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SummaryClimate change causes both temporal (e.g. advancing spring phenology) and geographic (e.g. range expansion poleward) species shifts, which affect the photoperiod experienced at critical developmental stages (‘experienced photoperiod’). As photoperiod is a common trigger of seasonal biological responses – affecting woody plant spring phenology in 87% of reviewed studies that manipulated photoperiod – shifts in experienced photoperiod may have important implications for future plant distributions and fitness. However, photoperiod has not been a focus of climate change forecasting to date, especially for early‐season (‘spring’) events, often assumed to be driven by temperature. Synthesizing published studies, we find that impacts on experienced photoperiod from temporal shifts could be orders of magnitude larger than from spatial shifts (1.6 h of change for expected temporal vs 1 min for latitudinal shifts). Incorporating these effects into forecasts is possible by leveraging existing experimental data; we show that results from growth chamber experiments on woody plants often have data relevant for climate change impacts, and suggest that shifts in experienced photoperiod may increasingly constrain responses to additional warming. Further, combining modeling approaches and empirical work on when, where and how much photoperiod affects phenology could rapidly advance our understanding and predictions of future spatio‐temporal shifts from climate change.

AbstractTemperature sensitivity—the magnitude of a biological response per °C—is a fundamental concept across scientific disciplines, especially biology, where temperature determines the rate of many plant, animal and ecosystem processes. Recently, a growing body of literature in global change biology has found temperature sensitivities decline as temperatures rise (Fuet al., 2015; Güsewell et al., 2017; Piao et al., 2017; Chen et al., 2019; Dai et al., 2019). Such observations have been used to suggest climate change is reshaping biological processes, with major implications for forecasts of future change. Here we present a simple alternative explanation for observed declining sensitivities: the use of linear models to estimate non-linear temperature responses. We show how linear estimates of sensitivities will appear to decline with warming for events that occur after a cumulative thermal threshold is met—a common model for many biological events. Corrections for the non-linearity of temperature response in simulated data and long-term phenological data from Europe remove the apparent decline. Our results show that rising temperatures combined with linear estimates based on calendar time produce observations of declining sensitivity—without any shift in the underlying biology. Current methods may thus undermine efforts to identify when and how warming will reshape biological processes.Significance statementRecently a growing body of literature has observed declining temperature sensitivities of plant leafout and other events with higher temperatures. Such results suggest that climate change is already reshaping fundamental biological processes. These temperature sensitivities are often estimated as the magnitude of a biological response per °C from linear regression. The underlying model for many events—that a critical threshold of warmth must be reached to trigger the event—however, is non-linear. We show that this mismatch between the statistical and biological models can produce the illusion of declining sensitivities with warming using current methods. We suggest simple alternative approaches that can better identify when and how warming will reshape biological processes.

SummaryPhenology is a major component of an organism's fitness. While individual phenological events affect fitness, there is growing evidence to suggest that the relationship between events could be equally or more important. This could explain why temperate deciduous woody plants exhibit considerable variation in the order of reproductive and vegetative events, or flower–leaf sequences (FLSs). There is evidence to suggest that FLSs may be adaptive, with several competing hypotheses to explain their function. Here, we advance existing hypotheses with a new framework that accounts for quantitative FLS variation at multiple taxonomic scales using case studies from temperate forests. Our inquiry provides several major insights towards a better understanding of FLS variation. First, we show that support for FLS hypotheses is sensitive to how FLSs are defined, with quantitative definitions being the most useful for robust hypothesis testing. Second, we demonstrate that concurrent support for multiple hypotheses should be the starting point for future FLS analyses. Finally, we highlight how adopting a quantitative, intraspecific approach generates new avenues for evaluating fitness consequences of FLS variation and provides cascading benefits to improving predictions of how climate change will alter FLSs and thereby reshape plant communities and ecosystems.