Quantitative approaches in climate change ecology

Publication: Research - peer-reviewJournal article – Annual report year: 2011

  • Author: Brown, Christopher J.

    University of Queensland

  • Author: Schoeman, David S.

    University of Ulster

  • Author: Sydeman, William J.

    Farallon Institute for Advanced Ecosystem Research

  • Author: Brander, Keith

    Section for Ocean Ecology and Climate, National Institute of Aquatic Resources, Technical University of Denmark, Kemitorvet, 2800, Kgs. Lyngby, Denmark

  • Author: Buckley, Lauren B.

    University of North Carolina

  • Author: Burrows, Michael

    Scottish Association for Marine Science

  • Author: Duarte, Carlos M.

    University of Western Australia

  • Author: Moore, Pippa J.

    Edith Cowan University

  • Author: Pandolfi, John M.

    University of Queensland

  • Author: Poloczanska, Elvira

    CSIRO Marine and Atmospheric Research

  • Author: Venables, William

    Commonwealth Scientific and Industrial Research Organisation

  • Author: Richardson, Anthony J.

    University of Queensland

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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.
Original languageEnglish
JournalGlobal Change Biology
Issue number12
Pages (from-to)3697-3713
StatePublished - 2011
CitationsWeb of Science® Times Cited: 75
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ID: 5895870