Density-dependent interactions in an Arctic char – brown trout system: competition, predation, or both?
Bibliographic record
Abstract
In the study of mechanisms structuring fish communities, mixed competition–predation interactions where large predators feed on prey fish versus those in which small predators compete with prey fish for a shared prey have been the focus of substantial research. We used a long-term data set from a system inhabited by brown trout (Salmo trutta) (predator) and Arctic char (Salvelinus alpinus) (prey) to evaluate whether mixed interspecific interactions were present in this system as suggested in other studies focusing on this species pair. We found no evidence for a negative interspecific density dependence in individual performance in either Arctic char or brown trout. In contrast, a negative intraspecific density dependence was present, especially in Arctic char. Furthermore, large brown trout condition showed a positive response to encounter rate with Arctic char (related to the density of small Arctic char). The most parsimonious interaction module to explain the Arctic char – brown trout interaction patterns in the studied system does therefore not need to include interspecific competition. We suggest that size-structured mixed competition–predation interactions in different systems are realized as being either mainly structured through interspecific predation or by competition depending on species life history characteristics and environmental conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".