Grow with the flow: Is phenotypic plasticity across hydrodynamic gradients common in seaweeds?
Bibliographic record
Abstract
Seaweeds are widely assumed to be phenotypically plastic across hydrodynamic gradients, yet while many marine macroalgae exhibit intraspecific phenotypic variation that correlates with flow, researchers often fail to test whether such variation is due to plasticity or another mechanism, such as local adaptation. In this minireview, we considered mechanisms for sensing flow in seaweeds that could facilitate adaptive phenotypic plasticity across hydrodynamic gradients. We then reviewed the literature from 1900 to 2024 to see how often phenotypic variation and plasticity across hydrodynamic gradients had been observed and demonstrated in different groups of seaweeds. In the last 124 years, phenotypic variation and plasticity in response to flow have been well documented in brown algae but scarcely documented in red and green algae. This could suggest that brown algae are better able to sense and respond to flow than red and green algae, perhaps due to the intercalary meristem of many brown algae, including most kelps. However, this skewed distribution could also be the result of publication bias, as most studies involving flow have been conducted on brown algae. Only 30% of 141 papers specifically investigated if observations of phenotypic variation along hydrodynamic gradients were due to plasticity. To date, phenotypic plasticity in response to flow has been demonstrated in 20 brown algal species, five red algal species, and two green algal species. Thus, the assumption that phenotypic plasticity to flow is common across seaweeds is not particularly well supported by the literature. Mechanisms underlying plasticity to flow are poorly understood and remain a critical avenue for future research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".