Reproduction Areas of Sea-Spawning Coregonids Reflect the Environment in Shallow Coastal Waters

Vol. 477: 231–250, 2013 MARINE ECOLOGY PROGRESS SERIES Published March 12 doi: 10.3354/meps10169 Mar Ecol Prog Ser

Reproduction areas of sea-spawning coregonids reflect the environment in shallow coastal waters

Lari Veneranta1,*, Richard Hudd1, Jarno Vanhatalo2

1Finnish Game and Fisheries Research Institute, 65100 Vaasa, Finland 2Fisheries and Environmental Management Group, Department of Environmental Sciences, University of Helsinki, 00014 Helsinki, Finland

ABSTRACT: We evaluated the distribution and the extent of sea-spawning whitefish Coregonus lavaretus (L.) s.l. and vendace Coregonus albula larval areas in the Gulf of Bothnia, northern Baltic Sea, and suggest that the distribution of the reproduction areas could be an indicator of the health of the Baltic Sea shores. Our Geographic Information System (GIS) based predictive spatial model of habitat selection covers nearly the whole distribution area of both species. Extensive sampling data on larval occurrence were combined with GIS raster layers on environmental variables and used in a Gaussian process model, which predicts the spatial probability of larval occurrence. Out of 22 studied variables, shore profile, distance to sandy shallow shore, distance to 20 m depth contour line and ice break-up week were the most important for describing larval areas of both species. The earliest larval stages of sea-spawning whitefish can be found in various habitats close to the shoreline, but the highest densities of larvae were observed along gently sloping, shallow sandy shores. Vendace reproduction occurs in the northernmost and less saline areas of the Bothnian Bay and larval stages use the shallow areas. Compared to previous studies from 1990s, the extent of whitefish larval areas has decreased. We discuss the possibility that long-term changes in the environment, such as more frequent iceless winters and increasing eutrophication, have reduced the reproductive success of sea-spawning coregonids. Larval distribution maps can be used to focus conservation measures in the most appropriate places. We propose to use this method as a monitoring tool, and produce maps to assist integrated coastal zone management and environmental protection

KEY WORDS: Whitefish · Vendace · Larvae · Filamentous algae · Eutrophication · Environmental change · Modeling

Resale or republication not permitted without written consent of the publisher

INTRODUCTION ing ecotype reproducing in the coastal areas (Lehto- nen 1981, Sõrmus & Turovski 2003). Sea-spawning Sea-spawning whitefish and vendace whitefish and vendace are economically and cultur- ally valuable and, as native Baltic species, they are The brackish water Baltic Sea is inhabited by 2 sea- also a key element of coastal fish fauna. spawning species of coregonids; whitefish Corego - The catch of both whitefish ecotypes has decreased nus lavaretus (L.) s.l. and vendace Coregonus albula since the 1980s, while the vendace harvest has re - (L.). At present, both species are found mainly in the mained relatively stable (Gårdmark et al. 2004, Urho northernmost basin of the Baltic Sea, the Gulf of 2011, Finnish Game and Fisheries Research Institute Bothnia (hereafter GoB). In the GoB, whitefish exists 2012). Especially in the southern GoB, catches of sea- as 2 reproductive ecotypes, an anadromous river- spawning ecotypes have collapsed (Anon 2011, Urho spawning ecotype and a more stationary sea-spawn- 2011). Until the 1970s, whitefish harvest contained

*Email: [email protected] © Inter-Research 2013 · www.int-res.com 232 Mar Ecol Prog Ser 477: 231–250, 2013

an equal number of sea-spawning and anadromous The distribution of important reproduction habitats ecotypes (Lehtonen 1981), but today the IUCN (Inter- of sea-spawning coregonids and the environmental national Union for Conservation of Nature) classifica- factors that determine them are generally poorly tion for sea-spawning ecotype is Vulnerable (Urho et known. Previous publications on larval sea-spawn- al. 2010). Recently, Sweden prohibited whitefish fishing whitefish in the GoB are 20 yr old (Hudd et al. ing (Anon 2011) to protect the sea-spawning popula- 1988, 1992, Leskelä et al. 1991) and there are very tions along the Swedish coast of the southern GoB. few studies on larval vendace in brackish water (but To fulfill the demands of the commercial fishery, sub- see Jokikokko 1993). In order to find the right combi- stantial numbers of the anadromous whitefish are nation of fishery and environmental management stocked in the GoB, especially in Finland, while sea- actions to safeguard these decreasing populations spawning whitefish are stocked in minor quantities, (which are likely to be genetically unique; Olsson mainly in the southern GoB. Sea-spawning vendace et al. 2012), new studies on their reproduction are are not stocked in the GoB. needed. Earlier studies suggest that the most important reproduction areas of the sea-spawning vendace are along the Swedish coast of the northernmost GoB, i.e. Large-scale changes in the GoB the Bothnian Bay (Enderlein 1989, Lehtonen & Joki - kokko 1995, Thoresson et al. 2001), while whitefish The amount of nutrients in the open areas of the reproduce along the entire coast of the GoB (Lehto- GoB has doubled during the last 30 years (HELCOM nen 1981, Sõrmus & Turovski 2003). However, these 1996, 2002, Fleming-Lehtinen et al. 2008), but it is studies are based on spawning time fisheries statis- still the least affected basin in the nutrient over- tics (e.g. Himberg 1995, authors’ unpubl. data from enriched Baltic Sea (Bernes 1988, Håkansson et al. informal surveys of fishermen) without validation, 1996, Lundberg et al. 2009). Coastal areas show and thus illustrate the need for more focused studies. more evidence of eutrophication than offshore areas Salinity restricts the reproduction areas of vendace, (Lund berg et al. 2005, Lundberg et al. 2009), and in since its larval stages can tolerate salinities up to 5 the GoB there is an increasing trend of eutrophica- (Jäger et al. 1981). The spawning time fishing of ven- tion from north to south (Nausch & Nausch 2011). dace is focused on estuaries (Enderlein 1989, Thores- Eutrophication is most visible in the shallow archi- son et al. 2001), suggesting that this tolerance may be pelago areas (Bonsdorff et al. 1997, Rönnberg & even lower. In the GoB, salinity is not considered to Bonsdorff 2004, Lundberg et al. 2009). However, in limit the reproduction of the sea-spawning whitefish, general, knowledge regarding the shallowest areas since it can hatch at salinities up to 10.2 (Jäger et al. is limited, and most of the wide-scale environmental 1981), although Albert et al. (2004) reported lower research in the GoB is focused on offshore areas (e.g. tolerance for freshwater whitefish. HELCOM 1996, 2009, Fleming-Lehtinen et al. 2008) Coregonids spawn in October to November or at least outside the littoral zone (Bonsdorff 2006, (Jokikokko 1993, Veneranta et al. in press) and their Lundberg et al. 2009). embryonic period is long. The demersal eggs remain Eutrophication has multidimensional direct and in the spawning grounds until hatching at ice indirect detrimental effects on coregonids (e.g. Ger- breakup in April to May. Thus, the embryos are deaux 2004, Gerdeaux et al. 2006). Nutrients in - exposed to environmental factors that influence their crease algal growth and vegetation, which leads to development and survival over an extended period emerging sedimentation in coastal areas (e.g. Isaks- (Lahti et al. 1979, Müller 1992, Veneranta et al. in son & Pihl 1992, Eriksson et al. 1998, Berglund et al. press). The sea-spawning whitefish larvae appear in 2003). Fine sediment settling in layers as thin as 1 to littoral areas soon after hatching, presumably due to 4 mm can decrease coregonid egg survival (Wilkon- the proximity of the spawning grounds (Ponton & ska & Zuromska 1982, Fudge & Bodaly 1984), pre- Müller 1989, Sarvala et al. 1994). In lakes, the spatial sumably due to the associated high oxygen depletion distribution patterns of larval coregonids vary widely rates (Lahti et al. 1979, Carlton et al. 1989, Ventling- (Ponton & Müller 1989, Wanzenböck & Jagsch 1998, Schwank & Livingstone 1994). In shallow areas, both Karjalainen et al. 2002, Lahnsteiner & Wanzenböck water exchange and exposure to waves and wind 2004) and sea living larvae have been observed in determine an area’s capacity to dissipate nutrient areas with clean, hard sand or gravel bottoms or discharge, which in turn influences the formation of stony bottoms with patches of sand (Leskelä et al. algal mats and sedimentation. In the littoral zone, the 1991). occurrence of algae is also regulated by currents, Veneranta et al.: Reproduction areas of sea-spawning coregonids 233

