Worldwide border interceptions provide a window into human‐mediated global insect movement
Bibliographic record
Abstract
As part of national biosecurity programs, cargo imports, passenger baggage, and international mail are inspected at ports of entry to verify compliance with phytosanitary regulations and to intercept potentially damaging nonnative species to prevent their introduction. Detection of organisms during inspections may also provide crucial information about the species composition and relative arrival rates in invasion pathways that can inform the implementation of other biosecurity practices such as quarantines and surveillance. In most regions, insects are the main taxonomic group encountered during inspections. We gathered insect interception data from nine world regions collected from 1995 to 2019 to compare the composition of species arriving at ports in these regions. Collectively, 8,716 insect species were intercepted in these regions over the last 25 yr, with the combined international data set comprising 1,899,573 interception events, of which 863,972 were identified to species level. Rarefaction analysis indicated that interceptions comprise only a small fraction of species present in invasion pathways. Despite differences in inspection methodologies, as well as differences in the composition of import source regions and imported commodities, we found strong positive correlations in species interception frequencies between regions, particularly within the Hemiptera and Thysanoptera. There were also significant differences in species frequencies among insects intercepted in different regions. Nevertheless, integrating interception data among multiple regions would be valuable for estimating invasion risks for insect species with high likelihoods of introduction as well as for identifying rare but potentially damaging species.
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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 | 0.002 | 0.002 |
| 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) | 0.003 | 0.001 |
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".