A highly aggregated geographical distribution of forest pest invasions in the<scp>USA</scp>
Bibliographic record
Abstract
Abstract Aim Geographical variation in numbers of established non‐native species provides clues to the underlying processes driving biological invasions. Specifically, this variation reflects landscape characteristics that drive non‐native species arrival, establishment and spread. Here, we investigate spatial variation in damaging non‐native forest insect and pathogen species to draw inferences about the dominant processes influencing their arrival, establishment and spread. Location The continental USA , including A laska ( H awaii not included). Methods We assembled the current geographical ranges (county‐level) of 79 species of damaging non‐indigenous forest insect and pathogen species currently established in the continental USA . We explored statistical associations of numbers of species per county with habitat characteristics associated with propagule pressure and with variables reflecting habitat invasibility. We also analysed relationships between the geographical area occupied by each pest species and the time since introduction and habitat characteristics. Results The geographical pattern of non‐native forest pest species richness is highly focused, with vastly more species in the north‐eastern USA . Geographical variation in species richness is associated with habitat factors related to both propagule pressure and invasibility. Ranges of the non‐native species are related to historical spread; range areas are strongly correlated with time since establishment. The average (all species) radial rate of range expansion is 5.2 km yr −1 , and surprisingly, this rate did not differ among foliage feeders, sap‐feeders, wood borers and plant pathogens. Main conclusions Forest pest species are much more concentrated in the north‐eastern region of the USA compared with other parts of the country. This pattern most likely reflects the combined effects of propagule pressure (pest arrival), habitat invasibility (pest establishment) and invasion spread. The similarity in historical spread among different types of organisms indicates the importance of anthropogenic movement in spread.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".