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Land use influence on 137Cs levels in perch (Perca fluviatilis L.) and roach (Rutilus rutilus L.)
Journal of Environmental Radioactivity 55 (2001) 125–143
Land use influence on 137Cs levels in perch (Perca fluviatilis L.) and roach (Rutilus rutilus L.) Lars Sonesten* Department of Limnology, Evolutionary Biology Centre, Uppsala University, Norbyva¨gen 20, SE-752 36 Uppsala, Sweden Received 8 April 2000; received in revised form 12 September 2000; accepted 18 September 2000
Abstract The environmental influence on Chernobyl-derived 137Cs levels in perch (Perca fluviatilis L.) and roach (Rutilus rutilus L.) was revealed using partial least-squares regression (PLS). The 53 environmental predictors used describe land use in catchment areas, various catchment area and lake characteristics, lake water chemistry, and fish stock composition. The study showed a profound effect of land use on the 137Cs levels in fish. Radiocaesium deposited on arable land was retained in the soil to a greater extent than was 137Cs deposited on wetlands, which more easily leached out to the lake ecosystems. The 137Cs deposition close to the lakes had a more pronounced effect on 137Cs levels in the fish than did more distant deposition. The radiocaesium bioavailability is mainly governed by lake water cation content, as hardwater lakes had significantly lower 137Cs content in fish. Resuspension of 137Cs contaminated sediments only had a limited influence on the observed levels in fish. # 2001 Elsevier Science Ltd. All rights reserved. Keywords: Radiocaesium; 137Cs; Fish; Partial least squares (PLS); Catchment area; Wetland; Farm land; Water chemistry; Lake morphometry; Resuspension
1. Introduction High amounts of 137Cs in the environment are still a major concern in areas of Sweden that were severely affected by the fallout from the Chernobyl accident in
*Present address: Department of Environmental Assessment, Swedish University of Agricultural Sciences, P.O. Box 7050, SE-750 07 Uppsala, Sweden. Tel.: +46-18-673145; fax: +46-18-673156. E-mail address: [email protected] (L. Sonesten). 0265-931X/01/$ - see front matter # 2001 Elsevier Science Ltd. All rights reserved. PII: S 0 2 6 5 - 9 3 1 X ( 0 0 ) 0 0 1 8 7 - 9
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Ukraine 1986. It was estimated that as much as 10% of the total amount of emitted 137 Cs from the damaged reactor might have fallen over Sweden (Persson, Rodhe, & De Geer, 1987). The deposition on Swedish ground was most severe in the eastern central and northern parts of the country. These areas were subjected to local rains, which efficiently washed out radioactive particles when the radioactive cloud reached Sweden (op. cit.). The main part of the 137Cs that entered aquatic ecosystems arrived during the first months after the reactor accident. After the first year, only small amounts of radiocaesium leached out from the catchment areas (Brittain, Bjo¨rnstad, & Sundblad, 1991). This main pulse of 137Cs originated from direct deposition on the lake surfaces and early surficial run-off (Andersson & Broberg, 1991). Due to the high affinity to particles, especially clay minerals (cf. Avery, 1996), the major proportion of the radiocaesium that entered the lakes was rapidly deposited in the sediments (Broberg & Andersson, 1991). However, the small proportion that entered the food web had serious consequences for the concentrations in biota, with high radiocaesium levels in a variety of organism groups. The 137Cs was readily taken up by low trophic levels, and sequentially a pulse of radiocaesium was transferred up through the food web (Meili, Forseth, Nordlinder, & Saxe´n, 1991; Broberg & Andersson, 1991), as the significance of 137Cs in ingested food becomes more prominent at higher trophic levels (cf. Avery, 1996). There was a pronounced time-lag that increased for every trophic level (Meili et al., 1991). Consequently, the maximum levels in planktivorous fish were reached in late summer 1986, but the corresponding maxima in piscivorous fish were not reached until 1987–1988 (Broberg & Andersson, 1991). Thereafter, the 137Cs content in fish has decreased to fairly stable levels, though the levels are species and size specific, and exhibit rather large differences between lakes (Sundbom, in prep.). Particularly, resuspension of 137Cs-contaminated sediments is believed to be responsible for a large part of the interlake differences, as shallow lakes, having a high degree of resuspension, tend to possess more radiocaesium in fish compared to deeper lakes (Broberg, Malmgren, & Jansson, 1995; Meili, Braf, & Konitzer, 1997). Another important factor is the 137Cs bioavailability for low trophic levels, which is considered to primarily be governed by the water cation concentration, especially the K+ content (cf. Avery, 1996; Hagstro¨m, 1999), via a chemical dilution (Kolehmainen, Ha¨sa¨nen, & Miettinen, 1967). This is commonly manifested as a negative correlation between the concentration of K+ (and other cations) in water and the 137Cs content in fish, due to the importance of ingested 137Cs at higher trophic levels (e.g. Carlsson, 1976; Smith, Kudelsky, Ryabov, & Hadderingh, 2000). This study aims to show the importance of various catchment area and lake characteristics on the 137Cs levels in perch (Perca fluviatilis L.) and roach (Rutilus rutilus L.). Partial least-squares regression (PLS) was used to evaluate the environmental influence on the 137Cs levels. PLS is a biased multivariate projection method, especially suited to use on multicollinear data (Eriksson, Hermens, Johansson, Verhaar, & Wold, 1995).
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2. Material and methods The 137Cs levels in perch (Perca fluviatilis L.) and roach (Rutilus rutilus L.), from a regional survey of 79 lakes in the county of Uppsala, Sweden, were related to 53 environmental variables describing land use in the catchment area, lake morphometry, lake water chemistry, and fish stock characteristics (Table 1). 2.1. Area description The county of Uppsala is situated north of Stockholm, Sweden (Fig. 1). It belongs to the boreal forest region, but in contrast to most other parts of the country, the soils are greatly influenced by calcareous moraines (Ingmar & Moreborg, 1976). Hence, even humic lakes are generally non-acidic, possessing high concentrations of dissolved ions. Sonesten (2000a) comprehensively describes the area. The county constitutes three different physical geographic regions (CABU, 1986; SNA, 1992). The southern part of the county is dominated by a mosaic-like landscape, with coniferous forests and arable land, in a fissure-valley terrain with clayey valleys, lakes and streams. An extension of the Stockholm and Roslagen archipelagos forms the eastern part of the county. Coniferous forests in a hilly fissure-valley landscape dominate this area. The north and northwestern part of the county belongs to the transitional zone to the Taiga terrain. It is a plain dominated by coniferous forests, large mires and eskers. The 137Cs deposition in the area, originating from the Chernobyl accident, varied greatly. The northwestern part of the county was most seriously affected, with up to 100 kBq m2 (SGC, 1986). The deposition declined in a southeastward direction, with about 20 kBq m2 in the central part of the county, to less than 5 kBq m2 in the southeastern areas (op. cit.). 2.2. Fish samples Fish were caught by means of standardized fishing during 1991–1993, starting in the end of July and continuing to the beginning of September each year (Nyberg, 1999). Every lake was fished on one occasion, but in the case of larger lakes, the fishing was extended for several days. The sampling was standardized in relation to lake morphometry to give comparable landings. To get representative specimens of varying size, benthic multi-mesh gillnets were used (Nyberg & Degerman, 1988). The fish were kept cool and, generally, total length and weight were measured and tissue samples from the fillets were taken on fresh samples. If the fish could not be processed the same day (esp. during 1991), they were measured and frozen (208C) as soon as possible. Occasionally, fish length and weight were measured only after thawing and thereafter corrected for size reduction during storage. The correction was made by applying a linear regression from other samples with known size reduction due to their respective storage time. If the specimens were large enough (>approx. 12 cm), tissue samples were taken solely from the dorsal muscle, but from smaller specimens as much muscle tissue as possible was taken. To enhance
Variable
m m m km2 km2
% % % % % % % % % km2 Mm3 km km
Cs levels in perch (Perca fluviatilis L.) and roach
Mean
Min
Max
Definition
13.0 10.5 153 645 492
1.07 1.96 3 0.09 0.42 1.17 0.00
7.15 8.78 40 85 2060 11865 9810
26 31 27 59 14 96 26 76 10 13 1 75 10 13 2 1 1.0 2.1 1.9 0.7
0.3 5 5 0.15 0.14 2.14 2.14 34 0 0 0 40 0 0 0 0 0.03 0.03 0.22 0.12
88 110 75 704 103 1840 205 100 65 57 11 95 46 57 15 5 9.42 26.5 15 2.55
The intercept of the linearly regressed 137Cs content in perch vs. fish length The intercept of the linearly regressed 137Cs content in roach vs. fish length Deposition of 137Cs per m2 ground surface Estimated total amount of 137Cs deposited on lake surface Estimated total amount of 137Cs deposited on catchment areaa Estimated total amount of 137Cs deposited on whole catchment area Difference between total and close deposition X co-ordinate according to the Swedish National Grid Y co-ordinate according to the Swedish National Grid Distance from the NW-SE separator (cf. Fig. 1) Lake altitude (meters above sea level) Altitude difference within the whole catchment area Altitude difference within the catchment areaa The surface of the whole catchment area The surface of catchment areaa The ratio between the whole catchment area and lake area The ratio between the catchment areaa and lake area Forest coverage of the catchment areaa Arable land coverage of the catchment areaa Wetland coverage of the catchment areaa Coverage of other kinds of land use in the catchmenta (mainly urban areas) Forest coverage of the whole catchment area Arable land coverage of the whole catchment area Wetland coverage of the whole catchment area Lake coverage of the whole catchment area Coverage of other kinds of land use of the whole catchment area Lake area Lake volume Maximum lake length Maximum lake width (perpendicular to the maximum length)
1
ln(Bq kg w.w.) ln(Bq kg1 w.w.) Bq m2 GBq GBq GBq GBq
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CsPerch CsRoach 137 Cs dep Lake dep Close dep Total dep Dist dep X Y Z MASL TotAlt AltDiff TCatchArea CatchArea TCatALak CatALak Forest% Arable land% Wetland% Urban% TForest% TArable% TWetland% TLakes% TUrban% Lake area Lake volume Lake length Lake width
Unit
128
Table 1 Definition, mean and range of environmental variables used to examine the environmental influence on (Rutilus rutilus L.) from 79 circumneutral lakes in the County of Uppsala, Sweden, 1991–1993
