Pest host expansion as a scale-free stepwise process across the host phylogeny
Bibliographic record
Abstract
The present and future host ranges of pests have important implications for ecology, economics and health. Most multi-host pests have phylogenetically conserved host symbioses, but jumps to phylogenetically distant hosts are surprisingly common and can have large fitness consequences. We introduce a model representing host distributions as outcomes of random jumps across a host phylogeny, with jump probabilities proportional to a power function of phylogenetic distance, parallel to Lévy flights in foraging behaviour. Using machine learning, we fit our model to empirical data on insect and pathogen pests of North American trees and estimate the parameter controlling the distribution of jump distance. We find that non-native insect pests are more likely to make jumps to phylogenetically distant hosts than native insect pests, although we did not observe a similar distinction between non-native and native pathogens. Finally, we use estimated jump parameters to describe the risk of future host expansion across the phylogeny of North American trees.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".