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Quantitative approaches in climate change ecology
Article first published online: 19 SEP 2011
© 2011 Blackwell Publishing Ltd
Global Change Biology
Volume 17, Issue 12, pages 3697–3713, December 2011
How to Cite
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. and Richardson, A. J. (2011), Quantitative approaches in climate change ecology. Global Change Biology, 17: 3697–3713. doi: 10.1111/j.1365-2486.2011.02531.x
- Issue published online: 17 OCT 2011
- Article first published online: 19 SEP 2011
- Accepted manuscript online: 24 AUG 2011 02:35AM EST
- Manuscript Accepted: 11 AUG 2011
- Manuscript Revised: 10 AUG 2011
- Manuscript Received: 15 MAR 2011
- National Center for Ecological Analysis and Synthesis
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.