Quantitative approaches in climate change ecology

Brown, Christopher J., Schoeman, David S., Sydeman, William J., Brander, Keith, Buckley, Lauren B., Burrows, Michael, Duarte, Carlos M., Moore, Pippa J., Pandolfi, John M., Poloczanska, Elvira, Venables, William and Richardson, Anthony J. (2011) Quantitative approaches in climate change ecology. Global Change Biology, 17 12: 3697-3713. doi:10.1111/j.1365-2486.2011.02531.x

Author Brown, Christopher J.
Schoeman, David S.
Sydeman, William J.
Brander, Keith
Buckley, Lauren B.
Burrows, Michael
Duarte, Carlos M.
Moore, Pippa J.
Pandolfi, John M.
Poloczanska, Elvira
Venables, William
Richardson, Anthony J.
Title Quantitative approaches in climate change ecology
Journal name Global Change Biology   Check publisher's open access policy
ISSN 1354-1013
Publication date 2011-12
Sub-type Critical review of research, literature review, critical commentary
DOI 10.1111/j.1365-2486.2011.02531.x
Volume 17
Issue 12
Start page 3697
End page 3713
Total pages 17
Place of publication Oxford, United Kingdom
Publisher Wiley-Blackwell Publishing
Collection year 2012
Language eng
Formatted 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.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Critical review of research, literature review, critical commentary
Collections: School of Mathematics and Physics
Official 2012 Collection
School of Biological Sciences Publications
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Citation counts: TR Web of Science Citation Count  Cited 57 times in Thomson Reuters Web of Science Article | Citations
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