The landscape genetics of yellow perch (<i>Perca flavescens</i>) in a large fluvial ecosystem
Bibliographic record
Abstract
Landscape genetics is being increasingly applied to elucidate the role of environmental features on the population structure of terrestrial organisms. However, the potential of this framework has been little explored in aquatic ecosystems such as large rivers. Here, we used a landscape genetics approach in order to (i) document the population structure of the yellow perch (Perca flavescens) by means of genetic variation at microsatellite markers, (ii) assess to what extent the structure was explained by landscape heterogeneity, and (iii) interpret the relevance of interactions between genetics and landscape for management and conservation. Analysis of the genetic variation among 1715 individuals from 16 localities and distributed over 310 km in the freshwater section of the Saint Lawrence River (Québec, Canada) revealed a relatively modest level of genetic structuring (F(ST) = 0.039). Application of the Monmonier's algorithm combining geographical and genetic information identified three zones of restricted gene flow defining four distinct populations. Physical barriers played a more important role on gene flow and genetic structure than waterway geographical distance. We found correlations between genetic differentiation and presence of distinct water masses in the sector of Lake Saint-Louis (r = 0.7177, P = 0.0340) and with fragmentation of spawning habitats in the sector of Lake Saint-Pierre (r = 0.8578, P = 0.0095). Our results support the treatment of four distinct biological units, which is in contrast with the current basis for yellow perch management. Finally, this study showed that landscape genetics is a powerful means to identify environmental barriers to gene flow causing genetic discontinuities in apparently highly connected aquatic landscapes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".