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ABSTRACT AimAll bees depend on angiosperms for survival, while many angiosperms depend on bees for reproduction. However, bee and flowering plant species richness do not peak in the same geographical regions of the world, suggesting that the flora in regions where bees are not as diverse, such as the tropics, may be relatively less bee‐dependent. We test this assumption by analysing whether local relative bee diversity can predict the proportion of angiosperm species that attract bees (i.e., “bee flowers”). LocationThe Americas. Time PeriodPresent. Major Taxa StudiedBees and angiosperms. MethodsWe map the proportion of bees to angiosperm species using recently available datasets of geographic distribution for both taxa. We then combine data from surveys on pollination systems for 56 floristic communities to estimate the proportion of angiosperm species with bee flowers in different regions. Finally, we test whether the proportion of bee flowers in a community can be predicted by a combination of relative bee species richness and abiotic environmental variables. ResultsBroad distribution maps show that the relative richness of bees in relation to angiosperms decreases in tropical areas; however, there is no evidence that tropical floristic communities are less dependent on bees. Interestingly, the proportion of angiosperm species with bee flowers was almost always found to be around 50% across biomes, with some variation depending on the habitat type and method of data collection. Main ConclusionsOur results suggest that plant communities can be highly bee‐dependent even where bees are relatively less diverse. While lower species richness does not mean lower abundance, and fewer bee species of specific life histories can still provide adequate pollination supply for a large number of angiosperm species, this pattern may impact how bee flowers interact with bees in different areas, and consequently how bees and bee flower specialisations have evolved over time. 

Abstract Insects are declining in abundance and species richness, globally. This has broad implications for the ecology of our planet, many of which we are only beginning to understand. Comprehensive, large-scale efforts are urgently needed to quantify and mitigate insect biodiversity loss. Because there is broad interest in this topic from a range of scientists, policymakers, and the general public, we posit that such endeavors will be most effective with precise and standardized terms. The Entomological Society of America is the world’s largest association of professional entomologists and is ideally positioned to lead the way on this front. We provide here a glossary of definitions for biodiversity loss terminology. This can be used to enhance and clarify communication among entomologists and others with an interest in addressing the multiple overlapping research, policy, and outreach challenges surrounding this urgent issue. 

Abstract Climate is a fundamental driver of macroecological patterns in functional trait variation. However, many of the traits that have outsized effects on thermal performance are complex, multi‐dimensional, and challenging to quantify at scale.To overcome this challenge, we leveraged techniques in deep learning and computer vision to quantify hair coverage and lightness of bees, using images of a diverse and widely distributed sample of museum specimens.We demonstrate that climate shapes variation in these traits at a global scale, with bee lightness increasing with maximum environmental temperatures (thermal melanism hypothesis) and decreasing with annual precipitation (Gloger's Rule).We found that deserts are hotspots for bees covered in light‐coloured hairs, adaptations that may mitigate heat stress and represent convergent evolution with other desert organisms.These results support major ecogeographical rules in functional trait variation and emphasize the role of climate in shaping bee phenotypic diversity. Read the freePlain Language Summaryfor this article on the Journal blog. 

ABSTRACT Community or volunteer participation in research has the potential to significantly help mobilize the wealth of biodiversity and functional ecological data housed in natural history collections. Many such projects recruit community scientists to transcribe specimen label data from images; a next step is to task community scientists with conducting straightforward morphological measurements (e.g., body size) from specimen images. We investigated whether community science could be an effective approach to generating significant body size datasets from specimen images generated by museum digitization initiatives. Using the community science platform Notes from Nature, we engaged community scientists in a specimen measurement task to estimate body size (i.e., intertegular distance) from images of bee specimens. Community scientists showed high engagement and completion of this task, with each user measuring 43.6 specimens on average and self‐reporting successful measurement of 98.0% of the images. Community scientist measurements were significantly larger than measurements conducted by trained researchers, though the average measurement error was only 2.3%. These results suggest that community science participation could be an effective approach for bee body size measurement, for descriptive studies or for research questions where this degree of expected error is deemed acceptable. For larger‐bodied organisms (e.g., vertebrates), where modest measurement errors represent a smaller proportion of body size, community science approaches may be particularly effective. Methods we present here may serve as a blueprint for future projects aimed at engaging the public in biodiversity and collections‐based research efforts. 

Abstract An animal’s diet breadth is a central aspect of its life history, yet the factors determining why some species have narrow dietary breadths (specialists) and others have broad dietary breadths (generalists) remain poorly understood. This challenge is pronounced in herbivorous insects due to incomplete host plant data across many taxa and regions. Here, we develop and validate machine learning models to predict pollen diet breadth in bees, using a bee phylogeny and occurrence data for 682 bee species native to the United States, aiming to better understand key drivers. We found that pollen specialist bees made an average of 72.9% of their visits to host plants and could be predicted with high accuracy (mean 94%). Our models predicted generalist bee species, which made up a minority of the species in our dataset, with lower accuracy (mean 70%). The models tested on spatially and phylogenetically blocked data revealed that the most informative predictors of diet breadth are plant phylogenetic diversity, bee species’ geographic range, and regional abundance. Our findings also confirm that range size is predictive of diet breadth and that both male and female specialist bees mostly visit their host plants. Overall, our results suggest we can use visitation data to predict specialist bee species in regions and for taxonomic groups where diet breadth is unknown, though predicting generalists may be more challenging. These methods can thus enhance our understanding of plant-pollinator interactions, leading to improved conservation outcomes and a better understanding of the pollination services bees provide. 

Abstract Climatic stressors are important drivers in the evolution of social behavior. Social animals tend to thrive in harsh and unpredictable environments, yet the precise benefits driving these patterns are often unclear. Here, we explore water conservation in forced associations of a solitary bee (Melissodes tepidus timberlakei Cockerell, 1926) to test the hypothesis that grouping can generate synergistic physiological benefits in an incipient social context. Paired bees displayed mutual tolerance and experienced reduced water loss relative to singleton bees when exposed to acute low-humidity stress, with no change in activity levels. While the mechanism underlying these benefits remains unknown, social advantages like these can facilitate the evolution of cooperation among nonrelatives and offer important insights into the social consequences of climate change. 

Abstract Climatic factors are known to shape the expression of social behaviours. Likewise, variation in social behaviour can dictate climate responses. Understanding interactions between climate and sociality is crucial for forecasting vulnerability and resilience to climate change across animal taxa.These interactions are particularly relevant for taxa like bees that exhibit a broad diversity of social states. An emerging body of literature aims to quantify bee responses to environmental change with respect to variation in key functional traits, including sociality. Additionally, decades of research on environmental drivers of social evolution may prove fruitful for predicting shifts in the costs and benefits of social strategies under climate change.In this review, we explore these findings to ask two interconnected questions: (a) how does sociality mediate vulnerability to climate change, and (b) how might climate change impact social organisation in bees? We highlight traits that intersect with bee sociality that may confer resilience to climate change (e.g. extended activity periods, diet breadth, behavioural thermoregulation) and we generate predictions about the impacts of climate change on the expression and distribution of social phenotypes in bees.The social evolutionary consequences of climate change will be complex and heterogeneous, depending on such factors as local climate and plasticity of social traits. Many contexts will see an increase in the frequency of eusocial nesting as warming temperatures accelerate development and expand the temporal window for rearing a worker brood. More broadly, climate‐mediated shifts in the abiotic and biotic selective environments will alter the costs and benefits of social living in different contexts, with cascading impacts at the population, community and ecosystem levels. 

Abstract Phenotypic divergence is an important consequence of restricted gene flow in insular populations. This divergence can be challenging to detect when it occurs through subtle shifts in morphological traits, particularly in traits with complex geometries, like insect wing venation. Here, we employed geometric morphometrics to assess the extent of variation in wing venation patterns across reproductively isolated populations of the social sweat bee,Halictus tripartitus. We examined wing morphology of specimens sampled from a reproductively isolated population ofH. tripartituson Santa Cruz Island (Channel Islands, Southern California). Our analysis revealed significant differentiation in wing venation in this island population relative to conspecific mainland populations. We additionally found that this population‐level variation was less pronounced than the species‐level variation in wing venation among three sympatric congeners native to the region,Halictus tripartitus,Halictus ligatus, andHalictus farinosus. Together, these results provide evidence for subtle phenotypic divergence in an island bee population. More broadly, these results emphasize the utility and potential of wing morphometrics for large‐scale assessment of insect population structure. 

