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Empirical estimation of dispersal resistance surfaces: a case study with red-cockaded woodpeckers
Authors:Anne M. Trainor  Jeffrey R. Walters  William F. Morris  Joseph Sexton  Aaron Moody
Affiliation:1. Department of Geography, University of North Carolina, Chapel Hill, NC, 27599, USA
2. School of Forestry and Environmental Studies, Yale University, New Haven, CT, 06511, USA
3. Department of Biological Sciences, Virginia Tech, Blacksburg, VA, 24061, USA
4. Biology Department, Duke University, Durham, NC, 27708, USA
5. Global Land Cover Facility, University of Maryland, College Park, MD, 20742, USA
6. Department of Geography and Curriculum for the Environment and Ecology, University of North Carolina, Chapel Hill, NC, 27599, USA
Abstract:Persistence of wildlife populations depends on the degree to which landscape features facilitate animal movements between isolated habitat patches. Due to limited data availability, the effect of landscape features on animal dispersal is typically estimated using expert opinion. With sufficient data, however, resistance surfaces can be estimated empirically. After modeling suitable prospecting habitat using an extensive dataset from the federally endangered red-cockaded woodpecker (Picoides borealis), we used data from over 800 prospecting events from 34 radio-tagged birds to identify the best relationship between habitat suitability and resistance surfaces. Our results demonstrated that juvenile female P. borealis prospecting for new territories beyond their natal territories preferred to traverse through forests with tall canopy and minimal midstory vegetation. The non-linear relationship between habitat suitability and resistance surfaces was the most biologically relevant transformation, which in turn identified the specific forest composition that promoted and inhibited prospecting and dispersal behavior. These results corresponded with over 60 % of dispersal events from an independent dataset of short-distance dispersal events. This new understanding of P. borealis prospecting behavior will help to identify areas necessary for maintaining habitat connectivity and to implement effective management strategies. Our approach also provides a framework to not only estimate and evaluate resistance surfaces based on species-specific responses to intervening landscape features, but also addresses an often-neglected step, selecting a biologically relevant function to transform habitat suitability model into a resistance surface.
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