Cheadle Center for Biodiversity and Ecological Restoration

Overview: The Big-Bee project will advance research on bee ecology and taxonomy through the creation and standardization of open datasets for taxonomic and computational analysis. It will overcome challenges in bee identification and discovery by linking ecological and anatomical traits. Today, the vast majority of digital insect records include only taxon names, dates, and locations. This paucity of data greatly limits opportunities for research. To achieve fundamental changes in how insect specimens are digitized, the project will supply innovative methods for deep digitization of target taxa, including high-resolution imaging methods. These image data will enable the scoring of life-history traits and will facilitate identification from digitized specimens. Big-Bee will further revolutionize processes of insect specimen digitization by enabling global bee data to be integrated and linked. The project will produce important partnerships between researchers, industry, and government agencies. Specifically, Big-Bee will:

1) Digitize over 0.5 million new specimens via an image-to-data workflow that will capture both labels and specimens of 5,509 agriculturally and phylogenetically important global bee species.

2) Create over 1.3M specimen images from which the project will capture detailed functional trait information at the specimen level. It will create a widely accessible and citable image datasets. The data will include 109K high-resolution, focal-stacked exemplar and diagnostic images, and 10,300 image suites each consisting of 64 images capturing 360 degrees views of specimens, including over 659,000 high-resolution images of bees.  This will enable the scientific-grade 3D reconstruction of specimens from images and will supply rich data for computational discovery and computer vision applications.

 3) Create and steward a new Symbiota portal, Bee Library, that will index all digitized bee occurrence data. Through it, the project will furnish new tools for annotating and sharing ecological and anatomical information. The Bee Library will facilitate identification by producing curated, and openly reviewed datasets about critical bee functional traits, biotic interactions (parasites, floral associates), geographic distributions, and identification features. Project data curators will steward the Bee Library, evaluating and correcting georeference data for all digitized bee species, and providing reports back to collections.

Intellectual Merit: Animal pollination accounts for 35% of global food production. Insects pollinate 80% of wild plants. Most of these insect pollinators are bees. Although limited in scope, extant data show bee abundance is declining due to anthropogenic disturbances. A growing body of research demonstrates that pollinator responses to anthropogenic disturbances, such as land use and climate change, are mediated by functional or life-history traits that impact the fitness of individual species within the community. These effects impact plant-pollinator interactions in both natural and agricultural systems. While the US currently possesses a wealth of digitized historic bee specimen data, most records do not include associated data reflecting functional traits. This greatly limits the mechanistic insights they can provide about factors driving long-term community response to anthropogenic disturbances. In order to accurately track how and why pollinators are declining, better tools are urgently needed to support bee identification, functional trait annotation, and the exchange of historical species and trait information among researchers, industry, and policymakers.

Broader Impacts: Big-Bee will collaborate on the creation of the US National Native Bee Monitoring Plan as part of the Bee Monitoring Research Coordination Network currently funded through the USDA. It will create an infrastructure to support hundreds of bee researchers in specimen digitization. The project published datasets contribute to the Open Traits Network, Global Biotic Interactions, and other downstream applications. It will develop computer science and collections-based undergraduate curricula, increasing capacity in entomology for bee identification and research. Participating institutions will develop undergraduate course materials that involve examination of bee specimens and the use of data derived from specimens, and will contribute lesson plans to BLUE (Biodiversity Literacy for Undergraduate Education). Extensive workforce training in biodiversity informatics, museum preparation, bee identification, and the creation of citizen science monitoring programs will be offered to collections within and outside the Big-Bee network. Big-Bee museums will contribute to a new syndicated college radio science program, The Buzz, that will be produced by undergraduate students in science communication.