Advances in quantifying space-use and habitat-selection of animals
Sunday, 26 June 2022 (full day)
Presenters: John Fieberg, University of Minnesota, USA; Tal Avgar, Utah State University, USA; Johannes Signer, University of Göttingen, Germany; Brian Smith, Utah State University, USA
We will illustrate strategies for fitting habitat-selection models (resource-selection and step-selection functions, RSFs and SSFs, respectively) to data from multiple tagged individuals, highlighting three recent developments:
- Integrated Step-Selection Functions (iSSFs), a simple framework for simultaneous modeling animal movement and habitat selection processes using conditional logistic regression (Avgar et al. 2016, Fieberg et al. 2021). These models allow one to relax the assumption that movement characteristics (i.e. step lengths and turn angles) are independent of habitat features.
- The amt (animal movement tools) package in R, which provides tools for exploratory analysis of animal location data, functions for data development prior to fitting RSFs or SSFs, and a simple tidyverse workflow for seamless fitting of RSF and SSF models to data from individual animals (Signer et al. in 2019).
- Methods for efficient estimation of mixed-effect RSFs and SSFs using INLA and the glmmTMB package (Muff et al. 2020).
We will include a mix of lectures and hands on applications (model fitting in R). A preliminary schedule and list of topics is given below:
- Short introduction to Resource-Selection Functions (lecture).
- Introduction to Step-Selection Functions (lecture).
- Relaxing Assumptions: Integrated Step-Selection Functions (lecture, hands on component).
- Exploratory data analysis using the amt R package (lecture and hands on component)
- Fitting models to individual animals using a tidyverse.workflow (lecture and hands on component)
- Fitting mixed effect RSFs and SSFs to multiple animals using INLA and glmmTMB (lecture and hands on component).
Cost: R1,500 per person in-person (US$105); R975 per person virtual (US$67,25)