Leveraging large biological interaction datasets to quantify plant specialization by bees

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Angel Chen, Mitchell K. Rapaport, Nicholas R. Bachelder, Samantha J. Solomon and Zoe Fang, University of California, Santa Barbara, Michelle J. Lee, Ecology, Evolution and Marine Biology, University of California, Santa Barbara, Santa Barbara, CA, Joshua Bang, Statistics and Applied Probability, University of California, Santa Barbara, Santa Barbara, CA, Katja C. Seltmann, Cheadle Center for Biodiversity and Ecological Restoration, University of California Santa Barbara, Santa Barbara, CA

Background/Question/Methods
Large, open-access biological datasets, like those hosted by Global Biotic Interactions (GloBI), have become increasingly accessible due to greater data collection, compilation, and storage. These databases serve to better inform our understanding of species occurrences, interactions, and ecosystem structure, broadly. In this work, we leverage GloBI data to better understand patterns of pollination, a biologically and economically essential biotic interaction between plants and pollinators. Specifically, we sought to develop a better understanding of bee specialization of pollen, an evolutionary trait in bees that underscores the stability and structure of pollinator interaction networks. We compared GloBI and expert-compiled data to better understand patterns in resource specialization.
Results/Conclusions
Through our exploration of GloBI, we found several sources of bias, including the limitations of community data collection and scarcity of rare bees. We found a strong positive correlation between the number of sources (i.e. literature, natural history collection) citing the interactions of a bee species and the number of plant families visited by that same bee species. We also found that while expert classification of bee specialists visit fewer plant families than other bees in the GloBI dataset, there are clusters of species that diverge from the expected trend. These findings indicate that observer bias, on a global scale, can skew our definition of resource specialization or generalization. Moreover, large, open-access datasets like GloBI can change our previous understanding of biological interactions and systems by accessing novel data sources and aggregation.