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.

<p>Land use change constitutes a primary driving force in shaping social-ecological systems world wide, and its effects reach far beyond the directly impacted areas. Graph based landscape ecological tools have become established as a promising way to efficiently explore and analyze the complex, spatial systems dynamics of ecological networks in physical landscapes. However, little attention has been paid to making these approaches operational within ecological assessments, physical planning, and design. This thesis presents a network based, landscape-ecological tool that can be implemented for effective use by practitioners within physical planning and design, and ecological assessments related to these activities. The tool is based on an ecological profile system, a common generalized network model of the ecological infrastructure, graph theoretic metrics, and a spatially explicit, geographically defined representation, deployable in a GIS. Graph theoretic metrics and analysis techniques are able to capture the spatio-temporal dynamics of complex systems, and the generalized network model places the graph theoretic toolbox in a geographically defined landscape. This provides completely new insights for physical planning, and environmental assessment activities. The design of the model is based on the experience gained through seven real-world cases, commissioned by different governmental organizations within Stockholm County. A participatory approach was used in these case studies, involving stakeholders of different backgrounds, in which the tool proved to be flexible and effective in the communication and negotiation of indicators, targets, and impacts. In addition to successful impact predictions for alternative planning scenarios, the tool was able to highlight critical ecological structures within the landscape, both from a system-centric, and a site-centric perspective. In already being deployed and used in planning, assessments, inventories, and monitoring by several of the involved organizations, the tool has proved to effectively meet some of the challenges of application in a multidisciplinary landscape.</p>

Land use change constitutes a primary driving force in shaping social-ecological systems world wide, and its effects reach far beyond the directly impacted areas. Graph based landscape ecological tools have become established as a promising way to efficiently explore and analyze the complex, spatial systems dynamics of ecological networks in physical landscapes. However, little attention has been paid to making these approaches operational within ecological assessments, physical planning, and design. This thesis presents a network based, landscape-ecological tool that can be implemented for effective use by practitioners within physical planning and design, and ecological assessments related to these activities. The tool is based on an ecological profile system, a common generalized network model of the ecological infrastructure, graph theoretic metrics, and a spatially explicit, geographically defined representation, deployable in a GIS. Graph theoretic metrics and analysis techniques are able to capture the spatio-temporal dynamics of complex systems, and the generalized network model places the graph theoretic toolbox in a geographically defined landscape. This provides completely new insights for physical planning, and environmental assessment activities. The design of the model is based on the experience gained through seven real-world cases, commissioned by different governmental organizations within Stockholm County. A participatory approach was used in these case studies, involving stakeholders of different backgrounds, in which the tool proved to be flexible and effective in the communication and negotiation of indicators, targets, and impacts. In addition to successful impact predictions for alternative planning scenarios, the tool was able to highlight critical ecological structures within the landscape, both from a system-centric, and a site-centric perspective. In already being deployed and used in planning, assessments, inventories, and monitoring by several of the involved organizations, the tool has proved to effectively meet some of the challenges of application in a multidisciplinary landscape.