Conductivity TotP TotN TOC TCPUE TNCPUE Pe-CPUE Pe-NCPUE Ro-CPUE Ro-NCPUE N species Dec O2 a
m m % days meq l1 meq l1 meq l1 meq l1 meq l1 mg Pt l1 mS m1 mg P l1 mg N l1 mg l1 kg kg kg
mg l1
3.7 1.9
1.0 0.5
12.5 6.4
1.63 190 1100 180 30 240 4.0 7.7 92
0.51 1.6 170 40 1 75 0.6 6.5 15
2.43 1570 2560 660 85 760 21 8.8 260
19.1 34 1020 18.5 3.7 85 0.9 30 0.8 38 6.2 8.1
4.4 3 400 5 0.3 8.8 0.0 0.0 0.0 0.0 2 0.2
49.3 275 2980 39.8 17.0 384 3.1 215 2.7 302 13 12.6
Maximum lake depth Average lake depth Relative depth, estimates lake stratification stability (Dmaxlake diam1)b Volume development=3 mean depth/max depth (describes lake shape)b Theoretical lake water retention time Total Ca contentc,d Total Mg contentc,d Total K contentc,d Total Na contentc,d Total Fe contentc,d pH measured on cold stored water (588C) within 48 h Water colour measured on filtered water (Whatman GF/C) with comparator 1991–1992 and calculated from absorbances 1993 (420 nm) Conductivity measured on unfiltered water within 48 h (stored at 58 8C) Total P content. Molybdate reactive phosphate analysis after K2S2O8 digestestionc Total N content. Measured by second derivate spectroscopyc Total organic carbon content after H+ addition and aerationc Total fish catch per unit effort Average total number of fish per unit effort Catch of perch (Perca fluviatilis) per unit effort Average number of perch per unit effort Catch of roach (Rutilus rutilus) per unit effort Average number of roach per unit effort Number of fish species caught O2 content at 0.5 m depth in December 1988 (after 1 month of ice coverage as a measure on the capacity to withstand oxygen depletion)
Refers to catchment area excl. eventual sub-catchments of upstream lakes. Confer Hakanson and Peters (1995). c Measured on unfiltered deep frozen water. d Analysed by AAS. b
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Dmax Dmean Drel VD Tr Ca Mg K Na Fe pH Water colour
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Fig. 1. The geographic locations of the investigated lakes in the County of Uppsala, Sweden, 1991–1993. Lakes are marked according to principal land use in the catchment areas. The NW–SE separator, used to divide the two geologically distinct areas, is also shown.
analytical precision on small specimens, 2–10 specimens of approximately the same size were mixed together. The samples were homogenized and lyophilized for subsequent 137Cs analyses. 2.3.
137
Cs analysis
The dried fish samples were compressed as much as possible to minimize their volumes prior to 137Cs analysis. The 137Cs activity was measured with an Intertechnique CG 4000 Gamma Counting System, equipped with a NaI detector. To avoid differences in activity between the different years due to physical decay, the 137 Cs contents obtained were decay-corrected to May 1st, 1986. In total, 2132 perch and 1787 roach samples, including the mixed samples, were analysed. 2.4.
137
Cs levels
Occasionally, a distinct size dependence of the 137Cs content in fish may be apparent, even though there is a large contrast between different studies. The
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variation seems to partly be attributable to fish species (Rowan, Chant, & Rasmussen, 1998), but also to whether the prevailing radiocaesium conditions are in steady state. To avoid this fish-size dependency in the present study, lake specific linear regressions of 137Cs content in fish muscle vs. the natural logarithm of the fish length were used. The regression intercepts are used to estimate the basal 137Cs levels in the fish, and these are assumed to estimate the 137Cs levels in small specimens. The regression slopes, which are believed to represent net accumulation due to the 137Cs content in the food and metabolic processes, were analysed in separate PLS models, to evaluate if the accumulation is affected by the environmental predictors used. This method, to separate lake specific regressions of a contaminant vs. fish length, if there is a prominent fish-size dependency, has earlier proved successful in estimating lakespecific levels of Hg in perch (Sonesten, 2000b, 2001). 2.5. Environmental data Data on land use and lake morphometry were obtained from the literature (Brunberg & Blomqvist, 1998, and refs. therein), with some minor additions and corrections. Because the immediate surroundings probably have the largest influence on lakes, distinction was made between the total catchment area and the area excluding any sub-catchments of upstream lakes. Lake water chemical composition was analysed on subsurface samples collected concurrently with the fish sampling. The capacity for the lakes to withstand oxygen depletion during wintertime was estimated by the dissolved oxygen content after 1 month of ice-cover, from an earlier survey (Sonesten, 1989). The chemical analyses were made according to Swedish Standard Methods or similar methods described in Goedkoop and Sonesten (1995). Data on the fish landings from the standardized fishing were used to assess the fish biomass (Nyberg & Degerman, 1988). The catch was given as total number and weight of all species caught, and separately for perch and roach. The lake X and Y co-ordinates, within the Swedish National Grid (Fig. 1), were used to describe its geographical position. Additionally, the lake distance (Z) to the NW–SE separator was used. This separator divides the county into two geologically different areas (Sonesten, 2000a). The hilly fissure-valley landscapes of the southern and eastern parts of Uppsala County form one area, and the Taiga plain, with coniferous forest, mires and eskers, constitutes the other area in the north and northwestern part of the county. The separator also describes the lake ontogenesis in the area, as the northwestern part emerged substantially earlier from the Litorina Sea, a predecessor to the Baltic Sea, than did the southeastern area (cf. Segerberg, 1999). 2.6. Statistical evaluation The environmental impact on the 137Cs levels in perch and roach was statistically revealed using partial least-squares regression models (also called projections on latent structures or simply PLS). PLS is a biased multivariate regression method with similarities to principal component analysis (PCA) and its more common regression
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extension, the principal component regression (PCR) (Ho¨skuldsson, 1988; Garthwaite, 1994). The PCA is, however, a maximum variance projection method in the X-space, whereas PLS is a maximum covariance model of the relationship between the X- and Y-space (Eriksson, Johansson, & Wold, 1999). All these methods use a few summarizing latent components, and the PLS components resemble the principal components used in the PCR analysis. However, there is one principal difference between the two different kinds of latent components. The PLS is a biased method as it uses the information in the response variable(s) to extract only the useful variance among the explanatory variables to form its components, i.e. the PLS maximizes the covariance between the explanatory and response variable(s). This has the implication that latent components of the PLS analysis are usually smaller than comparable PCA components, but on the other hand they give a better relationship between the X- and Y-spaces. In contrast, the PCA components of the PCR method are formed, irrespective of the response variable, in a previous principal component analysis. This procedure might even obstruct the analysis if the first PCA components are irrelevant to the response variable. Furthermore, the PLS and the PCA methods also produce two similar plots, the X-score and the loading plots, which are vital for model evaluation. These plots are complementary and superimposable on each other. The X-scores project the relationship between observations (lakes), i.e. observations close to each other in the plot are comparatively more similar than distant observations. The loadings show the relationship between the variables. In contrast to PCA, there are two kinds of loadings in PLS, the analogue to the PCA loadings and the PLS weights. Most frequently, the weights are used, as they summarize the correlation structure between the explanatory and response variables (environmental predictors and 137Cs levels, respectively). In addition to the PLS weights there are two other possibilities for model interpretation, especially for more complex models (op. cit.). The PLS regression coefficients are pooling the information over all PLS dimensions (components), which gives one vector of model information per response variable. The variable influence on projection (VIP) gives a summary of the information in the PLS weights and regression coefficients, i.e. it pools the information over all Yvariables and PLS dimensions, resulting in one value for every explanatory variable. In comparison, the PLS weights give both the correlation structure (multidimensional) and the strength. The regression coefficients give the direction (onedimensional) and strength of the influence, whereas the VIP only gives the strength of the relationship. The VIP might be used to find the most significant explanatory variables to the model. Unfortunately, no defined limit exists for the statistical significance, but limits of 0.7–0.8 or 1.0 are often used (Eriksson et al., 1999). In this study significant explanatory variables were defined as having a VIP>1.0, a moderately significant variable having VIP ¼ 0:821:0, whereas VIP 50.8 signifies low importance. The environmental predictors were not transformed, as transformations did not significantly enhance the models tested. All statistical analyses were made on autoscaled predictors, i.e. mean centred and scaled to unit variance. This gave them all the same weight in the analyses, even though the measurements were not on the
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same scale (Geladi & Kowalski, 1986). Cross-validation was used to appraise the number of significant PLS components and to estimate the predictive ability of the various PLS models (Eriksson, Hermens, Johansson, Verhaar, & Wold, 1995); Eriksson et al., 1999; Lindgren, Hansen, Karcher, Sjo¨stro¨m, & Eriksson, 1996). Components were judged to have a significant contribution to the model if the ratio between the PRESSa (predicted residual sum of squares) and SSa1 (residual sum of squares of the component before the current) was less than 0.9 (Eriksson et al., 1999). To avoid spurious results, caused by serious inherent background correlations in the data set, permutation tests were used. The test was used to reveal eventual latent structures by scrambling the response variable 25 times and retesting the model, with cross-validation, on every new data set. The background correlation is given by the intercepts of the scrambled R2 and Q2 (cross-validated R2 ) regressed against the R2 of the original 137Cs levels and the permuted levels (Lindgren, Hansen, Karcher, Sjo¨stro¨m, & Eriksson, 1996). The PLS models were made using Simca-P1 7.0 (Umetrics AB).
