Overview
Southeast Coastal Ocean Observing Regional Association (SECOORA) is soliciting proposals for a competitive opportunity focused on coastal and ocean HAB observing in the Southeast. The scope of this opportunity is broad, but applicants should use the SECOORA Harmful Algal Bloom Plan as a guide to SECOORA’s priorities. Specifically, applicants should review the sections identifying HAB species of concern by state and areas of potential investment. Proposals should address how the effort can contribute to a regional-scale enterprise and include measurable engagement with stakeholders.
Projects should focus one or both areas:
- Observing – Funds can be requested to support the collection of data to better study HABs issues within the region. Funding can be used for fixed stations, mobile platforms (e.g., drones, AUVs), and ships.
- Products – Transform raw and/or processed data into useful and meaningful decision-making information and tools for HAB tracking and monitoring or forecasting HAB occurrence.
Total funds available are $200,000. SECOORA intends to award between 1 – 3 proposals that will be funded for a 2 year timeline. Proposals are due September 15, 2022 at 5 PM ET.
Please review the frequently asked questions page. If you have additional questions, please email laura@secooraorgpact.wpengine.com.
Related news
Meet the Scientist Turning Ocean DNA Into Open Data
Meet Dr. Luke Thompson, the NOAA AOML scientist building open tools that turn ocean eDNA sequences into data anyone can search and use.
SECOORA Receives $25,000 Grant from the Curtis & Edith Munson Foundation to Support South Florida Monitoring
New funding will help sustain coastal monitoring and provide people with the data needed to address challenges facing South Florida’s marine ecosystems.
Reinventing the eDNA Wheel: Next-Generation Biodiversity Monitoring
Scientists from MBARI, NOAA AOML, and USGS are using environmental DNA to detect marine life from a single water sample. Learn how long-term MBON time series, autonomous samplers, and machine learning are shaping the future of biodiversity monitoring.