Habitat stratification to maximize the power to detect proportional declines in occupancy of an imperilled freshwater fish species
Bibliographic record
Abstract
Monitoring imperilled species provides critical information for decision-making, but the effort needed to detect significant changes in the occurrence of rare species often requires substantial resources. To address this challenge, we developed a sampling design that reduced the effort needed to detect proportional reductions in silver shiner ( Notropis photogenis) occupancy probability ( ψ) over time, a species listed as Threatened in Canada owing to its rarity and threats from urbanization and agriculture. A stratified-random site selection approach based on the probabilistic relationship between site depth and adult silver shiner ψ was implemented in the fall of 2022 and 2023. Stratified sampling increased estimated ψ by 72% in 2022 compared to previous non-stratified designs, with similar detection probabilities ( p ∼ 0.8), boosting power to detect future declines by 86.5%. However, a significant reduction in p between 2022 and 2023 negated these gains and prevented conclusions of within-river range contraction. These findings demonstrate the potential to improve the power of occupancy models with habitat-focused sampling designs and provide considerations around sample size when designing occupancy-based monitoring programs.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".