Using multiple data types and integrated population models to improve our knowledge of apex predator population...

Area of Science:

• Wildlife ecology
• Population dynamics
• Conservation biology

Background:

• Current large carnivore management relies on independent data types, limiting estimation accuracy.
• Observation biases and ecological factors are often not integrated across datasets.
• Improved estimation methods are crucial for effective wildlife conservation and management.

Purpose of the Study:

• To develop a general integrated population model (IPM) for brown bears (Ursus arctos).
• To incorporate multiple data types (counts, capture-mark-recapture, litter size) and account for ecological/observation biases.
• To enhance the accuracy and precision of population parameter estimation for informed management.

Main Methods:

• Developed a general integrated population model (IPM) for brown bears.
• Utilized repeated counts, capture-mark-recapture, and litter size data.
• Considered ecological and observation process factors affecting data.
• Validated the IPM on simulated data and applied it to historical Kodiak Island bear data.

Main Results:

• The IPM yielded more accurate and precise estimates compared to count data alone.
• Credible intervals for population estimates included true values 94% and 5% of the time.
• Estimated annual average litter size on Kodiak Island varied from 0.45 to 1.59.
• Detected a positive relationship between salmon availability and adult survival, with higher survival in females.

Conclusions:

• Integrating multiple data sources via IPMs enhances population estimation accuracy and precision for large carnivores.
• IPMs can optimize data collection efforts by facilitating information sharing.
• This approach is vital for effective brown bear management and adaptable to other species.