Species richness and community composition of songbirds in a tropical forest‐agricultural landscape
Bibliographic record
Abstract
Abstract Management strategies that attempt to mitigate tropical biodiversity loss require detailed studies of biodiversity in different land‐uses. In this study the community structure and species richness of songbirds was characterised along with the vegetation structure, in three land‐use types in and around a tropical forest reserve in Uganda (intact, mature forest; regenerating secondary forest; smallholder agriculture). Each land‐use type had 30–35 count stations that were sampled twice by means of a 15‐min recording session. In total, 118 bird species were recorded from 192 station counts. Number of species/station was similar in intact and regenerating forest and lower in smallholder agriculture. Songbird communities in intact forest were highly distinct from those in smallholder agriculture and were composed of forest‐dependent species. Communities in regenerating forest were intermediate between intact forest and agriculture, although much closer to intact forest. Generalised Linear Model (GLM) modelling revealed that tree density and distance to the nearest intact forest had strong positive and non‐linear effects on the community composition and forest species richness of songbirds. Simulations using these models showed that agroforestry programmes would not raise tree densities to levels that would shift agricultural songbird communities towards forest communities. Current and best‐case agricultural practices are therefore unlikely to contribute to the conservation of the songbird component of forest biodiversity in this area.
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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 | 0.000 |
| 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".