{"Abstract":["Last modified: January 09, 2025\n\nIntroductionThis dataset comprises all bee interactions indexed by Global Biotic Interactions (GloBI; Poelen et al. 2014). It is published quarterly by the Big Bee Project (Seltmann et al. 2021) to summarize all available knowledge about bee interactions from natural history collections, community science observations (i.e., iNaturalist), and the literature. Interactions include flower visitation, parasitic interactions (mite, viral), lecty, and many others.\n\nData DescriptionPlease see the integration process page to better understand how Global Biotic Interactions combines datasets from various sources. The complete interaction dataset for all species can be accessed via https://www.globalbioticinteractions.org/data.\n\nData is filtered for unique records based on the interaction description and source citation. Archives contain full data records and unique filtered records in tab-delimited format.\n\nDataset column name definitions https://api.globalbioticinteractions.org/interactionFields or https://api.globalbioticinteractions.org/interactionFields\n\nDuplicate records occur in the database because more than one provider shares information. This is most frequently occuring in museum specimen data and duplicates can be identified evaluating the institutionCode, collectionCode and catalogNumber fields. The file catalogNumber_counts.tsv groups records by these three fields for this dataset, but does not filter out duplicate records. Additionally, this dataset includes the citation information provided by the data publisher. The provided sourceCitation may not include information about the primary provider (often the natural history collection) the specimen data originates and the catalogNumber should be referenced to understand the original source of the data.\n\nIf you know of a missing dataset, or wish to share your dataset, please contact us!\n\n \n\nMetrics\n\n\n\n\nDate\n\n\nTotal bee records\n\n\n\n07-17-2020\n232,906\n\n\n01-24-2021\n257,738\n\n\n11-17-2021\n226,160\n\n\n06-01-2022\n286,818\n\n\n11-07-2022\n429,308\n\n\n01-18-2024\n842,819\n\n\n07-03-2024\n1,109,057\n\n\n01-09-2025\n1,223,768\n\n\n11-25-2025\n2,480,473\n\n\n\n\n \n\n\n\n\nDate\nAndrenidae\nApidae\nColletidae\nHalictidae\n\n\n07-17-2020\n73,463\n106,222\n20,821\n58,880\n\n\n01-24-2021\n77,824\n120,919\n21,376\n63,945\n\n\n11-17-2021\n25,535\n134,517\n10,568\n43,070\n\n\n06-01-2022\n78,016\n144,827\n20,409\n64,054\n\n\n11-07-2022\n84,172\n171,378\n30,792\n79,155\n\n\n01-18-2024\n166,473\n334,224\n63,847\n171,931\n\n\n07-03-2024\n\n\n289,400\n\n\n\n371,953\n\n\n\n83,337\n\n\n\n190,562\n\n\n\n01-09-2025\n\n\n204,565\n\n\n\n686,195\n\n\n\n70,724\n\n\n\n241,856\n\n\n\n11-25-2025\n\n\n269,191\n\n\n\n1,509,768\n\n\n\n129,763\n\n\n\n386,203\n\n\n\n\n\n \n\n\n\n\nDate\nMegachilidae\nMelittidae\nStenotritidae\n\n\n07-17-2020\n44,449\n2,511\n23\n\n\n01-24-2021\n48,856\n2,624\n18\n\n\n11-17-2021\n37,001\n995\n9\n\n\n06-01-2022\n54,516\n2,994\n18\n\n\n11-07-2022\n61,391\n2,396\n24\n\n\n01-18-2024\n100,814\n5,088\n442\n\n\n07-03-2024\n\n\n162,587\n\n\n\n4,964\n\n438\n\n\n01-09-2025\n\n\n126,113\n\n\n\n5,928\n\n441\n\n\n11-25-2025\n\n\n174,935\n\n\n\n9,764\n\n849\n\n\n\n\n \n\nIncluded files\n\n\n\nfilter_and_count_bee_families.sh - script for separating bee records into family and counting number of records for each family\n\ncitation-count.sh - script for counting citations\n\nfamily_counts.tsv - counts by family\n\nglobi-bees-filtered_file.tsv.gz - list of all bee interaction data indexed on Global Biotic Interactions from GloBI version 2025-Nov-25 produced by [filter_and_count_bee_families.sh].\n\ninteractions.tsv.gz - archive of the full Global Biotic Interaction dataset on November 25, 2025. Downloaded from https://www.globalbioticinteractions.org\n\n\n \n\nInteraction Sources\n\nBelow is a list of sources that contributed to this dataset, along with raw counts of unique interactions and links to the corresponding digital archives and reviews. These datasets are indexed and reviewed by Global Biotic Interactions (GloBI) using automated, reproducible workflows that extract species-interaction records, reconcile taxonomic names against authoritative catalogs, and summarize the findings. Each review produces a versioned digital archive to ensure long-term preservation and to document data provenance. For details, see the Methods section within each linked archive.\n\n\n\n\ncount\nsource\n\n\n276746\n\n\n\n\nhttp://iNaturalist.org is a place where you can record what you see in nature, meet other nature lovers, and learn about the natural world.\n\n\n\n\n266822\n\n\n\n\nReji Chacko, M., Albouy, C., Altermatt, F., Brändle, M., Casanelles Abella, J., Boussange, V., Campell, F., Ellis, W. N., Fopp, F., Gossner, M. M., Ho., H., Joss, A., Kipf, P., Neff, F., Petrović, A., Prié, V., Tomanović, Ž., Zimmerli, N., Pellissier, L. (2024). trophiCH v1 - a food web for Switzerland. EnviDat. https://www.doi.org/10.16904/envidat.467.\n\n\n\n\n258683\n\n\n\n\nUSGS Biodiversity Information Serving Our Nation (BISON) IPT\n\n\n\n\n180849\n\n\n\n\necdysis - a portal for live-managing arthropod occurrence data\n\n\n\n\n113301\n\n\n\n\nDigital Bee Collections Network, 2014 (and updates). Version: 2015-03-18. National Science Foundation grant DBI 0956388; PBI: Phytophagous Insects as a Model Group for Documenting Planetary Biodiversity (Insecta: Heteroptera: Miridae: Orthotylinae, Phylinae). Version: 08 Mar 2016. National Science Foundation grant DBI#0316495; Tri-Trophic Thematic Collection Network, 2014 (and updates). Version: 08 Mar 2016. http://tcn.amnh.org/. National Science Foundation grant(s) EF#1115081, EF#1115103, EF#1115080, EF#1115144, EF#1115191, EF#1115104, EF#1115115\n\n\n\n\n112006\n\n\n\n\nUniversity of Kansas Natural History Museum - Snow Entomological Museum Collection\n\n\n\n\n79134\n\n\n\n\nSymbiota Collections of Arthropods Network (SCAN)\n\n\n\n\n62736\n\n\n\n\nFrost Entomological Museum, Pennsylvania State University\n\n\n\n\n49513\n\n\n\n\nLanuza et al. (2025), EuPPollNet: A European Database of Plant-Pollinator Networks. Global Ecol Biogeogr, 34: e70000. https://doi.org/10.1111/geb.70000\n\n\n\n\n41298\n\n\n\n\nBalfour, N.J., Castellanos, M.C., Goulson, D., Philippides, A. and Johnson, C., 2022. DoPI: The Database of Pollinator Interactions. Ecology, 103, e3801.