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

Abstract

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 longterm funding for modelling and monitoring, and improved communication between research and practise to ensure optimal conservation and restoration outcomes.

Supporting the restoration of complex ecosystems requires long‐term and multi‐scale perspectives

“…They emphasize the need for integrating pluralistic values of nature in restoration research and planning in order to find the required support to implement restoration at scale (Quintero‐Uribe et al 2022). Effective restoration planning also needs to consider transient dynamics and future trajectories under environmental changes to restore resilient systems for the future (Bullock et al 2021, Zurell et al 2021). This is inevitable given ongoing human‐induced climate change, the increasing mixing of biota due to globalization, as well as the widespread human transformation of landscapes worldwide, as highlighted by the theoretical analysis of Storch et al (2021).…”

“…Quintero et al argue that exploring scenarios with co-benefits between multiple nature perspectives and increasing the use of spatially explicit quantitative models could help in mainstreaming rewilding in local decision-making. Zurell et al (2021) identify gaps and biases in the use of spatially-explicit models in restoration and animal conservation. They propose a typology for models at different ecological levels, from genes to ecosystems, to support decision-making and guide restoration efforts at multiple scales.…”

“…Zurell et al (2021) identify gaps and biases in the use of spatially‐explicit models in restoration and animal conservation. They propose a typology for models at different ecological levels, from genes to ecosystems, to support decision‐making and guide restoration efforts at multiple scales.…”

Regional opportunities for Siberian tundra conservation in the next 1000 years

“…, 2005). These models have become increasingly used lately to assist in formulating conservation management strategies (Zurell et al, 2021). For the northern Siberian treeline ecotone the vegetation model LAVESI was developed, which incorporates a full tree (Larix) life-cycle beginning with seed production and dispersal, germination, and establishment of seedlings which then grow based on abiotic (temperature, precipitation, active layer depth) and biotic (competition) conditions (Kruse et al, 2019a(Kruse et al, , 2016.…”

“…Models which contain processes to estimate competition between individuals for resources and seed dispersal in a spatially explicit environment and which handle each tree individually are called individual-based spatially explicit models ( DeAngelis and Mooij, 2005 ). These models have become increasingly used lately to assist in formulating conservation management strategies ( Zurell et al, 2021 ). For the northern Siberian treeline ecotone the vegetation model LAVESI was developed, which incorporates a full tree ( Larix ) life-cycle beginning with seed production and dispersal, germination, and establishment of seedlings which then grow based on abiotic (temperature, precipitation, active layer depth) and biotic (competition) conditions ( Kruse et al, 2019a , 2016 ).…”