Choosing species for reforestation in diverse forest communities: social preference versus ecological suitability
Bibliographic record
Abstract
Choosing species for reforestation programs or community forestry in species‐rich tropical rainforest ecosystems is a complex task. Reforestation objectives, social preferences, and ecological attributes must be balanced to achieve landscape restoration, timber production, or community forestry objectives. Here we develop a method to make better species choices for reforestation programs with native species when limited silvicultural information is available. We conducted community surveys to determine social preference of tree species and inferred their ecological suitability for open‐field plantations from growth rates and frequency in forest plots at different successional stages. Several species, for which silvicultural data was available, were correctly classified as promising or unsuitable for open‐field reforestation. Notably, we found a strong negative correlation between ecological suitability indicators and socioeconomic preference ranks. Only a single outlier species ranked very high in both categories. This result highlights the difficulty of finding suitable native species for community forestry and offers an explanation why reforestation efforts with native species often fail. We concluded that the approach should be a useful first screening of species‐rich forest communities for potential reforestation species. Our results also support the view that species‐rich tropical rainforests are not an easily renewable natural resource in a sense that secondary forests will not provide an equivalent resource value to local communities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".