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Abstract Species occurrence data are foundational for research, conservation, and science communication, but the limited availability and accessibility of reliable data represents a major obstacle, particularly for insects, which face mounting pressures. We presentBeeBDC, a newRpackage, and a global bee occurrence dataset to address this issue. We combined >18.3 million bee occurrence records from multiple public repositories (GBIF, SCAN, iDigBio, USGS, ALA) and smaller datasets, then standardised, flagged, deduplicated, and cleaned the data using the reproducibleBeeBDC R-workflow. Specifically, we harmonised species names (following established global taxonomy), country names, and collection dates and, we added record-level flags for a series of potential quality issues. These data are provided in two formats, “cleaned” and “flagged-but-uncleaned”. TheBeeBDCpackage with online documentation provides end users the ability to modify filtering parameters to address their research questions. By publishing reproducibleRworkflows and globally cleaned datasets, we can increase the accessibility and reliability of downstream analyses. This workflow can be implemented for other taxa to support research and conservation. 

Conservation and management of wild bees is hindered by the variety of ways wild bee occurrence data are recorded, managed, and shared. Here, we present solutions to address this issue and introduce The Wild Bee Data Standard, a standardized means of recording and reporting data associated with wild bee occurrences, including physical specimens and photo observations. This standard aligns with contemporary data management practices widely adopted by the broader biodiversity data community. We propose a set of terms for the standard that describe various features of bee occurrences, including collection method and location, taxonomic verification, and final record storage. We emphasize the importance of providing sampling protocol and effort information with wild bee occurrence data and offer guidance to make this a more common practice. We describe how to translate data not currently aligned with the standard to meet its conditions, and how to upload those data to an accessible online repository. We provide case studies, data entry templates, a glossary of terms, and additional resources to guide new users to implementing the standard. We also present a forum, established as a GitHub repository, to support continued development of the standard. Recognizing the significant change this represents for current data practices, we outline the benefits for the bee research and conservation community that will result from improved data standards. We advocate for making all historical, current, and future bee occurrence data openly available to facilitate more rigorous and comprehensive research, conservation, and management of wild bees. This contribution is part of a series developed in association with the U.S. National Native Bee Monitoring Network to standardize bee monitoring practices. 

We analysed the wild bee community sampled from 1921 to 2018 at a nature preserve in southern Michigan, USA, to study long-term community shifts in a protected area. During an intensive survey in 1972 and 1973, Francis C. Evans detected 135 bee species. In the most recent intensive surveys conducted in 2017 and 2018, we recorded 90 species. Only 58 species were recorded in both sampling periods, indicating a significant shift in the bee community. We found that the bee community diversity, species richness and evenness were all lower in recent samples. Additionally, 64% of the more common species exhibited a more than 30% decline in relative abundance. Neural network analysis of species traits revealed that extirpation from the reserve was most likely for oligolectic ground-nesting bees and kleptoparasitic bees, whereas polylectic cavity-nesting bees were more likely to persist. Having longer phenological ranges also increased the chance of persistence in polylectic species. Further analysis suggests a climate response as bees in the contemporary sampling period had a more southerly overall distribution compared to the historic community. Results exhibit the utility of both long-term data and machine learning in disentangling complex indicators of bee population trajectories. 

Native bee species in the United States provide invaluable pollination services. Concerns about native bee declines are growing, and there are calls for a national monitoring program. Documenting species ranges at ecologically meaningful scales through coverage completeness analysis is a fundamental step to track bees from species to communities. It may take decades before all existing bee specimens are digitized, so projections are needed now to focus future research and management efforts. From 1.923 million records, we created range maps for nearly 88% (3158 species) of bee species in the contiguous United States, provided the first analysis of inventory completeness for digitized specimens of a major insect clade, and perhaps most important, estimated spatial completeness accounting for all known bee specimens in USA collections, including undigitized bee specimens. Completeness analyses were very low (3–37%) across four examined spatial resolutions when using the currently available bee specimen records. Adding a subset of observations from community science data sources did not significantly increase completeness, and adding a projected 4.7 million undigitized specimens increased completeness by only an additional 12–13%. Assessments of data, including projected specimen records, indicate persistent taxonomic and geographic deficiencies. In conjunction with expedited digitization, new inventories that integrate community science data with specimen‐based documentation will be required to close these gaps. A combined effort involving both strategic inventories and accelerated digitization campaigns is needed for a more complete understanding of USA bee distributions. 

Megachilidae is one of the United States’ most diverse bee families, with 667 described species in 19 genera. Unlike other bee families, which are primarily ground nesters, most megachilid bees require biotic cavities for nesting (i.e., wood, pithy stems,etc.). For this group, the availability of woody-plant species may be as important as nectar/pollen resources in maintaining populations. We studied Megachilidae biodiversity in the continental United States. We confirmed that the highest species richness of Megachilidae was in the southwestern United States. We examined the relationship between species richness and climate, land cover, tree species richness, and flowering plant diversity. When examining environmental predictors across the conterminous United States, we found that forested habitats, but not tree diversity, strongly predicted Megachilidae richness. Additionally, Megachilidae richness was highest in areas with high temperature and low precipitation, however this was not linearly correlated and strongly positively correlated with flowering plant diversity. Our research suggests that the availability of nesting substrate (forested habitats), and not only flowering plants, is particularly important for these cavity-nesting species. Since trees and forested areas are particularly susceptible to climate change, including effects of drought, fire, and infestations, nesting substrates could become a potential limiting resource for Megachilidae populations.