Effect of Pre‐Harvest Mortality on Harvest Rates and Derived Population Estimates
Bibliographic record
Abstract
ABSTRACT Banding waterfowl, in combination with the citizen science provided by hunters that report marks from harvested birds, is a long‐standing, institutionalized practice for estimating probabilities of survival and exploitation (i.e., legal harvest from such populations). Range‐wide population abundance can also be estimated by combining the number of banded individuals with the number harvested from the population. Waterfowl marking with uniquely identifiable bands done during late summer in North America is often referred to as pre‐season banding. For example, mass capture of arctic geese for pre‐season banding is normally done in July (nonbreeders) or August (failed breeders and breeders with young) during flightless molt of respective groups. An important assumption for proper inference about harvest probability provided from such samples is that there is no mortality, natural or otherwise, during the interval between when individuals are marked and when hunting seasons begin. We evaluated the effect of variable mortality that could occur between marking and subsequent hunting seasons on estimates of survival, recovery, and harvest probabilities using simulation pertinent to a typical waterfowl species. We fit a Brownie tag‐recovery model to the simulated data and calculated the estimator bias that resulted from various pre‐harvest mortality scenarios. There was no effect on survival probability during the interval between annual banding in subsequent years, but recovery probability, and thus estimated harvest probability, was directly and inversely related to pre‐harvest mortality of juveniles. The magnitude of negative bias in harvest probability of juveniles increased further as the fraction of the population sampled declined. If the probability of pre‐harvest mortality differs between marked and unmarked individuals, the negative bias in harvest probability results in overestimates of derived abundance that increases as the proportion of marked individuals in the population declines. We used our observed results to propose an explanation for occasional biologically improbable estimates of abundance of juvenile lesser snow geese (Anser caerulescens). © 2021 The Authors. The Journal of Wildlife Management published by Wiley Periodicals LLC on behalf of 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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".