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Premise Biological outliers (observations that fall outside of a previously understood norm, e.g., in phenology or distribution) may indicate early stages of a transformative change that merits immediate attention. Collectors of biodiversity specimens such as plants, fungi, and animals are on the front lines of discovering outliers, yet the role collectors currently play in providing such data is unclear. Methods We surveyed 222 collectors of a broad range of taxa, searched 47 training materials, and explored the use of 170 outlier terms in 75 million specimen records to determine the current state of outlier detection and documentation in this community. Results Collectors reported observing outliers (e.g., about 80% of respondents observed morphological and distributional outliers at least occasionally). However, relatively few specimen records include outlier terms, and imprecision in their use and handling in data records complicates data discovery by stakeholders. This current state appears to be at least partly due to the absence of protocols: only one of the training materials addressed documenting and reporting outliers. Conclusions We suggest next steps to mobilize this largely untapped, yet ideally suited, community for early detection of biotic change in the Anthropocene, including community activities for building relevant best practices.

Distributions of taxa across time and space are central to understanding biodiversity and biotic change, yet currently available occurrence data, drawn from biodiversity specimen records and observational datasets, are often insufficient to answer many driving questions. Records of “associated taxa,” taxa co-occurring with a specimen at the time and place of collection, have the potential to fill data gaps and expand the spatiotemporal scope of current occurrence records. I developed a method to extract associated taxon records from 84,328 digitized specimen records and examined the potential of these data to improve the quantity and quality of existing species occurrence data. Adding associated taxon records increased the size of the test dataset by 18.5%, spanned multiple decades (1937–2016), and potentially extended the known range of 217 taxa in Florida and up to 1500 taxa in the United States, demonstrating the capacity of these records to deepen our understanding of changes in the distributions of taxa on Earth. These results suggest that increased attention to documenting associated taxa could be a promising way to maximize the impact of every collecting event.

The timing of phenological events, such as leaf-out and flowering, strongly influence plant success and their study is vital to understanding how plants will respond to climate change. Phenological research, however, is often limited by the temporal, geographic, or phylogenetic scope of available data. Hundreds of millions of plant specimens in herbaria worldwide offer a potential solution to this problem, especially as digitization efforts drastically improve access to collections. Herbarium specimens represent snapshots of phenological events and have been reliably used to characterize phenological responses to climate. We review the current state of herbarium-based phenological research, identify potential biases and limitations in the collection, digitization, and interpretation of specimen data, and discuss future opportunities for phenological investigations using herbarium specimens.

Premise of the Study: Phenological annotation models computed on large‐scale herbarium data sets were developed and tested in this study. Methods: Herbarium specimens represent a significant resource with which to study plant phenology. Nevertheless, phenological annotation of herbarium specimens is time‐consuming, requires substantial human investment, and is difficult to mobilize at large taxonomic scales. We created and evaluated new methods based on deep learning techniques to automate annotation of phenological stages and tested these methods on four herbarium data sets representing temperate, tropical, and equatorial American floras. Results: Deep learning allowed correct detection of fertile material with an accuracy of 96.3%. Accuracy was slightly decreased for finer‐scale information (84.3% for flower and 80.5% for fruit detection). Discussion: The method described has the potential to allow fine‐grained phenological annotation of herbarium specimens at large ecological scales. Deeper investigation regarding the taxonomic scalability of this approach is needed.

Premise of the Study A novel method of estimating phenology of herbarium specimens was developed to facilitate more precise determination of plant phenological responses to explanatory variables (e.g., climate). Methods and Results Simulated specimen data sets were used to compare the precision of phenological models using the new method and two common, alternative methods (flower presence/absence and ≥50% flowers present). The new "estimated phenophase" method was more precise and extracted a greater number of significant species-level relationships; however, this method only slightly outperformed the simple "binary" (e.g., flowers present/absent) method. Conclusions The new method enables estimation of phenological trends with greater precision. However, when time and resources are limited, a presence/absence method may offer comparable results at lower cost. Using a more restrictive approach, such as only including specimens in a certain phenophase, is not advised given the detrimental effect of decreased sample size on resulting models.

PREMISE OF THE STUDY: Herbarium specimens provide a robust record of historical plant phenology (the timing of seasonal events such as flowering or fruiting). However, the difficulty of aggregating phenological data from specimens arises from a lack of standardized scoring methods and definitions for phenological states across the collections community. METHODS AND RESULTS: To address this problem, we report on a consensus reached by an iDigBio working group of curators, researchers, and data standards experts regarding an efficient scoring protocol and a data-sharing protocol for reproductive traits available from herbarium specimens of seed plants. The phenological data sets generated can be shared via Darwin Core Archives using the Extended MeasurementOrFact extension. CONCLUSIONS: Our hope is that curators and others interested in collecting phenological trait data from specimens will use the recommendations presented here in current and future scoring efforts. New tools for scoring specimens are reviewed.