Sampling methodology influences habitat suitability modeling for chiropteran species

Area of Science:

• Wildlife ecology
• Conservation biology
• Ecological modeling

Background:

• Technological advancements offer new wildlife survey methods.
• Habitat suitability models (HSMs) are crucial for conservation planning.
• Using diverse data types for HSMs can introduce biases.

Purpose of the Study:

• To investigate how different data types influence habitat suitability models (HSMs).
• To assess the impact of detection bias on HSMs for three bat species.
• To compare HSMs derived from passive-only, active-only, and combined occurrence data.

Main Methods:

• Compared habitat suitability models (HSMs) for three bat species (Lasiurus borealis, Lasiurus cinereus, Perimyotis subflavus).
• Utilized passive-only (acoustic) and active-only (mist-netting, wind turbine mortalities) occurrence data.
• Analyzed model overlap and discriminatory ability across different data types.

Main Results:

• Active-only models demonstrated the highest discriminatory ability for occurrence and presence/absence.
• HSM niche overlaps varied greatly between data types (max 45%).
• Passive models favored agricultural areas; active models favored forests, indicating sampling bias.

Conclusions:

• Detection and survey biases significantly influence HSMs, especially when combining data.
• Different data types yield substantially different habitat suitability predictions.
• Carefully consider detection biases when using HSMs for management decisions.