INTERACTIVE EFFECTS OF VEGETATION AND PREDATORS ON THE SUCCESS OF NATURAL AND SIMULATED NESTS OF GRASSLAND SONGBIRDS
Bibliographic record
Abstract
Abstract We examined the influence of vegetation and predator community on nesting success of songbirds in the grasslands of eastern North Dakota, USA. Each year, eight sites were chosen: four were subject to predator removal, and four were non-removal sites. On each site, nests of grassland songbirds were monitored, and simulated nests were used to examine how vegetation characteristics at nests affect nest success. Vegetative characteristics at simulated nests did not differ from those at natural nests, but successful natural nests had greater forb and lesser grass cover than unsuccessful nests, whereas no differences in vegetation were detected between successful and depredated simulated nests. On non-removal sites, small mammals and ground squirrels (Spermophilus sp.) depredated nests in taller and denser cover when compared to nests destroyed by medium-sized mammals and birds. On removal sites, we found no difference in vegetation characteristics of nests depredated by different predator types. However, each group of mammalian predators depredated simulated nests with different vegetation characteristics on removal versus non-removal sites. On sites where predators were removed, small mammals and ground squirrels preyed on simulated nests in shorter vegetation containing fewer forbs, ground squirrels preyed on nests with higher grass cover and lower vertical density, and medium-sized carnivores preyed on nests in taller vegetation. These results support the hypothesis that high predator diversity may reduce the chance of “safe” nest sites, and suggest that the behavior of low-level predators may change when top-level predators are removed.
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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".