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Data from: Sampling methodology influences habitat suitability modeling for Chiropteran species

Gaulke, Sarah1; Hohoff, Tara1; Rogness, Brittany1; Davis, Mark1

Published Sep 05, 2023 on Dryad. https://doi.org/10.5061/dryad.t1g1jwt6r

Data files

Sep 05, 2023 version files 17.78 MB

Abstract

Technological advances increase opportunities for novel wildlife survey methods. With increased detection methods, many organizations and agencies are creating habitat suitability models (HSMs) to identify critical habitats and prioritize conservation measures. However, multiple occurrence data types are utilized independently to create these HSMs with little understanding of how biases inherent to those data might impact HSM efficacy.

We sought to understand how different data types can influence HSMs using three bat species (L. borealis, L. cinereus, and P. subflavus). We compared the overlap of models created from passive-only (acoustics), active-only (mist-netting and wind turbine mortalities), and combined occurrences to identify the effect of multiple data types and detection bias.

For each species, the active-only models had the highest discriminatory ability to tell occurrence from background points and for two of the three species, active-only models performed best at maximizing the discrimination between presence and absence values. By comparing the niche overlaps of HSMs between data types, we found a high amount of variation with no species having over 45% overlap between the models. Passive models showed more suitable habitat in agricultural lands, while active models showed higher suitability in forested land, reflecting sampling bias.

Overall, our results emphasize the need to carefully consider the influences of detection and survey biases on modeling, especially when combining multiple data types or using single data types to inform management interventions. Biases from sampling, behavior at the time of detection, false positive rates, and species life history intertwine to create striking differences among models. The final model output should consider biases of each detection type, particularly when the goal is to inform management decisions, as one data type may support very different management strategies than another.