strong winds and ice scraping (Sfriso et al. 1987, Lav- tribution in the GoB and (3) to assess the value of ery et al. 1991, Norkko & Bonsdorff, 1996). whitefish and vendace larvae as indicative species Climate warming has decreased the probability of for the purposes of coastal zone management and ice occurrence and advanced the date of ice breakup monitoring the status of shallow coastal waters in the in the Baltic Sea (Jevrejeva et al. 2004, Jaagus 2006, GoB. HELCOM 2007). These changes may also have changed the distribution of coregonids. Freeberg et al. (1990) and Brown et al. (1993) suggest that ice MATERIALS AND METHODS cover enhances the whitefish egg survival by reduc- ing the effects of wind generated wave action. Winds Study area may also transport eggs to unfavorable environments, damage them physically, transport harmful material The GoB consists of 2 major basins, the Bothnian and alter the sedimentation. Moreover, ice movement Sea and the Bothnian Bay, separated by the Quark, a can clean littoral areas from sediment, algal growth shallow sill between the basins (Fig. 1). The size of and macrovegetation (Barnes et al. 1993). the study area is approximately 600 × 120 km and it contains numerous river estuaries, distinct archipelago areas and exposed open shorelines. The east Coastal gradients for assessing coast is characterized by wide shallow areas with the reproduction areas evenly sloping bottoms and structures formed by moraines. The northeast coast is typified by open The Baltic Sea is located in the seasonal sea ice shores with large shallow sandy areas. In the west, zone and there are strong north-south gradients, for broad faults and rifts break the coastline, creating example, in salinity, river discharge, coastal types, deep hollows close to the coast. In archipelago areas, climate and pollution (Voipio 1981, Andersen et al. the outer islands are strongly exposed to wind while 2011). These gradients offer an excellent opportunity the middle and inner archipelago are more sheltered. to analyze the reasons for variance in species distri- The freshwater discharge from rivers has a strong bution. Moreover, the environmental variables can influence on the GoB. Due to the differences in water be coded into Geographical Information System (GIS) volume and retention time (Håkansson et al. 1996, as high resolution raster layers. Together with statis- Myrberg & Andrejev 2006), salinity ranges from tical modeling, these allow construction of reproduc- limnic waters (<1) in the innermost reaches in the tion area maps which may be used, for example, in north and in the estuaries to 6−7 in the south (Voipio conservation planning. In the northern Baltic Sea, 1981, Håkansson et al. 1996, HELCOM 2002). During such maps have been presented, for instance, for mild winters, ice occurs only in the Bothnian Bay, but pike Esox lucius, roach Rutilus rutilus (Härmä et. al. in cold winters nearly the entire Baltic Sea may 2008, Sundblad et al. 2009) and pikeperch Sander freeze. During normal winters, ice covers the coastal lucioperca (Veneranta et al. 2011) on smaller scales. areas of the GoB from October-November to April- In this work, we modeled nearly the entire present May, and breaks up from south to north in approxi- distribution area of 2 species that have an especially mately 1 mo. The average duration of ice cover varies sensitive stage in their early life cycle. The methodol- from 60 to 194 d (Seinä & Peltola 1991, Haapala & ogy for the species distribution model (SDM) used Leppäranta 1997). There are no notable tides, but here and early results for whitefish follow Vanhatalo water-level fluctuates occasionally over 1.5 m due et al. (2012). In this study, the modeling results for to internal waves and air pressure changes (Jerling both whitefish and vendace are set in a biological 1999). perspective and used to analyze the effect of changes in the environment. The main objectives are to (1) identify the characteristics of the potential larval Sampling procedure areas on a wide geographical scale, following the principles that are suggested for environmental gra- Larvae were sampled from 653 sampling sites in dients (Guisan & Zimmermann 2000, Kitsiou et al. 26 sub-areas during 2009 to 2011 (Table S1 in the 2001) and for dispersion and patchiness of aquatic supplement at www.int-res.com/articles/suppl/m477 organisms in different habitats (Cyr et al. 1992, Pepin p231_supp.pdf). In 2009, the sub-areas and sampling & Shears 1997, Claramunt et al. 2005), (2) estimate sites were dispersed mainly on the basis of exposure recent changes in whitefish and vendace larval dis- and bottom type, that were identified from ortho - 234 Mar Ecol Prog Ser 477: 231–250, 2013

Fig. 1. (a) Study area and division of regions. (b) Sampling sub-areas in the Gulf of Bothnia are marked with circles. The size of the circles indicates the count of sampling sites in each sub-area. (c) The reference station in the southern Gulf of Bothnia photo graphs. Also, the depth gradients, archipelago mized by locating the sub-areas, except for 9 and 20, areas and duration of ice winter were considered. outside large rivers and estuaries. The larval white- The sites were randomized as far as logistically pos- fish stocking sites were avoided. The sampling was sible to avoid spatial autocorrelation in the data. In designed to cover all close to shore habitat types in 2010, the coastal areas were classified to 8 strata the GoB as well as the main distribution area of sea- based on exposure, ice winter length in 2009 and spawning whitefish and vendace. length of shoreline in a 3 km circle. We also used data The sampling sites were visited once, approxi- from the sub-area 16 where stationary monitoring is mately 1 wk after ice break-up, in order to reach the conducted as a part of a separate sampling program. early larval stages that had already started feeding. The locations of sampling sites were randomized to In practice, sampling followed ice break-up as it pro- balance the number of samples in each class, de - ceeded from south to north, except in sub-area 16 pending on the attainability of sites. Stratified random where sampling was made 3 to 4 wk after ice-break. sampling was used since it was assumed to produce The sampling was made with a beach seine (509 sites) more precise estimates in spatially auto- correlated in all near shore sites (Hudd et. al. 1988), with a Gulf- circumstances than simple random sampling (Caeiro Olympia sampler (Hudd et al. 1984, Aneer et al. 1992) et al. 2003). In 2011, the sampling sites were selected in open water in sub-areas 10, 19 and 20 (100 sites) from the northern GoB to increase the sample size and with a tow net sampler in open water (44 sites) in and spatial coverage there, following the same strat- sub-areas 9−12 and 25. The majority of samples were ification as in 2010. The influence of rivers was mini- collected with a beach seine, since prior information Veneranta et al.: Reproduction areas of sea-spawning coregonids 235

