Population genomics of Sitka black-tailed deer supports invasive species management and ecological restoration on islands
Bibliographic record
Abstract
Abstract Invasive mammals represent a critical threat to island biodiversity; eradications can result in ecological restoration yet may fail in the absence of key population parameters. Over-browsing by invasive Sitka black-tailed deer (Odocoileus hemionus sitkensis) is causing severe ecological and cultural impacts across the Haida Gwaii archipelago (Canada). Previous eradication attempts demonstrate forest regeneration upon deer removal, but reinvasion reverses conservation gains. Here we use restriction-site associated DNA sequencing (12,947 SNPs) to investigate connectivity and gene flow of invasive deer (n = 181) across 15 islands, revealing little structure throughout Haida Gwaii and identifying the large, central island of Moresby (>2600 km2) as the greatest source of migrants. As a result, the archipelago itself should be considered the primary eradication unit, with the exception of geographically isolated islands like SGang Gwaay. Thus, limiting eradications to isolated islands combined with controlled culling and enhanced biosecurity may be the most effective strategies for achieving ecological restoration goals.
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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 | 0.000 | 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".