Effects of dynamic landscape elements on fish dispersal: the example of creek chub (<i>Semotilus atromaculatus</i>)
Bibliographic record
Abstract
Barriers along a watercourse and interconnections between drainage systems are dynamic landscape elements that are expected to play major roles in the dispersal and genetic structure of fish species. The objective of this study was to assess the role of these elements using creek chub (Semotilus atromaculatus) in the Mastigouche Wildlife Reserve (Québec, Canada) as model. Numerous impassable waterfalls and interconnections among drainage systems were inferred with geographic information systems and confirmed de visu. The analysis of 32 populations using seven nuclear microsatellites revealed the presence of three genetically distinct groups. Some groups were found upstream of impassable barriers and in adjacent portions of distinct drainage systems. Admixture among groups was also detected in some populations. Constraining phylogenetic procedures as well as Mantel correlation tests confirmed that the genetic structure is more likely to result from interconnections between the drainage systems than from the permanent network. This study indicates that landscape elements such as interconnections are of major importance for circumventing impassable barriers and colonizing lakes that are otherwise inaccessible. Such an approach could be relevant for determining the origins of fish species (i.e. native vs. introduced) in the context of conservation.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".