Simple rules for complex landscapes: the case of hilltopping movements and topography Academic Article uri icon


  • Empirical data on the signals and processes that direct animal movement during dispersal in heterogeneous landscapes are scarce. Our understanding could benefi t from utilising simulation approaches and searching for simple rules across species and landscapes. Th is study sought to identify general movement behaviours that optimise butterfl y movements during hilltopping. Th is widespread dispersal-like phenomenon in butterfl ies, where males and virgin females ascend to mountain summits for the purpose of mating, benefi ts from the uniqueness of knowing the purpose (mating) and the orientation signal (topography). We used an individualbased simulation model to search for movement rules that can optimise mating success, mating time, and the success of mated females in subsequently fi nding habitat patches across diff erently 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. Th e 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 fi eld studies. Notably, the parameter values identifi ed by the model as optimal corresponded with the observed behaviour of butterfl ies in the fi eld. Finally, the optimal movement behaviours were aff ected by the lifespan of butterfl ies 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.

publication date

  • January 1, 2013

published in