Distribution model transferability for a wide-ranging species, the Gray Wolf

Area of Science:

• Ecology and Conservation Biology
• Wildlife Distribution Modeling
• Spatial Analysis

Background:

• Predicting species distributions using existing data is cost-effective for species recovery.
• Gray wolf (Canis lupus) populations are recovering, necessitating accurate distribution models.
• Assessing the spatial transferability of models is vital for wide-ranging carnivores.

Purpose of the Study:

• To develop a current gray wolf distribution model for the western Great Lakes region.
• To evaluate the spatial transferability of single-state models to this region.
• To identify key environmental variables influencing wolf distribution.

Main Methods:

• Collected 3500 wolf locations from winter surveys in Minnesota, Wisconsin, and Michigan.
• Included 10 environmental variables (e.g., land cover, road density, snowfall).
• Created a regional ensemble model and single-state models, assessing transferability via cross-validation and extrapolation.

Main Results:

• The regional model identified 106,465 km² of suitable winter habitat, predicting 88% of wolf locations.
• Increasing natural cover and distance to crops were consistently important predictors.
• Extrapolation models showed higher performance metrics than cross-validation, but internal performance did not predict extrapolation success.

Conclusions:

• Accurate species distribution models are essential for effective conservation of recovering carnivores.
• Understanding scale-dependent factors and behavioral plasticity is critical for robust model transferability.
• This research provides a foundation for habitat management, connectivity assessments, and conflict mitigation for gray wolves.