Our world is in the midst of unprecedented change—climate shifts and sustained, widespread habitat degradation have led to dramatic declines in biodiversity rivaling historical extinction events. At the same time, new approaches to publishing and integrating previously disconnected data resources promise to help provide the evidence needed for more efficient and effective conservation and management. Stakeholders have invested considerable resources to contribute to online databases of species occurrences. However, estimates suggest that only 10% of biocollections are available in digital form. The biocollections community must therefore continue to promote digitization efforts, which in part requires demonstrating compelling applications of the data. Our overarching goal is therefore to determine trends in use of mobilized species occurrence data since 2010, as online systems have grown and now provide over one billion records. To do this, we characterized 501 papers that use openly accessible biodiversity databases. Our standardized tagging protocol was based on key topics of interest, including: database(s) used, taxa addressed, general uses of data, other data types linked to species occurrence data, and data quality issues addressed. We found that the most common uses of online biodiversity databases have been to estimate species distribution and richness, to outline data compilation and publication, and to assist in developing species checklists or describing new species. Only 69% of papers in our dataset addressed one or more aspects of data quality, which is low considering common errors and biases known to exist in opportunistic datasets. Globally, we find that biodiversity databases are still in the initial stages of data compilation. Novel and integrative applications are restricted to certain taxonomic groups and regions with higher numbers of quality records. Continued data digitization, publication, enhancement, and quality control efforts are necessary to make biodiversity science more efficient and relevant in our fast-changing environment.

Plant phenology-the timing of plant life cycle stages such as flowering or leaf senescence-plays a critical role in terrestrial ecosystems and is known to be responsive to environmental changes (Rathcke and Lacey, 1985; Ollerton and Lack, 1992; Cleland et al., 2007; Chuine, 2010). The fingerprint of accelerating global change, including both global-scale climatic changes and their local-scale outcomes, along with human disturbance, may show its first biotic signs in disrupted phenologies. These disruptions can have significant consequences if they lead to phenological mismatches between plants and the animals that depend on them (Kudo and Ida, 2013; Mayor et al., 2017). Plant phenology data that cover broad scales have until recently only been available through monitoring networks, such as the National Phenology Network (NPN) (Schwartz et al., 2012; Rosemartin et al., 2014) in the United States, which coordinates amateur and professional scientists to make phenological observations. Although such networks provide critical data, reporting remains sparse because such networks often focus on key taxa or repeat sampling at a relatively small number of locations. Promising new resources, as well as historical resources (e.g., imaged herbarium sheets), that provide wider taxonomic and spatial coverage are becoming available digitally (Davis et al., 2015; Willis et al., 2017; Silva et al., 2018); however, these have attendant issues with sampling protocols and with proper annotation of phenological traits and species identification. An alternative set of resources that has yet to be broadly tapped for phenology studies comprises repositories of naturalist citizen science images. Here we focus in particular on iNaturalist (http://www.inatu ralist.org/) as a source of phenology data because it: (1) enforces the provision of species occurrence metadata required for scientific use; (2) manages taxonomic resources, putting a premium on quality identification, and sets objective requirements for records to be considered "research grade"; (3) allows reporting of cultivation status along with annotation of traits including phenology in metadata fields, although trait annotation is still not often used; (4) is growing at a rapid and

Abstract Machine learning (ML) has great potential to drive scientific discovery by harvesting data from images of herbarium specimens—preserved plant material curated in natural history collections—but ML techniques have only recently been applied to this rich resource. ML has particularly strong prospects for the study of plant phenological events such as growth and reproduction. As a major indicator of climate change, driver of ecological processes, and critical determinant of plant fitness, plant phenology is an important frontier for the application of ML techniques for science and society. In the present article, we describe a generalized, modular ML workflow for extracting phenological data from images of herbarium specimens, and we discuss the advantages, limitations, and potential future improvements of this workflow. Strategic research and investment in specimen-based ML methods, along with the aggregation of herbarium specimen data, may give rise to a better understanding of life on Earth.

