Enrichment planting does not improve tree restoration when compared with natural regeneration in a former pine plantation in Kibale National Park, Uganda
Bibliographic record
Abstract
Abstract Given the high rates of deforestation and subsequent land abandonment, there are increasing calls to reforest degraded lands; however, many areas are in a state of arrested succession. Plantations can break arrested succession and the sale of timber can pay for restoration efforts. However, if the harvest damages native regeneration, it may be necessary to intervene with enrichment planting. Unfortunately, it is not clear when intervention is necessary. Here, we document the rate of biomass accumulation of planted seedlings relative to natural regeneration in a harvested plantation in Kibale National Park, Uganda. We established two 2‐ha plots and in one, we planted 100 seedlings of each of four native species, and we monitored all tree regeneration in this area and the control plot. After 4 years, naturally regenerating trees were much taller, larger and more common than the planted seedlings. Species richness and two nonparametric estimators of richness were comparable between the plots. The cumulative biomass of planted seedlings accounted for 0.04% of the total above‐ground tree biomass. The use of plantations facilitated the growth of indigenous trees, and enrichment planting subsequent to harvesting was not necessary to obtain a rich tree community with a large number of new recruits.
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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) | 0.000 | 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 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".