Spatiotemporal changes in genetic diversity and structure of a recent fish invasion in eastern North America
Bibliographic record
Abstract
Abstract Introduced and geographically expanding populations experience similar eco-evolutionary challenges, including founder events, genetic bottlenecks, and novel environments. Theory predicts that reduced genetic diversity resulting from such genetic phenomena limits the colonization success of introduced populations. We examined an invasive population of a Eurasian freshwater fish, Tench ( Tinca tinca ), that has been expanding geographically in eastern North America for three decades. Using genomic data, we evaluated evidence for single versus multiple introductions and the connectivity of the population across the entire range in which it has been spreading. Tench exhibited low levels of genetic diversity, a lack of marked population subdivision across time and space, and evidence of a recent genetic bottleneck. These results suggest that the invasion stemmed from a single introduction, consistent with the reported invasion history. Furthermore, the large genetic neighbourhood size and weak within-population genetic substructure suggest high connectivity across the invaded range, despite the large area occupied, and no evidence of substantial diminution of genetic diversity from the invasion core to the margins. As eradicating the species within a ~112 km radius would be necessary to prevent recolonization, eradicating Tench is likely not feasible at watershed—and possibly local—scales. Management should instead focus on reducing abundance in priority conservation areas to mitigate adverse impacts. Our study supports the argument that introduced populations can thrive despite recent bottlenecks and low levels of genetic diversity, and it suggests that landscape heterogeneity and population demographics can generate variability in spatial patterns of genetic diversity within a single range expansion.
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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 | 0.000 |
| 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".