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