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Abstract Phytoplankton blooms create harmful toxins, scums, and taste and odor compounds and thus pose a major risk to drinking water safety. Climate and land use change are increasing the frequency and severity of blooms, motivating the development of new approaches for preemptive, rather than reactive, water management. While several real-time phytoplankton forecasts have been developed to date, none are both automated and quantify uncertainty in their predictions, which is critical for manager use. In response to this need, we outline a framework for developing the first automated, real-time lake phytoplankton forecasting system that quantifies uncertainty, thereby enabling managers to adapt operations and mitigate blooms. Implementation of this system calls for new, integrated ecosystem and statistical models; automated cyberinfrastructure; effective decision support tools; and training for forecasters and decision makers. We provide a research agenda for the creation of this system, as well as recommendations for developing real-time phytoplankton forecasts to support management.
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This content will become publicly available on August 13, 2026
Integrating phytoplankton phenology, traits, and model‐data fusion to advance bloom prediction
Abstract While there is a diversity of approaches for modeling phytoplankton blooms, their accuracy in predicting the onset and manifestation of a bloom is still lagging behind what is needed to support effective management. We outline a framework that integrates trait theory and ecosystem modeling to improve bloom prediction. This framework builds on the concept that the phenology of blooms is determined by the dynamic interaction between the environment and traits within the phytoplankton community. Phytoplankton groups exhibit a collection of traits that govern the interplay of processes that ultimately control the phases of bloom initiation, maintenance, and collapse. An example of process‐trait mapping is used to demonstrate a more consistent approach to bloom model parameterization that allows better alignment with models and laboratory‐ and ecosystem‐scale datasets. Further approaches linking statistical‐mechanistic models to trait parameter databases are discussed as a way to help optimize models to better simulate bloom phenology and allow them to support a wider range of management needs.
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- PAR ID:
- 10643691
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Limnology and Oceanography Letters
- ISSN:
- 2378-2242
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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