveland, G.; Marthinsen, I.; Norheim, G.; Julshamn, K. Levels of environmental pollutants in flounder (Platichthys flesus L.) and cod (Gadus morhua L.) caught in the waterway of Glomma, Norway. II. Mercury and arsenic. Arch. Environ. Contam. Toxicol. 1993, 24, 187–193. [Google Scholar] [CrossRef] [PubMed]
  25. Park, J.; Curtis, L.R. Mercury distribution in sediments and bioaccumulation by fish in two Oregon reservoirs: Point-source and nonpoint-source impacted systems. Arch. Environ. Contam. Toxicol. 1997, 33, 423–429. [Google Scholar] [CrossRef] [PubMed]
  26. Gorski, P.R.; Lathrop, R.C.; Hill, S.D.; Herrin, R.T. Temporal mercury dynamics and diet composition in the mimic shiner. Trans. Am. Fish. Soc. 1998, 128, 701–712. [Google Scholar] [CrossRef]
  27. Farkas, A.; Salánki, J.; Speciár, A. Age- and size-specific patterns on heavy metals in the organs of freshwater fish Abramis brama L., populating a low-contaminated site. Water Res. 2003, 37, 959–964. [Google Scholar] [CrossRef]
  28. Murphy, G.W.; Newcomb, T.J.; Orth, O.J. Sexual and seasonal variations of mercury in smallmouth bass. J. Freshw. Ecol. 2007, 22, 35–143. [Google Scholar] [CrossRef]
  29. Zhang, L.; Campbell, L.M.; Johnson, T.B. Seasonal variation in mercury and food web biomagnification in Lake Ontario, Canada. Environ. Pollut. 2012, 161, 178–184. [Google Scholar] [CrossRef] [PubMed]
  30. Moreno, C.E.; Fjeld, E.; Deshar, M.K.; Lydesen, E. Seasonal variation of mercury and δ15N in fish from Lake Heddalsvatn, southern Norway. J. Limnol. 2015, 74, 21–30. [Google Scholar] [CrossRef]
  31. Farkas, A.; Saláki, J.; Varanka, I. Heavy metal concentrations in fish of Lake Balaton. Lakes Reserv. 2000, 5, 271–279. [Google Scholar] [CrossRef]
  32. Foster, E.P.; Drake, D.I.; DiDimenico, G. Seasonal changes and tissue distribution of mercury in largemouth bass (M. ceopterus salmoides) from Dorena Reservoir, Oregon. Arch. Environ. Contam. Toxicol. 2000, 38, 78–82. [Google Scholar] [CrossRef] [PubMed]
  33. Burger, J.; Jeitner, C.; Donio, M.; Shukla, S.; Gochfeld, M. Factors affecting mercury and selenium levels in New Jersey flatfish: Low risk to human consumers. J. Toxicol. Environ. Health 2009, 72, 853–860. [Google Scholar] [CrossRef] [PubMed]
  34. Peterson, B.J.; Fry, B. Stable isotopes in ecosystem studies. Ann. Rev. Ecol. Syst. 1987, 18, 293–320. [Google Scholar] [CrossRef]
  35. Cabana, G.; Rasmussen, J.B. Comparison of aquatic food chains using nitrogen isotopes. Proc. Natl. Acad. Sci. USA 1996, 93, 10844–10847. [Google Scholar] [CrossRef] [PubMed]
  36. Post, D.M. Using stable isotopes to estimate trophic position: Models, methods, and assumptions. Ecology 2002, 83, 703–718. [Google Scholar] [CrossRef]
  37. Hoefs, J. Stable Isotope Geochemistry; Springer Science & Business Media: New York, NY, USA, 2013. [Google Scholar]
  38. Minagawa, M.; Wada, E. Stepwise enrichment of 15N along food chains: Further evidence and the relation between delta15N and animal age. Geochim. Cosmochim. Acta 1984, 48, 1135–1140. [Google Scholar] [CrossRef]
  39. Vander Zanden, M.J.; Rasmussen, J.B. Primary consumer delta13C and delta15N and the trophic position of aquatic consumers. Ecology 1999, 80, 1395–1404. [Google Scholar] [CrossRef]
