Most countries are vulnerable to novel pest invasions and under‐report the diversity of tree pests
Bibliographic record
Abstract
Abstract Aim Tree pests have major impacts in both natural and agricultural systems. Despite their ecological and economic importance, it is likely that pests go unreported in many regions. Using a global dataset of tree pests and multiple metrics of pest diversity standardized for host availability, we describe the biogeography of under‐reporting and reveal potential for future pest invasions. Location Global. Time period Contemporary. Major taxa studied Pathogens, insect pests and native tree hosts. Methods We assembled the global distributions of 3,828 tree pests and 2,689 host tree species across 226 countries, and calculated two metrics of pest diversity that account for host availability: pest saturation (i.e., the proportion of the known pests of native host trees present with countries) and pest deficit (the number of known pests of native trees that have not yet been reported in countries). We used Bayesian regression models to identify how sampling, socio‐economic variables, climate and ecological drivers affect estimates of pest saturation and deficit. Results We show that most countries are reported to have fewer than 50% of the pests for which native hosts are available—corresponding to hundreds of additional pest species per country. Pest saturation was lowest in Africa and central Asia, while pest deficit was highest in eastern Europe. Accounting for research output, pest saturation was higher in warmer and wetter countries, while pest deficit was highest in countries with greater host phylogenetic diversity. Low saturation and high deficit even in well‐documented countries suggest a considerable potential for pest range expansion if barriers to dispersal are lowered. Main conclusions Our findings indicate that, although all countries could potentially host additional pests, countries with low research output and high host diversity should be prioritized for future pest discovery and surveillance to ensure timely detection and implementation of pest mitigation strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.004 | 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".