Minimizing invasion risk by reducing propagule pressure: a model for ballast-water exchange
Bibliographic record
Abstract
Biological invasions are a major and increasing agent of global biodiversity change. Theory and practice indicate that invasion risk can be diminished by reducing propagule pressure, or the quantity, quality, and frequency of introduced individuals. For aquatic invasions, the primary global invasion pathway is ballast-water transport, and the primary risk reduction strategy is currently open-ocean exchange. Exchange was developed with shipping between freshwater ports in mind, but the majority of shipping connects brackish and marine ports. A worldwide convention, adopted in 2004 by the International Maritime Organization, now mandates ballast-water exchange (or equivalent management) for its 164 member states. Will exchange be as effective in reducing invasion risk for euryhaline species (those capable of tolerating a wide range of salinity levels) in salt-water ports? Here we develop a simple mathematical framework for optimizing ballast-water exchange in terms of exchange level, timing, and species salinity tolerance. Our model shows that when species survival is worse in the post-exchange than in the pre-exchange water, exchange is always effective. However, when survival is equal or better following exchange, a critical level and timing are required for effective exchange. We illustrate the model's applications with a variety of introduced marine and estuarine organisms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".