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Abstract: Aim: Climate affects the flowering time of many species. Little is known, however, about how climate influences the properties of regional floras, including the rate at which taxa flower sequentially throughout the flowering season. This study is the first to detect geographical variation in this rate across North America. In addition, we tested for the independent effects of intraspecific variation in flowering time and taxonomic composition on the rate of sequential flowering among regional floras distributed across a temperature gradient. Location: North America. Time period: This study examined >59,000 herbarium specimens that were collected in flower from 1901 to 2013. Major taxa studied: 2,803 angiosperm taxa. Methods: We identified 51 climatically homogeneous regions across the continental, mostly western states of the USA, in each of which ≥100 species were represented by herbarium specimens. We then examined the effects of mean annual temperature (MAT) on the rate of sequential flowering among species in each region. We also evaluated whether geographical variation in the rate of sequential flowering was attributable to intraspecific variation in the flowering time and/or the taxonomic composition of regional floras. Results: As MAT increased over space, the rate of sequential flowering (standardized by the absolute length of the flowering season in each region) increased among relatively early‐flowering taxa but decreased among the latest‐flowering taxa. Both intraspecific variation and shifts in taxonomic composition among floras contributed to this pattern. Main conclusions: Among floras throughout North America, the rate of sequential flowering among co‐occurring taxa changes with MAT. Intraspecific phenological variation primarily affects the rate of sequential flowering during the first half of the growing season, consistent with the inference that future warming will most strongly affect flowering synchrony among early‐flowering taxa.

Forecasting the impacts of changing climate on the phenology of plant populations is essential for anticipating and managing potential ecological disruptions to biotic communities. Herbarium specimens enable assessments of plant phenology across broad spatiotemporal scales. However, specimens are collected opportunistically, and it is unclear whether their collection dates – used as proxies of phenological stages – are closest to the onset, peak, or termination of a phenophase, or whether sampled individuals represent early, average, or late occurrences in their populations. Despite this, no studies have assessed whether these uncertainties limit the utility of herbarium specimens for estimating the onset and termination of a phenophase. Using simulated data mimicking such uncertainties, we evaluated the accuracy with which the onset and termination of population‐level phenological displays (in this case, of flowering) can be predicted from natural‐history collections data (controlling for biases in collector behavior), and how the duration, variability, and responsiveness to climate of the flowering period of a species and temporal collection biases influence model accuracy. Estimates of population‐level onset and termination were highly accurate for a wide range of simulated species' attributes, but accuracy declined among species with longer individual‐level flowering duration and when there were temporal biases in sample collection, as is common among the earliest and latest‐flowering species. The amount of data required to model population‐level phenological displays is not impractical to obtain; model accuracy declined by less than 1 day as sample sizes rose from 300 to 1000 specimens. Our analyses of simulated data indicate that, absent pervasive biases in collection and if the climate conditions that affect phenological timing are correctly identified, specimen data can predict the onset, termination, and duration of a population's flowering period with similar accuracy to estimates of median flowering time that are commonplace in the literature. [ABSTRACT FROM AUTHOR]

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 (R2 = 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. 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. [ABSTRACT FROM AUTHOR]