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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.

 

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. 

Temperate deciduous forests by definition include a large proportion of woody species that shed their leaves each autumn and are completely leafless during winter months. Leaf senescence in deciduous trees is an active, complex process typically involving the physiological formation of an abscission layer causing the petiole to mechanically detach from the branch. However, several deciduous species retain all or some senesced leaves on branches through much of winter, a phenomenon called leaf marcescence. Marcescence has long fascinated botanists, including Pehr Kalm as early as 1749. Yet, surprisingly little research has been done to date. Here, we review and explore patterns and mechanisms of leaf marcescence in temperate forests, bringing together six nonmutually exclusive but separately proposed hypotheses: (1) Marcescence has no adaptive function but rather an evolutionary byproduct; (2) Marcescent leaves deter winter browsing herbivores; (3) Leaf retention through winter improves nutrient resorption during autumn senescence; (4) Prolonged leaf shedding into spring minimizes nutrient leaching and promotes decomposition; (5) Marcescent leaves protect overwintering buds from frost or desiccation; and (6) Marcescent canopies provide winter cover for animals (including insects, birds, bats), thereby affecting plant nutrient availability via excrement. No hypothesis has complete support and few tests of multiple hypotheses have been done. It is likely that any adaptive value of marcescence is species and context dependent. Despite increased interest in plant phenology and prevalence of this trait, much remains to be understood on the physiology, evolution, function, and ecological implications of leaf marcescence.

 

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. 

The widespread digitization of natural history collections, combined with novel tools and approaches is revolutionizing biodiversity science. The ‘extended specimen’ concept advocates a more holistic approach in which a specimen is framed as a diverse stream of interconnected data. Herbarium specimens that by their very nature capture multispecies relationships, such as certain parasites, fungi and lichens, hold great potential to provide a broader and more integrative view of the ecology and evolution of symbiotic interactions. This particularly applies to parasite–host associations, which owing to their interconnectedness are especially vulnerable to global environmental change.

Here, we present an overview of how parasitic flowering plants is represented in herbarium collections. We then discuss the variety of data that can be gathered from parasitic plant specimens, and how they can be used to understand global change impacts at multiple scales. Finally, we review best practices for sampling parasitic plants in the field, and subsequently preparing and digitizing these specimens.

Plant parasitism has evolved 12 times within angiosperms, and similar to other plant taxa, herbarium collections represent the foundation for analysing key aspects of their ecology and evolution. Yet these collections hold far greater potential. Data and metadata obtained from parasitic plant specimens can inform analyses of co‐distribution patterns, changes in eco‐physiology and species plasticity spanning temporal and spatial scales, chemical ecology of tripartite interactions (e.g. host–parasite–herbivore), and molecular data critical for species conservation. Moreover, owing to the historic nature and sheer size of global herbarium collections, these data provide the spatiotemporal breadth essential for investigating organismal response to global change.

Parasitic plant specimens are primed to serve as ideal examples of extended specimen concept and help motivate the next generation of creative and impactful collection‐based science. Continued digitization efforts and improved curatorial practices will contribute to opening these specimens to a broader audience, allowing integrative research spanning multiple domains and offering novel opportunities for education.

 

Abstract Natural history collections (NHCs) are the foundation of historical baselines for assessing anthropogenic impacts on biodiversity. Along these lines, the online mobilization of specimens via digitization—the conversion of specimen data into accessible digital content—has greatly expanded the use of NHC collections across a diversity of disciplines. We broaden the current vision of digitization (Digitization 1.0)—whereby specimens are digitized within NHCs—to include new approaches that rely on digitized products rather than the physical specimen (Digitization 2.0). Digitization 2.0 builds on the data, workflows, and infrastructure produced by Digitization 1.0 to create digital-only workflows that facilitate digitization, curation, and data links, thus returning value to physical specimens by creating new layers of annotation, empowering a global community, and developing automated approaches to advance biodiversity discovery and conservation. These efforts will transform large-scale biodiversity assessments to address fundamental questions including those pertaining to critical issues of global change.