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Turbidity characterizes the reproduction areas of pikeperch (Sander lucioperca (L.)) in the northern Baltic Sea
Estuarine, Coastal and Shelf Science 95 (2011) 199e206
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Turbidity characterizes the reproduction areas of pikeperch (Sander lucioperca (L.)) in the northern Baltic Sea L. Venerantaa, *, L. Urhob, A. Lappalainenb, M. Kallasvuob a b
Finnish Game and Fisheries Research Institute, Quark, Korsholmanpuistikko 16, 65320 Vaasa, Finland Finnish Game and Fisheries Research Institute, P.O. Box 2, FI-00791 Helsinki, Finland
a r t i c l e i n f o
a b s t r a c t
Article history: Received 19 February 2011 Accepted 24 August 2011 Available online 29 August 2011
The pikeperch (Sander lucioperca (L.)) is an economically important fish species occurring in the fresh and brackish waters of Europe. To evaluate the distribution and extent of the reproduction areas in the northern Baltic Sea, a field survey was carried out in two separate coastal areas. Presence/absence data were used to develop a geographic information system (GIS)-based predictive spatial distribution model, where high resolution raster maps of the focal environmental variables and a logistic regression equation were used to predict the probability of larval occurrence. The results indicated that the pikeperch reproduction areas are located in the innermost archipelago zone where high water turbidity best explained their presence. Turbidity was related to several other variables such as fetch and depth. Contrary to our preliminary hypothesis, surface water temperatures measured during the survey had no significant effect in the model due to the low spatial variation in the measured values. Since turbidity is possible to determine by remote sensing methods, the probability maps can be cost-effectively extended to more extensive coastal areas with proper validation. Ó 2011 Elsevier Ltd. All rights reserved.
Keywords: prediction pikeperch larval habitat turbidity Baltic Sea
1. Introduction The pikeperch (Sander lucioperca (L.)) is a valuable species for both recreational and commercial fisheries in the fresh and brackish waters of many European countries. Young and adult pikeperch prefer pelagic sheltered coastal areas close to the shoreline (Urho et al., 1990; Repecka and Mileriene, 1991; Winkler et al., 1994). In the Baltic Sea, the largest catches are from oligohaline bays that are often also eutrophicated (Lehtonen et al., 1996). Large natural fluctuations in yeareclass strength and catches are typical for pikeperch populations (Lappalainen and Lehtonen, 1995; Lappalainen et al., 1995). In general, high summer temperatures have been shown to have a positive influence (e.g. Svärdson and Molin, 1973; Lehtonen and Lappalainen, 1995; Gröger et al., 2007), and turbidity and eutrophication have also been connected to the survival and distribution of offspring (Hansson and Rudstam, 1990; Sandström and Karås, 2002; Pekcan-Hekim and Lappalainen, 2006).
* Corresponding author. E-mail addresses: lari.veneranta@rktl.fi (L. Veneranta), lauri.urho@rktl.fi (L. Urho), antti.lappalainen@rktl.fi (A. Lappalainen), meri.kallasvuo@rktl.fi (M. Kallasvuo). 0272-7714/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.ecss.2011.08.032
The spawning of pikeperch in the Baltic Sea takes place in shallow eutrophicated bays. The depth of the spawning ground ranges between 0.7 and 3 m (Filuk, 1962; Virbickas et al., 1974; Lehtonen and Lappalainen, 1995), although larvae may also reach the surface from deeper water layers (Belyy, 1968). However, only some local reproduction areas have earlier been confirmed by sampling larvae (Urho et al., 1990) or juveniles (Winkler et al., 1994; Kjellman et al., 2001, 2003; Lappalainen and Urho, 2006). The spawning of pikeperch in the Baltic Sea takes place between late April and the beginning of July, depending on the latitude (e.g. Filuk, 1962; Winkler et al., 1989; Lappalainen et al., 2003) once water temperatures exceed 10 C. At an incubation temperature of 12 C, the larvae hatch less than 14 days after fertilization (Lappalainen et al., 2003). Thus, in the northern Baltic Sea area, the spawning and hatching period varies temporally depending on the early summer temperatures. There is a need for information on the distribution of larval areas that have high importance for the fish production, and also on a wider spatial scale, since changes in the climate are expected to have an influence on pikeperch (Lehtonen et al., 1996; Lappalainen et al., 1997; Lappalainen et al., 1997). Pikeperch stocks are also currently affected by fishing pressure, not only from fishermen but possibly additionally from cormorants and seals, whose populations have rapidly increased. In Finland, fishing of
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pikeperch is only regulated by minimum size restrictions and is also targeted at spawning pikeperch. A question has arisen as to whether more fisheries management actions should be directed to spawning areas, although their locations have not been well known. Due to high water turbidity and inconsistent knowledge of pikeperch reproduction areas in the Baltic Sea, the exact location of spawning grounds based on egg observations using means such as scuba diving would be laborious for large-scale mapping purposes. Instead, the location of the key reproduction areas can be assessed by focussing the catch effort on the pelagic early life stages of pikeperch, i.e. newly-hatched larvae, which are relatively easy to catch. A combination of predictive spatial models and a geographic information system (GIS) can be used to construct thematic reproduction area maps from stratified random sampling data. In recent years, many studies have focused on predicting the relationship between species distribution and environmental variables with statistical modelling and/or GIS applications. For fish larvae in the northern Baltic Sea, distribution maps have been produced for pike (Esox lucius) and roach (Rutilus rutilus) (Härmä et al., 2008; Lappalainen et al., 2008; Sundblad et al., 2009). In these studies, the georeferenced species occurrence data have been combined with environmental variables and the potential distribution of species has been predicted in a predefined geographical range to locate the areas where a species occurs. For coastal pelagic predators such as pikeperch, larval area prediction maps based on environmental factors have not been previously constructed. The aim of this study was to (1) identify the larval areas of pikeperch in the northern Baltic Sea and the environmental factors that determine these areas, (2) construct a modelled predictive spatial map of the distribution of larval pikeperch on the basis of larval occurrence and geographically expandable environmental factors and (3) to evaluate the estimation accuracy of the model with external validation in an independent coastal area. The hypothesis was that newly-hatched larvae will be located in shallow waters with high temperature and high turbidity that were hypothesized to be important for the occurrence of pikeperch larvae. 2. Material and methods 2.1. Study area The sampling area for the training data set was located in the Archipelago Sea (Fig. 1, area A) and the sampling area used for validation of the model in the western Gulf of Finland, in the northern Baltic Sea (Fig. 1, area B). Training data was used to construct the model and validation data to evaluate the model. Both were fragmented coastal areas and covered the inner, intermediate and outer archipelago zones (Häyren, 1900; Granö, 1981). The water quality and geomorphology were quite similar in both sampling areas and both were influenced by fresh, nutrient-rich outflows from small rivers. Thus, especially in the inner parts of the study areas, the water was very turbid and eutrophic, with a decreasing trend towards the outer archipelago. In the inner parts of archipelago, the early summer salinity varied between 3 and 4. In the Archipelago Sea and Northern Gulf of Finland, salinity usually fluctuates between 4.5 and 6 (Weckström et al., 2002; Suominen et al., 2009). 2.2. Field surveys In the training area the sampling stations were randomly selected within three strata based on turbidity values obtained
Fig. 1. Location of the study areas (A ¼ training area and B ¼ validation area) in the northern Baltic Sea.
from EOS Terra-MODIS satellite images (resolution 250 m) captured in summer 2005. The total number of sampling stations was 134 in the Archipelago Sea in 2007, of which 74 stations were randomly selected from areas with high turbidity (over 7 Formazin Nephelometric Units, FNU), 40 stations from areas with moderate turbidity (2e7 FNU) and 20 stations from areas with low turbidity (less than 2 FNU). In the validation area, in the western Gulf of Finland in 2008, the total number of sampling stations was 50 and they were randomly selected to cover all archipelago zones in the sampling area. The training area was sampled from 17 to 28 June 2007 and the validation area from 4 to 22 June 2008. The sampling of pikeperch larvae was carried out during the day with two pelagic ichthyoplankton Gulf Olympia samplers with an inflow diameter of 190 mm (Hudd et al., 1984; Aneer et al., 1992). The samplers were fitted in parallel to both sides of the bow of the boat approx. 2 m apart and at depths of 0.75 m and 1.00 m from the surface. The net behind the sampler cone had a mesh size of 300 mm. The hauling speed was set to 2 ms1 with GPS and the hauls were 400 m long straight lines. Due to depth limitations of the Gulf Olympia sampling method, no samples were taken in water shallower than approximately 1.5 m. Each sampling station was visited two times in a span of at least one week to ensure the right timing for larval occurrence. All larval samples were fixed in 4% formaldehyde solution in the field and larvae were identified, counted and measured for total length (mm) in the laboratory after preservation to 96% ethanol. The water turbidity was measured at every sampling station and visit with a Eutech TN100ir turbidity metre near the water surface. The water depth, bottom type (soft or hard), sampling position and surface water temperature were defined with a combined GPS plotter and sounder at the time of sampling. To obtain an estimate of salinities in the area at the spawning time, the salinity values were measured in early spring in both areas. Eight temperature data loggers (Onset Hobo UA-002-64) were placed in the training area at a depth of 2.5 m in spring 2007 and ten loggers were placed at a depth of 2 m in the validation area in 2008 to follow the temperature development and possible summer upwelling. The loggers were located so that they covered all the archipelago zones. Data from three loggers for both, training and validation area were used to describe the temperature development.
