Complex interactions of climatic and ecological controls on macroalgal recruitment
Bibliographic record
Abstract
Little is known about the cumulative effects of multiple (.2) environmental controls on species performance and interactions in aquatic ecosystems. We asked how changes in climatic (temperature, ultraviolet radiation) and ecological controls (nutrients, grazing) affect recruitment of the green macroalga Enteromorpha intestinalis, which forms destructive algal blooms in coastal ecosystems worldwide. We designed factorial laboratory experiments to analyze the recruitment response to (1) single and combined effects of nutrient enrichment, grazing pressure, and grazer species composition and (2) the cumulative effects of ultraviolet (UV) radiation, temperature, nutrients, and grazing. Recruitment of E. intestinalis increased exponentially with nutrient enrichment. Grazers could control algal recruitment until a nutrient threshold was reached depending on grazer species composition. Snails (Littorina littorea) had strong negative effects on recruit density, whereas amphipods (Gammarus oceanicus) had weak grazing effects and favored algal recruitment through excretion when nutrient supply was low. Temperature and nutrients both enhanced algal recruitment but also the effects of grazers, which led to a significant three‐way interaction among these factors. Similarly, effects of UV radiation depended on grazer presence and temperature. When grazers were absent, UV radiation reduced recruitment at 11 and 17°C but enhanced recruitment at 5°C. No effects were seen in the presence of grazers. Our results indicate that multiple human influences, such as climate change, eutrophication, and food web alterations, have interdependent effects and the potential for synergistically enhancing the development of macroalgal blooms in coastal ecosystems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".