DOI: 10.6084/m9.figshare.16577354.v1
title: Datasets for: Continental risk assessment for understudied taxa post catastrophic wildfire indicates severe impacts on the Australian bee fauna

description: <i>Data acquisition</i>Occurrence data for bee species were downloaded from ALA<sup>60</sup> using <i>ALA4R</i> version 1.8.0<sup>64</sup> in <i>R</i> version 3.6.2<sup>65</sup>.Floral visitation data were obtained from ALA<sup>60</sup>, Museums Victoria, the Western Australian Museum<sup>66,67</sup>, and publications (Tables S1 and S2). Floral visitation records were checked for errors and synonymies using the Australian Plant Name Index<sup>68</sup>. Life-history traits for bee species were sourced, in most cases, from the most recent taxonomic descriptions, or other publications (Tables S1 and S2). A one-hectare resolution Major Vegetation Subgroup (MVS) map was sourced from Geoscience Australia’s National Mapping Division (NMD)<sup>61</sup>. Fire frequency data from 1988 to 2016 were downloaded from the Department of Environment and Energy (DEE)<sup>69</sup>, 2019–20 wildfire occurrence data (National Indicative Aggregated Fire Extent Dataset — NIAFED — version 20200623) were sourced from the Department of Agriculture, Water and the Environment (DAWE)<sup>36</sup>, and 2019–20 wildfire intensity data (Google Earth Engine Burnt Area Map — GEEBAM) were sourced from the Department of Planning, Industry and Environment (DPIE)<sup>62</sup>. All raster data sources were matched in resolution to the one-hectare MVS map. These GIS data sources may vary in spatial uncertainty or resolution and their caveats can be found at their respective locations online.<br><i>Data filtering and analyses</i>Occurrence data from ALA were filtered to include only reliable (“preserved specimens”, “machine observations” — e.g., malaise traps, — and data from published datasets) and “present” (compared to “absent”) records. Records without geographic locations or that did not align with base maps were excluded from GIS analyses. Species were then filtered for minimum sample size (n = 30) and minimum number of unique localities (n = 5). However, if there were 15 or more unique localities and a sample size of less than 30, the species was included.<br>The MVS map was reprojected to a world geodetic system (WGS 1984, EPSG:4326) and clipped to the 2019–20 wildfire map in <i>QGIS</i> version 3.12<sup>70</sup>. The NIAFED and GEEBAM maps were aligned and matched to the resolution of the MVS map using the package <i>raster</i> version 3.0-12<sup>71</sup> in <i>R</i> version 3.6.2<sup>65</sup>. Major vegetation subgroups<sup>61</sup>, 2019–20 wildfire status<sup>36</sup>, and fire frequency<sup>69</sup> were extracted for each ALA record using <i>raster</i>. The proportion of each MVS burnt was calculated by clipping MVS maps with the 2019–20 burn map in <i>ArcMap</i> Version 10.6.1<sup>72</sup>. All map files used in our analyses are available at (<i>html location to be confirmed upon acceptance</i>) for use with our <i>R </i>script.<br>We complemented species distributional data (ALA<sup>60</sup> point data) with spatial information on their associated habitat (MVS<sup>61</sup>), to avoid reliance on the limited data for some species. To determine the potential distribution of each species we buffered the latitudinal and longitudinal extents of the raster datasets (MVS, fire frequency, NIAFED, and GEEBAM) by 20% in each direction. For geographically-restricted species with latitudinal or longitudinal ranges less than one degree (~111 km), we buffered their extent by one degree in each direction along that axis or axes. These values were chosen as conservative estimates of species distributional extents, but we recognize that this treatment may over-inflate the distribution of some species with highly-localized ranges. <br>These data are broken into four files:Map_data — hosts all of the map files used in the analysesBee-plant_point_data — hosts the ALA download data, combined bee dataset, and the life history and plant data spreadsheetWard_comparison_data — hosts some of the data used for the Ward co-analysis using our methodAll_other_R_data — hosts many of the runfiles from our main analysis