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Analysis of local habitat selection and large-scale attraction/avoidance based on animal tracking data: is there a
Moritz Mercker1,2, Philipp Schwemmer3, Verena Peschko3
1Bionum GmbH - Consultants in Biostatistics, Hamburg, Finkenwerder Norderdeich 15 A, Hamburg, Germany. mmercker@bionum.de.
Movement Ecology
|April 24, 2021
Summary
Integrated step selection models (iSSMs) and spatio-temporal point process models (ST-PPMs) accurately analyze animal tracking data. iSSMs offer superior statistical power and efficiency for understanding habitat selection and movement patterns.
Area of Science:
- Ecology
- Wildlife Biology
- Statistical Modeling
Background:
- Advancements in wildlife telemetry have generated vast amounts of high-resolution animal tracking data.
- A variety of statistical methods have been developed to analyze this complex data.
Purpose of the Study:
- To compare the performance of different statistical methods for inferring resource selection and attraction/avoidance from tracking data.
- To evaluate the interplay between statistical models, habitat characteristics, and animal movement.
Main Methods:
- Simulated habitat and tracking data were used for comparative analysis.
- Spatial logistic regression models (SLRMs), spatio-temporal point process models (ST-PPMs), step selection models (SSMs), and integrated step selection models (iSSMs) were compared.
Main Results:
- Integrated step selection models (iSSMs) and ST-PPMs maintained nominal type I error rates across all scenarios.
- SSMs exhibited minor exceedances, while SLRMs frequently and significantly exceeded type I error rates.
- iSSMs demonstrated greater statistical power and robustness compared to ST-PPMs.
Conclusions:
- Integrated step selection models (iSSMs) are recommended for analyzing habitat selection and large-scale attraction/avoidance from animal tracking data.
- iSSMs provide advantages including faster computation, predictive capabilities, and the development of mechanistic movement models.
