Non‐native tree pests have a broader host range than native pests and differentially impact host lineages
Bibliographic record
Abstract
Abstract Non‐native pests in North America are a growing threat to native trees and forest communities. Theory suggests that a less specialised niche, as reflected by a pest's broad host range, may facilitate successful pest invasions. Incorporating a pest's host range characteristics along with life‐history traits may thus help us better predict future invasions and impacts. We quantified the host range of North American tree pests using indices of host taxonomic richness and the standard effect size of the mean pairwise distance between hosts, then compared the host ranges of non‐native and native pests. We next examined whether non‐native and native pests tended to be associated with phylogenetically distinct sets of host tree lineages. Finally, we evaluated whether pest impacts were associated with pest host ranges, nativity or pest type (i.e. insect vs. pathogen). We found that the set of tree hosts impacted by non‐native pests was phylogenetically distinct from hosts of native pests, and that non‐native insect pests had significantly broader host ranges than native insect pests, perhaps due to an innate ability to associate with diverse tree species. We also found that pathogens generally had broader host ranges than insect pests—possibly a result of more frequent host range expansion. While severity was not associated with pest nativity per se, non‐native pathogens had the greatest impact on their hosts across all pest subsets. Synthesis . We identified important differences in the host ranges of native and non‐native pests, with non‐native pests associated with a broader range of hosts, and this difference was most evident in comparisons between insect pests. We suggest a broad host range might facilitate non‐native pest establishment. We also highlight the need for further research on pest life histories and impacts to better understand the consequences of pest invasions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".