Non‐native fruit trees facilitate colonization of native forest on abandoned farmland
Bibliographic record
Abstract
Abstract Ecological restoration of abandoned, formerly forested farmland can improve the delivery of ecosystem services and benefit biodiversity conservation. Restoration programs can involve removing isolated, non‐native trees planted by farmers for fruit or wood. As such “legacy” trees can attract seed dispersers and create microclimates that help native seedlings to establish, removing them may actually slow forest recovery. Working on abandoned farmland in Kibale National Park, Uganda, we evaluated the effect of legacy trees on forest recovery by measuring the number, diversity, and biomass of native seedlings and saplings regenerating in plots centered on avocado (Persea americana), mango (Mangifera indica), and Eucalyptus legacy trees compared with adjacent plots without legacy trees. The assemblages of native, forest‐dependent tree species in plots around avocado and mango trees were distinct from each other and from those around eucalyptus and all the near‐legacy plots. In particular, avocado plots had higher stem density and species richness of forest‐dependent species than near‐avocado plots, particularly large‐seeded, shade‐tolerant, and animal‐dispersed species—key targets of many restoration plans. Furthermore, many of the species found in high numbers were among those failing to establish in ongoing large‐scale forest restoration in Kibale. Taken together, our results demonstrate that the legacy trees facilitate the dispersal and establishment of native tree species. Retaining the existing legacy trees for a number of years could usefully complement existing management strategies to restore more biodiverse native forest in degraded lands. However, careful monitoring is needed to ensure that the legacy trees do not themselves establish.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".