Low growth resilience of subarctic rhodoliths (Lithothamnion glaciale) to coastal eutrophication
Bibliographic record
Abstract
Eutrophication is one of the most important drivers of change in coastal marine ecosystems worldwide. Given their slow growth, rhodoliths and the biodiverse communities they support are regarded as non-renewable resources threatened by human activity. Consequences of nutrient enrichment on growth and calcification in crustose coralline algae are equivocal, and even more so in cold-water rhodoliths. We paired a 183 d laboratory mesocosm experiment with a 193 d field experiment on Newfoundland (Canada) rhodoliths (Lithothamnion glaciale) to test the hypothesis that nutrient (nitrate, ammonia, and phosphate) enrichment and biofouling reduce rhodolith growth. Rhodoliths in the laboratory were exposed to 1 of 3 nutrient concentrations (ambient, intermediate, or high) and either of 2 levels of manual cleaning (cleaned or uncleaned) to control biofouling. We exposed rhodoliths in the field to 1 of 2 nutrient concentrations (ambient or elevated). Eutrophication in the laboratory did not affect biofouling; however, manual cleaning reduced biofouling by ~4 times relative to uncleaned rhodoliths. Rhodoliths grew 2 times slower at elevated than ambient nutrient concentrations, and ~27% more in cleaned than uncleaned rhodoliths at all concentrations. Rhodoliths in the field also grew significantly slower under elevated than ambient phosphate concentrations, but only during the first 6 wk, indicating some capacity for long-term recovery. We conclude that despite some growth resilience to low and infrequent increases in nutrient concentrations, subarctic L. glaciale rhodoliths cannot cope with prolonged exposure to modest eutrophication.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".