Abstract
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.
Original language | English |
---|---|
Pages (from-to) | 6551-6567 |
Number of pages | 17 |
Journal | Global Change Biology |
Volume | 27 |
Issue number | 24 |
Early online date | Sept 2021 |
DOIs | |
Publication status | Published - Dec 2021 |
Bibliographical note
Funding Information:We thank the funding bodies (Playford Trust, Flinders University, and the Australian Government) for providing scholarship funding to many of the authors. We would also like to thank the data providers and uploaders whose quality data made these analyses possible. Additionally, we would like to thank Gergana Daskalova and Mitchell Hearn for their help in construction of our graphical abstract. Finally, we thank our editor and two anonymous reviewers who provided very helpful comment on our manuscript—we are truly thankful.
Publisher Copyright:
© 2021 John Wiley & Sons Ltd
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.