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dc.contributor.authorDe Cáceres, Miquel
dc.contributor.authorColl Mir, Lluís
dc.contributor.authorLegendre, Pierre
dc.contributor.authorAllen, Robert B.
dc.contributor.authorWiser, Susan
dc.contributor.authorFortin, Marie-Josée
dc.contributor.authorCondit, Richard
dc.contributor.authorHubbell, Stephen P.
dc.date.accessioned2019-10-07T12:48:00Z
dc.date.available2019-10-07T12:48:00Z
dc.date.issued2019
dc.identifier.issn0012-9615
dc.identifier.urihttp://hdl.handle.net/10459.1/66748
dc.description.abstractEcologists have long been interested in how communities change over time. Addressing questions about community dynamics requires ways of representing and comparing the variety of dynamics observed across space. Until now, most analytical frameworks have been based on the comparison of synchronous observations across sites and between repeated surveys. An alternative perspective considers community dynamics as trajectories in a chosen space of community resemblance and utilizes trajectories as objects to be analyzed and compared using their geometry. While methods that take this second perspective exist, for example to test for particular trajectory shapes, there is a need for formal analytical frameworks that fully develop the potential of this approach. By adapting concepts and procedures used for the analysis of spatial trajectories, we present a framework for describing and comparing community trajectories. A key element of our contribution is the means to assess the geometric resemblance between trajectories, which allows users to describe, quantify, and analyze variation in community dynamics. We illustrate the behavior of our framework using simulated data and two spatiotemporal community data sets differing in the community properties of interest (species composition vs. size distribution of individuals). We conclude by evaluating the advantages and limitations of our community trajectory analysis framework, highlighting its broad domain of application and anticipating potential extensions.
dc.description.sponsorshipM. De Cáceres was supported by projects CGL2014-59742-C2-2-R and CGL2017-89149-C2-2-R (Spanish Ministry of Economy and Competitiveness) and by a Spanish “Ramon y Cajal” fellowship (RYC-2012-11109). S. K. Wiser was supported by the Strategic Science Investment Fund of the New Zealand Ministry of Business, Innovation and Employment’s Science and Innovation Group.
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherEcological Society of America
dc.relationMINECO/PN2013-2016/CGL2014-59742-C2-2-R
dc.relationMINECO/PN2017-2020/CGL2017-89149-C2-2-R
dc.relation.isformatofReproducció del document publicat a: https://doi.org/10.1002/ecm.1350
dc.relation.ispartofEcological Monographs, 2019, vol. 89, num. 2, e01350
dc.rights(c) Ecological Society of America, 2019
dc.subjectBeta diversity
dc.subjectForest dynamics
dc.subjectStand size structure
dc.subjectTrajectory data mining
dc.titleTrajectory analysis in community ecology
dc.typeinfo:eu-repo/semantics/article
dc.date.updated2019-10-07T12:48:02Z
dc.identifier.idgrec028887
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.identifier.doihttps://doi.org/10.1002/ecm.1350


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