(Hudd et al. 1988; Leskelä et al. 1991) suggested that ples taken with Gulf-Olympia or tow net 0.15 to the shallow areas are the main habitats for whitefish 47.5 m, focusing on depths over 1 m. larvae. Using different gear in shallow and deep water The beach seine had 8 m long arms with a mesh was necessary in order to cover all coastal types in size of 5 mm and a cod end with 1 mm netting. The our sampling. For the modeling purposes, we seine was set in a half-circle by fording out from the assume that the probability to catch one or more lar- shore and returning back with one end of the rope val fish, assuming that they exist in the sampling (length 10 m). Then, the seine was hauled back to site, is equal with all gear. This is a reasonable shore. If the first haul was without whitefish larvae, a assumption when modeling the binary presence– second haul was done. The Gulf-Olympia samplers absence observations, since all larvae that hit the were fitted in parallel to both sides of the bow of the net are caught irrespective of the gear. The area of boat approximately 2 m apart and at depths of 0.3 m net and the length of haul affect the number of and 0.5 m from the surface. The net behind the sam- caught fish and only in areas with very sparse larval pler cone had a mesh size of 300 µm. The hauling populations can they affect the probability of pres- speed was set to 2 m s−1 with GPS and the hauls were ence observation. 500 m long straight lines. For data-analysis, the mid- All larval samples were fixed in a 4% formalde- dle point of the line was indicated to be the sampling hyde solution in the field and identified, counted and point and the parallel samples were combined. The measured in the laboratory after preservation with tow net was similar to the Gulf-Olympia, except that 96% ethanol. The developmental stages of larvae it had only one sampler that was towed with a 20 m were identified according to Evropejtseva (1949) on rope just below the water surface behind the boat. a 5 level scale (Fig. S1 in the supplement). In addi- Due to sampling gear limitations, the depth range of tion to larvae, various background variables were the beach seine samples was 0.1 to 2 m and in sam- measured at each sampling site (Table 1).

Table 1. Variables measured in field. Numbers in parentheses: no. of classes in classification

Variable Near Open Range/ Unit Method/ shore water classes device

Position X x Continuous Lat./Long. (°) Garmin GPSMap (WGS84) 76CSx/Oregon 300 Turbidity X x Continuous FNU Eutech TN100ir Water temperature X x Continuous °C Eutech ECTestr11 Conductivity X – Continuous mS m−1 Eutech ECTestr11 Waveheight X – Flat, ripples, Classification (5) Visual 0.02−0.1 m 0.1−0.3 m >0.3 m Wind direction X – 0−360 ° Visual Wind speed X – 0, 2, 4, 6, 8, 10, 14+ Classification (7) Visual Depth X x m Shoreline profile X – I = open water, II = steep, Classification (6) Visual III = gently sloping with steep edge, IV = gently sloping, V = shallow, VI = shallow with sand bar Bottom type at X – Soft, silt, sand, Classification (7) Visual 0.3 m depth sand/stone, stones (≤30 cm diam.), rocks (>30cm diam.), cliff Bottom coverage at 0.3 m X – No coverage, Classification (5) Visual depth (algae and <10%, 10−25%, macrovegetation) 25−50%, >50%

Sampling speed – x – m s−1 Garmin GPSMap Gulf–Olympia/tow net 76CSx 236 Mar Ecol Prog Ser 477: 231–250, 2013

Environmental variables in Geographical Information System (GIS)

The environmental variables used in the SDM are summarized in Table 2. The data were obtained from the Finnish Meteorological

Institute (FMI), Finnish Environ- FGFRI model, Baleco model, Baleco ment Institute (FEI), Swedish Envi- ronmental Protection Agency (SEPA) 2 and Helcom Map and Data Service (HELCOM) or constructed for this e available as GIS layers and the vari- study in the Finnish Game and Fish- eries Research Institute (FGFRI). For the modeling, all variables were converted to a grid with a resolution of 300 m. All GIS analyses were performed using the ESRI ArcGIS (ArcMap 9.3.1 or 10.0 and Spatial Analyst extension) or ERDAS ER Mapper 7.0 software packages. Andersen et al. (2011) described the water quality index variables phosphorus, nitrogen, chlorophyll a (chl a) and secchi-depth. The shallow areas that were approximately less than 1 m in depth were digi- tized using coastal area orthophoto - graphy at a resolution of 0.5 m (Na - tional Land Survey of Finland and Swedish Land Survey) and SPOT 5−satellite images (Google Earth). The visible bottom in shallow areas was classified into (1) sand, (2) sand and mud, (3) sand and stones or rocks and (4) other types. These data were used to construct raster maps of bottom type, water distance to shallow areas and to shallow ables with lowercase letters are measured at the sampling site. I denotes index value sandy areas. The distance to shallow and shallow sandy areas was weighted with the surface area of shallow depths at the scale of the entire study area. Water area relative to shoreline , summer phytoplankton concentration cont. 2000 11−45 (higher value = better status) I HELCOM length was calculated from basic a

maps (scale 1:20000, Lantmäteriet in winter (2003−2007) in winter (2003−2007) Sweden and National Land Survey of Finland) at a frame size of 900 m. Basic maps were also used to calculate the amount of shoreline in a ISLANDNDSAND Number of islands in a circle of 10000 mDSHALLOPE900 Area Area weighted distance to shallow weighted distance to sandSALSPR cont. area Water per shoreline lengthSALWIN Spring salinity (May-June) salinity Winter ICEWIN cont.ICELAST cont. 100EKOSTAT Length of ice winter in 2009 Last ice cover (concentration <30%)PHOSP Ecological status of coastal waters cont.NITROG Dissolved inorganic phosphorus (DIP) 60 cont. 90 Dissolved inorganic nitrogenCHLA (DIN) cont. class cont. 90 0−549 class Chl 10 000 cont. 1852 cont. vector 0−6168 2000 1852 0−116 I 14−49 (higher value = better status) 2000 10 000 I 0−1046 0−6.2 10−49 (higher value = better status) FGFRI I I 7−21 HELCOM 0−4 I 0−24 HELCOM FGFRI I FGFRI 0−6.0 psu wk FGFRI 3d biogeochemical FMI, type m FMI FEI/SEPA psu FMI 3d biogeochemical FMI, SECCHIRIVERS Secchi depth, summer average (2003−2007)SHAREABOTCLS Distance to the nearest river mouth cont. Shallow area Bottom class cont. 2000 10−43 (higher value = better status) I 150 HELCOM cont. class 0−56700 200 200 m FGFRI 0−1122 0−5 index FGFRI NA FGFRI Variableshoprofile Descriptiondepth Shore profileFE300WFE300ME DepthD20M Fetch weighted Fetch meanLINED Distance to 20 m depth curve Shoreline density in a circle of 3000 m cont. Type cont. class cont. 100 Resol. (m) cont. 200 NA cont. 300 300 range Value 0−323 NA 0−27 473 Unit 1−6 90−260 084 km/km 9−124 Source 247 m 0.1−47 m NA FGFRI m FGFRI FGFRI m FGFRI FGFRI

