R code, spatial and tabular data to fully reproduce STEPS simulations of population change for common brushtail possum, grassland melomys and northern brown bandicoot in northern Australia

The development of effective fire management for biodiversity conservation
is a global challenge. The highly dynamic nature of fire, the difficulty
in replicating ‘real-world’ fire experiments, and the need to understand
population changes at large spatiotemporal scales make computer
simulations particularly useful for identifying optimal fire management
regimes for biodiversity conservation. We aimed to develop a flexible
modelling approach with which to investigate how the spatiotemporal
application of fire (i.e. management scenarios) influences savanna
biodiversity. We used existing data from a landscape-scale fire experiment
to develop population simulations for the common brushtail possum
(Trichosurus vulpecula), grassland melomys (Melomys burtoni) and northern
brown bandicoot (Isoodon macrourus) across the Kapalga area of Kakadu
National Park in northern Australia. We simulated how populations were
expected to change between 1995 and 2015 in response to the fire patterns
observed at Kapalga over this period, and under a hypothetical management
scenario of extensive prescribed burning. Our models predicted a
substantial decline in all three species in response to the observed fire
regime at Kapalga, suggesting that the fire patterns observed at Kapalga,
with the associated mechanisms and interactions with other ecological
processes, were not conducive with the persistence of native mammal
populations. Our prescribed burning scenario had little effect on the
predicted population trajectory of the common brushtail possum and
grassland melomys, but markedly improved the population trajectory of the
northern brown bandicoot. These inconsistencies highlight the need for a
nuanced approach to fire management across northern Australian savannas,
that is tailored to local conditions and management objectives. Synthesis
and applications. The modelling approach outlined here, provides a basis
for identifying fire patterns that are beneficial for conserving
biodiversity, thereby increasing our capacity to establish clear targets
for prescribed fire management. Importantly, this approach is flexible and
can be easily adapted to other taxa and fire-prone ecosystems.