UC Santa Barbara Reports Title Extending Anthophila research through image and trait digitization (Big-Bee) proposal. Permalink https://escholarship.org/uc/item/2vm761mv Author Seltmann, Katja C Publication Date 2021-08-16 eScholarship.org Powered by the California Digital Library University of California I. Vision: We propose to catalyze a fundamental shift in how insects are digitized in the ADBC program by establishing Big-Bee, a Thematic Collection Network to provide bee research communities with deep digitization of focal taxa, and a network-specific data portal, The Bee Library. We will focus on outreach to the broader ecological, taxonomic and systematic bee research communities with our involvement in the USDA National Bee Monitoring Network, workforce training, and the creation of 1.2M images of over 572,000 bee specimens to capture global morphological and trait variation. Our aim is to publish openly available image and trait datasets that are easily accessible to expand the research value of the specimens beyond biology and foster partnerships in computer science, industry, and engineering. II. Introduction and Urgent Need: Animal pollination accounts for 35% of global food production and 80% of wild plants rely on insect pollination (Klein et al. 2007; Potts et al. 2010). Most of these insect pollinators are bees, which contribute 5% to 8% of total world crop production through pollination services (Klein et al. 2007). In the late 1980s, the value to US crops alone was estimated at $4.6 - $18.9 billion (Michener 2000; O’Toole 1993), which is approximately $14 - $60 billion today, adjusted for inflation. However, where data are available, bee abundance is declining dramatically, especially in agricultural regions, potentially creating deficits in pollination services (National Research Council et al. 2007; Koh et al. 2016). A recently published study by Reilly et al. (2020) also shows that US crops are frequently limited by the lack of pollinators. To avert this looming crisis, researchers need to be able to accurately track pollinator declines. Recent anthropogenic loss of biodiversity and changes in species distributions are already affecting our economy (Calvo-Agudo et al. 2019; Janzen & Hallwachs 2019; Lister & Garcia 2018; Sánchez-Bayo & Wyckhuys 2019). Broadly, these declines are being driven by habitat loss, pesticide use, and disease, among other factors (reviewed in Potts et al. 2010; Goulson et al. 2015). However, the reasons for taxon- or region- specific declines in pollinators are often unknown. Our ability to respond to the crisis of global pollinator declines requires historic and integrated data in order to track how species distributions and flight phenology are changing over time (Cameron et al. 2011; Bartomeus et al. 2013), to monitor the spread of non-native bee species (Gibbs & Sheffield 2009), to elucidate historical trends in abundance over decadal timescales (Biesmeijer et al. 2006; Colla et al. 2012; Bartomeus et al. 2013; Burkle et al. 2013), and to confirm recent bee extinction events (Ollerton et al. 2014). A recent gap analysis presented by Cobb & Seltmann et al. 2020 at the 4th Annual Digital Data Conference revealed that over 40% of US bee specimens already have digitized label data. This equals approximately 2,171,588 specimens out of the estimated 5,045,313 that exist in US collections. This is a much larger number than other groups of insects (except ants) and is largely due to great interest in bee research, federal pollinator initiatives and prior NSf funding, such as the “Collaborative Research: Collaborative Databasing of North American Bee Collections Within a Global Informatics Network” that produced 369,654 new specimen records now available on iDigBio (Ascher et al. 2016). Many collections already prioritize the digitization of bee label data in projects like the Bumblebee Project (Smithsonian Transcription Center 2020), and ongoing digitization by the USGS and USDA. Yet, the challenge is that even with this data, researchers can only assess the status of a fraction of the 20,000 known global bee species because we lack mechanistic understanding of factors leading from species- to community-scale pollinator decline, driven in part by a paucity of data on important species-level traits related to pollinator response to anthropogenic stresses. Thus, we need to fundamentally change the direction of arthropod specimen digitization to extend specimen digitization beyond the label data and create resources that include researchers early in the process. Historical specimen label data are critical in understanding long- term pollinator declines, but also contain well documented biases, and will not alone answer fundamental questions about insect declines, especially