Many unreported crop pests and pathogens are probably already present
Bibliographic record
Abstract
Summary Biotic invasions threaten global biodiversity and ecosystem function. Such incursions present challenges to agriculture where invasive pest species cause significant production losses require major economic investment to control and can cause significant production losses. Pest Risk Analysis (PRA) is key to prioritizing agricultural biosecurity efforts, but is hampered by incomplete knowledge of current crop pest and pathogen distributions. Here we develop predictive models of current pest distributions and test these models using new observations at sub-national resolution. We apply generalized linear models (GLM) to estimate presence probabilities for 1739 crop pests in the CABI pest distribution database. We test model predictions for 100 unobserved pest occurrences in the People’s Republic of China (PRC), against observations of these pests abstracted from the Chinese literature. This resource has hitherto been omitted from databases on global pest distributions. Finally, we predict occurrences of all unobserved pests globally. Presence probability increases with host presence, presence in neighbouring regions, per capita GDP, and global prevalence. Presence probability decreases with mean distance from coast and known host number per pest. The models were good predictors of pest presence in Provinces of the PRC, with area under the ROC curve (AUC) values of 0.75 – 0.76. Large numbers of currently unobserved, but probably present pests (defined here as unreported pests with a predicted presence probability > 0.75), are predicted in China, India, southern Brazil and some countries of the former USSR. Our results shows that GLMs can predict presences of pseudo-absent pests at sub-national resolution. The Chinese scientific literature has been largely inaccessible to Western academia but contains important information that can support PRA. Prior studies have often assumed that unreported pests in a global distribution database represents a true absence. Our analysis provides a method for quantifying pseudo-absences to enable improved PRA and species distribution modelling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".