Fringe effects: detecting bull trout (<i>Salvelinus confluentus</i>) at distributional boundaries in a montane watershed
Bibliographic record
Abstract
Robust assessment and monitoring programs are critical for effective conservation, yet for many taxa we fail to understand how trade-offs in sampling design affect power to detect population trends and describe spatial patterns. We tested an occupancy-based sampling approach to evaluate design considerations for detecting watershed-scale population trends associated with juvenile bull trout (Salvelinus confluentus) distributions. Electrofishing surveys were conducted across 275 stream sites from the Prairie Creek watershed, Northwest Territories, Canada. Site-level detectability of juvenile bull trout was not uniform, and imperfect detection affected modelled occupancy probabilities most in fringe habitats near distributional boundaries in steep reaches and large streams. We show that detecting a 30% change in watershed-level occupancy ≥78% of the time, as conservation guidelines suggest, may require three repeat surveys (i.e., temporal replicates) and increased spatial sampling intensity of fringe habitats. Additional sampling effort in fringe sites could be offset by sampling fewer sites in core habitats to optimize designs for detecting demographic shifts in bull trout, while still minimizing risk of nondetection for this cryptic species.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".