Gene flow in an arctic wetland: modelling landscape effects on fine-scale genetic variation in an isolated muskrat «Ondatra zibethicus» population
Bibliographic record
Abstract
This thesis evaluates the genetic diversity, the population genetic structure as well as the functional connectivity of an isolated muskrat (Ondatra zibethicus) population at the northernmost limit of its geographic distribution in North America. In the first chapter, nine microsatellite markers were used to assess the genetic characteristics of muskrats sampled from 21 different lakes spread across the Old Crow Flats (OCF), an arctic wetland complex in Northern Yukon. Genetic diversity was relatively low, while patterns of genetic variation were structured into two population clusters within the OCF, suggesting reduced levels of gene flow in arctic habitats. In chapter 2, the relationship between genetic differentiation and the connectivity of wetland features in the OCF was investigated using circuit theory to model isolation by resistance (IBR) based on landscape cover. Resistance surfaces were parameterized using a machine learning algorithm to optimize the fit between an IBR model and the observed genetic distance between lakes. The optimized IBR model was subsequently compared to the alternative models of isolation by distance (IBD) and isolation by barrier (IBB) using two complementary approaches: (1) causal modelling, and (2) partitioning of genetic variation explained by spatial eigenfunctions (Moran's eigenvector maps). The optimized IBR model revealed that hydrological features facilitated gene flow in the landscape. Gene flow through most terrestrial habitats was relatively unimpeded, though woodlands were severely obstructive. Causal modelling showed greater support for the optimized IBR model then for IBD or IBB models. Partitioning of variation suggested that both IBR and IBB models contributed unique explanatory spatial structures, while the IBD model was redundant. This thesis highlights the first muskrat population genetic structures detected at fine spatial scales and identifies the landscape variables that drive this spatial pattern using new modelling approaches.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".