Method for Estimating Detection Probabilities of Nonmigrant Tagged Fish: Applications for Quantifying Residualization Rates
Bibliographic record
Abstract
Abstract Detection probabilities are commonly accounted for in spatial mark–recapture studies to estimate quantities of biological interest such as survival, movement, or abundance, but they generally require large numbers of tagged animals to be detected. In some studies, few tagged animals are present at a particular time; the ability to estimate detection probability despite small sample sizes during some time periods would greatly improve inferences of ecologically relevant attributes. We developed a method for estimating time‐varying detection probabilities of tagged fish at acoustic or radio receiver stations during periods in which few tagged fish are present and mark–recapture methods are thus prohibitive. We quantified how an index of detection probability varies with an environmental covariate, and then calibrated this index against mark–recapture detection probability estimates derived from detection data collected when tagged fish were abundant. With a known time series of the environmental covariate, the method generates a time series of predicted detection probabilities for each receiver station in a study. We apply the method to a case study involving steelhead Oncorhynchus mykiss smolts tagged with acoustic transmitters to estimate the proportion of tagged fish residualizing in a river (i.e., remaining in freshwater instead of migrating seaward). Despite few detections of tagged fish in the months after the primary downstream migration period, we were able to estimate a residualization rate of 5% (95% confidence interval, 3–12%), which is comparable to residualization rate estimates from studies employing sampling methods that do not allow the survival and movement patterns of tagged fish to also be quantified. This method can be used in conjunction with mark–recapture survival estimation methods to better isolate probabilities of residualization and survival, which are otherwise confounded.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".