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dc.contributor.authorBrown, Christopher J
dc.contributor.authorSchoeman, David S
dc.contributor.authorSydeman, William J
dc.contributor.authorBrander, Keith
dc.contributor.authorBuckley, Lauren B
dc.contributor.authorBurrows, Michael
dc.contributor.authorDuarte, Carlos M
dc.contributor.authorMoore, Pippa J
dc.contributor.authorPandolfi, John M
dc.contributor.authorPoloczanska, Elvira
dc.contributor.authorVenables, William
dc.contributor.authorRichardson, Anthony J
dc.date.accessioned2018-01-05T04:30:02Z
dc.date.available2018-01-05T04:30:02Z
dc.date.issued2011
dc.identifier.issn1354-1013
dc.identifier.doi10.1111/j.1365-2486.2011.02531.x
dc.identifier.urihttp://hdl.handle.net/10072/173581
dc.description.abstractContemporary impacts of anthropogenic climate change on ecosystems are increasingly being recognized. Documenting the extent of these impacts requires quantitative tools for analyses of ecological observations to distinguish climate impacts in noisy data and to understand interactions between climate variability and other drivers of change. To assist the development of reliable statistical approaches, we review the marine climate change literature and provide suggestions for quantitative approaches in climate change ecology. We compiled 267 peer-reviewed articles that examined relationships between climate change and marine ecological variables. Of the articles with time series data (n = 186), 75% used statistics to test for a dependency of ecological variables on climate variables. We identified several common weaknesses in statistical approaches, including marginalizing other important non-climate drivers of change, ignoring temporal and spatial autocorrelation, averaging across spatial patterns and not reporting key metrics. We provide a list of issues that need to be addressed to make inferences more defensible, including the consideration of (i) data limitations and the comparability of data sets; (ii) alternative mechanisms for change; (iii) appropriate response variables; (iv) a suitable model for the process under study; (v) temporal autocorrelation; (vi) spatial autocorrelation and patterns; and (vii) the reporting of rates of change. While the focus of our review was marine studies, these suggestions are equally applicable to terrestrial studies. Consideration of these suggestions will help advance global knowledge of climate impacts and understanding of the processes driving ecological change.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherBlackwell Publishing
dc.relation.ispartofpagefrom3697
dc.relation.ispartofpageto3713
dc.relation.ispartofjournalGlobal Change Biology
dc.relation.ispartofvolume17
dc.subject.fieldofresearchEcological Applications not elsewhere classified
dc.subject.fieldofresearchEnvironmental Sciences
dc.subject.fieldofresearchBiological Sciences
dc.subject.fieldofresearchcode050199
dc.subject.fieldofresearchcode05
dc.subject.fieldofresearchcode06
dc.titleQuantitative approaches in climate change ecology
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttps://creativecommons.org/licenses/by/3.0/
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© The Author(s) 2011. This is an Open Access article distributed under the terms of the Creative Commons Attribution 3.0 Unported (CC BY 3.0) License (https://creativecommons.org/licenses/by/3.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorBrown, Chris J.


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