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2.3. Data analysis and spatial modelling 2.3.1. Explanatory variables The spatial turbidity data measured in 2007 (134 in situ measurements) in the Archipelago Sea and in 2008 (50 in situ measurements) in the western Gulf of Finland area were imported into the GIS and interpolated by using inverse distance weighting (IDW) to create a turbidity map with a spatial resolution of 50 m. In interpolation, the maximum turbidity of sampling rounds was used. IDW determines cell values using a linearly weighted set of combinations (Watson and Philip, 1985). A high resolution thematic turbidity map was needed, since the spatial resolution of EOS TerraMODIS images was not sufficient for the complex archipelago areas. The bathymetry map with a spatial resolution of 50 m was obtained from the Finnish Maritime Administration. In the training area, the depth data was compared to in situ depth measurements in the sampling stations and the correlation was 0.82 (RMSE 4.07). The geographical characteristics of study areas were described with GIS-constructed variables. The distance to areas with a depth greater than 20 m was calculated for each grid with a spatial resolution of 50 m. Shoreline density, which describes the complexity of the archipelago, was calculated as the total shoreline length within a circle of radius 5000 m, the units being length per unit area at a spatial resolution of 50 m. The exposure of a site to wave action was estimated using an effective wave exposure index, fetch (Isaeus, 2004), with a spatial resolution of 25 m. All GIS analyses were performed using the ArcGIS (ArcMap 9.3.1 and Spatial Analyst extension, ESRI, Redlands, California) software package. The average temperature of two sampling rounds in 2007 at each sampling station was used to describe the surface water temperature, but these values were not interpolated to thematic temperature map. 2.3.2. Modelling A logistic regression model for the training data was constructed to examine the relationship between the occurrence of pikeperch larvae and the in situ measured or GIS-constructed environmental variables. Data on the presence/absence of pikeperch larvae were used as a binary response variable. The environmental factors (depth, surface water temperature, turbidity, shoreline density, distance to 20 m depth curve and fetch) were used as continuous explanatory variables, except for the bottom type which was set as classification variable. The parallel samples of the two consecutive sampling occasions were combined, and the binary response variable therefore corresponds to the catch effort of four separate samples. A stepwise backward elimination method was used to select the variables for the model from among the main effects and interactions. A significance level of 0.05 was used as a criterion for a variable to remain in the model. The model for the training data
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was used to score the validation data and data for the GIS probability layer. The overall fit of the model was assessed using the Hosmer and Lemeshow goodness-of-fit test and with separate ROC (Reveiver Operator Characteristics) curves and AUC (Area Under Curve) e values for the validation and training data sets. Deviance residual plots were used to identify influential outlying observations and to check the model structure. The logistic regression model was fitted using the logistic procedure and spatial autocorrelation was tested with empirical semivariogram of model deviance residuals using the variogram procedure of SAS 9.2 software package (SAS Institute Inc, Cary, North Carolina). 3. Results 3.1. Abiotic characteristics of the study areas and the occurrence of larvae In both areas there was a clear decreasing gradient in turbidity and increasing gradient in early spring salinity from the inner to outer parts of the archipelago. In the innermost archipelago, the shallow areas dominated and caused early warming of the water layer compared to the exposed, deeper and thus colder outer areas archipelago zone. The fetch values were highest in the outer archipelago and lowest in the inner archipelago. As with shoreline density, the wave exposure also varies depending on the geomorphometry and level of isolation. Turbidity correlated with all other variables, fetch with depth and depth with the distance to 20 m depth curve. The surface temperature measured at each sampling station correlated only with distance to 20 m depth curve (Table 2). The correlation of variables was adequately low (0.61), suggesting that backward elimination approach was suitable (Wintle et al., 2005; Heinänen et al., 2008). The range of in situ measured surface water temperatures in the training area was narrow considering the spatial and temporal scale (Table 1). However, the measured logger temperatures indicated significant temperature differences between the three archipelago zones (Fig. 2). A temperature level of 12 C was used to describe the minimum threshold value for larval growth (Alabaster and Lloyd, 1980). In the training area, from 15 May to 30 June 2007, water temperatures exceeded this threshold in the innermost areas on all days, while in the middle archipelago the temperature increased more slowly, but nearly all days were warmer than the growth threshold. In the outer parts of archipelago, however, the temperature only exceeded the threshold at the end of May and one third of days were below this limit. Although there was a significant upwelling occasion in the middle of June 2007, the water temperature did not drop under the threshold value. In the same period in 2008 in the validation area, the water temperature exceeded 12 C in the outer parts of study
Table 1 Ranges (minemax) of the abiotic variables at all the sampling stations and at those stations where pikeperch larvae were present, calculated separately for the training and validation areas. Spring salinity was measured in training area 7e18.5 and in validation area 6e16.5. Variable/Area
Spring salinity, (ppt) Surface water temperature, ( C) Sum of June degree days in the innermost area Water turbidity, (FNU) Fetch, index Bottom type Depth, (m) Shoreline density, (index) Distance to 20 m depth curve, (m)
Training area
Validation area
Total range (minemax)
Range when larvae present (minemax)
Total range (minemax)
Range when larvae present (minemax)
0.9e6.2 13.4e20.7 539 0.8e33.7 2873e40,276 Softehard 1.7e67 0.3e3.2 0e20,687
3.8e5.4 14.2e20.7 NA 5.0e33.7 3569e20,486 Soft 2e17 0.3e3.0 250e18,137
1.1e5.3 10.6e17.9 505 1.44e19.7 3948e307,744 NA 2e27 0.1e2.66 0e13,523
3.3e4.2 14.7e17.9 NA 5.0e19.7 3948e9128 NA 2e9.8 0.4e2.4 3859e12,192
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Table 2 Correlation of environmental variables in the training area (n ¼ 134).
Turbidity Fetch Depth Distance to 20 m depth Line density Surface temperature
Turbidity
Fetch
Depth
Distance to 20 m depth
Line density
Surface temperature
1.00 0.41*** 0.50*** 0.61*** 0.41*** 0.18ns
1.00 0.59*** 0.21* 0.01ns 0.10ns
1.00 0.46*** 0.11ns 0.08ns
1.00 0.28** 0.52***
1.00 0.11ns
1.00
Significance level *** < 0.0001; ** < 0.01; * < 0.05;
ns
not significant.
areas on only 6% of days, and in the middle area the temperature was warmer than the threshold on 66% of the follow-up days. Upwelling caused the temperature to drop under the critical level two times, in the middle and at the end of June in 2008. In the innermost area, the temperature was highest and 85% of days were warmer than 12 C. After warming there were also no sudden decreases in temperature (Fig. 2). In the training area in 2007, pikeperch larvae were present at 51 of the 134 stations, and in the validation area in 2008 at 10 out of 50 stations. The highest larval densities occurred in the innermost archipelago and no larvae were caught in the outer archipelago. The total number of larvae caught was 597 individuals in the training area and 272 in the validation area. The average length of pikeperch larvae was 7.1 1.9 mm in the training area in 2007 and 6.2 0.8 mm in the validation area in 2008, and the size distributions were rather uniform in both years except for the lack of larger-sized larvae in 2008 (Fig. 3). In sampling stations where pikeperch were present the catch consisted also of pikeperch in length of 6e7 mm or smaller. In 2007, the larvae were caught during the whole sampling period, while in 2008 the main larval catch was obtained at the beginning of June.
The HosmereLemeshow goodness-of-fit test (c2 ¼ 5.747, df ¼ 8, p ¼ 0.676) indicated that the model fit in the training area was reasonably good. The model had no consistent patterns when the semivariance of the model deviance residuals was plotted against the distance between sampling points that indicates no significant spatial autocorrelation. According to this model, pikeperch larvae only occurred at turbidities greater than 9.0 FNU (Fig. 4). The threshold value of p(x) > 0.5 was used to classify observation as presence to achieve the best correct classification ratio for both, training and validation area. In the training area, the model correctly predicted 85.1% of the observations (n ¼ 134), the sensitivity was 78.4% (11 false negatives) and specificity 89.4% (9 false positives). AUC for training data was 0.94. The specific model fitted for the training area was validated in the validation area to assess the predictive performance of the model and the possibility to extend it to wider coastal areas. In the validation area, the model correctly predicted 82% of observations (n ¼ 50) and the values for sensitivity and specificity were 90.0% (8 false positives) and 80.0% (1 false negative), respectively. AUC for validation data was 0.89. The results were scored according to the interpolated turbidity values and a thematic map describing the probability of pikeperch presence was constructed (Fig. 5a and b).
