Phenological change is widely regarded as an important biological indicator of contemporary climate change. Increasing global temperatures have been identified as driving changes in the timing of key life-cycle events across a wide range of organisms. Estimates of phenological change are often based on single measure of phenology, such as the date of the first flower to bloom or the first migrant of the season to arrive. However, this approach is unlikely to be representative of the population as a whole and ignores important information regarding, for example, the duration of the phenomenon, its temporal skew, and its shape. A method of analysis that accounts for the variation in the response of individuals and focusses on the population-level dynamics provides a more complete picture of the extent of phenological change. This thesis presents a novel method of analysis that quantifies three essential aspects (or parameters) of the phenological time distribution. It describes an R package produced to automate the fitting of the model to varied phenological datasets and offer researchers a tool to facilitate the standardised comparison of phenological data. The utility of the model is explored using three detailed phenological datasets. The thorough analysis of the germination response of three high-elevation, perennial plant species to temperature demonstrates the accuracy of the model and its ability to quantify subtle variation in the phenology of three closely related species. The capacity of all three parameters to describe the effect of established temperature-mediated processes also demonstrates their biological interpretability. Investigation into the effects of climate change on marine plankton over several decades reveals that successive trophic levels (or functional groups) are responding differently to changes in sea surface temperature. The advancement of each functional group’s bloom phenology is shown to result from the modification of different parameters of the phenological time distribution. Analysis of the parameters reveals that different aspects of sea surface temperature are driving the modification of plankton functional group bloom phenology, both directly and indirectly. Finally, examination of the famous Japanese cherry tree flowering records shows that the novel method of phenological analysis can reliably estimate phenological responses to specific environmental stimuli using first occurrence data occurring along an environmental gradient. A method of phenological analysis that characterises the diversity of the phenological response and quantifies the influence that biological and environmental factors have on the shape of the time distribution provides a detailed understanding of the extent, and potential driving mechanisms, of phenological change.

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Creative Commons Attribution-Share Alike 4.0 International License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.