Data from: Resource dispersion promotes kin selection in a solitary predator

Resource dispersion or kin selection are commonly used to explain animal spatial organisation. Despite this, studies examining how these factors interact in wild populations are extremely rare. We used 16 years of individual-level spatial and genetic data to disentangle how resources and relatedness influence spatial organisation of a solitary predator, the Eurasian lynx (Lynx lynx). As expected, space-use overlap between neighbouring individuals increased when food resources were heterogeneous and unpredictably distributed (resource dispersion) or when neighbours were closely related (kin selection). However, these patterns were highly dependent on each other. Increased spatial overlap was restricted to mother-daughter dyads, with this effect only occurring in areas and during seasons when prey were clumped and irregularly distributed in the landscape. Additionally, full-siblings with similar levels of genetic relatedness did not show these patterns, suggesting that kin selection is mediated through mother-daughter recognition, and is only beneficial under specific resource dispersion circumstances. Our results provide key insights into the flexibility of spatial organisation of solitary animals, and clearly show the importance of considering the interaction between resources and kinship when assessing animal space use patterns.

Identifier
DOI https://doi.org/10.5061/dryad.82rv8q9
PID https://nbn-resolving.org/urn:nbn:nl:ui:13-8w-zr9i
Metadata Access https://easy.dans.knaw.nl/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:easy.dans.knaw.nl:easy-dataset:116451
Provenance
Creator Aronsson, Malin; Åkesson, Mikael; Low, Matthew; Persson, Jens; Andrén, Henrik
Publisher Data Archiving and Networked Services (DANS)
Publication Year 2018
Rights info:eu-repo/semantics/openAccess; License: http://creativecommons.org/publicdomain/zero/1.0; http://creativecommons.org/publicdomain/zero/1.0
OpenAccess true
Representation
Resource Type Dataset
Discipline Life Sciences; Medicine