3.2. Spatial modelling of pikeperch larvae In the final logistic regression model fitted to the training area, the only variable explaining the occurrence of pikeperch larvae was turbidity (p < 0.001) (Appendix). The effect of depth was close to significance (p ¼ 0.064), but as it is correlated with turbidity, it was dropped from the model. The parameter estimates for logistic regression model p(x) ¼ 1e1/(1þee(a þ b(turbidity))), where p(x) is the probability for larval pikeperch occurrence, are intercept a ¼ 5.24 (SE 0.87), and the parameter estimate for turbidity b ¼ 0.58 (SE 0.10).
4. Discussion 4.1. Reproduction in inshore areas We only found pikeperch larvae at stations where the surface temperature during the survey time was higher than 14.1 C. Previous studies on the recruitment of juvenile pikeperch (e.g. Buijse et al., 1992; Lappalainen and Lehtonen, 1995; Kjellman et al., 2001) have also highlighted temperature as the most important
Fig. 2. Temperature development in the training (A) and validation area (B) in the different archipelago zones. Data are daily average values. The black horizontal line marks the threshold for larval growth (12 C) and vertical lines with arrows mark the sampling periods.
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Fig. 3. The lengthefrequency distribution of pikeperch larvae in the training area A (black bars) and validations area B (grey bars).
factor determining the size of next yeareclass. Kokurewicz (1969) suggested that larval pikeperch favour temperatures between 12 and 16 C for best growth, while even as high a range as 24e29 C has been suggested as optimal (Hokanson, 1977; Marshall, 1977; Hilge, 1990; Wang et al., 2009). Temperatures below 12 C may reduce hatching success and values below 6 C are lethal for embryos (Alabaster and Lloyd, 1980). In the training area in 2007, the similarity in the water surface temperatures between the inner and outer parts of the archipelago was due to the high summer air temperatures and mild winds. Neither significant wind-induced circulation nor upwellings were observed during the sampling period according to the temperature logger data. However, in the validation area in 2008, the temperature gradient between inner and outer archipelago was more evident. The time period when larvae were detected was windy and colder than in the training area the previous year, and especially in the middle and outer archipelago the temperature was significantly lower than in 2007. The influence of upwelling was notable in the middle and outer archipelago areas, where a sudden decrease in temperature took place in the middle of June, during the occurrence of early pikeperch larval stages. The validation area sampled in 2008 had,
Fig. 4. Modelled probability of pikeperch larval occurrence with 95% prediction confidence limits in relation to turbidity. Circles indicate the observed presence (1) and absence (0) of larvae.
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indeed, a lack of larger (>15 mm) pikeperch larvae, which may reflect the low survival rate of smaller larvae in the cold water conditions compared to warmer years and areas, such as in 2007. Not only water temperature but also salinity may contribute to the suitability of pikeperch spawning and hatching areas. The salinity limits of pikeperch larvae have been reported to vary between 2.5 (Deelder and Willemsen, 1964; Kukuradze, 1974) and 10 (Klinkhardt and Winkler, 1989). In laboratory studies Olifan (1940) and Tanasijcuk and Vonokov (1955), both referenced in Klinkhardt and Winkler (1989) recorded substantially increased mortality of larvae at a salinity of over 3.75 while Klinkhardt and Winkler (1989) determined a salinity limit of 7 for successful reproduction. The salinity range measured in early spring varied between 3.3 and 5.4, but this may underestimate the true salinity values of the larval period. The runoff of several small rivers reduces the springtime surface salinity within the inner archipelago compared to more saline open sea areas (Suominen et al., 2009). In this study, the core larval areas were focused in the low salinity zone, while larvae found at more saline (>5) stations can be considered to be uncommon and the stations suboptimal in the sense of early larval development. Nevertheless, the salinity values in the larval areas were higher or at the same level as what the studies of Olifan (1940) and Tanasijcuk and Vonokov, 1955; ref. in Klinkhardt and Winkler (1989)
Fig. 5. (a) and (b). Predicted probability map showing the larval distribution in the training area (a) and in the validation area (b). The map describes the probability of catching at least one pikeperch larva in a water volume of 45 m3. Circles indicate sampling stations where pikeperch larvae were present and crosses indicate stations where pikeperch were absent.
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indicated to be the successful survival limit of embryos. Overall, the salinities in the northern Baltic Sea do not exceed the 7e10 salinity limit that has been reported to restrict juvenile and adult pikeperch feeding areas (Boiko and Kozlitina, 1975; Janovskaya, 1977). However, these more saline areas are also colder than the less saline and turbid areas, and the effect of these factors on pikeperch reproduction cannot be separated. This study emphasized the importance of turbid estuaries for the reproduction of pikeperch, but the exact role of turbidity in larval survival remains undefined. The results indicate that high turbidity best describes the distribution of the larval areas on a wide scale. In the sampling areas, turbidity includes the indirect effects of temperature, salinity, depth and shoreline length. The less saline and more turbid innermost archipelago areas are typically shallow, and warm up more rapidly in spring than the surrounding areas. The turbidity in these areas may also be induced by wind, which causes sediment disturbance. High turbidity may protect the pikeperch larvae from predators during the critical first 3e4 days after hatching (Svärdson and Molin, 1973). Pekcan-Hekim and Lappalainen (2006) found that pikeperch larvae should have lower predation risk in highly turbid waters due to the decreased reaction distance of the predators. The visual adaptations of pikeperch may also increase their foraging abilities in turbid environments (Ali and Ryder, 1977), and pikeperch larvae are thus able to take advantage of the high production of suitable prey (Sandström and Karås, 2002).
Regardless of the modelling techniques, the sampling design greatly affects the accuracy of final result, i.e. the modelled probability map. It is necessary that the sampling design covers all environmental gradients present in the area (Guisan and Zimmermann, 2000). In the few coastal larval studies based on presence/absence field observations, the predictability of observations has been around 90% (e.g. Eastwood et al., 2001; Härmä et al., 2008; Sundblad et al., 2009). Larval modelling studies with external validation have been scarce. In this study, the model fit was reasonably high. The high proportion of false positives in the validation area indicates that the pikeperch larvae were not as widely distributed there as in the training area, where the range in turbidity was greater. This was due to differences between years and areas. The colder temperatures measured in 2008 in the validation area suggest that the year class was not as successful as in 2007, and this may therefore reduce the predictive ability of the model. However, the environmental variables measured in this study may indirectly or directly influence the ecological niche of larval pikeperch, but they do not include the influences on a wider scale, e.g. species interactions or changes over time. Thus, the resulting probability map describes of the potential geographical distribution of larvae under the environmental conditions that prevailed during the sampling.
4.2. Sampling design and spatial modelling
Gröger et al. (2007) indicated that climate, salinity and temperature have a significant effect on the pikeperch population response in terms of recruitment and adult stock fluctuations with substantial lag periods of from 3 to 5 years and Pekcan-Hekim et al. (2011) have shown that summers warmer than 18.5 C contribute most to pikeperch catches. It seems that the larval period is critical for pikeperch stock success, as with many other fish species (Braum, 1966; Johnston and Mathias, 1994). For the pikeperch occurring at the northern limit of their distribution, changes in the climate may be crucial. The salinity of the Baltic Sea is predicted to decrease in the 21st century due to an increased freshwater supply from rivers (Meier, 2006; Meier et al., 2006). In particular, the coastal archipelago areas with estuaries and increasing river runoff are expected to have lower salinity and higher turbidity in the future. Moreover, if the winter ice period becomes shorter (Jevrejeva et al., 2004; Jaagus, 2006) and spring and summer temperatures increase, it may induce a spatial enlargement of the coastal pikeperch reproduction areas. Thus, it would be important to follow-up the fish reproduction areas to attain better understanding of the effects of changing climate to fish populations. In this study, the turbidity value used for the logistic model was obtained from field sampling. New satellite instruments and satellite image interpretation techniques may in the future enable the use of high resolution remote sensing data for turbidity estimates. This type of remote sensing data makes it possible to extend the prediction maps to a wider spatial and temporal scale, covering larger coastal areas. Easily interpretable species distribution modelling has great potential to develop into a well-established tool for resource management and conservation planning purposes, and will therefore promote the sustainable use of coastal areas.
