Plant Specimens in the Future

A sample of herbarium images used for training an AI model for recognizing leaf shape (Hussein et al., 2019)

In the first post in this series, I described ideas Mason Heberling (2022) presents in his paper on the role of herbaria in plant trait studies, including an outline of why specimens have been almost ignored by ecologists and evolutionary biologists in studies of genetic and environmental influences on plant characteristics.  After this survey and a convincing argument for why specimens would be valuable in this research, he discusses how herbaria could become centers for such work.  He begin this topic with a great quote from the corn systematist Edgar Anderson (1952):  “Making a good herbarium record . . . is something like trying to stable a camel in a dog kennel” (p. 47).  I imagine Anderson attempting to wrestle a corn plant, or parts thereof, onto a herbarium sheet.  But Heberling is also thinking about how plant trait studies might need not one specimen, but a number representing different parts of a plant’s life cycle or the variations found within a population.  He is realistic in considering how much more work this would mean for herbarium staff and how much more space would be needed to store all these specimens.  That’s why he argues for a reframing of the work of herbaria, which might seem like overreaching for an article on plant traits, but he makes clear that this type of research ties in nicely with the herbarium community’s present interest in the extended specimen network (ESN):  digitally tying together many types of genetic, ecological, and morphological data with specimen data (Lendemer et al., 2019). 

Heberling deals with what information should be on a herbarium sheet for trait research beyond the basics of plant name and collector as well as date and location.  Phenological data—presence of flower or fruit—is becoming more standard, but what if leaf areas have been measured or chemical analysis done?  This information is usually fed into trait databases such as Morphobank, but is not at present often linked to a specimen.  This is why Heberling calls for the participation of the functional trait researchers in building the ESN.  It would be helpful in convincing this community of the importance of vouchers to substantiate trait data.  This might not always be feasible, but at least photographic evidence could be linked.  In the other direction, it’s important for herbarium curators to be involved in developing the Open Traits Network that is attempting to standardize and integrate trait data.          

Heberling contends that rather than declaring specimens as too imperfect a form of evidence to use in trait studies, researchers should seek to change collection practices:  “We must ask how herbaria can better address the needs of new and unanticipated specimen uses.  What information do we wish that collectors a century ago had provided with their specimens?”  Then he gets more daring:  “I propose an open reevaluation of the very collection event” (p. 108).  Decisions have to be made in the digital age about what information is on the specimen itself and what is linked to it.  As one example, he cites work that he and his colleague Bonnie Isaac (2018) have done in linking online specimen data to information including photographs they input into iNaturalist at the time of a collection event. 

As to what information is actually recorded on the specimen, Heberling notes that research shows that data fields in taxonomic software are well-standardized, but the information in those fields may not be.  Anyone who compares label data to the digital record can attest to this.  Sometimes the problem may be just a random input error, but there is also the problem of fields without controlled vocabularies, or OCR difficulties, or a particular individual’s own take on what goes where.  These problems are being resolved as best practices become more widely standardized and employed.

Then there is also the issue of intensive collecting for life history or extent of variation studies.  Heberling admits that this cannot be done in all circumstances and requires budgeting for increased curatorial work and storage that might not be possible for all institutions.  But these issues definitely need to be part of conversations on the future of herbaria.  He ends by enumerating several moves that will lead to increased effectiveness and use of plant collections including archiving population-level and ontogenetic or developmental variation.  Also there needs to be more environmental context on labels.  This has become more common with habitat descriptions and associated species often listed, but available light and other abiotic conditions should be noted, and to make this information optimally useful, a standardized vocabulary should be adopted.

Also, the ENS should be built into specimen collection itself, as in the iNaturalist case; collectors should leverage the ability to create “born digital” specimens as much as possible.  The accession should also include storage of material such as silica dried leaved in fragment packets for future research requiring destructive testing.  Finally, and perhaps most importantly, collection should be planned well into the future in order to track traits at a time of climate and habitat change.  This outline for the future is a great way for Heberling to end his article that is both rich in data and in good ideas about why herbaria are important and how they can become even more significant in the future.   

References

Anderson, E. (1952). Plants, Man and Life. University of California Press.

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

Heberling, J. M., & Isaac, B. L. (2018). INaturalist as a tool to expand the research value of museum specimens. Applications in Plant Sciences, 6(11), e01193. https://doi.org/10.1002/aps3.1193

Hussein, B. R., Malik, O. A., Ong, W.-H., & Slik, J. W. F. (2021). Automated Extraction of Phenotypic Leaf Traits of Individual Intact Herbarium Leaves from Herbarium Specimen Images Using Deep Learning Based Semantic Segmentation. Sensors, 21(13), 4549. https://doi.org/10.3390/s21134549

Lendemer, J., Thiers, B., Monfils, A. K., Zaspel, J., Ellwood, E. R., Bentley, A., LeVan, K., Bates, J., Jennings, D., Contreras, D., Lagomarsino, L., Mabee, P., Ford, L. S., Guralnick, R., Gropp, R. E., Revelez, M., Cobb, N., Seltmann, K., & Aime, M. C. (2020). The Extended Specimen Network: A Strategy to Enhance US Biodiversity Collections, Promote Research and Education. BioScience, 70(1), 23–30. https://doi.org/10.1093/biosci/biz140

Collections in the Future

Methods for measuring leaf shape in butterfly weed, Asclepias tuberosa, (Woodson 1947, p. 368)

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. 

References

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.