Rapidly mapping fire effects on biodiversity at a large-scale using citizen science

Abstract

The unprecedented scale of the 2019-2020 eastern Australian bushfires exemplifies the challenges that scientists and conservation biologists face monitoring the effects of biodiversity in the aftermath of large-scale environmental disturbances. After a large-scale disturbance there are conservation policy and management actions that need to be both timely and informed by data. By working with the public, often widely spread out over such disturbed areas, citizen science offers a unique opportunity to collect data on biodiversity responses at the appropriate scale. We detail a citizen science project, hosted through iNaturalist, launched shortly after the 2019-2020 bushfire season in eastern Australia. It rapidly (1) provided accurate data on fire severity, relevant to future recovery; and (2) delivered data on a wide range (mosses to mammals) of biodiversity responses at a scale that matched the geographic extent of these fires.

Widespread short‐term persistence of frog species after the 2019–2020 bushfires in eastern Australia revealed by citizen science

“…Citizen science has the capacity not only to collect large datasets across a broad geographic area, but to generate data rapidly, allowing a timely response to help understand the impact of stochastic events such as landslides, floods and severe weather (Hicks et al, 2019). The 2019/20 bushfires were of a scale too large for a rapid response to using conventional biodiversity monitoring methods (Kirchhoff et al, 2020). Much of the area burnt was on private properties, and movement restrictions due to COVID‐19 in the months after fires presented further logistical problems for professional surveys across the fire grounds.…”

“…This data gap exists due to logistical challenges involved in collecting postfire data in a timely fashion across a wide area. Citizen science offers a new and powerful approach to this problem, capable of rapidly responding to catastrophic events such as fires across a large spatial scale (Kirchhoff et al, 2020). This is particularly true for ongoing citizen science projects that have likely collected “before” data, providing the necessary temporal comparison with “after” data following catastrophic events.…”