Uncovering the hidden within shipping containers: Molecular biosurveillance confirms a pathway for introducing multiple regulated and invasive species
Bibliographic record
Abstract
The negative ramifications of invasive alien species (IAS) are considered the second-most cause of biodiversity extinction and endangerment after habitat modification. IAS movements are mainly anthropogenically driven (e.g., transport of shipping containers) and require fast detection to minimize damage and cost. The present study is the first to use molecular biosurveillance of international shipping containers to detect IAS and regulated species identification in Canada. Thirty-eight samples were collected from debris (soil, stems, seeds, individual specimens) found in containers arriving in Canada. A multi-marker approach using COI, ITS, ITS2, and 16S was used to identify four main taxonomic groups: arthropods, fungi, plants, and bacteria, respectively. Eleven IAS species were identified via metabarcoding based on environmental DNA samples, including two arthropods, six fungi, two plants, and one bacteria. The origin of the eDNA detected from each species was linked to their native distribution and country of origin, except for Lymantria dispar. Additionally, four physical specimens were collected from shipping container debris and DNA barcoded, identifying three non-regulated species (two arthropods and one fungus). Altogether, these results demonstrate the importance of integrating molecular identification into current toolkits for the biosurveillance of invasive alien species. Additionally, it reaffirms international shipping containers as a pathway for multiple invasive aliens and regulated species introduction in Canada. It also highlights the need to establish regular and effective molecular biosurveillance at the Canadian border to avoid new or recurrent 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".