Remote sensing shows south-east Queensland koalas (Phascolarctos cinereus) prefer areas of higher tree canopy height within their home ranges
Dave L. Mitchell
A B * , Mariela Soto-Berelov B and Simon D. Jones B
+ Author Affiliations
- Author Affiliations
A Australian Koala Foundation, GPO Box 2659, Brisbane, Qld 4001, Australia.
B School of Science, RMIT University, Melbourne, Vic. 3000, Australia.
Wildlife Research 50(11) 939-953 https://doi.org/10.1071/WR22093
Submitted: 1 June 2022 Accepted: 22 December 2022 Published: 27 January 2023
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)
Abstract
Context: Home range studies allow investigation of faunal habitat use within a well-defined area, and for some species, the concept of ‘core’ and ‘non-core’ home ranges provides the means to examine how resource use varies within home ranges. Taking this approach, we investigated whether koalas preferentially used areas of taller forest canopy within home ranges. After an extensive examination of data quality and home range estimation methods, we used remote sensing techniques to provide canopy height information at high resolution.
Aims: In many areas, koalas prefer taller individual trees at the plot scale; our aim was to investigate whether koalas prefer forest areas with higher canopy height within their home ranges.
Methods: In our southeast Queensland study area, we developed a canopy height model (CHM) from airborne LiDAR (Light Detection and Ranging) data. Existing radio telemetry and GPS data from 135 koalas were used to generate home ranges using 95% kernel density estimators, and 50% kernels represented core home ranges. Some home ranges occupied more than one forest type (Regional Ecosystem – RE); we treated each RE as an individual patch, and used 225 patches in our analysis. We intersected the 95% kernels with the CHM, and used hierarchical spatial clustering to derive four categorical canopy height classes within each patch. We then compared differences in height class area proportions between core and non-core areas for each patch.
Key results: The highest of the four canopy height classes comprised a significantly higher proportion of core areas (42.3%) than non-core areas (30.7%). Classes 2 and 3 were evenly distributed, and the proportion of Class 4 (lowest canopy height) was 20.3% of non-core areas and 11.0% in core areas. Results were similar for REs grouped by Land Zone and individual REs.
Conclusions and implications: We conclude that areas of higher canopy are an important habitat resource for koalas. We have, for the first time, examined resource variability within entire koala home ranges using remote sensing, and our methods demonstrate an avenue for further research using other forms of remote sensing. Classified canopy height models could also be used for strategic conservation planning, and at population-level koala habitat management when combined with other relevant habitat factors.
Keywords: ecology, habitat preference, habitat use, koala, LiDAR, radio telemetry, spatial clustering, spatial ecology, wildlife management.
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