Spatially explicit power analysis for detecting occupancy trends for multiple species.

Area of Science:

• Ecology
• Conservation Biology
• Statistical Modeling

Background:

• Designing effective wildlife monitoring programs requires assessing statistical power to detect population changes.
• Existing methods often overlook spatial explicit analysis and dynamic landscape factors.

Purpose of the Study:

• To develop a spatially explicit simulation framework for statistical power analysis in biological monitoring programs.
• To evaluate the power of detecting temporal occupancy trends for multiple species under various scenarios.

Main Methods:

• Developed a simulation framework using raster layers for occupancy and detectability.
• Modeled changes in occupancy over space and time, including stochastic disturbances.
• Estimated power to detect occupancy trends across landscapes and management units for specified monitoring designs.

Main Results:

• Power to detect trends was sensitive to occupancy changes, detectability, initial occupancy, and species rarity.
• 80% power to detect a 50% occupancy decline was achieved for 22% of species over 15 years.
• 80% power to detect a 50% occupancy increase was achieved for 87% of species.
• Camera trapping improved power to detect mammal occupancy declines by 63% compared to live trapping.

Conclusions:

• The framework provides a flexible tool for designing and evaluating monitoring programs for numerous species.
• It explicitly considers species distribution and alternative sampling methods for informed conservation decisions.
• The study highlights the importance of power analysis in optimizing wildlife monitoring efforts.