Role of visual barriers on mitigation of interspecific interference competition between native and non-native salmonid species
Bibliographic record
Abstract
Interspecific competition is a mechanism by which native salmonids can be replaced by non-native species. According to the two-species Lotka–Volterra competition model, replacement of the native species would occur when the non-native species has a competitive advantage over the native species and interspecific competition is more intense than competition within each of the two species. However, field observations have implied that visual barriers such as woody debris may slow down the replacement by mitigating interspecific competition. Using an experimental stream with white-spotted charr ( Salvelinus leucomaenis (Pallas, 1814)) as the native species and brown trout ( Salmo trutta L., 1758) as the non-native species, this study examined aggressive actions within and between species to assess the relative intensities of interspecific and intraspecific competitons within native species and the effect of visual barriers for reducing the relative intensity. In a sympatric and no-barrier treatment where trout was the dominant species, interspecific competition occurred more intensely than intraspecific competition among native charr. However, the relative intensity of interspecific competition decreased in a sympatric and with-barrier treatment. Our results suggest that interspecific competition may contribute to the replacement of native species by more competitive, non-native species. However, restoring visual barriers is a potential method to mitigate interference interactions and may deter the replacement.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".