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.

Preparing students to explore, understand, and resolve societal challenges such as global climate change is an important task for evolutionary and ecological biologists that will require novel and innovative pedagogical approaches. Recent calls to reform undergraduate science education emphasize the importance of engaging students in inquiry-driven, active, and authentic learning experiences. We believe that the vast digital resources (i.e., “big data”) associated with natural history collections provide invaluable but underutilized opportunities to create such experiences for undergraduates in biology. Here, we describe an online, open-access educational module that we have developed that harnesses the power of collections-based information to introduce students to multiple conceptual and analytical elements of climate change, evolutionary, and ecological biology research. The module builds upon natural history specimens and data collected over the span of nearly a century in Yosemite National Park, California, to guide students through a series of exercises aimed at testing hypotheses regarding observed differences in response to climate change by two closely related and partially co-occurring species of chipmunks. The content of the module can readily be modified to meet the pedagogical goals and instructional levels of different courses while the analytical strategies outlined can be adapted to address a wide array of questions in evolutionary and ecological biology. In sum, we believe that specimen-based natural history data represent a powerful platform for reforming undergraduate instruction in biology. Because these efforts will result in citizens who are better prepared to understand complex biological relationships, the benefits of this approach to undergraduate education will have widespread benefits to society.

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.

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.