Harmful Algal Blooms (HABs) are a growing global concern, Increasing in frequency and intensity in recent years. HABs pose significant risks to human health, aquatic ecosystems, and economic activities, especially in regions that rely on aquaculture.
A study published by researchers from Yamaguchi University, Hiroshima University, Hokkaido University, the Fisheries Development Institute (IFOP), the Salmon Technological Institute (INTESAL), Ritsumeikan University, and Universidad de La Frontera has shed new light on HAB dynamics by analyzing interactions between specific algal species and environmental factors using a cutting-edge statistical tool called Empirical Dynamic Modeling (EDM). This research offers a promising step toward predicting and mitigating HABs in the future.
The growing threat of Harmful Algal Blooms
HABs are a natural phenomenon, but their recent surge is alarming. In Chile, HABs have caused severe economic losses, particularly in the salmon and mussel farming industries. These blooms are often dominated by toxic species such as Pseudo-nitzschia, which produces amnesic shellfish toxins, and Alexandrium catenella, responsible for paralytic shellfish poisoning. Non-toxic species can also cause damage by depleting oxygen levels in the water, creating “dead zones” that suffocate marine life.
Scientific publications have reported that climate change and other environmental factors are exacerbating these events. Factors such as rising sea temperatures, changes in salinity, and nutrient inputs from precipitation are altering conditions that favor harmful algal bloom species. Understanding how these factors interact with each other and the phytoplankton community is crucial for developing effective prediction and mitigation strategies.
The Role of Species Interactions and Environmental Factors
The study focused on two groups of Pseudo-nitzschia species—P. delicatissima and P. seriata—and their interactions with other phytoplankton species, as well as the influence of temperature and salinity. Using a 28-year dataset from three coastal stations in southern Chile, researchers applied EDM to identify causal relationships within the phytoplankton community.
Pseudo-nitzschia species have been reported to produce a neurotoxin called domoic acid. This toxin contaminates shellfish and fish and can cause a rare illness known as amnesic shellfish poisoning (ASP) if ingested. ASP symptoms include vomiting, nausea, diarrhea, headache, confusion, short-term memory loss, seizures, and, in rare cases, death.
The findings revealed that each Pseudo-nitzschia group had unique pairing species, suggesting that interactions between specific algae play a significant role in HAB dynamics. For example, competition for nutrients or the production of allelochemicals (chemicals that inhibit the growth of other species) could influence which species dominate during a bloom.
Interestingly, according to the study’s results, salinity showed a marginally significant effect on the P. seriata group in Melinka, while temperature did not appear to significantly influence either group of species.
“This study aimed to understand how harmful algal species interact with other phytoplankton and environmental factors such as temperature and salinity. This understanding is crucial because HABs have increased in frequency and intensity in Chile, causing substantial damage to the aquaculture industry, particularly salmon farming, which is vital to Chile’s economy as the world’s second-largest salmon producer,” said So Fujiyoshi, an assistant professor at The IDEC Institute of Hiroshima University (Japan).
Empirical Dynamic Modeling: A game changer for HAB prediction
Empirical Dynamic Modeling (EDM) is a powerful tool for analyzing complex and nonlinear systems such as ecological communities. Unlike traditional methods that rely on linear correlations, EDM uses time-series data to uncover causal relationships. By applying EDM, the study identified specific phytoplankton species and environmental factors that influence Pseudo-nitzschia growth.
“We found that Pseudo-nitzschia has complex interactions with other algal species. Our findings also suggest that salinity could have a more significant influence than temperature, which was previously thought to be a key factor. This discovery could improve our ability to predict HABs,” Fujiyoshi stated.
This approach is particularly valuable for HAB prediction because it accounts for the dynamic and interconnected nature of aquatic ecosystems. For example, the study highlighted how changes in salinity due to freshwater inputs from precipitation or glacier melt could alter vertical water stratification, creating conditions that favor certain HAB species over others.
Implications for Chile’s aquaculture industry
Chile is the world’s second-largest producer of salmon and trout, making it highly vulnerable to the impacts of HABs. The country has implemented extensive monitoring systems to detect HABs and measure toxin levels, but these measures are largely reactive. The findings of this study could pave the way for more proactive strategies by incorporating species interactions and environmental data into predictive models.
For instance, understanding how Pseudo-nitzschia interacts with other phytoplankton species could help identify early warning signs of an imminent bloom. Similarly, monitoring changes in salinity and nutrient levels could provide insights into conditions that favor HAB formation. By integrating this knowledge into existing monitoring systems, Chile could shift from early warnings to true prediction and mitigation of HABs.
A step toward biological prediction models
The study represents a novel approach to HAB research by focusing on biological interactions within phytoplankton communities. While much attention has been given to environmental factors such as temperature and salinity, species interactions have been understudied. This research highlights the importance of considering both biotic and abiotic factors to understand HAB dynamics.
Looking ahead, the research team hopes to use their findings to create a biological prediction model for HABs. “The next steps would include incorporating additional environmental parameters, particularly nutrient variations due to upwelling, investigating the specific mechanisms behind how different phytoplankton species influence Pseudo-nitzschia, and developing these findings into a practical prediction model that could help protect the aquaculture industry,” said Fujiyoshi.
The use of EDM in this study is particularly innovative. By analyzing species interactions and environmental conditions, EDM provides a more nuanced understanding of HAB formation. This could serve as a foundation for developing biological prediction models that go beyond traditional monitoring methods.
Conclusion
Harmful algal blooms are a complex and multifaceted problem, but advances in scientific tools such as Empirical Dynamic Modeling are opening new avenues for understanding and predicting these events. The recent study on Pseudo-nitzschia species in Chile underscores the importance of species interactions and environmental factors in HAB dynamics. By leveraging long-term ecological data and innovative analytical techniques, researchers are moving closer to developing predictive models that could help mitigate the impacts of HABs on ecosystems and industries.
As climate change continues to alter marine environments, this type of research will be crucial for safeguarding aquatic ecosystems and the livelihoods that depend on them. This study not only provides valuable insights into Chilean HABs but also lays the groundwork for future research that could benefit regions worldwide facing similar challenges.
The study was funded by the Science and Technology Research Partnership for Sustainable Development – Monitoring Algae in Chile, JSPS KAKENHI Fostering Joint International Research, and the JSPS Bilateral Program.
Contact
So Fujiyoshi
Assistant Professor, Hiroshima University, The IDEC Institute
E-mail: sofu62＊hiroshima-u.ac.jp
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Reference (open access)
Perera, I. U., Fujiyoshi, S., Kumakura, D., Medel, C., Yarimizu, K., Espinoza-González, O., Guzmán, L., Nakaoka, S., Tucca, F., Jaramillo-Torres, A., Tohsato, Y., Acuña, J. J., Jorquera, M. A., Lee, H., & Maruyama, F. (2025). Causal interactions among phytoplankton and Pseudo-nitzschia species revealed by empirical dynamic modelling. Marine Pollution Bulletin, 211, 117432. https://doi.org/10.1016/j.marpolbul.2024.117432
