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. 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.

Mobilizing the community of biodiversity specimen collectors to effectively detect and document outliers in the Anthropocene.

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.

A new method and insights for estimating phenological events from herbarium specimens.

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.

Toward a large-scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras.

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.

Spring- and fall-flowering species show diverging phenological responses to climate in the Southeast USA.

Plant phenological shifts (e.g., earlier flowering dates) are known consequences of climate change that may alter ecosystem functioning, productivity, and ecological interactions across trophic levels. Temperate, subalpine, and alpine regions have largely experienced advancement of spring phenology with climate warming, but the effects of climate change in warm, humid regions and on autumn phenology are less well understood. In this study, nearly 10,000 digitized herbarium specimen records were used to examine the phenological sensitivities of fall- and spring-flowering asteraceous plants to temperature and precipitation in the US Southeastern Coastal Plain. Climate data reveal warming trends in this already warm climate, and spring- and fall-flowering species responded differently to this change. Spring-flowering species flowered earlier at a rate of 1.8-2.3 days per 1 °C increase in spring temperature, showing remarkable congruence with studies of northern temperate species. Fall-flowering species flowered slightly earlier with warmer spring temperatures, but flowering was significantly later with warmer summer temperatures at a rate of 0.8-1.2 days per 1 °C. Spring-flowering species exhibited slightly later flowering times with increased spring precipitation. Fall phenology was less clearly influenced by precipitation. These results suggest that even warm, humid regions may experience phenological shifts and thus be susceptible to potentially detrimental effects such as plant-pollinator asynchrony.

Digitization protocol for scoring reproductive phenology from herbarium specimens of seed plants.

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.

Worldwide Engagement for Digitizing Biocollections (WeDigBio): The Biocollections Community's Citizen-Science Space on the Calendar.

The digitization of biocollections is a critical task with direct implications for the global community who use the data for research and education. Recent innovations to involve citizen scientists in digitization increase awareness of the value of biodiversity specimens; advance science, technology, engineering, and math literacy; and build sustainability for digitization. In support of these activities, we launched the first global citizen-science event focused on the digitization of biodiversity specimens: Worldwide Engagement for Digitizing Biocollections (WeDigBio). During the inaugural 2015 event, 21 sites hosted events where citizen scientists transcribed specimen labels via online platforms (DigiVol, Les Herbonautes, Notes from Nature, the Smithsonian Institution's Transcription Center, and Symbiota). Many citizen scientists also contributed off-site. In total, thousands of citizen scientists around the world completed over 50,000 transcription tasks. Here, we present the process of organizing an international citizen-science event, an analysis of the event's effectiveness, and future directions-content now foundational to the growing WeDigBio event.

Old Plants, New Tricks: Phenological Research Using Herbarium Specimens.

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.