Applications of step-selection functions in ecology and conservation

Area of Science:

• Ecology and Wildlife Biology
• Movement Ecology
• Spatial Data Analysis

Background:

• Advancements in positioning technology enable large-scale collection of animal movement data.
• Investigating animal behavior and habitat selection presents new opportunities and challenges with this data.
• Step Selection Functions (SSFs) are emerging as powerful tools for analyzing animal movement and resource selection.

Purpose of the Study:

• To review and clarify the application and methodology of Step Selection Functions (SSFs) in animal movement studies.
• To address current debates and uncertainties in SSF data analysis, covariate consideration, random step selection, and individual variation.
• To identify areas for future research and development in SSF modeling approaches.

Main Methods:

• Comparison of environmental attributes between observed animal movement steps and simulated random steps from the same origin.
• Review of existing literature and common practices in applying SSFs to diverse ecological questions.
• Discussion of statistical considerations including data analysis, covariate selection (path vs. endpoint), number of random steps, and individual variation.

Main Results:

• SSFs are versatile for studying habitat selection, human-wildlife interactions, movement corridors, and dispersal.
• SSFs can potentially model resource selection across multiple spatial and temporal scales.
• Key areas requiring further research and consensus include data analysis, covariate application, random step generation, and accounting for individual differences.

Conclusions:

• This review synthesizes current understanding and highlights critical areas for advancing SSF methodology.
• Future research should focus on resolving methodological ambiguities and enhancing the robustness of SSFs.
• Integration of SSFs with state-space models is proposed as a promising avenue for classifying animal behavioral states during movement analysis.