Data from: Global origins of invasive brown rats (Rattus norvegicus) in the Haida Gwaii archipelago
Bibliographic record
Abstract
AbstractBrown rats (Rattus norvegicus) have commensally spread from northern China and Mongolia to become among the most invasive species on the planet. Understanding the proximate source(s) of invasion can inform biosecurity plans and eradication strategies for preventing or mitigating impacts to native biodiversity. The Haida Gwaii archipelago, located off the coast of British Columbia, Canada, is a significant nesting site for 1.5 million seabirds across 12 species, half of which are now threatened by brown rats. Local knowledge points to a European origin in the late 1800’s to early 1900’s, though the true source(s) and firm date(s) of invasion remain unknown. To fill these knowledge gaps, we analyzed genotypic data (16,598 SNPs) for 280 brown rats sampled throughout Haida Gwaii relative to a published global database of potential source populations. Principle component analysis and population assignment tests supported multiple potential invasion sources from Europe and North America. Likewise, demographic modelling best supported two invasions into the islands. The first invasion likely occurred in the early 1900’s into the south-central archipelago from Western Europe followed by a more recent invasion in the early 2000’s from Vancouver, British Columbia, into northern Haida Gwaii. The northern invasion of Haida Gwaii could also be indicative of contemporary gene flow between Haida Gwaii and the mainland, representing a significant biosecurity risk. Our results will inform management strategies for invasive rats in Haida Gwaii, and serve as a guide for studies in other isolated systems worldwide.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".