Overlooked climate parameters best predict flowering onset: Assessing phenological models using the elastic net. (9th October 2018)

• Record Type: Journal Article
• Authors: Park, Isaac W.; Mazer, Susan J.
• Is Part Of: Global change biology. Volume 24:Number 12(2018)
• Page Start: 5972
• Page End: 5984
• Publication Date: 2018-10-09
• Subjects: flowering time -- herbarium specimen -- phenoclimate modeling -- phenology; Climatic changes -- Environmental aspects -- Periodicals; Troposphere -- Environmental aspects -- Periodicals; Biodiversity conservation -- Periodicals; Eutrophication -- Periodicals; 551.5
• DOI: 10.1111/gcb.14447
• Languages: English
• ISSNs: 1354-1013

Abstract

Abstract: Determining the manner in which plant species shift their flowering times in response to climatic conditions is essential to understanding and forecasting the impacts of climate change on the world's flora. The limited taxonomic diversity and duration of most phenological datasets, however, have impeded a comprehensive, systematic determination of the best predictors of flowering phenology. Additionally, many studies of the relationship between climate conditions and plant phenology have included only a limited set of climate parameters that are often chosen a priori and may therefore overlook those parameters to which plants are most phenologically sensitive. This study harnesses 894, 392 digital herbarium records and 1, 959 in situ observations to produce the first assessment of the effects of a large number (25) of climate parameters on the flowering time of a very large number (2, 468) of angiosperm taxa throughout North America. In addition, we compare the predictive capacity of phenological models constructed from the collection dates of herbarium specimens vs. repeated in situ observations of individual plants using a regression approach—elastic net regularization—that has not previously been used in phenological modeling, but exhibits several advantages over ordinary least squares and stepwise regression. When herbarium‐derived data and in situ phenological observations were used to predict flowering onset, the multivariate models based on each of these data sources had similar predictive capacity ( R 2 = 0.27). Further, apart from mean maximum temperature (TMAX), the two best predictors of flowering time have not commonly been included in phenological models: the number of frost‐free days (NFFD) and the quantity of precipitation as snow (PAS) in the seasons preceding flowering. By vetting these models across an unprecedented number of taxa, this work demonstrates a new approach to phenological modeling. Abstract : This study harnesses 894, 392 digital herbarium records and 1, 959 in situ observations to produce the first assessment of the effects of a 25 of climate parameters on the flowering time of 2, 468 of angiosperm taxa throughout North using a regression approach—elastic net regularization—that has not previously been used in phenological modeling, but exhibits several advantages over ordinary least squares and stepwise regression. Multivariate models based on herbarium‐derived data and in situ phenological observations had similar predictive capacity. By vetting these models across an unprecedented number of taxa, this work demonstrates a new approach to phenological modeling.