  40. Lavoie, R.A.; Hebert, C.E.; Rail, J.-F.; Braune, B.M.; Yumvihoze, E.; Hill, L.G.; Lean, D.R.S. Trophic structure and mercury distribution in a Gulf of St. Lawrence (Canada) food web using stable isotope analysis. Sci. Total Environ. 2010, 408, 5529–5539. [Google Scholar] [CrossRef] [PubMed]
  41. Power, M.; Klein, G.M.; Guiguer, K.; Kwan, M.K.H. Mercury accumulation in the fish community of a sub-Arctic lake in relation to trophic position and carbon sources. J. Appl. Ecol. 2002, 39, 819–830. [Google Scholar] [CrossRef]
  42. Gorski, P.R.; Cleckner, L.B.; Hurley, J.P.; Sierzen, M.E.; Armstrong, D.E. Factors affecting enhanced mercury bioaccumulation in inland lakes of Isle Royale National Park, USA. Sci. Total Environ. 2003, 304, 327–348. [Google Scholar] [CrossRef]
  43. Stewart, A.R.; Saiki, M.K.; Kuwabara, J.S.; Alpers, C.N.; Marvin-Dipasquale, M.; Krabbenhoft, D.P. Influence of plankton mercury dynamics and trophic pathways on mercury concentrations of top predator fish of a mining-impacted reservoir. Can. J. Fish. Aquat. Sci. 2008, 65, 2351–2366. [Google Scholar] [CrossRef]
  44. Chumchal, M.M.; Hambright, K.D. Ecological factors regulating mercury contamination of fish from Caddo Lake, Texas, USA. Environ. Toxicol. Chem. 2009, 28, 962–972. [Google Scholar] [CrossRef] [PubMed]
  45. Holtan, H. Norsjø: En Limnologisk Undersøkelse Utført I 1967; Norwegian Institute for Water Research NIVA: Oslo, Norway, 1968; p. 40. [Google Scholar]
  46. Vann-Nett. Available online: http://www.vann-nett.no/portal/water?waterbodyID=016-6-L (accessed on 28 July 2016).
  47. Nordeng, H. On the biology of char (Salmo alpinus L.) in Salangen, North Norway. Age and spawning frequency determined from scales and otoliths. Nytt Mag. Zool. 1961, 10, 67–123. [Google Scholar]
  48. Shanks, A.L. Surface slicks associated with tidally forced internal waves may transport pelagic larvae of benthic invertebrates and fishes shoreward. Mar. Ecol. Prog. Ser. 1983, 13, 311–315. [Google Scholar] [CrossRef]
  49. Raastad, J.E.; Olsen, L.-H. Insekter og Småkryp I Vann og Vassdrag; Aschehoughs Naturbøker: Oslo, Norway, 1999. [Google Scholar]
  50. Craig, H. Isotopic standards for carbon and oxygen and correction factors for mass-spectrometric analysis of carbon dioxide. Geochim. Cosmochim. Acta 1957, 12, 133–149. [Google Scholar] [CrossRef]
  51. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2016. [Google Scholar]
  52. NLME: Linear and Nonlinear Mixed Effects Models, R-Package Version 3.1-127, 2016. Available online: http://CRAN.R-project.org/package=nlme (accessed on 28 July 2016).
  53. Zuur, A.F.; Ieno, E.N.; Walker, N.J.; Saveliev, A.A.; Smith, G.M. Mixed Effects Models and Extensions in Ecology with R; Springer Science & Business Media: New York, NY, USA, 2009; p. 574. [Google Scholar]
  54. RGL: 3D Visualization Using Open GL, R-Package Version 0.95.1441, 2016. Available online: https://CRAN.R-project.org/package=rgl (accessed on 28 July 2016).
  55. RGLWIDGET: “RGL” in “HTMLWIDGETS” Framework, R-Package Version 0.1.1434, 2015. Available online: https://CRAN.R-project.org/package=rglwidget (accessed on 28 July 2016).
