TY - JOUR
T1 - Habitat in flames
T2 - How climate change will affect fire risk across koala forests
AU - Shabani, Farzin
AU - Shafapourtehrany, Mahyat
AU - Ahmadi, Mohsen
AU - Kalantar, Bahareh
AU - Özener, Haluk
AU - Clancy, Kieran
AU - Esmaeili, Atefeh
AU - da Silva, Ricardo Siqueira
AU - Beaumont, Linda J.
AU - Llewelyn, John
AU - Jones, Simon
AU - Ossola, Alessandro
PY - 2023/11
Y1 - 2023/11
N2 - Aim: Generate fire susceptibility maps for the present and 2070, to identify the threat wildfires pose to koalas now and under future climate change. Location: Australia. Time period: Present and 2070. Major taxa studied: 60 main tree species browsed by koalas. Method: The Decision Tree machine learning algorithm was applied to generate a fire susceptibility index (a measure of the potential for a given area or region to experience wildfires) using a dataset of conditioning factors, namely: altitude, aspect, rainfall, distance from rivers, distance from roads, forest type, geology, koala presence and future dietary sources, land use-land cover (LULC), normalized difference vegetation index (NDVI), slope, soil, temperature, and wind speed. Results: We found a general increase in susceptibility of Australian vegetation to bushfires overall. The simulation for current conditions indicated that 39.56% of total koala habitat has a fire susceptibility rating of “very high” or “high”, increasing to 44.61% by 2070. Main conclusions: Wildfires will increasingly impact koala populations in the future. If this iconic and vulnerable marsupial is to be protected, conservation strategies need to be adapted to deal with this threat. It is crucial to strike a balance between ensuring that koala habitats and populations are not completely destroyed by fire while also allowing for forest rejuvenation and regeneration through periodic burns.
AB - Aim: Generate fire susceptibility maps for the present and 2070, to identify the threat wildfires pose to koalas now and under future climate change. Location: Australia. Time period: Present and 2070. Major taxa studied: 60 main tree species browsed by koalas. Method: The Decision Tree machine learning algorithm was applied to generate a fire susceptibility index (a measure of the potential for a given area or region to experience wildfires) using a dataset of conditioning factors, namely: altitude, aspect, rainfall, distance from rivers, distance from roads, forest type, geology, koala presence and future dietary sources, land use-land cover (LULC), normalized difference vegetation index (NDVI), slope, soil, temperature, and wind speed. Results: We found a general increase in susceptibility of Australian vegetation to bushfires overall. The simulation for current conditions indicated that 39.56% of total koala habitat has a fire susceptibility rating of “very high” or “high”, increasing to 44.61% by 2070. Main conclusions: Wildfires will increasingly impact koala populations in the future. If this iconic and vulnerable marsupial is to be protected, conservation strategies need to be adapted to deal with this threat. It is crucial to strike a balance between ensuring that koala habitats and populations are not completely destroyed by fire while also allowing for forest rejuvenation and regeneration through periodic burns.
KW - 60 main tree species browsed by koalas
KW - Climate change
KW - Decision Tree machine learning algorithm
KW - Fire
UR - http://www.scopus.com/inward/record.url?scp=85168555854&partnerID=8YFLogxK
U2 - 10.1016/j.eti.2023.103331
DO - 10.1016/j.eti.2023.103331
M3 - Article
AN - SCOPUS:85168555854
SN - 2352-1864
VL - 32
JO - Environmental Technology and Innovation
JF - Environmental Technology and Innovation
M1 - 103331
ER -
