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

被引:11
作者
Aben, Job [1 ]
Signer, Johannes [2 ]
Heiskanen, Janne [3 ,4 ]
Pellikka, Petri [3 ,4 ]
Travis, Justin M. J. [5 ]
机构
[1] Univ Antwerp, Dept Biol, Antwerp, Belgium
[2] Univ Goettingen, Wildlife Sci, Gottingen, Germany
[3] Univ Helsinki, Dept Geosci & Geog, Helsinki, Finland
[4] Univ Helsinki, Fac Sci, Inst Atmospher & Earth Syst Res, Helsinki, Finland
[5] Univ Aberdeen, Sch Biol Sci, Aberdeen, Scotland
关键词
animal movement behaviour; viewshed analysis; perceptual range; step-selection function; habitat selection; LiDAR; SELECTION FUNCTIONS; RESOURCE SELECTION; ANIMAL MOVEMENT; LANDSCAPE; ECOLOGY; GAP; INFORMATION; BEHAVIOR;
D O I
10.1098/rsbl.2020.0478
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
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页数:6
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