amt

amt provides R-based tools for managing animal telemetry data and performing step-selection-based habitat selection and movement analyses.

Key Features:

• Data Management: Organizes and manages telemetry and tracking datasets for animal movement analyses.
• Step-Selection Functions (SSFs): Implements SSFs that compare observed steps between successive locations with random steps using a likelihood approach akin to the Cox proportional hazards model and link environmental covariates to fine-temporal-resolution location data.
• Statistical Modeling: Models step length and turn angle distributions with common statistical distributions and incorporates habitat- and movement-related covariates, including functions of distances between points and angular deviations.
• Inference and Simulation: Estimates utilization distributions and mechanistic home ranges from fitted models and simulates animal space use based on those models.

Scientific Applications:

• Movement Ecology: Links environmental variables with movement data to analyze how animals interact with their habitats.
• Habitat Selection Analysis: Infers habitat preferences and selection pressures by relating animal locations to environmental covariates using SSFs.
• Behavioral Studies: Examines movement behaviors such as foraging patterns and migratory routes by modeling step lengths and turn angles.
• Case Study: Applied to fisher (Pekania pennanti) telemetry data to illustrate analysis of real-world movement datasets.

Methodology:

Follows a four-step computational approach: data management of telemetry datasets; exploratory data analysis; model fitting using SSFs to relate environmental covariates to observed movements; and simulation of movements from fitted models.