3. Results There were large variations in 137Cs content between lakes regarding both perch and roach. Perch had higher contents than roach. Maximum recorded 137Cs content in perch was 23 750 Bq kg1 (w.w.), whereas the highest noted radiocaesium content in roach was 3000 Bq kg1 (w.w.). In a majority of the lakes, there was a pronounced loglinear relationship between the 137Cs content and fish length. Generally, this relationship was positive, but occasionally it was negative or absent. This was especially true for roach, particularly if only a few and/or small specimens were caught. Consequently, also the lake-specific 137Cs levels, here defined as the intercept of the 137Cs content regressed against the fish length, were greatly variable for both species. 3.1. The PLS model on
137
Cs levels in fish
The radiocaesium levels in perch and roach responded virtually identically to the impact of the environmental variables describing various lake and catchment area characteristics. Hence, the environmental influence will be presented here in a combined PLS model, with the 137Cs levels in both perch and roach as response variables. The resultant model contains two significant PLS components. The model explains about 77% of the variation in 137Cs in perch and roach, by using approximately 30% of the variation in the environmental predictors (Table 2). There is a good predictive ability, as measured by cross-validation, as Q2 is close to R2 (Table 2). Moreover, no serious coincidental latent background-correlation exists in the explanatory data set, as the model R2 and Q2 are well separated from the permuted models (Fig. 2). There are two lakes that strongly deviate from the others, as depicted by their X-scores (Fig. 3). These two lakes, Lake Alstasjo¨n and Bjo¨rklinge-Langsjo¨n, are extreme in both dimensions of the relationship between
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Table 2 Cumulative percent variation explained (R2 ) and predictive ability (Q2 ) of PLS models on 137Cs levels in perch and roach vs. 53 environmental predictors (cf. Table 1). A combined model of the regression intercepts of 137Cs content vs. fish length, as well as the species in separate models of the intercepts and slopes, are shown. The variation adjusted for degrees of freedom (R2adj ) and the amount of variation in the X-matrix (X) used by the models is also given. Significant (PRESS=SSres > 0:9) and non-significant PLS components are denoted s and ns, respectively PLS component
Percent variation X
R2
s/ns R2adj
Q2
137
Combined model on regression intercepts of Cs in perch and roach vs. fish length 1 21 65 64 61 2 30 77 76 68 3 39 80 80 68 4 46 83 82 67 5 52 85 84 66 Intercept of 1 2 3
137
Intercept of 1 2 3
137
Cs in perch vs. fish length 21 29 38 Cs in roach vs. fish length 21 31 39
Slope of 1 2
137
Slope of 1
137
Cs in perch vs. fish length 19 31 Cs in roach vs. fish length 17
s s ns ns ns
69 83 86
69 83 86
66 71 71
s s ns
62 74 79
61 74 78
57 63 62
s ns ns
33 43
32 42
22 21
s ns
19
18
6
ns
Fig. 2. The PLS model on 137Cs levels in perch and roach vs. 53 environmental predictors is well separated from any eventual background correlation, as measured by the intercepts of the permuted R2’s and Q2’s regressed against the observed 137Cs levels and the permuted levels.
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Fig. 3. Similarities between the lakes among the investigated environmental variables. The relationship is given by the X-scores of the combined PLS model for 137Cs levels in perch (Perca fluviatilis L.) and roach (Rutilus rutilus L.) vs. 53 environmental predictors. Symbols as depicted in Fig. 1. Note! The figure is superimposable on Fig. 4.
lakes (X-scores). They are the only ones having the combination of very high coverage of arable land (low X-scores in the first component), and very little forested areas (high X-scores in the second component), in their catchment areas. Their major contribution to the model is to justify the inclusion of the second PLS component, which is non-significant if the potential outliers are excluded. The exclusion of the outliers would increase the R2 of the first component (68% compared to 65%), whereas the impact on which environmental predictors are important and the direction of their influence is negligible. Hence, due to their modest impact, these two lakes are kept within the model. 3.2. Effects of
137
Cs deposition on the
137
Cs levels in fish
Naturally, an immense part of the variation in 137Cs levels in the fish derives from the differences in radiocaesium that have reached the lakes. In the model, the most important measure describes the 137Cs deposition per unit area (137Cs-fallout in Figs. 4 and 5). The other measures, which distinguish between the amounts of 137Cs that were deposited on the lakes, on their close vicinity and on distant parts of the catchment areas, have no statistically significant contribution to the observed differences in fish radiocaesium levels (Figs. 4 and 5). However, there is a tendency that the influence from 137Cs deposition in a lake’s vicinity is comparatively more important than the more distant deposition. Particularly, the direct deposition on the lake surface has a comparatively higher impact on the observed 137Cs levels in the fish (Fig. 4).
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Fig. 4. The correlation structure between 53 environmental predictors (cf. Table 1) and the 137Cs levels in perch (Perca fluviatilis L.) and roach (Rutilus rutilus L.), as given by the PLS weights (w*c1 and w*c2) for the two first PLS components. Abbreviations according to Table 1. Note! The figure is superimposable on Fig. 3.
3.3. Impact of land use/catchment area on the
137
Cs levels in fish
Except for the high impact of the 137Cs fallout, the first PLS component chiefly distinguishes between lakes heavily affected by wetlands or arable land in their catchment areas (Fig. 4). This is also visualized among the X-scores, which describes the relationship between the lakes, as lakes primarily impounded by arable land are generally found on the left side of the X-score plot and wetland lakes are essentially found on the opposite side (Fig. 3). On the contrary, forest lakes only possess a detectable influence through the second PLS component (Figs. 3 and 4), even though the accumulated influence (VIP) over the two components is not significant (Fig. 5). Other kinds of land use, i.e. urban areas and the amount of upstream lakes, have no major influence on the observed 137Cs levels. This is also true for the other measures that describe the size of the catchment area and its relation to lake size. However, one exception is the size of the most adjacent catchment area, i.e. without eventual sub-catchments of upstream lakes, and its ratio to the lake surface. These predictors have a moderate impact on the radiocaesium levels in the fish (Figs. 4 and 5). 3.4. Influence of lake characteristics on the
137
Cs levels in fish
The significance of various dissolved ions in lake water on the observed 137Cs levels in fish is clearly demonstrated by their closely connected and numerically large
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Fig. 5. PLS regression coefficients summarizing the influence of the 53 environmental predictors, over the significant two first PLS components, on the 137Cs levels in perch (Perca fluviatilis L.) and roach (Rutilus rutilus L.). Bold typeface shows highly significant influence (variable influence on projection, VIP>1.0), Bold and italic typeface illustrates modest impact (VIP=0.8–1.0), Other predictors have low influence (VIP 5 0.8).
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PLS weights (Fig. 4). The importance of the dissolved ion complex is chiefly in the first PLS component, but its summarized influence over both significant PLS components shows that it is one of the most influential factors (Fig. 5). Other important lake characteristics belong to the humic matter complex (water colour, TOC and Fe). On the contrary, the lake morphometric descriptors, like the volume development (Vd ) and relative depth (Drel ), did not possess any major influence on the 137Cs levels in perch and roach (Figs. 4 and 5). 3.5. Environmental impact on the regression slopes of
137
Cs vs. fish length
The environmental predictors had divergent effects on the regression slopes for the two species. No substantial influence on the regression slopes of roach was detected, whereas the PLS analysis on perch resulted in a statistically weak and unstable model with comparatively low predictive ability (R2 ¼ 0:33, Q2 ¼ 0:22). The model weakness is also illustrated by the immense variation of the scrambled R2 ’s in the permutation test (Fig. 6), implying that the model can hardly be separated from the inherent background correlation among the explanatory variables. In comparison to the amount of variation explained by the model (33%), the model can therefore be suspected to be an artefact caused by a latent structure in the data set. The model needs to be evaluated further, to reveal any possible environmental effect on the regression slope.
