Livestock Landscapes as Ecological Filters: Effects of the Tree Cover Gradient on the Taxonomic and Functional Diversity of Granivorous Birds in the Colombian Amazon
Bibliographic record
Abstract
Abstract Livestock landscapes in the Colombian Amazon are undergoing rapid land-use changes, leading to the structural simplification of vegetation and the loss of avifauna and its ecological functions. This study evaluated the effect of tree cover as an ecological filter on the taxonomic and functional diversity of granivorous birds within livestock mosaics. Eight livestock landscape mosaics were analyzed and classified based on tree cover into open, semi-open, and semi-closed categories. Bird surveys were conducted using point counts and mist netting, and eight morphological traits were measured across the 22 recorded species. Five functional diversity indices were calculated to assess the response to the tree cover gradient through their morphological traits. A reduction in tree cover excludes functionally specialized species and favors generalist communities with redundant traits. Semi-closed and semi-open covers harbored higher functional and taxonomic diversity, showing high levels of richness and functional evenness, with niche complementarity. Open pastures exhibited lower trait variability and clustering with reduced ecological functionality. Larger granivorous species were associated with denser vegetation, whereas smaller species were linked to more open, highly disturbed areas. Greater tree cover promotes the coexistence of granivorous bird species by increasing the availability of microhabitats and resource diversity, which are exploited by species with different functional traits. This highlights the importance of conserving structurally complex vegetation within livestock matrices, using silvopastoral systems to enhance the resilience and functionality of productive Amazonian landscapes.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".