Cue identification in phenology: A case study of the predictive performance of current statistical tools

Abstract

Changes in the timing of life‐history events (phenology) are a widespread consequence of climate change. Predicting population resilience requires knowledge of how phenology is likely to change over time, which can be gained by identifying the specific environmental cues that drive phenological events. Cue identification is often achieved with statistical testing of candidate cues. As the number of methods used to generate predictions increases, assessing the predictive accuracy of different approaches has become necessary. This study aims to (a) provide an empirical illustration of the predictive ability of five commonly applied statistical methods for cue identification (absolute and relative sliding time window analyses, penalized signal regression, climate sensitivity profiles and a growing degree‐day model) and (b) discuss approaches for implementing cue identification methods in different systems. Using a dataset of mean clutch initiation timing in wild great tits (Parus major), we explored how the days of the year identified as most important, and the aggregate statistic identified as a cue, differed between statistical methods and with respect to the time span of data used. Each method's predictive capacity was tested using cross‐validation and assessed for robustness to varying sample size. We show that the identified critical time window of cue sensitivity was consistent across four of the five methods. The accuracy and precision of predictions differed by method with penalized signal regression resulting in the most accurate and most precise predictions in our case. Accuracy was maximal for near‐future predictions and showed a relationship with time. The difference between predictions and observations systematically shifted across the study from preceding observations to lagging. This temporal trend in prediction error suggests that the current statistical tools either fail to capture a key component of the cue–phenology relationship, or the relationship itself is changing through time in our system. These two influences need to be teased apart if we are to generate realistic predictions of phenological change. We recommend future phenological studies to challenge the idea of a static cue–phenology relationship and should cross‐validate results across multiple time periods.

Best environmental predictors of breeding phenology differ with elevation in a common woodland bird species

“…In addition, our results concur with other studies on woodland passerines indicating that air temperature has a strong causal effect on egg‐laying dates (Pereyra, 2011; Phillimore, Leech, Pearce‐Higgins, & Hadfield, 2016; Shutt, Burgess, et al, 2019; Shutt, Cabello, et al, 2019; Simmonds, Cole, & Sheldon, 2019; Visser, Holleman, & Caro, 2009). Our study improves the understanding of mechanisms involved in the determining factors of bird breeding phenology by showing that air temperature has a negative indirect effect through plant phenology or snow melt‐out date, depending on the elevation.…”

Phenological asynchrony: a ticking time‐bomb for seemingly stable populations?

“…The phenological cues for each species were identified using an absolute sliding time window approach implemented in the package ‘ climwin’ (Bailey and van de Pol, 2016; van de Pol et al ., 2016) in R. This method would not identify a true cue, but a proxy for predictive purposes; the true cues may be more complex and could even change over time (Buse et al ., 1999; Simmonds et al ., 2019a). But for the purposes of this study, we made use of one of the best tools we have currently available (Simmonds et al ., 2019a). An exhaustive search of windows was performed with a reference date of 20th May for both species, searching for a maximum length window of 365 days, with all variables standardised to z‐scores.…”

“…A detailed data description is present in the Supporting Information from Simmonds et al . (2019b, http://www.oikosjournal.org/appendix/oik-06985). The focal phenological trait in our model was hatch date.…”

Cascading effects of temperature alterations on trophic ecology of European grayling (Thymallus thymallus)

“…The month variable was excluded from this part of the analysis to avoid covariation with temperature 8 . Appearance of each group of organisms constituting diet of grayling may be affected by the ambient air temperature within different periods (varying durations and time lags) 49 , therefore series of sliding window analyses 50,51 were conducted on the biological and climatic data from the years 2005–2016 in order to identify the optimal time window of the air temperature predictor (see Supplementary Materials for details).…”