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Models are useful tools for understanding and predicting ecological patterns and processes. Under ongoing climate and biodiversity change, they can greatly facilitate decision-making in conservation and restoration and help designing adequate management strategies for an uncertain future. Here, we review the use of spatially explicit models for decision support and to identify key gaps in current modelling in conservation and restoration. Of 650 reviewed publications, 217 publications had a clear management application and were included in our quantitative analyses. Overall, modelling studies were biased towards static models (79%), towards the species and population level (80%) and towards conservation (rather than restoration) applications (71%). Correlative niche models were the most widely used model type. Dynamic models as well as the gene-to-individual level and the community-to-ecosystem level were underrepresented, and explicit cost optimisation approaches were only used in 10% of the studies. We present a new model typology for selecting models for animal conservation and restoration, characterising model types according to organisational levels, biological processes of interest and desired management applications. This typology will help to more closely link models to management goals. Additionally, future efforts need to overcome important challenges related to data integration, model integration and decision-making. We conclude with five key recommendations, suggesting that wider usage of spatially explicit models for decision support can be achieved by 1) developing a toolbox with multiple, easier-to-use methods, 2) improving calibration and validation of dynamic modelling approaches and 3) developing best-practise guidelines for applying these models. Further, more robust decision-making can be achieved by 4) combining multiple modelling approaches to assess uncertainty, and 5) placing models at the core of adaptive management. These efforts must be accompanied by long-term funding for modelling and monitoring, and improved communication between research and practise to ensure optimal conservation and restoration outcomes.
Silvicultural practices lead to changes in forest composition and structure and may impact species diversity from the overall regional species pool to stand-level species occurrence. We explored to what extent fine-scale occupancy patterns in differently managed forest stands are driven by environment and ecological traits in three regions in Germany using a multi-species hierarchical model. We tested for the possible impact of environmental variables and ecological traits on occupancy dynamics in a joint modelling exercise while taking possible variation in coefficient estimates over years and plots into account. Bird species richness differed across regions and years, and trends in species richness across years were different in the three regions. On the species level, forest management affected occupancy of species in all regions, but only 35% of the total assemblage-level variation in occurrence probability was explained by either forest type and successional stage and <?1% by forest edge. On the assemblage level, bird occurrence decreased with body mass in all regions. Species with smaller breeding ranges had lower occurrence probabilities in one region, while later spring arrival decreased occurrence probabilities in the two other regions. Spatial variation in the effect size of trait covariates such as species phylogeny and breeding strata showed that variation in patch occupancy due to fine-scale differences in forest management is, to some extent, predictable from ecological traits. Our results show that environmental factors and ecological traits jointly predict variation in bird occupancy patterns and their response to forest management. Observations at the fine scale of forest stands, at which conservation efforts can be arranged along with forest management practices in heterogeneous environments, have been shown to provide meaningful insights despite the difficulties involved in monitoring mobile organisms such as birds at the plot level.