A wave of green leaves and multi‐colored flowers advances from low to high latitudes each spring. However, little is known about how flowering offset (i.e., ending of flowering) and duration of populations of the same species vary along environmental gradients. Understanding these patterns is critical for predicting the effects of future climate and land‐use change on plants, pollinators, and herbivores. Here, we investigated potential climatic and landscape drivers of flowering onset, offset, and duration of 52 plant species with varying key traits. We generated phenology estimates using >270,000 community‐science photographs and a novel presence‐only phenometric estimation method. We found longer flowering durations in warmer areas, which is more obvious for summer‐blooming species compared to spring‐bloomers driven by their strongly differing offset dynamics. We also found that higher human population density and higher annual precipitation are associated with delayed flowering offset and extended flowering duration. Finally, offset of woody perennials was more sensitive than herbaceous species to both climate and urbanization drivers. Empirical forecast models suggested that flowering durations will be longer in 2030 and 2050 under representative concentration pathway (RCP) 8.5, especially for summer‐blooming species. Our study provides critical insight into drivers of key flowering phenophases and confirms that Hopkins’ Bioclimatic Law also applies to flowering durations for summer‐blooming species and herbaceous spring‐blooming species.

We are in the midst of unprecedented change—climate shifts and sustained, widespread habitat degradation have led to dramatic declines in biodiversity rivaling historical extinction events. At the same time, new approaches to publishing and integrating previously disconnected data resources promise to help provide the evidence needed for more efficient and effective conservation and management. Stakeholders have invested considerable resources to contribute to online databases of species occurrences and genetic barcodes. However, estimates suggest that only 10% of biocollections are available in digital form. The biocollections community must therefore continue to promote digitization efforts, which in part requires demonstrating compelling applications of the data. Our overarching goal is therefore to determine trends in use of mobilized species occurrence data since 2010, as online systems have grown and now provide over one billion records. To do this, we characterized 501 papers that use openly accessible biodiversity databases. Our standardized tagging protocol was based on key topics of interest, including: database(s) used, taxa addressed, general uses of data, other data types linked to species occurrence data, and data quality issues addressed. We found that the most common uses of online biodiversity databases have been to estimate species distribution and richness, to outline data compilation and publication, and to assist in developing species checklists or describing new species. Only 69% of papers in our dataset addressed one or more aspects of data quality, which is low considering common errors and biases known to exist in opportunistic datasets. Globally, we find that biodiversity databases are still in the initial stages of data compilation. Novel and integrative applications are restricted to certain taxonomic groups and regions with higher numbers of quality records. Continued data digitization, publication, enhancement, and quality control efforts are necessary to make biodiversity science more efficient and relevant in our fast-changing world.

Interdisciplinary collaborations and data sharing are essential to addressing the long history of human-environmental interactions underlying the modern biodiversity crisis. Such collaborations are increasingly facilitated by, and dependent upon, sharing open access data from a variety of disciplinary communities and data sources, including those within biology, paleontology, and archaeology. Significant advances in biodiversity open data sharing have focused on neontological and paleontological specimen records, making available over a billion records through the Global Biodiversity Information Facility. But to date, less effort has been placed on the integration of important archaeological sources of biodiversity, such as zooarchaeological specimens. Zooarchaeological specimens are rich with both biological and cultural heritage data documenting nearly all phases of human interaction with animals and the surrounding environment through time, filling a critical gap between paleontological and neontological sources of data within biodiversity networks. Here we describe technical advances for mobilizing zooarchaeological specimen-specific biological and cultural data. In particular, we demonstrate adaptations in the workflow used by biodiversity publisher VertNet to mobilize Darwin Core formatted zooarchaeological data to the GBIF network. We also show how a linked open data approach can be used to connect existing biodiversity publishing mechanisms with archaeoinformatics publishing mechanisms through collaboration with the Open Context platform. Examples of ZooArchNet published datasets are used to show the efficacy of creating this critically needed bridge between biological and archaeological sources of open access data. These technical advances and efforts to support data publication are placed in the larger context of ZooarchNet, a new project meant to build community around new approaches to interconnect zoorchaeological data and knowledge across disciplines.

Premise of the Study The Plant Phenology Ontology ( PPO ) was originally developed to integrate phenology observations of whole plants across different global observation networks. Here we describe a new release of the PPO and associated data pipelines that supports integration of phenology observations from herbarium specimens, which provide historical and modern phenology data. Methods and Results Critical changes to the PPO include key terms that describe how measurements from parts of plants, which are captured in most imaged herbarium specimens, relate to whole plants. We provide proof of concept for ingesting annotations from imaged herbarium sheets of Prunus serotina , the common black cherry. We then provide an example analysis of changes in flowering timing over the past 125 years, demonstrating the value of integrating herbarium and observational phenology data sets. Conclusions These conceptual and technical advances will support the addition of phenology data from herbaria, but also could be expanded upon to facilitate the inclusion of data from photograph‐based citizen science platforms. With the incorporation of herbarium phenology data, new historical baseline data will strengthen the capability to monitor, model, and forecast plant phenology changes.

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