Habitat heterogeneity and climatic seasonality structure the avifauna trophic guilds in the Brazilian Pantanal wetland
Bibliographic record
Abstract
Landscape heterogeneity and annual flood pulses characterize the Brazilian Pantanal, the largest floodplain in South America. The objective of this work was to explain spatial and temporal variations in the trophic structure of a bird assemblage consisting of 316 species of terrestrial and aquatic birds, out of which 88 are visitors. The food items potentially consumed by these species were combined into 12 trophic guilds and were compared based on habitats (terrestrial or aquatic), periods of the year, and visitor or resident species. Nonmetric multidimensional scaling was used to determine which trophic guilds characterize the different habitats and months. The habitats were separated into a gradient of trophic guild similarity ranging from woodlands to purely aquatic, with swamps and floodable fields in intermediate positions. Species that consume invertebrates and plants predominate in the terrestrial habitats, whereas the consumption of terrestrial and (or) aquatic invertebrates, vertebrates, and plants predominate in the aquatic habitats. The monthly similarities in trophic structure vary with rainfall, and the period of receding waters is characterized by an increase in the number of species in guilds that consume nectar, invertebrates, vertebrates, and (or) plant parts obtained or captured in the drying landscape and terrestrial habitats. Visitor species do not exploit new resource types; instead they accommodate themselves in the pre-existing trophic guilds.
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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 | 0.000 | 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".