3 km circle and the number of 2. EnvironmentalTable variables used to analyze whitefish and vendace larval occurrence. The variables with capital letters ar Veneranta et al.: Reproduction areas of sea-spawning coregonids 237

islands in a 10 km circle. The depth data from the ables is mis-specified or geographic factors are influ- Finnish Transport Agency and Swedish Maritime ential (Elith & Leathwick 2009). Administration were used to calculate the water dis- The SDM was used (1) to detect the environmental tance to a 20 m depth curve. The exposure to wind variables that best describe the occurrence, and (2) to was calculated as an average over 32 directions and predict the probability of occurrence for the whole as a weighted average, where the maximum expo- study area. In both tasks, the environmental variables sure had an effect of 30% and the average 70%. The should be stable over sampling period, and, in (2), ad- calculations of the exposure to wind variables are ditionally available as GIS layers. Table 2 summarizes described, for example, by Ekebom et al. (2003). Year available variables fulfilling both conditions (see also 2009 was selected to represent the ice cover, since it previous section) together with 2 in-site measured was assumed to best describe the average duration variables, depth and shoreline profile (shoprofile), and extent of the available in press. that were considered a priori important for the occurrence. We built 2 models with different sets of environmental variables. The full model contained all the Species distribution model variables summarized in Table 2 and it is used for task (1). The reduced model used only variables that are Following Vanhatalo et al. (2012), we modeled the available as GIS layers and it is used for task (2). presence–absence observations as Bernoulli trials We assessed the relevance of an environmental with occurrence probability π. The occurrence prob- variable to the occurrence with the average predic- ability is related to the environmental variables x at tive comparison (APC) method (Gelman & Pardoe location s through 2007, Vanhatalo et al. 2012). In case of a continuous variable, the method estimated the expected differ- π(x,s) = g(ƒ[x] + ρ[s]) ence in the probability of occurrence associated with Here ƒ(x) is a predictive function, ρ(s) is a spatial a unit difference in the covariate, which has an inter- random field and g is a logistic link function that pretation similar to the weights in a linear model. shrinks the prediction between zero and one. The With unordered categorical variables (EKOSTAT, predictive function and the spatial random field are BOTTOMCLS and shoprofile), the method estimated given Gaussian process (GP) priors with neural net- the expected absolute difference in the probability of work and Mátern covariance functions (Vanhatalo et occurrence with a change in the categorical variable. al. 2012) respectively. GP is a non-parametric model The APC took into account the uncertainty of model which defines probability distributions over func- parameters and summarized the average effect of an tions, and its properties are controlled by the covari- environmental variable over the whole study region. ance function. The neural network covariance func- We considered a variable influential if the 95% cred- tion is a reasonable choice for the predictive function, ible interval of APC does not cross zero. since it gives rise to non-linear functions which can Due to the interactions between the variables, the incorporate interactions between environmental va- predictive effect of a variable depends on the values ri ables and have good extrapolation power. The of the other variables and may be different in differ- Mátern covariance function is a common choice ent regions. For this reason, we studied the variable when modeling spatial random fields. response curves separately for each 5 regions shown With Bayes theorem, we calculate the posterior in Fig. 1. We plotted the response curves along 2 en - distribution of ƒ(x) and ρ(s) which are then used to vi ronmental variables. The response curves were calculate the posterior predictive probability of oc - calculated so that the variables that are not altered currence for the entire coastline. The posterior pre- were set to their average value over the study region. dictive probability contains all information about oc - The model’s performance was evaluated using 10- currence conveyed from the data by the model. The fold cross validation (CV). The test statistics were the predictive function extrapolates the occurrence proportion of true classifications (PTC) and true pos- probability from sample sites into unsampled areas itive or true negative rate (TPR or TNR, respectively) and the spatial random field explicitly models the (the proportion of true positive or negative classifica- spatial structure in the observations that is unex- tions to the number of positive or negative observa- plained by the environmental variables. A strong tions). The classification was conducted according to spatial pattern in the spatial random field indicates the class of highest posterior predictive probability. that either key environmental variables are missing, Values >50% indicate that the model worked better the predictive model from the environmental vari- than a random classifier, but otherwise it was hard to 238 Mar Ecol Prog Ser 477: 231–250, 2013

define any absolute level for good or bad perform- (Fig. 1, Table S2 in the supplement). Leskelä et al. ance. In comparison, recent smaller scale larval (1991) sampled sandy beaches with a similar beach SDMs from the northern Baltic Sea report TPRs rang- seine to ours. The original dataset was not available, ing from 0.78 to 1.0 and TNRs from 0.56 to 0.98 which substantially increased the uncertainty of the (Härmä et al. 2008, Sundblad et al. 2009, Veneranta comparison. Eight sub-areas in the Leskelä et al. et al. 2011). If all the values are close to each other, (1991) study (in Fig. 1b, sub-areas 1, 2, 3, 6, 10, 14, 19 the performance of the classifier was balanced and 25) were the same as in our study. In an external between the 2 classes, which is desirable from the reference sub-area (26) in the southernmost GoB, we point of view of a model’s purpose. For each test, we sampled exactly the same locations as Leskelä et al. calculated the mean and 95% central posterior cred- (1991). We compared the larval catch by Leskelä et al. ible interval using Bayesian bootstrap as described (1991) with ours, even though the data are not com- by Vehtari & Lampinen (2002). All the results for this pletely comparable as Leskelä et al. (1991) targeted paper were computed with Matlab using the GPstuff later developmental stages. Due to the differences in toolbox (Vanhatalo et al. in press). size and development, and the high mortality of larvae in early stages (Karjalainen et al. 2000), the quan- tity of larvae should be much higher in our samples Model for the effect of bottom coverage than in the samples of Leskelä et al. (1991). on occurrence probability

In order to examine the effect of bottom coverage RESULTS on occurrence probability, we analyzed the bottom class specific occurrence probabilities conditional on Distribution area, occurrence and size of larvae the bottom coverage level. For this, the occurrence probability for a bottom class with a specific coverage Most of the larvae caught (99% of vendace and level was assumed to be equal throughout the study 93% of whitefish) came from regions I and II. White- region. For example, it should be equally likely to fish were present in 53% and vendace in 26% of sam- find whitefish from a sandy uncovered bottom every- ples. In the main distribution area of vendace, regions where in the GoB, but the probability would be dif- I and II, vendace larvae were present in 40% of sites. ferent for rocky or sandy covered bottom. Since ven- Thus, the prevalence is suitable for modeling pur- dace were caught almost entirely from regions I and poses for both species (Maggini et al. 2006). The total II (see results section), only these regions were used number of whitefish and vendace larvae in the beach in the vendace analysis. seine catch was 37 953 and 50 445, respectively, and The occurrence probability of covered bottom is in the Gulf-Olympia catch 52 and 71, respectively. denoted with, πc (bottom coverage over 50%) and the The average length of whitefish larvae was 14.9 ± uncovered bottom with πu (bottom coverage under 1.8 mm (mean ± SD) and that of vendace larvae 12.5 ± 50%). Then, the number of occurrence observations 1.3 mm, apart from sub-area 16, where average is modeled with a binomial distribution with the length for whitefish was 24.5 ± 3.2 mm and for ven- probability πc or πu (covered/uncovered). The occur- dace 20.1 ± 4.1 mm. The size distribution of larvae rence probability was given an uninformative uni- was unimodal with the exception of sub-area 16, and form prior on the interval from 0 to 1, and by using catches contained mostly early developmental stages the Bayes theorem, we condition the observations to (1 and 2) (Fig. 2). In open water sampling, 75% of calculate the posterior distribution of πc and πu. The whitefish and 28% of vendace had a yolk sac, while posterior densities were visualized and the probabil- close to shore only 6% of whitefish and 0.5% of ven- ity that πu > πc calculated. dace larvae had a yolk sac in sub-areas where both beach seine and open water samplers were used.