4. Discussion This study clearly shows the effect of the first 137Cs pulse shortly after the Chernobyl accident. A major part of the differences in radiocaesium levels, observed
Fig. 6. The PLS model on the regression slope of 137Cs activity in perch vs. fish length and 53 environmental predictors is severely affected by an inherent background correlation, as measured by the intercepts of the permuted R20 s and Q20 s regressed against the observed 137Cs levels and the permuted levels.
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in perch and roach from various lakes, can be explained by the 137Cs deposition on the lake’s vicinity. The difference between radiocaesium deposited on arable land and on wetlands seems to be particularly important (Figs. 4 and 5). Due to its high affinity for particles, especially clay minerals (cf. Avery, 1996), the 137Cs fraction that was deposited on farmland is comparatively immobile. In contrast, the portion that fell on wetlands seems to have leached out to the lake systems to a much higher degree, probably due to less suitable binding sites for the radiocaesium. This is in agreement with several field and laboratory studies that have shown higher 137Cs mobility in organic soils compared to mineral soils (e.g. Fredriksson, Garner, & Scott Russell, 1966; Hilton, Livens, Spezzano, & Leonard, 1993; Comans et al., 1998). On the other hand, in this study, 137Cs deposition on forested areas appears to have had a lesser impact on the levels encountered in the fish (Fig. 4). A comparatively higher transport of 137Cs from wetlands, compared to other kinds of soil, is also indicated by soil samples from one of the catchment areas. The radiocaesium content in samples from forested and cultivated soils was generally about 6 times higher than the content in wetland samples (Broberg, unpublished). Likewise, the 137Cs in wetland samples was also typically found at deeper strata in the soil profile than in other kinds of soil (op. cit.). The low mobility of 137Cs from surrounding soils is also implied by the modest influence of the deposition on distant parts of the catchments (Fig. 4). The significance of lake geographic location (X, Y, and Z) probably is an expression of the prominent southeastern-ward gradient in 137Cs deposition (SGC, 1986). Additionally, there is also a concomitant gradient in lake onthogeny, as the NW lakes are substantially older than the SE lakes, and they are located on a plain at a comparatively higher altitude (Segerberg, 1999). This is manifested by the positive relationship between the 137Cs levels in fish and lake altitude (MASL in Figs. 4 and 5), as well as its negative relation to catchment area inclination (altitude difference in Figs. 4 and 5). The differences in lake onthogeny also have the implication that the northwestern lakes are predominantly more influenced by wetlands and consequently they are more humic and low in dissolved ions. The concentrations of major cations are believed to a great extent to govern the bioavailability of 137Cs in lakes, whereas humic matter seems to have only a minor influence (Penttila¨, Kairesalo, & Uusi-Rauva, 1993). This is in agreement with the present study, which distinctly shows the negative relationship between the concentrations of major cations and the observed 137Cs levels in perch and roach (Figs. 4 and 5). However, the positive correlation with the humic matter complex (water colour, TOC and Fe) is less clear. It could be an expression of the commonly observed negative relationship between humic matter and lake water Ca and Mg content (cf. Stumm & Morgan, 1981), but it could also reflect the strong coupling between the 137Cs levels in the fish and the influence of the wetlands. The lake pH seems to have only a minor impact on the radiocaesium levels in the fish, which is in accordance with other studies (Ha˚kanson & Andersson, 1992). In comparison to other investigations (Broberg, Malmgren, & Jansson, 1995; Meili, Braf, & Konitzer, 1997), morphometric parameters only have a modest influence (Figs. 4 and 5). Except the strong effect of lake altitude and catchment area
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inclination (altitude difference), only the size of the adjacent catchment area and maximum water depth have a moderate impact on the 137Cs levels in perch and roach. Probably, the catchment area influence reflects the more ‘‘accessible’’ radiocaesium that was deposited in the lake’s vicinity. The inverse relationship between maximum depth and 137Cs levels in the fish presumably expresses the detected effect that fish in shallow lakes have comparatively higher radiocaesium levels than those in deeper lakes (Broberg et al., 1995). Contrastingly, the volume development (Vd ) and relative depth (Drel ), which describes the lake shape and lake protection against resuspension (Ha˚kanson & Peters, 1995), did not have any major influence on the 137Cs levels in the fish (Figs. 4 and 5). Nevertheless, there is a tendency that the radiocaesium levels were higher in comparatively larger and shallower lakes, as indicated by the negative relationship between 137Cs levels and maximum depth, mean depth and Drel , whereas the relationship between 137Cs and Vd is positive (Fig. 4). This is in agreement with Broberg et al. (1995), who found resuspension of 137Cs-contaminated sediment maintained high 137Cs levels in fish in shallow, wind-exposed lakes, especially in combination with an extensive water retention time. In contrast to the reported effects of resuspended 137Cs-contaminated sediments, a remobilization of sediment-bound 137Cs may occur under reduced conditions, especially with elevated NH+ 4 concentrations (Evans et al., 1983; Pardue, DeLaune, Patrick, & Whitcomb, 1989; Davison, Spezzano, & Hilton, 1993). In this study, no indications of such remobilization can be found. In contrast, there is a positive correlation between the capacity to withstand oxygen depletion (Dec. O2 in Fig. 4) and the observed 137Cs levels in the fish. This positive relationship could be caused by the previously discussed more profound resuspension in shallow lakes. The influence of sampling year is weak, even though there is an indication of declining 137Cs levels with increasing year (1991–1993), as there is a negative relationship between sampling year and radiocaesium levels (Figs. 4 and 5). Other studies in the area show fairly stable 137Cs levels in small specimens of perch and roach during this time period (Sundbom, in prep.), which probably causes the nonsignificant relationship depicted in this study. The 137Cs content in both perch and roach possessed a prominent size dependency, which made the use of e.g. geometric means unsuitable as the fish-size composition in the samples would heavily affect the means. Other kinds of measures to handle fishsize dependencies of environmental contaminants, e.g. mercury, include normalization (Johnels, Westermark, Berg, Persson, & Sjo¨strand, 1967) and the use of adjusted means in ANOVA’s (Watras, Back, Halvorsen, Hudson, Morrison, & Wente, 1998). However, these methods have been criticized as they do not completely remove the size dependencies when the slopes of the Hg to fish-size regression lines are not equal (Somers & Jackson, 1993; Tremblay, Legendre, Doyon, Verdon, & Schetagne, 1998; Sonesten, 2001). The method used here, i.e. using regression intercepts to estimate the 137Cs levels in fish, has successfully been used earlier to estimate Hg levels in perch, which also had a prominent fish-size dependency (Sonesten, 2000b).
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5. Conclusions This study clearly shows the prominent effects on 137Cs levels in fish in lakes having a high proportion of wetlands in their catchment areas, after a pulse in radiocaesium deposition. Contrarily, the deposited 137Cs only marginally affected fish in lakes surrounded by arable land, as the radiocaesium was retained in the surrounding soils. Additionally, the 137Cs deposition on a lake’s vicinity was found to have a more profound effect than more distant deposition. The 137Cs bioavailability seems to be controlled predominantly by the lake water cation content, as hardwater lakes generally possessed significantly lower 137Cs levels in the fish. In contrast to other studies, the present investigation only shows a minor influence of resuspended 137Cs-contaminated sediments on the radiocaesium levels in fish.
Acknowledgements The author wishes to express his gratitude to Anders Broberg, Marcus Sundbom, and two anonymous reviewers who substantially improved the manuscript.
