A population model for management of Atlantic flyway resident population Canada geese
Bibliographic record
Abstract
ABSTRACT Highly abundant resident Canada geese ( Branta canadensis ) cause property damage throughout their range. Effective reduction and management of these populations requires knowledge of their population dynamics and responses to management actions. We used data from New Jersey, USA, and other resident Canada goose populations to produce stage‐structured matrix models for resident Canada geese from both urban and rural landscapes. We ran stochastic simulations to assess 3 management activities for Atlantic Flyway Resident Population Canada geese: harvest, nest treatment, and cull. Unrealistic harvest rates, in excess of 10% for urban geese, would be needed to reduce the urban population to target levels within 10 years in the absence of other management activities. Nest treatment to prevent hatching is less controversial than culling adults, but as many as 62% of eggs in urban areas would need to be treated annually to sufficiently reduce the mean stochastic population growth rate. Cull would be the most effective way to achieve the population goal, but current cull rates are insufficient to reduce the urban population. Although reduction of urban geese was a challenge, current management activities in rural populations appeared to be sufficient to reduce populations. We also provide a simple spreadsheet tool for managers who want to explore management options for other resident Canada goose populations by inserting relevant vital rate estimates for their populations and manipulating management activities. © 2016 The Wildlife Society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".