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Societal Impact StatementThe practice of writing science blogs benefits both the scientist and society alike by providing professional development opportunities and delivering information in a format that is accessible to large and diverse audiences. By designing a project that introduced upper‐level undergraduate students to science blog writing with a focus on plant biology, we piqued students' interest in science writing and the content of a popular plant science blog website. If adopted more widely, this work could broaden the scope of science education and promote the development of effective science communication skills for the next generation of scientists. SummarySuccessful scientists must communicate their research to broad audiences, including distilling key scientific concepts for the general public. Students pursuing careers in Science, Technology, Engineering, and Mathematics (STEM) fields benefit from developing public communication skills early in their careers, but opportunities are limited in traditional biology curricula.We created the “Plant Science Blogging Project” for a Plant Biology undergraduate course at the University of Pittsburgh in Fall 2018 and 2019. Students wrote blog posts merging personal connections with plants with plant biology concepts for the popular science blogsPlant Love StoriesandEvoBites. By weaving biology into their narratives, students learned how to share botanical knowledge with the general public.The project had positive impacts on student learning and public engagement. In post‐assignment surveys, the majority of students reported that they enjoyed the assignment, felt it improved their understanding of plant biology, and piqued their interest in reading and writing science blogs in the future. Approximately one‐third of the student‐authored blogs were published, including two that rose to the top 10 most‐read posts on Plant Love Stories. Some dominant themes in student blogs, including medicine and culture, differed from common story themes published on the web, indicating the potential for students to diversify science blog content.Overall, the Plant Science Blogging Project allows undergraduate students to engage with plant biology topics in a new way, sharpen their scientific communication skills in accordance with today's world of mass information sharing, and contribute to the spread of scientific knowledge for public benefit. 

The herbaceous layer accounts for the majority of plant biodiversity in eastern North American forests, encompassing substantial variation in life history strategy and function. One group of early‐season herbaceous understory species, colloquially referred to as spring ephemeral wildflowers, are ecologically and culturally important, but little is known about the prevalence and biogeographic patterns of the spring ephemeral strategy. Methods: We used observations collected by the Global Biodiversity Information Facility (GBIF) to quantify the ephemerality of 559 understory forb species across eastern North America and classify them according to a continuous ephemerality index (ranging from 0 = never ephemeral to 1 = always ephemeral). We then used this information to model where ephemeral forbs were most common across the landscape with the goal of identifying geographic and environmental drivers important to their distributions and ranges. Results: Only 3.4% of all understory wildflower species were spring ephemerals in all parts of their range, and 18.4% (103 species) were ephemeral in at least part of their range. Spring ephemerals peaked in absolute species richness and relative proportion at mid latitudes. Conclusions: Spring ephemeral phenology is an important shade‐avoidance strategy for a large segment of the total understory species in temperate deciduous forests. In North America, the strategy is relatively most important for forest understories at mid latitudes. The definitions of spring ephemerality we provide here serve as an important ecological context for conservation priorities and to evaluate responses of this biodiverse group to future environmental change. 

Abstract Temperate understory plant species are at risk from climate change and anthropogenic threats that include increased deer herbivory, habitat loss, pollinator declines and mismatch, and nutrient pollution. Recent work suggests that spring ephemeral wildflowers may be at additional risk due to phenological mismatch with deciduous canopy trees. The study of this dynamic, commonly referred to as “phenological escape”, and its sensitivity to spring temperature is limited to eastern North America. Here, we use herbarium specimens to show that phenological sensitivity to spring temperature is remarkably conserved for understory wildflowers across North America, Europe, and Asia, but that canopy trees in North America are significantly more sensitive to spring temperature compared to in Asia and Europe. We predict that advancing tree phenology will lead to decreasing spring light windows in North America while spring light windows will be maintained or even increase in Asia and Europe in response to projected climate warming. 

Community science image libraries offer a massive, but largely untapped, source of observational data for phenological research. The iNaturalist platform offers a particularly rich archive, containing more than 49 million verifiable, georeferenced, open access images, encompassing seven continents and over 278,000 species. A critical limitation preventing scientists from taking full advantage of this rich data source is labor. Each image must be manually inspected and categorized by phenophase, which is both time-intensive and costly. Consequently, researchers may only be able to use a subset of the total number of images available in the database. While iNaturalist has the potential to yield enough data for high-resolution and spatially extensive studies, it requires more efficient tools for phenological data extraction. A promising solution is automation of the image annotation process using deep learning. Recent innovations in deep learning have made these open-source tools accessible to a general research audience. However, it is unknown whether deep learning tools can accurately and efficiently annotate phenophases in community science images. Here, we train a convolutional neural network (CNN) to annotate images of Alliaria petiolata into distinct phenophases from iNaturalist and compare the performance of the model with non-expert human annotators. We demonstrate that researchers can successfully employ deep learning techniques to extract phenological information from community science images. A CNN classified two-stage phenology (flowering and non-flowering) with 95.9% accuracy and classified four-stage phenology (vegetative, budding, flowering, and fruiting) with 86.4% accuracy. The overall accuracy of the CNN did not differ from humans ( p = 0.383), although performance varied across phenophases. We found that a primary challenge of using deep learning for image annotation was not related to the model itself, but instead in the quality of the community science images. Up to 4% of A. petiolata images in iNaturalist were taken from an improper distance, were physically manipulated, or were digitally altered, which limited both human and machine annotators in accurately classifying phenology. Thus, we provide a list of photography guidelines that could be included in community science platforms to inform community scientists in the best practices for creating images that facilitate phenological analysis.