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Penttila¨, S., Kairesalo, T., & Uusi-Rauva, A. (1993). The occurence and bioavailability of radioactive 137 Cs in small forest lakes in southern Finland. Environmental Pollution, 82, 47–55. Persson, C., Rodhe, H., & De Geer, L. -E. (1987). The Chernobyl accident } a meteorological analysis of how radionuclides reached and were deposited in Sweden. Ambio, 16, 20–31. Rowan, D. J., Chant, L. A., & Rasmussen, J. B. (1998). The fate of radiocesium in freshwater communities } Why is biomagnification variable both within and between species?. Journal of Environmental Radioactivity, 40(1), 15–36. Segerberg, A. (1999). Ba¨linge mossar } Kustbor i Uppland under yngre stenaldern (The Ba¨linge bogs } Neolithic coastal settlement in Uppland, eastern middle Sweden, In Swedish with English summary). AUN 26. Ph.D. thesis, Dept. of Archeology and Ancient History, Uppsala University, Sweden. 243 pp. SGC. (1986). Swedish Geological Company. Map of 137Csm2 ground surface. Results from aerial surveys May to October 1986. Swedish Radiation Protection Institute. Smith, J. T., Kudelsky, A. V., Ryabov, I. N., & Hadderingh, R. H. (2000). Radiocaesium concentration factors of Chernobyl-contaminated fish: a study of the influence of potassium, and ‘‘blind’’ testing of a previously developed model. Journal of Environmental Radioactivity, 48, 359–369. SNA. (1992). National atlas of Sweden } the environment (1st ed.). Stockholm, Sweden; Almqvist & Wiksell International, ISBN 91-87760-10-X. Somers, K. M., & Jackson, D. A. (1993). Adjusting mercury concentration for fish-size covariation: A multivariate alternative to bivariate regression. Canadian Journal of Fisheries and Aquatic Science, 50, 2388–2396. Sonesten, L. (1989). Sammansta¨llning och utva¨rdering av syrgasdata fra˚n ett hundratal sjo¨ar i Uppsala la¨n. Stencil, Upplandsstiftelsen, Uppsala 12 pp. (Compilation and evaluation of oxygen measurements from some hundred lakes in the county of Uppsala, Sweden, In Swedish. The Uppland foundation, Uppsala, Sweden). Sonesten, L. (2000a). Mercury content in roach (Rutilus rutilus L.) in circumneutral lakes } effects of catchment area and water chemistry. Environmental Pollution, in press. Sonesten, L. (2000b). Catchment area composition and water chemistry heavily affects mercury levels in perch (Perca fluviatilis L.) in circumneutral lakes. Submitted for publication. Sonesten, L. (2001). A comparison of fish mercury levels in lakes } adjusting for Hg and fish-size covariation. Manuscript. Stumm, W., & Morgan, J.J. (1981). Aquatic chemistry (2nd ed.) (780 pp.). New York: John Wiley & Sons. Tremblay, G., Legendre, P., Doyon, J. -F., Verdon, R., & Schetagne, R. (1998). The use of polynomial regression analysis with indicator variables for interpretation of mercury in fish data. Biogeochemistry, 40, 189–201. Watras, C. J., Back, R. C., Halvorsen, S., Hudson, R. J. M., Morrison, K. A., & Wente, S. P. (1998). Bioaccumulation of mercury in pelagic freshwater food webs. The Science of the Total Environment, 219, 183–208.
Land use influence on 137Cs levels in perch (Perca fluviatilis L.) and roach (Rutilus rutilus L.) Lars Sonesten* Department of Limnology, Evolutionary Biology Centre, Uppsala University, Norbyva¨gen 20, SE-752 36 Uppsala, Sweden Received 8 April 2000; received in revised form 12 September 2000; accepted 18 September 2000
Abstract The environmental influence on Chernobyl-derived 137Cs levels in perch (Perca fluviatilis L.) and roach (Rutilus rutilus L.) was revealed using partial least-squares regression (PLS). The 53 environmental predictors used describe land use in catchment areas, various catchment area and lake characteristics, lake water chemistry, and fish stock composition. The study showed a profound effect of land use on the 137Cs levels in fish. Radiocaesium deposited on arable land was retained in the soil to a greater extent than was 137Cs deposited on wetlands, which more easily leached out to the lake ecosystems. The 137Cs deposition close to the lakes had a more pronounced effect on 137Cs levels in the fish than did more distant deposition. The radiocaesium bioavailability is mainly governed by lake water cation content, as hardwater lakes had significantly lower 137Cs content in fish. Resuspension of 137Cs contaminated sediments only had a limited influence on the observed levels in fish. # 2001 Elsevier Science Ltd. All rights reserved. Keywords: Radiocaesium; 137Cs; Fish; Partial least squares (PLS); Catchment area; Wetland; Farm land; Water chemistry; Lake morphometry; Resuspension
1. Introduction High amounts of 137Cs in the environment are still a major concern in areas of Sweden that were severely affected by the fallout from the Chernobyl accident in
*Present address: Department of Environmental Assessment, Swedish University of Agricultural Sciences, P.O. Box 7050, SE-750 07 Uppsala, Sweden. Tel.: +46-18-673145; fax: +46-18-673156. E-mail address: [email protected] (L. Sonesten). 0265-931X/01/$ - see front matter # 2001 Elsevier Science Ltd. All rights reserved. PII: S 0 2 6 5 - 9 3 1 X ( 0 0 ) 0 0 1 8 7 - 9
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Ukraine 1986. It was estimated that as much as 10% of the total amount of emitted 137 Cs from the damaged reactor might have fallen over Sweden (Persson, Rodhe, & De Geer, 1987). The deposition on Swedish ground was most severe in the eastern central and northern parts of the country. These areas were subjected to local rains, which efficiently washed out radioactive particles when the radioactive cloud reached Sweden (op. cit.). The main part of the 137Cs that entered aquatic ecosystems arrived during the first months after the reactor accident. After the first year, only small amounts of radiocaesium leached out from the catchment areas (Brittain, Bjo¨rnstad, & Sundblad, 1991). This main pulse of 137Cs originated from direct deposition on the lake surfaces and early surficial run-off (Andersson & Broberg, 1991). Due to the high affinity to particles, especially clay minerals (cf. Avery, 1996), the major proportion of the radiocaesium that entered the lakes was rapidly deposited in the sediments (Broberg & Andersson, 1991). However, the small proportion that entered the food web had serious consequences for the concentrations in biota, with high radiocaesium levels in a variety of organism groups. The 137Cs was readily taken up by low trophic levels, and sequentially a pulse of radiocaesium was transferred up through the food web (Meili, Forseth, Nordlinder, & Saxe´n, 1991; Broberg & Andersson, 1991), as the significance of 137Cs in ingested food becomes more prominent at higher trophic levels (cf. Avery, 1996). There was a pronounced time-lag that increased for every trophic level (Meili et al., 1991). Consequently, the maximum levels in planktivorous fish were reached in late summer 1986, but the corresponding maxima in piscivorous fish were not reached until 1987–1988 (Broberg & Andersson, 1991). Thereafter, the 137Cs content in fish has decreased to fairly stable levels, though the levels are species and size specific, and exhibit rather large differences between lakes (Sundbom, in prep.). Particularly, resuspension of 137Cs-contaminated sediments is believed to be responsible for a large part of the interlake differences, as shallow lakes, having a high degree of resuspension, tend to possess more radiocaesium in fish compared to deeper lakes (Broberg, Malmgren, & Jansson, 1995; Meili, Braf, & Konitzer, 1997). Another important factor is the 137Cs bioavailability for low trophic levels, which is considered to primarily be governed by the water cation concentration, especially the K+ content (cf. Avery, 1996; Hagstro¨m, 1999), via a chemical dilution (Kolehmainen, Ha¨sa¨nen, & Miettinen, 1967). This is commonly manifested as a negative correlation between the concentration of K+ (and other cations) in water and the 137Cs content in fish, due to the importance of ingested 137Cs at higher trophic levels (e.g. Carlsson, 1976; Smith, Kudelsky, Ryabov, & Hadderingh, 2000). This study aims to show the importance of various catchment area and lake characteristics on the 137Cs levels in perch (Perca fluviatilis L.) and roach (Rutilus rutilus L.). Partial least-squares regression (PLS) was used to evaluate the environmental influence on the 137Cs levels. PLS is a biased multivariate projection method, especially suited to use on multicollinear data (Eriksson, Hermens, Johansson, Verhaar, & Wold, 1995).