Due to depth limitations of the Gulf Olympia ichthyoplankton sampling method, the occurrence of early stage larval in shallow (<2 m) areas and in the immediate vicinity of shoreline and reed belts remained unknown. Urho et al. (1990) have noted that the early larval stages of pikeperch favour open water areas in estuaries, while larger larvae and juvenile fish are more focused in littoral areas. The uniform size distribution and relatively small size of pikeperch larvae in both sampling areas also indicates that our timing in larval sampling was set to the right period. Sampling immediately after the hatching period also enables evaluation of the potential spawning areas. The habitat changes of freshwater fish larvae, in particular, are often connected to the developmental stages (Urho, 1996, 1999). It is most probable that the early stages of pikeperch larvae remain near the spawning areas, since the water currents in the area are relatively weak. The larvae are not evenly distributed in the water column, and to avoid the negative influence of patchiness on the sampling, the parallel samples in this study were combined. However, due to the hypothesized patchiness of larvae, the presence/absence data were considered more suitable for spatial modelling purposes than quantitative estimates of larval abundance. In this study, we only used environmental factors that were available and could be expanded to wider areas at a high spatial resolution as predictor variables. The measured data set shows some correlations between variables, due to parallel gradients through the archipelago zones. Therefore, the pikeperch larval areas could be modelled with only variable, turbidity. However, though the turbidity best describes the occurrence of larval pikeperch in this study, other additional variables might improve the model. Adding temperature to the model successfully would require a thorough follow-up of temperature development on a wide scale with high resolution satellite images. Spatial salinity information at a resolution adequate for pikeperch modelling purposes in archipelago areas would require extensive flow information on the Baltic basin and small rivers. High resolution spatial environmental factors of this kind were unfortunately not available. Habitat models have been popular and quite widely used for modelling species distributions (Guisan and Zimmermann, 2000).
4.3. Future aspects
Acknowledgements We thank J. Salmi for assistance in the field and I. Kallio-Nyberg and R. Hudd for valuable comments. Referees provided valuable comments that improved this paper substantially. The study was financed by the Maj and Tor Nessling Foundation, the Finnish Game and Fisheries Research Institute and the Finnish Inventory Programme for the Underwater Marine Environment, VELMU.
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Appendix The model selection process with main effects. The standard error of variable estimates is shown in parentheses. Max-rescaled r2
Intercept
e
0.73
6.61 (6.65)NS
Fetch Dist. 20 m Surface temperature
0.73 0.73 0.73
6.61 (6.61)NS 6.76 (5.33)NS 1.91 (1.55)NS
Model selection step
Removed
0 1 2 3 NS
Variable estimate (SE) Fetch
Dist. 20 m
Surface temperature
Lineden
Depth
Turbidity
7.6 108 (0.00)NS e e e
1.1 107 (0.00)NS
0.31 (0.43)NS
0.72 (0.54)NS
0.16 (0.09)NS
0.40 (0.11)**
1.1 107 (0.00)NS e e
0.31 (0.43)NS 0.32 (0.34)NS e
0.72 (0.54)NS 0.72 (0.53)NS 0.64 (0.52)NS
0.16 (0.09)NS 0.16 (0.09)NS 0.16 (0.09)NS
0.40 (0.11)** 0.40 (0.11)** 0.44 (0.11)**
¼ not significant (>0.05), * <0.05, ** <0.01.
References Alabaster, J.S., Lloyd, R., 1980. Water Quality Criteria for Freshwater Fish. Butterworths (FAO-EIFAC), London, Boston, p. 384. Ali, M.A., Ryder, R.A., 1977. Photoreceptors and visual pigments as related to behavioral responses and preferred habitats of perches (Perca spp.) and pikeperches (Stizostedion spp.). Journal of the Fisheries Research Board of Canada 34, 1475e1480. Aneer, G., Blomqvist, E.M., Hallbäck, H., Mattila, J., Nellbring, S., Skora, K., Urho, L., 1992. Methods for Sampling of Shallow Water Fish, vol. 13. The Baltic Marine Biologist Publication. Belyy, N.D., 1968. Behavior and settling of free pikeperch Lucioperca lucioperca (L.) embryos hatching out in deep water. Problems of Ichthyology 8, 712e718. Boiko, E.G., Kozlitina, S.V., 1975. Osnovnye zakonomernosti kolebanii zapasa produkcii I ulova azovskogo sudaka. Trudy VNIRO tom CIX, 52e71 (in Russian). Braum, E., 1966. The survival of fish larvae with reference to their feeding behavior and to the food supply. In: Gerking, S. (Ed.), The Biological Basis of Freshwater Fish Production. Blackwell, Oxford, pp. 113e131. Buijse, A.D., Pet, J.S., Van Densen, W.L.T., Machiels, M.A.M., Rabbinge, R., 1992. A size- and age-structured simulation model for evaluating management strategies in a multispecies gill net fishery. Fisheries Research 13, 95e117. Deelder, C., Willemsen, J., 1964. Synopsis of biological data on pikeperch, Lucioperca lucioperca (Linnaeus) 1758. FAO Fisheries Synopsis 28, 1e58. Eastwood, P., Meaden, G., Grioche, A., 2001. Modelling spatial variations in spawning habitat suitability for the sole Solea solea using regression quantiles and GIS procedures. Marine Ecology Progress Series 224, 251e266. Filuk, J., 1962. Studies on the biology and catches of the Vistula Firth pikeperch. Prace MIR t.11/A. Gdynia. 225e274. Granö, O., 1981. The zone concept applied to the Finnish coast in the light of scientific traditions. Fennia 159 (1), 63e68. Gröger, J.P., Winkler, H.M., Rountree, R.A., 2007. Population dynamics of pikeperch (Sander lucioperca) and its linkage to fishery driven and climatic influences in a southern Baltic lagoon of the Darss-Zingst Bodden Chain. Fisheries Research 84 (2), 189e201. Guisan, A., Zimmermann, N.E., 2000. Predictive habitat distribution models in ecology. Ecological Modelling 135 (2e3), 147e186. Hansson, S., Rudstam, L., 1990. Eutrophication and the Baltic fish communities. Ambio 19, 123e125. Härmä, M., Lappalainen, A., Urho, L., 2008. Reproduction areas of roach (Rutilus rutilus) in the northern Baltic Sea: potential effects of climate change. Canadian Journal of Fisheries and Aquatic Sciences 65, 2678e2688. Häyren, E., 1900. Längs-zonerna i Ekenäs skärgå rd. Geografiska Föreningens Tidskrift 12, 222e234. Heinänen, et al., 2008. Modelling the occurrence and abundance of a colonial species, the arctic tern Sterna paradisaea in the archipelago of SW Finland. Ecography 31, 601e611. Hilge, V., 1990. Beobachtungen zur Aufzucht von Zandern (Stizostedion lucioperca L.) im Labor. Archiv fuer Fischereiwissenschaft 40, 167e173. Hokanson, K.E.F., 1977. Temperature requirements of some percids and adaptations to the seasonal temperature cycle. Journal of the Fisheries Research Board of Canada 34, 1524e1550. Hudd, R., Hilden, M., Urho, L., Axell, M.-B., Jåfs, L.-A., 1984. Fiskeriundersökning av Kyrö älvs mynnings- och influensområde 1980e82. ((Fishery Investigation in 1980e1982 of the Kyrönjoki Estuary and its Influence Area in the Northern Quark of the Baltic Sea)). Report 242B, 1e277. National Board of Waters, Helsinki, Finland (In Swedish with English summary). Isaeus, M., 2004. Factors structuring Fucus communities at open and complex coastlines in the Baltic Sea. Ph. D. thesis. Department of Botany, Stocholm University, Stockholm. Jaagus, J., 2006. Trends in sea ice conditions in the Baltic Sea near the Estonian coast during the period 1949/1950e2003/2004 and their relationship to large-scale atmospheric circulation. Boreal Environment Research 11, 169e183. Janovskaya, L.I., 1977. Zakonomernosti opredeljajushtshie velitshiny pokolenii volzhskogo poluprokhodnogo sudaka v usloviakh izmenennogo rezhima volgi. Izvestija GosHIIORKH tom 111, 74e84 (in Russian).
