Spatially‐explicit land use effects on nesting of Atlantic Flyway resident Canada geese in New Jersey
Bibliographic record
Abstract
Atlantic Flyway resident population (AFRP) Canada geese Branta canadensis in New Jersey, USA, have grown dramatically during the last thirty years and are considered as overabundant in many areas. Development of corporate parks and urban areas with manicured lawns and artificial ponds offer ideal nesting habitat for AFRP geese, with limited pressure from hunting or natural predators. As a result, spatial heterogeneity in reproduction must be taken into account in managing the population. We identified the site and landscape spatial scale extents at which land use features influenced nest site selection and nest success. Nest searches were conducted throughout the State during 2009—2010, and 309 nests were monitored through hatch to determine their fates. We ran a spatial correlation analysis of land use composition to identify spatial scale extents at which geese most considerably respond to their environment for nest site selection and nest success. All significant spatial scale extents were at or below 2.25 km for the five classified land use types. We emphasize that habitat‐goose associations in densely urban areas were strongest at extents < 1 km, while rural and natural areas were strongest at extents > 1 km. Geese responded to human‐dominated land uses at a smaller spatial scale extent than land uses with low human density. The strength of all nest‐land use univariate relationships was low; however, our primary objective was to identify the scales extent at which geese associate with land use, rather than the intensity. We encourage managers to consider these scale‐dependent associations in identifying important habitat variables in multivariate models; and if population control of AFRP Canada geese is of primary interest, then focusing on local habitat management will most likely have the largest influence in managing this population.
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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 | 0.001 |
| 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) | 0.001 | 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".