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2. Material and methods The 137Cs levels in perch (Perca fluviatilis L.) and roach (Rutilus rutilus L.), from a regional survey of 79 lakes in the county of Uppsala, Sweden, were related to 53 environmental variables describing land use in the catchment area, lake morphometry, lake water chemistry, and fish stock characteristics (Table 1). 2.1. Area description The county of Uppsala is situated north of Stockholm, Sweden (Fig. 1). It belongs to the boreal forest region, but in contrast to most other parts of the country, the soils are greatly influenced by calcareous moraines (Ingmar & Moreborg, 1976). Hence, even humic lakes are generally non-acidic, possessing high concentrations of dissolved ions. Sonesten (2000a) comprehensively describes the area. The county constitutes three different physical geographic regions (CABU, 1986; SNA, 1992). The southern part of the county is dominated by a mosaic-like landscape, with coniferous forests and arable land, in a fissure-valley terrain with clayey valleys, lakes and streams. An extension of the Stockholm and Roslagen archipelagos forms the eastern part of the county. Coniferous forests in a hilly fissure-valley landscape dominate this area. The north and northwestern part of the county belongs to the transitional zone to the Taiga terrain. It is a plain dominated by coniferous forests, large mires and eskers. The 137Cs deposition in the area, originating from the Chernobyl accident, varied greatly. The northwestern part of the county was most seriously affected, with up to 100 kBq m2 (SGC, 1986). The deposition declined in a southeastward direction, with about 20 kBq m2 in the central part of the county, to less than 5 kBq m2 in the southeastern areas (op. cit.). 2.2. Fish samples Fish were caught by means of standardized fishing during 1991–1993, starting in the end of July and continuing to the beginning of September each year (Nyberg, 1999). Every lake was fished on one occasion, but in the case of larger lakes, the fishing was extended for several days. The sampling was standardized in relation to lake morphometry to give comparable landings. To get representative specimens of varying size, benthic multi-mesh gillnets were used (Nyberg & Degerman, 1988). The fish were kept cool and, generally, total length and weight were measured and tissue samples from the fillets were taken on fresh samples. If the fish could not be processed the same day (esp. during 1991), they were measured and frozen (208C) as soon as possible. Occasionally, fish length and weight were measured only after thawing and thereafter corrected for size reduction during storage. The correction was made by applying a linear regression from other samples with known size reduction due to their respective storage time. If the specimens were large enough (>approx. 12 cm), tissue samples were taken solely from the dorsal muscle, but from smaller specimens as much muscle tissue as possible was taken. To enhance
Variable
m m m km2 km2
% % % % % % % % % km2 Mm3 km km
Cs levels in perch (Perca fluviatilis L.) and roach
Mean
Min
Max
Definition
13.0 10.5 153 645 492
1.07 1.96 3 0.09 0.42 1.17 0.00
7.15 8.78 40 85 2060 11865 9810
26 31 27 59 14 96 26 76 10 13 1 75 10 13 2 1 1.0 2.1 1.9 0.7
0.3 5 5 0.15 0.14 2.14 2.14 34 0 0 0 40 0 0 0 0 0.03 0.03 0.22 0.12
88 110 75 704 103 1840 205 100 65 57 11 95 46 57 15 5 9.42 26.5 15 2.55
The intercept of the linearly regressed 137Cs content in perch vs. fish length The intercept of the linearly regressed 137Cs content in roach vs. fish length Deposition of 137Cs per m2 ground surface Estimated total amount of 137Cs deposited on lake surface Estimated total amount of 137Cs deposited on catchment areaa Estimated total amount of 137Cs deposited on whole catchment area Difference between total and close deposition X co-ordinate according to the Swedish National Grid Y co-ordinate according to the Swedish National Grid Distance from the NW-SE separator (cf. Fig. 1) Lake altitude (meters above sea level) Altitude difference within the whole catchment area Altitude difference within the catchment areaa The surface of the whole catchment area The surface of catchment areaa The ratio between the whole catchment area and lake area The ratio between the catchment areaa and lake area Forest coverage of the catchment areaa Arable land coverage of the catchment areaa Wetland coverage of the catchment areaa Coverage of other kinds of land use in the catchmenta (mainly urban areas) Forest coverage of the whole catchment area Arable land coverage of the whole catchment area Wetland coverage of the whole catchment area Lake coverage of the whole catchment area Coverage of other kinds of land use of the whole catchment area Lake area Lake volume Maximum lake length Maximum lake width (perpendicular to the maximum length)
1
ln(Bq kg w.w.) ln(Bq kg1 w.w.) Bq m2 GBq GBq GBq GBq
137
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CsPerch CsRoach 137 Cs dep Lake dep Close dep Total dep Dist dep X Y Z MASL TotAlt AltDiff TCatchArea CatchArea TCatALak CatALak Forest% Arable land% Wetland% Urban% TForest% TArable% TWetland% TLakes% TUrban% Lake area Lake volume Lake length Lake width
Unit
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Table 1 Definition, mean and range of environmental variables used to examine the environmental influence on (Rutilus rutilus L.) from 79 circumneutral lakes in the County of Uppsala, Sweden, 1991–1993
Conductivity TotP TotN TOC TCPUE TNCPUE Pe-CPUE Pe-NCPUE Ro-CPUE Ro-NCPUE N species Dec O2 a
m m % days meq l1 meq l1 meq l1 meq l1 meq l1 mg Pt l1 mS m1 mg P l1 mg N l1 mg l1 kg kg kg
mg l1
3.7 1.9
1.0 0.5
12.5 6.4
1.63 190 1100 180 30 240 4.0 7.7 92
0.51 1.6 170 40 1 75 0.6 6.5 15
2.43 1570 2560 660 85 760 21 8.8 260
19.1 34 1020 18.5 3.7 85 0.9 30 0.8 38 6.2 8.1
4.4 3 400 5 0.3 8.8 0.0 0.0 0.0 0.0 2 0.2
49.3 275 2980 39.8 17.0 384 3.1 215 2.7 302 13 12.6
Maximum lake depth Average lake depth Relative depth, estimates lake stratification stability (Dmaxlake diam1)b Volume development=3 mean depth/max depth (describes lake shape)b Theoretical lake water retention time Total Ca contentc,d Total Mg contentc,d Total K contentc,d Total Na contentc,d Total Fe contentc,d pH measured on cold stored water (588C) within 48 h Water colour measured on filtered water (Whatman GF/C) with comparator 1991–1992 and calculated from absorbances 1993 (420 nm) Conductivity measured on unfiltered water within 48 h (stored at 58 8C) Total P content. Molybdate reactive phosphate analysis after K2S2O8 digestestionc Total N content. Measured by second derivate spectroscopyc Total organic carbon content after H+ addition and aerationc Total fish catch per unit effort Average total number of fish per unit effort Catch of perch (Perca fluviatilis) per unit effort Average number of perch per unit effort Catch of roach (Rutilus rutilus) per unit effort Average number of roach per unit effort Number of fish species caught O2 content at 0.5 m depth in December 1988 (after 1 month of ice coverage as a measure on the capacity to withstand oxygen depletion)
Refers to catchment area excl. eventual sub-catchments of upstream lakes. Confer Hakanson and Peters (1995). c Measured on unfiltered deep frozen water. d Analysed by AAS. b
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Dmax Dmean Drel VD Tr Ca Mg K Na Fe pH Water colour
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Fig. 1. The geographic locations of the investigated lakes in the County of Uppsala, Sweden, 1991–1993. Lakes are marked according to principal land use in the catchment areas. The NW–SE separator, used to divide the two geologically distinct areas, is also shown.
analytical precision on small specimens, 2–10 specimens of approximately the same size were mixed together. The samples were homogenized and lyophilized for subsequent 137Cs analyses. 2.3.
137
Cs analysis
The dried fish samples were compressed as much as possible to minimize their volumes prior to 137Cs analysis. The 137Cs activity was measured with an Intertechnique CG 4000 Gamma Counting System, equipped with a NaI detector. To avoid differences in activity between the different years due to physical decay, the 137 Cs contents obtained were decay-corrected to May 1st, 1986. In total, 2132 perch and 1787 roach samples, including the mixed samples, were analysed. 2.4.
137
Cs levels
Occasionally, a distinct size dependence of the 137Cs content in fish may be apparent, even though there is a large contrast between different studies. The
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variation seems to partly be attributable to fish species (Rowan, Chant, & Rasmussen, 1998), but also to whether the prevailing radiocaesium conditions are in steady state. To avoid this fish-size dependency in the present study, lake specific linear regressions of 137Cs content in fish muscle vs. the natural logarithm of the fish length were used. The regression intercepts are used to estimate the basal 137Cs levels in the fish, and these are assumed to estimate the 137Cs levels in small specimens. The regression slopes, which are believed to represent net accumulation due to the 137Cs content in the food and metabolic processes, were analysed in separate PLS models, to evaluate if the accumulation is affected by the environmental predictors used. This method, to separate lake specific regressions of a contaminant vs. fish length, if there is a prominent fish-size dependency, has earlier proved successful in estimating lakespecific levels of Hg in perch (Sonesten, 2000b, 2001). 2.5. Environmental data Data on land use and lake morphometry were obtained from the literature (Brunberg & Blomqvist, 1998, and refs. therein), with some minor additions and corrections. Because the immediate surroundings probably have the largest influence on lakes, distinction was made between the total catchment area and the area excluding any sub-catchments of upstream lakes. Lake water chemical composition was analysed on subsurface samples collected concurrently with the fish sampling. The capacity for the lakes to withstand oxygen depletion during wintertime was estimated by the dissolved oxygen content after 1 month of ice-cover, from an earlier survey (Sonesten, 1989). The chemical analyses were made according to Swedish Standard Methods or similar methods described in Goedkoop and Sonesten (1995). Data on the fish landings from the standardized fishing were used to assess the fish biomass (Nyberg & Degerman, 1988). The catch was given as total number and weight of all species caught, and separately for perch and roach. The lake X and Y co-ordinates, within the Swedish National Grid (Fig. 1), were used to describe its geographical position. Additionally, the lake distance (Z) to the NW–SE separator was used. This separator divides the county into two geologically different areas (Sonesten, 2000a). The hilly fissure-valley landscapes of the southern and eastern parts of Uppsala County form one area, and the Taiga plain, with coniferous forest, mires and eskers, constitutes the other area in the north and northwestern part of the county. The separator also describes the lake ontogenesis in the area, as the northwestern part emerged substantially earlier from the Litorina Sea, a predecessor to the Baltic Sea, than did the southeastern area (cf. Segerberg, 1999). 2.6. Statistical evaluation The environmental impact on the 137Cs levels in perch and roach was statistically revealed using partial least-squares regression models (also called projections on latent structures or simply PLS). PLS is a biased multivariate regression method with similarities to principal component analysis (PCA) and its more common regression
