Effects of riffle length on the short-term movement of fishes among stream pools
Bibliographic record
Abstract
Recent research has suggested that the within-habitat dynamics of fish populations and assemblages can be affected by the spatial distribution of habitats within streams. In this study, we determined the extent to which pool isolation (length of riffles connecting adjacent pools) influenced fish movement in two Arkansas streams. We marked individuals from 12 pools assigned to two treatment categories: pools separated by long riffles (>50 m) and those separated by short riffles (<10 m). Repeatedly snorkeling pools for 3 days in 1995 and 1997, we discovered substantial emigration (>20%) and significant effects of riffle length. Total emigration from short-riffle pools was three times higher (29%) than movement from long-riffle pools (10%). Further, marked fish in short-riffle pools moved upstream and downstream with equal frequency, whereas fish in long-riffle pools moved twice as often downstream. Collectively, these results indicate significant effects of habitat spacing on short-term movement patterns by fish. In streams, where fish are distributed within a mosaic of habitats of varying quality, such movements may allow individuals to assess spatial variability in resource conditions (e.g., food, predators). Because land-use activities can alter habitat spacing, these findings have important implications for fish conservation in degraded streams.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".