Macroalgae exhibit diverse responses to human disturbances on coral reefs
Bibliographic record
Abstract
Scientists and managers rely on indicator taxa such as coral and macroalgal cover to evaluate the effects of human disturbance on coral reefs, often assuming a universally positive relationship between local human disturbance and macroalgae. Despite evidence that macroalgae respond to local stressors in diverse ways, there have been few efforts to evaluate relationships between specific macroalgae taxa and local human-driven disturbance. Using genus-level monitoring data from 1205 sites in the Indian and Pacific Oceans, we assess whether macroalgae percent cover correlates with local human disturbance while accounting for factors that could obscure or confound relationships. Assessing macroalgae at genus level revealed that no genera were positively correlated with all human disturbance metrics. Instead, we found relationships between the division or genera of algae and specific human disturbances that were not detectable when pooling taxa into a single functional category, which is common to many analyses. The convention to use percent cover of macroalgae as an indication of local human disturbance therefore likely obscures signatures of local anthropogenic threats to reefs. Our limited understanding of relationships between human disturbance, macroalgae taxa, and their responses to human disturbances impedes the ability to diagnose and respond appropriately to these threats.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".