Changes in larval abundance during the last decades Spatial distribution of larval areas

To compare the abundance of whitefish larvae in The larval distributions, in terms of large scale the 1980s and 1990s with our study, the number of habitat preferences, were well described with a high samples and the number of whitefish caught by Les - model fit (Fig. 3, Table 3). However, the predictive kelä et al. (1991) was summarized for our 5 regions accuracy of the reduced whitefish model was clearly Veneranta et al.: Reproduction areas of sea-spawning coregonids 239

worse than that of the full model. This is reasonable since both depth and shoprofile are important in explaining occurrence (Fig. 4). With vendace, the predictive power reduced only slightly from full to reduced model. The extent of whitefish larval areas covers regions I to IV and partially region V, whereas vendace larval areas are in the northernmost parts of regions I and II (Bothnian Bay) (Fig. 3). There were only a few estuarine influenced vendace observations in regions III, IV and V. Most of the predictive power came from the environmental variables, and the spatial component had only a slight influence on the prediction. In the whitefish model, the spatial correlation length, distance at which the correlation dropped to 5% from its maximum, was about 160 km along an east−west direction Fig. 2. Length–frequency distribution of (a) whitefish and and about 640 km along a south−north direction. In (b) vendace larvae in samples. The whitefish length distri - bution data from sub-area 16 is shown separately in gray the vendace model, the respective correlation lengths since it was collected as a part of separate sampling were 200 and 700 km, respectively. Correlation lengths program indicate a very smooth spatial random field.

Fig. 3. Distribution of (a) whitefish and (b) vendace modelled larval areas with prediction probability. Note that variables depth and shoreline profile are not used in the prediction, therefore the map overestimates the width of larval areas away from the coast. Circles indicate capture frequency of whitefish and vendace in each sub-area per sampling effort 240 Mar Ecol Prog Ser 477: 231–250, 2013

Table 3. Predictive performance of the full (using all en - beaches, but larvae were also caught from steep vironmental variables in Table 2) and reduced model (with- shores (Fig. 5a) with stones, rocks or vegetation. out depth and shore profile environmental variables). PTC: proportion of true classifications; TPR: true positive rate; Nearly all larvae were caught from a sampling depth TNR: true negative rate. The 10-fold CV results summarize of <1 m (Fig. 5b) and most from depths less than the mean and approximate central 95% credible interval 0.3 m. The highest densities of larvae were observed (paren theses) in gently sloping shores with a sheltering offshore sand bar. Vendace larvae were occasionally found in Species PTC (%) TPR (%) TNR (%) open water areas, though the highest densities were caught close to the shoreline from depths <1 m. The Full model Whitefish 81 (78–84) 81 (77–85) 81 (77–84) larvae were found from both exposed shores and Vendace 87 (85–89) 71 (66–77) 93 (91–95) sheltered inner bays. Only the most vegetated and Reduced model Whitefish 71 (68–74) 75 (72–79) 67 (63–71) Vendace 85 (83–88) 66 (60–72) 92 (90–94)

Enviromental variables and larval occurrence

APC indicated that shoprofile, depth and distance to shallow sandy shore (DISTSAND) were the most relevant covariates for whitefish occurrence over the entire study area (Fig. 4). Other relevant covariates were bottom type (BOTTOMCLS), ecological status (EKOSTAT), last week of ice cover in 2009 (ICE- LAST09), distance to 20 m depth (DIST20M) and surface area of shallow depths (SHAREA). For vendace occurrence, the most relevant covariates were sho - profile, ecological status (EKOSTAT) and BOTTOM- CLS. Other important covariates were last week of ice cover in 2009 (ICELAST09), secchi depth (SEC- CHI), distance to shallow areas and spring and winter salinity (SAL09SPR and SAL09WIN) (Fig. 4). We also tested field measured variables, turbidity, conductivity, wind and waves, but they did not have any significant importance to larval presence. Though sea-spawning whitefish larvae were caught within a temperature range of 0.9 to 19.6°C and vendace within the range of 1.3 to 19.4°C, the influence of depth and shoprofile indicates that higher temperature is preferable. Water temperature flucu- ates depending on the day, wind and sampling time so that in open shores, the daytime temperature behind a sheltering sand bar can be up to 10°C higher than in the surrounding outer area. The variables describing nutrients (NITROG and CHLA) had minor influence on the whitefish occurrence Fig. 4. Average predictive comparison (APC) of the environ- so that a higher NITROG index (high dissolved in - mental variables that describes the importance of the vari- organic nitrogen content) and a higher CHLA index able in the entire study area. The x-axis shows the posterior (low phytoplankton concentration) increased the summary of change in occurrence probability with unit probability of occurrence. change of a (normalized) environmental variable and the y- Whitefish larvae appeared most frequently in the axis shows the environmental variables. The line shows the 95% central posterior credible interval and the circle shows shallowest littoral areas. The highest catch per the posterior mean of APC. The categorical variables are unit effort (CPUE) was caught from shallow sandy below and the continuous variables above the dashed line Veneranta et al.: Reproduction areas of sea-spawning coregonids 241

Fig. 5. (a) Shoreline profile (I = open water, II = steep, III = gently sloping with steep edge, IV = gently sloping, V = shallow, VI = shallow with sand bar) and (b) depth in relation to larval CPUE per site of whitefish (black bars) and vendace (gray bars) larvae shallowest, innermost bays did not have whitefish or they were found at all bottom types but the highest vendace larvae (see ‘Region-scale changes in the CPUE was caught from shores with gently sloping abundance of whitefish larvae’). sand bottom. Decreasing EKOSTAT increased the Fig. 6 shows the region specific response curves. A probability of vendace larvae occurrence. Depth did gentle shoprofile increased the probability of white- not influence vendace occurrence as much as whitefish occurrence in all regions, though in regions I and fish larval occurrence. II higher densities of larvae were also found on According to the visual bottom coverage classifica- steeper shores than in southern regions III–V. An in - tion, sampling sites in regions I, II and IV had on creasing depth and greater distance to a sandy shore average less algal growth and vegetation than in (DISTSAND) decreased the probability of whitefish regions III and V (Table 4). Fig. 7 summarizes the larval occurrence in all regions. DIST20M had only a posterior distributions of the occurrence probabilities minor effect in region IV, but decreased probability of covered and uncovered bottoms. For whitefish, it of occurrence in other regions. Nutrient levels (lower was higher in uncovered than in covered bottoms NITROG) had an opposite response in regions III, with high certainty. The difference between covered IV and V than in regions I and II. A better EKO - and uncovered bottom was less certain for vendace STAT increased the probability of whitefish larvae larvae and its sign depended on BOTTOMCLS. The occurrence, but decreased that of vendace larvae occurrence probability of vendace larvae was higher occurrence. in uncovered than in covered mud and sand bottoms, The vicinity of wide shallow sandy areas (SHAREA) but lower in uncovered than in covered cliff, rock and later ice break (ICELAST09) in creased both spe- and stone bottoms (Fig. 7). cies’ probability of occurrence. The length of ice Table 5 shows the average CPUE of larval white- cover had a weak positive effect only for vendace. fish and the percentage of presence for regions When examining sites with <5 wk ice cover in grouped to a sea-area level and Fig. 8 shows the regions IV and V, no whitefish larvae were caught interaction between the last ice cover week (ICE- even if the sites were similar, in terms of exposure, LAST09) and exposure (FE300ME) in these sea- BOTTOMCLS and shoprofile, to sites with high prob- areas. In regions I and II, the CPUE was high with all ability of occurrence in regions I and II. combinations of last ice cover week and exposure The response curves for vendace were shown only and the highest in open shores with a late last ice for regions I and II since those cover its main distribu- cover week. In regions I and II, the probability of tion area. Except for BOTTOMCLS, vendace larvae occurrence decreased with earlier ice break-up and preferred similar habitats in both regions. In region I, higher exposure so that exposure alone had a smaller where the observed densities of larvae were highest, impact. Region III is a transition zone between the 2 242 Mar Ecol Prog Ser 477: 231–250, 2013

