Functional eradication as a framework for invasive species control
Bibliographic record
Abstract
Invasive species continue to drive major losses in biodiversity and ecosystem function across the globe. Dealing with the effects of invasion is particularly problematic in marine and freshwater habitats, because the pace at which invaders establish often greatly outstrips the resources available for their eradication. While most managers in North America now focus on ongoing containment and suppression interventions, they often lack quantitative guidance from which to set targets and evaluate success. We propose practical guidelines for identifying management targets for invasions for which eradication is unfeasible, based on achieving “functional” eradication – defined as suppressing invader populations below levels that cause unacceptable ecological effects – within high‐priority locations. We summarize key ecological information needed to inform this strategy, including density–impact functions and recolonization rates. We illustrate the framework's application for setting local suppression targets using three globally invasive species as examples: red lionfish (Pterois spp), European green crab (Carcinus maenas), and rusty crayfish (Faxonius rusticus). Identifying targets for suppression allows managers to estimate the degree of removal required to mitigate ecological impacts and the management resources needed to achieve sufficient control of an invader.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".