Closure violation in DNA-based mark-recapture estimation of grizzly bear populations
Bibliographic record
Abstract
We use methods in the program MARK to explore the effects of closure violation when DNA-based markrecapture methods are used to estimate grizzly bear (Ursus arctos) populations. Our approach involves the use of Pradel models in MARK to explore the relationship between recruitment, apparent survival rates, recapture rates, and distance between mean bear-capture locations and the edge of the sampling grid. If the population is demographically closed, it can be assumed that apparent survival estimates the fidelity of bears to the grid area and recruitment estimates rates of addition of bears to the grid area. A core bear population is defined from the Pradel analysis and is used to approximate the grid-based population size. The Huggins closed-population model in MARK is used to provide robust superpopulation estimates by explicitly modeling the relationship between capture probability and distance of bear-capture location from the grid edge. Data from a grizzly bear DNA-based markrecapture inventory conducted in British Columbia is used to illustrate this method. The results of the Pradel analysis suggest that bears with mean capture locations within 10 km of the grid edge exhibit reduced fidelity rates and higher addition rates. Using the population of bears captured more than 10 km from the grid edge, a core-extrapolated estimate is derived, which is substantially lower than naïve CAPTURE superpopulation estimates. The Huggins model superpopulation estimate displays superior precision compared with CAPTURE model estimates. Our results illustrate the danger of naïve interpretation of closed-model estimates. This method allows further inferences to be made concerning the spatial causes of closure violation, and the degree of bias caused by closure violation to be explored.
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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 | 0.037 | 0.180 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".