Fig. 6. Whitefish (a) and vendace (b) response curves to the environmental predictors (see Table 2 for definitions) for each region (shown in Fig. 1) expressed in the occurrence probability scale. The rows top to bottom represent regions I–V for whitefish and I–II for vendace (regions III–V not included due to the low abundance of vendace larvae). Shoreline profile, ecological status and bottom type are classificatory variables, and the graph shows the posterior mean with a 95% central posterior credible interval. The solid line is the posterior mean and the dashed lines show the limits of the 95% central posterior credible interval main basins. There, the average CPUE and percent- increased. In regions IV and V, the average CPUE age of presence were notably smaller than in regions was similar in magnitude to region III, but the per- I and II. The last ice cover week had a similar effect centage of presence was smaller. The probability of as in regions I and II, but along increasing exposure occurrence decreased fast along earlier ice break the occurrence probability first decreased and then and increasing exposure. Veneranta et al.: Reproduction areas of sea-spawning coregonids 243

Table 4. Distribution of bottom coverage classification at region level Leskelä et al. (1991) study. In general, the catch frequency and CPUE in our Region Bottom coverage class study were higher in northern regions No growth% <10% 10−25% 25−50% >50% Sample count (I and II) than in the south (see also I 20.0 28.7 13.3 6.7 31.3 150 Table 5). II 18.8 18.8 16.1 11.6 34.8 119 III 6.0 7.0 6.0 19.0 62.0 100 IV 10.4 20.8 33.3 14.6 20.8 48 DISCUSSION V 4.3 6.5 7.6 8.7 72.8 92 The validity of large-scale species Whitefish Vendace distribution model in coastal areas a 1 b 1 Median Uncovered Catch frequency Covered Descriptions of habitat preferences )

c 0.8 0.8 are scale dependent with respect to π /

u the size of the sampling unit and the π 0.6 0.6 extent of the study (Levin 1992, Jack- son et al. 2001). Our data covers prac- 0.4 0.4 tically the entire distribution area of both species with high resolution, and Probability ( 0.2 0.2 our model’s predictive performance was good (Table 4) compared to previ-

0 0 ous studies in the northern Baltic Sea. Mud Silt Sand Sa/st Stones Rocks Cliff Mud Silt Sand Sa/st Stones Rocks Cliff The aim of the sampling strategy was p(π >π ): 0.92 0.97 1.00 0.99 0.88 0.93 0.66 0.70 0.63 0.82 0.38 0.15 0.14 0.42 u c to produce a complete data set de -

Fig. 7. Posterior distribution of the occurrence probability in an uncovered, πu, scribing the 3 larval periods (2009 to π π π and covered, c, bottom together with the probability that u > c for (a) white- 2011), and thus the strategy was fine- fish and (b) vendace. Crosses: percentage of presences (catch frequency) in tuned after the first sampling year. each bottom type when coverage is <50% (black) and >50% (gray). Circles: median value of posterior distribution and lines indicate the 95% central The results describe the essential posterior credible interval. Sa/st: sand/stone information about the distribution of sea-spawning whitefish and vendace larvae under the environmental con- Region-scale changes in the abundance ditions that prevailed during the sampling. For both of whitefish larvae species, the spatial component had only a small effect on the predictions. Therefore, the prediction Table 6 compares our study to that of Leskelä et al. resolution depends on the grid resolution of the envi- (1991). In regions IV and V, the present catches have ronmental variables. Since depth and shore profile decreased multiple times, while in regions I to III variables improved the model’s predictive per - they have increased significantly. In the reference formance, the probability of occurrence maps could sub-area (26) outside the modeling area, no whitefish be improved if they were available in GIS raster larvae were found, although the CPUE was 7.2 in the layers.

Table 5. Average CPUE and catch frequency per site (FREQ) of larval whitefish caught with beach seine in region level related to ice winter length and exposure

Class Icelasta Exposureb REGIONS I and II REGION III REGIONS IV and V Sites CPUE FREQ Sites CPUE FREQ Sites CPUE FREQ

1 LONG OPEN 192 114.5 87.5 29 3.6 62.1 28 3.2 42.8 2 LONG SHELTERED 69 119.9 78.3 41 1.0 46.3 33 1.3 42.4 3 SHORT OPEN 8 58.2 87.5 42 17.1 64.3 51 1.0 25.5 4 SHORT SHELTERED – – – 1 0 0 15 4.8 53.3

aIcelast = last week of ice cover in winter 2009; long >14 weeks and short ≤14 weeks bExposure = FE300ME average exposure; OPEN > 10 000 m and SHELTERED ≤10 000 m 244 Mar Ecol Prog Ser 477: 231–250, 2013

Fig. 8. Interaction of ice pe - riod (ICELAST09) and ex po - sure (FE300ME) variables on whitefish prediction for regions I and II, III, and IV and V, expressed in the predictor (f(x)), scale when other variables are fixed at their empiri- cal mean. The colour reflects the height of the mesh surface in graphs. Note the different scale of variables ICELAST09 AND FE300ME between the regions

Table 6. Average beach seine CPUE comparison for results presented in Leskelä et al. (1991) and present study in 5 regions. CPUE: catch per unit effort; FREQ: catch frequency per unit effort

Region Samples Range Range CPUE white- CPUE CPUE Whitefish Vendace whitefish vendace fish (Leskelä whitefish vendace FREQ FREQ this study this study et al. 1991) (this study) (this study) (this study) (this study)

I 176 0−4717 0−10834 7.4 117.9 237.7 65.9 54.0 II 141 0−2956 0−2489 53.0 104.2 60.9 78.7 39.7 III 173 0−651 0−4 0.8 12.2 <0.1 38.7 2.3 IV 70 0−42 0−1 15.9 3.5 <0.1 40.0 2.9 V 135 0−65 0−4 2.2 1.1 <0.1 14.1 0.7

