An Improved Method for the Estimation and Comparison of Mortality Rates in Fish from Catch-Curve Data
Bibliographic record
Abstract
Abstract Catch-curve analyses are routinely used to estimate instantaneous mortality (Z) in fish, and as the age-frequency data are often overdispersed, the application of a variance bias-correction factor has been recommended. The extensions of the Poisson generalized linear model (GLMPoisson) may, however, constitute a better alternative, as they model the variance (SE) in counts more adequately with their specific dispersion parameter for more accurate estimations and statistical comparisons. To test this idea, simulated age-frequency data generated under four dispersion scenarios were analyzed according to six currently available methods and compared with the results of a GLMPoisson and five of its extensions to evaluate each method-specific bias in Z ± SE estimates. Empirical age-frequency data from sampled Walleye Sander vitreus and Arctic Char Salvelinus alpinus populations in Québec, Canada, were then used to illustrate the applicability of our GLM-based method, which relies on the behavior of Pearson residuals to assess model adequacy and an information-theoretic approach for model selection. All analyses revealed that Z-estimates were generally accurate among the methods considered, except under the most likely situation of quadratic overdispersion met in ecological studies, for which only the negative binomial type 2 and the mean-parametrized Conway–Maxwell–Poisson (CMP) extensions were adequate to estimate both Z and its SE. Linearly overdispersed data were best modeled by the negative binomial type 1 and generalized Poisson (GLMGP) extensions; the GLMCMP and GLMGP were the most appropriate to model underdispersed data, whereas the GLMPoisson adequately modeled equi-dispersed data, similar to the Chapman and Robson (1960) method. Statistical comparisons of Z ± SE for grouping factors, such as year or site, were correctly achieved when the most adequate and statistically supported GLMPoisson extension was applied. Altogether, the proposed GLM-based method should help to circumvent the identified issues related to SE estimation for statistical inferences about mortality rates for fisheries management decision making.
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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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".