Mapping marine benthic biological traits to facilitate future sustainable development.

Escalating societal demands placed on the seabed mean there has never been such a pressing need to align our understanding of the relationship between the physical impact of anthropogenic activities (e.g., installation of wind turbines, demersal fishing) and the structure and function of the seabed assemblages. However, spatial differences in benthic assemblages based on empirical data are currently not adequately incorporated into decision-making processes regarding future licensable activities or wider marine spatial planning frameworks. This study demonstrates that, through harnessing a Big Data approach, large-scale, continuous coverage maps revealing differences in biological traits expressions of benthic assemblages can be produced. We present independent maps based on a suite of response traits (depicting differences in responses to natural or anthropogenically induced change) and effects traits (reflecting different functional potential), although maps derived using single traits or combinations of a range of traits are possible. Models predicting variations in response traits expression provide greater confidence than those predicting effects traits. We discuss how such maps may be used to assist in the decision-making process for the licensing of anthropogenic activities and as part of marine spatial planning approaches. The confidence in such maps to reflect spatial variations in marine benthic trait expression may, in the future, inherently be improved through (1) the inclusion of more empirical macrofaunal assemblage field data; (2) an improved knowledge of marine benthic taxa trait expression; and (3) a greater understanding of the traits responsible for determining a taxon's response to an anthropogenic pressure and a taxon's functional potential.

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.