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dc.contributor.authorLong, Jed
dc.contributor.authorNelson, Trisalyn
dc.contributor.authorWebb, Stephen
dc.contributor.authorGee, Kenneth
dc.date.accessioned2015-02-22T00:01:44Z
dc.date.available2015-02-22T00:01:44Z
dc.date.issued2014-09
dc.identifier.citationLong , J , Nelson , T , Webb , S & Gee , K 2014 , ' A critical examination of indices of dynamic interaction for wildlife telemetry studies ' , Journal of Animal Ecology , vol. 83 , no. 5 , pp. 1216-1233 . https://doi.org/10.1111/1365-2656.12198en
dc.identifier.issn0021-8790
dc.identifier.otherPURE: 140558303
dc.identifier.otherPURE UUID: f9808e3d-79f2-452e-8739-26fab58c9a3d
dc.identifier.otherScopus: 84906315210
dc.identifier.otherWOS: 000340877700022
dc.identifier.urihttp://hdl.handle.net/10023/6131
dc.descriptionThe deer data used in this study were supported by funding from The Samuel Roberts Noble Foundation.en
dc.description.abstract1. Wildlife scientists continue to be interested in studying ways to quantify how the movements of animals are interdependent – dynamic interaction. While a number of applied studies of dynamic interaction exist, little is known about the comparative effectiveness and applicability of available methods used for quantifying interactions between animals. 2. We highlight the formulation, implementation and interpretation of a suite of eight currently available indices of dynamic interaction. Point- and path-based approaches are contrasted to demonstrate differences between methods and underlying assumptions on telemetry data. 3. Correlated and biased correlated random walks were simulated at a range of sampling resolutions to generate scenarios with dynamic interaction present and absent. We evaluate the effectiveness of each index at identifying different types of interactive behaviour at each sampling resolution. Each index is then applied to an empirical telemetry data set of three whitetailed deer (Odocoileus virginianus) dyads. 4. Results from the simulated data show that three indices of dynamic interaction reliant on statistical testing procedures are susceptible to Type I error, which increases at fine sampling resolutions. In the white-tailed deer examples, a recently developed index for quantifying local-level cohesive movement behaviour (the di index) provides revealing information on the presence of infrequent and varying interactions in space and time. 5. Point-based approaches implemented with finely sampled telemetry data overestimate the presence of interactions (Type I errors). Indices producing only a single global statistic (7 of the 8 indices) are unable to quantify infrequent and varying interactions through time. The quantification of infrequent and variable interactive behaviour has important implications for the spread of disease and the prevalence of social behaviour in wildlife. Guidelines are presented to inform researchers wishing to study dynamic interaction patterns in their own telemetry data sets. Finally, we make our code openly available, in the statistical software R, for computing each index of dynamic interaction presented herein
dc.format.extent18
dc.language.isoeng
dc.relation.ispartofJournal of Animal Ecologyen
dc.rights© 2014 The Authors. Journal of Animal Ecology © 2014 British Ecological Society, This is the accepted version of the following article: A critical examination of indices of dynamic interaction for wildlife telemetry studies Long, J., Nelson, T., Webb, S. & Gee, K. Sep 2014 In : Journal of Animal Ecology. 83, 5, p. 1216-1233, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/1365-2656.12198/pdfen
dc.subjectBiased random walken
dc.subjectContact rateen
dc.subjectGPS telemetryen
dc.subjectOdocoileus virginianusen
dc.subjectProximityen
dc.subjectSampling resolutionen
dc.subjectSimulationen
dc.subjectStatic interactionen
dc.subjectG Geography (General)en
dc.subjectBDCen
dc.subject.lccG1en
dc.titleA critical examination of indices of dynamic interaction for wildlife telemetry studiesen
dc.typeJournal articleen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.Geography & Sustainable Developmenten
dc.contributor.institutionUniversity of St Andrews.Centre for Geoinformaticsen
dc.contributor.institutionUniversity of St Andrews.Bell-Edwards Geographic Data Instituteen
dc.identifier.doihttps://doi.org/10.1111/1365-2656.12198
dc.description.statusPeer revieweden
dc.date.embargoedUntil2015-02-22


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