Insect Species Occurrence Data from Multiple Projects Worldwide with Focus on Bees and Wasps in North America
Bibliographic record
Abstract
Species occurrence records for native and non-native bees, wasps and other insects collected for many different projects using mainly bowl, malaise, and vane trapping and insect netting methods in Canada, Mexico, the non-contiguous United States, U.S. Territories (specifically U.S. Virgin Islands), U.S. Minor Outlying Islands and other global locations with the bulk of the specimens coming from projects requested by partners in the Eastern United States often from Federal lands such as USFWS, NPS, DOD, USFS. Some records also contain notes regarding plants or substrates from which insects were collected or that were present and/or in flower at the time the insects were collected. Unless otherwise noted, taxonomic determinations (identifications) were completed by Sam Droege (USGS Bee Lab at the Eastern Ecological Science Center (EESC)) and Clare Maffei (USFWS, National Wildlife Refuge System, Inventory and Monitoring Branch).The USGS Bee Lab currently keeps only a small synoptic collection, rare and voucher specimens are deposited in the Smithsonian National Collection (NMNH) and widely distributed to other institutions for DNA, revisions, and augmentation of existing collections. Surplus specimens are also made available to students to learn their identifications. Corrections to any of our determinations are always welcomed. Common species that are not in demand for surplus are usually destroyed and the pins recycled. Recent revisions to Lasioglossum, Ceratina, and to a much lesser extent Triepeolus and Epeolus and other small groups have rendered determinations prior to those revisions out of date for species involved in name changes and users should account for that during analyses. Current data (included information on specimen codes without identifications) are always available without charge directly from Sam Droege.----------While these data were collected using many different techniques, time periods, and regions they all follow the Wild Bee Data Standard (WBDS) core fields which uses controlled language adapted from Darwin Core (DwC). While the collection methods are unstandardized, the data presentation is.We highly recommend using the “Darwin Core Archive” package for this dataset. This can be downloaded from the GBIF annotated archive. The “Simple” download includes only specimen identification, date, and location. <b>To understand the full context of those observations—including collection methods and associated measurements—download the “Darwin Core Archive” files.</b>Files in the archive are linked by the <b>"eventID"</b> field. Each “eventID” represents a single collection event using one method. For example, a transect of bowl traps and a hand netting event on the same day and location will have separate “eventID’s”. All files include the date and location of the collection.<b>Occurrence file:</b> Provides the complete representation of identifications. This is the best place to explore how uncertain identifications and taxonomic changes affect the dataset. “scientificName” and “taxonRank” are treated conservatively; any specimen with an uncertain species-level identification is rolled up to genus for these fields“verbatimIdentification” shows the estimated identification or, in the case of taxonomic splits, the species group. These are often qualified in some way using terms such as “?”, “aff”, “group”, “near”, etc.“identificationRemarks” provides notes on any changes made.“basisOfRecord” uses controlled language from DwC to describe the nature of the record.<b>Event file:</b>Contains information on the collection method and any associated data recorded by the project (such as weather or flora).“samplingEffort” is the only exception to WBDS. This field cannot be interpreted for the entire dataset because these data are aggregated from multiple sources. Researchers may calculate values for specific collection events.<b>Extended Measurement or Fact file:</b> Adds details related to the controlled-language “samplingProtocol” field from the “Event” file, such as collection medium (e.g.: type of fluid used in traps).<b>Important Notes:</b>Taxonomic uncertainty and changes over time may affect interpretation. Review the Occurrence file for details. These data are aggregated from multiple projects, so some fields may vary in completeness. Absence of bee species from this dataset does not indicate absence of the species from the collecting location. Number of times a species is recorded in this dataset does not represent actual species abundance or common-ness but does offer an indication of fluctuations in communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".