Transferring biodiversity models for conservation: Opportunities and challenges
Phil Bouchet | Mar 06, 2018
Many ecological models are used to make predictions in areas where empirical data are non-existent. Photo: OxyMotion
Sequeira AMM, Bouchet PJ, Yates KL, Mengersen K, Caley MJ. 2018. Transferring biodiversity models for conservation: Opportunities and challenges. Methods in Ecology and Evolution, 9(5): 1250-1264.
- Predictive models are widespread in ecology as they provide a mechanism for generating knowledge about systems for which data are often lacking.
- The ability to make both accurate and precise predictions in novel conditions (e.g. unsampled areas, future years) is termed ‘transferability’ and should be a key feature of models used in conservation planning and management applications.
- The factors that enhance or undermine transferability, however, remain little known.
- ‘How transferable is my model?’ is also often a difficult question to answer as a consistent framework for measuring transferability is lacking.
- We propose such a framework and review the literature to summarise best practices in developing ecological models that achieve maximum reliability when projected in novel environments.
After decades of extensive surveying, knowledge of the global distribution of species still remains inadequate for many purposes. In the short to medium term, such knowledge is unlikely to improve greatly given the often prohibitive costs of surveying and the typically limited resources available. By forecasting biodiversity patterns in time and space, predictive models can help fill critical knowledge gaps and prioritize research to support better conservation and management. The ability of a model to predict biodiversity metrics in novel environments is termed ‘transferability’, and models with high transferability will be the most useful in this context. Despite their potential broad utility, little guidance exists on what confers high transferability to biodiversity models. We synthesise recent advances in biodiversity model transfers to facilitate increased understanding of what underpins successful model transferability, demonstrating that a consistent approach has so far been lacking but is essential for achieving high levels of repeatability, transparency, and accountability of model transfers. We provide a set of guidelines to support efficient learning and the improvement of model transferability.
Examples of range of conditions where model transferability might be tested. ‘Internal’ refers to conditions modelled in the reference system, while ‘External’ to model transfers to a target system. Figure: Sequeira et al. 2018.
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