What you see is where you go: visibility influences movement decisions of a forest bird navigating a three-dimensional-structured matrix

Abstract

Animal spatial behaviour is often presumed to reflect responses to visual cues. However, inference of behaviour in relation to the environment is challenged by the lack of objective methods to identify the information that effectively is available to an animal from a given location. In general, animals are assumed to have unconstrained information on the environment within a detection circle of a certain radius (the perceptual range; PR). However, visual cues are only available up to the first physical obstruction within an animal's PR, making information availability a function of an animal's location within the physical environment (the effective visual perceptual range; EVPR). By using LiDAR data and viewshed analysis, we modelled forest birds' EVPRs at each step along a movement path. We found that the EVPR was on average 0.063% that of an unconstrained PR and, by applying a step-selection analysis, that individuals are 1.55 times more likely to move to a tree within their EVPR than to an equivalent tree outside it. This demonstrates that behavioural choices can be substantially impacted by the characteristics of an individual's EVPR and highlights that inferences made from movement data may be improved by accounting for the EVPR.

Keywords: LiDAR; animal movement behaviour; habitat selection; perceptual range; step-selection function; viewshed analysis.

Figure 1.

The agricultural matrix greenbuls had to navigate while returning to their territories in forest fragments (photo: J. Aben, 2009).

Figure 2.

Graphical overview of the analytical approach. A greenbul selects one tree to move to next from its current position (red dot; all panels). The decision on which tree to move to may be influenced by the percentage of field to cross (a), on tree height (b), and if a tree is visible (c; viewshed was truncated at 65 m for illustration). To test our hypothesis, a step-selection function is fitted to compare the covariates between the observed step and the alternative steps (d).