Factors influencing nest survival in resident Canada geese
Bibliographic record
Abstract
ABSTRACT Overpopulation of Canada goose ( Branta canadensis ) that make up the Atlantic Flyway Resident Population (AFRP) in New Jersey led to the implementation of a management program that includes hunter harvest, culling programs, and efforts to reduce recruitment through nest destruction. We investigated clutch size, hatchability, and nest survival of Canada goose nests in the AFRP in New Jersey during 1985–1989, 1995–1997, and 2009–2010, and identified ecological, temporal, and spatial variables associated with nest survival to better understand the factors influencing population growth. Mean (±SE) clutch size was 4.86 eggs (±0.04), and mean hatchability of all eggs was 0.61 ± 0.04 across the study. Mean hatchability in 2009–2010 was significantly lower than in the 1980s and 1990s, whereas we did not detect any significant differences in mean clutch size across the decades. Nest survival decreased across the decades, with survival probabilities ranging from 0.68 ± 0.03 in 1988 to 0.45 ± 0.02 in 2010, likely related to reproductive control programs. Nest survival was influenced by date within the nesting season, decade, precipitation, and extreme high temperature. Further, nest survival was associated with commercial‐industrial, agricultural, and urban residential land use at a site level (0.25 km), and natural and urban residential land use at a landscape level (2.25 km and 0.75 km, respectively). Commercial land use (e.g., corporate parks and golf courses) offers favorable Canada goose nesting habitat at the site level, with manicured lawns, man‐made ponds, and decreased predator habitat (e.g., dense tree, shrub cover). We recommend targeting population management efforts in commercial, industrial, and urban residential areas these land uses were associated with increased nest survival. © 2016 The Wildlife Society.
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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".