Plant Phenological Responses Research Articles

Seasonal and vertical patterns of water availability and variability determine plant reproductive phenology.

Changing precipitation regimes can influence terrestrial plants and ecosystems. However, plant phenological responses to changing precipitation temporal patterns and the underlying mechanisms are largely unclear. This study was conducted to explore the effects of seasonal precipitation redistribution on plant reproductive phenology in a temperate steppe. A field experiment with control (C), advanced (AP) and delayed (DP) growing-season precipitation peaks, and the combination of AP and DP (ADP) were employed. Seven dominant plant species were selected and divided into two functional groups (early- vs. middle-flowering species, shallow- vs. deep-rooted species) to monitor reproductive phenology including budding, flowering, and fruiting date, as well as reproductive duration for four growing seasons from 2015 to 2017, and 2022. The AP, but not DP treatment advanced the phenological (i.e., budding, flowering, and fruiting) dates and lengthened the reproductive duration across the 4 growing seasons and 7 monitored species. In addition, the phenological responses showed divergent patterns among different plant functional groups, which could be attributed to shifts in soil moisture and its variability in different months and soil depths. Moreover, species with lengthened reproductive duration increased phenological overlap with other species, which could have a negative impact on their dominance under the AP treatment. Our findings reveal that changing precipitation seasonality could have considerable impacts on plant phenology by affecting soil water availability and variability. Incorporating these two factors simultaneously in the phenology models will help us understand the response of plant phenology under intensified changing precipitation scenarios. In addition, the observations of decreased dominance for the species with lengthened reproductive duration suggest that changing reproductive phenology can have a potential to affect community composition in grasslands under global change.

Mycorrhizal status regulates plant phenological mismatch caused by warming

Mycorrhiza is an important functional feature of plants, which plays a vital role in regulating plant phenology in response to environmental changes. However, the effect of mycorrhiza on plant phenological asymmetry in response to climate changes is still rarely reported. Based on a global database of mycorrhizal statuses (obligately mycorrhizal, OM and facultatively mycorrhizal, FM) and phenology, we demonstrated that mycorrhizas reduce the phenological mismatches between above- and below-ground plant responses to climate warming under OM status. The mismatch of above- and below-ground growing season length of FM plants to warming was as high as 10.65 days, 9.1925 days and 12.36 days in total, herbaceous and woody plants, respectively. The mismatch of growing season length of OM plants was only 2.12 days, −0.61 days and 7.64 days among plant groups, which was much lower than that of FM plants. Correlation analysis indicated that OM plants stabilized plant phenology by regulating the relationship between the start of the growing season and the length of the growing season. Path analysis found that herbaceous plants and woody plants reduced phenological mismatches by stabilizing below-ground and above-ground phenology, respectively. In exploring the effects of mycorrhizal status on early- or late-season phenophases, we found that different mycorrhizal statuses affected the response of early- or late-season phenophase to warming. OM promoted the advance of early-season phenophase, and FM promoted the delay of late-season phenophase among different plant groups. In different regions, OM and FM promoted the advance of early-season phenophase in temperate and boreal regions, respectively. Our results indicate that mycorrhizal status mediates plant phenological response to warming, so the potential effects of mycorrhizal status should be considered when studying plant phenology changes.

Controlled experiments fail to capture plant phenological response to chilling temperature

AbstractAimControlled experiments are increasingly important for investigating how and to what degree plant phenology responds to global climate change. Current experiments underline that chilling and forcing temperatures are two major environmental cues shaping the budburst date of temperate species, but whether experiments could reflect the observed responses to chilling has rarely been examined.LocationEurope and North America.Time periods1951–2021.Major taxa studiedTemperate trees and shrubs.MethodsUsing an experimental database of budburst dates for 50 species derived from previous literature and observational data of the same species at 12,579 stations in Europe and 1469 stations in the USA, we compared the response of forcing requirement (FR) of the budburst date to chilling accumulation (CA) between observations and experiments using a common measure of FR and CA.ResultsThe median, variance and probability distribution of CA‐FR curves differed significantly between experiments and observations in most cases. The distinction in chilling effects between experiments and observations could be attributed to the difference in thermal space, heat stress, genetic variation among provenances, different forcing treatments adopted and plant materials used in the experiments.Main conclusionsOur results suggest that the uncertainty of phenological models based solely on the experimental data needs to be re‐evaluated when predicting future spring phenological responses across broad spatial scales.

