Larger Fish Upstream: Testing the Drivers of Fish Longitudinal Size Distribution in a Stream
Bibliographic record
Abstract
ABSTRACT The “larger fish upstream pattern” (LFUP), where fish size increases from river mouths toward headwaters, is widely documented, but the drivers remain unclear. Here, we tested whether LFUP is related to species reproductive strategies or environmental factors (differential growth, predation, competition). Using historical data (1994–1998) from six sites along a stream, we analysed the size distributions of eight fish species with three distinct spawning strategies (pelagic, lithopelagic, nest‐spawning). Linear and mixed‐effects models were used to test the effect of distance from the stream mouth, productivity (food resources), predation and intraspecific competition on body size distribution of each species. Pelagic species (Deuterodon janeiroensis, D. hastatus) and the lithopelagic spawner Pimelodella lateristriga consistently exhibited LFUP, driven primarily by distance from the river mouth. The LFUP in the lithopelagic Mimagoniates microlepis was explained by productivity, not distance. Nest spawners (e.g., Geophagus brasiliensis) showed no LFUP or inverse trends. In all species, competition reduced body size, while predation effects were variable. We found that LFUP pattern is strongly linked to reproductive strategies: species with drifting eggs/larvae (pelagic/lithopelagic) require upstream movement to maintain population, while environmental factors had weak or inconsistent effects. Our findings highlight that upstream movement is critical for small‐stream fish conservation, especially for species vulnerable to downstream drift. Barriers (e.g., dams) disrupting connectivity threaten LFUP‐dependent species globally, exposing the need for fish passage solutions when managing small 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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".