This month’s series of posts will diverge from usual where I keep to one theme. This time, I’ll seesaw between two topics that I, at least, see as related. Each stems from a recent publication I found particularly noteworthy on how plant collections will be used and curated in the future. The first is an article by Mason Heberling (2022) on “Herbaria as Big Data Sources of Plant Traits” and appeared in the International Journal of Plant Sciences. It is a review of a topic that hasn’t received enough attention: how herbaria can be used in functional trait analysis. The other is Issue 8 of The Ethnobotany Assembly or T.E.A., a quarterly online journal about plant-people relationships. It’s entitled Plant Humanities: Where Arts, Humanities, and Plants Meet and is edited and with a contribution by Felix Driver and Caroline Cornish. There is a great deal coming out about the plant humanities and some of it I find disappointing, but not this publication. The articles are varied and thought-provoking, but I’ll save further comments until the next post.
Why am I juxtaposing such different types of work? Precisely because they are so different. Yet they both speak volumes about the possible future of herbaria if researchers of this caliber continue to give their attention to the amazing resources that plant specimens provide to so many fields. I begin with plant traits because this topic fits squarely within biological inquiry, where recent reports like those on the Extended Specimen Network clearly put herbaria. However, Heberling argues that specimens have been neglected by plant trait researchers who tend to look elsewhere for data. He lays out his case in the first part of the article and also provides examples of where herbaria have made significant contributions to the field. In the second part, which I’ll discuss later, he outlines how in the future herbaria and collecting might adapt to support this area of research.
After his introduction, Heberling discusses community ecologists’ growing use of plant functional traits in their research during the past 20 years. Functional traits include morphological, chemical, phenological, and physiological attributes that serve as surrogates in understanding individual fitness. He uses as an example work on the leaf economics spectrum (LES) where characteristics such as leaf mass per area, construction costs, photosynthetic rate, and leaf life span have been found to relate to each other. This quartet varies along a spectrum from long-lived, high construction costs, low photosynthetic rates, and large area to the other end with opposite traits. Patterns falling outside the spectrum are thought to be maladaptive.
Heberling notes that in most of this work “little or no explicit attention has been paid toward specimens as primary sources of trait data (p. 90).” One reason he gives is that functional trait analyses are a recent development in plant science and preparing specimens is a technology that has been around for a long time. In the past, noting traits like phenological status was not necessarily considered important, especially because it would be apparent to someone looking at the specimen. This made sense until the age of digital data when researchers can be searching online databases for label information on phenological status and not finding it. An image may not be available, and even if it were, it would be much more time efficient to simply search the data files. Other changes in processing specimens that could aid trait research include preserving plants in different stages of development for life history research.
Also tackled are the arguments ecologists and evolutionary biologists have against using specimens, including issues of collection bias, such as toward ignoring young and immature plants and choosing those with flower and fruit. This makes sense for taxonomic studies but not for plant life history work. However, he contends that “we cannot assume the limits of herbaria without trying (p. 101).” In this case, awareness of the issue could lead to changes in collection practices, with a broader selection of material chosen. As for the idea that leaf area changes markedly over time in dried specimens, the assumption has been disproven in comparative studies. Several traits, including amino acid and metal contents have also been validated for herbarium-based measurement.
Heberling provides an extensive table citing findings where specimens have been studied in trait research and describes many ingenious approaches used both in early studies and also more recently. One of my favorites is a 2002 paper by Teece et al. on 11 Lewis and Clark specimens, among the earliest collected in the Western United States before development and industrialization caused substantive environmental changes. Leaf fatty acid content was measured as was stable carbon isotope composition. These results served as an important baseline for comparison to later specimens. Heberling also discusses the vast literature on stomata, leaf area, herbivory, and other traits that is based on specimens. It is a fascinating review. However, he then notes that there is little herbarium specimen data in the two major trait databases TRY, begun in 2007 and BIEN, started in 2016. With about 12 million records, TRY has only 10.4% of North American woody plants represented by even one specific leaf area measurement. It is this dearth that Heberling addresses in the latter part of his article and that I’ll look at in the third post of this series.
Heberling, J. M. (2022). Herbaria as Big Data Sources of Plant Traits. International Journal of Plant Sciences, 183(2), 87–118. https://doi.org/10.1086/717623.
Teece, M. A., Fogel, M., Tuross, N., McCourt, R. M., & Spamer, E. (2002). The Lewis and Clark Herbarium of The Academy of Natural Sciences. Part 3. Modern environmental applications of a historic nineteenth century botanical collection. Notulae Naturae, 477, 1–20.
Woodson, R. E. (1947). Some Dynamics of Leaf Variation in Asclepias tuberosa. Annals of the Missouri Botanical Garden, 34(4), 353. https://doi.org/10.2307/2394774.