Nest Survival of American Coots Relative to Grazing, Burning, and Water Depths
Bibliographic record
Abstract
Water and emergent vegetation are key features influencing nest site selection and success for many marsh-nesting waterbirds.Wetland management practices such as grazing, burning, and waterlevel manipulations directly affect these features and can influence nest survival.We used model selection and before-after-control-impact approaches to evaluate the effects of water depth and four common landmanagement practices or treatments, i.e., summer grazing, fall grazing, fall burning, and idle (no active treatment) on nest survival of American coots (Fulica americana) nesting at Grays Lake, a large montane wetland in southeast Idaho.The best model included the variables year × treatment, and quadratic functions of date, water depth, and nest age; height of vegetation at the nest did not improve the best model.However, results from the before-after-control-impact analysis indicate that management practices affected nest success via vegetation and involved interactions of hydrology, residual vegetation, and habitat composition.Nest success in idled fields changed little between pre-and post-treatment periods, whereas nest success declined in fields that were grazed or burned, with the most dramatic declines the year following treatments.The importance of water depth may be amplified in this wetland system because of rapid water-level withdrawal during the nesting season.Water and land-use values for area ranchers, management for nesting waterbirds, and long-term wetland function are important considerations in management of water levels and vegetation. 1 U.S. Geological Survey, Northern Prairie Wildlife Research Center Avian Conservation and Ecology 6(2): 1 http://www.ace-eco.org/vol6/iss2/art1/pour les propriétaires de ranch de la région, l'aménagement de l'habitat pour les oiseaux aquatiques nicheurs et le fonctionnement à long terme des milieux humides représentent des éléments essentiels à considérer dans la gestion des niveaux d'eau et de la végétation.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".