\n\n\n\n\n28517\n\n\n\n\nPaDIL Bee records from the Pests and Diseases Image Library, http://www.padil.gov.au.\n\n\n\n\n27114\n\n\n\n\nGuzman, Laura Melissa; Kelly, Tyler; Elle, Elizabeth, 2022, "A dataset for pollinator diversity and their interactions with plants in the Pacific NorthWest", https://doi.org/10.5683/SP3/WTEZNH, Borealis, V1\n\n\n\n\n24564\n\n\n\n\nUniversity of Michigan Museum of Zoology Insect Division. Full Database Export 2020-11-20 provided by Erika Tucker and Barry Oconner.\n\n\n\n\n23727\n\n\n\n\nCarril OM, Griswold T, Haefner J, Wilson JS. (2018) Wild bees of Grand Staircase-Escalante National Monument: richness, abundance, and spatio-temporal beta-diversity. PeerJ 6:e5867 https://doi.org/10.7717/peerj.5867\n\n\n\n\n18757\n\n\n\n\nA. Thessen. 2014. Species associations extracted from EOL text data objects via text mining.\n\n\n\n\n18003\n\n\n\n\nPensoft Darwin Core Archives available via Integrated Publication Toolkit\n\n\n\n\n17603\n\n\n\n\nDorey, J.B., Fischer, E.E., Chesshire, P.R. et al. A globally synthesised and flagged bee occurrence dataset and cleaning workflow. Sci Data 10, 747 (2023). https://doi.org/10.1038/s41597-023-02626-w\n\n\n\n\n17088\n\n\n\n\nVandame R, Mérida J, Sagot P, Madrigal González D, Bedolla García B Y, González-Vanegas P A, Cultid-Medina C A, Barrios J M (2023). Potential host plant records recovered from ECOAB wild bee collection, Mexico. Version 1.10. Comisión nacional para el conocimiento y uso de la biodiversidad.\n\n\n\n\n15763\n\n\n\n\nSchwarz, Benjamin et al. (2021). Data from: Temporal scale-dependence of plant-pollinator networks [Dataset]. Dryad. https://doi.org/10.5061/dryad.qz612jmbp\n\n\n\n\n10211\n\n\n\n\nPensoft Darwin Core Archives with associateTaxa columns\n\n\n\n\n9104\n\n\n\n\nAmerican Museum of Natural History Hymenoptera\n\n\n\n\n8678\n\n\n\n\nAubouin, L., Genoud, D., Givord-Coupeau, B. et al. BeeFunc, a comprehensive trait database for French bees. Sci Data 12, 1302 (2025). https://doi.org/10.1038/s41597-025-05626-0\n\n\n\n\n8657\n\n\n\n\nWeb of Life. http://www.web-of-life.es .\n\n\n\n\n6600\n\n\n\n\nUniversity of Michigan Museum of Zoology, Division of Insects\n\n\n\n\n6331\n\n\n\n\nAllen-Perkins, Alfonso, Magrach, Ainhoa, Dainese, Matteo, Garibaldi, Lucas A., Kleijn, David, Rader, Romina, Reilly, James R., et al. 2022. "CropPol: A Dynamic, Open and Global Database on Crop Pollination." Ecology 103(3): e3614. https://doi.org/10.1002/ecy.3614\n\n\n\n\n6290\n\n\n\n\nPurdue Entomological Research Collection\n\n\n\n\n6178\n\n\n\n\nRedhead, J.W.; Coombes, C.F.; Dean, H.J.; Dyer, R.; Oliver, T.H.; Pocock, M.J.O.; Rorke, S.L.; Vanbergen, A.J.; Woodcock, B.A.; Pywell, R.F. (2018). Plant-pollinator interactions database for construction of potential networks. NERC Environmental Information Data Centre. https://doi.org/10.5285/6d8d5cb5-bd54-4da7-903a-15bd4bbd531b\n\n\n\n\n5535\n\n\n\n\n@article{Hale_2024, title={A highly resolved network reveals the role of terrestrial herbivory in structuring aboveground food webs}, volume={379}, ISSN={1471-2970}, url={http://dx.doi.org/10.1098/rstb.2023.0180}, DOI={10.1098/rstb.2023.0180}, number={1909}, journal={Philosophical Transactions of the Royal Society B: Biological Sciences}, publisher={The Royal Society}, author={Hale, Kayla R. S. and Curlis, John David and Auteri, Giorgia G. and Bishop, Sasha and French, Rowan L. K. and Jones, Lance E. and Mills, Kirby L. and Scholtens, Brian G. and Simons, Meagan and Thompson, Cody and Tourville, Jordon and Valdovinos, Fernanda S.}, year={2024}, month=jul }\n\n\n\n\n5531\n\n\n\n\nhttps://mangal.io - the ecological interaction database.\n\n\n\n\n5316\n\n\n\n\nClint Otto, Russ Bryant, and Ned H. Euliss Jr., 2020, The U.S. Geological Survey Pollinator Library Dataset: U.S. Geological Survey. https://doi.org/10.5066/P9DSS3VL\n\n\n\n\n4688\n\n\n\n\nUniversity of Colorado Museum of Natural History Entomology Collection\n\n\n\n\n4680\n\n\n\n\nNational Database Plant Pollinators. Center for Plant Conservation at San Diego Zoo Global. Accessed via https://saveplants.org/national-collection/pollinator-search/ on 2020-06-05.\n\n\n\n\n4284\n\n\n\n\nSeltmann, K., Van Wagner, J., Behm, R., Brown, Z., Tan, E., & Liu, K. (2020). BID: A project to share biotic interaction and ecological trait data about bees (Hymenoptera: Anthophila). UC Santa Barbara: Cheadle Center for Biodiversity and Ecological Restoration. Retrieved from https://escholarship.org/uc/item/1g21k7bf\n\n\n\n\n4169\n\n\n\n\nEardley C, Coetzer W. 2016. Catalogue of Afrotropical Bees.\n\n\n\n\n3709\n\n\n\n\nArizona State University Hasbrouck Insect Collection\n\n\n\n\n3619\n\n\n\n\nMaiorano, L., Montemaggiori, A., Ficetola, G.F., O’Connor, L. & Thuiller, W. (2020), Data from: Tetra-EU 1.0: a species-level trophic meta-web of European tetrapods, Dryad, Dataset, https://doi.org/10.5061/dryad.jm63xsj7b hash://md5/40b3d2de829d5f6d98ab71b0b5aa87fd\n\n\n\n\n3547\n\n\n\n\nMycology Collections Data Portal (MyCoPortal). https://mycoportal.org\n\n\n\n\n3140\n\n\n\n\nUniversity of New Hampshire Donald S. Chandler Entomological Collection\n\n\n\n\n3124\n\n\n\n\nCaraDonna, P.J. 2020. Temporal variation in plant-pollinator interactions, Rocky Mountain Biological Laboratory, CO, USA, 2013 - 2015 ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/27dc02fe1655e3896f20326fed5cb95f (Accessed 2021-04-16).\n\n\n\n\n3120\n\n\n\n\nLaManna, JA, Burkle, LA, Belote, RT, Myers, JA. Biotic and abiotic drivers of plant–pollinator community assembly across wildfire gradients. J Ecol. 2020; 00: 1– 14. https://doi.org/10.1111/1365-2745.13530 .\n\n\n\n\n3039\n\n\n\n\nOllerton, J., Trunschke, J. ., Havens, K. ., Landaverde-González, P. ., Keller, A. ., Gilpin, A.-M. ., Rodrigo Rech, A. ., Baronio, G. J. ., Phillips, B. J., Mackin, C. ., Stanley, D. A., Treanore, E. ., Baker, E. ., Rotheray, E. L., Erickson, E. ., Fornoff, F. ., Brearley, F. Q. ., Ballantyne, G. ., Iossa, G. ., Stone, G. N., Bartomeus, I. ., Stockan, J. A., Leguizamón, J., Prendergast, K. ., Rowley, L., Giovanetti, M., de Oliveira Bueno, R., Wesselingh, R. A., Mallinger, R., Edmondson, S., Howard, S. R., Leonhardt, S. D., Rojas-Nossa, S. V., Brett, M., Joaqui, T., Antoniazzi, R., Burton, V. J., Feng, H.-H., Tian, Z.-X., Xu, Q., Zhang, C., Shi, C.-L., Huang, S.