A framework for identifying factors controlling cyanobacterium
Microcystis flos-aquae blooms by coupled CCM-ECCM Bayesian networks
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.