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Creators/Authors contains: "Heberling, J. Mason"

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  1. Understory forest plants are often limited by shade from the canopy above them. Many such species therefore make use of a shade avoidance strategy referred to as “phenological escape” to access ephemeral light availability during periods when the canopy above them is open (e.g., in early spring). In this primer, we review past literature on phenological escape and related topics. We discuss (1) the physiological importance of this shade avoidance strategy, (2) the effects that climate change may have on species performance via changes in phenological escape, (3) the potential for climate change to result in phenological mismatch related to shade avoidance, and (4) the potential avenues of future research in this area of study. Phenological escape is an important strategy used by spring-active plant species ranging from spring ephemeral wildflowers to deciduous tree seedlings, allowing them to assimilate 50%–100% of their annual carbon budgets before the canopy closes above them. Access to spring light, and thus success of this shade avoidance strategy, is projected to change in response to climate change. Change in access to light, and therefore change in spring performance, likely depends on functional group (woody vs. nonwoody plants), continent, and other geographic and environmental drivers. 
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    Free, publicly-accessible full text available July 1, 2025
  2. Abstract Recent evidence suggests that community science and herbarium datasets yield similar estimates of species' phenological sensitivities to temperature. Despite this, two recent studies by Alecrim et al. (2023) and Miller et al. (2022) found very different results when using different data sources (community science and herbarium specimens, respectively) to investigate whether warming threatens wildflowers with phenological mismatch in relation to shading by deciduous trees.Here, we investigated whether differences between the two studies' results could be reconciled by testing four hypotheses related to model design, species, spatiotemporal data extent and phenophase.Hybrid model structures brought results from the two datasets closer together but did not fully reconcile the differences between the studies. Neither the species nor the phenophase selected for analysis seemed to be responsible for differences in results. Cropping the datasets to match spatial and temporal extents appeared to reconcile most differences but only at the cost of much higher uncertainty associated with reduced sample size.Synthesis: Our analysis suggests that although species‐level estimates of phenological sensitivity may be similar between community science and herbarium datasets, inherent differences in the types and extent of data may lead to contradictory inference about complex biotic interactions. We conclude that, until community science data repositories expand to match the range of climate conditions present in herbarium collections or until herbarium collections match the spatial extent and temporal frequency of community science repositories, ecological studies should ideally be evaluated using both datasets to test the possibility of biased results from either. 
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  3. When scientists study plants, they often collect, preserve, and store parts of the plants in a big collection called an herbarium. These plant specimens serve as proof that a species was growing in a certain place at a certain time. Herbaria (“herbaria” is the plural of herbarium) are where scientists describe new plant species and study how different species are related. Herbaria also contain lots of information about where certain plant species grow, what type of habitats species like, and at what time of year plants bloom and make fruits. Finally, herbaria are powerful tools for helping us understand how plants are affected by disturbances like habitat destruction and climate change. For all of these reasons, herbaria allow us to better understand and protect plant species all over the world. To continue benefitting from herbaria, we need to keep collecting plants and make these collections accessible to the world. 
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  4. 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. 
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  5. 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. 
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  6. 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. 
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