Turbot (Psetta maxima) and flounder (Platichthys flesus) are two species of flatfish both having their nursery areas around the shores of Gotland in the Baltic Sea. The common brown shrimp (Crangon crangon) is a known predator on newly settled plaice (Pleuronectes platessa) in the North Sea area and is also found in the Baltic Sea. Experiments were carried out to see if the brown shrimp is predating on juvenile turbot and flounder, and if so on which sizes, and if the brown shrimp prefers any of the flatfish species, and also to see if there is a difference between day and night in density of the shrimp, i.e. when the fish might be subjected to predation. The results showed that predation decreased with size for both turbot and flounder. The brown shrimps prefer small flatfishes, size class <30 mm, without any species preference. The shrimp abundance was higher during the night at one out of three locations around Gotland. More data is, however, needed to conclude that it is more active during the night.

Piggvarens (Scophthalmus maximus) val av habitat i Östersjön har modellerats utifrån provfiskedata och miljövariabler. Vid totalt 435 stationer i Östersjön har data samlats in i form av provfiske, CTD-mätningar (konduktivitet, temperatur och djup) och videofilmer. Genom att analysera videofilmerna från havsbotten i Östersjön har den klassificerats efter fyra olika förklaringsvariabler: täckningsgrad mjukbotten, strukturbildande växter, övriga alger och täckningsgrad blåmusslor. Ytterligare sex förklaringsvariabler har samlats in från mätningar och befintliga kartor: bottensalinitet, bottentemperatur, djup, siktdjup, vågexponering och bottenlutning. Dessa tio förklaringsvariabler har använts i tio olika enskilda statistiska modelleringsmetoder med förekomst/icke-förekomst av piggvar som responsvariabel. Nio av tio modeller visade på bra resultat (AUC > 0,7) där CTA (Classification Tree Analysis) och GBM (Global Boosting Model) hade bäst resultat (AUC > 0,9). Genom att kombinera modeller med bra resultat på olika sätt skapades sex ensemblemodeller för att minska varje enskild modells svagheter. Ensemblemodellerna visade tydligt fördelarna med denna typ av modellering då de gav ett mycket bra resultat (AUC > 0,949). Den sämsta ensemblemodellen var markant bättre än den bästa enskilda modellen. Resultaten från modellerna visar att största sannolikheten för piggvarsförekomst i Östersjön är vid grunt (< 20 meter) och varmt (> 10 oC) vatten med hög vågexponering (> 30 000 m²/s). Dessa tre variabler var de med högst betydelse för modellerna. Täckningsgrad mjukbotten och de två växtlighetsvariablerna från videoanalyserna var de tre variabler som hade lägst påverkan på piggvarens val av habitat. Med en högre kvalitet på videofilmerna hade de variablerna kunnat klassificeras i mer specifika grupper vilket eventuellt gett ett annat resultat. Generellt visade modellerna att denna typ av habitatmodellering med provfiske och miljödata både är möjlig att utföra. / The turbots’ (Scophthalmus maximus) selection of habitat in the Baltic Sea has been modeled on the basis of fish survey data and environmental variables. At a total of 435 stations in the Baltic Sea, data was collected in the form of fish survey data, CTD (Conductivity, Temperature and Depth) measurements and videos. By analyzing the videos from the seabed of the Baltic Sea, four different explanatory variables have been classified: coverage of soft bottom, structure-forming plants, other algae and coverage of mussels. Another six explanatory variables have been collected from measurements and existing rasters: salinity, temperature, depth, water transparency, wave exposure and the bottom slope. These ten explanatory variables have been used in ten different species distribution modeling methods with the presence/absence of turbot as a response variable. Nine out of ten models showed good results (AUC > 0.7) where the CTA (Classification Tree Analysis) and GBM (Global Boosting Model) performed the best (AUC > 0.9). By combining the models with good performance in six different ensemble models each individual models’ weaknesses were decreased. The ensemble models clearly showed strength as they gave a very good performance (AUC > 0.94). The worst ensemble model was significantly better than the best individual model. The results of the models show that the largest probability of occurrence of turbot in the Baltic Sea is in shallow (< 20 m) and warm (> 10 ° C) water with high wave exposure (> 30,000 m²/s). These three variables were those with the highest significance for the models. Coverage of soft bottom and the two vegetation variables, from the video analyzes, had the lowest impact on the turbots’ choice of habitat. A higher quality of the videos would have made it possible to classify these variables in more specific groups which might have given a different result. Generally, the models showed that this type of modeling of habitat is possible to perform with fish survey and environmental monitoring data and generates useful results.

It has been shown that fish of both marine and limnetic origin display increased growth at intermediate salinities. Furthermore, it has been shown that fish in the brackish water Baltic Sea are smaller compared to their conspecifics in the Atlantic, where salinities are higher. Also, it has been suggested that fish produce more eggs at the edges of their distribution range as a response to inferior environmental conditions. In this study, I investigated if there is a trade-off in energy investment between somatic and gonad growth in relation to salinity. To do this, I performed a growth experiment and a literature review. In the growth experiment, juvenile turbot were reared in salinities of 6, 10.5, 15 and 30 ‰. I found that turbot juveniles from Gotland grew equally well in all salinities investigated. In the literature review, data from the Baltic Sea was tested against data from the North and Black Seas. Data of turbot total energy investment (somatic and gonad growth) was analyzed. I found that energy content at age differed significantly between the populations investigated with lower energy content for the Baltic Sea populations. Also, growth rate in relation to energy content (size) was analyzed for the different populations, but no difference for growth rate in relation to energy content occurred. The result of the analysis of growth rate indicates that the change in allocated energy is the same, regardless of population, and thus that fish from the Baltic Sea display growth rates similar to those of other populations. It was also established that energy investment in gonads increased along with decreasing salinities. The smaller size of turbot in the Baltic Sea is therefore probably the result of a difference in size at maturity, possibly because less energy is allocated to somatic growth and more energy to start producing eggs. It is probably also the consequence of that the Baltic Sea turbot, post sexual maturity, continue to invest more energy in egg production.