Life histories predict coral community disassembly under multiple stressors
Bibliographic record
Abstract
Climate change is reshaping biological communities against a background of existing human pressure. Evaluating the impacts of multiple stressors on community dynamics can be particularly challenging in species-rich ecosystems, such as coral reefs. Here, we investigate whether life-history strategies and cotolerance to different stressors can predict community responses to fishing and temperature-driven bleaching using a 20-year time series of coral assemblages in Kenya. We found that the initial life-history composition of coral taxa largely determined the impacts of bleaching and coral loss. Prior to the 1998 bleaching event, coral assemblages within no-take marine reserves were composed of three distinct life histories - competitive, stress-tolerant and weedy- and exhibited strong declines following bleaching with limited subsequent recovery. In contrast, fished reefs had lower coral cover, fewer genera and were composed of stress-tolerant and weedy corals that were less affected by bleaching over the long term. Despite these general patterns, we found limited evidence for cotolerance as coral genera and life histories were variable in their sensitivities to fishing and bleaching. Overall, fishing and bleaching have reduced coral diversity and led to altered coral communities of 'survivor' species with stress-tolerant and weedy life histories. Our findings are consistent with expectations that climate change interacting with existing human pressure will result in the loss of coral diversity and critical reef habitat.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".