Predicting species distributions for conservation decisions

TLDR

Species distribution models are increasingly used to support conservation decisions, yet evidence of their effectiveness in real‑world conservation is scarce and the decision framework is rarely explicit. The authors aim to show that successful SDM examples exist in grey literature, propose tailoring SDMs to diverse decision contexts through a structured transparent process, and encourage modellers to engage directly with decision makers. This can be facilitated by individuals or institutions acting as translators between modellers and decision makers. The study finds that while successful SDM applications exist, they are largely hidden in grey literature, and that active involvement of modellers in real decision processes can bridge theory and practice and improve conservation outcomes.

Abstract

Species distribution models (SDMs) are increasingly proposed to support conservation decision making. However, evidence of SDMs supporting solutions for on-ground conservation problems is still scarce in the scientific literature. Here, we show that successful examples exist but are still largely hidden in the grey literature, and thus less accessible for analysis and learning. Furthermore, the decision framework within which SDMs are used is rarely made explicit. Using case studies from biological invasions, identification of critical habitats, reserve selection and translocation of endangered species, we propose that SDMs may be tailored to suit a range of decision-making contexts when used within a structured and transparent decision-making process. To construct appropriate SDMs to more effectively guide conservation actions, modellers need to better understand the decision process, and decision makers need to provide feedback to modellers regarding the actual use of SDMs to support conservation decisions. This could be facilitated by individuals or institutions playing the role of 'translators' between modellers and decision makers. We encourage species distribution modellers to get involved in real decision-making processes that will benefit from their technical input; this strategy has the potential to better bridge theory and practice, and contribute to improve both scientific knowledge and conservation outcomes.