A global review of the downstream effects of small impoundments on stream habitat conditions and macroinvertebrates
Bibliographic record
Abstract
The downstream ecological effects of large impoundments have previously been reviewed; however, little is known about the downstream ecological effects of small man-made impoundments. In this review, we examine 94 papers focusing on the effects of small impoundments on stream habitat conditions and macroinvertebrates. Most studies (89.3%) address the effects of small impoundments on physical and chemical habitat conditions, while fewer studies (48.9%) address the effects on macroinvertebrates. In general, most studies report no significant downstream effects of small impoundments on physico-chemical variables, whereas macroinvertebrates richness and abundance increased or decreased. Mean effect sizes (as Cohen’s d) for physico-chemical variables range from −0.82 to 0.68 (small weir: −0.21 to 0.35; run-of-river dam: −0.82 to 0.64; low head dam: −0.49 to 0.68), and from −0.03 to 0.63 for macroinvertebrates abundance and richness. Our assessment of the published literature demonstrates the advantage of combining qualitative and quantitative analyses, and that, while small impoundments may have minimal significant effects on most physico-chemical variables, macroinvertebrates’ richness and density may be affected. This review is relevant for management and scientific communities to identify potential alterations of stream habitats and biota by small impoundments.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".