  56. Schoener, T.W. Nonsynchronous spatial overlap of lizards in patchy habitats. Ecology 1970, 51, 408–418. [Google Scholar] [CrossRef]
  57. Wallace, R.K. An Assessment of diet overlap indices. Trans. Am. Fish. Soc. 1981, 110, 72–76. [Google Scholar] [CrossRef]
  58. Nilsson, N.A. Interactive segregation between fish species. In The Biological Basis of Freshwater Fish Production; Gerking, S.D., Ed.; Blackwell Scientific: Oxford, UK, 1967; pp. 295–313. [Google Scholar]
  59. Amundsen, P.-A.; Knudsen, R.; Bryhni, H.T. Niche use and resource partitioning of Arctic charr, European whitefish and grayling in a subarctic lake. Hydrobiologia 2010, 650, 3–14. [Google Scholar] [CrossRef]
  60. Borgstrøm, R.; Saltveit, S.J. En Vurdering av Fisketap Gjennom Tappetunnellene fra Nedre Norsjø til Rafnes og Porsgrunn Fabrikker; Institutt for Naturforvaltning NHL: Ås, Norway, 1981; 18p. [Google Scholar]
  61. Degerman, E.; Hammar, J.; Nyberg, P.; Svardson, G. Human impact on the fish diversity in the four largest lakes of Sweden. Ambio 2001, 30, 522–528. [Google Scholar] [CrossRef] [PubMed]
  62. Sandlund, O.T.; Haugerud, E.; Rognerud, S.; Borgstrøm, R. Arctic charr (Salvelinus alpinus) squeezed in a complex fish community dominated by Perch (Perca fluviatilis). Fauna Nor. 2013, 33, 1–11. [Google Scholar] [CrossRef]
  63. Gu, B.; Schell, D.M.; Alexander, V. Stable carbon and nitrogen isotopic analysis of the plankton food web on a subarctic lake. Can. J. Fish. Aquat. Sci. 1994, 51, 1338–1344. [Google Scholar] [CrossRef]
  64. Yoshioka, T.; Wada, E.; Hayashi, H. A stable isotope study on seasonal food web dynamics in a eutrophic lake. Ecology 1994, 75, 835–846. [Google Scholar] [CrossRef]
  65. Toda, H.; Wada, E. Use of 15N/14N ratios to evaluate the food source of the mysid, Neomysis intermedia Czeriawsky, in a eutrophic lake in Japan. Hydrobiologia 1990, 194, 85–90. [Google Scholar] [CrossRef]
  66. Grey, J.; Jones, R.I.; Sleep, D. Seasonal changes in the importance of the source of organic matter to the diet of zooplankton in Loch Ness, as indicated by stable isotope analysis. Limnol. Oceanogr. 2001, 46, 505–513. [Google Scholar] [CrossRef]
  67. Klemetsen, A.; Amundsen, P.-A.; Dempson, J.B.; Jonsson, B.; Jonsson, N.; O’Connell, M.F.; Mortensen, E. Atlantic Salmon Salmo salar L., Brown trout Salmo trutta L. and Arctic charr Salvelinus alpinus L.: A review of aspects of their life histories. Ecol. Freshw. Fish 2003, 12, 1–59. [Google Scholar] [CrossRef]
  68. Jonsson, B. Fisker; Cappelens Naturhåndbøker: Oslo, Norway, 2006. [Google Scholar]
  69. Granås, E. Krøkle (Osmerus eperlanus (L.)) i Nordfjorden, Tyrifjordsundersøkelsen 1970–1986; Direktoratet for vilt og Ferskvannsfisk: Oslo, Norway, 1979. [Google Scholar]
  70. Nellbring, S. The ecology of smelts (genus Osmerus): A literature review. Nord. J. Freshw. Res. 1989, 65, 116–145. [Google Scholar]
  71. Horppila, J.; Malinen, T.; Nurminen, L.; Tallberg, P.; Vinni, M. A metalimnetic oxygen minimum indirectly contributing to the low biomass of cladocerans in Lake Hiidenvesi—A diurnal study on the refugee effect. Hydrobiologia 2000, 436, 81–90. [Google Scholar] [CrossRef]
