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- Lindegarth, Mats, 1965, et al.
(författare)
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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)
- 2014
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Ingår i: Ambio. - : Springer Science and Business Media LLC. - 0044-7447 .- 1654-7209. ; 43:1, s. 82-93
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 2. |
- Bergström, Ulf, et al.
(författare)
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Evaluating eutrophication management scenarios in the Baltic Sea using species distribution modelling
- 2013
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Ingår i: Journal of Applied Ecology. - : Wiley. - 1365-2664 .- 0021-8901. ; 50:3, s. 680-690
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Tidskriftsartikel (refereegranskat)abstract
- Eutrophication is severely affecting species distributions and ecosystem functioning in coastal areas. Targets for eutrophication reduction have been set in the Baltic Sea Action Plan (BSAP) using Secchi depth, a measure of water transparency, as the main status indicator. Despite the high economic costs involved, the potential effects of this political decision on key species and habitats have not been assessed. In a case study including species central to coastal ecosystem functioning, we modelled the effects of changing Secchi depth on the distribution of bladderwrack Fucus vesiculosus and eelgrass Zostera marina vegetation as well as recruitment areas of the main predatory fish species, perch Perca fluviatilis and pikeperch Sander lucioperca. Specifically, we explored the effects of changing Secchi depth on species distributions under a set of scenarios based on the BSAP, using three fundamentally different modelling techniques: maximum entropy, generalized additive and random forest modelling. Improved Secchi depth (reduced eutrophication) was predicted to cause a substantial increase in the distribution of bladderwrack, while the distribution of eelgrass remained largely unaffected. For the fish, a large increase in perch recruitment areas was predicted and a concurrent decrease in recruitment areas of pikeperch. These changes are likely to have effects on biodiversity and ecosystem services. The three modelling methods exposed differences in the quantitative predictions for species with a weaker coupling to Secchi depth. Qualitatively, however, the results were consistent for all species. Synthesis and applications. We show how ecological effects of environmental policies can be evaluated in an explicit spatial context using species distribution modelling. The model-specific responses to changes in eutrophication status emphasize the importance of using ensemble modelling for exploring how species distributions may respond to alternative management regimes. A pronounced difference in response between species suggests that eutrophication mitigation will have consequences for ecosystem functioning, and thus ecosystem goods and services, by inducing changes in the simple food webs of the Baltic Sea. These model predictions form a basis for spatially explicit cost-benefit estimates under different scenarios, providing valuable information for both decision-makers and the wider society.
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