Watershed-level sampling effort requirements for determining riverine fish species composition
Bibliographic record
Abstract
Accurate assessments of watershed-level species composition are necessary for comparative ecological studies, ecosystem health assessments, monitoring, and aquatic conservation prioritization. Several studies have addressed sampling effort requirements for characterizing fish species composition at a section of stream, but none have examined watershed-level requirements. In the spring and summer of 2002, we extensively sampled nine Great Lakes watersheds to assess sampling-effort requirements. Sampling requirements increased with the targeted percentage of estimated species richness. Sampling 15119 randomly selected reaches of stream, stratified by stream order, was on average sufficient to detect 80%100% of estimated species richness. Watershed size (km2) and estimated species richness each showed a weak, negative correlation with sampling-effort requirements in our study streams, with Pearson's correlation coefficients of 5.06 and 0.590, respectively. Because of among-watershed variability in sampling effort requirements, field crews should plot species accumulation curves onsite to determine adequate inventory completion. Based on the difficulty of detecting the last 10% of species, random sampling should be conducted in conjunction with targeted sampling of rare 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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".