ReviewHarmful algae2020
Modeling harmful algal blooms in a changing climate.
Review in Harmful algae, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed.
- Advancing Near-Term Water Quality Forecasting with Explainable Machine Learning and Probabilistic Uncertainty Quantification.ACS ES&T water · 2026Article
- Potential Increase in Known and Emerging Biotoxins in Marine Ecosystem Due to Climate Change and Subsequent Health Issues.Foods (Basel, Switzerland) · 2026Review
- Turbidity and streamflow as real-time indicators of microbial risk for aquatic recreators.Environmental monitoring and assessment · 2026Article
- Pathogenic effects ofApplied and environmental microbiology · 2026Article
- Estimating Operational Costs of Activated Carbon for Water Treatment Plants by Predicting the Rise of Harmful Algal Blooms Under Climate Change in Korea Using Machine Learning.Water environment research : a research publication of the Water Environment Federation · 2026Article
- More realistic plankton simulation models will improve projections of ocean ecosystem responses to global change.Nature ecology & evolution · 2025Review
- Article
- Harmful algal blooms are preceded by a predictable and quantifiable shift in the oceanic microbiome.Nature communications · 2025Article
- Climate Change and Marine Food Webs: Navigating Structural Uncertainty Using Qualitative Network Analysis With Insights for Salmon Survival.Global change biology · 2025Article
- Article
- Coastal generalized ecosystem model (CGEM) 1.0: Flexible model formulations for simulating complex biogeochemical processes in aquatic ecosystems.Ecological modelling · 2024Article
- Harmful algal blooms in inland waters.Nature reviews. Earth & environment · 2024Article
- Planktonic eukaryotes in the Chesapeake Bay: contrasting responses of abundant and rare taxa to estuarine gradients.Microbiology spectrum · 2024Article
- Toward a Predictive Understanding of Cyanobacterial Harmful Algal Blooms through AI Integration of Physical, Chemical, and Biological Data.ACS ES&T water · 2024Review
- Article
- Identification of aMarine drugs · 2023Article
- Dinoflagellate vertical migration fuels an intense red tide.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Microbial removal of nutrients from anaerobic digestate: assessing product-coupled and non-product-coupled approaches.Frontiers in microbiology · 2023Article
- Impacts of Climate Change on the Biogeography of Three Amnesic Shellfish Toxin Producing Diatom Species.Toxins · 2022Article
- Oxidative Stress Parameters and Morphological Changes in Japanese Medaka (Life (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
This review assesses harmful algal bloom (HAB) modeling in the context of climate change, examining modeling methodologies that are currently being used, approaches for representing climate processes, and time scales of HAB model projections. Statistical models are most commonly used for near-term HAB forecasting and resource management, but statistical models are not well suited for longer-term projections as forcing conditions diverge from past observations. Process-based models are more complex, difficult to parameterize, and require extensive calibration, but can mechanistically project HAB response under changing forcing conditions. Nevertheless, process-based models remain prone to failure if key processes emerge with climate change that were not identified in model development based on historical observations. We review recent studies on modeling HABs and their response to climate change, and the various statistical and process-based approaches used to link global climate model projections and potential HAB response. We also make several recommendations for how the field can move forward: 1) use process-based models to explicitly represent key physical and biological factors in HAB development, including evaluating HAB response to climate change in the context of the broader ecosystem; 2) quantify and convey model uncertainty using ensemble approaches and scenario planning; 3) use robust approaches to downscale global climate model results to the coastal regions that are most impacted by HABs; and 4) evaluate HAB models with long-term observations, which are critical for assessing long-term trends associated with climate change and far too limited in extent.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.