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Machine learning (ML) can accelerate the extraction of phenological data from herbarium specimens; however, no studies have assessed whether ML-derived phenological data can be used reliably to evaluate ecological patterns. In this study, 709 herbarium specimens representing a widespread annual herb, Streptanthus tortuosus, were scored both manually by human observers and by a mask R-CNN object detection model to (1) evaluate the concordance between ML and manually-derived phenological data and (2) determine whether ML-derived data can be used to reliably assess phenological patterns. The ML model generally underestimated the number of reproductive structures present on each specimen; however, when these counts were used to provide a quantitative estimate of the phenological stage of plants on a given sheet (i.e., the phenological index or PI), the ML and manually-derived PI’s were highly concordant. Moreover, herbarium specimen age had no effect on the estimated PI of a given sheet. Finally, including ML-derived PIs as predictor variables in phenological models produced estimates of the phenological sensitivity of this species to climate, temporal shifts in flowering time, and the rate of phenological progression that are indistinguishable from those produced by models based on data provided by human observers. This study demonstrates that phenological data extracted using machine learning can be used reliably to estimate the phenological stage of herbarium specimens and to detect phenological patterns. 

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.

 

Abstract Machine learning (ML) has great potential to drive scientific discovery by harvesting data from images of herbarium specimens—preserved plant material curated in natural history collections—but ML techniques have only recently been applied to this rich resource. ML has particularly strong prospects for the study of plant phenological events such as growth and reproduction. As a major indicator of climate change, driver of ecological processes, and critical determinant of plant fitness, plant phenology is an important frontier for the application of ML techniques for science and society. In the present article, we describe a generalized, modular ML workflow for extracting phenological data from images of herbarium specimens, and we discuss the advantages, limitations, and potential future improvements of this workflow. Strategic research and investment in specimen-based ML methods, along with the aggregation of herbarium specimen data, may give rise to a better understanding of life on Earth. 

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 thatDOYaccurately represents any particular phenophase. Ignoring this variation can reduce the explanatory power of pheno‐climatic models (PCMs) designed to predict the effects of climate on flowering date.

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 inPCMs to control for phenological variation among specimens ofStreptanthus tortuosus(Brassicaceeae) when testing for the effects of climate onDOY. We demonstrate that includingPIas an independent variable improves model fit.

IncludingPIinPCMs increased the modelR²relative toPCMs that excludedPI; regression coefficients for climatic parameters, however, remained constant.

Our protocol provides a simple, quantitative phenological metric for any observed plant. IncludingPIinPCMs increasesR²and enables predictions of theDOYof any phenophase under any specified climatic conditions.