Is Biodiversity Able to Buffer Ecosystems from Climate Change? What We Know and What We Don’t
Bibliographic record
Abstract
Climate change alters ecosystems and their functioning, but biodiversity can buffer such changes. Previous syntheses suggest that biodiversity confers insurance; however, it is not clear whether this effect extends to climatic stressors. Here, we analyze 342 measures of the effects of biodiversity on stability in order to compare the response to climatic versus nonclimatic stressors. In general, the stabilizing effect of biodiversity is weaker for climatic than for nonclimatic stressors. We suggest that this reflects species pools being compiled at a small spatial scale for experiments testing climatic stressors. Some bias in the representation of biomes and stability metrics in biodiversity–climate studies may also distort the perceived effect of biodiversity on stability. We recommend that in future studies, researchers increase the spatial scale of experiments, manipulate multiple facets of biodiversity, simulate climate change in more realistic ways, and focus on underrepresented combinations of biomes and climatic stressors.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.018 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".