Abstract
Phenological models are widely used to estimate the influence of weather and climate on plant development. The goodness of fit of phenological models often is assessed by considering the root-mean-square error (RMSE) between observed and predicted dates. However, the spatial patterns and temporal trends derived from models with similar RMSE may vary considerably. In this paper, we analyse and compare patterns and trends from a suite of temperature-based phenological models, namely extended spring indices, thermal time and photothermal time models. These models were first calibrated using lilac leaf onset observations for the period 1961–1994. Next, volunteered phenological observations and daily gridded temperature data were used to validate the models. After that, the two most accurate models were used to evaluate the patterns and trends of leaf onset for the conterminous US over the period 2000–2014. Our results show that the RMSEs of extended spring indices and thermal time models are similar and about 2 days lower than those produced by the other models. Yet the dates of leaf out produced by each of the models differ by up to 11 days, and the trends differ by up to a week per decade. The results from the histograms and difference maps show that the statistical significance of these trends strongly depends on the type of model applied. Therefore, further work should focus on the development of metrics that can quantify the difference between patterns and trends derived from spatially explicit phenological models. Such metrics could subsequently be used to validate phenological models in both space and time. Also, such metrics could be used to validate phenological models in both space and time.
Highlights
Climate change is influencing the timing of key biological events
Further work should focus on the development of metrics that can quantify the difference between patterns and trends derived from spatially explicit phenological models
Our results show that errors produced by running SI-x lilac leafing model (SI-xLM) and thermal time (TT) models are similar, and that these models are 2 days more accurate than those provided by other spring phenology
Summary
Climate change is influencing the timing of key biological events. Increasingly warm springs are advancing the time of leaf onset of plants (Ellwood et al 2013; Schwartz et al 2013) and the migration of animals (Marra et al 2005; Ault et al 2011). Monitoring and analysing the timing of plants and animal development events is essential. Plant phenological models support the study of the impacts of climate change and inter-annual weather variability on vegetated canopies (Badeck et al 2004; Schwartz et al 2006; Allstadt et al 2015). Phenological models are often calibrated using ground and weather observations.
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