Factors Affecting Bull Trout (<scp><i>Salvelinus confluentus</i></scp>) Abundance and Potential Displacement by Non‐Native Brook Trout (<scp><i>Salvelinus fontinalis</i></scp>) and Brown Trout (<scp><i>Salmo trutta</i></scp>) in Headwater Streams
Bibliographic record
Abstract
ABSTRACT The invasion of non‐native fish species poses a major threat to native salmonids in freshwater ecosystems. We investigated the potential displacement of threatened Bull Trout (Salvelinus confluentus) by non‐native Brook Trout (Salvelinus fontinalis) and Brown Trout (Salmo trutta) in 44 headwater streams across Alberta, Canada. Using electrofishing surveys and a combination of single‐ and multi‐species N‐mixture models, we examined how fish abundance responds to instream habitat characteristics, landscape features and species interactions, while accounting for imperfect detection. Results revealed that reduced Bull Trout abundance was correlated with the abundance of Brook Trout in cold streams (mean August temperature < 11 °C) that lack habitat complexity, particularly where large woody debris and heterogeneous channel features are limited. Conversely, Brown Trout were associated with reduced Bull Trout abundance in larger, lower‐elevation systems, likely due to competitive dominance in deeper and slower habitats. These findings highlight that temperature alone likely does not dictate displacement risk and that habitat structure and species‐specific traits also play critical roles. Management strategies should prioritize the protection of cold, structurally complex streams and consider targeted removals or exclusion strategies to limit further invasion. Habitat restoration and assisted colonization to suitable, non‐invaded refugia may further enhance Bull Trout conservation under ongoing climate and land‐use change.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".