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extension, the principal component regression (PCR) (Ho¨skuldsson, 1988; Garthwaite, 1994). The PCA is, however, a maximum variance projection method in the X-space, whereas PLS is a maximum covariance model of the relationship between the X- and Y-space (Eriksson, Johansson, & Wold, 1999). All these methods use a few summarizing latent components, and the PLS components resemble the principal components used in the PCR analysis. However, there is one principal difference between the two different kinds of latent components. The PLS is a biased method as it uses the information in the response variable(s) to extract only the useful variance among the explanatory variables to form its components, i.e. the PLS maximizes the covariance between the explanatory and response variable(s). This has the implication that latent components of the PLS analysis are usually smaller than comparable PCA components, but on the other hand they give a better relationship between the X- and Y-spaces. In contrast, the PCA components of the PCR method are formed, irrespective of the response variable, in a previous principal component analysis. This procedure might even obstruct the analysis if the first PCA components are irrelevant to the response variable. Furthermore, the PLS and the PCA methods also produce two similar plots, the X-score and the loading plots, which are vital for model evaluation. These plots are complementary and superimposable on each other. The X-scores project the relationship between observations (lakes), i.e. observations close to each other in the plot are comparatively more similar than distant observations. The loadings show the relationship between the variables. In contrast to PCA, there are two kinds of loadings in PLS, the analogue to the PCA loadings and the PLS weights. Most frequently, the weights are used, as they summarize the correlation structure between the explanatory and response variables (environmental predictors and 137Cs levels, respectively). In addition to the PLS weights there are two other possibilities for model interpretation, especially for more complex models (op. cit.). The PLS regression coefficients are pooling the information over all PLS dimensions (components), which gives one vector of model information per response variable. The variable influence on projection (VIP) gives a summary of the information in the PLS weights and regression coefficients, i.e. it pools the information over all Yvariables and PLS dimensions, resulting in one value for every explanatory variable. In comparison, the PLS weights give both the correlation structure (multidimensional) and the strength. The regression coefficients give the direction (onedimensional) and strength of the influence, whereas the VIP only gives the strength of the relationship. The VIP might be used to find the most significant explanatory variables to the model. Unfortunately, no defined limit exists for the statistical significance, but limits of 0.7–0.8 or 1.0 are often used (Eriksson et al., 1999). In this study significant explanatory variables were defined as having a VIP>1.0, a moderately significant variable having VIP ¼ 0:821:0, whereas VIP 50.8 signifies low importance. The environmental predictors were not transformed, as transformations did not significantly enhance the models tested. All statistical analyses were made on autoscaled predictors, i.e. mean centred and scaled to unit variance. This gave them all the same weight in the analyses, even though the measurements were not on the
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same scale (Geladi & Kowalski, 1986). Cross-validation was used to appraise the number of significant PLS components and to estimate the predictive ability of the various PLS models (Eriksson, Hermens, Johansson, Verhaar, & Wold, 1995); Eriksson et al., 1999; Lindgren, Hansen, Karcher, Sjo¨stro¨m, & Eriksson, 1996). Components were judged to have a significant contribution to the model if the ratio between the PRESSa (predicted residual sum of squares) and SSa1 (residual sum of squares of the component before the current) was less than 0.9 (Eriksson et al., 1999). To avoid spurious results, caused by serious inherent background correlations in the data set, permutation tests were used. The test was used to reveal eventual latent structures by scrambling the response variable 25 times and retesting the model, with cross-validation, on every new data set. The background correlation is given by the intercepts of the scrambled R2 and Q2 (cross-validated R2 ) regressed against the R2 of the original 137Cs levels and the permuted levels (Lindgren, Hansen, Karcher, Sjo¨stro¨m, & Eriksson, 1996). The PLS models were made using Simca-P1 7.0 (Umetrics AB).
3. Results There were large variations in 137Cs content between lakes regarding both perch and roach. Perch had higher contents than roach. Maximum recorded 137Cs content in perch was 23 750 Bq kg1 (w.w.), whereas the highest noted radiocaesium content in roach was 3000 Bq kg1 (w.w.). In a majority of the lakes, there was a pronounced loglinear relationship between the 137Cs content and fish length. Generally, this relationship was positive, but occasionally it was negative or absent. This was especially true for roach, particularly if only a few and/or small specimens were caught. Consequently, also the lake-specific 137Cs levels, here defined as the intercept of the 137Cs content regressed against the fish length, were greatly variable for both species. 3.1. The PLS model on
137
Cs levels in fish
The radiocaesium levels in perch and roach responded virtually identically to the impact of the environmental variables describing various lake and catchment area characteristics. Hence, the environmental influence will be presented here in a combined PLS model, with the 137Cs levels in both perch and roach as response variables. The resultant model contains two significant PLS components. The model explains about 77% of the variation in 137Cs in perch and roach, by using approximately 30% of the variation in the environmental predictors (Table 2). There is a good predictive ability, as measured by cross-validation, as Q2 is close to R2 (Table 2). Moreover, no serious coincidental latent background-correlation exists in the explanatory data set, as the model R2 and Q2 are well separated from the permuted models (Fig. 2). There are two lakes that strongly deviate from the others, as depicted by their X-scores (Fig. 3). These two lakes, Lake Alstasjo¨n and Bjo¨rklinge-Langsjo¨n, are extreme in both dimensions of the relationship between
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Table 2 Cumulative percent variation explained (R2 ) and predictive ability (Q2 ) of PLS models on 137Cs levels in perch and roach vs. 53 environmental predictors (cf. Table 1). A combined model of the regression intercepts of 137Cs content vs. fish length, as well as the species in separate models of the intercepts and slopes, are shown. The variation adjusted for degrees of freedom (R2adj ) and the amount of variation in the X-matrix (X) used by the models is also given. Significant (PRESS=SSres > 0:9) and non-significant PLS components are denoted s and ns, respectively PLS component
Percent variation X
R2
s/ns R2adj
Q2
137
Combined model on regression intercepts of Cs in perch and roach vs. fish length 1 21 65 64 61 2 30 77 76 68 3 39 80 80 68 4 46 83 82 67 5 52 85 84 66 Intercept of 1 2 3
137
Intercept of 1 2 3
137
Cs in perch vs. fish length 21 29 38 Cs in roach vs. fish length 21 31 39
Slope of 1 2
137
Slope of 1
137
Cs in perch vs. fish length 19 31 Cs in roach vs. fish length 17
s s ns ns ns
69 83 86
69 83 86
66 71 71
s s ns
62 74 79
61 74 78
57 63 62
s ns ns
33 43
32 42
22 21
s ns
19
18
6
ns
Fig. 2. The PLS model on 137Cs levels in perch and roach vs. 53 environmental predictors is well separated from any eventual background correlation, as measured by the intercepts of the permuted R2’s and Q2’s regressed against the observed 137Cs levels and the permuted levels.
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Fig. 3. Similarities between the lakes among the investigated environmental variables. The relationship is given by the X-scores of the combined PLS model for 137Cs levels in perch (Perca fluviatilis L.) and roach (Rutilus rutilus L.) vs. 53 environmental predictors. Symbols as depicted in Fig. 1. Note! The figure is superimposable on Fig. 4.
lakes (X-scores). They are the only ones having the combination of very high coverage of arable land (low X-scores in the first component), and very little forested areas (high X-scores in the second component), in their catchment areas. Their major contribution to the model is to justify the inclusion of the second PLS component, which is non-significant if the potential outliers are excluded. The exclusion of the outliers would increase the R2 of the first component (68% compared to 65%), whereas the impact on which environmental predictors are important and the direction of their influence is negligible. Hence, due to their modest impact, these two lakes are kept within the model. 3.2. Effects of
137
Cs deposition on the
137
Cs levels in fish
Naturally, an immense part of the variation in 137Cs levels in the fish derives from the differences in radiocaesium that have reached the lakes. In the model, the most important measure describes the 137Cs deposition per unit area (137Cs-fallout in Figs. 4 and 5). The other measures, which distinguish between the amounts of 137Cs that were deposited on the lakes, on their close vicinity and on distant parts of the catchment areas, have no statistically significant contribution to the observed differences in fish radiocaesium levels (Figs. 4 and 5). However, there is a tendency that the influence from 137Cs deposition in a lake’s vicinity is comparatively more important than the more distant deposition. Particularly, the direct deposition on the lake surface has a comparatively higher impact on the observed 137Cs levels in the fish (Fig. 4).
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Fig. 4. The correlation structure between 53 environmental predictors (cf. Table 1) and the 137Cs levels in perch (Perca fluviatilis L.) and roach (Rutilus rutilus L.), as given by the PLS weights (w*c1 and w*c2) for the two first PLS components. Abbreviations according to Table 1. Note! The figure is superimposable on Fig. 3.
3.3. Impact of land use/catchment area on the
137
Cs levels in fish
Except for the high impact of the 137Cs fallout, the first PLS component chiefly distinguishes between lakes heavily affected by wetlands or arable land in their catchment areas (Fig. 4). This is also visualized among the X-scores, which describes the relationship between the lakes, as lakes primarily impounded by arable land are generally found on the left side of the X-score plot and wetland lakes are essentially found on the opposite side (Fig. 3). On the contrary, forest lakes only possess a detectable influence through the second PLS component (Figs. 3 and 4), even though the accumulated influence (VIP) over the two components is not significant (Fig. 5). Other kinds of land use, i.e. urban areas and the amount of upstream lakes, have no major influence on the observed 137Cs levels. This is also true for the other measures that describe the size of the catchment area and its relation to lake size. However, one exception is the size of the most adjacent catchment area, i.e. without eventual sub-catchments of upstream lakes, and its ratio to the lake surface. These predictors have a moderate impact on the radiocaesium levels in the fish (Figs. 4 and 5). 3.4. Influence of lake characteristics on the
137
Cs levels in fish
The significance of various dissolved ions in lake water on the observed 137Cs levels in fish is clearly demonstrated by their closely connected and numerically large
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Fig. 5. PLS regression coefficients summarizing the influence of the 53 environmental predictors, over the significant two first PLS components, on the 137Cs levels in perch (Perca fluviatilis L.) and roach (Rutilus rutilus L.). Bold typeface shows highly significant influence (variable influence on projection, VIP>1.0), Bold and italic typeface illustrates modest impact (VIP=0.8–1.0), Other predictors have low influence (VIP 5 0.8).