  72. Pethon, P. Aschehougs Store Fiskebok; Løvaas Lito: Sarpsborg, Norway, 2005. [Google Scholar]
  73. Klemetsen, A.; Vasshaug, Ø. Et forsøk på å overføre krøkle til Vestlandet. Fauna 1966, 19, 92–99. [Google Scholar]
  74. Jensen, K.W. Fish and Fisheries in Lake Norsjø; AS Norsk Bergverk: Ulefoss, Norway, 1954; p. 68. [Google Scholar]
  75. Borgstrøm, R. Oppsamlingsskjønn for Norsjø m.v. Ovenforliggende Reguleringers Virkning på Fiskebestander og Utøvelsen av Fisket; Universitet i Oslo: Oslo, Norway, 1974; p. 49. [Google Scholar]
  76. Finstad, A.G.; Ugedal, O.; Berg, O.K. Growing large in a low grade environment: Size dependent foraging gain and niche shifts to cannibalism in Arctic char. Oikos 2006, 112, 73–82. [Google Scholar] [CrossRef]
  77. Hammar, J. Cannibals and parasites: Conflicting regulators of bimodality in high latitude Arctic char, Salvelinus alpinus. Oikos 2000, 88, 33–47. [Google Scholar] [CrossRef]
  78. Svenning, M.A.; Borgstrøm, R. Population structure in landlocked Spitzbergen Arctic char. Sustained by cannibalism? Nord. J. Freshwater Res. 1995, 71, 424–431. [Google Scholar]
  79. Parker, H.H.; Johnson, L. Population structure, ecological segregation and reproduction in non-anadromous Arctic charr, Salvelinus alpinus (L.), in four unexploited lakes in the Canadian high Arctic. J. Fish Biol. 1991, 38, 123–147. [Google Scholar] [CrossRef]
  80. Hindar, K.; Ryman, N.; Stahl, G. Genetic differentiation among local populations and morphotypes of Arctic charr, Salvelinus alpinus. Biol. J. Linn. Soc. 1986, 27, 269–285. [Google Scholar] [CrossRef]
  81. Snorrason, S.S.; Skúlason, S.; Sandlund, O.T.; Malmquist, H.J.; Jonsson, B.; Jonasson, P.M. Shape polymorphism in Arctic charr, Salvelinus alpinus, in Thingvallavatn, Iceland. In Biology of Charr and Masu Salmon; Kawanabe, H., Ed.; Kyoto University: Kyoto, Japan, 1989. [Google Scholar]
  82. Danzmann, R.G.; Ferguson, M.M.; Skúlason, S.S.; Noakes, D.L.G. Mitochondrial DNA diversity among four sympatric morphs of Arctic charr, Salvelinus alpinus L., from Thingvallavatn, Iceland. J. Fish Biol. 1991, 39, 649–659. [Google Scholar] [CrossRef]
  83. Hartley, S.E.; MCGowan, C.; Greer, R.B.; Walker, A.F. The genetics of sympatric Arctic charr (Salvelinus alpinus (L.)) populations from Loch Rannoch, Scotland. J. Fish Biol. 1992, 41, 1021–1031. [Google Scholar] [CrossRef]
  84. Nordeng, H. Solution to the “Char problem” based on Arctic char (Salvelinus alpinus) in Norway. Can. J. Fish. Aquat. Sci. 1983, 40, 1372–1387. [Google Scholar] [CrossRef]
  85. Nordeng, H.; Bratland, P.; Skurdal, J. Patterns of smolt transformation in the resident fraction on anadromous Arctic charr Salvelinus alpinus; genetic and environmental influence. In Biology of Charrs and Masu Salmon; Kawanabe, H., Ed.; Kyoto University: Kyoto, Japan, 1989. [Google Scholar]
  86. Westgaard, J.L.; Klemetsen, A.; Knudsen, R. Genetic differences between two sympatric morphs of Arctic charr confirmed by microsatellite DNA. J. Fish Biol. 2004, 65, 1185–1191. [Google Scholar] [CrossRef]
