Landscape variability explains spatial pattern of population structure of northern pike (<i><scp>E</scp>sox lucius</i>) in a large fluvial system
Bibliographic record
Abstract
A growing number of studies have been investigating the influence of contemporary environmental factors on population genetic structure, but few have addressed the issue of spatial patterns in the variable intensity of factors influencing the extent of population structure, and particularly so in aquatic ecosystems. In this study, we document the landscape genetics of northern pike (Esox lucius), based on the analysis of nearly 3000 individuals from 40 sampling sites using 22 microsatellites along the Lake Ontario - St. Lawrence River system (750 km) that locally presents diverse degrees of interannual water level variation. Genetic structure was globally very weak (F ST = 0.0208) but spatially variable with mean level of differentiation in the upstream section of the studied area being threefold higher (F ST = 0.0297) than observed in the downstream sector (F ST = 0.0100). Beside interannual water level fluctuation, 19 additional variables were considered and a multiple regression on distance matrices model (R (2) = 0.6397, P < 0.001) revealed that water masses (b = 0.3617, P < 0.001) and man-made dams (b = 0.4852, P < 0.005) reduced genetic connectivity. Local level of interannual water level stability was positively associated to the extent of genetic differentiation (b = 0.3499, P < 0.05). As water level variation impacts on yearly quality and localization of spawning habitats, our study illustrates how temporal variation in local habitat availability, caused by interannual water level fluctuations, may locally decrease population genetic structure by forcing fish to move over longer distances to find suitable habitat. This study thus represents one of the rare examples of how environmental fluctuations may influence spatial variation in the extent of population genetic structure within a given species.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".