An advance in phenology—the timing and duration of biological events—of numerous species has been widely recorded and linked to climate change. Phenological variability within a population is widespread in nature and arises from underlying differences in performance, metabolism or behavior among individuals. The “pace-of-life syndrome” explains differences in performance by characterizing slow and fast individual strategies within a population, but has yet to be explored under a phenological context. We hypothesized that taking into account thermal pace-of-life strategies in development rate models would improve phenological predictions in ectotherms. To test our hypothesis, we reared populations of the quinoa moth (Copitarsia incommoda) at several temperatures (4619 phenological observations), and by individually following each individuals, we characterized the proportion of slow and fast individuals within the populations. We then integrated the observed thermal pace-of-life strategies in development into a predictive phenological model through the transformation of the commonly used thermal performance curve into a thermal performance probability. The predictive power of our models were evaluated by confronting the predicted phenology with the observed one, through a cross-validation process. We finally explored our models by generating data for a virtual species with various life-history traits.
Results/Conclusions
Our rearing experiments revealed the occurrence of thermal pace-of-life strategies in development rates that accounted for 37% to 59% of the populations. Slow and fast individuals occurred for every rearing temperatures at a similar proportion. By individually following every individuals, our study highlighted strategy shifts across life stages. These shifts from slow to fast or fast to slow strategies occurred without a clear and repeatable pattern. Nevertheless, implementing the observed strategies within our individual-based model greatly improved phenological predictions in the quinoa moth, with an average overlapping of 64% between the observed and predicted distributions of emergence times. Moreover, the model performed better when incorporating slow-fast strategies than when accounting for intra-populational variability alone. The model exploration with generated data suggested that ectotherm species with a higher number of life stages and with a high proportion of slow and/or fast individuals should exhibit a wider variance of populational phenology, resulting in a longer potential window of interaction with other species. Together, these lines of evidence highlight the importance of inter-individual differences in phenological predictions, and illustrates the suitability of the pace-of-life framework to integrate these differences.