Phenology, the timing of biological events, is a key determinant of lifetime fitness, species interactions, and ecosystem functioning. Developing useful measures of phenology is therefore important, especially for predicting how populations and communities will respond to evolutionary or environmental change. Traditionally, phenology is measured as one or a series of discrete events. For example, if one were interested in flowering phenology, a typical measure for an individual plant would be the day of first flowering. While such discrete measures can be useful, they often describe only a small fraction of the relevant phenological activity. For instance, individual plants can have multiple flowers and so a first flowering day may not be sufficient for inferring lifetime fitness or interactions with pollinators. Similarly, the first flowering day within a population may not be useful for predicting how the entire population will respond to climate change.
In response to growing interest in alternative approaches for measuring plant phenology, we propose a flexible modeling framework that describes phenology across a growing season as a distribution rather than as a series of discrete events. We test our approach using a unique, 5-year dataset on winegrape phenology from California.
Results/Conclusions
The modeling framework describes the full phenology of an individual, population, or locality across a growing season using probability distributions that can be parameterized using either frequentist or Bayesian inference. The resulting parameter estimates are interpretable and biologically meaningful. Further, they allow for straightforward comparisons of phenological activity between individuals or across space/time.
Using our high resolution winegrape data, we parameterized models built using this framework, finding that they effectively described phenology within individual plants, including bud burst activity and the ripening of grapes. We also found that they accurately described phenology at the population level, allowing us to compare phenological activity across winegrape varieties. Our results highlight how more comprehensive measures of plant phenology can enhance our understanding of how phenology varies across space and time.