Jevrejeva, S., Drabkin, V.V., Kostjukov, V., Lebedev, A.A., Leppäranta, M., Mironov, Y.U., Schmelzel, N., Sztobryn, M., 2004. Baltic Sea ice seasons in the twentieth century. Climate Research 25, 217e227. Johnston, T.A., Mathias, J.A., 1994. Feeding ecology of walleye, Stizostedion vitreum, larvae: effects of body size, zooplankton abundance, and zooplankton community composition. Canadian Journal of Fisheries and Aquatic Sciences 51, 2077e2089. Kjellman, J., Lappalainen, J., Urho, L., 2001. Influence of temperature on size and abundance dynamics of age 0 perch and pikeperch. Fisheries Research 53 (1), 47e56. Kjellman, J., Lappalainen, J., Urho, L., Hudd, R., 2003. Early determination of perch and pikeperch recruitment in the Northern Baltic Sea. Hydrobiologia 495, 181e191. Klinkhardt, M., Winkler, H.M., 1989. Einfluß der Salinität auf die Befruchtungs- und Entwicklungsfähigkeit der Eier von vier Süßwasserfischarten Plötz (Rutilus rutilus), Barsch (Perca fluviatilis), Kaulbarsch (Gymnocephalus cernua) und Zander (Stizostedion lucioperca), Wissenschaftliche Zeitschrift der Universität Rostock. Naturwissenschaftliche Reihe 38 (5), 23e30. Kokurewicz, B., 1969. The influence of temperature on the embryonic development of the perches: Perca fluviatilis L. and Lucioperca lucioperca (L.). Zoologica Poloniae 19, 47e67. Kukuradze, A.M., 1974. A description of the spawning stock and reproduction of pikeperch (Lucioperca lucioperca) of the Danube delta and the waters in the Danubian region. Journal of Icthyology 14, 385e392. Lappalainen, A., Härmä, M., Kuningas, S., Urho, L., 2008. Reproduction of pike (Esox lucius) in reed belt shores of the SW coast of Finland, Baltic Sea: a new survey approach. Boreal Environment Research 13, 370e380. Lappalainen, A., Urho, L., 2006. Young-of-the-year fish species composition in small coastal bays in the northern Baltic Sea, surveyed with beach seine and small underwater detonations. Boreal Environmental Research 11, 431e440. Lappalainen, J., Lehtonen, H., 1995. Year-class strength of pikeperch (Stizostedion lucioperca (L.)) in relation to environmental factors in a shallow Baltic Bay. Annales Zoologici Fennici 32, 411e420. Lappalainen, J., Erm, V., Lehtonen, H., 1995. Pikeperch, Stizostedion lucioperca (L.), catch in relation to juvenile density and water temperature in Pärnu Bay, Estonia. Fisheries Management and Ecology 2, 113e120. Lappalainen, J., Lehtonen, H., Erm, V., 1997. Possible effects of climate warming on the timing of spawning, abundance and catches of pikeperch, Stizostedion lucioperca (L. Boreal Environmental Research 2, 85e91. Lappalainen, J., Dörner, H., Wysujack, K., 2003. Reproduction biology of pikeperch (Sander lucioperca (L.)) e a review. Ecology of Freshwater Fish 12, 95e106. Lehtonen, H., Lappalainen, J., 1995. The effects of climate on the year-class variations of certain freshwater fish species. In: Beamish, R.J. (Ed.), Climate Change and Northern Fish Populations, vol. 121. Canadian Special Publication of Fisheries and Aquatic Sciences, pp. 37e44. Lehtonen, H., Hansson, S., Winkler, H., 1996. Biology and exploitation of pikeperch, Stizostedion lucioperca (L.), in the Baltic Sea area. Annales Zoologici Fennici 33, 525e535. Marshall, T.R., 1977. Morphological, physiological and ethological differences between walleye Stizostedion vitreum vitreum and pikeperch S. lucioperca. Journal of the Fisheries Research Board of Canada 34, 1515e1523. Meier, H., 2006. Baltic Sea climate in the late twenty-first century: a dynamical downscaling approach using two global models and emission scenarios. Climate Dynamics 27, 39e68. Meier, H., Kjellström, E., Graham, L.P., 2006. Estimating uncertainties of projected Baltic Sea salinity in the late 21st century. Geophysical Research Letters 33. Olifan, V.I., 1940. Eksperimental’nye ekologo-fiziologiceskie issledovanija nad ikroj i licinkami ryb. 1 Vlijanie solenosti na rannie stadii razvitija azovskich lesca, sudaka i volzkoj sel’di. Zoologiceskij zurnal tom XIX (vyp. 1), 73e97. Pekcan-Hekim, Z., Lappalainen, J., 2006. Effects of clay turbidity and density of pikeperch (Sander lucioperca) larvae on predation by perch (Perca fluviatilis). Naturwissenschaften 93, 356e359. Pekcan-Hekim, Z., Urho, L., Auvinen, H., Raitaniemi, J., Söderkultalahti, P., Heikinheimo, O., Lappalainen, J., 2011. Climate warming and pikeperch year-
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class catches in the Baltic Sea. Ambio: A Journal of the Human Environment 40 (5), 447e456. Repecka, R., Mileriene, E., 1991. Species composition and abundance in shore-zone fish communities of the Kurshiu Bay during 1985-1989. Ecology 5, 65e80. Vilnius. Sandström, A., Karås, P., 2002. Effects of eutrophication on young-of-the-year freshwater fish communities in coastal areas of Baltic. Environmental Biology of Fishes 63, 89e101. Sundblad, G., Härmä, M., Lappalainen, A., Urho, L., Bergström, U., 2009. Transferability of predictive fish distribution models in two coastal systems. Estuarine, Coastal and Shelf Science 83, 90e96. Suominen, T., Tolvanen, H., Kalliola, R., 2009. Surface layer salinity gradients and flow patterns in the archipelago coast of SW Finland, northern Baltic Sea. Marine Environmental Research 69 (4), 216e226. Svärdson, G., Molin, G., 1973. The Impact of Climate on Scandinavian Populations of Sander (Stizostedion lucioperca (L.), vol. 53 Report Institute of Fresh-water Research Drottningholm112e139. Tanasijcuk, V.S., Vonokov, I.K., 1955. Vlijanija vody raznoj solenosti na spermu, ikru, licinok i mal’kov sudaka Lucioperca lucioperca (Linné). Voprosy Ichtyologii 5, 39e48. Urho, L., Hildén, M., Hudd, R., 1990. Fish reproduction and the impact of acidification in the Kyrönjoki River estuary in the Baltic Sea. Environmental Biology of Fishes 27, 273e283. Urho, L., 1996. Habitat shifts of perch larvae as survival strategy. Annales Zoologici Fennici 33, 329e340.
Urho, L., 1999. Relationship between dispersal of larvae and nursery areas in the Baltic Sea. ICES Journal of Marine Science 56 (Suppl.), 114e121. Virbickas, J., Gerulaitis, A., Misiünienë, D., Sinevicienë, D., 1974. Biology and Fishery of the Pike-Perch in the Water Bodies of Lithuania. State Publishing House ‘Mintis’, Vilnius, p. 277. (in Russian with English summary). Wang, N., Xu, X., Kestemont, P., 2009. Effect of temperature and feeding frequency on growth performances, feed efficiency and body composition of pikeperch juveniles (Sander lucioperca). Aquaculture 289, 1e2. Watson, D.F., Philip, G.M., 1985. A refinement of inverse distance weighted interpolation. Geoprocessing 2, 315e327. Weckström, K., Korhola, A., Shemeikka, P., 2002. Physical and chemical characteristics of shallow embayments on the southern coast of Finland. Hydrobiologia 477, 115e127. Winkler, H.M., Debus, L.R., Thiel, R., Franek, D., 1994. Variability of young and small sized fish community in a southern Baltic estuary from 1983 to 1992. In: Proceedings of VIII Congress of SEI. Oviedo Spain, vol. 26 Sept to 2. Oct 1994. Winkler, H.M., Klinkhardt, M., Buuk, B., 1989. Zur Fruchtbarkeit und Reifenentwicklung des Zanders (Stizostedion lucioperca (L.)) aus Brackgewässern der südlichen Ostsee. Wiss. Z, vol. 38. University of Rostock, N-Reihe. 31e37. Wintle, B.A., et al., 2005. Fauna habitat modelling and mapping: a review and case study in the Lower Hunter Central Coast region of NSW. Austral Ecology 30, 719e738.
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Estuarine, Coastal and Shelf Science journal homepage: www.elsevier.com/locate/ecss
Turbidity characterizes the reproduction areas of pikeperch (Sander lucioperca (L.)) in the northern Baltic Sea L. Venerantaa, *, L. Urhob, A. Lappalainenb, M. Kallasvuob a b
Finnish Game and Fisheries Research Institute, Quark, Korsholmanpuistikko 16, 65320 Vaasa, Finland Finnish Game and Fisheries Research Institute, P.O. Box 2, FI-00791 Helsinki, Finland
a r t i c l e i n f o
a b s t r a c t
Article history: Received 19 February 2011 Accepted 24 August 2011 Available online 29 August 2011
The pikeperch (Sander lucioperca (L.)) is an economically important fish species occurring in the fresh and brackish waters of Europe. To evaluate the distribution and extent of the reproduction areas in the northern Baltic Sea, a field survey was carried out in two separate coastal areas. Presence/absence data were used to develop a geographic information system (GIS)-based predictive spatial distribution model, where high resolution raster maps of the focal environmental variables and a logistic regression equation were used to predict the probability of larval occurrence. The results indicated that the pikeperch reproduction areas are located in the innermost archipelago zone where high water turbidity best explained their presence. Turbidity was related to several other variables such as fetch and depth. Contrary to our preliminary hypothesis, surface water temperatures measured during the survey had no significant effect in the model due to the low spatial variation in the measured values. Since turbidity is possible to determine by remote sensing methods, the probability maps can be cost-effectively extended to more extensive coastal areas with proper validation. Ó 2011 Elsevier Ltd. All rights reserved.
Keywords: prediction pikeperch larval habitat turbidity Baltic Sea
1. Introduction The pikeperch (Sander lucioperca (L.)) is a valuable species for both recreational and commercial fisheries in the fresh and brackish waters of many European countries. Young and adult pikeperch prefer pelagic sheltered coastal areas close to the shoreline (Urho et al., 1990; Repecka and Mileriene, 1991; Winkler et al., 1994). In the Baltic Sea, the largest catches are from oligohaline bays that are often also eutrophicated (Lehtonen et al., 1996). Large natural fluctuations in yeareclass strength and catches are typical for pikeperch populations (Lappalainen and Lehtonen, 1995; Lappalainen et al., 1995). In general, high summer temperatures have been shown to have a positive influence (e.g. Svärdson and Molin, 1973; Lehtonen and Lappalainen, 1995; Gröger et al., 2007), and turbidity and eutrophication have also been connected to the survival and distribution of offspring (Hansson and Rudstam, 1990; Sandström and Karås, 2002; Pekcan-Hekim and Lappalainen, 2006).