Developing a simple and efficient modeling solution for predicting key phenological stages of table grapes in a non-traditional viticulture zone in south Asia.

Phenological shifts are one of the most visible signs of climatic variability and change in the biosphere. However, modeling plant phenological responses has always been a key challenge due to climatic variability and plant adaptation. Grapevine is a phenologically sensitive crop and, thus, its developmental stages are affected by the increase in temperature. The goal of this study was to develop a temperature-based grapevine phenology model (GPM) for predicting key developmental stages for different table grape cultivars for a non-traditional viticulture zone in south Asia. Experiments were conducted in two vineyards at two locations (Chakwal and Islamabad) in the Pothawar region of Pakistan during the 2019 and 2020 growing seasons for four cultivars including Perlette, King's Ruby, Sugraone and NARC Black. Detailed phenological observations were obtained starting in January until harvest of the grapes. The Mitscherlich monomolecular equation was used to develop the phenology model for table grapes. There was a strong non-linear correlation between the Eichhorn and Lorenz phenological (ELP) scale and growing degree days (GDD) for all cultivars with coefficient of determinations (R2) ranging from 0.90 to 0.94. The results for model development indicated that GPM was able to predict phenological stages with high skill scores, i.e., a root mean square (RMSE) of 2.14 to 2.78 and mean absolute error (MAE) of 1.86 to 2.26 days. The prediction variability of the model for the onset timings of phenological stages was up to 3 days. The results also reveal that the phenology model based on GDD approach provides an efficient planning tool for viticulture industry in different grape growing regions. The proposed methodology, being a simpler one, can be easily applied to other regions and cultivars as a predictor for grapevine phenology.

Incorporating plant phenological responses into species distribution models reduces estimates of future species loss and turnover.

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.

Effects of short- and long-term experimental warming on plant–pollinator interactions and floral rewards in the Low Arctic

Plant phenological and growth responses to experimental warming are widely documented, but less is known about warming effects on plant–pollinator interactions. We investigated the effects of short- and long-term passive warming on flowering phenology, insect visitation, fruit production, and floral rewards in the Low Arctic in northern Alaska. To better understand the role of insect visitors in plant reproductive success, we quantified pollen loads on floral visitors and tested for pollen limitation in four species. Long-term warming advanced flowering onset in evergreen shrubs and forbs. Warming, in general, increased the duration of flowering for forbs, evergreen shrubs, and deciduous shrubs. Considering all growth forms together, long-term warming increased floral density. This pattern was primarily driven by deciduous and evergreen shrubs. Dipterans accounted for more visits than Hymenopterans, although Hymenopterans had higher pollen loads. Insect exclusion and warming decreased fruit set in the forb, Bistorta officinalis Delarbre. Nectar volume in the deciduous shrub, Vaccinium uliginosum, was higher in the warmed plots than the control, but nectar quality did not differ. Advanced flowering onset, longer flowering duration, and increased flower density and nectar volume may have important implications for the pollinator community, warranting further research on long-term warming effects on tundra ecosystems.

Adapting a process-oriented cold hardiness model to conifers

Plant phenology has been and continues to be impacted by climate change. Process-oriented modeling of phenology reveals biological characteristics through interpretation of model results and parameter values. This paper aims to implement a cold hardiness model using historical Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) data, determine if model results improve with data clustering by seed source or growing location, and interpret model results into biological meaning. These interpretations have applications for reforestation and studies of plant phenological response to climate change.Cold hardiness data were compiled through literature review. A dynamic process-oriented cold hardiness model driven by thermal time based on daily temperatures was applied to multiple data clustering scenarios. Model results were analyzed for goodness of fit to determine model error, efficiency, and bias. A sensitivity analysis was performed to determine parameter sensitivity, and further validate the model calibration.Data clustering by seed source improved fit compared to clustering by growing location or no clustering, when applied to the full dataset. Using only temperature inputs, model results had low error when data modeled were similar. Results show that for cold hardiness acclimation a linear growing degree function with a threshold of 10 °C was adequate across testing data, as was an upper limit cold hardiness temperature of –3 °C.Interpretation of model results show that both acclimation to growing sites and seed source genetics impact cold hardiness response, though clustering by seed source improved model performance. This model could be applied to mitigate cold related risks to seedlings during production and establishment, and serve as a template for predicting phenological responses in simulated future climate scenarios.

Effects of Temperature, Precipitation, and CO2 on Plant Phenology in China: A Circular Regression Approach

Leveraging circular regression, this study analyzed phenological data from China spanning the period 2003 to 2015, meticulously examining the effects of temperature, precipitation, and CO2 concentrations on the phenological patterns of woody and herbaceous plants. For woody plants, the results showed that rising temperatures and increased precipitation notably advanced early growth phases, such as budburst, leaf unfolding, and first flowering (p < 0.001). Specifically, CO2 concentrations had a pronounced impact on the leaf unfolding phase (p < 0.001). In contrast, autumnal events, particularly fruit maturity, autumn coloring, and leaf fall, were delayed by warmer temperatures and higher precipitation (p < 0.001), Of these events, only fruit maturity demonstrated sensitivity to CO2 concentration variations. In the realm of herbaceous plants, elevated temperatures and precipitation collectively hastened the budburst phase (p < 0.001), which is an effect further accentuated by high CO2 levels (p < 0.001). Moreover, rising temperatures and augmented precipitation were instrumental in advancing the flowering phase (p < 0.001). Conversely, warmer conditions slowed down the fruiting process (p < 0.001), with this delay somewhat mitigated by the effects of increased precipitation. Interestingly, while CO2 concentrations had negligible influence on the flowering and fruiting stages, they noticeably delayed seed dispersal and the initiation of senescence (p < 0.001). Overall, the prevailing trend suggests that plants, whether woody or herbaceous in nature, tend to prolong their growth season under warmer and more humid conditions. The influence of CO2 concentration, however, is contingent upon the specific phenological phase and plant type. Our findings emphasize the nuanced and stage-specific responses of plant phenology to temperature, precipitation, and CO2, highlighting the value of using circular regression in ecological studies.

Divergent responses of reproductive phenology to asymmetric warming: Evidence from a 10-year grassland experiment in the inner mongolian steppe

It is well documented that asymmetric daytime and nighttime warming have profound influences on plant growth and ecosystem carbon cycling of terrestrial ecosystems. However, the effects of long-term asymmetric warming on plant phenology and species-specific responses remain unclear. We conducted a ten-year field experiment with daytime and nighttime warming in a temperate steppe in northern China and examined how species-specific reproductive phenology (RP) of six coexisting herbaceous species responded to asymmetric warming during two time periods: early (2006–2009) and late (2013–2015). Overall, nighttime warming advanced the flowering and fruiting time of early-blooming species by 1.2 and 1.6 days, respectively. The responses of reproductive duration for early-blooming species to nighttime warming were species-specific (-3.6 days for Potentilla bifurca, +1.6 days for Allium bidentatum). Nighttime warming significantly shortened reproductive duration of P. bifurca and advanced the flowering and fruiting time of Heteropappus altaicus during the late period relative to the early period. Such species-specific responses of plant phenology and differences in response to short- vs long-term warming could be ascribed to water deficit and increasing coverage of species during the late period, respectively. The findings highlight the critical role of functional traits in mediating the response and adaptation of plant phenology in temperate grasslands under climate warming.

Effects of intra-annual precipitation patterns on grassland productivity moderated by the dominant species phenology.

Phenology and productivity are important functional indicators of grassland ecosystems. However, our understanding of how intra-annual precipitation patterns affect plant phenology and productivity in grasslands is still limited. Here, we conducted a two-year precipitation manipulation experiment to explore the responses of plant phenology and productivity to intra-annual precipitation patterns at the community and dominant species levels in a temperate grassland. We found that increased early growing season precipitation enhanced the above-ground biomass of the dominant rhizome grass, Leymus chinensis, by advancing its flowering date, while increased late growing season precipitation increased the above-ground biomass of the dominant bunchgrass, Stipa grandis, by delaying senescence. The complementary effects in phenology and biomass of the dominant species, L. chinensis and S. grandis, maintained stable dynamics of the community above-ground biomass under intra-annual precipitation pattern variations. Our results highlight the critical role that intra-annual precipitation and soil moisture patterns play in the phenology of temperate grasslands. By understanding the response of phenology to intra-annual precipitation patterns, we can more accurately predict the productivity of temperate grasslands under future climate change.

Contrasting responses of early‐ and late‐season plant phenophases to altered precipitation

Precipitation is a key driver of plant phenology in addition to temperature and photoperiod. Although a few studies have explored phenological responses to altered precipitation, the general patterns of sequential phenophase responses and their potential drivers remain elusive. Here, we conducted a meta‐analysis of the responses of ten phenophases to altered precipitation from 63 manipulative experiments. We show that early‐season (leaf out, first flowering, last flowering and first fruiting) and late‐season phenophases (last fruiting and leaf colouring) shifted in opposite directions with precipitation changes. Advanced early‐season phenophases and delayed late‐season phenophases led to extensions of the reproductive phase and growing season with precipitation increases. Similarly, delayed leaf out and advanced leaf colouring resulted in a shorter length of the growing season with precipitation decreases. We further found that the responses of phenophases were less pronounced in wetter regions than in drier regions, regardless of the precipitation increase or decrease treatments. In addition, the phenophase responses were mediated by the seasons when the precipitation changes were imposed. For instance, early‐season phenophases were more responsive to winter or spring precipitation increases, but late‐season phenophases were only significantly affected by spring–autumn precipitation increases. These findings will help improve the forecasts of plant phenological responses to precipitation changes and will assist in the incorporation of precipitation representations into next‐generation phenological models.

Combining Solar-Induced Chlorophyll Fluorescence and Optical Vegetation Indices to Better Understand Plant Phenological Responses to Global Change

Recent advances in the satellite retrieval of solar-induced chlorophyll fluorescence (SIF) provide new opportunities for understanding the phenological responses of ecosystems to global climate change. Because of the strong link between SIF and plant gross photosynthesis, phenological events derived from SIF represent the seasonal variation of ecosystem functioning (photosynthetic phenology) and differ from phenologies derived from traditional vegetation indices. We provide an overview of recent advances in remotely sensed photosynthetic phenologies, with a focus on their driving factors, their impact on the global carbon cycle, and their relationships with vegetation index-derived land surface phenology metrics. We also discuss future research directions on how to better use various phenological metrics to understand the responses of plants to global change.

Testing machine learning algorithms on a binary classification phenological model

AbstractAimPhenological models have become a vital tool for predicting future phenological responses to global climate change. Recently, machine learning (ML) has been used successfully to develop phenological models based on ground observations. However, fitting an observation series that has been observed for only a few years (or even a decade) can easily lead to overfitting, and it is still a great challenge to predict future phenology accurately based on a short observation series. Here, based on historical ground phenological observations, we construct an ML‐based binary classification phenological model that can be applied to temperate trees.InnovationWe thoroughly describe the construction process of a species‐specific ML‐based binary classification phenological model that is suitable for phenological predictions in both spring and autumn. Through experiments, we evaluate 18 commonly used ML classification algorithms and the effects of two parameters on the prediction performance of the model. Finally, we compare the performance between the binary classification model and six widely used ecophysiological models for accuracy of spring phenological prediction.Main conclusionsThe median root mean square error (RMSE) of the binary classification model for the first flowering date (93 observation series) was only 2.99 days, which proves that it is a good method of phenological prediction for temperate trees. This model can effectively overcome the insufficient sample size of ground observations for specific species and provide new insights of phenological predictions for tropical and subtropical trees. The comparison of different ML algorithms showed that the median root mean square errors of the six algorithms were <3.5 days, and lower than those of six ecophysiological models (>3.7 days) in prediction of spring phenology. In addition, this model can help us to infer plant physiological mechanisms and drivers of phenological change and provide more accurate predictions of plant phenological responses to climate change.

Phenological and physiological responses of the terrestrial ecosystem to the 2019 drought event in Southwest China: Insights from satellite measurements and the SSiB2 model

• The drought in Southwest China severely suppressed plant phenology and physiology. • Chlorophyll fluorescence sensitively responds to changes in water conditions. • Satellite chlorophyll fluorescence contributes to model evaluation and improvement. Understanding plant phenological and physiological changes in response to drought will provide key insight into the response of terrestrial ecosystems to climate change, but is still limited due to the increased drought severity and frequency in recent decades. Here, we combine solar-induced chlorophyll fluorescence (SIF) along with SSiB2 (Simplified Simple Biosphere Model) simulations to investigate the plant phenological and physiological responses to the 2019 drought in southwestern China. Our results show that this 2019 drought had substantial impacts on vegetation phenology and photosynthesis due to the soil moisture deficit in spring, while the rewatering process in July alleviated the water deficit and reduced drought damage to plants. Moreover, SIF observations provide a physiology-related vegetation response, as the recovery of plant photosynthesis indicated by fluorescence yield ( S I F yield ) is much stronger than the recovery of greenness described by vegetation indices during the rewatering in July. The SSiB2 simulations captured the physiological response of plants to moisture deficit during drought period, while the lack of realistic energy dissipation mechanisms under stressed conditions may lead to discrepancies in the timing of peak response to drought. Our findings highlight the prospective application of remote sensing SIF measurements in monitoring the timely response of plant physiology to changes in water conditions and to provide important information for model evaluation and improvement.

Phenological trajectories of Caribbean very dry tropical forests diverge under different geologic formations

AbstractTropical dry forests experience pronounced seasonal changes in precipitation manifested in varied plant phenologies. At landscape scales, geologic substrate—one of the least understood abiotic factors interacting with precipitation—may modulate phenological responses in these forests through a combination of mechanisms regulating water and nutrient use. We leveraged a phenological dataset from the semiarid island of Curaçao to examine the extent to which plant phenology at multiple levels of biological organization diverge under different geologies. Monthly observations over a 30‐month period of leaves, flowers, and fruits of 69 plant species of different life forms at three nearby sites differing in their underlaying geology were used to examine intra‐ and inter‐annual plant responses at species, community, and system levels. The integration of leaf, flower, and fruit observations at intra‐annual scales revealed diverse phenological strategies among species, broad associations with geologic substrate, and the extent of intra‐specific variation as a function of geology. The community‐ and system‐level analyses at inter‐annual scales showed a reduction in mean leaf scores during the 30‐month period, a weak and strong leafless period in 1993 and 1994, respectively, and differences among geologic substrates. Finally, we observed significant and positive relationships between precipitation and the phenophase scores; the strength of the relationships varied with phenophase and geologic substrate. Results of this work emphasize the importance of geologic substrate, and more broadly speaking landscape heterogeneity, in modulating plant phenological responses in tropical dry forests. Ultimately, this information will become important to understand and mitigate global climate change impacts.Abstract in Spanish is available with online material.

Responses of plant phenology to warming and nitrogen addition under different precipitation conditions in a desert steppe of Nei Mongol, China

Responses of plant phenology to warming and nitrogen addition under different precipitation conditions in a desert steppe of Nei Mongol, China

Does Climate Warming Favour Early Season Species?

Plant species that start early in spring are generally more responsive to rising temperatures, raising concerns that climate warming may favour early season species and result in altered interspecific interactions and community structure and composition. This hypothesis is based on changes in spring phenology and therefore active growing season length, which would not be indicative of possible changes in growth as would changes in cumulative forcing temperatures (growing degree days/hours) in the Northern Hemisphere. In this study we analysed the effects of a moderate climate warming (2°C warmer than the 1981–2010 baseline) on the leaf-out of hypothetical species without chilling restriction and actual plant species with different chilling and forcing requirements in different parts of the globe. In both cases, early season species had larger phenological shifts due to low leaf-out temperatures, but accumulated fewer forcing gains (changes in cumulative forcing temperatures by warming) from those shifts because of their early spring phenology. Leaf-out time was closely associated with leaf-out temperatures and therefore plant phenological responses to climate warming. All plant species would be equally affected by climate warming in terms of total forcing gains added from higher temperatures when forcing gains occurring between early and late season species are included. Our findings will improve the understanding of possible mechanisms and consequences of differential responses in plant phenology to climate warming.

Artificial light pollution inhibits plant phenology advance induced by climate warming

Natural photic regime has been drastically altered by the artificial night sky luminance. Despite evidence of sufficient light brightness inducing plant physiology and affecting phenology, generalization regarding effects of light pollution on plant phenology across species and locations is less clear. Meanwhile, the relative contributions and joint effects of artificial light pollution and climate change or other anthropic stressors still remain unknown. To fill this knowledge gap, we utilized in situ plant phenological observations of seven tree species during 1991–2015 in Europe, night-time light dataset and gridded temperature dataset to investigate the impacts of the artificial light pollution on spatial-temporal shifts of plant phenological phases under climatic warming. We found 70% of the observation sites were exposed to increased light pollution during 1992–2015. Among them, plant phenological phases substantially delayed at 12–39% observation sites of leaf-out, and 6–53% of flowering. We also found plant species appeared to be more sensitive to artificial light pollution, and phenology advancement was hindered more prominently and even delay phenomenon exhibited when the color level showed stronger sky brightness. Linear mixed models indicate that although temperature plays a dominant role in shifts of plant phenological phases at the spatial scale, the inhibitory effect of artificial light pollution is evident considering the interactions. To our knowledge, this study is the first to quantitatively establish the relationship between artificial light pollution and plant phenology across species and locations. Meanwhile, these findings provide a new insight into the ecological responses of plant phenology to the potential but poorly understood environmental stressors under this warmer world and call for light pollution to be accorded the equal status as other global change phenomena.

Plant phenological responses to experimental warming-A synthesis.

Although there is abundant evidence that plant phenology is shifting with climatic warming, the magnitude and direction of these shifts can depend on the environmental context, plant species, and even the specific phenophase of study. These disparities have resulted in difficulties predicting future phenological shifts, detecting phenological mismatches and identifying other ecological consequences. Experimental warming studies are uniquely poised to help us understand how climate warming will impact plant phenology, and meta-analyses allow us to expose broader trends from individual studies. Here, we review 70 studies comprised 1226 observations of plant phenology under experimental warming. We find that plants are advancing their early-season phenophases (bud break, leaf-out, and flowering) in response to warming while marginally delaying their late-season phenophases (leaf coloration, leaf fall, and senescence). We find consistency in the magnitude of phenological shifts across latitude, elevation, and habitat types, whereas the effect of warming on nonnative annual plants is two times larger than the effect of warming on native perennial plants. Encouragingly for researchers, plant phenological responses were generally consistent across a variety of experimental warming methods. However, we found numerous gaps in the experimental warming literature, limiting our ability to predict the effects of warming on phenological shifts. In particular, studies outside of temperate ecosystems in the Northern Hemisphere, or those that focused on late-season phenophases, annual plants, nonnative plants, or woody plants and grasses, were underrepresented in our data set. Future experimental warming studies could further refine our understanding of phenological responses to warming by setting up experiments outside of traditionally studied biogeographic zones and measuring multiple plant phenophases (especially late-season phenophases) across species of varying origin, growth form, and life cycle.

A New Method for Counting Reproductive Structures in Digitized Herbarium Specimens Using Mask R-CNN.

Phenology—the timing of life-history events—is a key trait for understanding responses of organisms to climate. The digitization and online mobilization of herbarium specimens is rapidly advancing our understanding of plant phenological response to climate and climatic change. The current practice of manually harvesting data from individual specimens, however, greatly restricts our ability to scale-up data collection. Recent investigations have demonstrated that machine-learning approaches can facilitate this effort. However, present attempts have focused largely on simplistic binary coding of reproductive phenology (e.g., presence/absence of flowers). Here, we use crowd-sourced phenological data of buds, flowers, and fruits from >3,000 specimens of six common wildflower species of the eastern United States (Anemone canadensis L., A. hepatica L., A. quinquefolia L., Trillium erectum L., T. grandiflorum (Michx.) Salisb., and T. undulatum Wild.) to train models using Mask R-CNN to segment and count phenological features. A single global model was able to automate the binary coding of each of the three reproductive stages with >87% accuracy. We also successfully estimated the relative abundance of each reproductive structure on a specimen with ≥90% accuracy. Precise counting of features was also successful, but accuracy varied with phenological stage and taxon. Specifically, counting flowers was significantly less accurate than buds or fruits likely due to their morphological variability on pressed specimens. Moreover, our Mask R-CNN model provided more reliable data than non-expert crowd-sourcers but not botanical experts, highlighting the importance of high-quality human training data. Finally, we also demonstrated the transferability of our model to automated phenophase detection and counting of the three Trillium species, which have large and conspicuously-shaped reproductive organs. These results highlight the promise of our two-phase crowd-sourcing and machine-learning pipeline to segment and count reproductive features of herbarium specimens, thus providing high-quality data with which to investigate plant responses to ongoing climatic change.