Annual survival and seasonal hunting mortality of midcontinent snow geese
Bibliographic record
Abstract
ABSTRACT Annual banding programs have allowed the estimation of key demographic parameters for many populations of harvested wildlife, yet they often provide little insight into vital rate variation among seasonally occupied habitats or among regions with differing hunting regulations. For lesser snow geese (Chen caerulescens caerulescens) in the midcontinent of North America, rapid growth in abundance and the consequent implementation of special conservation measures present a scenario where seasonal mortality estimates would be highly valuable. We evaluated variation in hunting mortality of adult and young geese among 3 seasons (autumn, winter, and spring) based on annual banding data from breeding colonies north and south of 60°N latitude, and seasonal hunting recoveries from the United States and Canada. Using band‐recovery models and data covering 1999 through 2015, we first estimated annual survival for geese of both age classes and breeding locations, and then subsequently used seasonal hunting recoveries to derive estimates of seasonal hunting mortality and annual non‐hunting mortality. Simulation models validated the accuracy of this approach. Hunting mortality in winter generally exceeded that during spring and autumn, but our estimates suggested that hunting mortality represented a small fraction of annual mortality for adult and young birds. Consistent with recent studies, our estimates pointed to a greater harvest effect for the smaller subarctic population breeding near southern Hudson Bay, Canada, than for the large arctic population breeding farther north. Although mean kill rates were higher for young than adult geese, natural mortality for young was high and temporally variable, implying that some hunting mortality experienced by young geese during their first year could be compensated by natural causes of death. Natural (non‐hunting) mortality and annual survival showed greater temporal variation than seasonal kill rates in adults and young geese, highlighting the importance of non‐harvest factors (e.g., climate, habitat, population density) to the dynamics of these populations. These novel estimates of seasonal kill rates and non‐hunting mortality contribute further support to the notion that internal dynamics of the lesser snow goose midcontinent population, including natural mortality and recruitment, currently influence population trajectory more than ongoing interventions through harvest management. © 2017 The Wildlife Society.
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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 | 0.001 | 0.001 |
| 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 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".