Carry‐over effects of spring hunt and climate on recruitment to the natal colony in a migratory species
Bibliographic record
Abstract
Summary In long‐lived species, temporal variation in recruitment, defined as the entry of new individuals into the breeding population, can have a large effect on population growth rate. While hunting, as a management tool, is generally expected to control population size via increased mortality, it may also act by affecting recruitment. Although the impact of hunting on survival is well studied, less attention has been paid to the non‐lethal impacts of hunting on recruitment. To control the population size of the greater snow goose C hen caerulescens atlantica , an overabundant arctic‐nesting species, a spring hunting season was implemented from 1999 onwards in addition to the traditional autumn and winter hunting seasons. We investigated the potential carry‐over effect of spring hunting on recruitment of females to their natal colony on Bylot Island, Nunavut, Canada from 1992 to 2005 while accounting for other potential confounding factors, primarily climatic effects. We applied a multistate capture‐Mark‐Recapture recruitment model to a dataset of known‐age individuals ( n = 12 100), combining live recaptures at the breeding colony with dead recoveries from hunters. Annual variation in recruitment probability was best explained by spring hunt and a synthetic variable combining the climatic conditions experienced during migration (extreme values of the North Atlantic Oscillation index) with conditions upon arrival at the breeding grounds (snow cover). This model accounted for 58% of the temporal variation in recruitment, while the harvest rate or the climatic index taken alone accounted for 38% each. In the year with the highest spring hunting pressure (adult harvest rate ≈6%), recruitment was reduced by up to 50% compared to years with no hunt and similar average climatic conditions. Synthesis and applications . We conclude that there was a negative impact of the spring hunt not only on survival but also on recruitment in greater snow geese. These non‐lethal effects of hunting must be considered in management decisions aimed at controlling overabundant populations where recruitment is an important driver of population growth, as occurs in geese. Our study is also relevant to other situations such as in threatened species still exposed to hunting, as consideration of non‐lethal effects of hunting may be critical for their conservation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".