-Q., Cole, L. J., Bendifallah, L., Ellis, E. E., Hegland, S. J., Straffon Díaz, S., Lander, T. A. ., Mayr, A. V., Dawson, R. ., Eeraerts, M. ., Armbruster, W. S. ., Walton, B. ., Adjlane, N. ., Falk, S. ., Mata, L. ., Goncalves Geiger, A. ., Carvell, C. ., Wallace, C. ., Ratto, F. ., Barberis, M. ., Kahane, F. ., Connop, S. ., Stip, A. ., Sigrist, M. R. ., Vereecken, N. J. ., Klein, A.-M., Baldock, K. ., & Arnold, S. E. J. . (2022). Pollinator-flower interactions in gardens during the COVID-19 pandemic lockdown of 2020. Journal of Pollination Ecology, 31, 87–96. https://doi.org/10.26786/1920-7603(2022)695\n\n\n\n\n2831\n\n\n\n\nRobert L. Minckley San Bernardino Valley from the year 2000 to 2011.\n\n\n\n\n2778\n\n\n\n\nHarvard University M, Morris P J (2021). Museum of Comparative Zoology, Harvard University. Museum of Comparative Zoology, Harvard University.\n\n\n\n\n2252\n\n\n\n\nGiselle Muschett & Francisco E. Fontúrbel. 2021. A comprehensive catalogue of plant – pollinator interactions for Chile\n\n\n\n\n2068\n\n\n\n\nCohen JM, Sauer EL, Santiago O, Spencer S, Rohr JR. 2020. Divergent impacts of warming weather on wildlife disease risk across climates. Science. doi:10.1126/science.abb1702\n\n\n\n\n2038\n\n\n\n\nSarah E. Miller. 07/06/2017. Information extracted from dataset https://www.idigbio.org/portal/recordsets/db4bb0df-8539-4617-ab5f-eb118aa3126b.\n\n\n\n\n1884\n\n\n\n\nInternational Council for the Exploration of the Sea (ICES). Year of The Stomach Datasets.\n\n\n\n\n1815\n\n\n\n\nhttp://gomexsi.tamucc.edu\n\n\n\n\n1812\n\n\n\n\n@article {Keck2025.01.24.634685, author = {Keck, Fran{\\c c}ois and Broadbent, Henry and Altermatt, Florian},title = {Extracting massive ecological data on state and interactions of species using large language models},year = {2025},doi = {10.1101/2025.01.24.634685},journal = {bioRxiv}}\n\n\n\n\n1766\n\n\n\n\nFricke, E.C., Svenning, J. Accelerating homogenization of the global plant–frugivore meta-network. Nature 585, 74–78 (2020). https://doi.org/10.1038/s41586-020-2640-y\n\n\n\n\n1654\n\n\n\n\nKlompen H, Johnson N (2018). Ohio State Acarology Laboratory (OSAL), Ohio State University. Museum of Biological Diversity, The Ohio State University. Accessed via https://mbd-db.osu.edu/hol/taxon_name/05fbf4bb-f8e1-404e-a27c-759d345aa4d0 on 2023-11-06 hash://sha256/fb23140e60f4889de35ae174b2570cf294012bff4f2c8c419c292af51c98c25f\n\n\n\n\n1619\n\n\n\n\nGrundler MC (2020) SquamataBase: a natural history database and R package for comparative biology of snake feeding habits. Biodiversity Data Journal 8: e49943. https://doi.org/10.3897/BDJ.8.e49943\n\n\n\n\n1510\n\n\n\n\nBrose, U. (2018). GlobAL daTabasE of traits and food Web Architecture (GATEWAy) version 1.0 [Data set]. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig. https://doi.org/10.25829/IDIV.283-3-756\n\n\n\n\n1502\n\n\n\n\nFlorida State Collection of Arthropods\n\n\n\n\n1431\n\n\n\n\nSarah E Miller. 6/19/2015. Species associations manually extracted from datasets https://www.nceas.ucsb.edu/interactionweb/resources.html.\n\n\n\n\n1351\n\n\n\n\nRubinigg M. 2023 Data on economic dependence of pollination by animals in crops as well as observations of pollinators on plant species, inon economic dependence of pollination by animals in crops as well as observations of pollinators on plant species, in particular crops. EU Pollinator Hub. [2025-08-28] app.pollinatorhub.eu\n\n\n\n\n1091\n\n\n\n\nMagrach, Ainhoa et al. (2017), Data from: Plant-pollinator networks in semi-natural grasslands are resistant to the loss of pollinators during blooming of mass-flowering crops, Dryad, Dataset, https://doi.org/10.5061/dryad.k0q1n\n\n\n\n\n1057\n\n\n\n\nSarah E Miller. 4/18/2016. Species associations from Wardeh, M. et al. Database of host-pathogen and related species interactions, and their global distribution. Sci. Data 2:150049 doi: 10.1038/sdata.2015.49 (2015)\n\n\n\n\n1057\n\n\n\n\nWardeh, M., Risley, C., McIntyre, M. et al. Database of host-pathogen and related species interactions, and their global distribution. Sci Data 2, 150049 (2015). https://doi.org/10.1038/sdata.2015.49\n\n\n\n\n958\n\n\n\n\nSarah E Miller. 5/30/2016. Interations from various papers.\n\n\n\n\n945\n\n\n\n\nUniversity of California Santa Barbara Invertebrate Zoology Collection\n\n\n\n\n873\n\n\n\n\nEd Baker; Ian J. Kitching; George W. Beccaloni; Amoret Whitaker et al. (2016). Dataset: NHM Interactions Bank. Natural History Museum Data Portal (data.nhm.ac.uk). https://doi.org/10.5519/0060767\n\n\n\n\n870\n\n\n\n\nDel Risco, A.A., Montoya, Á.M., García, V. et al. Data synthesis and dynamic visualization converge into a comprehensive biotic interaction network: a case study of the urban and rural areas of Bogotá D.C.. Urban Ecosyst (2021). https://doi.org/10.1007/s11252-021-01133-3\n\n\n\n\n857\n\n\n\n\nThe International Barcode of Life Consortium (2016). International Barcode of Life project (iBOL). Occurrence dataset https://doi.org/10.15468/inygc6\n\n\n\n\n762\n\n\n\n\nOllerton J, Liede-Schumann S, Endress ME, Meve U, Rech AR, Shuttleworth A, Keller HA, Fishbein M, Alvarado-Cárdenas LO, Amorim FW, Bernhardt P, Celep F, Chirango Y, Chiriboga-Arroyo F, Civeyrel L, Cocucci A, Cranmer L, da Silva-Batista IC, de Jager L, Deprá MS, Domingos-Melo A, Dvorsky C, Agostini K, Freitas L, Gaglianone MC, Galetto L, Gilbert M, González-Ramírez I, Gorostiague P, Goyder D, Hachuy-Filho L, Heiduk A, Howard A, Ionta G, Islas-Hernández SC, Johnson SD, Joubert L, Kaiser-Bunbury CN, Kephart S, Kidyoo A, Koptur S, Koschnitzke C, Lamborn E, Livshultz T, Machado IC, Marino S, Mema L, Mochizuki K, Morellato LPC, Mrisha CK, Muiruri EW, Nakahama N, Nascimento VT, Nuttman C, Oliveira PE, Peter CI, Punekar S, Rafferty N, Rapini A, Ren ZX, Rodríguez-Flores CI, Rosero L, Sakai S, Sazima M, Steenhuisen SL, Tan CW, Torres C, Trøjelsgaard K, Ushimaru A, Vieira MF, Wiemer AP, Yamashiro T, Nadia T, Queiroz J, Quirino Z. The diversity and evolution of pollination systems in large plant clades: Apocynaceae as a case study. Ann Bot. 2019 Jan 23;123(2):311-325. doi: 10.1093/aob/mcy127. PMID: 30099492; PMCID: PMC6344220.