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September 10, 2025Open Access

Prey Distribution Mapping to Support Conservation Planning

RKRachel A. KingSan Diego State University JBJenna BraunYork University MWMichael WestphalBureau of Land Management

Key Points

Mapping prey distribution revealed critical hotspots of high prey richness, which can support successful conservation outcomes.
Over 60% of the study area contained suitable habitat for at least one prey item, benefiting more than half of the listed predator species.
The study utilized species distribution models (SDMs) combined with Bayesian regression trees to analyze prey density across California.
This approach suggests significant regions for conservation efforts, highlighting the importance of integrating prey data with existing species models.

Abstract

ABSTRACT Aim Biodiversity conservation broadly relies on protecting suitable habitat for species of concern, and species distribution models (SDM) are a common method for classifying potential habitat suitability. However, SDMs frequently omit resource availability and are therefore missing potentially useful information for planning successful conservation areas. Here, we aim to identify regions of high prey availability, and thus high resource availability, for several listed predator species across California dryland regions. This information could be used to support more refined estimates of valuable habitat for prey species that support listed species within Central California drylands. Location California, USA. Time Period 1945–2022. Major Taxa Studied Arthropoda. Methods We used a prey list for 11 listed species found in California drylands and compiled occurrence records for those species from the Global Biodiversity Information Facility. We fit individual SDMs for all species using Bayesian additive regression trees. These individual SDMs were then stacked to identify hotspots of prey density across California. Results We found the highest observed and predicted prey richness along the Southern California coast in grassland, savanna, and urban landcover classes. Only a small region (2.9%) contained suitable habitat for more than 50% of prey species. In contrast, 60% of the study region contained suitable habitat for at least 1 prey item for more than 50% of listed predator species. Main Conclusions Observed hotspots of high prey richness and regions where predicted prey richness could support multiple listed species identify potential regions for conservation efforts. Our results also highlight how mapping prey in addition to listed species can support planning, as our stacked‐SDMs identified a wide geographic extent that can support the listed species. Future research could use these stacked‐SDMs to identify sites for standardised field surveys for key variables including whether predicted prey items are present but also in high abundance.

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