Mechanisms of coexistence between native bull trout (<i>Salvelinus confluentus</i>) and non-native lake trout (<i>Salvelinus namaycush</i>): inferences from pattern-oriented modeling
Bibliographic record
Abstract
Determining the ecological mechanisms that control population abundances is an important issue for the conservation of endangered and threatened species. We examined whether a threatened bull trout ( Salvelinus confluentus ) population could coexist at observed levels with the ecologically similar introduced species, lake trout ( Salvelinus namaycush ), using a pattern-oriented analysis of population dynamics models. We used a large suite of stage- and age-structured models to examine how both competitive and predatory interactions, combined with differing life-history strategies and species vital rates, drove salmonid coexistence patterns. In our models, an ontogenetic shift in juvenile bull trout resource use was the most important factor contributing to the two species coexistence; however, this coexistence occurred with reduced abundances in bull trout that increase the chances of extirpation for the native species. Observed levels of competition were found to have stronger effects than predation on population abundances. We used a pattern-oriented modeling approach to inference; this approach assumes process models that can generate patterns similar to the observed patterns are better supported than those that cannot. This methodology may find wide use on a number of data-limited fishery management and conservation problems.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".