An annotated registry of established forest pathogens in the continental USA, Canada, and Hawaiian Islands
Bibliographic record
Abstract
General understanding of disease epiphytotics caused by introduced agents (pathogens) in forest systems, as well as the ability to assess risk of future introductions within a process-barrier framework of biological invasion, are impeded by a lack of systematically compiled information on origins and functional traits of pathogens that have become established outside their range. To address these gaps, substantially update previous registries, and provide critical biosecurity information, we assembled a list of established forest phytopathogens that are present, but not thought to be native in the continental United States (CONUS), Canada, and the Hawaiian islands under working hypotheses of their origins. We restricted the present list to phytopathogens that cause disease on native tree or woody shrub species, excluding the larger number of species of non-native phytopathogens of trees that are exclusive to agricultural and horticultural species and landscapes which are already well-represented in pest databases (e.g., CABI, EPPO, APHIS, etc.). We used previous databases as a scaffold and supplemented those lists with additional taxa by cross-referencing with lists of forest pathogens and hypothetical origins for other regions (Australia and Europe) as well as by reviewing taxonomic, host, and distribution history of pathogen species that have been recorded in both the study area (CONUS-Canada-Hawaii) and at least one other continent. For each of the 93 species in our database, we provide a relational database of a) taxonomic information, b) invasion status in each region, c) first year on record in each region, d) working hypotheses of original range (where possible), e) traits including disease type and name, dispersal mode, and organs and host life stages infected, f) major hosts, g) and > 7,000 chronological records of potential location-year and host-location-year combinations for each pathogen. We also provide a reference-annotated classification system for types of evidence for first years (c), original ranges (d), and major hosts (f). This represents a significant expansion of our knowledge of non-native infectious microorganisms of forest trees compared to previous registries, particularly in terms of its comprehensiveness, precision, and accuracy of information that includes a way to assess and compare the level of uncertainty associated with key information.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.020 | 0.029 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".