A solution to the challenges of interdisciplinary aggregation and use of specimen-level trait data.

Understanding variation of traits within and among species through time and across space is central to many questions in biology. Many resources assemble species-level trait data, but the data and metadata underlying those trait measurements are often not reported. Here, we introduce FuTRES (Functional Trait Resource for Environmental Studies; pronounced few-tress), an online datastore and community resource for individual-level trait reporting that utilizes a semantic framework.

A community-developed extension to Darwin Core for reporting the chronometric age of specimens.

Darwin Core, the data standard used for sharing modern biodiversity and paleodiversity occurrence records, has previously lacked proper mechanisms for reporting what is known about the estimated age range of specimens from deep time. This has led to data providers putting these data in fields where they cannot easily be found by users, which impedes the reuse and improvement of these data by other researchers. Here we describe the development of the Chronometric Age Extension to Darwin Core, a ratified, community-developed extension that enables the reporting of ages of specimens from deeper time and the evidence supporting these estimates.

Analyzing a phenological anomaly in Yucca of the southwestern United States.

Yucca in the American desert Southwest typically flowers in early spring, but a well-documented anomalous bloom event occurred during an unusually cold and wet late fall and early winter 2018-2019. We used community science photographs to generate flowering presence and absence data. We fit phenoclimatic models to determine which climate variables are explanatory for normal flowering, and then we tested if the same conditions that drive normal blooming also drove the anomalous blooming event.

Climate, urbanization, and species traits interactively drive flowering duration.

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.

Machine Learning Using Digitized Herbarium Specimens to Advance Phenological Research.

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.

Maximizing human effort for analyzing scientific images: A case study using digitized herbarium sheets.

Premise: Digitization and imaging of herbarium specimens provides essential historical phenotypic and phenological information about plants. However, the full use of these resources requires high-quality human annotations for downstream use. Here we provide guidance on the design and implementation of image annotation projects for botanical research.

Methods for broad-scale plant phenology assessments using citizen scientists' photographs.

Premise: Citizen science platforms for sharing photographed digital vouchers, such as iNaturalist, are a promising source of phenology data, but methods and best practices for use have not been developed. Here we introduce methods using flowering phenology as a case study, because drivers of phenology are not well understood despite the need to synchronize flowering with obligate pollinators. There is also evidence of recent anomalous winter flowering events, but with unknown spatiotemporal extents.

Research applications of primary biodiversity databases in the digital age.

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.

ZooArchNet: Connecting zooarchaeological specimens to the biodiversity and archaeology data networks.

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.

Integrating herbarium specimen observations into global phenology data systems.

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.