Identifying the best climatic predictors in ecology and evolution

TY - JOUR

T1 - Identifying the best climatic predictors in ecology and evolution

AU - Van de Pol, M.

AU - Bailey, Liam

AU - McLean, Nina

AU - Rijsdijk, Laurie

AU - Lawson, C.R.

AU - Brouwer, L.

N1 - 6109, AnE; Data Archiving: data archived: The Chaffinch data set and weather data are available as part of R package climwin. The R code used to generate the simulation data and results are available as part of online Appendix S4.

PY - 2016

Y1 - 2016

N2 - 1.Ecologists and many evolutionary biologists relate variation in physiological, behavioral, life-history, demographic, population and community traits to 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.2.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.3.We propose a four-step approach which allows for more rigorous identification and quantification of weather signals (or any other predictor variable for which data is available at high temporal resolution), easily implementable with our new R package ‘climwin’. We compare our approach with conventional approaches and provide worked examples.4.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.5.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.

AB - 1.Ecologists and many evolutionary biologists relate variation in physiological, behavioral, life-history, demographic, population and community traits to 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.2.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.3.We propose a four-step approach which allows for more rigorous identification and quantification of weather signals (or any other predictor variable for which data is available at high temporal resolution), easily implementable with our new R package ‘climwin’. We compare our approach with conventional approaches and provide worked examples.4.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.5.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.

KW - international

U2 - 10.1111/2041-210X.12590

DO - 10.1111/2041-210X.12590

M3 - Article

SN - 2041-210X

VL - 7

SP - 1246

EP - 1257

JO - Methods in Ecology and Evolution

JF - Methods in Ecology and Evolution

IS - 10

ER -