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

• Published in: The Journal of animal ecology Vol. 88; no. 9; pp. 1428 - 1440
• Main Authors: Simmonds, Emily G., Cole, Ella F., Sheldon, Ben C., Bouwhuis, Sandra
• Format: Journal Article
• Language: English
• Published: England Blackwell Publishing Ltd 01.09.2019; John Wiley and Sons Inc
• Subjects: Accuracy; Animals; Climate Change; climate sensitivity profile; cue identification; Cues; Empirical analysis; GAM; great tits; growing degree days; Identification; Identification methods; Life Histories; penalized signal regression; Performance prediction; Phenology; Regression analysis; Seasons; Sensitivity; Sensitivity analysis; sliding windows; Statistical analysis; Statistical methods; Statistical prediction; Statistics; Windows (intervals); GAM; great tits; growing degree days; penalized signal regression; phenology; cue identification; climate sensitivity profile; sliding windows
• ISSN: 0021-8790; 1365-2656
• DOI: 10.1111/1365-2656.13038

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. The authors give a case study of the predictive performance of five different phenological cue identification methods. They identify a temporal bias in prediction error, suggesting 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. Photo credit: Emily G. Simmonds.