Overlap in spawning habitat characteristics between two salmonids in relation to stream size: redd superimposition hypothesis on longitudinal species replacement
Bibliographic record
Abstract
Redd superimposition, spawning on a previous spawner's redd by a later spawner, reduces reproductive success of the previous spawner. Therefore, in streams where redd superimposition frequently occurs, late-spawning species have a competitive advantage over early-spawning species. We hypothesised that smaller channels of upper reaches would have higher potential of redd superimposition owing to lower availability of spawning habitat, thereby providing a competitive advantage to late-spawning species, to explain a displacement of native masu salmon ( Oncorhynchus masou ishikawae) by non-native white-spotted char ( Salvelinus leucomaenis) (late spawner) specific to small upper reaches in a Japanese river. We examined (1) the availability of spawning habitat and (2) overlap in spawning habitat characteristics between the two species in streams with different channel size. The results showed that (1) the habitat availability decreased upstream as channel size decreased, and (2) characteristics of spawning habitat highly overlapped between the two species in small channels, whereas those differed significantly between the two species in larger channels, supporting our hypothesis. Our results provide a new perspective on longitudinal changes in competitive advantages in salmonids.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".