# Considering landscape heterogeneity improves the inference of inter-individual interactions from movement data

## TL;DR

This study shows that ignoring landscape features when analyzing animal movement can lead to incorrect conclusions about social interactions between individuals.

## Contribution

The study introduces a method to reduce bias in inferring inter-individual interactions by considering landscape heterogeneity.

## Key findings

- Neglecting environmental features leads to biased inference of inter-individual interactions.
- Including landscape data improves the accuracy of movement analysis.
- The 'Spatial+' method helps reduce bias when landscape data is unavailable.

## Abstract

Animal movement is influenced by both the physical environment and social environment. The effects of both environments are not independent from each other and identifying whether the resulting movement trajectories are shaped by interactions between individuals or whether they are the result of their physical environment, is important for understanding animal movement decisions.

Here, we assessed whether the commonly used methods for inferring interactions between moving individuals could discern the effects of environment and other moving individuals on the movement of the focal individual. We used three statistical methods: dynamic interaction index, and two methods based on step selection functions. We created five scenarios in which the animals' movements were influenced either by their physical environment alone or by inter-individual interactions. The physical environment is constructed such that it leads to a correlation between the movement trajectories of two individuals.

We found that neglecting the effects of physical environmental features when analysing interactions between moving animals leads to biased inference, i.e. inter-individual interactions spuriously inferred as affecting the movement of the focal individual. We suggest that landscape data should always be included when analysing animal interactions from movement data. In the absence of landscape data, the inference of inter-individual interactions is improved by applying ‘Spatial+’, a recently introduced method that reduces the bias of unmeasured spatial factors.

This study contributes to improved inference of biotic and abiotic effects on individual movement obtained by telemetry data. Step selection functions are flexible tools that offer the possibility to include multiple factors of interest as well as combine it with Spatial+.

The online version contains supplementary material available at 10.1186/s40462-025-00567-0.