Show simple item record Christopher J. en_US David S. en_US William J. en_US Keith en_US Lauren B. en_US Michael en_US Carlos M. en_US P. J. en_US John M. en_US Elvira en_US William en_US Anthony J. en_US 2012-01-16T09:23:33Z 2012-01-16T09:23:33Z 2011-12-01 en_US
dc.identifier en_US
dc.identifier.citation Brown , C J , Schoeman , D S , Sydeman , W J , Brander , K , Buckley , L B , Burrows , M , Duarte , C M , Moore , P J , Pandolfi , J M , Poloczanska , E , Venables , W & Richardson , A J 2011 , ' Quantitative approaches in climate change ecology ' Global Change Biology , vol 17 , no. 12 , pp. 3697-3713 . , 10.1111/j.1365-2486.2011.02531.x en_US
dc.identifier.other PURE: 174828 en_US
dc.identifier.other dspace: 2160/7739 en_US
dc.description.abstract Contemporary 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. en_US
dc.format.extent 17 en_US
dc.relation.ispartof Global Change Biology en_US
dc.title Quantitative approaches in climate change ecology en_US
dc.contributor.pbl Institute of Biological, Environmental and Rural Sciences en_US

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