Defining a species in fungal plant pathology: beyond the species level
Bibliographic record
Abstract
In plant pathology, the correct naming of a species is essential for determining the causal agents of disease. Species names not only serve the general purpose of concise communication, but also are critical for effective plant quarantine, preventing the introduction of new pathogens into a territory. Many phytopathogenic genera have multiple species and, in several genera, disagreements between the multiple prevailing species concept definitions result in numerous cryptic species. Some of these species were previously called by various names; forma speciales (specialised forms), subspecies, or pathotypes. However, based on new molecular evidence they are being assigned into new species. The frequent name changes and lack of consistent criteria to delineate cryptic species, species, subspecies, forms, and races create increasing confusion, often making communication among biologists arduous. Furthermore, such ambiguous information can convey misleading evolutionary concepts and species boundaries. The aim of this paper is to review these concepts, clarify their use, and evaluate them by referring to existing examples. We specifically address the question, “Do plant pathogens require a different ranking system?” We conclude that it is necessary to identify phytopathogens to species level based on data from multiple approaches. Furthermore, this identification must go beyond species level to clearly classify hitherto known subspecies, forms and races. In addition, when naming phytopathogenic genera, plant pathologists should provide more information about geographic locations and host ranges as well as host specificities for individual species, cryptic species, forms or races. When describing a new phytopathogen, we suggest that authors provide at least three representative strains together with pathogenicity test results. If Koch’s postulates cannot be fulfilled, it is necessary to provide complementary data such as associated disease severity on the host plant. Moreover, more sequenced collections of species causing diseases should be published in order to stabilise the boundaries of cryptic species, species, subspecies, forms, and races.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.007 | 0.024 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".