From Science to Management: Using Bayesian Networks to Learn about Lyngbya

TL;DR

This paper develops a Bayesian network model to understand the complex environmental factors leading to Lyngbya blooms, aiming to improve management and research prioritization.

Contribution

It introduces an integrated Bayesian network approach to model ecological complexity and stakeholder knowledge in Lyngbya bloom management.

Findings

Identified key environmental factors influencing Lyngbya blooms.

Prioritized research gaps for future investigation.

Provided a framework for evaluating management strategies.

Abstract

Toxic blooms of Lyngbya majuscula occur in coastal areas worldwide and have major ecological, health and economic consequences. The exact causes and combinations of factors which lead to these blooms are not clearly understood. Lyngbya experts and stakeholders are a particularly diverse group, including ecologists, scientists, state and local government representatives, community organisations, catchment industry groups and local fishermen. An integrated Bayesian network approach was developed to better understand and model this complex environmental problem, identify knowledge gaps, prioritise future research and evaluate management options.