Spatial variation in benthic community composition on a minimally disturbed coral reef in the years following a prolonged marine heatwave
Bibliographic record
Abstract
With the increased frequency of marine heatwaves and related coral mass mortality events, it is imperative that we improve our understanding of coral reef recovery processes including how benthic community compositions can change over time. Coral reef benthic communities are influenced by many abiotic factors, but on most reefs local anthropogenic disturbances overshadow these factors thus obscuring their influence. Here, we leverage a dataset from a coral reef with very minimal local anthropogenic disturbance - the uninhabited southern coast of the world's largest atoll (Kiritimati) - to assess spatial variation in benthic community composition three years after the mass coral mortality event driven by the 2015-2016 El Niño. Across forereef sites ranging from 7 to 22 m, scleractinian coral cover remained very low (6.9 +/- 0.4 % SE) while soft coral cover was < 1%. Coral cover was highest at deep sites (18-22 m compared to sites at 7-10 m depth) and exposed locations, where stress-tolerant corals likely made up a larger proportion of the coral community before the mass mortality event. Higher cover of crustose coralline algae at shallow exposed sites, fleshy macroalgae at deep sites, and turf algae at exposed locations were consistent with taxa-specific preferences for light, wave action, and sedimentation. The abundances of the most common genera of juvenile coral (Acropora and Pocillopora) were still low (< 1 juvenile colony per genus per site) and varied only with depth. These findings demonstrate how variation in pre-mortality coral composition can lead to differences in post-mortality benthic communities on low disturbance coral reefs.
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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".