\n\n\n\n\n760\n\n\n\n\nIllinois Natural History Survey Insect Collection\n\n\n\n\n721\n\n\n\n\nPoelen, J. H. (2024). A biodiversity dataset graph: Biological Associations in TaxonWorks hash://sha256/e4a47c067d6c125da60c9a1b92b5eecdea539cb8666cd3aed99db347ae5b8ed0 hash://md5/686007de79cc2a49ab23fd3debe56e3f [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11151783\n\n\n\n\n711\n\n\n\n\nTexas A&M University Insect Collection\n\n\n\n\n703\n\n\n\n\nNatural History Collections managed by Arctos (https://arctosdb.org) accessed via https://vertnet.org .\n\n\n\n\n688\n\n\n\n\nRusso, Laura; Fitzpatrick, Una; Larkin, Michelle et al. (2022). Database of plant-flower visitor interactions from Ireland [Dataset]. Dryad. https://doi.org/10.5061/dryad.kwh70rz47\n\n\n\n\n634\n\n\n\n\nClassen, Alice; Steffan-Dewenter, Ingolf (2020): Plant-pollinator interactions along an elevational gradient on Mt. Kilimanjaro. PANGAEA, https://doi.org/10.1594/PANGAEA.911390\n\n\n\n\n627\n\n\n\n\nA review of the status of web-based African Plant-Pollinator Interaction data.\n\n\n\n\n607\n\n\n\n\nScott L. Gardner and Gabor R. Racz (2021). University of Nebraska State Museum - Parasitology. Harold W. Manter Laboratory of Parasitology. University of Nebraska State Museum.\n\n\n\n\n590\n\n\n\n\nNational Museum of Natural History, Smithsonian Institution IPT RSS Feed\n\n\n\n\n563\n\n\n\n\nRaymond, B., Marshall, M., Nevitt, G., Gillies, C., van den Hoff, J., Stark, J.S., Losekoot, M., Woehler, E.J., and Constable, A.J. (2011) A Southern Ocean dietary database. Ecology 92(5):1188. Available from http://dx.doi.org/10.1890/10-1907.1 . Data set supplied by Ben Raymond.\n\n\n\n\n555\n\n\n\n\nSarah E Miller. 6/22/2015. Species associations manually extracted from datasets https://www.nceas.ucsb.edu/interactionweb/resources.html.\n\n\n\n\n492\n\n\n\n\nRCPol: Online Pollen Catalogs Network. 2016. https://rcpol.org.br/\n\n\n\n\n480\n\n\n\n\nPinnegar, J.K. (2014). DAPSTOM - An Integrated Database & Portal for Fish Stomach Records. Version 4.7. Centre for Environment, Fisheries & Aquaculture Science, Lowestoft, UK. February 2014, 39pp.\n\n\n\n\n459\n\n\n\n\nPardee, G.L., Ballare, K.M., Neff, J.L., Do, L.Q., Ojeda, D., Bienenstock, E.J., Brosi, B.J., Grubesic, T.H., Miller, J.A., Tong, D. and Jha, S., 2023. Local and Landscape Factors Influence Plant-Pollinator Networks and Bee Foraging Behavior across an Urban Corridor. Land, 12(2), p.362. https://www.mdpi.com/2073-445X/12/2/362\n\n\n\n\n437\n\n\n\n\nThe Albert J. Cook Arthropod Research Collection\n\n\n\n\n409\n\n\n\n\nSarah E Miller. 6/25/2015. Species associations manually extracted from Robertson, C. 1929. Flowers and insects: lists of visitors to four hundred and fifty-three flowers. Carlinville, IL, USA, C. Robertson.\n\n\n\n\n384\n\n\n\n\nBoreux, Virginie; Klein, Alexandra-Maria (2019). Global pollinator database. figshare. Dataset. https://doi.org/10.6084/m9.figshare.9980471.v1\n\n\n\n\n334\n\n\n\n\nFroese, R. and D. Pauly. Editors. 2018. FishBase. World Wide Web electronic publication. www.fishbase.org, version (10/2018).\n\n\n\n\n320\n\n\n\n\nHurlbert, A. H., Olsen, A. M., Sawyer, M. M., and Winner, P. M. 2021. Avian Diet Database. https://doi.org/10.5281/zenodo.5151056\n\n\n\n\n296\n\n\n\n\nGlobal Web Database (http://globalwebdb.com): an online collection of food webs. Accessed via https://www.globalwebdb.com/Service/DownloadArchive on 2017-10-12.\n\n\n\n\n290\n\n\n\n\nCalifornia Academy of Sciences Entomology and Entomology Type Collection\n\n\n\n\n290\n\n\n\n\nSarah E Miller. 06/10/2015. Species associations manually extracted from Chamberlin, W. J. The Buprestidae of North America, Exclusive of Mexico, a Catalogue including Synonomy, Bibliography, Distribution, Type Locality and Hosts of Each Species,. 1926.\n\n\n\n\n274\n\n\n\n\nBENSCH, S., HELLGREN, O. and PÉREZ‐TRIS, J. (2009), MalAvi: a public database of malaria parasites and related haemosporidians in avian hosts based on mitochondrial cytochrome b lineages. Molecular Ecology Resources, 9: 1353-1358. https://doi.org/10.1111/j.1755-0998.2009.02692.x\n\n\n\n\n263\n\n\n\n\nCanterbury Museum. (2025). Canterbury Museum (CMNZ) collection insect specimen-plant flower interactions (0.4) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15429172\n\n\n\n\n263\n\n\n\n\nFood Webs and Species Interactions in the Biodiversity of UK and Ireland (Online). 2017. Data provided by Malcolm Storey. Also available from http://bioinfo.org.uk.\n\n\n\n\n259\n\n\n\n\nhttp://invertebrates.si.edu/parasites.htm\n\n\n\n\n248\n\n\n\n\nWorldFAIR pilot data from: VisitationData_Luisa_Carvalheiro.\n\n\n\n\n246\n\n\n\n\nEneida L. Hatcher, Sergey A. Zhdanov, Yiming Bao, Olga Blinkova, Eric P. Nawrocki, Yuri Ostapchuck, Alejandro A. Schäffer, J. Rodney Brister, Virus Variation Resource – improved response to emergent viral outbreaks, Nucleic Acids Research, Volume 45, Issue D1, January 2017, Pages D482–D490, https://doi.org/10.1093/nar/gkw1065 .\n\n\n\n\n230\n\n\n\n\nUniversity of Hawaii Insect Museum\n\n\n\n\n211\n\n\n\n\nPalomares, M.L.D. and D. Pauly. Editors. 2018. SeaLifeBase. World Wide Web electronic publication. www.sealifebase.org, version (10/2018).\n\n\n\n\n204\n\n\n\n\n@article{Sabino_2022, doi = {10.1016/j.agee.2022.108012}, url = {https://doi.org/10.1016%2Fj.agee.2022.108012}, year = 2022, month = {sep}, publisher = {Elsevier {BV}}, volume = {335}, pages = {108012}, author = {William Sabino and Luciano Costa and Tamires Andrade and Juliana Teixeira and Gustavo Araújo and André Luís Acosta and Luísa Carvalheiro and Tereza Cristina Giannini}, title = {Status and trends of pollination services in Amazon agroforestry systems}, journal = {Agriculture, Ecosystems & Environment}}\n\n\n\n\n181\n\n\n\n\nBrigham Young University Arthropod Museum\n\n\n\n\n179\n\n\n\n\nStokland, J.; Dahlberg, A.; Meyke, E.; Schigel, D.; Siitonen, J. (2006) The Nordic saproxylic database - a comprehensive overview of the biological diversity in dead wood. 1st European Congress of Conservation Biology - "Diversity for Europe". August 2006, Hungary. Book of Abstracts. Society of Conservation Biology (USA) & Blackwell Publishing (UK) p. 159 .\n\n\n\n\n169\n\n\n\n\nUniversity of Wisconsin Stevens Point, Stephen J. Taft Parasitological Collection\n\n\n\n\n164\n\n\n\n\nStephens, P. R., Pappalardo, P. , Huang, S. , Byers, J. E., Farrell, M. J., Gehman, A. , Ghai, R. R., Haas, S. E., Han, B. , Park, A. W., Schmidt, J. P., Altizer, S. , Ezenwa, V. O. and Nunn, C. L. (2017), Global Mammal Parasite Database version 2.0. Ecology, 98: 1476-1476. doi:10.1002/ecy.1799\n\n\n\n\n162\n\n\n\n\nBrose, U. et al., 2005. Body sizes of consumers and their resources. Ecology, 86(9), pp.2545–2545. Available at: https://doi.org/10.1890/05-0379.