Close-to-nature management of tropical timber plantations is economically viable and provides biodiversity benefits
Bibliographic record
Abstract
Abstract Reforestation of tropical forests is crucial to mitigate the climate crisis and restore ecosystems. However, past efforts have been criticized for establishing monoculture timber plantations with exotic tree species. Close-to-nature (CTN) practices aim to minimize negative forest management impacts on forests ecosystems by mimicking natural dynamics. So far, CTN management practices are rarely applied in tropical plantation forestry. This study evaluates the economic, carbon sequestration, and biodiversity potential of CTN management in tropical mixed-species plantations in Central America using a simulation-optimization approach. To our knowledge, this study is the first to assess the potential of tropical CTN-managed plantations on the basis of detailed process-based forest growth simulations. CTN practices such as selective harvesting, retention forestry, and shelterwood cutting of mixed-species stands were compared to even-aged mixtures and conventional monoculture practices. Results showed that CTN management was economically viable for certain species mixtures and management practices at an 8 % discount rate and had the potential to increase carbon storage and biodiversity in the modeled plantations. At current carbon prices, CTN-managed plantations may only become financially competitive with monocultures, if monocultures are excluded from carbon certification schemes that increasingly aim at co-producing non-carbon benefits like biodiversity conservation. If carbon prices increase, the sale of carbon credits could finance the transformation of monocultures to CTN-managed mixed-species stands. The competitiveness of CTN management could also be improved through performance-based biodiversity payments, such as the sale of biodiversity credits.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".