Reassessing the Cowbird Threat
Bibliographic record
Abstract
Abstract The demographic significance of parasitism by Brown-headed Cowbirds (Molothrus ater; hereafter “cowbirds”) has been debated for years, because manipulative studies are few and mathematical models of cowbird-host systems have not led to tests of their predictions. We combine results from a cowbird- removal experiment (Smith et al. 2002, 2003) with a stochastic simulation model that we developed, to reassess the effect of cowbirds on the annual reproductive success (ARS) and nesting success in Song Sparrows (Melospiza melodia). Our model followed the breeding success of individual Song Sparrow nests and extrapolated to output variables including ARS, nest survival, and parasitism rates. We parameterized the model with field data from cowbird control sites (i.e., cowbirds not manipulated) and found that output variables matched those observed. We used the reduced parasitism rates observed on cowbird-removal sites, reran the model, and compared output with observed values. On removal sites, ARS was greater than predicted by the model, which indicates that the model failed to account for some biological phenomenon that occurred when cowbirds were in the system. To assess what this phenomenon might be, we conducted further analyses that indicated that cowbirds may facilitate nest predation by “conventional” predators. Using elasticity analyses, we show that cowbirds have their largest effect on ARS through egg removal and that conventional predators are more important than cowbirds in directly affecting nest survival rates. We report that to assess the effect of cowbirds on nest survival, researchers must avoid using the common technique of comparing survival rates of parasitized and unparasitized nests within populations. Réévaluation de la Menace du Vacher
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".