Detecting declines in the abundance of a bull trout (Salvelinus confluentus) population: understanding the accuracy, precision, and costs of our efforts
Bibliographic record
Abstract
Using empirical field data for bull trout ( Salvelinus confluentus ), we evaluated the trade-off between power and sampling effort–cost using Monte Carlo simulations of commonly collected mark–recapture–resight and count data, and we estimated the power to detect changes in abundance across different time intervals. We also evaluated the effects of monitoring different components of a population and stratification methods on the precision of each method. Our results illustrate substantial variability in the relative precision, cost, and information gained from each approach. While grouping estimates by age or stage class substantially increased the precision of estimates, spatial stratification of sampling units resulted in limited increases in precision. Although mark–resight methods allowed for estimates of abundance versus indices of abundance, our results suggest snorkel surveys may be a more affordable monitoring approach across large spatial scales. Detecting a 25% decline in abundance after 5 years was not possible, regardless of technique (power = 0.80), without high sampling effort (48% of study site). Detecting a 25% decline was possible after 15 years, but still required high sampling efforts. Our results suggest detecting moderate changes in abundance of freshwater salmonids requires considerable resource and temporal commitments and highlight the difficulties of using abundance measures for monitoring bull trout populations.
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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 | 0.027 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".