The genetic signature left by the range expansion of red foxes in Australia is detectable after more than 80 years of population stability
Bibliographic record
Abstract
Abstract Reconstructing biological invasions from historical sources can provide insights into how they occur but are difficult to do when invasions are poorly documented. Genetic signatures left by invaders can also offer insights into invasion routes, points of origin and general biology but often present conclusions that are contradictory to expectations. Here, we test the ability of continental‐wide microsatellite genotype data from 29 loci and 3122 samples to reconstruct the well‐documented invasion of red foxes Vulpes vulpes from the United Kingdom into Australia over 150 years ago, an invasion that has led to the extinction of many native species. Our analysis reveals several key signals of invasion evident in Australian foxes. They display lower levels of diversity than foxes sampled from the UK, exhibit clines in diversity from the point of introduction (south‐east Australia) to the edge of their range, and show strong evidence of allele surfing in westerly and north‐easterly directions. These characteristics are consistent with a single point of origin followed by rapid expansion in westerly and north‐easterly directions as suggested by historical records. We also find little genetic structure in foxes across Australia with only the vast Nullarbor Plains and Great Victoria Desert region presenting a detectable barrier to their dispersal. As such, no mainland region within the current range of foxes can be considered genetically isolated and therefore appropriate for localised eradication efforts. Overall, our analyses demonstrate the ability of comprehensive population genetic studies to reconstruct invasion histories even after more than 80 years since colonisation was stabilised.
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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 | 0.001 | 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 | 0.000 |
| 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".