Seed Dispersal Services by Frugivorous Birds in Tropical Forest Regeneration Following Selective Logging
Bibliographic record
Abstract
Selective logging alters forest structure and can disrupt even the most critical ecosystem processes, such as seed dispersal, which is critical for natural regeneration. This is an evaluation of frugivorous bird seed dispersal and regeneration of tropical forests that have been selectively logged. The field experiment compared logged and unlogged control sites over 12 months, including bird surveys, seed rain sampling, and a germination experiment. One thousand two hundred and forty-eight observations of birds were made to constitute 37 frugivorous species, and the richness of species decreased by a quarter in the logged regions. Nevertheless, seed dispersal was also high: frugivorous birds scattered an average of 18,600 seeds per hectare per year in logged forests, compared with 24,300 in unlogged areas (a 23% reduction). It is interesting to note that the medium- and large-bodied birds together accounted for approximately 86% of total seed dispersal, highlighting their dominant role despite a 31% decline in abundance post-logging. The distribution of seed deposition showed a higher percentage (46%) of early-successional plant species in the logged regions than in the controls (28%). The success of seed germination of the dispersed seeds (bird-favored) was found to be much better (by 17%, p < 0.05) as compared to the non-dispersed. The findings show that, despite selective logging decreasing bird diversity and dispersal rates, frugivorous birds remain an important part of the forest regeneration process by driving seed flow and facilitating plant growth. The conservation plans must prioritize safeguarding important frugivore species and the habitat structures that facilitate their survival. The further increase in the structural complexity of logged forests could also enhance seed dispersal services and accelerate the restoration of the ecosystem.
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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 | 0.000 | 0.000 |
| 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".