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Examination of conflicts and improved strategies for the management of an endangered Eucalypt species using Bayesian networks

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  • Pollino, Carmel A.
  • White, Andrea K.
  • Hart, Barry T.

Abstract

Bayesian decision support tools are becoming increasingly popular as a modelling framework that can analyse complex problems, resolve controversies, and support future decision-making in an adaptive management framework. This paper introduces a model designed to assist the management of an endangered Eucalypt species, the Swamp Gum (Eucalyptus camphora). This tree species is found in the Yellingbo Nature Conservation Reserve (YNCR), an isolated patch of forest in the Yarra Valley (Victoria, Australia), where E. camphora has become increasingly threatened by dieback. In order to maintain and rehabilitate existing trees and encourage regeneration, management strategies and action plans have concentrated on restoring the hydrological regime, which has been altered due to agricultural activities within the catchment. However, research suggests that nutrient enrichment from surrounding horticulture and livestock is having a greater impact on the health of the trees. A Bayesian network model has been developed for E. camphora and used to explore the differences between these two hypotheses. Model outputs suggest that the influencing factors of E. camphora condition are (a) spatially specific and (b) differ according to the group conducting the study in the YNCR. Given the poor quality of data and knowledge available, further research is required to identify the causal factors of dieback. The model offers a framework to guide future integrative and iterative monitoring and research in the YNCR.

Suggested Citation

  • Pollino, Carmel A. & White, Andrea K. & Hart, Barry T., 2007. "Examination of conflicts and improved strategies for the management of an endangered Eucalypt species using Bayesian networks," Ecological Modelling, Elsevier, vol. 201(1), pages 37-59.
    • Handle: RePEc:eee:ecomod:v:201:y:2007:i:1:p:37-59
    DOI: 10.1016/j.ecolmodel.2006.07.032
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    Cited by:

    1. Guo, Kai & Zhang, Xinchang & Kuai, Xi & Wu, Zhifeng & Chen, Yiyun & Liu, Yi, 2020. "A spatial bayesian-network approach as a decision-making tool for ecological-risk prevention in land ecosystems," Ecological Modelling, Elsevier, vol. 419(C).
    2. Ropero, R.F. & Renooij, S. & van der Gaag, L.C., 2018. "Discretizing environmental data for learning Bayesian-network classifiers," Ecological Modelling, Elsevier, vol. 368(C), pages 391-403.
    3. Liedloff, Adam C. & Smith, Carl S., 2010. "Predicting a ‘tree change’ in Australia's tropical savannas: Combining different types of models to understand complex ecosystem behaviour," Ecological Modelling, Elsevier, vol. 221(21), pages 2565-2575.
    4. Mulazzani, Luca & Manrique, Rosa & Malorgio, Giulio, 2017. "The Role of Strategic Behaviour in Ecosystem Service Modelling: Integrating Bayesian Networks With Game Theory," Ecological Economics, Elsevier, vol. 141(C), pages 234-244.
    5. Johnson, Sandra & Mengersen, Kerrie & de Waal, Alta & Marnewick, Kelly & Cilliers, Deon & Houser, Ann Marie & Boast, Lorraine, 2010. "Modelling cheetah relocation success in southern Africa using an Iterative Bayesian Network Development Cycle," Ecological Modelling, Elsevier, vol. 221(4), pages 641-651.
    6. Hamilton, Serena H. & Pollino, Carmel A. & Jakeman, Anthony J., 2015. "Habitat suitability modelling of rare species using Bayesian networks: Model evaluation under limited data," Ecological Modelling, Elsevier, vol. 299(C), pages 64-78.
    7. Vilizzi, L. & Price, A. & Beesley, L. & Gawne, B. & King, A.J. & Koehn, J.D. & Meredith, S.N. & Nielsen, D.L. & Sharpe, C.P., 2012. "The belief index: An empirical measure for evaluating outcomes in Bayesian belief network modelling," Ecological Modelling, Elsevier, vol. 228(C), pages 123-129.

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