A globally synthesised and flagged bee occurrence dataset and cleaning workflow.

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 present BeeBDC, a new R package, 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 reproducible BeeBDC 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". The BeeBDC package with online documentation provides end users the ability to modify filtering parameters to address their research questions. By publishing reproducible R workflows 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.

Continental risk assessment for understudied taxa post-catastrophic wildfire indicates severe impacts on the Australian bee fauna.

The 2019-2020 Australian Black Summer wildfires demonstrated that single events can have widespread and catastrophic impacts on biodiversity, causing a sudden and marked reduction in population size for many species. In such circumstances, there is a need for conservation managers to respond rapidly to implement priority remedial management actions for the most-affected species to help prevent extinctions. To date, priority responses have been biased towards high-profile taxa with substantial information bases. Here, we demonstrate that sufficient data are available to model the extinction risk for many less well-known species, which could inform much broader and more effective ecological disaster responses. Using publicly available collection and GIS datasets, combined with life-history data, we modelled the extinction risk from the 2019-2020 catastrophic Australian wildfires for 553 Australian native bee species (33% of all described Australian bee taxa). We suggest that two species are now eligible for listing as Endangered and nine are eligible for listing as Vulnerable under IUCN criteria, on the basis of fire overlap, intensity, frequency, and life-history traits: this tally far exceeds the three Australian bee species listed as threatened prior to the wildfire. We demonstrate how to undertake a wide-scale assessment of wildfire impact on a poorly understood group to help to focus surveys and recovery efforts. We also provide the methods and the script required to make similar assessments for other taxa or in other regions.