Monitoring dynamic spatio-temporal ecological processes optimally

TL;DR

This paper presents a framework that integrates animal movement models with survey design to optimize monitoring of dynamic ecological processes, demonstrated through a case study on sea otters in Glacier Bay.

Contribution

It introduces a cohesive, general framework for designing optimal ecological surveys by explicitly linking movement models with monitoring objectives.

Findings

Reduced uncertainty in occupancy, abundance, and distribution estimates.

Successfully applied to monitor sea otter population spread.

Framework is adaptable to various ecological systems.

Abstract

Population dynamics varies in space and time. Survey designs that ignore these dynamics may be inefficient and fail to capture essential spatio-temporal variability of a process. Alternatively, dynamic survey designs explicitly incorporate knowledge of ecological processes, the associated uncertainty in those processes, and can be optimized with respect to monitoring objectives. We describe a cohesive framework for monitoring a spreading population that explicitly links animal movement models with survey design and monitoring objectives. We apply the framework to develop an optimal survey design for sea otters in Glacier Bay. Sea otters were first detected in Glacier Bay in 1988 and have since increased in both abundance and distribution; abundance estimates increased from 5 otters to >5,000 otters, and they have spread faster than 2.7 km per year. By explicitly linking animal movement models and survey design, we were able to reduce uncertainty associated with predicted occupancy, abundance, and distribution. The framework we describe is general, and we outline steps to applying it to novel systems and taxa.