Richard Rabeler, associate research scientist, U-M Herbarium, verifying the determination of a specimen. Image credit: Dale Austin

The University of Michigan Herbarium has been awarded seven National Science Foundation grants over the past four years. Six of the grants involve Thematic Collections Networks (TCN), which are collaborative projects administered by the Advancing Digitization of Biodiversity Collections (ADBC) project.  

Each TCN is a network of institutions with a strategy for digitizing information that addresses a particular research theme, according to iDigBio, the nationwide coordinator for the program based at the University of Florida.  Once digitized, data are easily accessed and available for other research and educational use. 

Since the first TCN project at the Herbarium (Tri-Trophic TCN) began in January 2012, over 475,000 specimens from the collection have been imaged as part of these projects.  Most of the images, either of the specimen labels or of the specimens themselves, are available online. Another aspect involves digitizing the data about the individual specimens and georeferencing localities.

Tri-trophic update


The first TCN project at the University of Michigan Herbarium was "Plants, Herbivores, and Parasitoids: A Model System for the study of Tri-trophic Associations”, or, abbreviated, the Tri-trophic TCN. Dr. Richard Rabeler, associate research scientist at the Herbarium, is the principal investigator.


The project goal is to digitize specimen records for 20 families of vascular plants, the Hemiptera (plant bugs) that eat them, and the parasitoid wasps that feed on the Hemiptera; hence the name "tri-trophic.” There are 34 institutions involved, including 15 herbaria and 19 insect collections. The lead institution is the American Museum of Natural History.

Our contribution to the project involved imaging all of our North American and Mexican specimens of Cyperaceae (sedges) and Poaceae (grasses); approximately 116,000 specimens have been imaged.  Data records have been completed where none existed before using a combination of optical character recognition and transcription. Records will be georeferenced prior to the project’s completion in January 2016