Spatially explicit models for decision-making in animal conservation and restoration

Anne-Kathleen Malchow, Guillermo Fandos-Guzman, Christian König, Simon Kapitza, Justin M.J. Travis, Greta Bocedi, Damaris Zurell* (Corresponding Author)

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)
11 Downloads (Pure)


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 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 bestpractise 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.
Original languageEnglish
Number of pages16
Issue number4
Early online date8 Oct 2021
Publication statusPublished - 1 Apr 2022

Bibliographical note

DZ, CK, AKM and GF were supported by the German Science Foundation (DFG) under grant agreement no. ZU 361/1-1. GB was supported by a Royal Society University Research Fellowship (UF160614). We acknowledge the support of the Deutsche Forschungsgemeinschaft and Open Access Publishing Fund of University of Potsdam.

Data Availability Statement

Data are available from the Dryad Digital Repository: <> (Zurell et al. 2021).


  • adaptive management
  • biodiversity conservation
  • cost optimisation
  • ecosystem restoration
  • global change
  • predictive models


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