Multi-mode movement decisions across widely ranging behavioral processes.

Area of Science:

• Ecology
• Movement Ecology
• Behavioral Ecology

Background:

• Animal movement is crucial for evolution and biodiversity.
• Understanding movement patterns requires distinguishing behavioral states.
• Current methods for state discrimination have limitations, overlooking statistical uncertainty.

Purpose of the Study:

• To develop a novel population-level, multi-state step selection function (HMM-SSF) model.
• To simultaneously identify distinct behavioral bouts and their associated behavior-habitat relationships.
• To assess the robustness, versatility, and predictive ability of HMM-SSF using diverse animal data.

Main Methods:

• Developed population-level, multi-state step selection functions (HMM-SSF).
• Utilized simulated data and empirical relocation data from mule deer, plains bison, and plains zebra.
• Applied the HMM-SSF model to analyze movement patterns during foraging, migration, and predator avoidance.

Main Results:

• HMM-SSF successfully identified different behavioral bouts and their habitat selection patterns.
• Animals exhibited distinct habitat preferences during 'encamped' versus 'travelling' phases.
• Habitat attribute selection varied based on movement phase; predator presence triggered a shift to travelling behavior.

Conclusions:

• HMM-SSF provides a robust and versatile tool for analyzing animal movement behavior and habitat use.
• The model accurately captures state-dependent habitat selection and predator-induced movement responses.
• HMM-SSF advances mechanistic understanding of how animals interact with their environment amidst complex ecological factors.