A Bayesian framework to spatially constrain habitat suitability maps from species distribution models

Species Distribution Models (SDMs) are widely used to predict where species could potentially thrive based on environmental conditions. Their outputs, known as Habitat Suitability scores, are essential for applications like reintroduction planning, locating rare species, or assessing biodiversity responses to climate change. However, these scores often overestimate the actual areas occupied by species, as not all suitable sites are used due to biotic constraints, dispersal limitations, or historical contingencies. Over-prediction of habitat suitability reduces reliability for critical tasks like extinction risk assessments. To address this issue, spatial filtering methods are often applied to better approach realized species distributions. Most commonly, this involves clipping habitat suitability maps with binary spatial masks such as IUCN (International Union for Conservation of Nature) range maps or from minimal convex polygons around observations. Although easy to apply, these methods assumes full independence between the suitability map and the spatial mask, which can introduce inaccuracies and limit interpretability. Here, we introduce Permanence of Ratios, a geostatistical method that relaxes this assumption by adopting a conditional independence perspective. Using a Bayesian framework, Permanence of Ratios clarifies each map’s role in the filtering process, avoids artificial renormalization, and produces results that are more accurate and interpretable. Using data from European bird species, we compare this method to traditional clipping across several spatial constraints, including expert range maps and observation-based techniques. Our results demonstrate Permanence of Ratios as a practical and effective tool to refine SDM outputs without additional data, making it beneficial for research in ecology and conservation. Supplementary Material File (main document (2).pdf) - Download - 9.39 MB Information & Authors Information Version history Copyright This work is licensed under a Non Exclusive No Reuse License.

Authors Metrics & Citations Metrics Article Usage 318views 317downloads Citations Download citation Maxime HOAREAU, Sara SI-MOUSSI, Wilfried THUILLER. A Bayesian framework to spatially constrain habitat suitability maps from species distribution models. Authorea. 16 October 2025. DOI: https://doi.org/10.22541/au.176061482.24539650/v1 DOI: https://doi.org/10.22541/au.176061482.24539650/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.