Using expert elicitation to identify effective combinations of management actions for koala conservation in different regional landscapes
Emma B. Camus A B , Jonathan R. Rhodes A B , Clive A. McAlpine A B , Daniel Lunney C D E , John Callaghan F , Ross Goldingay F , Angie Brace G , Murray Hall A B , Scott Benitez Hetherington H , Marama Hopkins B , Marek J. Druzdzel I and Helen J. Mayfield
A B *
+ Author Affiliations
- Author Affiliations
A Centre for Biodiversity and Conservation Science, University of Queensland, Brisbane, Qld 4072, Australia.
B School of Earth and Environmental Sciences, University of Queensland, Brisbane, Qld 4072, Australia.
C Department of Planning and Environment, Locked Bag 5022, Parramatta, NSW 2124, Australia.
D Faculty of Science, School of Life and Environmental Sciences, University of Sydney, Sydney, NSW 2006, Australia.
E Australian Museum, 1 William Street, Sydney, NSW 2010, Australia.
F Faculty of Science and Engineering, Southern Cross University, Lismore, NSW 2480, Australia.
G Lismore City Council, 43 Oliver Avenue, Goonellabah, NSW 2480, Australia.
H Tweed Shire Council, PO Box 816, Murwillumbah, NSW 2484, Australia.
I Faculty of Computer Science, Bialystok University of Technology, Bialystok, Poland.
Wildlife Research 50(7) 537-551 https://doi.org/10.1071/WR22038
Submitted: 26 February 2022 Accepted: 3 July 2022 Published: 21 July 2022
© 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: Choosing the most effective combinations of actions to manage threatened species is difficult.
Aims: This study aimed to identify the most effective combinations of six management actions for reversing population declines of koala populations in three regional landscapes (Coastal, Riverine and Hinterland) in north-east New South Wales, Australia, based on expert-elicited knowledge.
Methods: A Bayesian network was used to combine continuous probability distributions representing the impact of specific management actions and combinations of actions. Data were derived from expert elicitation. Variation within and among expert judgements was captured by incorporating submodels of individual responses. We evaluated alternative management solutions to address four mortality factors (disease and injury, vehicle strikes, domestic dogs and wild dogs/dingoes (Canis familiaris), and two habitat-related management actions (restoration and protection).
Key results: We show that there are marked differences in the expected response of the koala populations to the various management options in the three regional landscapes over a 20-year period (2019–2039), and that multiple management actions are required to arrest and reverse the decline in koala populations of north-east NSW.
Conclusions: Management actions for koala conservation should be based on regional context. Our model, in conjunction with robust expert-elicitation procedures, allows decision makers to distinguish effective from ineffective combinations of management actions for threatened species management, and has been structured so that new data can be incorporated into the model.
Implications: Model design could be easily adapted to different species or conservation contexts, and updated as new evidence becomes available, making it valuable in adaptive management for local to regional-scale conservation problems.
Keywords: adaptive management, Bayesian network, expert elicitation, habitat restoration, koala conservation, roadkill, targeted management, threatened species recovery, wild dogs.
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