Landscape characteristics influencing habitat use of grassland birds in the Pampas ecoregion of Argentina
Bibliographic record
Abstract
Understanding the influence of landscape composition on grassland bird habitat use is vital for predicting their fate in heavily fragmented ecosystems. During the breeding season, grassland patch metrics and matrix composition are driving factors in grassland bird habitat use. We modeled the use probability of grassland birds based on habitat characteristics during one breeding season in the Tandilia Mountains of Argentina. Between September 2020 and March 2021, we visited 126 field points monthly, recording detected birds within a 100 m radius (sampling unit). For each sampling unit, we calculated (a) the covered area by each land-use type (i.e., groves, crops, pastures, and natural grasslands), (b) the distance to landscape elements (i.e., streams, human settlements, groves, and natural grassland remnants), and (c) the area and shape of the nearest grassland remnant. We analyzed the effect of habitat variables on the use probability for each species through occupancy models. We detected 18 grassland bird species, of which 80% used sites near more circular grassland patches. Sites located on natural grassland remnants showed use probabilities greater than 40%. Other land uses such as perennial pastures, groves, and streams strongly affected the habitat use of grassland birds. Granivores and omnivores used sites with greater perennial pasture coverage, while insectivores and also granivores used sites near groves and away from streams. These results confirm that natural grassland remnants are the main drivers of grassland bird habitat use during the breeding season. Maintaining the integrity of these remnants is essential for grassland bird conservation.
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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".