Emerging frontiers in phenological research
Libby Ellwood, Katelin Pearson, and Gil Nelson
Herbarium specimens record phenology—the study of cyclical events in an organism's life cycle—with such features as young flower buds, senescing leaves, and even bare branches on an herbarium sheet. There are other ways of recording phenology, such as records of observations and satellite imagery, but specimen data provide historical information that is verifiable, spans the globe, and is becoming increasingly accessible thanks to digitization efforts. Plant phenology is often sensitive to changes in temperature and precipitation, making phenology data especially critical for climate change research.
The urgency of biodiversity research relating to environmental change, the ready availability of phenological data, along with continually improving computational capabilities, make this an ideal time to be conducting phenological research. In the Special Issue, “Emerging Frontiers in Phenological Research”, in the journal, Applications in Plant Sciences, nine research groups present innovative phenology projects, all of which make use of, or can be applied to, herbarium specimens.
Digitized herbarium specimens most often require the addition of phenological classification to be useful for phenological research, and this can be resource‐intensive and time‐consuming. Lorieul et al. (2019) describe advances in fine- and coarse-scale phenological scoring of herbarium specimens using computer neural networks, thereby streamlining the process of annotating specimens for phenophases. Fine‐scale phenophases may be necessary for addressing certain research questions, although as Pearson (2019) models and Ellwood et al. (2019) demonstrate, binary annotations (e.g., flowering/not flowering) provide nearly as much value as more precise estimations of fine‐scale phenophases. In these two studies, more precise estimates of phenophase led to slightly stronger models. However, the effort required to produce accurate, fine‐scale phenophase annotations may not always be worth the small statistical advantage.
Most phenology studies require a substantial amount of data collection, though Park et al. (2019) present software they developed, PhenoForecaster, that predicts the flowering times of more than 2300 angiosperm species. This software improves the accessibility of phenological data for use in modeling studies. Understudied species and localities are also highlighted in this collection. Andrew et al. (2019) used collections to find that fungal diversity is higher in forests than in urban areas. Daru et al. (2019) examined a taxonomically understudied taxon, Protea L., in a phenologically understudied part of the world—subtropical Africa, and found that even these botanically unique species respond to temperature in ways that are similar to northern temperate species. Panchen et al.'s (2019) research investigates another geographically understudied area, the Canadian Arctic. Panchen et al. evaluate the degree to which the underrepresentation of arctic species in herbaria impacts one's ability to use collections of arctic plants for phenology research. Their findings point to spatial, temporal, and phenological biases that must be considered by researchers using these data.
Richardson et al. (2019) present a study using another transformative tool for phenological research: landscape‐scale images. Using PhenoCam images of vegetative phenology, Richardson et al. evaluated Hopkins’ Bioclimatic Law, which posits that phenology is directly related to latitude, longitude, and elevation. They find that ecotypes vary in how strongly they follow the Bioclimatic Law. Integrating data from image‐based studies (e.g., Richardson et al., 2019) with data from herbarium‐based studies (e.g., Daru et al., 2019; Ellwood et al., 2019; Park et al., 2019), and even aggregating data between studies with similar data sources, can be a challenge due to the many ways phenology can be described. Fortunately, a standardized, relational vocabulary is being developed in the Plant Phenology Ontology (PPO), which enables integration of phenological data across platforms, data sources, and species (Stucky et al., 2018). Brenskelle et al. (2019) describe important updates to the PPO that will allow data from herbarium‐based studies to be represented more accurately by including descriptive terms for how measurements of plant parts relate to full plants—a relationship that was previously unaddressed in this standardized vocabulary.
This suite of articles represents work at the cutting edge of phenological research. We anticipate that these articles will provide even the most experienced phenological researcher new insights into research methods, software packages, and foundational standards and practices. Likewise, we are hopeful that this work will inspire new and experienced researchers to continue to push the boundaries of what is possible when using herbarium specimens in phenological research.
All articles in this Special Issue are open access and can be found here. This post contains excerpts from the Introduction article of the issue. We are appreciative of funding from iDigBio (U.S. National Science Foundation grants EF‐1115210 and DBI‐1547229).
Image accessed on: idigbio.org/portal/search
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