Detecting Harmful Algae with Metabarcoding

Harmful algal blooms (HABs) are a longstanding problem in Florida and cost the state billions of dollars in lost revenue. HAB impacts stem from a number of causes including toxins that negatively impact human and ecosystem health, reduction of light penetration and the degradation of natural habitats, and hypoxic conditions that can result in wildlife mortalities. In response, the state has invested tens of millions of dollars to improve monitoring and mitigation.

However, monitoring efforts and any hopes of mitigation are contingent on the proper taxonomic identification of species involved. These efforts have been hampered by recent challenges including emergent HAB taxa, expanded distributions of problematic taxa, and coincident HABs. Moreover, monitoring efforts typically depend on the identification and enumeration of HAB species using morphological characters and light microscopy.

Although these techniques remain the standard, they have limitations as some species cannot be easily distinguished based on morphological characteristics alone. Here, in a collaborative effort between UCF and FWCFWRI scientists, we propose to leverage the existing HAB monitoring program at FWC-FWRI to develop an optimized molecular toolkit for the accurate and efficient detection and quantification of HAB taxa across the state. The result of this project will be new tools that can be used statewide for HAB event response and more effective monitoring strategies for mitigating HAB impacts. Ultimately outputs from this project will advance our ability to monitor and predict HABs in Florida and thus promote Healthy Coastal Ecosystems and improve the resilience of coastal communities.