Landscape, colonization, and life history: their effects on genetic diversity in four sympatric species inhabiting a dendritic system
Bibliographic record
Abstract
To what degree are patterns of genetic structure in fragmented systems the result of contemporary landscape versus history? We examined the distribution of genetic diversity as a function of colonization history and contemporary landscape in four fish species inhabiting a hierarchically fragmented, unaltered system, the Kogaluk drainage (Labrador): lake trout (Salvelinus namaycush), longnose sucker (Catostomus catostomus), round whitefish (Prosopium cylindraceum), and lake chub (Couesius plumbeus). The footprint of colonization history was still observable in the three species where this issue was examined regardless of the generations since their arrival. Approximate Bayesian computation (ABC) analyses suggest colonization took place from the southwest. The species exhibit similar diversity patterns despite different [Formula: see text] values and generation intervals. Contemporary gene flow was largely negligible except for gene flow from a centrally located lake. These results suggest landscape has driven colonization history, which still has influence on genetic structuring. The species are widespread. Understanding how they behave in the pristine Kogaluk provides a baseline against which to evaluate how other anthropogenically perturbed systems are performing. Improved understanding of historical and contemporary processes is required to fully explain diversity patterns in complex metapopulations.
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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".