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Scientific background

Strong species-environment relationships

PREHAB has reviewed and synthesised published records on species-environment relationships in coastal areas of the Baltic Sea region. This provides a scientific basis for evaluating the usefulness of environmental predictors for modelling benthic species and habitats.

Focusing on vegetation, invertebrates and fish as response groups, the relative importance of predictors from the six general categories were assessed. The review clearly demonstrates strong species-environment relationships in benthic habitats of the Baltic Sea. Results highlight the potential for successful mapping and predictive modelling of benthic assemblages in the Baltic Sea, while at the same time they point at the complexity of the species-environment relationships, which makes the modelling process demanding.

In the review, a total of 137 peer-reviewed field studies covering three decades (from 1979 to 2010) and six sub-regions were analysed. The literature search identified a total of 17 predictors, which were divided into six general categories, including bottom topography (water depth and slope), biotic features (biological processes, vegetation cover and cover of filamentous algae), hydrography (pH, nutrient content, oxygen, salinity, Secchi depth, sedimentation and water temperature), exposure (wave exposure), substrate (sediment type and substrate) and spatiotemporal variability (site and time). THESE DON´T OVERLAP ENTIRELY WITH THE FIVE CATEGORIES MENTIONED EARLIER!?

Consistency between modelling techniques

To evaluate the performance of different types of predictor variables, PREHAB performed subsequent predictive modelling of large data sets from four case-study regions in the Baltic Sea.

Models of quantitative (regression) and qualitative (classification) responses were analysed and evaluated. Regression models were used to assess abundance or percent cover of macrovegetation, macroinvertebrates and fish, while classification models were used to assess distribution (presence/absence or presence only) data using the same three response categories.

Different modelling methods have been applied (see section xxx in the web resource) and all of them appear to be useful and robust. The explanatory power of predictor variables differed among response variables and regions, but there was a large agreement in the importance of predictors among methods within regions.

In summary, environmental factors identified in the literature are often useful as predictors in species distribution models, but explicit focus on prediction as a success criterion may result in alternative priorities for species distribution modelling compared to basic, traditional ecological research.

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