Watershed characteristics shape the landscape genetics of brook stickleback (<i>Culaea inconstans</i>) in shallow prairie lakes
Bibliographic record
Abstract
Abstract Investigating the consequences of landscape features on population genetic patterns is increasingly important to elucidate the ecological factors governing connectivity between populations and predicting the evolutionary consequences of landscapes. Small prairie lakes in Alberta, Canada, and the brook stickleback (Culaea inconstans) that inhabit them, provide a unique aquatic system whereby populations are highly isolated from one another. These heterogeneous and extreme environments are prone to winterkills, an event whereby most of the fish die and frequent bottlenecks occur. In this study, we characterized the genetic population structure of brook stickleback among several lakes, finding that the species is hierarchically influenced by within‐lake characteristics in small‐scale watersheds. Landscape genetic analyses of the role of spatial features found support for basin characteristics associated with genetic diversity and bottlenecks in 20% of the sampled lakes. These results suggest that brook stickleback population genetic patterns may be driven, at least in part, by ecological processes that accelerate genetic drift and landscape patterns associated with reduced dispersal. Collectively, these results reinforce the potential importance of connectivity in the maintenance of genetic diversity, especially in fragmented landscapes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".