Refining predictions of metacommunity dynamics by modeling species non-independence.

Predicting the dynamics of biotic communities is difficult because species' environmental responses are not independent, but covary due to shared or contrasting ecological strategies and the influence of species interactions. We used latent-variable joint species distribution models to analyze paired historical and contemporary inventories of 585 vascular plant species on 471 islands in the southwest Finnish archipelago. Larger, more heterogeneous islands were characterized by higher colonization rates and lower extinction rates. Ecological and taxonomical species groups explained small but detectable proportions of variance in species' environmental responses. To assess the potential influence of species interactions on community dynamics, we estimated species associations as species-to-species residual correlations for historical occurrences, for colorizations, and for extinctions. Historical species associations could to some extent predict joint colonization patterns, but the overall estimated influence of species associations on community dynamics was weak. These results illustrate the benefits of considering metacommunity dynamics within a joint framework, but also suggest that any influence of species interactions on community dynamics may be hard to detect from observational data.

Testing the potential for predictive modeling and mapping and extending its use as a tool for evaluating management scenarios and economic valuation in the Baltic Sea (PREHAB).

We evaluated performance of species distribution models for predictive mapping, and how models can be used to integrate human pressures into ecological and economic assessments. A selection of 77 biological variables (species, groups of species, and measures of biodiversity) across the Baltic Sea were modeled. Differences among methods, areas, predictor, and response variables were evaluated. Several methods successfully predicted abundance and occurrence of vegetation, invertebrates, fish, and functional aspects of biodiversity. Depth and substrate were among the most important predictors. Models incorporating water clarity were used to predict increasing cover of the brown alga bladderwrack Fucus vesiculosus and increasing reproduction area of perch Perca fluviatilis, but decreasing reproduction areas for pikeperch Sander lucioperca following successful implementation of the Baltic Sea Action Plan. Despite variability in estimated non-market benefits among countries, such changes were highly valued by citizens in the three Baltic countries investigated. We conclude that predictive models are powerful and useful tools for science-based management of the Baltic Sea.

A multivariate assessment of coastal eutrophication. Examples from the Gulf of Finland, northern Baltic Sea.

The Gulf of Finland is the sub-basin of the Baltic Sea that is most seriously affected by the effects and consequences of eutrophication. In this study, physical, chemical and biological long-term data (1980-2002) from the Finnish environmental monitoring programme is used to detect possible gradients of eutrophication in the Gulf. The Finnish coastal area of the Gulf of Finland is divided into three parts in an east-west direction, and into three zones (inner, middle, outer) according to differences in descriptive parameters. We use principal component analysis (PCA) to study spatial and temporal differences in relation to eutrophication. Clear differences between coastal and offshore areas are seen. Differences between eastern and western Gulf are not as evident. The changes due to eutrophication are larger for the inner archipelago, whereas the outer areas have been more stable over time. The concentration of oxygen is the strongest driving factor for eutrophication in the region.