Predicting local and non-local effects of resources on animal space use using a mechanistic step selection model

TL;DR

This paper introduces a mechanistic step selection model that accounts for both local and non-local resource effects on animal space use, providing a unified approach to predict animal distribution patterns from movement data.

Contribution

It develops a novel model that naturally incorporates non-local resource effects from local movement processes, unifying resource selection and mechanistic movement models.

Findings

Complex animal use patterns emerge from the model, influenced by habitat size and isolation.

Large, high-quality habitats are used more intensively than smaller or isolated patches.

The framework can be applied with various environmental covariates, unifying different modeling approaches.

Abstract

1. Predicting space use patterns of animals from their interactions with the environment is fundamental for understanding the effect of habitat changes on ecosystem functioning. Recent attempts to address this problem have sought to unify resource selection analysis, where animal space use is derived from available habitat quality, and mechanistic movement models, where detailed movement processes of an animal are used to predict its emergent utilization distribution. Such models bias the animal's movement towards patches that are easily available and resource-rich, and the result is a predicted probability density at a given position being a function of the habitat quality at that position. However, in reality, the probability that an animal will use a patch of the terrain tends to be a function of the resource quality in both that patch and the surrounding habitat. 2. We propose a mechanistic model where this non-local effect of resources naturally emerges from the local movement processes, by taking into account the relative utility of both the habitat where the animal currently resides and that of where it is moving. We give statistical techniques to parametrize the model from location data and demonstrate application of these techniques to GPS data of caribou in Newfoundland. 3. Steady-state animal probability distributions arising from the model have complex patterns that cannot be expressed simply as a function of the local quality of the habitat. In particular, large areas of good habitat are used more intensively than smaller patches of equal quality habitat, whereas isolated patches are used less frequently. 4. Whilst we focus on habitats in this study, our modelling framework can be readily used with any environmental covariates and therefore represents a unification of mechanistic modelling and step selection approaches to understanding animal space use.