Molecular insights into the invasion dynamics of Carcinus crabs in South Africa
Bibliographic record
Abstract
Abstract Knowledge of the introduction history and spread dynamics of invasive species can provide important insights for management (Ens et al. in Environ Rev (in press), 2022), however such information is often unavailable for accidental introductions. Here we infer how the European shore crab, Carcinus maenas , and its congener, the Mediterranean shore crab, C. aestuarii , were introduced to and spread within South Africa. We do this using nuclear microsatellite data and Bayesian assignment tests and Approximate Bayesian Computation (ABC) modelling that included samples from the native and other invasive ranges of these two species. We also compared the genetic diversity and structure of one of the South African populations during and after intensive management, with that of another, unmanaged, population. South African populations had higher genetic diversity than invasive Carcinus populations from elsewhere in the world. Moreover, the ABC analyses suggest that South African populations originated from an admixture event between individuals of C. maenas from a population in the native range and an invasive population from Canada. We also identified instances of hybridisation between Carcinus maenas and C. aestuarii in South Africa. South African populations showed no genetic structure, suggesting either extensive migration between them or that populations arose from the same initial introduction. Management of Carcinus did not affect genetic diversity or structure, and we suspect that the management duration was insufficient to target a full generation of crabs. Together these results suggest multiple introductions and/or high propagule pressure to South Africa, crab (larval or adult) movement between existing populations, and some hybridisation. For eradication from South Africa to be achieved, management would need to concurrently target all known invasive populations and clearly establish that new introductions could be prevented.
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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".