Dispersal strategies, secondary range expansion and invasion genetics of the nonindigenous round goby, <i>Neogobius melanostomus</i>, in Great Lakes tributaries
Bibliographic record
Abstract
Dispersal strategies are important mechanisms underlying the spatial distribution and colonizing ability of all mobile species. In the current study, we use highly polymorphic microsatellite markers to evaluate local dispersal and colonization dynamics of the round goby (Neogobius melanostomus), an aquatic invader expanding its range from lake to river environments in its introduced North American range. Genetic structure, genotype assignment and genetic diversity were compared among 1262 round gobies from 20 river and four lake sites in three Great Lakes tributaries. Our results indicate that a combination of short-distance diffusion and long-distance dispersal, collectively referred to as 'stratified dispersal', is facilitating river colonization. Colonization proceeded upstream yearly (approximately 500 m/year; 2005-2009) in one of two temporal replicates while genetic structure was temporally stable. Contiguous dispersal from the lake was observed in all three rivers with a substantial portion of river fish (7.3%) identified as migrants. Genotype assignment indicated a separate introduction occurred upstream of the invasion front in one river. Genetic diversity was similar and relatively high among lake and recently colonized river populations, indicating that founder effects are mitigated through a dual-dispersal strategy. The remarkable success of round goby as an aquatic invader stresses the need for better diffusion models of secondary range expansion for presumably sessile invasive species.
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 | 0.000 |
| 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) | 0.000 | 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".