Step selection functions with non-linear and random effects

Natasha J. Klappstein, Théo Michelot, John Fieberg, Eric J. Pedersen, Joanna Mills Flemming

Research output: Contribution to journalArticlepeer-review

Abstract

Step selection functions (SSFs) are used to jointly describe animal movement patterns and habitat preferences. Recent work has extended this framework to model inter-individual differences, account for unexplained structure in animals' space use and capture temporally varying patterns of movement and habitat selection. In this paper, we formulate SSFs with penalised smooths (similar to generalised additive models) to unify new and existing extensions, and conveniently implement the models in the popular, open-source mgcv R package. We explore non-linear patterns of movement and habitat selection, and use the equivalence between penalised smoothing splines and random effects to implement individual-level and spatial random effects. This framework can also be used to fit varying-coefficient models to account for temporally or spatially heterogeneous patterns of selection (e.g. resulting from behavioural variation), or any other non-linear interactions between drivers of the animal's movement decisions. We provide the necessary technical details to understand several key special cases of smooths and their implementation in mgcv, showcase the ecological relevance using two illustrative examples and provide R code to facilitate the adoption of these methods. This paper offers a broad overview of how smooth effects can be applied to increase the flexibility and biological realism of SSFs.

Original languageEnglish (US)
JournalMethods in Ecology and Evolution
DOIs
StateAccepted/In press - 2024

Bibliographical note

Publisher Copyright:
© 2024 The Author(s). Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society.

Keywords

  • animal movement
  • generalised additive models
  • habitat selection
  • integrated step selection analysis
  • mgcv
  • penalised splines
  • random effects
  • step selection functions

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