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Abstract Global warming has caused widespread shifts in plant phenology among species in the temperate zone, but it is unclear how population‐level responses will scale to alter the structure of the flowering season at the community level. This knowledge gap exists largely because—while the climatic sensitivity of first flowering within populations has been studied extensively—little is known about the responsiveness of the duration of a population's flowering period. This limits our ability to anticipate how the entire flowering periods of co‐occurring species may continue to change under warming. Nonetheless, flowering sensitivity to temperature often varies predictably among species between and within communities, which may help forecast temperature‐related changes to a community's flowering season. However, no studies—empirical or theoretical—have assessed how patterns of variation in flowering sensitivity among species could scale to alter community‐level flowering changes under warming. Here, we provide a conceptual overview of how variation in the sensitivity of flowering onset and duration among species can mediate changes to a community's flowering season due to warming trends. Specifically, we focus on the effects of differences in (1) the mean sensitivity of flowering onset and duration among communities and (2) the sensitivity of flowering onsets and durations among species flowering sequentially through the season within a community. We evaluated the manner and degree in which these forms of between‐species variation in sensitivity might affect the structure of the flowering season—both independently and interactively—using simulations, which covered a wide but empirically informed range of parameter values and combinations representing distinct community‐level patterns. Our findings predict that communities across the temperate zone will exhibit varied and often contrasting flowering responses to warming across biomes, underscoring that accounting for the temperature sensitivity of both phenological onset and duration among species is essential for understanding community‐level flowering dynamics in a warming world. 

Summary Phenological response to global climate change can impact ecosystem functions. There are various data sources from which spatiotemporal and taxonomic phenological data may be obtained: mobilized herbaria, community science initiatives, observatory networks, and remote sensing. However, analyses conducted to date have generally relied on single sources of these data. Siloed treatment of data in analyses may be due to the lack of harmonization across different data sources that offer partially nonoverlapping information and are often complementary. Such treatment precludes a deeper understanding of phenological responses at varying macroecological scales. Here, we describe a detailed vision for the harmonization of phenological data, including the direct integration of disparate sources of phenological data using a common schema. Specifically, we highlight existing methods for data harmonization that can be applied to phenological data: data design patterns, metadata standards, and ontologies. We describe how harmonized data from multiple sources can be integrated into analyses using existing methods and discuss the use of automated extraction techniques. Data harmonization is not a new concept in ecology, but the harmonization of phenological data is overdue. We aim to highlight the need for better data harmonization, providing a roadmap for how harmonized phenological data may fill gaps while simultaneously being integrated into analyses. 

Summary Anthropogenetic climate change has caused range shifts among many species. Species distribution models (SDMs) are used to predict how species ranges may change in the future. However, most SDMs rarely consider how climate‐sensitive traits, such as phenology, which affect individuals' demography and fitness, may influence species' ranges.Using > 120 000 herbarium specimens representing 360 plant species distributed across the eastern United States, we developed a novel ‘phenology‐informed’ SDM that integrates phenological responses to changing climates. We compared the ranges of each species forecast by the phenology‐informed SDM with those from conventional SDMs. We further validated the modeling approach using hindcasting.When examining the range changes of all species, our phenology‐informed SDMs forecast less species loss and turnover under climate change than conventional SDMs. These results suggest that dynamic phenological responses of species may help them adjust their ecological niches and persist in their habitats as the climate changes.Plant phenology can modulate species' responses to climate change, mitigating its negative effects on species persistence. Further application of our framework will contribute to a generalized understanding of how traits affect species distributions along environmental gradients and facilitate the use of trait‐based SDMs across spatial and taxonomic scales. 

PremiseHerbarium 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. MethodsHere 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. ResultsIncludingPIinPCMs increased the modelR²relative toPCMs that excludedPI; regression coefficients for climatic parameters, however, remained constant. DiscussionOur protocol provides a simple, quantitative phenological metric for any observed plant. IncludingPIinPCMs increasesR²and enables predictions of theDOYof any phenophase under any specified climatic conditions. 

PremiseHerbarium specimens represent an outstanding source of material with which to study plant phenological changes in response to climate change. The fine‐scale phenological annotation of such specimens is nevertheless highly time consuming and requires substantial human investment and expertise, which are difficult to rapidly mobilize. MethodsWe trained and evaluated new deep learning models to automate the detection, segmentation, and classification of four reproductive structures ofStreptanthus tortuosus(flower buds, flowers, immature fruits, and mature fruits). We used a training data set of 21 digitized herbarium sheets for which the position and outlines of 1036 reproductive structures were annotated manually. We adjusted the hyperparameters of amask R‐CNN(regional convolutional neural network) to this specific task and evaluated the resulting trained models for their ability to count reproductive structures and estimate their size. ResultsThe main outcome of our study is that the performance of detection and segmentation can vary significantly with: (i) the type of annotations used for training, (ii) the type of reproductive structures, and (iii) the size of the reproductive structures. In the case ofStreptanthus tortuosus, the method can provide quite accurate estimates (77.9% of cases) of the number of reproductive structures, which is better estimated for flowers than for immature fruits and buds. The size estimation results are also encouraging, showing a difference of only a few millimeters between the predicted and actual sizes of buds and flowers. DiscussionThis method has great potential for automating the analysis of reproductive structures in high‐resolution images of herbarium sheets. Deeper investigations regarding the taxonomic scalability of this approach and its potential improvement will be conducted in future work.