* Corresponding author. E-mail addresses: lari.veneranta@rktl.fi (L. Veneranta), lauri.urho@rktl.fi (L. Urho), antti.lappalainen@rktl.fi (A. Lappalainen), meri.kallasvuo@rktl.fi (M. Kallasvuo). 0272-7714/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.ecss.2011.08.032
The spawning of pikeperch in the Baltic Sea takes place in shallow eutrophicated bays. The depth of the spawning ground ranges between 0.7 and 3 m (Filuk, 1962; Virbickas et al., 1974; Lehtonen and Lappalainen, 1995), although larvae may also reach the surface from deeper water layers (Belyy, 1968). However, only some local reproduction areas have earlier been confirmed by sampling larvae (Urho et al., 1990) or juveniles (Winkler et al., 1994; Kjellman et al., 2001, 2003; Lappalainen and Urho, 2006). The spawning of pikeperch in the Baltic Sea takes place between late April and the beginning of July, depending on the latitude (e.g. Filuk, 1962; Winkler et al., 1989; Lappalainen et al., 2003) once water temperatures exceed 10 C. At an incubation temperature of 12 C, the larvae hatch less than 14 days after fertilization (Lappalainen et al., 2003). Thus, in the northern Baltic Sea area, the spawning and hatching period varies temporally depending on the early summer temperatures. There is a need for information on the distribution of larval areas that have high importance for the fish production, and also on a wider spatial scale, since changes in the climate are expected to have an influence on pikeperch (Lehtonen et al., 1996; Lappalainen et al., 1997; Lappalainen et al., 1997). Pikeperch stocks are also currently affected by fishing pressure, not only from fishermen but possibly additionally from cormorants and seals, whose populations have rapidly increased. In Finland, fishing of
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pikeperch is only regulated by minimum size restrictions and is also targeted at spawning pikeperch. A question has arisen as to whether more fisheries management actions should be directed to spawning areas, although their locations have not been well known. Due to high water turbidity and inconsistent knowledge of pikeperch reproduction areas in the Baltic Sea, the exact location of spawning grounds based on egg observations using means such as scuba diving would be laborious for large-scale mapping purposes. Instead, the location of the key reproduction areas can be assessed by focussing the catch effort on the pelagic early life stages of pikeperch, i.e. newly-hatched larvae, which are relatively easy to catch. A combination of predictive spatial models and a geographic information system (GIS) can be used to construct thematic reproduction area maps from stratified random sampling data. In recent years, many studies have focused on predicting the relationship between species distribution and environmental variables with statistical modelling and/or GIS applications. For fish larvae in the northern Baltic Sea, distribution maps have been produced for pike (Esox lucius) and roach (Rutilus rutilus) (Härmä et al., 2008; Lappalainen et al., 2008; Sundblad et al., 2009). In these studies, the georeferenced species occurrence data have been combined with environmental variables and the potential distribution of species has been predicted in a predefined geographical range to locate the areas where a species occurs. For coastal pelagic predators such as pikeperch, larval area prediction maps based on environmental factors have not been previously constructed. The aim of this study was to (1) identify the larval areas of pikeperch in the northern Baltic Sea and the environmental factors that determine these areas, (2) construct a modelled predictive spatial map of the distribution of larval pikeperch on the basis of larval occurrence and geographically expandable environmental factors and (3) to evaluate the estimation accuracy of the model with external validation in an independent coastal area. The hypothesis was that newly-hatched larvae will be located in shallow waters with high temperature and high turbidity that were hypothesized to be important for the occurrence of pikeperch larvae. 2. Material and methods 2.1. Study area The sampling area for the training data set was located in the Archipelago Sea (Fig. 1, area A) and the sampling area used for validation of the model in the western Gulf of Finland, in the northern Baltic Sea (Fig. 1, area B). Training data was used to construct the model and validation data to evaluate the model. Both were fragmented coastal areas and covered the inner, intermediate and outer archipelago zones (Häyren, 1900; Granö, 1981). The water quality and geomorphology were quite similar in both sampling areas and both were influenced by fresh, nutrient-rich outflows from small rivers. Thus, especially in the inner parts of the study areas, the water was very turbid and eutrophic, with a decreasing trend towards the outer archipelago. In the inner parts of archipelago, the early summer salinity varied between 3 and 4. In the Archipelago Sea and Northern Gulf of Finland, salinity usually fluctuates between 4.5 and 6 (Weckström et al., 2002; Suominen et al., 2009). 2.2. Field surveys In the training area the sampling stations were randomly selected within three strata based on turbidity values obtained
Fig. 1. Location of the study areas (A ¼ training area and B ¼ validation area) in the northern Baltic Sea.
from EOS Terra-MODIS satellite images (resolution 250 m) captured in summer 2005. The total number of sampling stations was 134 in the Archipelago Sea in 2007, of which 74 stations were randomly selected from areas with high turbidity (over 7 Formazin Nephelometric Units, FNU), 40 stations from areas with moderate turbidity (2e7 FNU) and 20 stations from areas with low turbidity (less than 2 FNU). In the validation area, in the western Gulf of Finland in 2008, the total number of sampling stations was 50 and they were randomly selected to cover all archipelago zones in the sampling area. The training area was sampled from 17 to 28 June 2007 and the validation area from 4 to 22 June 2008. The sampling of pikeperch larvae was carried out during the day with two pelagic ichthyoplankton Gulf Olympia samplers with an inflow diameter of 190 mm (Hudd et al., 1984; Aneer et al., 1992). The samplers were fitted in parallel to both sides of the bow of the boat approx. 2 m apart and at depths of 0.75 m and 1.00 m from the surface. The net behind the sampler cone had a mesh size of 300 mm. The hauling speed was set to 2 ms1 with GPS and the hauls were 400 m long straight lines. Due to depth limitations of the Gulf Olympia sampling method, no samples were taken in water shallower than approximately 1.5 m. Each sampling station was visited two times in a span of at least one week to ensure the right timing for larval occurrence. All larval samples were fixed in 4% formaldehyde solution in the field and larvae were identified, counted and measured for total length (mm) in the laboratory after preservation to 96% ethanol. The water turbidity was measured at every sampling station and visit with a Eutech TN100ir turbidity metre near the water surface. The water depth, bottom type (soft or hard), sampling position and surface water temperature were defined with a combined GPS plotter and sounder at the time of sampling. To obtain an estimate of salinities in the area at the spawning time, the salinity values were measured in early spring in both areas. Eight temperature data loggers (Onset Hobo UA-002-64) were placed in the training area at a depth of 2.5 m in spring 2007 and ten loggers were placed at a depth of 2 m in the validation area in 2008 to follow the temperature development and possible summer upwelling. The loggers were located so that they covered all the archipelago zones. Data from three loggers for both, training and validation area were used to describe the temperature development.
