Robust and Defensible Mark–Recapture Methodologies for Salmonid Escapement: Modernizing the Use of Data and Resources
Bibliographic record
Abstract
Abstract Estimates of population size, required for most ecological investigations, are often achieved by mark–recapture experiments, frequently by applying pooled or stratified Petersen estimators. Unfortunately, the closure assumption required by Petersen estimators is frequently violated in the estimation of salmonid escapement, even though the consequences of this violation have been known for decades. We illustrate how biologists and analysts can and should make better use of statistical, mathematical, and computational advances in their analysis of mark–recapture data. Modern, easily applied approaches address and minimize the effects of violations to the model assumptions on which abundance estimators are based. Using examples from research estimating the numbers of Chinook Salmon Oncorhynchus tshawytscha escaping fisheries to spawn, this study demonstrates and provides evidence in support of the use of a robust and defensible approach to salmonid escapement estimation based on the analysis of individual encounter histories. The main attributes of the approach include (1) testing for demographic closure, (2) allowing different hypotheses about the demographic attributes and capture history of the studied population to be expressed within a model selection framework, encompassing suites of open- or closed-population approaches, and (3) optimizing the use of information by embracing the opportunities that mark–recapture experiments generate to increase our knowledge of salmonid ecology and hence improve both future study designs and management decisions. This study also demonstrates that discrepancies (positive) in abundance estimates produced with the Petersen estimator relative to those produced by the “best models” from robust estimators are inversely proportional to sampling rates. Received May 20, 2015; accepted October 22, 2015
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".