Ecophysiological traits explain species dominance patterns in macroalgal blooms
Bibliographic record
Abstract
Macroalgal blooms are a growing environmental problem in eutrophic coastal ecosystems world wide. These blooms are dominated typically by only one out of several co‐occurring opportunistic species, which are all favored by increased nutrient loads. We asked whether pronounced dominance of filamentous Pilayella littoralis Kjellm. (Phaeophyceae) over foliose Enteromorpha intestinalis L. (Chlorophyceae) in the Baltic Sea can be explained by interspecific physiological differences. In laboratory experiments, we analyzed uptake kinetics of nitrate, ammonium, and phosphate and the time dependency of uptake rates for both species. We further examined growth rates and nutrient assimilation in relation to single and combined enrichment with nitrate and phosphate, and three different nitrogen sources. Overall, we did not detect distinct differences in uptake, growth, and assimilation rates between P. littoralis and E. intestinalis. Minor differences and the related advantages for single species are discussed. Highest maximal uptake rates were found for ammonium, followed by nitrate and phosphate. Strong time dependency of uptake occurred, with the highest rates during the first 15 to 30 min. Nitrate enrichment had far more of an effect on growth than phosphate. Enrichment with urea, ammonium, and nitrate significantly increased growth rates without interspecific differences. A larger surface area to volume (SA/V) ratio in Pilayella compared with Enteromorpha did not translate into greater physiological capacity. We conclude that species dominance patterns in macroalgal blooms are not always a direct result of different ecophysiological traits among species. Ecological traits such as susceptibility to herbivory are important factors in determining species distribution in the field.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".