Hunting and seagrass affect fall stopover Canada goose distribution in eastern Canada
Bibliographic record
Abstract
Abstract Canada geese (Branta canadensis) migrating along coastal flyways are reliant on natural coastal ecosystems. Within these stopover sites, eelgrass (Zostera marina), the most common and widespread seagrass species in North America, is an important food resource for migrating waterfowl. Given the growing anthropogenic pressure on coastal ecosystems, geese migrating along coastal regions may find it increasingly difficult to access suitable stopover sites where food is abundant and human disturbance is low. We assessed the influence of hunting and eelgrass on the spatiotemporal distribution of Canada geese in the Tabusintac Bay, New Brunswick, Canada, a wetland of international importance. We surveyed Canada geese at 6 stations from mid‐September to late October, 2016 and 2017. We used 2‐part hurdle models consisting of generalized linear mixed models with binomial and negative binomial response distributions to model Canada geese presence and abundance, respectively, in relation to eelgrass abundance, distance to the mainland coastline, water depth, and tidal conditions in 3 different hunting intensity periods. Eelgrass abundance is a significant predictor of Canada geese presence early in the season, when hunting activity is low. At the onset of the hunting period, geese shifted diurnal distribution to areas farther offshore, indicating a response to avoid disturbance, and the abundance of Canada geese increased with increasing eelgrass availability, emphasizing the importance of eelgrass as a food source during fall migration in that region. Thus, our results highlight the effects of human disturbance and eelgrass abundance in influencing stopover behavior of Canada geese during fall migration in eastern Canada.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".