Congruent Long‐Term Declines in Carbon and Biodiversity Are a Signature of Forest Degradation
Bibliographic record
Abstract
ABSTRACT Recent global policy initiatives aimed at reducing forest degradation require practical definitions of degradation that are readily monitored. However, consistent approaches for monitoring forest degradation over the long term and at broad scales are lacking. We quantified the long‐term effects of intensive wood harvest on above‐ground carbon and biodiversity at fine resolutions (30 m2) and broad scales (New Brunswick, Canada; 72,908 km2). Model predictions for above‐ground biomass were highly correlated with independent data (r = 0.77). After accounting for carbon stored in wood products, net CO2 emissions from forests for the region from 1985 to 2020 were 141 CO2e Tg (4.02 TgCO2e year−1; 32% of all reported emissions). We found strong positive correlations between locations with declines in above‐ground carbon and habitats for old‐forest bird species, which have lost > 20% habitat over 35 years. High congruence between biodiversity and forest carbon offers potential for policy incentives to conserve both objectives simultaneously and slow rates of forest degradation. These methods could be used to track forest degradation for managed forest regions worldwide.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".