Bright spots of carbon storage in temperate forests
Bibliographic record
Abstract
Abstract Mitigating climate change is an urgent challenge for society. Increasing carbon storage in forests, which cover more than 30% of the global land surface, presents a key opportunity to meet this challenge. Although the biophysical and ecological factors that affect carbon storage have been well studied, the relative importance of social factors in privately owned forests, such as people's goals and management actions, is less well understood. We examine how well typical biophysical and ecological variables can explain differences in above‐ground carbon storage across 1,561 plots in temperate forests of southern Quebec, Canada. We then identify bright spots and dark spots of above‐ground carbon storage, where forests are performing much better or worse than predicted based on biophysical and ecological conditions alone. We conducted surveys with forest owners to assess whether their individual goals, values and management actions explain the differences in carbon storage between bright and dark spots. Biophysical and ecological variables collectively explained a substantial fraction of the variation in carbon storage between forest plots ( R 2 = 0.42). The ecological variables of stand age, species richness and functional diversity within the plots were the most important variables in explaining carbon storage. Surveys showed that bright spots (plots that stored more carbon than predicted) were often managed by forest owners who expressed a strong connection to, or dependence on, their forested property. Dark spots were often associated with forest harvesting and hunting. Synthesis and applications . Ecologically based policies that aim to increase the average forest age, species richness and functional diversity—and socially based policies that incentivise long‐term forest ownership, stronger connections between people and their properties, and maple syrup production—could help increase carbon storage over multidecadal time‐scales and thereby reduce the harmful effects of climate change.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".