  87. Adams, C.; Fraser, D.; Wilson, A.J.; Alexander, G.; Ferguson, M.M.; Skúlason, S. Patterns of phenotopic and genetic variability show hidden diversity in Scottish Arctic charr. Ecol. Freshw. Fish 2007, 16, 78–86. [Google Scholar] [CrossRef]
  88. Gomez-Uchida, D.; Dunphy, K.P.; O’Connell, M.F.; Ruzzante, D.E. Genetic divergence between sympatric Arctic charr Salvelinus alpinus morphs in Gander Lake, Newfoundland: Roles of migration and unequal effective population sizes. J. Fish Biol. 2008, 73, 2040–2057. [Google Scholar] [CrossRef]
  89. Power, M.; Power, G.; Reist, J.D.; Bajno, R. Ecological and genetic differentiation among the Arctic charr of Lake Aigneau, Northern Québec. Ecol. Freshw. Fish 2009, 18, 445–460. [Google Scholar] [CrossRef]
  90. Conejeros, P.; Phan, A.; Power, M.; O’Connell, M.; Alekseyev, S.; Salinas, I.; Dixon, B. Differentiation of sympatric Arctic char morphotypes using major histocompability class II genes. Trans. Am. Fish. Soc. 2014, 143, 586–594. [Google Scholar] [CrossRef]
  91. May-MCNally, S.L.; Quinn, T.P.; Woods, P.J.; Taylor, E.B. Evidence for genetic distinction among sympatric ecotypes of Arctic char (Salvelinus alpinus) in south-western Alaskan lakes. Ecol. Freshw. Fish 2015, 24, 562–574. [Google Scholar] [CrossRef]
  92. Foe, C.; Louie, S. Statewide Mercury Control Program for Reservoirs, Appendix A: Importance of Primary and Secondary Production in Controlling Fish Tissue Mercury Concentrations; Californian Environmental Protection Agency, State Water Resource Control Board: Sacramento, CA, USA, 2014.
  93. Werner, E.E. Ontogenetic scaling of competitive relations: Size dependent effects and response in two anuran larvae. Ecology 1994, 75, 197–213. [Google Scholar] [CrossRef]
  94. Persson, L.; Leonardson, K.; Roos, A.M.; Gyllenberg, M.; Christensen, C.S. Ontogenetic scaling of foraging rates and the dynamics of a size-scaled consumer-resource model. Theor. Popul. Biol. 1998, 54, 270–297. [Google Scholar] [CrossRef] [PubMed]
  95. Aljetlavi, A.A.; Leonardsson, K. Size-dependent competitive ability in a deposit-feeding amphipod, Monoporeia affinis. Oikos 2002, 97, 31–44. [Google Scholar] [CrossRef]
  96. Byström, P.; Andersson, J. Size-dependent foraging capacities and inter cohort competition in an ontogenetic omnivore (Arctic char). Oikos 2005, 110, 523–536. [Google Scholar] [CrossRef]
  97. Perssin, L.; Byström, P.; Wahlström, E.; Nijlunsing, A.; Rosema, S. Resource limitation during early ontogeny-constraints induced by growth capacity in larval and juvenile fish. Oecologia 2000, 122, 459–469. [Google Scholar] [CrossRef]
  98. Byström, P.; Andersson, J.; Kiessling, A.; Eriksson, L.-O. Size and temperature dependent foraging capacities and metabolism: Consequences for winter starvation mortality in fish. Oikos 2006, 115, 43–52. [Google Scholar] [CrossRef]
Environments 03 00029 g001 550
Figure 1. Map of the study area.
Figure 1. Map of the study area.
Environments 03 00029 g001
Environments 03 00029 g002 550
Figure 2. Seasonal variation in stomach content of (a) E. smelt (Winter n = 13; Summer n = 6; Autumn n = 12); (b) A. charr (Winter n = 8; Spring n = 12; Summer n = 10; Autumn n = 11); (c) Whitefish (Winter n = 11; Spring n = 4; Autumn n = 7).
