A Practitioner-Informed Decision Tree for Selecting Harmful Cyanobacteria Bloom Control and Mitigation Techniques

Area of Science:

• Environmental Science
• Ecology
• Water Resource Management

Background:

• Harmful cyanobacterial blooms (HCBs) pose significant ecological and human health risks, with increasing global incidence.
• Existing HCB control techniques lack clear guidance on selection and effectiveness across diverse water bodies.
• There is a need for a systematic framework to evaluate and choose appropriate HCB mitigation strategies.

Purpose of the Study:

• To develop a decision tree framework for selecting effective in situ Harmful Cyanobacterial Bloom control techniques.
• To synthesize scientific literature and practitioner experience for HCB management guidance.
• To address the lack of clear, evidence-based strategies for HCB control in different aquatic environments.

Main Methods:

• A comprehensive review of peer-reviewed and gray literature on HCB control techniques.
• A national survey of practitioners regarding HCB control methods and their application.
• Development of a decision tree based on literature review and survey data, incorporating key influencing factors.

Main Results:

• Key factors for HCB control feasibility include nutrient load source (external vs. internal), treatment area size, and water body regulations.
• The decision tree framework categorizes HCB control techniques based on environmental conditions and effectiveness.
• Survey results revealed significant regional variations in HCB control technique application.

Conclusions:

• The developed decision tree provides a science-based framework to guide the selection of appropriate and effective HCB control strategies.
• Understanding site-specific factors is crucial for successful Harmful Cyanobacterial Bloom mitigation.
• The framework aids water resource managers in making informed decisions for HCB management, supported by case studies and a review of current techniques.