Using indicators to assess the status of biological invasions and their management on islands─the Prince Edward Islands, South Africa as an example
Bibliographic record
Abstract
Abstract Addressing the challenge biological invasions pose to island biodiversity is pivotal to achieving Target 6 of the Kunming–Montreal Global Biodiversity Framework. Using a suite of 24 indicators, we evaluated the current status of biological invasions and their management on the Prince Edward Islands, South Africa’s sub-Antarctic territories, and provide recommendations for management. There are 45 established alien taxa on Marion Island, of which 25 are invasive, and nine invasive taxa on the less frequently visited Prince Edward Island. However, despite stringent biosecurity, new alien taxa continue to arrive, potentially through ten introduction pathways, but particularly as contaminants on goods and stowaways on transport vectors. Not all detected taxa have been systematically recorded or identified—identifying incursions to species level may help pinpoint gaps in biosecurity. Three invasive plant species have caused Major environmental impacts (as per the Environmental Impact Classification for Alien Taxa categories), and Massive impacts have been recorded for the house mouse. An ambitious plan to eradicate the house mouse is being developed. A further eight taxa are controlled and four monitored to determine whether they have been eradicated. We argue that systematically tracking and documenting biological invasions is vital to improve the appropriateness, adaptability, and responsiveness of management; and we recommend a dedicated, integrated reporting process involving all stakeholders. Such monitoring is particularly important for remote sites given competing demands to reduce the human footprint, manage biological invasions, and allow access. This article is part of the theme issue ‘Managing biological invasions in protected areas: moving towards the new Global Biodiversity Framework targets’.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".