Selection for Overhead Concealment Improves Nest Survival of a Ground Nesting Bird in Argentinian Rangelands
Bibliographic record
Abstract
Grassland ecosystems have suffered intense modification worldwide, resulting in a loss of biodiversity. Birds that breed in grasslands have experienced steep population declines over recent decades. When modifications of grasslands reduce the available breeding habitat, birds may select habitat features that do not favor their breeding success. However, the relationship between selected nesting habitat and nest survival is not well established for many grassland birds. We studied the nest site selection and nest survival of a common grassland bird, the Grassland Yellow-Finch Sicalis luteola, in the Flooding Pampa of Argentina, a region comprised mostly of large natural rangelands. We searched for nests over three breeding seasons (2017–2020) and used linear models to analyze whether finches selected nest sites according to distance from grassland edges, type of grassland community, vegetation density, visual concealment, and grass height. We modeled daily nest survival rates (DSR) to assess whether these variables influenced breeding success. We confirmed the fate of 133 nests, of which 93 (70%) failed, predation being the principal cause (84% of failures). Our models showed that finches selected shrubby grasslands over other types available, and sites with high overhead visual concealment. Only overhead concealment was positively correlated with DSR. This may indicate that their nests are affected by avian predators that search for prey from above and that they benefit from tall and dense vegetation that provides good overhead cover. We believe that preserving areas of heterogeneous and dense shrubby grasslands within grazing plots is a good starting point that could benefit this bird species and others with similar nesting strategies.
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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".