IMPACT OF SPECIAL EARLY HARVEST SEASONS ON SUBARCTIC-NESTING AND TEMPERATE-NESTING CANADA GEESE
Bibliographic record
Abstract
Dramatic changes in wintering distributions of Canada geese (Branta canadensis) have occurred over the past 50 years in eastern North America. Declines in numbers of subarctic-nesting geese wintering in southern states, and increases in numbers wintering in northern regions, have resulted in a northern shift in winter distributions. In contrast, numbers of temperate-nesting geese have increased throughout eastern North America. Management efforts to control overabundant temperate-nesting flocks have included the establishment of special early harvest seasons in September. However, the effect of early seasons on survival and harvest of subarctic-nesting populations has not been documented. Understanding the timing of migration movements and the fidelity of subarctic-nesting flocks to terminal winter refuges in the Southeast also is necessary to design early harvest seasons that target temperate-nesting flocks and protect subarctic-nesting populations. We used recoveries of marked geese to estimate survival and harvest rates before and after implementation of early harvest seasons within the Mississippi Flyway during 1976–1999. In addition, we used observations of neck-banded geese from the Southern James Bay Population (SJBP) to evaluate the hypothesis that subarctic-nesting geese arriving prior to mid-December on several key terminal winter refuges in the Southeast (early arriving migrants) were more likely to return to those refuges in subsequent years than were migrants arriving after mid-December (late arriving migrants). September seasons during 1987–1994 were a minor source of mortality for subarctic-nesting populations and accounted for <10% of their annual harvest mortality. The effectiveness of early seasons for increasing mortality of temperate-nesting flocks varied among the states we examined and was tempered by concurrent changes in state-specific harvest regulations during the regular harvest season. For SJBP Canada geese, annual fidelity to southeastern refuges was 10% higher for early arrivers than for late arriving geese. However, in any given year only 47–57% of the surviving geese were expected to return to the refuges the following year. Although early arriving migrants had higher survival and higher return probabilities than did late arriving migrants or geese that failed to return, numbers of geese wintering on southeastern refuges likely declined because <60% of the surviving geese affiliated with the refuges would return in a given year and because of lower survival for geese that did not return to the refuges.
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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 | 0.000 | 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".