Ecosystem recovery after climatic extremes enhanced by genotypic diversity
Bibliographic record
Abstract
Contemporary climate change is characterized both by increasing mean temperature and increasing climate variability such as heat waves, storms, and floods. How populations and communities cope with such climatic extremes is a question central to contemporary ecology and biodiversity conservation. Previous work has shown that species diversity can affect ecosystem functioning and resilience. Here, we show that genotypic diversity can replace the role of species diversity in a species-poor coastal ecosystem, and it may buffer against extreme climatic events. In a manipulative field experiment, increasing the genotypic diversity of the cosmopolitan seagrass Zostera marina enhanced biomass production, plant density, and faunal abundance, despite near-lethal water temperatures due to extreme warming across Europe. Net biodiversity effects were explained by genotypic complementarity rather than by selection of particularly robust genotypes. Positive effects on invertebrate fauna suggest that genetic diversity has second-order effects reaching higher trophic levels. Our results highlight the importance of maintaining genetic as well as species diversity to enhance ecosystem resilience in a world of increasing uncertainty.
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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 | 0.000 | 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".