Effect of environmental variables on tolerate and take advantage of fast warming areas larval occurrence (Eckmann & Pusch 1989). The shallow water close to a shoreline, and especially between a sand bar Shore profile and depth are strongly related, with a and shoreline, is warmer than offshore water and trend from a gentle shore profile and shallow water seems to attract the larvae. We caught larvae in to a steep profile and deep water. Both species temperatures ranging from 0.9 to 19.6°C. There is clearly prefer nearshore areas, but depth explains no information on the extent of sea-spawning white- only whitefish larvae occurrence. The reason may be fish or vendace larvae dispersion from spawning that vendace larvae have a wider horizontal distribu- sites. However, dispersion of river spawning white- tion, as indicated also by the lower proportion of yolk fish larvae does not extend far from the river (Leh- sac larvae of vendace compared to whitefish in open tonen et al. 1992), and, thus, it is reasonable to water catch. Karjalainen et al. (2000) observed a sim- assume that the larvae of sea-spawning whitefish ilar pattern of horizontal distribution of vendace lar- and vendace remain close to the spawning site in vae in some relatively large lakes. Only a minor pro- the early stages. Therefore, we presume that high portion of whitefish or vendace larvae were caught probability of larvae occurrence indicates a high further away from the shore. These offshore larvae probability of spawning site as well. At hatching were newly hatched, and possibly searching for time, the outer exposed areas are often still partially nursery areas. ice covered, so the iceless, shallowest shores pro- The highest densities of larvae were caught from vide more optimal conditions for early growth. A gently sloping, shallow sand shores with a shelter- decreasing DIST20M curve increased the probabil- ing offshore sand bar. The importance of sandy ity of whitefish occurrence, suggesting that white- open shores to larvae occurrence might be con- fish favor areas that have deeper and colder water nected to the long ice winter in regions I and II, close to their reproduction habitat. which enables stable conditions for egg develop- ICELAST09 influences whitefish occurrence both ment in wind exposed shores. Coregonids are in the whole study area and on a regional scale. In adapted to cold-water, but in larval stages they can the southern regions IV and V, the highest probabil- Veneranta et al.: Reproduction areas of sea-spawning coregonids 245

ity of occurrence and the highest whitefish larval material from rivers and is connected to eutrophica- densities are in sheltered areas that are ice covered tion (Pettersson et al. 1997, Lundberg et al. 2009). longer than the wind-exposed areas at the same lat- The available large-scale nutrient data describe the itude. However, the length of the ice covered period natural gradients of the GoB more than the eutro- varies annually and, therefore, its importance to the phication development. However, there are differ- probability of occurrence cannot be thoroughly ana- ences between sea-areas as well as inner and outer lyzed. In general, the length depends on latitude archipelagos. For example, Lundberg et al. (2009) and exposure to wind. In the northernmost parts of reported the average total phosphorus concentration the GoB, the length of the ice covered period is in the inner archipelago of the Bothnian Sea (region longer and ice loss happens later than in the south V) to be 26.1 µg l−1 (range 5 to 87), and in the outer and, therefore, has no effect on vendace occurrence. archipelago 15.7 µg l−1 (range 2 to 59). In the north- In regions IV and V, most of the observations and ern GoB, the average total phosphorus concentration highest whitefish larval densities were found in is lower. The effect of nutrients on whitefish has been sheltered areas that are also covered by ice for studied in lakes. Müller (1992) showed that at phos- longer periods than wind-exposed areas at the same phorus concentrations above 80 µg l−1, whitefish eggs latitude. never reached the hatching stage. At concentrations Nutrient levels or primary production (NITROG, between 35 and 80 µg l−1, embryonic development CHLA and PHOSP) have no unambiguous influence fluctuated widely, ranging from 0 to 80% of viable on whitefish or vendace prediction. The validity of eggs. These results support the spatial pattern in these variables as true response variables can be the probability of sea-spawning whitefish occurrence challenged due to large scale and coarse precision of shown here. these measurements. The ecological status of the coastal waters does not strictly follow the south-north gradient of eutrophication described in Lundberg et al. (2009). Regardless The multifaceted role of eutrophication of the assumed inaccuracy of the variable, it was included in the model since it separates the coastal Changes in the environment due to eutrophication gradients and indicates the differences between the may reduce the survival of embryos and diminish inner and outer archipelago. In regions I and II, suitable larval habitats. Along with increasing decreasing EKOSTAT increased the probability of eutrophication, enhanced growth of filamentous occurrence for both species. This is explained by algae is a widely recognized problem (Raffaelli et al. the better EKOSTAT in the open sea areas than in 1998, Vahteri et al. 2000, Berglund et al. 2003). The the coastal areas. The patchiness of sedimentation eastern coast of GoB is more prone to eutrophication (Downing & Rath 1988) in eutrophication-affected than the west, due to shallower water and higher areas may explain the general trend of decreasing nutrient load. Therefore, the signs of eutrophication larval whitefish occurrence from north to south, are not as evident in the west as in the east (Håkans- especially in region V. The probability of whitefish son et al. 1996, HELCOM 2009, Andersen et al. larvae occurrence is higher in region IV than V, 2011). Our visual bottom coverage classification indi- which may be explained by region IV’s better cates the same phenomenon on a regional level. The EKOSTAT, as indicated by less vegetation covered sampling sites in regions III and V had higher bottom bottoms. coverage than in regions I, II and IV. The probability The studies by Müller (1992) on lakes and Joki - of whitefish larvae occurrence is higher in uncovered kokko (1993) and Veneranta et al. (in press) on the areas than in areas that are covered by algae or veg- GoB connect the successful reproduction of corego- etation, regardless of bottom structure. The response nids to oligotrophy, which suggests that extensive of vendace larvae to bottom cover was parallel to sedimentation caused by eutrophication decreases whitefish but with more variation, except for stony egg survival. In the southern Baltic Sea, autumn bottoms where a higher coverage indicated a higher sedimentation layers as thick as 1 cm have been occurrence probability. reported (Eriksson et al. 1998, Isaeus et al. 2004). Basal production in the Bothnian Bay (regions I and Therefore, sedimentation likely explains the present II) is phosphorus limited (Humborg et al. 2003) and weak reproductive success of sea-spawning white- phytoplankton concentration in the north is low com- fish in the southern Baltic Sea. Sedimentation in the pared to other regions. The turbidity of waters (low northern part of GoB (region II) (Jokikokko 1993) is SECCHI index) results from high inflow of humic much lower than in the south (Eriksson et al. 1998, 246 Mar Ecol Prog Ser 477: 231–250, 2013

