Open AccessSpatially explicit models for decision‐making in animal conservation and restoration
Author(s) -
Zurell Damaris,
König Christian,
Malchow AnneKathleen,
Kapitza Simon,
Bocedi Greta,
Travis Justin,
Fandos Guillermo
Publication year - 2022
Publication title -
ecography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.973
H-Index - 128
eISSN - 1600-0587
pISSN - 0906-7590
DOI - 10.1111/ecog.05787
Subject(s) - toolbox , computer science , typology , management science , risk analysis (engineering) , adaptive management , selection (genetic algorithm) , population , environmental resource management , ecology , geography , engineering , environmental science , business , machine learning , demography , archaeology , sociology , biology , programming language
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.