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2.3. Data analysis and spatial modelling 2.3.1. Explanatory variables The spatial turbidity data measured in 2007 (134 in situ measurements) in the Archipelago Sea and in 2008 (50 in situ measurements) in the western Gulf of Finland area were imported into the GIS and interpolated by using inverse distance weighting (IDW) to create a turbidity map with a spatial resolution of 50 m. In interpolation, the maximum turbidity of sampling rounds was used. IDW determines cell values using a linearly weighted set of combinations (Watson and Philip, 1985). A high resolution thematic turbidity map was needed, since the spatial resolution of EOS TerraMODIS images was not sufficient for the complex archipelago areas. The bathymetry map with a spatial resolution of 50 m was obtained from the Finnish Maritime Administration. In the training area, the depth data was compared to in situ depth measurements in the sampling stations and the correlation was 0.82 (RMSE 4.07). The geographical characteristics of study areas were described with GIS-constructed variables. The distance to areas with a depth greater than 20 m was calculated for each grid with a spatial resolution of 50 m. Shoreline density, which describes the complexity of the archipelago, was calculated as the total shoreline length within a circle of radius 5000 m, the units being length per unit area at a spatial resolution of 50 m. The exposure of a site to wave action was estimated using an effective wave exposure index, fetch (Isaeus, 2004), with a spatial resolution of 25 m. All GIS analyses were performed using the ArcGIS (ArcMap 9.3.1 and Spatial Analyst extension, ESRI, Redlands, California) software package. The average temperature of two sampling rounds in 2007 at each sampling station was used to describe the surface water temperature, but these values were not interpolated to thematic temperature map. 2.3.2. Modelling A logistic regression model for the training data was constructed to examine the relationship between the occurrence of pikeperch larvae and the in situ measured or GIS-constructed environmental variables. Data on the presence/absence of pikeperch larvae were used as a binary response variable. The environmental factors (depth, surface water temperature, turbidity, shoreline density, distance to 20 m depth curve and fetch) were used as continuous explanatory variables, except for the bottom type which was set as classification variable. The parallel samples of the two consecutive sampling occasions were combined, and the binary response variable therefore corresponds to the catch effort of four separate samples. A stepwise backward elimination method was used to select the variables for the model from among the main effects and interactions. A significance level of 0.05 was used as a criterion for a variable to remain in the model. The model for the training data
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was used to score the validation data and data for the GIS probability layer. The overall fit of the model was assessed using the Hosmer and Lemeshow goodness-of-fit test and with separate ROC (Reveiver Operator Characteristics) curves and AUC (Area Under Curve) e values for the validation and training data sets. Deviance residual plots were used to identify influential outlying observations and to check the model structure. The logistic regression model was fitted using the logistic procedure and spatial autocorrelation was tested with empirical semivariogram of model deviance residuals using the variogram procedure of SAS 9.2 software package (SAS Institute Inc, Cary, North Carolina). 3. Results 3.1. Abiotic characteristics of the study areas and the occurrence of larvae In both areas there was a clear decreasing gradient in turbidity and increasing gradient in early spring salinity from the inner to outer parts of the archipelago. In the innermost archipelago, the shallow areas dominated and caused early warming of the water layer compared to the exposed, deeper and thus colder outer areas archipelago zone. The fetch values were highest in the outer archipelago and lowest in the inner archipelago. As with shoreline density, the wave exposure also varies depending on the geomorphometry and level of isolation. Turbidity correlated with all other variables, fetch with depth and depth with the distance to 20 m depth curve. The surface temperature measured at each sampling station correlated only with distance to 20 m depth curve (Table 2). The correlation of variables was adequately low (0.61), suggesting that backward elimination approach was suitable (Wintle et al., 2005; Heinänen et al., 2008). The range of in situ measured surface water temperatures in the training area was narrow considering the spatial and temporal scale (Table 1). However, the measured logger temperatures indicated significant temperature differences between the three archipelago zones (Fig. 2). A temperature level of 12 C was used to describe the minimum threshold value for larval growth (Alabaster and Lloyd, 1980). In the training area, from 15 May to 30 June 2007, water temperatures exceeded this threshold in the innermost areas on all days, while in the middle archipelago the temperature increased more slowly, but nearly all days were warmer than the growth threshold. In the outer parts of archipelago, however, the temperature only exceeded the threshold at the end of May and one third of days were below this limit. Although there was a significant upwelling occasion in the middle of June 2007, the water temperature did not drop under the threshold value. In the same period in 2008 in the validation area, the water temperature exceeded 12 C in the outer parts of study
Table 1 Ranges (minemax) of the abiotic variables at all the sampling stations and at those stations where pikeperch larvae were present, calculated separately for the training and validation areas. Spring salinity was measured in training area 7e18.5 and in validation area 6e16.5. Variable/Area
Spring salinity, (ppt) Surface water temperature, ( C) Sum of June degree days in the innermost area Water turbidity, (FNU) Fetch, index Bottom type Depth, (m) Shoreline density, (index) Distance to 20 m depth curve, (m)
Training area
Validation area
Total range (minemax)
Range when larvae present (minemax)
Total range (minemax)
Range when larvae present (minemax)
0.9e6.2 13.4e20.7 539 0.8e33.7 2873e40,276 Softehard 1.7e67 0.3e3.2 0e20,687
3.8e5.4 14.2e20.7 NA 5.0e33.7 3569e20,486 Soft 2e17 0.3e3.0 250e18,137
1.1e5.3 10.6e17.9 505 1.44e19.7 3948e307,744 NA 2e27 0.1e2.66 0e13,523
3.3e4.2 14.7e17.9 NA 5.0e19.7 3948e9128 NA 2e9.8 0.4e2.4 3859e12,192
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Table 2 Correlation of environmental variables in the training area (n ¼ 134).
Turbidity Fetch Depth Distance to 20 m depth Line density Surface temperature
Turbidity
Fetch
Depth
Distance to 20 m depth
Line density
Surface temperature
1.00 0.41*** 0.50*** 0.61*** 0.41*** 0.18ns
1.00 0.59*** 0.21* 0.01ns 0.10ns
1.00 0.46*** 0.11ns 0.08ns
1.00 0.28** 0.52***
1.00 0.11ns
1.00
Significance level *** < 0.0001; ** < 0.01; * < 0.05;
ns
not significant.
areas on only 6% of days, and in the middle area the temperature was warmer than the threshold on 66% of the follow-up days. Upwelling caused the temperature to drop under the critical level two times, in the middle and at the end of June in 2008. In the innermost area, the temperature was highest and 85% of days were warmer than 12 C. After warming there were also no sudden decreases in temperature (Fig. 2). In the training area in 2007, pikeperch larvae were present at 51 of the 134 stations, and in the validation area in 2008 at 10 out of 50 stations. The highest larval densities occurred in the innermost archipelago and no larvae were caught in the outer archipelago. The total number of larvae caught was 597 individuals in the training area and 272 in the validation area. The average length of pikeperch larvae was 7.1 1.9 mm in the training area in 2007 and 6.2 0.8 mm in the validation area in 2008, and the size distributions were rather uniform in both years except for the lack of larger-sized larvae in 2008 (Fig. 3). In sampling stations where pikeperch were present the catch consisted also of pikeperch in length of 6e7 mm or smaller. In 2007, the larvae were caught during the whole sampling period, while in 2008 the main larval catch was obtained at the beginning of June.
The HosmereLemeshow goodness-of-fit test (c2 ¼ 5.747, df ¼ 8, p ¼ 0.676) indicated that the model fit in the training area was reasonably good. The model had no consistent patterns when the semivariance of the model deviance residuals was plotted against the distance between sampling points that indicates no significant spatial autocorrelation. According to this model, pikeperch larvae only occurred at turbidities greater than 9.0 FNU (Fig. 4). The threshold value of p(x) > 0.5 was used to classify observation as presence to achieve the best correct classification ratio for both, training and validation area. In the training area, the model correctly predicted 85.1% of the observations (n ¼ 134), the sensitivity was 78.4% (11 false negatives) and specificity 89.4% (9 false positives). AUC for training data was 0.94. The specific model fitted for the training area was validated in the validation area to assess the predictive performance of the model and the possibility to extend it to wider coastal areas. In the validation area, the model correctly predicted 82% of observations (n ¼ 50) and the values for sensitivity and specificity were 90.0% (8 false positives) and 80.0% (1 false negative), respectively. AUC for validation data was 0.89. The results were scored according to the interpolated turbidity values and a thematic map describing the probability of pikeperch presence was constructed (Fig. 5a and b).
3.2. Spatial modelling of pikeperch larvae In the final logistic regression model fitted to the training area, the only variable explaining the occurrence of pikeperch larvae was turbidity (p < 0.001) (Appendix). The effect of depth was close to significance (p ¼ 0.064), but as it is correlated with turbidity, it was dropped from the model. The parameter estimates for logistic regression model p(x) ¼ 1e1/(1þee(a þ b(turbidity))), where p(x) is the probability for larval pikeperch occurrence, are intercept a ¼ 5.24 (SE 0.87), and the parameter estimate for turbidity b ¼ 0.58 (SE 0.10).
4. Discussion 4.1. Reproduction in inshore areas We only found pikeperch larvae at stations where the surface temperature during the survey time was higher than 14.1 C. Previous studies on the recruitment of juvenile pikeperch (e.g. Buijse et al., 1992; Lappalainen and Lehtonen, 1995; Kjellman et al., 2001) have also highlighted temperature as the most important
Fig. 2. Temperature development in the training (A) and validation area (B) in the different archipelago zones. Data are daily average values. The black horizontal line marks the threshold for larval growth (12 C) and vertical lines with arrows mark the sampling periods.
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Fig. 3. The lengthefrequency distribution of pikeperch larvae in the training area A (black bars) and validations area B (grey bars).
factor determining the size of next yeareclass. Kokurewicz (1969) suggested that larval pikeperch favour temperatures between 12 and 16 C for best growth, while even as high a range as 24e29 C has been suggested as optimal (Hokanson, 1977; Marshall, 1977; Hilge, 1990; Wang et al., 2009). Temperatures below 12 C may reduce hatching success and values below 6 C are lethal for embryos (Alabaster and Lloyd, 1980). In the training area in 2007, the similarity in the water surface temperatures between the inner and outer parts of the archipelago was due to the high summer air temperatures and mild winds. Neither significant wind-induced circulation nor upwellings were observed during the sampling period according to the temperature logger data. However, in the validation area in 2008, the temperature gradient between inner and outer archipelago was more evident. The time period when larvae were detected was windy and colder than in the training area the previous year, and especially in the middle and outer archipelago the temperature was significantly lower than in 2007. The influence of upwelling was notable in the middle and outer archipelago areas, where a sudden decrease in temperature took place in the middle of June, during the occurrence of early pikeperch larval stages. The validation area sampled in 2008 had,
Fig. 4. Modelled probability of pikeperch larval occurrence with 95% prediction confidence limits in relation to turbidity. Circles indicate the observed presence (1) and absence (0) of larvae.
