Major Lineages and Metapopulations in Columbia River <i>Oncorhynchus mykiss</i> Are Structured by Dynamic Landscape Features and Environments
Bibliographic record
Abstract
Abstract It is widely recognized that genetic diversity within species is shaped by dynamic habitats. The quantitative and molecular genetic patterns observed are the result of demographics, mutation, migration, and adaptation. The populations of rainbow trout Oncorhynchus mykiss in the Columbia River basin (including both resident and anadromous forms and various subspecies) present a special challenge to understanding the relative roles of those factors. Standardized microsatellite data were compiled for 226 collections (15,658 individuals) from throughout the Columbia and Snake River basins to evaluate the genetic patterns of structure and adaptation. The data were primarily from fish of the anadromous life history form, and we used a population grouping procedure based on principal components and hierarchical k‐means clustering to cluster populations into eight aggregates or groups with similar allele frequencies. These aggregates approximated geographic regions, and the two largest principal components corresponded to ancestral lineages of Sacramento redband trout O. m. stonei, coastal rainbow trout O. m. irideus, and interior Columbia River redband trout O. m. gairdneri. Genetic data were partitioned among primary aggregates (lower Columbia, middle–upper Columbia, and Snake rivers), and the magnitude of genetic divergence relative to genetic diversity was analyzed (per locus) to test for evidence of selection and subsequent signals of adaptation. Two loci showed higher divergence than expected by chance (i.e., positive selection); however, both of these loci were on the fringe of the 99% confidence level and are potential false positives. Genetic patterns were also significantly correlated with certain environmental and habitat parameters (e.g., precipitation), but the extent to which those correlations are causal as opposed to effectual remains unclear. Despite the remaining questions, these data provide a foundation for more detailed investigations of harvest, admixture, and introgression between hatchery‐ and natural‐origin fish and differences in reproductive success among individuals as well as monitoring trends in productivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".