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A framework for identifying factors controlling cyanobacterium Microcystis flos-aquae blooms by coupled CCM-ECCM Bayesian networks
  • Ofir Tal,
  • Ilia Ostrovsky,
  • Gideon Gal
Ofir Tal
Israel Oceanographic and Limnological Research

Corresponding Author:[email protected]

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Ilia Ostrovsky
Israel Oceanographic and Limnological Research
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Gideon Gal
Israel Oceanographic and Limnological Research
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1. Cyanobacterial blooms in freshwater sources are a global concern, and gaining insight into their causes is crucial for effective resource management and control. 2. In this study, we present a computational framework for the causal analysis of cyanobacterial harmful algal blooms (cyanoHABs) in Lake Kinneret. Our framework integrates Convergence Cross Mapping (CCM) and Extended CCM (ECCM) causal networks with Bayesian Network (BN) models. 3. The constructed CCM - ECCM causal networks and BN models unveil significant interactions among factors influencing cyanoHAB formation. These interactions have been validated by domain experts and supported by evidence from peer-reviewed publications. Our findings suggest that M. flos-aquae levels are influenced not only by community structure but also by nitrate, nitrite, ammonium, phosphate, oxygen, and temperature levels in the weeks preceding bloom occurrences. 4. We have demonstrated a non-parametric computational framework for the causal analysis of a multivariate ecosystem. Our framework offers a more comprehensive understanding of the underlying mechanisms driving M. flos-aquae in Lake Kinneret. It captures complex interactions and provides an explainable prediction model. By considering causal relationships, temporal dynamics, and joint probabilities of environmental factors, the proposed framework enhances our understanding of cyanoHABs in Lake Kinneret.
20 Jun 2023Submitted to Ecology and Evolution
21 Jun 2023Submission Checks Completed
21 Jun 2023Assigned to Editor
06 Jul 2023Reviewer(s) Assigned
12 Mar 2024Editorial Decision: Revise Minor