Isaeus et al. 2004), making conditions more favorable Isaksson & Pihl 1992, Norkko et al. 2000). The chang- for sea-spawning whitefish more beneficial. ing environmental conditions can be seen as a grad- Exposure affects sedimentation in the coastal areas ual change or even as a cascade-effect in a northward where water exchange is low (Pihl et al. 1999), and in direction and from the inner to outer areas (Lundberg archipelago areas that may work as a buffer or filter et al. 2009). Eriksson et al. (1998) found that the between the coastline and open sea (Bonsdorff et al. annual algal vegetation had increased over several 1997, Humborg et al. 2003, Vahtera et al. 2007). Sedi - decades. Also, in the inner archipelago in regions V mentation is especially high in late autumn and early and III, the negative changes had already begun in winter in the outer archipelago and open sea, while the 1980s at the latest, whereas the corresponding in sheltered areas it is lower in winter and constantly outer archipelago and exposed areas have remained high in summer (Heiskanen & Tallberg 1999). In more stable over time (Lundberg et al. 2009). In our eutrophic and exposed areas, such as region V, the study, whitefish larvae were less frequent and abun- high sedimentation period may overlap with the dant in the more eutrophic southern areas. spawning time of sea-spawning coregonids and incubation time of eggs. The timing of sedimentation may ex plain the clustering of the few larval observa- Region-scale changes in the distribution tions in the sheltered archipelago areas in region V. of whitefish larval areas However, in regions I and II where the occurrence probability of larvae is highest, the only places where Considering the developmental stages, the smaller whitefish or vendace larvae were not abundant were CPUEs found in our study relative to the study of the most closed and vegetation-covered bays. The Leskelä et al. (1991) in regions IV, V and in sub- southernmost area (sub-area 26), which was com- area (26) are consistent with our other results in the pared to the earlier study of Leskelä et al. (1991), is context of temporal changes in the environment. In presently covered by dense growth of filamentous regions I, II and III, CPUE and the probability of lar- algae or reed Phragmites australis spreading to the val occurrence are high, and show no decrease in shallowest shores (see Fig. 9). Shallow soft bottoms the extent of reproduction compared to Leskelä et accumulate loose-lying algae as a direct conse- al. (1991). In the sheltered archipelago areas with a quence of nutrient enrichment (Bonsdorff 1992, long ice winter, the currents and wave formation are less influential than in the outer areas. There- fore, since the bottoms were fairly uncovered and the eutrophication status was relatively good in region IV, the low CPUE and abundance of whitefish larvae can be assumed to be caused by the unsheltered coastline and short ice winter, rather than by eutrophication. Since coastal eutrophication is higher in the east than in the west (Håkansson et al. 1996, Andersen et al. 2011), the lack of whitefish larvae from outer, more exposed sites in region V may stem from eutrophication and sedimentation in winter in conjunction with shortened ice winter. Egg incubation studies done in situ (Müller 1992, Jokikokko 1993, Veneranta et al. in press) indicate that the response of whitefish reproduction to environmental changes is proportional and not abrupt. Therefore, whitefish reproduction can still be successful in the most optimal areas in region V, which remain suitable for spawning as a result of currents, low sedimentation and sheltering ice cover. How- ever, the extent of these areas has decreased significantly. Even though the large scale geomorphologi- cal properties do not limit areas that the earliest Fig. 9. Growth of filamentous algae and reed Phragmites australis along the shoreline in reference sub-area 26. The stage coregonid larvae can use, small scale changes bottom sand is seen at the front of the picture can have a detrimental effect. Veneranta et al.: Reproduction areas of sea-spawning coregonids 247

CONCLUSIONS ported by the fact that environmental research has not targeted the shallowest littoral areas in the GoB. We have shown that, at present, the main whitefish More precise studies with focus on the requirements reproduction areas are located in the northern GoB. for successful reproduction of sea-spawning corego- Early developmental stages of whitefish can be found nids should be done in areas where the probability in various habitats close to the shoreline, but the best for larval occurrence is high. Using more precise larval habitats are shallow sandy beaches, as empha- environmental data, the explicit effect of variables sized also in earlier studies (Hudd et al. 1988, Leskelä could be revealed. This knowledge could be further et al. 1991). Vendace larvae follow a similar occur- used to develop restoration methods for spawning rence pattern, but larval areas are more restricted to and larval areas, especially when applied for sea- the northernmost GoB, presumably due to too high spawning whitefish in regions IV and V. salinity in the southern areas. The reduction in the extent of successful repro - Acknowledgements. We thank H. Harjunpää and A. Huh- duction has been notable in the last 30 yr, though marniemi for helping in field and giving thoughts on larval catches of stocked anadromous whitefish have likely behavior. Thanks to E. Ruotsalainen and J. Vaskivuori for sustained the illusion of a healthy whitefish fishery, assistance in the field. Special thanks to Dr. L. Urho for valuable comments on our work and manuscript. The manu- especially in the southern GoB. Our results show that script was substantially improved by comments from Prof. T. increased bottom coverage decreases the occurrence Miller and 2 anonymous reviewers. The project had finan- probability of sea-spawning whitefish larvae. The cial support from the European Regional Developmental results suggest that the degradation of natural sea- Fund, Botnia Atlantica (304-9030-2008 BA 68748), the Re- spawning whitefish stocks is connected to ongoing gional Council of Ostrobothnia Finland, the County Admin- istrative Board of Norrbotten and Västerbotten, the city of large scale changes in shallow coastal areas, such as Piteå, Sweden, and the project ‘Effective Use of Ecosystem eutrophication, climate change and more common and Biological Knowledge in Fisheries’ (ECOKNOWS) iceless and warmer winters. Eutrophication leads to funded by the European Community’s 7th Framework Pro- sediment and decomposing algal accumulation in gramme (FP7/2007-2013) under grant agreement 244706. The funding sponsors did not take part in the study design, shallow littoral areas in the archipelago, and reduces data collection, analysis or interpretation of data in any form. potential spawning grounds and larval areas for sea- spawning whitefish. In the long run, this may deplete the spawning stocks due to decreasing larval recruit- LITERATURE CITED ment. Our results do not show a relationship effect Albert A, Vetemaa M, Saat T (2004) Effects of salinity on the between bottom coverage and vendace larvae, pre- development of Peipsi whitefish Coregonus lavaretus sumably since the distribution of vendace is largely maraenoides Poljakow embryos. Ann Zool Fenn 41: restricted to northernmost GoB, where signs of 85−88 Andersen JH, Axe P, Backer H, Carstensen J and others eutrophication are less visible. Our results indicate (2011) Getting the measure of eutrophication in the that the long development period of sea-spawning Baltic Sea: towards improved assessment principles and whitefish from egg fertilization to larvae makes it methods. Biogeochemistry 106:137−156 vulnerable to environmental changes, but we cannot Aneer G, Blomqvist EM, Hallbäck H, Mattila J, Nellbring S, Skóra K, Urho L (1992) Methods for sampling of shallow answer when and where the effects of sedimentation, water fish. Baltic Marine Biologists Publication 13 algal accumulation and long iceless periods are most Anon (2011) Beteckning Dnr 13-2144-11. Förslag till ändring detrimental for the sea-spawning coregonids. av Fiskeriverkets föreskrifter (FIFS 2004: 36) om fisket i Since sea-spawning coregonids, especially white- Skagerrak, Kattegatt och Östersjön; Ett fiskefritt område fish, appear to be sensitive to poor bottom quality and i södra Bottenhavet för att stärka bestånden av havs le - kande sik [Suggestion to change the regulations (FIFS climate change, a follow-up study of larval abun- 2004: 36) for fishing in Skagerrak, Kattegatt and the dance and occurrence could be used as an indicator Baltic Sea; prohibition of fishing for the area in the to monitor the large-scale environmental changes. It Southern Bothnian Sea to enhance the stocks of sea- is also known that the visible changes in vegetation spawning whitefish]. Fiskeriverket, avdelningen för resursförvaltning 2011-06-01 (in Swedish) can occur rapidly and not as a gradual change Barnes PW, Kempema EW, Reimnitz E, McCormick M, (Dahlgren & Kautsky 2004). Therefore, the less eu- Weber WS, Hayden EC (1993) Beach profile modification trophic regions I and II with long ice winters, should and sediment transport by ice: an overlooked process on be seen as a particularly important reserve for sea- Lake Michigan. J Coast Res 9: 65−86 Berglund J, Mattila J, Rönnberg O, Heikkilä J, Bonnsdorf E spawning coregonids. A relevant follow-up study (2003) Seasonal and inter-annual variation in occurrence should be established to detect changes that poten- and biomass of rooted macrophytes and drift algae in tially affect the recruitment success. This is also sup- shallow bays. Estuar Coast Shelf Sci 56: 1167−1175 248 Mar Ecol Prog Ser 477: 231–250, 2013

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Editorial responsibility: Hans Heinrich Janssen, Submitted: April 23, 2012; Accepted: November 8, 2012 Oldendorf/Luhe, Germany Proofs received from author(s): February 22, 2013