Characterization of Macroinvertebrate Communities in the Hyporheic Zone of River Ecosystems Reflects the Pump-Sampling Technique Used
Bibliographic record
Abstract
The hyporheic zone of river ecosystems provides a habitat for a diverse macroinvertebrate community that makes a vital contribution to ecosystem functioning and biodiversity. However, effective methods for sampling this community have proved difficult to establish, due to the inaccessibility of subsurface sediments. The aim of this study was to compare the two most common semi-quantitative macroinvertebrate pump-sampling techniques: Bou-Rouch and vacuum-pump sampling. We used both techniques to collect replicate samples in three contrasting temperate-zone streams, in each of two biogeographical regions (Atlantic region, central England, UK; Continental region, southeast France). Results were typically consistent across streams in both regions: Bou-Rouch samples provided significantly higher estimates of taxa richness, macroinvertebrate abundance, and the abundance of all UK and eight of 10 French common taxa. Seven and nine taxa which were rare in Bou-Rouch samples were absent from vacuum-pump samples in the UK and France, respectively; no taxon was repeatedly sampled exclusively by the vacuum pump. Rarefaction curves (rescaled to the number of incidences) and non-parametric richness estimators indicated no significant difference in richness between techniques, highlighting the capture of more individuals as crucial to Bou-Rouch sampling performance. Compared to assemblages in replicate vacuum-pump samples, multivariate analyses indicated greater distinction among Bou-Rouch assemblages from different streams, as well as significantly greater consistency in assemblage composition among replicate Bou-Rouch samples collected in one stream. We recommend Bou-Rouch sampling for most study types, including rapid biomonitoring surveys and studies requiring acquisition of comprehensive taxon lists that include rare taxa. Despite collecting fewer macroinvertebrates, vacuum-pump sampling remains an important option for inexpensive and rapid sample collection.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".