Predicting food web responses to biomanipulation using Bayesian Belief Network : Assessment of accuracy and applicability using in-situ exclosure experiments

R. B.H. LIM, J. H. LIEW, J. T.B. KWIK, D. C.J. YEO*

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

1 Scopus Citations

Abstract

Ecological networks are useful for describing the complex trophic interactions within an ecosystem and hold great potential for ecosystem-based management. However, owing to the complexity and limited knowledge on the trophic interactions of natural food webs, it is challenging to make quantitative predictions about ecological community response to management interventions. Here, we use stable isotope mixing models in conjunction with Bayesian Belief Networks (BBN) to develop and examine the trophic interactions for six empirically determined aquatic food webs in tropical reservoirs. Using BBN, we predicted potential trophic cascade outcomes to predator removals, validated the predictions against data observed from in-situ biomanipulation experiments, and identified influential species using sensitivity analyses. Comparisons among various food web modelling frameworks demonstrated the importance of weighted connectance and network-centric approach for quantitative predictions, suggesting that the Bayesian Belief Network framework can play an important role in ecosystem-based management.

Original languageEnglish
Pages (from-to)308-315
Number of pages8
JournalEcological Modelling
Volume384
Early online date17 Jul 2018
DOIs
Publication statusPublished - 24 Sep 2018
Externally publishedYes

Bibliographical note

We thank the two anonymous reviewers whose insights and suggestions greatly improved this manuscript. We also thank Chen Mingli, Ng Wen Qing, and Yvonne Kwang and the PUB site staff at the six reservoirs for their support and field assistance. The research leading to this paper was funded by PUB, Singapore’s Water Agency [National University of Singapore grant number R154-000-619-490].

Keywords

  • Environmental management
  • Freshwater
  • Sensitivity analysis
  • Stable isotope
  • Trophic interactions
  • Urban lakes

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