Brown, Christopher J.; Schoeman, David S.; Sydeman, William J.; Brander, Keith; Buckley, Lauren B.; Burrows, Michael T.; Duarte, Carlos M.; Moore, Pippa J.; Pandolfi, John M.; Poloczanska, Elvira; Venables, William; Richardson, Anthony J. 2011. Quantitative approaches in climate change ecology. Global Change Biology. Vol: 17(12). Pages 3697-3713. (Abstract)
Contemporary impacts of anthropogenic climate change on ecosystems are increasingly being recognized. Document- ing the extent of these impacts requires quantitative tools for analyses of ecological observations to distinguish cli- mate 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 pro- vide 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 sev- eral 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 met- rics. We provide a list of issues that need to be addressed to make inferences more defensible, including the consider- ation 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 auto- correlation 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.