Coral Recruit-Algae Interactions in Coral Reef Lagoons Are Mediated by Riverine Influences
Bibliographic record
Abstract
Coral recruit and algae abundance and diversity were studied in Kenyan reefs to determine the influence of terrestrial discharge (nutrients and sediments) and the recovery potential of coral reefs after disturbances. Reefs affected by sediments and nutrients were found to have high total, turf, and macroalgae but reduced coralline algae abundance and coral recruit density. Interestingly, this response was found to be the greatest in reefs close to nutrient sources relative to “pristine” reefs and those affected simultaneously by sediments and nutrients. Further, enhanced levels of brown algae and pocilloporid recruits were observed in reefs affected by terrestrial run-off whereas acroporid recruit, coralline, and calcareous algae abundance was high in reefs under low terrestrial input. Our results show that whereas increased sediment levels negatively affect coral recruit density individually, their interaction with nutrients improves recruit density in reefs simultaneously affected by sediment and nutrients. These findings suggest that the assessment of local factors that enhance inhibitory and those that suppress promotional processes involved in coral settlement and recruitment is an important aspect to consider in the conservation and management of coral reefs in the face of local anthropogenic stress as well as future climate disturbance dynamics and their interaction.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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 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".