Relative Invasion Risk for Plankton across Marine and Freshwater Systems: Examining Efficacy of Proposed International Ballast Water Discharge Standards
Bibliographic record
Abstract
Understanding the implications of different management strategies is necessary to identify best conservation trajectories for ecosystems exposed to anthropogenic stressors. For example, science-based risk assessments at large scales are needed to understand efficacy of different vector management approaches aimed at preventing biological invasions associated with commercial shipping. We conducted a landscape-scale analysis to examine the relative invasion risk of ballast water discharges among different shipping pathways (e.g., Transoceanic, Coastal or Domestic), ecosystems (e.g., freshwater, brackish and marine), and timescales (annual and per discharge event) under current and future management regimes. The arrival and survival potential of nonindigenous species (NIS) was estimated based on directional shipping networks and their associated propagule pressure, environmental similarity between donor-recipient ecosystems (based on salinity and temperature), and effects of current and future management strategies (i.e., ballast water exchange and treatment to meet proposed international biological discharge standards). Our findings show that current requirements for ballast water exchange effectively reduce invasion risk to freshwater ecosystems but are less protective of marine ecosystems because of greater environmental mismatch between source (oceanic) and recipient (freshwater) ecoregions. Future requirements for ballast water treatment are expected to reduce risk of zooplankton NIS introductions across ecosystem types but are expected to be less effective in reducing risk of phytoplankton NIS. This large-scale risk assessment across heterogeneous ecosystems represents a major step towards understanding the likelihood of invasion in relation to shipping networks, the relative efficacy of different invasion management regimes and seizing opportunities to reduce the ecological and economic implications of biological invasions.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".