Young mixed planted forests store more carbon than monocultures—a meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although decades of research suggest that higher species richness improves ecosystem functioning and stability, planted forests are predominantly monocultures. To determine whether diversification of plantations would enhance aboveground carbon storage, we systematically reviewed over 11,360 publications, and acquired data from a global network of tree diversity experiments. We compiled a maximum dataset of 79 monoculture to mixed comparisons from 21 sites with all variables needed for a meta-analysis. We assessed aboveground carbon stocks in mixed-species planted forests vs. (a) the average of monocultures, (b) the best monoculture, and (c) commercial species monocultures, and examined potential mechanisms driving differences in carbon stocks between mixtures and monocultures. On average, we found that aboveground carbon stocks in mixed planted forests were 70% higher than the average monoculture, 77% higher than commercial monocultures, and 25% higher than the best performing monocultures, although the latter was not statistically significant. Overyielding was highest in four-species mixtures (richness range 2–6 species), but otherwise none of the potential mechanisms we examined (nitrogen-fixer present vs. absent; native vs. non-native/mixed origin; tree diversity experiment vs. forestry plantation) consistently explained variation in the diversity effects. Our results, predominantly from young stands, thus suggest that diversification could be a very promising solution for increasing the carbon sequestration of planted forests and represent a call to action for more data to increase confidence in these results and elucidate methods to overcome any operational challenges and costs associated with diversification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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 it