Evolution increases ecosystem temporal stability and recovery from a flood in grassland communities
Bibliographic record
Abstract
Understanding factors that increase ecosystem stability is critical in the face of environmental change. Biodiversity plays a key role in buffering ecosystems against disturbances such as extreme climatic events. The evolution of biological communities within their local environment may also increase ecosystem stability and resilience, but this has yet to be tested. Here, we provide evidence for such evolutionary effects using a long-term grassland biodiversity experiment. Communities of plants with a history of co-occurrence (co-selected communities) were temporally more stable at low diversity than the same communities of plants with no such history (naïve communities). Furthermore, co-selected communities exhibited greater recovery following a major flood, resulting in more stable post-flood productivity. These results demonstrate that community evolution can increase ecosystem stability under normal circumstances and in response to extreme disturbance, but also suggest that high diversity can in part compensate for evolutionary naïvety.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".