The extent and predictability of the biodiversity–carbon correlation
Bibliographic record
Abstract
Protecting biomass carbon stocks to mitigate climate change has direct implications for biodiversity conservation. Yet, evidence that a positive association exists between carbon density and species richness is contrasting. Here, we test how this association varies (1) across spatial extents and (2) as a function of how strongly carbon and species richness depend on environmental variables. We found the correlation weakens when moving from larger extents, e.g. realms, to narrower extents, e.g. ecoregions. For ecoregions, a positive correlation emerges when both species richness and carbon density vary as functions of the same environmental variables (climate, soil, elevation). In 20% of tropical ecoregions, there are opportunities to pursue carbon conservation with direct biodiversity co-benefits, while other ecoregions require careful planning for both species and carbon to avoid potentially perverse outcomes. The broad assumption of a linear relationship between carbon and biodiversity can lead to undesired outcomes.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".