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

Area of Science:

• Ecology
• Animal Behavior
• Movement Ecology

Background:

• Animal movement is shaped by complex interactions between physical and social environments.
• Distinguishing between environmental and social drivers of movement is key to understanding animal decisions.

Purpose of the Study:

• To evaluate statistical methods for inferring animal interactions.
• To determine if common methods can differentiate between environmental and social influences on movement.

Main Methods:

• Assessed dynamic interaction index and two step selection function methods.
• Simulated animal movement scenarios influenced by environment and/or interactions.
• Investigated the impact of correlated movement trajectories due to physical environments.

Main Results:

• Neglecting physical environment data leads to biased inference of inter-individual interactions.
• Including landscape data is essential for accurate analysis of animal interactions.
• The 'Spatial+' method improves inference of interactions when landscape data is absent.

Conclusions:

• Improved inference of biotic and abiotic effects on animal movement using telemetry data.
• Step selection functions are versatile tools for incorporating multiple factors.
• Combining step selection functions with 'Spatial+' enhances the analysis of animal movement patterns.