ArticleEcology and evolution2024
The devil is in the detail: Environmental variables frequently used for habitat suitability modeling lack information for forest-dwelling bats in Germany.
Article in Ecology and evolution, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Predicting nepetalactone accumulation in Nepeta persica using machine learning algorithms and geospatial analysis.Scientific reports · 2025Article
- Predicted climate-induced range shifts and conservation challenges of the western barbastelle bat (Barbastella barbastellus).Scientific reports · 2025Article
- The devil is in the detail: Environmental variables frequently used for habitat suitability modeling lack information for forest-dwelling bats in Germany.Ecology and evolution · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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 (
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Registered trials
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