Identifying the best climatic predictors in ecology and evolution

Abstract

Summary1. 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.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 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. 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.

Associations between sex, age and species-specific climate sensitivity in migration

“…Average values are reported with ± SD. Climwin [48,49] is an R package created to determine a climate sensitivity window, or time period where climate most affects a biological response of interest. Climwin uses a sliding window approach, that is, the program varies the start and end dates of an interval of days to examine every possible window of climate.…”

“…Climwin uses a sliding window approach, that is, the program varies the start and end dates of an interval of days to examine every possible window of climate. The program then allows users to determine which of these climate windows best explains the occurrence of a response of interest, by ranking the windows via model goodness-of-fit (AICc weights) [48,49] . In our research, the climate data was the mean daily temperature, and the response of interest was the passage of a species at the bird observatory in southern Québec.…”

“…We used the R package Climwin [48,49] to determine species-specific periods of sensitivity to local temperature via a sliding window method [50][51][52][53]. Previous research used large and somewhat arbitrary time periods, usually the months during and preceding migration, therefore using a single fixed window to explain effects for many species [19,24,42,44].…”

Opposing responses to drought shape spatial population dynamics of declining grassland birds

“…Most research has focused on the influence of climate on species distributions across different spatial scales, but temporal dimensions of climate (e.g., resolution, extent and lags) are increasingly noted as critical elements that govern the ability to detect species responses (Adrian, Gerten, Huber, Wagner, & Schmidt, 2012;Bateman et al, 2016;Chan et al, 2016). Prior research suggests that there is no one-size-fits-all when considering temporal scale of variables as biologically relevant scales will be informed by the characteristics of the species and the dynamics of the ecosystem (Briga & Verhulst, 2015;van de Pol & Cockburn, 2011;van de Pol et al, 2016). In many cases, species may respond immediately to short-term weather events; however, lags between the onset of an environmental cue and ecological response constitute an important and often overlooked temporal dimension (van de Pol & Cockburn, 2011;van de Pol et al, 2016).…”

“…Prior research suggests that there is no one-size-fits-all when considering temporal scale of variables as biologically relevant scales will be informed by the characteristics of the species and the dynamics of the ecosystem (Briga & Verhulst, 2015;van de Pol & Cockburn, 2011;van de Pol et al, 2016). In many cases, species may respond immediately to short-term weather events; however, lags between the onset of an environmental cue and ecological response constitute an important and often overlooked temporal dimension (van de Pol & Cockburn, 2011;van de Pol et al, 2016).…”