Young mixed planted forests store more carbon than monocultures—a meta-analysis
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.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.020 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".