on a global scale, highlighting the need for efforts to capture data beyond what is on the labels (Shirey et al. 2020). Arguably, molecular methods are one tool for the identification of bees, but the scale at which they can be deployed for widespread monitoring is questionable. Analyses of mitochondrial and nuclear DNA sequence polymorphism have been widely used to discriminate among bee species and subspecies (francisco et al. 2001; Whitfield et al. 2006; Ramirez et al. 2010; Rasmussen & Cameron, 2010; Theeraapisakkun et al. 2010; Quezada- Euán et al. 2012). These methods require specialized personal knowledge and a well-equipped laboratory (francoy et al. 2008), or outside services (i.e., Barcode of Life). With these barriers, morphology and identification using local expertise continues to be the default means for identification, and morphometric variation in bee anatomy is sufficient to successfully identify bees, given enough image data (Rattanawannee et al. 2015; Buschbacher 2019), especially when linked with geographic information. In addition, technologies such as geometric morphometrics and computer vision have become increasingly useful in identifying species from images and have sparked interest in non-destructive methods of determination. 1 III. Intellectual Merit: A Thematic Network focused on functional Traits and Images: functional traits are phenological, physiological and anatomical characteristics that can be measured at the specimen level, which are not only related to the fitness of an organism, but also its function within the ecosystem (Violle et al. 2007). Bee functional traits are used to understand effects of pesticides (Brittain & Potts 2010), sensitivity of bees to land use pressures (De Palma et al. 2015; Harriston et al. 2018), susceptibility to parasites (fries et al. 2007), and many other critical questions. These human created pressures are unlikely to affect all species similarly, and the specific effects will be mediated by traits unique to each species (Murray et al. 2009; Roulston & Goodell 2011). for example, species with narrow ecological niches (e.g., restricted geographies, brief adult flight seasons, or narrow floral specializations) are predicted to be more sensitive than species with broader ecological niches (e.g., wide ranging species, with longer flight periods, or more generalized floral associations) (Den Boer 1968; Kassen 2002). However, compiling species-specific ecological traits for researchers is extremely difficult and time consuming, and for many species lacking entirely. This, combined with the known taxonomic difficulties of identifying contemporary specimens (Gonzalez et al. 2013), constitutes a serious impediment to researchers trying to explore ecological mechanisms driving pollinator declines. Here, we propose to create a specimen digitization project specifically designed to benefit both bee ecological research and taxonomy by providing open datasets that link ecological and anatomical traits and tackle the problem of specimen identification through traditional and computational methods. The digitization of traits from specimens is captured in the concept of the "Extended Specimen," and a growing number of studies are published that explore the relationship between trait diversity, species composition and environmental change in bees (Lendemer et al. 2020; McCravy et al. 2019). functional diversity of bees in an ecosystem may increase seed set and other measures of pollination success. Blitzer et al. (2016) showed that bee diversity based on nesting habits, sociality, and body size was associated with increased seed set in commercial farms. The commonly used functional traits used in bee research that this project will focus on are hairiness (pilosity), body color, body size, phenology (e.g., seasonality, flight period), nesting biology, sociality, and biotic associations (e.g., floral associates, parasites, pathogens). The ability to score and record other traits, including pollen-collecting structures, surface sculpture and trophic specialization will also be supported by the project. We have enlisted a Research Advisory Board to provide recommendations for potential targets and other traits during the project (see Trait and Character Digitization Targets, below). IV. Building datasets, identification tools and partnerships in a focused Symbiota Portal: We will create a Symbiota-based Bee Library portal to aggregate bee data and develop novel functionality to manage, search for, and share images and trait data. functional trait data from specimens is available on labels (e.g., biotic interactions, habitat, floral associates), and some traits can be estimated using label data from many specimens (e.g., flight period, phenology), or scored directly