Recovery of tree and mammal communities during large‐scale forest regeneration in Kibale National Park, Uganda
Bibliographic record
Abstract
Abstract Tropical landscapes are changing rapidly as a result of human modifications; however, despite increasing deforestation, human population growth, and the need for more agricultural land, deforestation rates have exceeded the rate at which land is converted to cropland or pasture. For deforested lands to have conservation value requires an understanding of regeneration rates of vegetation, the rates at which animals colonize and grow in regenerating areas, and the nature of interactions between plants and animals in the specific region. Here, we present data on forest regeneration and animal abundance at four regenerating sites that had reached the stage of closed canopy forest where the average dbh of the trees was 17 cm. Overall, 20.3 percent of stems were wind‐dispersed species and 79.7 percent were animal‐dispersed species, while in the old‐growth forest 17.3 percent of the stems were wind‐dispersed species. The regenerating forest supported a substantial primate population and encounter rate (groups per km walked) in the regenerating sites was high compared to the neighboring old‐growth forests. By monitoring elephant tracks for 10 yr, we demonstrated that elephant numbers increased steadily over time, but they increased dramatically since 2004. In general, the richness of the mammal community detected by sight, tracks, feces, and/or camera traps, was high in regenerating forests compared to that documented for the national park. We conclude that in Africa, a continent that has seen dramatic declines in the area of old‐growth forest, there is ample opportunity to reclaim degraded areas and quickly restore substantial animal populations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".