Identifying the best climatic predictors in ecology and evolution

van de Pol, Martijn, Bailey, Liam D., Mclean, Nina, Rijsdijk, Laurie, Lawson, Callum R., and Brouwer, Lyanne (2016) Identifying the best climatic predictors in ecology and evolution. Methods in Ecology and Evolution, 7 (10). pp. 1246-1257.

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Ecologists and many evolutionary biologists relate the variation in physiological, behavioural, life-history, demographic, population and community traits to the variation in weather, a key environmental driver. However, identifying which weather variables (e.g. rain, temperature, El Niño index), over which time period (e.g. recent weather, spring or year-round weather) and in what ways (e.g. mean, threshold of temperature) they affect biological responses is by no means trivial, particularly when traits are expressed at different times among individuals. A literature review shows that a systematic approach for identifying weather signals is lacking and that the majority of studies select weather variables from a small number of competing hypotheses that are founded on unverified a priori assumptions. This is worrying because studies that investigate the nature of weather signals in detail suggest that signals can be complex. Using suboptimal or wrongly identified weather signals may lead to unreliable projections and management decisions. We propose a four-step approach that allows for more rigorous identification and quantification of weather signals (or any other predictor variable for which data are available at high temporal resolution), easily implementable with our new R package ‘climwin’. We compare our approach with conventional approaches and provide worked examples. Although our more exploratory approach also has some drawbacks, such as the risk of overfitting and bias that our simulations show can occur at low sample and effect sizes, these issues can be addressed with the right knowledge and tools. By developing both the methods to fit critical weather windows to a wide range of biological responses and the tools to validate them and determine sample size requirements, our approach facilitates the exploration and quantification of the biological effects of weather in a rigorous, replicable and comparable way, while also providing a benchmark performance to compare other approaches to.

Item ID: 69639
Item Type: Article (Research - C1)
ISSN: 2041-210X
Keywords: bias, climate change, climate sensitivity, cross-validation, false positive, precision, R package climwin, sample size, sliding window, weather
Copyright Information: © 2016 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Date Deposited: 26 Oct 2021 02:11
FoR Codes: 49 MATHEMATICAL SCIENCES > 4905 Statistics > 490502 Biostatistics @ 50%
31 BIOLOGICAL SCIENCES > 3104 Evolutionary biology > 310406 Evolutionary impacts of climate change @ 25%
31 BIOLOGICAL SCIENCES > 3103 Ecology > 310307 Population ecology @ 25%
SEO Codes: 19 ENVIRONMENTAL POLICY, CLIMATE CHANGE AND NATURAL HAZARDS > 1905 Understanding climate change > 190501 Climate change models @ 100%
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