Integrated step selection analysis: bridging the gap between resource selection and animal movement

By Tal Avgar, Jonathan R. Potts, Mark A. Lewis, M. Boyce. 2015

453 citations recorded by the source corpus.

A resource selection function is a model of the likelihood that an available spatial unit will be used by an animal, given its resource value. But how do we appropriately define availability? Step selection analysis deals with this problem at the scale of the observed positional data, by matching each ‘used step’ (connecting two consecutive observed positions of the animal) with a set of ‘available steps’ randomly sampled from a distribution of observed steps or their characteristics. Here we present a simple extension to this approach, termed integrated step selection analysis (iSSA), which relaxes the implicit assumption that observed movement attributes (i.e. velocities and their temporal autocorrelations) are independent of resource selection. Instead, iSSA relies on simultaneously estimating movement and resource selection parameters, thus allowing simple likelihood‐based inference of resource selection within a mechanistic movement model. We provide theoretical underpinning of iSSA, as well as practical guidelines to its implementation. Using computer simulations, we evaluate the inferential and predictive capacity of iSSA compared to currently used methods. Our work demonstrates the utility of iSSA as a general, flexible and user‐friendly approach for both evaluating a variety of ecological hypotheses, and predicting future ecological patterns.

Subfields: Ecology, Nature and Landscape Conservation, Small Animals

View Paper: https://www.semanticscholar.org/paper/0be9b08a11f72533930abc6996233f6ac04be84f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

1. J. Signer
2. Tal Avgar
3. J. Fieberg
4. S. Boutin
5. A. C. Burton
6. E. Neilson
7. Kate Broadley
8. Mark Hebblewhite

In Nature and Landscape Conservation

1. J. Signer
2. J. Fieberg
3. A. C. Burton
4. S. Boutin
5. Tal Avgar

In Small Animals

1. J. Signer
2. Tal Avgar
3. J. Fieberg
4. A. C. Burton
5. S. Boutin
6. E. Neilson
7. Kate Broadley

Citations in the corpus

• Accounting for individual-specific variation in habitat-selection studies: Efficient estimation of mixed-effects models using Bayesian or frequentist computation (2018, background): “SSFs with individual-specific coefficients are particularly attractive since they can provide insights into movement and habitat-selection processes at fine-spatial and temporal scales (Avgar et al., 2016; Signer et al., 2018), but these models had previously been very challenging to fit.”
• Estimating utilization distributions from fitted step-selection functions (2017, methodology): “Under certain formulations, fitting an SSF results in a parametrized mechanistic movement model (also termed an integrated SSF; Avgar et al. 2016), a model for the pattern-generating process of the observed relocation data.”
• Animal movement affects interpretation of occupancy models from camera‐trap surveys of unmarked animals (2018, methodology): “Based on the theoretical work by Moorcroft and Barnett (2008) and more recently by Avgar et al. (2016), asymptotic UD should scale linearly with the square of the underlying habitat selection function (as long as movement capacity is much shorter than the scale of habitat variation) and hence the…”
• Animal movement tools (amt): R package for managing tracking data and conducting habitat selection analyses (2018, methodology): “For further details we refer the reader to Duchesne et al. (2015) and Avgar et al. (2016).”