203
indeed, a lack of larger (>15 mm) pikeperch larvae, which may reflect the low survival rate of smaller larvae in the cold water conditions compared to warmer years and areas, such as in 2007. Not only water temperature but also salinity may contribute to the suitability of pikeperch spawning and hatching areas. The salinity limits of pikeperch larvae have been reported to vary between 2.5 (Deelder and Willemsen, 1964; Kukuradze, 1974) and 10 (Klinkhardt and Winkler, 1989). In laboratory studies Olifan (1940) and Tanasijcuk and Vonokov (1955), both referenced in Klinkhardt and Winkler (1989) recorded substantially increased mortality of larvae at a salinity of over 3.75 while Klinkhardt and Winkler (1989) determined a salinity limit of 7 for successful reproduction. The salinity range measured in early spring varied between 3.3 and 5.4, but this may underestimate the true salinity values of the larval period. The runoff of several small rivers reduces the springtime surface salinity within the inner archipelago compared to more saline open sea areas (Suominen et al., 2009). In this study, the core larval areas were focused in the low salinity zone, while larvae found at more saline (>5) stations can be considered to be uncommon and the stations suboptimal in the sense of early larval development. Nevertheless, the salinity values in the larval areas were higher or at the same level as what the studies of Olifan (1940) and Tanasijcuk and Vonokov, 1955; ref. in Klinkhardt and Winkler (1989)
Fig. 5. (a) and (b). Predicted probability map showing the larval distribution in the training area (a) and in the validation area (b). The map describes the probability of catching at least one pikeperch larva in a water volume of 45 m3. Circles indicate sampling stations where pikeperch larvae were present and crosses indicate stations where pikeperch were absent.
204
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indicated to be the successful survival limit of embryos. Overall, the salinities in the northern Baltic Sea do not exceed the 7e10 salinity limit that has been reported to restrict juvenile and adult pikeperch feeding areas (Boiko and Kozlitina, 1975; Janovskaya, 1977). However, these more saline areas are also colder than the less saline and turbid areas, and the effect of these factors on pikeperch reproduction cannot be separated. This study emphasized the importance of turbid estuaries for the reproduction of pikeperch, but the exact role of turbidity in larval survival remains undefined. The results indicate that high turbidity best describes the distribution of the larval areas on a wide scale. In the sampling areas, turbidity includes the indirect effects of temperature, salinity, depth and shoreline length. The less saline and more turbid innermost archipelago areas are typically shallow, and warm up more rapidly in spring than the surrounding areas. The turbidity in these areas may also be induced by wind, which causes sediment disturbance. High turbidity may protect the pikeperch larvae from predators during the critical first 3e4 days after hatching (Svärdson and Molin, 1973). Pekcan-Hekim and Lappalainen (2006) found that pikeperch larvae should have lower predation risk in highly turbid waters due to the decreased reaction distance of the predators. The visual adaptations of pikeperch may also increase their foraging abilities in turbid environments (Ali and Ryder, 1977), and pikeperch larvae are thus able to take advantage of the high production of suitable prey (Sandström and Karås, 2002).
Regardless of the modelling techniques, the sampling design greatly affects the accuracy of final result, i.e. the modelled probability map. It is necessary that the sampling design covers all environmental gradients present in the area (Guisan and Zimmermann, 2000). In the few coastal larval studies based on presence/absence field observations, the predictability of observations has been around 90% (e.g. Eastwood et al., 2001; Härmä et al., 2008; Sundblad et al., 2009). Larval modelling studies with external validation have been scarce. In this study, the model fit was reasonably high. The high proportion of false positives in the validation area indicates that the pikeperch larvae were not as widely distributed there as in the training area, where the range in turbidity was greater. This was due to differences between years and areas. The colder temperatures measured in 2008 in the validation area suggest that the year class was not as successful as in 2007, and this may therefore reduce the predictive ability of the model. However, the environmental variables measured in this study may indirectly or directly influence the ecological niche of larval pikeperch, but they do not include the influences on a wider scale, e.g. species interactions or changes over time. Thus, the resulting probability map describes of the potential geographical distribution of larvae under the environmental conditions that prevailed during the sampling.
4.2. Sampling design and spatial modelling
Gröger et al. (2007) indicated that climate, salinity and temperature have a significant effect on the pikeperch population response in terms of recruitment and adult stock fluctuations with substantial lag periods of from 3 to 5 years and Pekcan-Hekim et al. (2011) have shown that summers warmer than 18.5 C contribute most to pikeperch catches. It seems that the larval period is critical for pikeperch stock success, as with many other fish species (Braum, 1966; Johnston and Mathias, 1994). For the pikeperch occurring at the northern limit of their distribution, changes in the climate may be crucial. The salinity of the Baltic Sea is predicted to decrease in the 21st century due to an increased freshwater supply from rivers (Meier, 2006; Meier et al., 2006). In particular, the coastal archipelago areas with estuaries and increasing river runoff are expected to have lower salinity and higher turbidity in the future. Moreover, if the winter ice period becomes shorter (Jevrejeva et al., 2004; Jaagus, 2006) and spring and summer temperatures increase, it may induce a spatial enlargement of the coastal pikeperch reproduction areas. Thus, it would be important to follow-up the fish reproduction areas to attain better understanding of the effects of changing climate to fish populations. In this study, the turbidity value used for the logistic model was obtained from field sampling. New satellite instruments and satellite image interpretation techniques may in the future enable the use of high resolution remote sensing data for turbidity estimates. This type of remote sensing data makes it possible to extend the prediction maps to a wider spatial and temporal scale, covering larger coastal areas. Easily interpretable species distribution modelling has great potential to develop into a well-established tool for resource management and conservation planning purposes, and will therefore promote the sustainable use of coastal areas.
Due to depth limitations of the Gulf Olympia ichthyoplankton sampling method, the occurrence of early stage larval in shallow (<2 m) areas and in the immediate vicinity of shoreline and reed belts remained unknown. Urho et al. (1990) have noted that the early larval stages of pikeperch favour open water areas in estuaries, while larger larvae and juvenile fish are more focused in littoral areas. The uniform size distribution and relatively small size of pikeperch larvae in both sampling areas also indicates that our timing in larval sampling was set to the right period. Sampling immediately after the hatching period also enables evaluation of the potential spawning areas. The habitat changes of freshwater fish larvae, in particular, are often connected to the developmental stages (Urho, 1996, 1999). It is most probable that the early stages of pikeperch larvae remain near the spawning areas, since the water currents in the area are relatively weak. The larvae are not evenly distributed in the water column, and to avoid the negative influence of patchiness on the sampling, the parallel samples in this study were combined. However, due to the hypothesized patchiness of larvae, the presence/absence data were considered more suitable for spatial modelling purposes than quantitative estimates of larval abundance. In this study, we only used environmental factors that were available and could be expanded to wider areas at a high spatial resolution as predictor variables. The measured data set shows some correlations between variables, due to parallel gradients through the archipelago zones. Therefore, the pikeperch larval areas could be modelled with only variable, turbidity. However, though the turbidity best describes the occurrence of larval pikeperch in this study, other additional variables might improve the model. Adding temperature to the model successfully would require a thorough follow-up of temperature development on a wide scale with high resolution satellite images. Spatial salinity information at a resolution adequate for pikeperch modelling purposes in archipelago areas would require extensive flow information on the Baltic basin and small rivers. High resolution spatial environmental factors of this kind were unfortunately not available. Habitat models have been popular and quite widely used for modelling species distributions (Guisan and Zimmermann, 2000).
4.3. Future aspects
Acknowledgements We thank J. Salmi for assistance in the field and I. Kallio-Nyberg and R. Hudd for valuable comments. Referees provided valuable comments that improved this paper substantially. The study was financed by the Maj and Tor Nessling Foundation, the Finnish Game and Fisheries Research Institute and the Finnish Inventory Programme for the Underwater Marine Environment, VELMU.
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205
Appendix The model selection process with main effects. The standard error of variable estimates is shown in parentheses. Max-rescaled r2
Intercept
e
0.73
6.61 (6.65)NS
Fetch Dist. 20 m Surface temperature
0.73 0.73 0.73
6.61 (6.61)NS 6.76 (5.33)NS 1.91 (1.55)NS
Model selection step
Removed
0 1 2 3 NS
Variable estimate (SE) Fetch
Dist. 20 m
Surface temperature
Lineden
Depth
Turbidity
7.6 108 (0.00)NS e e e
1.1 107 (0.00)NS
0.31 (0.43)NS
0.72 (0.54)NS
0.16 (0.09)NS
0.40 (0.11)**
1.1 107 (0.00)NS e e
0.31 (0.43)NS 0.32 (0.34)NS e
0.72 (0.54)NS 0.72 (0.53)NS 0.64 (0.52)NS
0.16 (0.09)NS 0.16 (0.09)NS 0.16 (0.09)NS
0.40 (0.11)** 0.40 (0.11)** 0.44 (0.11)**
¼ not significant (>0.05), * <0.05, ** <0.01.
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