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

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 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.