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.
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Free, publicly-accessible full text available April 12, 2025
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Phenology varies widely over space and time because of its sensitivity to climate. However, whether phenological variation is primarily generated by rapid organismal responses (plasticity) or local adaptation remains unresolved. Here we used 1,038,027 herbarium specimens representing 1,605 species from the continental United States to measure flowering-time sensitivity to temperature over time (Stime) and space (Sspace). By comparing these estimates, we inferred how adaptation and plasticity historically influenced phenology along temperature gradients and how their contributions vary among species with different phenology and native climates and among ecoregions differing in species composition. Parameters Sspace and Stime were positively correlated (r = 0.87), of similar magnitude and more frequently consistent with plasticity than adaptation. Apparent plasticity and adaptation generated earlier flowering in spring, limited responsiveness in late summer and delayed flowering in autumn in response to temperature increases. Nonetheless, ecoregions differed in the relative contributions of adaptation and plasticity, from consistently greater importance of plasticity (for example, southeastern United States plains) to their nearly equal importance throughout the season (for example, Western Sierra Madre Piedmont). Our results support the hypothesis that plasticity is the primary driver of flowering-time variation along temperature gradients, with local adaptation having a widespread but comparatively limited role.more » « lessFree, publicly-accessible full text available March 1, 2025
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Abstract In recent decades, the final frost dates of winter have advanced throughout North America, and many angiosperm taxa have simultaneously advanced their flowering times as the climate has warmed. Phenological advancement may reduce plant fitness, as flowering prior to the final frost date of the winter/spring transition may damage flower buds or open flowers, limiting fruit and seed production. The risk of floral exposure to frost in the recent past and in the future, however, also depends on whether the last day of winter frost is advancing more rapidly, or less rapidly, than the date of onset of flowering in response to climate warming. This study presents the first continental‐scale assessment of recent changes in frost risk to floral tissues, using digital records of 475,694 herbarium specimens representing 1,653 angiosperm species collected across North America from 1920 to 2015. For most species, among sites from which they have been collected, dates of last frost have advanced much more rapidly than flowering dates. As a result, frost risk has declined in 66% of sampled species. Moreover, exotic species consistently exhibit lower frost risk than native species, primarily because the former occupy warmer habitats where the annual frost‐free period begins earlier. While reducing the probability of exposure to frost has clear benefits for the survival of flower buds and flowers, such phenological advancement may disrupt other ecological processes across North America, including pollination, herbivory, and disease transmission.
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Premise Herbarium specimens have been used to detect climate‐induced shifts in flowering time by using the day of year of collection (
DOY ) as a proxy for first or peak flowering date. Variation among herbarium sheets in their phenological status, however, undermines the assumption thatDOY accurately represents any particular phenophase. Ignoring this variation can reduce the explanatory power of pheno‐climatic models (PCM s) designed to predict the effects of climate on flowering date.Methods Here we present a protocol for the phenological scoring of imaged herbarium specimens using an ImageJ plugin, and we introduce a quantitative metric of a specimen's phenological status, the phenological index (
PI ), which we use inPCM s to control for phenological variation among specimens ofStreptanthus tortuosus (Brassicaceeae) when testing for the effects of climate onDOY . We demonstrate that includingPI as an independent variable improves model fit.Results Including
PI inPCM s increased the modelR 2relative toPCM s that excludedPI ; regression coefficients for climatic parameters, however, remained constant.Discussion Our protocol provides a simple, quantitative phenological metric for any observed plant. Including
PI inPCM s increasesR 2and enables predictions of theDOY of any phenophase under any specified climatic conditions.