Bayesian methods for comparing species physiological and ecological response curves

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datacite.alternateIdentifier.citationEcological informatics, 34, 43-35, 2016
datacite.alternateIdentifier.doi10.1016/j.ecoinf.2016.03.001
datacite.alternateIdentifier.issn1574-9541
datacite.creatorAshcroft, Michael B.
datacite.creatorCasanova-Katny, Angelica
datacite.creatorMengersen, Kerrie
datacite.creatorRosenstiel, Todd N.
datacite.creatorTurnbull, Johanna D.
datacite.creatorWasley, Jane
datacite.creatorWaterman, Melinda J.
datacite.creatorZuniga, Gustavo E.
datacite.creatorRobinson, Sharon A.
datacite.date2016
datacite.rightsRegistro bibliográfico
datacite.subjectAntarctic moss
datacite.subjectCommunity ecology
datacite.subjectNiche partitioning
datacite.subjectPhotosynthesis
datacite.subjectPhysiological response
datacite.subjectUncertainty
datacite.titleBayesian methods for comparing species physiological and ecological response curves
dc.contributor.authorCASANOVA KATNY, MARIA ANGELICA
dc.description.abstractMany ecological questions require information on species' optimal conditions or critical limits along environmental gradients. These attributes can be compared to answer questions on niche partitioning, species coexistence and niche conservatism. However, these comparisons are unconvincing when existing methods do not quantify the uncertainty in the attributes or rely on assumptions about the shape of species' responses to the environmental gradient. The aim of this study was to develop a model to quantify the uncertainty in the attributes of species response curves and allow them to be tested for substantive differences without making assumptions about the shape of the responses. We developed a model that used Bayesian penalised splines to produce and compare response curves for any two given species. These splines allow the data to determine the shape of the response curves rather than making a priori assumptions. The models were implemented using the R2OpenBUGS package for R, which uses Markov Chain Monte Carlo simulation to repetitively fit alternative response curves to the data. As each iteration produces a different curve that varies in optima, niche breadth and limits, the model estimates the uncertainty in each of these attributes and the probability that the two curves are different. The models were tested using two datasets of mosses from Antarctica. Both datasets had a high degree of scatter, which is typical of ecological research. This noise resulted in considerable uncertainty in the optima and limits of species response curves, but substantive differences were found. Schistidium antarctici was found to inhabit wetter habitats than Ceratodon purpureus, and Polytrichastrum alpinum had a lower optimal temperature for photosynthesis than Chorisodontium aciphyllum under high light conditions. Our study highlights the importance of considering uncertainty in physiological optima and other attributes of species response curves. We found that apparent differences in optima of 7.5 degrees C were not necessarily substantive when dealing with noisy ecological data, and it is necessary to consider the uncertainty in attributes when comparing the curves for different species. The model introduced here could increase the robustness of research on niche partitioning, species coexistence and niche conservatism. (C) 2016 Elsevier B.V. All rights reserved.
dc.description.ia_keywordspecies, curves, response, attributes, uncertainty, niche, ecological
dc.identifier.issn1878-0512
dc.identifier.urihttps://repositoriodigital.uct.cl/handle/10925/3998
dc.language.isoen
dc.publisherElsevier BV
dc.relationinstname: ANID
dc.relationreponame: Repositorio Digital RI2.0
dc.rights.driverinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/
dc.sourceEcological informatics
dc.subject.ia_oecd1nCiencias Naturales
dc.subject.ia_oecd2nCiencias Biológicas
dc.subject.ia_oecd3nEcología
dc.type.driverinfo:eu-repo/semantics/article
dc.type.driverhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.type.openaireinfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublication
oaire.citationEdition2016
oaire.citationEndPage43
oaire.citationStartPage35
oaire.citationTitleEcological informatics
oaire.citationVolume34
oaire.fundingReferenceAustralian Research Council DP110101714
oaire.fundingReferenceAustralian Antarctic Science AAS1313, AAS4046
oaire.fundingReferenceANID FONDECYT 1120895, 1140189 (Regular)
oaire.fundingReferenceINACH FR-0112
oaire.fundingReferenceUSACH VRIDEI, CEDENNA, Proyectos Basales
oaire.fundingReferenceNSF 1341742, 1258225
oaire.fundingReferenceARC DP
oaire.fundingReferenceCOE
oaire.fundingReferenceQUT Institute for Future Environments
oaire.fundingReferenceAustralian Postgraduate Awards
oaire.fundingReferenceAINSE Awards
oaire.licenseConditionCopyright © 2016 Elsevier B.V.
oaire.resourceTypeArtículo
oaire.resourceType.enArticle
relation.isAuthorOfPublicationa9399b7c-ca2e-4330-b761-690bd7ef26d8
relation.isAuthorOfPublication.latestForDiscoverya9399b7c-ca2e-4330-b761-690bd7ef26d8
uct.catalogadorjvu
uct.comunidadRecursos Naturalesen_US
uct.departamentoDepartamento de Ciencias Ambientales
uct.facultadFacultad de Recursos Naturales
uct.indizacionScience Citation Index Expanded - SCIE
uct.indizacionScopus
uct.indizacionCAB Abstracts
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