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  • Quantitative approaches in climate change ecology

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    BrownPUB739.pdf (238.9Kb)
    Author(s)
    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
    Griffith University Author(s)
    Brown, Chris J.
    Year published
    2011
    Metadata
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    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 ...
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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.
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    Journal Title
    Global Change Biology
    Volume
    17
    DOI
    https://doi.org/10.1111/j.1365-2486.2011.02531.x
    Copyright Statement
    © The Author(s) 2011. This is an Open Access article distributed under the terms of the Creative Commons Attribution 3.0 Unported (CC BY 3.0) License (https://creativecommons.org/licenses/by/3.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
    Subject
    Ecological Applications not elsewhere classified
    Environmental Sciences
    Biological Sciences
    Publication URI
    http://hdl.handle.net/10072/173581
    Collection
    • Journal articles

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