Monitoring dynamic spatio-temporal ecological processes optimally.

Area of Science:

• Ecology
• Wildlife Management
• Spatial Statistics

Background:

• Population dynamics exhibit significant spatio-temporal variation.
• Traditional survey designs often overlook these dynamics, leading to inefficiencies and missed variability.
• Dynamic survey designs offer a more robust approach by integrating ecological process knowledge and uncertainty.

Purpose of the Study:

• To present a cohesive framework for monitoring spreading populations by integrating animal movement models with survey design and monitoring objectives.
• To develop an optimized survey design for tracking sea otter populations in Glacier Bay.

Main Methods:

• Developed a framework linking animal movement models with survey design and monitoring objectives.
• Applied the framework to design an optimal survey for sea otters in Glacier Bay, a population that has grown from 5 to over 5,000 individuals since 1988.
• Compared the proposed dynamic design against random designs to assess its effectiveness in reducing uncertainty.

Main Results:

• The dynamic survey design significantly reduced uncertainty in forecasting sea otter occupancy, abundance, and distribution.
• The framework demonstrated superior performance compared to conventional random survey designs.
• Sea otter populations in Glacier Bay have shown rapid increase and spread, exceeding 2.7 km/yr.

Conclusions:

• Integrating animal movement models into survey design provides a powerful, optimized approach for monitoring dynamic populations.
• The described framework is adaptable for application to diverse species and ecosystems.
• This methodology enhances the accuracy and efficiency of wildlife monitoring and management strategies.