Environment and shipping drive environmental <scp>DNA</scp> beta‐diversity among commercial ports
Bibliographic record
Abstract
The spread of nonindigenous species by shipping is a large and growing global problem that harms coastal ecosystems and economies and may blur coastal biogeographical patterns. This study coupled eukaryotic environmental DNA (eDNA) metabarcoding with dissimilarity regression to test the hypothesis that ship-borne species spread homogenizes port communities. We first collected and metabarcoded water samples from ports in Europe, Asia, Australia and the Americas. We then calculated community dissimilarities between port pairs and tested for effects of environmental dissimilarity, biogeographical region and four alternative measures of ship-borne species transport risk. We predicted that higher shipping between ports would decrease community dissimilarity, that the effect of shipping would be small compared to that of environment dissimilarity and shared biogeography, and that more complex shipping risk metrics (which account for ballast water and stepping-stone spread) would perform better. Consistent with our hypotheses, community dissimilarities increased significantly with environmental dissimilarity and, to a lesser extent, decreased with ship-borne species transport risks, particularly if the ports had similar environments and stepping-stone risks were considered. Unexpectedly, we found no clear effect of shared biogeography, and that risk metrics incorporating estimates of ballast discharge did not offer more explanatory power than simpler traffic-based risks. Overall, we found that shipping homogenizes eukaryotic communities between ports in predictable ways, which could inform improvements in invasive species policy and management. We demonstrated the usefulness of eDNA metabarcoding and dissimilarity regression for disentangling the drivers of large-scale biodiversity patterns. We conclude by outlining logistical considerations and recommendations for future studies using this approach.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".