# The devil is in the detail: Environmental variables frequently used for habitat suitability modeling lack information for forest‐dwelling bats in Germany

## TL;DR

This study shows that using detailed environmental data improves habitat models for forest-dwelling bats in Germany, highlighting the need for more precise data in conservation planning.

## Contribution

The study demonstrates that targeted land cover and LiDAR data improve habitat suitability models for forest bat species.

## Key findings

- Incorporating tree species composition and LiDAR data improved model performance for three bat species.
- Distribution maps varied significantly based on the level of detail in environmental variables.
- Coarse environmental variables may inadequately represent habitat needs of specialized species.

## Abstract

In response to the pressing challenges of the ongoing biodiversity crisis, the protection of endangered species and their habitats, as well as the monitoring of invasive species are crucial. Habitat suitability modeling (HSM) is often treated as the silver bullet to address these challenges, commonly relying on generic variables sourced from widely available datasets. However, for species with high habitat requirements, or for modeling the suitability of habitats within the geographic range of a species, variables at a coarse level of detail may fall short. Consequently, there is potential value in considering the incorporation of more targeted data, which may extend beyond readily available land cover and climate datasets. In this study, we investigate the impact of incorporating targeted land cover variables (specifically tree species composition) and vertical structure information (derived from LiDAR data) on HSM outcomes for three forest specialist bat species (Barbastella barbastellus, Myotis bechsteinii, and Plecotus auritus) in Rhineland‐Palatinate, Germany, compared to commonly utilized environmental variables, such as generic land‐cover classifications (e.g., Corine Land Cover) and climate variables (e.g., Bioclim). The integration of targeted variables enhanced the performance of habitat suitability models for all three bat species. Furthermore, our results showed a high difference in the distribution maps that resulted from using different levels of detail in environmental variables. This underscores the importance of making the effort to generate the appropriate variables, rather than simply relying on commonly used ones, and the necessity of exercising caution when using habitat models as a tool to inform conservation strategies and spatial planning efforts.

Traditional habitat suitability models, relying on variables with coarse level of detail, may inadequately represent the complexities of specialized species. This study shows that habitat suitability modeling for forest specialist bat species can be improved by integrating more targeted data, such as tree species classification map and LiDAR‐derived vertical structure information. Furthermore, the results show a high difference in resulting distribution maps when using different levels of detail in environmental predictors, which underscores the importance of exercising caution when using habitat models as a tool to inform conservation strategies and spatial planning efforts.