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PLS weights (Fig. 4). The importance of the dissolved ion complex is chiefly in the first PLS component, but its summarized influence over both significant PLS components shows that it is one of the most influential factors (Fig. 5). Other important lake characteristics belong to the humic matter complex (water colour, TOC and Fe). On the contrary, the lake morphometric descriptors, like the volume development (Vd ) and relative depth (Drel ), did not possess any major influence on the 137Cs levels in perch and roach (Figs. 4 and 5). 3.5. Environmental impact on the regression slopes of
137
Cs vs. fish length
The environmental predictors had divergent effects on the regression slopes for the two species. No substantial influence on the regression slopes of roach was detected, whereas the PLS analysis on perch resulted in a statistically weak and unstable model with comparatively low predictive ability (R2 ¼ 0:33, Q2 ¼ 0:22). The model weakness is also illustrated by the immense variation of the scrambled R2 ’s in the permutation test (Fig. 6), implying that the model can hardly be separated from the inherent background correlation among the explanatory variables. In comparison to the amount of variation explained by the model (33%), the model can therefore be suspected to be an artefact caused by a latent structure in the data set. The model needs to be evaluated further, to reveal any possible environmental effect on the regression slope.
4. Discussion This study clearly shows the effect of the first 137Cs pulse shortly after the Chernobyl accident. A major part of the differences in radiocaesium levels, observed
Fig. 6. The PLS model on the regression slope of 137Cs activity in perch vs. fish length and 53 environmental predictors is severely affected by an inherent background correlation, as measured by the intercepts of the permuted R20 s and Q20 s regressed against the observed 137Cs levels and the permuted levels.
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in perch and roach from various lakes, can be explained by the 137Cs deposition on the lake’s vicinity. The difference between radiocaesium deposited on arable land and on wetlands seems to be particularly important (Figs. 4 and 5). Due to its high affinity for particles, especially clay minerals (cf. Avery, 1996), the 137Cs fraction that was deposited on farmland is comparatively immobile. In contrast, the portion that fell on wetlands seems to have leached out to the lake systems to a much higher degree, probably due to less suitable binding sites for the radiocaesium. This is in agreement with several field and laboratory studies that have shown higher 137Cs mobility in organic soils compared to mineral soils (e.g. Fredriksson, Garner, & Scott Russell, 1966; Hilton, Livens, Spezzano, & Leonard, 1993; Comans et al., 1998). On the other hand, in this study, 137Cs deposition on forested areas appears to have had a lesser impact on the levels encountered in the fish (Fig. 4). A comparatively higher transport of 137Cs from wetlands, compared to other kinds of soil, is also indicated by soil samples from one of the catchment areas. The radiocaesium content in samples from forested and cultivated soils was generally about 6 times higher than the content in wetland samples (Broberg, unpublished). Likewise, the 137Cs in wetland samples was also typically found at deeper strata in the soil profile than in other kinds of soil (op. cit.). The low mobility of 137Cs from surrounding soils is also implied by the modest influence of the deposition on distant parts of the catchments (Fig. 4). The significance of lake geographic location (X, Y, and Z) probably is an expression of the prominent southeastern-ward gradient in 137Cs deposition (SGC, 1986). Additionally, there is also a concomitant gradient in lake onthogeny, as the NW lakes are substantially older than the SE lakes, and they are located on a plain at a comparatively higher altitude (Segerberg, 1999). This is manifested by the positive relationship between the 137Cs levels in fish and lake altitude (MASL in Figs. 4 and 5), as well as its negative relation to catchment area inclination (altitude difference in Figs. 4 and 5). The differences in lake onthogeny also have the implication that the northwestern lakes are predominantly more influenced by wetlands and consequently they are more humic and low in dissolved ions. The concentrations of major cations are believed to a great extent to govern the bioavailability of 137Cs in lakes, whereas humic matter seems to have only a minor influence (Penttila¨, Kairesalo, & Uusi-Rauva, 1993). This is in agreement with the present study, which distinctly shows the negative relationship between the concentrations of major cations and the observed 137Cs levels in perch and roach (Figs. 4 and 5). However, the positive correlation with the humic matter complex (water colour, TOC and Fe) is less clear. It could be an expression of the commonly observed negative relationship between humic matter and lake water Ca and Mg content (cf. Stumm & Morgan, 1981), but it could also reflect the strong coupling between the 137Cs levels in the fish and the influence of the wetlands. The lake pH seems to have only a minor impact on the radiocaesium levels in the fish, which is in accordance with other studies (Ha˚kanson & Andersson, 1992). In comparison to other investigations (Broberg, Malmgren, & Jansson, 1995; Meili, Braf, & Konitzer, 1997), morphometric parameters only have a modest influence (Figs. 4 and 5). Except the strong effect of lake altitude and catchment area
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inclination (altitude difference), only the size of the adjacent catchment area and maximum water depth have a moderate impact on the 137Cs levels in perch and roach. Probably, the catchment area influence reflects the more ‘‘accessible’’ radiocaesium that was deposited in the lake’s vicinity. The inverse relationship between maximum depth and 137Cs levels in the fish presumably expresses the detected effect that fish in shallow lakes have comparatively higher radiocaesium levels than those in deeper lakes (Broberg et al., 1995). Contrastingly, the volume development (Vd ) and relative depth (Drel ), which describes the lake shape and lake protection against resuspension (Ha˚kanson & Peters, 1995), did not have any major influence on the 137Cs levels in the fish (Figs. 4 and 5). Nevertheless, there is a tendency that the radiocaesium levels were higher in comparatively larger and shallower lakes, as indicated by the negative relationship between 137Cs levels and maximum depth, mean depth and Drel , whereas the relationship between 137Cs and Vd is positive (Fig. 4). This is in agreement with Broberg et al. (1995), who found resuspension of 137Cs-contaminated sediment maintained high 137Cs levels in fish in shallow, wind-exposed lakes, especially in combination with an extensive water retention time. In contrast to the reported effects of resuspended 137Cs-contaminated sediments, a remobilization of sediment-bound 137Cs may occur under reduced conditions, especially with elevated NH+ 4 concentrations (Evans et al., 1983; Pardue, DeLaune, Patrick, & Whitcomb, 1989; Davison, Spezzano, & Hilton, 1993). In this study, no indications of such remobilization can be found. In contrast, there is a positive correlation between the capacity to withstand oxygen depletion (Dec. O2 in Fig. 4) and the observed 137Cs levels in the fish. This positive relationship could be caused by the previously discussed more profound resuspension in shallow lakes. The influence of sampling year is weak, even though there is an indication of declining 137Cs levels with increasing year (1991–1993), as there is a negative relationship between sampling year and radiocaesium levels (Figs. 4 and 5). Other studies in the area show fairly stable 137Cs levels in small specimens of perch and roach during this time period (Sundbom, in prep.), which probably causes the nonsignificant relationship depicted in this study. The 137Cs content in both perch and roach possessed a prominent size dependency, which made the use of e.g. geometric means unsuitable as the fish-size composition in the samples would heavily affect the means. Other kinds of measures to handle fishsize dependencies of environmental contaminants, e.g. mercury, include normalization (Johnels, Westermark, Berg, Persson, & Sjo¨strand, 1967) and the use of adjusted means in ANOVA’s (Watras, Back, Halvorsen, Hudson, Morrison, & Wente, 1998). However, these methods have been criticized as they do not completely remove the size dependencies when the slopes of the Hg to fish-size regression lines are not equal (Somers & Jackson, 1993; Tremblay, Legendre, Doyon, Verdon, & Schetagne, 1998; Sonesten, 2001). The method used here, i.e. using regression intercepts to estimate the 137Cs levels in fish, has successfully been used earlier to estimate Hg levels in perch, which also had a prominent fish-size dependency (Sonesten, 2000b).
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5. Conclusions This study clearly shows the prominent effects on 137Cs levels in fish in lakes having a high proportion of wetlands in their catchment areas, after a pulse in radiocaesium deposition. Contrarily, the deposited 137Cs only marginally affected fish in lakes surrounded by arable land, as the radiocaesium was retained in the surrounding soils. Additionally, the 137Cs deposition on a lake’s vicinity was found to have a more profound effect than more distant deposition. The 137Cs bioavailability seems to be controlled predominantly by the lake water cation content, as hardwater lakes generally possessed significantly lower 137Cs levels in the fish. In contrast to other studies, the present investigation only shows a minor influence of resuspended 137Cs-contaminated sediments on the radiocaesium levels in fish.
Acknowledgements The author wishes to express his gratitude to Anders Broberg, Marcus Sundbom, and two anonymous reviewers who substantially improved the manuscript.
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