Figure 2. Seasonal variation in stomach content of (a) E. smelt (Winter n = 13; Summer n = 6; Autumn n = 12); (b) A. charr (Winter n = 8; Spring n = 12; Summer n = 10; Autumn n = 11); (c) Whitefish (Winter n = 11; Spring n = 4; Autumn n = 7).
Environments 03 00029 g002
Environments 03 00029 g003 550
Figure 3. The diet of A. charr individuals larger than 140 mm (n = 20) compared to the diet of A. charr individuals smaller than 140 mm (n = 21).
Figure 3. The diet of A. charr individuals larger than 140 mm (n = 20) compared to the diet of A. charr individuals smaller than 140 mm (n = 21).
Environments 03 00029 g003
Environments 03 00029 g004 550
Figure 4. Partial linear regressions using the centered, transformed length as explanatory variable, and logarithmically transformed Tot-Hg as response variable, both corrected for all other variables included in the models for; (a) A. charr; (b) E. smelt; (c) whitefish.
Figure 4. Partial linear regressions using the centered, transformed length as explanatory variable, and logarithmically transformed Tot-Hg as response variable, both corrected for all other variables included in the models for; (a) A. charr; (b) E. smelt; (c) whitefish.
Environments 03 00029 g004
Environments 03 00029 g005 550
Figure 5. Partial linear regressions between the centered, transformed age and logarithmically transformed Tot-Hg-concentration; (a) added variable plot for E. smelt; (b) added variable plot for whitefish.
Figure 5. Partial linear regressions between the centered, transformed age and logarithmically transformed Tot-Hg-concentration; (a) added variable plot for E. smelt; (b) added variable plot for whitefish.
Environments 03 00029 g005
Environments 03 00029 g006 550
Figure 6. Partial linear regressions using centered, transformed weight as an explanatory variable and logarithmically transformed Tot-Hg-concentration as a response variable. Both variables are corrected for all other explanatory variables in their respective models; (a) for E. smelt; (b) for whitefish.
Figure 6. Partial linear regressions using centered, transformed weight as an explanatory variable and logarithmically transformed Tot-Hg-concentration as a response variable. Both variables are corrected for all other explanatory variables in their respective models; (a) for E. smelt; (b) for whitefish.
Environments 03 00029 g006
Environments 03 00029 g007 550
Figure 7. Partial linear regression between δ13C and Tot-Hg for E. smelt. All variables are corrected for the other explanatory variables included in the model.
Figure 7. Partial linear regression between δ13C and Tot-Hg for E. smelt. All variables are corrected for the other explanatory variables included in the model.
Environments 03 00029 g007
Environments 03 00029 g008 550
Figure 8. Partial linear regression between centered δ15N and logarithmically transformed Tot-Hg in A. charr. This regression was not significant, however, it was included due to heterogeneous residuals of the multiple linear regression model for Tot-Hg in A. charr.
Figure 8. Partial linear regression between centered δ15N and logarithmically transformed Tot-Hg in A. charr. This regression was not significant, however, it was included due to heterogeneous residuals of the multiple linear regression model for Tot-Hg in A. charr.
Environments 03 00029 g008
Environments 03 00029 g009 550
Figure 9. Linear regressions for A. charr, using centered, transformed length as an explanatory variable and logarithmically transformed Tot-Hg-concentration as a response. Seasons are coloured as green (spring), orange (summer), blue (autumn) and black (winter).
Figure 9. Linear regressions for A. charr, using centered, transformed length as an explanatory variable and logarithmically transformed Tot-Hg-concentration as a response. Seasons are coloured as green (spring), orange (summer), blue (autumn) and black (winter).
Environments 03 00029 g009
Table
Table 1. Catch of each species each month, and analysed fish per month.
Table 1. Catch of each species each month, and analysed fish per month.