\n\n\n\n\n159\n\n\n\n\nCruz, G.L.T., Winck, G.R., D’Andrea, P.S. et al. Integrating databases for spatial analysis of parasite-host associations and the novel Brazilian dataset. Sci Data 10, 757 (2023). https://doi.org/10.1038/s41597-023-02636-8\n\n\n\n\n143\n\n\n\n\nYale University Peabody Museum Collections Data Portal\n\n\n\n\n141\n\n\n\n\nWIRC / University of Wisconsin Madison WIS-IH / Wisconsin Insect Research Collection\n\n\n\n\n134\n\n\n\n\nBartomeus, Ignasi (2013): Plant-Pollinator Network Data. figshare. Dataset. https://doi.org/10.6084/m9.figshare.154863.v1\n\n\n\n\n133\n\n\n\n\nSarah E Miller. 12/13/2016. Species associations manually extracted from Onstad, D.W. EDWIP: Ecological Database of the World's Insect Pathogens. Champaign, Illinois: Illinois Natural History Survey, [23/11/2016]. http://insectweb.inhs.uiuc.edu/Pathogens/EDWIP.\n\n\n\n\n128\n\n\n\n\nLlewelyn, J., Strona, G., Dickman, C.R., Greenville, A.C., Wardle, G.M., Lee, M.S.Y., Doherty, S., Shabani, F., Saltré, F. and Bradshaw, C.J.A. (2023), Predicting predator–prey interactions in terrestrial endotherms using random forest. Ecography e06619. https://doi.org/10.1111/ecog.06619\n\n\n\n\n119\n\n\n\n\nSpecies Interactions of Australia Database (SIAD): Helping us to understand species interactions in Australia and beyond. http://www.discoverlife.org/siad/ .\n\n\n\n\n116\n\n\n\n\nField Museum of Natural History IPT\n\n\n\n\n106\n\n\n\n\nGroom, Q.J., Maarten De Groot, M. & Marčiulynienė, D. (2020) Species interation data manually extracted from literature for species .\n\n\n\n\n106\n\n\n\n\nLintulaakso, K., Tatti, N. and Žliobaitė, I., 2023. Quantifying mammalian diets. Mammalian Biology, 103(1), pp.53-67. https://doi.org/10.1007/s42991-022-00323-6\n\n\n\n\n99\n\n\n\n\nMihara, T., Nishimura, Y., Shimizu, Y., Nishiyama, H., Yoshikawa, G., Uehara, H., Hingamp, P., Goto, S., and Ogata, H.; Linking virus genomes with host taxonomy. Viruses 8, 66 doi:10.3390/v8030066 (2016).\n\n\n\n\n92\n\n\n\n\nSan Diego Natural History Museum\n\n\n\n\n79\n\n\n\n\nCarlson, C.J. et al., 2021. The Global Virome in One Network (VIRION): an atlas of vertebrate-virus associations. Available at: http://dx.doi.org/10.1101/2021.08.06.455442\n\n\n\n\n79\n\n\n\n\nCristina Preda and Quentin Groom. 2014. Species associations manually extracted from literature.\n\n\n\n\n72\n\n\n\n\nFaulwetter S, Markantonatou V, Pavloudi C, Papageorgiou N, Keklikoglou K, Chatzinikolaou E, Pafilis E, Chatzigeorgiou G, Vasileiadou K, Dailianis T, Fanini L, Koulouri P, Arvanitidis C (2014) Polytraits: A database on biological traits of marine polychaetes. Biodiversity Data Journal 2: e1024. doi:10.3897/BDJ.2.e1024 . Available at http://polytraits.lifewatchgreece.eu.\n\n\n\n\n62\n\n\n\n\nUS National Museum of Natural History Ixodes Records\n\n\n\n\n60\n\n\n\n\nAlmeida, F. (2005) Trophic Ecology of Atlantic Cod, off Cape Cod, MA, from F/V Riena Marie NEC-FA2001-1 in the Gulf of Maine from 2001-2004 (NEC-CoopRes project). Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version final) Version Date 2005-10-01 [if applicable, indicate subset used]. http://lod.bco-dmo.org/id/dataset/3087\n\n\n\n\n54\n\n\n\n\nFarr, David F.; Rossman, Amy Y.; Castlebury, Lisa A. (2021). United States National Fungus Collections Fungus-Host Dataset. Ag Data Commons. https://doi.org/10.15482/USDA.ADC/1524414.\n\n\n\n\n53\n\n\n\n\nSoleto-Casas RC and Simões N (2020). Parasitic and commensal invertebrates of echinoderms from American Tropical And Subtropical Atlantic manually extracted from literature.\n\n\n\n\n48\n\n\n\n\nSemantic Prototypes in Research Ecoinformatics (SPIRE). Data provided by Joel Sachs. See also http://ebiquity.umbc.edu/get/a/publication/297.pdf .\n\n\n\n\n45\n\n\n\n\nOlito, Colin; Fox, Jeremy W. (2015), Data from: Species traits and abundances predict metrics of plant–pollinator network structure, but not pairwise interactions, Dryad, Dataset, https://doi.org/10.5061/dryad.7st32\n\n\n\n\n43\n\n\n\n\nPrice Institute of Parasite Research, School of Biological Sciences, University of Utah\n\n\n\n\n40\n\n\n\n\nSarah E Miller. 9/19/2017. Species associations manually extracted from Benesh, D. P., Lafferty, K. D. and Kuris, A. (2017), A life cycle database for parasitic acanthocephalans, cestodes, and nematodes. Ecology, 98: 882. doi:10.1002/ecy.1680\n\n\n\n\n37\n\n\n\n\nPocock, Michael J. O.; Evans, Darren M.; Memmott, Jane (2012), Data from: The robustness and restoration of a network of ecological networks, Dryad, Dataset, https://doi.org/10.5061/dryad.3s36r118\n\n\n\n\n36\n\n\n\n\nQuentin J. Groom. 2020. Species interactions of species on the List of invasive alien species of Union concern\n\n\n\n\n34\n\n\n\n\nSarah E Miller. 6/20/2015. Species associations manually extracted from datasets https://www.nceas.ucsb.edu/interactionweb/resources.html.\n\n\n\n\n32\n\n\n\n\nBallantyne, Gavin; Baldock, Katherine C. R.; Willmer, Pat G. (2015), Data from: Constructing more informative plant-pollinator networks: visitation and pollen deposition networks in a heathland plant community, Dryad, Dataset, https://doi.org/10.5061/dryad.17pp3\n\n\n\n\n31\n\n\n\n\nShaw, LP, Wang, AD, Dylus, D, et al. The phylogenetic range of bacterial and viral pathogens of vertebrates. Mol Ecol. 2020; 29: 3361– 3379. https://doi.org/10.1111/mec.15463\n\n\n\n\n27\n\n\n\n\nMuseum for Southwestern Biology (MSB) Parasite Collection\n\n\n\n\n27\n\n\n\n\nSarah E Miller. 5/17/2016. Wenzel, Rupert L., and Vernon J. Tipton. Appendix: Classified List of Hosts and Parasites. Chicago, Ill.: Field Museum of Natural History, 1966.\n\n\n\n\n26\n\n\n\n\nSarah E Miller. 9/19/2016. Species associations extracted from Graystock, P., Blane, E.J., McFrederick, Q.S., Goulson, D. and Hughes, W.O., 2016. Do managed bees drive parasite spread and emergence in wild bees?. International Journal for Parasitology: Parasites and Wildlife, 5(1), pp.64-75.\n\n\n\n\n23\n\n\n\n\nAgosti, Donat. 2020. Transcription of Linné, C. von, 1758. Systema naturae per regna tria naturae secundum classes, ordines, genera, species, cum characteribus, differentiis, synonymis, locis. Available at: http://dx.doi.org/10.5962/bhl.title.542 .