The Promise of Genetics and Genomics for Improving Invasive Mammal Management on Islands
Bibliographic record
Abstract
Invasive species are major contributors to global biodiversity decline. Invasive mammalian species (IMS), in particular, have profound negative effects in island systems that contain disproportionally high levels of species richness and endemism. The eradication and control of IMS have become important conservation tools for managing species invasions on islands, yet these management operations are often subject to failure due to knowledge gaps surrounding species- and system-specific characteristics, including invasion pathways and contemporary migration patterns. Here, we synthesize the literature on ways in which genetic and genomic tools have effectively informed IMS management on islands, specifically associated with the development and modification of biosecurity protocols, and the design and implementation of eradication and control programs. In spite of their demonstrated utility, we then explore the challenges that are preventing genetics and genomics from being implemented more frequently in IMS management operations from both academic and non-academic perspectives, and suggest possible solutions for breaking down these barriers. Finally, we discuss the potential application of genome editing to the future management of invasive species on islands, including the current state of the field and why islands may be effective targets for this emerging technology.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".