An introduction to predictive distribution modelling for conservation to encourage novel perspectives

Abstract

The rapid pace and potentially irreversible consequences of global change create an urgent need to predict the spatial responses of biota for conservation to better inform the prioritization and management of terrestrial habitats and prevent future extinctions. Here, we provide an accessible entry point to the field to guide near-future work building predictive species distribution models (SDMs) by synthesizing a technical framework for the proactive conservation of avian biodiversity. Our framework offers a useful approach to navigate the challenges surrounding the large spatio-temporal resolution of datasets and datasets that favor hypothesis testing at broad spatio-temporal scales and coarse resolutions, which can affect our ability to assess the validity of current predicted distributions. We explain how to improve the accuracy of predictive models by determining the extent to which: 1) dispersal limitation impacts the rate of range shifts, 2) taxa are rare at their range limits, and 3) land use and climate change interact. Finally, we offer approaches to filling knowledge gaps by creatively leveraging existing methods and data sources.