\n\n\n\n\n23\n\n\n\n\nUdy, Kristy; Reininghaus, Hannah; Scherber, Christoph; Tscharntke, Teja (2020), Data from: Plant-pollinator interactions along an urbanization gradient from cities and villages to farmland landscapes, Dryad, Dataset, https://doi.org/10.5061/dryad.4mw6m906s\n\n\n\n\n20\n\n\n\n\nIPBES. (2016). The assessment report of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services on pollinators, pollination and food production. Table 2.4.3 p88 Zenodo. https://doi.org/10.5281/zenodo.3402857\n\n\n\n\n20\n\n\n\n\nSherman, Aja C.; Geiselman, Cullen; Simons, Nancy B.; Upham, Nathan S.; Poelen, Jorrit H.; Reeder, DeeAnn M.; Bertolino, Sandro; Groom, Quentin; Phelps, Kendra; Agosti, Donat; Willoughby, Anna R. In Preparation. Bat-Co-Roosting Database develop by the Biodiversity-related knowledge hub on COVID-19.\n\n\n\n\n19\n\n\n\n\nSeltzer, Carrie; Wysocki, William; Palacios, Melissa; Eickhoff, Anna; Pilla, Hannah; Aungst, Jordan; Mercer, Aaron; Quicho, Jamie; Voss, Neil; Xu, Man; J. Ndangalasi, Henry; C. Lovett, Jon; J. Cordeiro, Norbert (2015): Plant-animal interactions from Africa. figshare. https://dx.doi.org/10.6084/m9.figshare.1526128\n\n\n\n\n18\n\n\n\n\nJakovos Demetriou and Quentin Groom 2014. Species associations of Sceliphron manually extracted from literature.\n\n\n\n\n17\n\n\n\n\nSpecies Connect. https://speciesconnect.com\n\n\n\n\n16\n\n\n\n\nGeiselman, Cullen K. & Sarah Younger. 2020. Bat Eco-Interactions Database. www.batbase.org\n\n\n\n\n14\n\n\n\n\nFabricia Sousa Paz, Carlos Eduardo Pinto, Rafael Melo de Brito, Vera Lucia Imperatriz-Fonseca, Tereza Cristina Giannini, Edible Fruit Plant Species in the Amazon Forest Rely Mostly on Bees and Beetles as Pollinators, Journal of Economic Entomology, Volume 114, Issue 2, April 2021, Pages 710–722, https://doi.org/10.1093/jee/toaa284\n\n\n\n\n14\n\n\n\n\nScientific Committee on Antarctic Research. (2023). SCAR Southern Ocean Diet and Energetics Database (2023-04-04) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7796465 hash://md5/e41e29d8fb3c2d731f292ec08798cf6b hash://md5/05abf23c0b9e5f4bc721ff407455af0a hash://sha256/7a344b858ab8d1daeca1da49843e8bf957f1116ff9e10a29176ab5c02cb49bef\n\n\n\n\n12\n\n\n\n\nBernice Pauahi Bishop Museum, J. Linsley Gressitt Center for Research in Entomology\n\n\n\n\n12\n\n\n\n\nGaden S. Robinson; Phillip R. Ackery; Ian Kitching; George W Beccaloni; Luis M. Hernández (2023). HOSTS (from HOSTS - a Database of the World's Lepidopteran Hostplants) [Data set resource]. Natural History Museum. https://data.nhm.ac.uk/dataset/hosts/resource/877f387a-36a3-486c-a0c1-b8d5fb69f85a via Natural History Museum (2023). Data Portal query on 1 resources created at 2023-05-24 11:19:42.032183 PID https://doi.org/10.5519/qd.bsucrxdz\n\n\n\n\n12\n\n\n\n\nGandhi, K. J. K., & Herms, D. A. (2009). North American arthropods at risk due to widespread Fraxinus mortality caused by the Alien Emerald ash borer. Biological Invasions, 12(6), 1839–1846. doi:10.1007/s10530-009-9594-1.\n\n\n\n\n12\n\n\n\n\nMeyer R.S., et al., Beach environmental DNA fills gaps in photographic biomonitoring to track spatiotemporal community turnover across 82 phyla. Environmental DNA, submitted June 3, 2019.\n\n\n\n\n10\n\n\n\n\nConsortium of Small Vertebrate Collections\n\n\n\n\n10\n\n\n\n\nLee, Leshon; Tan, David J. X.; Oboňa, Jozef; Gustafsson, Daniel R.; Ang, Yuchen; Meier, Rudolf (2021). Phoresy Records Appendix.xlsx. figshare. Dataset. https://doi.org/10.6084/m9.figshare.12671711.v1\n\n\n\n\n9\n\n\n\n\nC. Anela Choy, Steven H. D. Haddock, Bruce H. Robison. 2017. Deep pelagic food web structure as revealed by in situ feeding observations. Proc. R. Soc. B 2017 284 20172116; DOI:10.1098/rspb.2017.2116.\n\n\n\n\n8\n\n\n\n\nSarah E Miller. 7/6/2016. Arctos collection.\n\n\n\n\n6\n\n\n\n\nGeiselman, Cullen K. and Tuli I. Defex. 2015. Bat Eco-Interactions Database. www.batplant.org\n\n\n\n\n5\n\n\n\n\nNEON Biorepository Portal at Arizona State University (ASU)\n\n\n\n\n4\n\n\n\n\nSarah E Miller. 4/20/2015. Species associations manually extracted from various papers and articles from site https://repository.si.edu\n\n\n\n\n4\n\n\n\n\nSarah E Miller. 5/28/2015. Arnaud, Paul Henri. A Host-parasite Catalog of North American Tachinidae (Diptera). Washington, D.C.: U.S. Dept. of Agriculture, Science and Education Administration, 1978.\n\n\n\n\n4\n\n\n\n\nSarah E Miller. 7/7/2016. Text gathered from Wirta, H.K., Vesterinen, E.J., Hambäck, P.A., Weingartner, E., Rasmussen, C., Reneerkens, J., Schmidt, N.M., Gilg, O. and Roslin, T., 2015. Exposing the structure of an Arctic food web. Ecology and evolution, 5(17), pp.3842-3856.\n\n\n\n\n4\n\n\n\n\nUniversity of California Santa Barbara Herbarium\n\n\n\n\n3\n\n\n\n\nGippet, J.M.W., Bates, O.K., Moulin, J. et al. The global risk of infectious disease emergence from giant land snail invasion and pet trade. Parasites Vectors 16, 363 (2023). https://doi.org/10.1186/s13071-023-06000-y\n\n\n\n\n3\n\n\n\n\nJorrit H. Poelen. 2017. Species interactions associated with known species interaction datasets.\n\n\n\n\n3\n\n\n\n\nMinisterio del Ambiente, Agua y Transición Ecológica de Ecuador - MAATE.\n\n\n\n\n3\n\n\n\n\nSarah E Miller. 9/15/2016. Species associations extracted from http://parasiticplants.siu.edu/index.html.\n\n\n\n\n3\n\n\n\n\nSarah E Miller. 9/3/2015. Species associations manually extracted from JSTOR.\n\n\n\n\n3\n\n\n\n\nSchriml, L. M., Arze, C., Nadendla, S., Ganapathy, A., Felix, V., Mahurkar, A., … Hall, N. (2009). GeMInA, Genomic Metadata for Infectious Agents, a geospatial surveillance pathogen database. Nucleic Acids Research, 38(Database), D754–D764. doi:10.1093/nar/gkp832\n\n\n\n\n2\n\n\n\n\nCarnegie Invertebrate Zoology Collection\n\n\n\n\n2\n\n\n\n\nF. Gabriel. Muñoz. 2017. Palm-Animal frugivore associations extracted from literature with Biodiversity Observations Miner for SouthEast Asia.\n\n\n\n\n2\n\n\n\n\nFerrer-Paris, José R.; Sánchez-Mercado, Ada Y.; Lozano, Cecilia; Zambrano, Liset; Soto, José; Baettig, Jessica; Leal, María (2014): A compilation of larval host-plant records for six families of butterflies (Lepidoptera: Papilionoidea) from available electronic resources. figshare. http://dx.doi.org/10.6084/m9.figshare.1168861\n\n\n\n\n2\n\n\n\n\nInouye, David (2017). An Access database of records collated from the literature about flies pollinating or at least visiting flowers, updated 2017. https://doi.org/10.13016/M2SZ73 http://hdl.handle.net/1903/19193 hash://sha256/a9ab0a6173d34695c85f5fb8947e196478d1253d9d79b0662921ef4e36639c05\n\n\n\n\n2\n\n\n\n\nPaleo Digitization Working Group. Biological associations extracted from fossil specimens.