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PREHAB top-10 conclusions for scenario modelling

We have explored how predictive modelling and mapping can be used to assess the ecological effects of changing human pressures. The distribution of key habitats for five species of vegetation and fish were predicted and mapped under different management scenarios, involving the two pressures ‘coastal construction’ and ‘eutrophication’. The assessments have resulted in a number of general conclusions and recommendations about the combined use of predictive mapping and scenario analyses in coastal habitats of the Baltic Sea.

  1. Predictive modelling in combination with scenario analyses is a powerful tool for exploring the effects of management options. Quantitative ecological effects of alternative management strategies can be obtained by including changes in the human pressure variable in spatially explicit predictive models.
  2. A spatial approach is necessary. In order to assess scenario-based changes in the distribution of species and habitats, map predictions for the region of interest, are absolutely necessary to obtain a quantitative estimate of expected future distribution changes.
  3. Full coverage maps of the human pressure variable, and its development under the scenario, must be available. For predictive models to be spatially explicit, full coverage maps of all explanatory variables are needed. In addition, the main scenario variable (i.e. the human pressure variable) needs to be available for the various scenario developments.
  4. A mechanistic understanding of the relationship between the study species and the scenario pressure variable is needed. Species distribution models are correlative by nature and it can be perilous to extrapolate observed relationships into the future. However, with a solid scientific understanding of the link between the scenario pressure variable and the response the likelihood of predictions can be assessed.
  5. An ensemble modelling approach gives more reliable predictions. The uncertainties of the modelling process can be minimized and quantified by applying several modelling techniques in an ensemble approach. For instance, different methods can give different relative weights to the explanatory variables, which may have large effects on the predicted distribution of the species and habitat in question.
  6. “Historical” knowledge about the distribution of the study species can be helpful. If historical data is available it can be used to hindcast the model as a validation step before performing future scenario assessments.
  7. There are many sources of uncertainty to consider. In scenario analysis, uncertainty comes from the data used, the modelling process as well as future changes in the environment. To end up with useful scenario predictions, it is crucial to assess and try to minimize these different sources of uncertainty.
  8. Scenarios on reduced eutrophication show both gains and losses in fish habitat distributions. If the target level of the Baltic Sea Action Plan is reached, reproduction areas for perch will increase, while reproduction areas for pikeperch will decrease. Both species are highly valuable for both recreational and commercial fishery, and these changes in reproduction habitats may have vast effects on catches as well as ecosystem functions.
  9. Reduced eutrophication will increase the distribution of the key species bladderwrack. Stands of this alga constitute the “forests” of Baltic Sea rocky shores, which are essential for the coastal ecosystem.
  10. Scenarios for coastal construction demonstrate a steady decline in fish reproduction areas. The analyses show that shoreline exploitation is typcially concentrated to areas that are important for the reproduction of many fish species. Each year, approximately 1 percent of previously unaffected habitats are exploited.


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