Effects of sampling effort, assemblage similarity, and habitat heterogeneity on estimates of species richness and relative abundance of stream fishes
Bibliographic record
Abstract
We estimated the sampling effort required to accurately estimate species richness and to detect changes in catch-per-unit-effort (CPUE) in four Great Plains, USA, streams. The number of sampled reaches (i.e., <1 km) required to estimate stream-segment (i.e., 20–28 km) species richness decreased with increased sampled reach length (i.e., 10, 20, 40, or 60 mean stream widths, MSW), whereas total sampling effort decreased with a greater number of shorter sampled reaches. Collecting all species in a stream segment required all sampled reaches (i.e., 10) of a length equal to 40 or 60 MSW. The number of stream reaches sampled with lengths equal to 40 MSW required to detect a 50% change in CPUE of common species (i.e., total abundance > 1% of total catch) with β = 0.80 ranged from 7 to 630 (mean = 99) and decreased with longer sampled reaches. A greater number of sampled reaches were needed to detect 90% of species richness and 25% changes in CPUE when Jaccard’s similarity of samples of stream fish assemblages and habitat heterogeneity was lower within streams. Our results suggest that homogeneous stream segments require more sampled reaches to characterize fish assemblages and monitor trends in fish abundance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".