\n\n\n\n\n2\n\n\n\n\nQuentin J. Groom. 2020. Bat interation data manually extracted from literature.\n\n\n\n\n2\n\n\n\n\nSarah E. Miller. 04/14/2015. Extracted from literature Scott, J.A. 1986.  The Butterflies of North America.  Stanford University Press, Stanford, CA\n\n\n\n\n2\n\n\n\n\nStrona, G., Palomares, M. L. D., Bailly, N., Galli, P., & Lafferty, K. D. (2013). Host range, host ecology, and distribution of more than 11 800 fish parasite species. Ecology, 94(2), 544–544. doi:10.1890/12-1419.1\n\n\n\n\n2\n\n\n\n\nStrong, Justin S., and Shawn J. Leroux. 2014. "Impact of Non-Native Terrestrial Mammals on the Structure of the Terrestrial Mammal Food Web of Newfoundland, Canada." PLOS ONE 9 (8): e106264. https://doi.org/10.1371/journal.pone.0106264\n\n\n\n\n2\n\n\n\n\nThessen AE. 2017. Biotic Interactions in Greenland. GloBI. 10.5281/zenodo.266824\n\n\n\n\n1\n\n\n\n\nBourlat SJ, Koch M, Kirse A, Langen K, Espeland M, Giebner H, Decher J, Ssymank A, Fonseca VG (2023) Metabarcoding dietary analysis in the insectivorous bat Nyctalus leisleri and implications for conservation. Biodiversity Data Journal 11: e111146. https://doi.org/10.3897/BDJ.11.e111146\n\n\n\n\n1\n\n\n\n\nCamargo-Sanabria, A.A., Fernández, J.A., Hernández-Quiroz, N.S., Buitrago-Torres, D.L. and Álvarez-Córdova, F. (2025), Ecological Interactions of Terrestrial Mammals in the Chihuahuan Desert: A Systematic Map. Mam Rev e70001. https://doi.org/10.1111/mam.70001\n\n\n\n\n1\n\n\n\n\nDe Rojas M, Doña J, Dimov I (2020) A comprehensive survey of Rhinonyssid mites (Mesostigmata: Rhinonyssidae) in Northwest Russia: New mite-host associations and prevalence data. Biodiversity Data Journal 8: e49535. https://doi.org/10.3897/BDJ.8.e49535\n\n\n\n\n1\n\n\n\n\nDeans, Andrew (2021). Catalog of Rose Gall, Herb Gall, and Inquiline Gall Wasps (Hymenoptera: Cynipidae) of the United States, Canada, and Mexico\n\n\n\n\n1\n\n\n\n\nGunther KA et al. 2014 Dietary breadth of grizzly bears in the Greater Yellowstone Ecosystem. Ursus 25(1):60-72\n\n\n\n\n1\n\n\n\n\nHiveTracks WorldFAIR Test Data.\n\n\n\n\n1\n\n\n\n\nSarah E Miller. 3/4/2015. Species associations manually extracted from http://onlinelibrary.wiley.com/doi/10.1111/j.1474-919X.2009.00907.x/suppinfo.\n\n\n\n\n1\n\n\n\n\nSarah E Miller. 5/21/2015. Text gathered from http://www.biodiversitylibrary.org/\n\n\n\n\n1\n\n\n\n\nVanderweyen A, Fraiture A, Groom Q, Desmet P, Reyserhove L (2019). Catalogue of the Rust Fungi of Belgium. Botanic Garden Meise.\n\n\n\n\n1\n\n\n\n\nVanderweyen, A., & Fraiture, A. (2009). Catalogue des Uredinales de Belgique, 1re partie, Chaconiaceae, Coleosporiaceae, Cronartiaceae, Melampsoraceae, Phragmidiaceae, Pucciniastraceae, Raveneliaceae et Uropyxidaceae. Lejeunia, Revue de Botanique|Vanderweyen, A., & Fraiture, A. (2009). Catalogue des Uredinales de Belgique, 2ème partie, Pucciniaceae (sauf Puccinia)(suite 2). Lejeunia, Revue de Botanique.|Vanderweyen, A., & Fraiture, A. (2012). CATALOGUE DES UREDINALES DE Belgique 3ème partie Pucciniaceae (genre Puccinia). Lejeunia, Revue de Botanique.\n\n\n\n\n1\n\n\n\n\nZeke Marshall. 2021. Species interactions manually extracted from literature.\n\n\n\n\n\n\n \n\nReferences\n\nPoelen JH, Simons JD, Mungall CJ (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2014.08.005\n\nSeltmann KC, Allen J, Brown BV, Carper A, Engel MS, Franz N, Gilbert E, Grinter C, Gonzalez VH, Horsley P, Lee S, Maier C, Miko I, Morris P, Oboyski P, Pierce NE, Poelen J, Scott VL, Smith M, Talamas EJ, Tsutsui ND, Tucker E (2021) Announcing Big-Bee: An initiative to promote understanding of bees through image and trait digitization. Biodiversity Information Science and Standards 5: e74037. https://doi.org/10.3897/biss.5.74037\n\nPoelen, JS & Seltmann, KS (2024) Bees Only Please: Bees Only Please: Selecting Hundreds of Thousands of Possible Bee Interactions Using a Laptop, Open Datasets, and Small (but Mighty) Commandline Tools. https://www.globalbioticinteractions.org/2024/06/07/bees-only-please\n\nAscher, J. S. and J. Pickering (2020) Discover Life bee species guide and world checklist (Hymenoptera: Apoidea: Anthophila). http://www.discoverlife.org/mp/20q?guide=Apoidea_species.\n\nAcknowledgements\n\nThis project is supported by the National Science Foundation. Award numbers: DBI:2102006, DBI:2101929, DBI:2101908, DBI:2101876, DBI:2101875, DBI:2101851, DBI:2101345, DBI:2101913, DBI:2101891 and DBI:2101850 "],"Other":["Please cite the resources, natural history collections and publications where the data originated as found in uniq_citations.tsv file above. Also, please cite Poelen et. al (above in References) to cite Global Biotic Interactions."]} 

Body size is a salient functional trait in bees, with implications for reproductive fitness, pollination ecology, and responses to environmental change. Methods for quantifying bee body size commonly rely on indirect estimates and vary widely across studies, particularly in studies of the large carpenter bees (Xylocopa Latreille) (Apidae: Xylocopinini). We evaluate the robustness of three common body size parameters (intertegular distance, head width, and costal vein length) as predictors of dry body mass within and among 11 species of Xylocopa (and 5 subspecies). We found that all three size measurements provide robust body size estimates, accounting for 92–93% of intraspecific variation in body mass. Within species, however, these measurements were considerably less predictive of body mass, explaining on average only 36.8% (intertegular distance), 57.4% (head width), and 38.8% (costal vein length) of the variation in body mass. We also highlight a novel application of photogrammetry and 3D modeling to estimate surface area and volume across species, and comment on the utility of these methods for body size estimates in Xylocopa and in insects more broadly. These findings provide practical guidelines for body size estimation methods within and among carpenter bee species.