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Simple rules for complex landscapes: the case of hilltopping movements and topography

Authors


G. Pe’er, Dept. of Conservation Biology, UFZ - Helmholtz Centre for Environmental Research, Permoserstr. 15, DE-04318 Leipzig, Germany. E-mail: guy.peer@ufz.de

Abstract

Empirical data on the signals and processes that direct animal movement during dispersal in heterogeneous landscapes are scarce. Our understanding could benefit from utilising simulation approaches and searching for simple rules across species and landscapes. This study sought to identify general movement behaviours that optimise butterfly movements during hilltopping. This widespread dispersal-like phenomenon in butterflies, where males and virgin females ascend to mountain summits for the purpose of mating, benefits from the uniqueness of knowing the purpose (mating) and the orientation signal (topography). We used an individual-based simulation model to search for movement rules that can optimise mating success, mating time, and the success of mated females in subsequently finding habitat patches across differently structured landscapes. We found that a strong response to topography was optimal for males and virgin females to reach summits, but slight deviation from ‘perfect’, namely a certain level of randomness in response to topography, was inherently essential to avoid the risk of being trapped on local summits. The optimal response of mated females deviated only slightly from a random movement, indicating a potential weak response to topography which could be easily overlooked by field studies. Notably, the parameter values identified by the model as optimal corresponded with the observed behaviour of butterflies in the field. Finally, the optimal movement behaviours were affected by the lifespan of butterflies and the spatial distribution of host plant patches, but less so by landscape structure. We therefore suggest that modelling approaches that build on simple biological rules can facilitate the development and parameterisation of models for understanding and potentially predicting dispersal and connectivity in complex landscapes, also in circumstances where observed patterns seem complex and empirical data are scarce.

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