Spring hunting changes the regional movements of migrating greater snow geese
Bibliographic record
Abstract
Summary Human‐induced disturbance such as hunting may influence the migratory behaviour of long‐distance migrants. In 1999 and 2000 a spring hunt of greater snow geese Anser caerulescens atlanticus occurred for the first time in North America since 1916, aimed at stopping population growth to protect natural habitats. We evaluated the impact of this hunt on the staging movements of geese along a 600‐km stretch of the St Lawrence River in southern Quebec, Canada. We tracked radio‐tagged female geese in three contiguous regions of the staging area from the south‐west to the north‐east: Lake St Pierre, Upper Estuary and Lower Estuary, in spring 1997 ( n = 37) and 1998 ( n = 70) before the establishment of hunting, and in 1999 ( n = 60) and 2000 ( n = 59) during hunting. We used multi‐state capture–recapture models to estimate the movement probabilities of radio‐tagged females among these regions. To assess disturbance level, we tracked geese during their feeding trips and estimated the probability of completing a foraging bout without being disturbed. In the 2 years without hunting, migration was strongly unidirectional from the south‐west to the north‐east, with very low westward movement probabilities. Geese gradually moved from Lake St Pierre to Upper Estuary and then from Upper Estuary to Lower Estuary. In contrast, during the 2 years with hunting westward movement was more than four times more likely than in preceding years. Most of these backward movements occurred shortly after the beginning of the hunt, indicating that geese moved back to regions where they had not previously experienced hunting. Overall disturbance level increased in all regions in years with hunting relative to years without hunting. Synthesis and applications. We conclude that spring hunting changed the stopover scheduling of this long‐distance migrant and might further impact population dynamics by reducing prenuptial fattening. The spring hunt may also have increased crop damage. We propose that staggered hunt opening dates could attenuate secondary effects of such management actions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 teacher head, 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".