MonthA. CharrE. Smelt
n Caught		WhitefishA. CharrE. Smelt
n Analysed		Whitefish
14 January23182115
14 February9202051515
14 March6214251515
14 April000000
14 May27941384
14 June2901000
14 July1210810
14 August1010000
14 September4323013140
14 October2020215152
14 November000000
14 December324010151510
15 January1202011515
Total191158117779976
Table
Table 2. Descriptive statistics for A. charr, E. smelt and whitefish including the variables age, length, weight, δ13C, δ15N and Tot-Hg (ww.).
Table 2. Descriptive statistics for A. charr, E. smelt and whitefish including the variables age, length, weight, δ13C, δ15N and Tot-Hg (ww.).
VariableSpeciesnMedianMean ± SDMinMaxMin–Max
Age (year)A. charr7799 ± 431916
E. smelt9922 ± 1187
Whitefish7645 ± 311615
Length (mm)A. charr77129145 ± 5174283209
E. smelt999899 ± 68711326
Whitefish76253247 ± 36130310180
Weight (g)A. charr772038 ± 453178175
E. smelt9944 ± 1286
Whitefish76131.5131 ± 4913265252
δ13C (‰)A. charr77−29.15−29.64 ± 1.51−34.74−27.796.95
E. smelt99−29.08−29.14 ± 0.55−32.38−27.604.78
Whitefish76−29.14−29.12 ± 0.49−30.21−27.612.60
δ15N (‰)A. charr7712.0111.69 ± 1.226.8913.516.62
E. smelt9910.1910.41 ± 0.977.6413.605.97
Whitefish768.358.60 ± 1.256.3912.636.24
Tot-Hg (ppm ww.)A. charr770.140.24 ± 0.210.071.131.06
E. smelt990.200.22 ± 0.080.090.540.44
Whitefish760.180.20 ± 0.090.050.490.45
ww.: Wet weight; SD: Standard deviation.
Table
Table 3. Descriptive statistics for the stable isotope ratios δ13C and δ15N of the pooled benthic invertebrate samples.
Table 3. Descriptive statistics for the stable isotope ratios δ13C and δ15N of the pooled benthic invertebrate samples.
VariableGroupnMedianMean ± SDMinMaxMin−Max
δ13C (‰)Trichoptera3−28.10−27.98 ± 0.43−28.66−27.171.49
Chironomidae5−29.40−30.00 ± 1.20−33.61−26.726.89
Asellus Aquaticus8−28.63−28.92 ± 0.78−32.21−25.256.96
δ15N (‰)Trichoptera35.135.46 ± 1.363.287.964.68
Chironomidae59.099.32 ± 0.428.2110.692.48
Asellus Aquaticus86.226.13 ± 0.314.377.322.86

Share and Cite

MDPI and ACS Style

Olk, T.R.; Karlsson, T.; Lydersen, E.; Økelsrud, A. Seasonal Variations in the Use of Profundal Habitat among Freshwater Fishes in Lake Norsjø, Southern Norway, and Subsequent Effects on Fish Mercury Concentrations. Environments 2016, 3, 29. https://doi.org/10.3390/environments3040029

AMA Style

Olk TR, Karlsson T, Lydersen E, Økelsrud A. Seasonal Variations in the Use of Profundal Habitat among Freshwater Fishes in Lake Norsjø, Southern Norway, and Subsequent Effects on Fish Mercury Concentrations. Environments. 2016; 3(4):29. https://doi.org/10.3390/environments3040029

Chicago/Turabian Style

Olk, Tom Robin, Tobias Karlsson, Espen Lydersen, and Asle Økelsrud. 2016. "Seasonal Variations in the Use of Profundal Habitat among Freshwater Fishes in Lake Norsjø, Southern Norway, and Subsequent Effects on Fish Mercury Concentrations" Environments 3, no. 4: 29. https://doi.org/10.3390/environments3040029

APA Style

Olk, T. R., Karlsson, T., Lydersen, E., & Økelsrud, A. (2016). Seasonal Variations in the Use of Profundal Habitat among Freshwater Fishes in Lake Norsjø, Southern Norway, and Subsequent Effects on Fish Mercury Concentrations. Environments, 3(4), 29. https://doi.org/10.3390/environments3040029

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop