Potential Landscape Connectivity for Invasive Wild Pigs (<em>Sus scrofa</em>) across the Northern Prairies of North America
Bibliographic record
Abstract
Understanding landscape scale connectivity is an essential component in the management of invasive species since connectivity facilitates their invasion potential. Invasive wild pigs (Sus scrofa) are among the most prolific invaders on the planet, causing billions of dollars in agricultural and environmental damage annually. Newly introduced to Canada in the 1980s, we examined wild pig invasion potential across the northern prairies from western Canada into the currently wild pig-free northern U.S. states. We used GPS collar data collected in the Canadian prairies to quantify resource selection and incorporated results into an electric circuit theory framework to evaluate potential regional landscape connectivity. While available landcover types in this region were dominated by crops and grasslands, wild pigs were predominately located in deciduous forest, crops, and wetlands. Resource selection modelling indicated wild pigs selected deciduous forest and wetlands over other landcover types. These selection tendencies resulted in areas at greater risk of occupation in an intermixture dominated by crops interspersed with waterbodies and deciduous forest fragments, which facilitated movement. Given the pervasiveness of this intermixture across the northern prairies, there was a high potential for invasive wild pigs to move throughout much of the region with areas in southern Saskatchewan and Manitoba, northeastern Montana, North and South Dakota, and western portions of Minnesota being particularly vulnerable. Our work highlights a need for monitoring and science-based response strategies for likely southward spread of this invasive species to prevent or reduce potential crop damage, risks to native species, and disease transmission to humans, pets, livestock, and wildlife.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".