A flexible survey design for monitoring spatiotemporal fish richness in nonwadeable rivers: optimizing efficiency by integrating gears
Bibliographic record
Abstract
We designed a flexible protocol for monitoring fish species richness in nonwadeable rivers. Nine sites were sampled seasonally with six gears in two physiographic regions in Missouri (USA). Using resampling procedures and mixed-effects modeling, we quantified richness and compositional overlap among gears, identified efficient gear combinations, and evaluated protocol performance across regions and seasons. We detected 25–75 species per sample and 89 185 fish. On average, no single gear detected >62% of observed species, but an optimized, integrated-gear protocol with four complementary gears on average detected 90% of species while only requiring 51.9% of initial sampling effort. Neither season nor physiographic region explained low spatiotemporal variation in percent richness detected by the integrated-gear protocol. In contrast, equivalent effort with an electrofishing-only protocol was 53.5% less efficient, seasonally biased and imprecise (36.1%–82.3% of richness), and on average detected 15.9% less of observed richness. Altogether, riverine fish richness is likely underestimated with single-gear survey designs. When paired with existing wadeable-stream inventories, our customizable approach could benefit regional monitoring by comprehensively documenting riverine contributions to riverscape biodiversity.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".