Larger fish upstream in a small stream: what are the causes of this longitudinal pattern?
Bibliographic record
Abstract
Abstract Stream fishes often display distinct spatial patterns in size distribution, with one possible pattern being larger individuals dominating the headwaters. This “larger fish upstream pattern” (LFUP) has been widely documented, yet the ecological drivers remain unclear. We investigated factors contributing to LFUP in the fish community of a South American stream. Using historical data (1994–1998) from electrofishing surveys across six sites, we analyzed size distribution across eight species, with predictors including upstream distance, food availability, predation pressure, and intraspecific competition. Linear mixed models indicated that four species exhibit consistent LFUP, primarily driven by upstream distance or food availability. Species with pelagic or lithopelagic spawning displayed LFUP, supporting the hypothesis that LFUP is linked to reproductive strategies requiring upstream movement to maintain population stability. Our findings support that habitat connectivity and complexity are crucial for conserving small pelagic and lithopelagic fish species, as suggested in previous works. This study contributes to the field by highlighting mobile behavior in small tropical stream fishes and underscores the need for conservation strategies that account for movement patterns and habitat requirements in stream systems worldwide.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".