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dc.contributor.authorEngen, Steinar
dc.contributor.authorSæther, Bernt-Erik
dc.date.accessioned2020-02-10T15:20:32Z
dc.date.available2020-02-10T15:20:32Z
dc.date.created2019-07-02T20:01:32Z
dc.date.issued2019
dc.identifier.citationTheoretical Population Biology. 2019, 127 133-143.nb_NO
dc.identifier.issn0040-5809
dc.identifier.urihttp://hdl.handle.net/11250/2640865
dc.description.abstractSpatial differentiation of phenotypes is assumed to be determined by a combination of fluctuating selection producing adaptations to the local environment and a homogenizing effect of migration. We present a model with density regulation and a density-dependent fitness function affected by spatio-temporal variability in population size driven by spatially correlated fluctuations in the environment causing fluctuating - and -selection on a set of traits. We derive the variance in local mean phenotypes and show how the spatial scales of the correlations between the components of the mean phenotype depend on ecological parameters. The degree of spatial differentiation of phenotypes is strongly influenced by parameters affecting ecological dynamics. In the case of a one-dimensional character the geographical scale of variation in the mean phenotype has simply an additive term corresponding to the Moran effect in population dynamics as well as a term determined by dispersal and strength of local selection. The degree of phenotypic differentiation increases with decreasing strength of local density dependence and decreasing strength of local selection. These results imply that the form of the spatial autocorrelation function can reveal important information about ecological and evolutionary processes causing phenotypic differentiation in space.nb_NO
dc.language.isoengnb_NO
dc.publisherElseviernb_NO
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleEcological dynamics and large scale phenotypic differentiation in density-dependent populationsnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.pagenumber133-143nb_NO
dc.source.volume127nb_NO
dc.source.journalTheoretical Population Biologynb_NO
dc.identifier.doi10.1016/j.tpb.2019.04.005
dc.identifier.cristin1709597
dc.relation.projectNorges forskningsråd: 267511nb_NO
dc.relation.projectNorges forskningsråd: 274930nb_NO
dc.relation.projectNorges forskningsråd: 23257nb_NO
dc.description.localcode© 2019 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)nb_NO
cristin.unitcode194,63,15,0
cristin.unitcode194,66,10,0
cristin.unitnameInstitutt for matematiske fag
cristin.unitnameInstitutt for biologi
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal