Complex climate-mediated effects of urbanization on plant reproductive phenology and frost risk.

Urbanization can affect the timing of plant reproduction (i.e. flowering and fruiting) and associated ecosystem processes. However, our knowledge of how plant phenology responds to urbanization and its associated environmental changes is limited. Herbaria represent an important, but underutilized source of data for investigating this question. We harnessed phenological data from herbarium specimens representing 200 plant species collected across 120 yr from the eastern US to investigate the spatiotemporal effects of urbanization on flowering and fruiting phenology and frost risk (i.e. time between the last frost date and flowering). Effects of urbanization on plant reproductive phenology varied significantly in direction and magnitude across species ranges. Increased urbanization led to earlier flowering in colder and wetter regions and delayed fruiting in regions with wetter spring conditions. Frost risk was elevated with increased urbanization in regions with colder and wetter spring conditions. Our study demonstrates that predictions of phenological change and its associated impacts must account for both climatic and human effects, which are context dependent and do not necessarily coincide. We must move beyond phenological models that only incorporate temperature variables and consider multiple environmental factors and their interactions when estimating plant phenology, especially at larger spatial and taxonomic scales.

New directions in tropical phenology.

Earth's most speciose biomes are in the tropics, yet tropical plant phenology remains poorly understood. Tropical phenological data are comparatively scarce and viewed through the lens of a 'temperate phenological paradigm' expecting phenological traits to respond to strong, predictably annual shifts in climate (e.g., between subfreezing and frost-free periods). Digitized herbarium data greatly expand existing phenological data for tropical plants; and circular data, statistics, and models are more appropriate for analyzing tropical (and temperate) phenological datasets. Phylogenetic information, which remains seldom applied in phenological investigations, provides new insights into phenological responses of large groups of related species to climate. Consistent combined use of herbarium data, circular statistical distributions, and robust phylogenies will rapidly advance our understanding of tropical - and temperate - phenology.

Phenological displacement is uncommon among sympatric angiosperms.

Interactions between species can influence successful reproduction, resulting in reproductive character displacement, where the similarity of reproductive traits - such as flowering time - among close relatives growing together differ from when growing apart. Evidence for the overall prevalence and direction of this phenomenon, and its stability under environmental change, remains untested across large scales. Using the power of crowdsourcing, we gathered phenological information from over 40 000 herbarium specimens, and investigated displacement in flowering time across 110 animal-pollinated species in the eastern USA. Overall, flowering time displacement is not common across large scales. However, displacement is generally greater among species pairs that flower close in time, regardless of direction. Furthermore, with climate change, the flowering times of closely related species are predicted, on average, to shift further apart by the mid-21st century. We demonstrate that the degree and direction of phenological displacement among co-occurring closely related species pairs varies tremendously. However, future climate change may alter the differences in reproductive timing among many of these species pairs, which may have significant consequences for species interactions and gene flow. Our study provides one promising path towards understanding how the phenological landscape is structured and may respond to future environmental change.

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.