Assessing introduction risk using species’ rank-abundance distributions
Bibliographic record
Abstract
Mixed-species assemblages are often unintentionally introduced into new ecosystems. Analysing how assemblage structure varies during transport may provide insights into how introduction risk changes before propagules are released. Characterization of introduction risk is typically based on assessments of colonization pressure (CP, the number of species transported) and total propagule pressure (total PP, the total abundance of propagules released) associated with an invasion vector. Generally, invasion potential following introduction increases with greater CP or total PP. Here, we extend these assessments using rank-abundance distributions to examine how CP : total PP relationships change temporally in ballast water of ocean-going ships. Rank-abundance distributions and CP : total PP patterns varied widely between trans-Atlantic and trans-Pacific voyages, with the latter appearing to pose a much lower risk than the former. Responses also differed by taxonomic group, with invertebrates experiencing losses mainly in total PP, while diatoms and dinoflagellates sustained losses mainly in CP. In certain cases, open-ocean ballast water exchange appeared to increase introduction risk by uptake of new species or supplementation of existing ones. Our study demonstrates that rank-abundance distributions provide new insights into the utility of CP and PP in characterizing introduction risk.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".