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Published on: August 2, 2018
A general framework for the analysis of animal resource selection from telemetry data
Devin S Johnson1, Dana L Thomas2, Jay M Ver Hoef1
1National Marine Mammal Laboratory, Alaska Fisheries Science Center, National Marine Fisheries Service, Seattle, Washington 98115, U.S.A.
Biometrics
|December 1, 2007
Summary
We introduce a flexible weighted distribution framework for analyzing animal telemetry data. This approach unifies existing resource selection models and effectively handles correlated animal movement data, improving ecological analysis.
Area of Science:
- Ecology
- Wildlife Biology
- Statistical Modeling
Background:
- Animal telemetry data, often collected using technologies like global positioning systems (GPS), frequently exhibit high autocorrelation.
- Existing resource selection models may not fully capture the complexities of animal movement and habitat use patterns.
- A unified framework is needed to integrate various interpretations of resource selection functions.
Purpose of the Study:
- To propose a general framework for analyzing animal telemetry data using weighted distributions.
- To demonstrate how popular resource selection models are special cases within this framework.
- To extend the framework to accommodate autocorrelated telemetry data and illustrate its application.
Main Methods:
- Development of a general framework based on weighted distributions for telemetry data analysis.
- Derivation of resource selection functions from the ratio of use and availability distributions.
- Incorporation of assumptions about animal movement and behavior to derive specific models.
- Extension of the framework to handle highly autocorrelated data, common with GPS technology.
Main Results:
- The weighted distribution framework provides a unified approach to understanding resource selection functions.
- Several existing resource selection models are shown to be specific instances of the general weighted distribution model.
- The framework effectively accounts for autocorrelation in telemetry data, a common issue with modern tracking technologies.
- Simulated data analysis demonstrates the potential benefits and flexibility of the proposed modeling approach.
Conclusions:
- The proposed weighted distribution framework offers a powerful and flexible tool for analyzing animal telemetry data.
- This approach enhances the understanding of resource selection by unifying diverse models and accommodating data complexities.
- The framework's successful application to a brown bear dataset in Alaska highlights its practical utility in wildlife research.
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