Growth Response and Cell Permeability of the Fish-Killing Phytoflagellate Heterosigma akashiwo Under Projected Climate Conditions
Bibliographic record
Abstract
Climate change and anthropogenic alterations in biogeochemical cycles are intensifying the frequency, duration, and potential toxicity of harmful algal blooms (HABs) in marine ecosystems. However, these effects are highly variable and depend on species identity, strain-specific traits, and local environmental conditions. Key drivers include rising sea surface temperatures, changes in salinity resulting from altered precipitation patterns and runoff, and elevated CO2 levels leading to ocean acidification. Heterosigma akashiwo, a euryhaline raphidophyte responsible for the widespread killing of fish, is particularly responsive to these changes. This study investigated the combined effects of temperature, salinity, and CO2 concentration on the growth, yield, and cell membrane permeability of H. akashiwo using a Design of Experiment (DOE) approach. DOE facilitates a detailed and systematic analysis of multifactorial interactions, enabling a deeper understanding of complex relationships while maximizing efficiency and minimizing the use of experimental resources. The results revealed that growth and yield were maximized at higher temperatures and salinities, whereas cell permeability increased under cooler, less saline, and lower CO2 conditions. These findings suggest that projected future ocean conditions may enhance biomass production while potentially reducing cellular permeability and, by extension, toxicity. This study highlights the value of the DOE framework in identifying key interactions among environmental drivers of HABs, offering a practical foundation for future predictive modeling under climate change scenarios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".