Standing genetic diversity and selection at functional gene loci are associated with differential invasion success in two non‐native fish species
Bibliographic record
Abstract
Invasive species are expected to experience a unique combination of high genetic drift due to demographic factors while also experiencing strong selective pressures. The paradigm that reduced genetic diversity should limit the evolutionary potential of invasive species, and thus, their potential for range expansion has received little empirical support, possibly due to the choice of genetic markers. Our goal was to test for effects of genetic drift and selection at functional genetic markers as they relate to the invasion success of two paired invasive goby species, one widespread (successful) and one with limited range expansion (less successful). We genotyped fish using two marker types: single nucleotide polymorphisms (SNPs) in known-function, protein-coding genes and microsatellites to contrast the effects of neutral genetic processes. We identified reduced allelic variation in the invaded range for the less successful tubenose goby. SNPs putatively under selection were responsible for the observed differences in population structure between marker types for round goby (successful) but not tubenose goby (less successful). A higher proportion of functional loci experienced divergent selection for round goby, suggesting increased evolutionary potential in invaded ranges may be associated with round goby's greater invasion success. Genes involved in thermal tolerance were divergent for round goby populations but not tubenose goby, consistent with the hypothesis that invasion success for fish in temperate regions is influenced by capacity for thermal tolerance. Our results highlight the need to incorporate functional genetic markers in studies to better assess evolutionary potential for the improved conservation and management of 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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".