Genetic diversity of <i>Colletotrichum siamense</i> causing mango anthracnose in the state of Jalisco, Mexico, based on ISSR markers
Bibliographic record
Abstract
Anthracnose caused by species of the genus Colletotrichum is a significant threat to mango production worldwide. Recently, an increase in preharvest anthracnose has been observed in commercial orchards in Jalisco, Mexico. This study aimed to identify the causal agent and assess its genetic diversity. A total of 51 mango fruits exhibiting anthracnose symptoms, including dark, hard, and dry lesions, were collected from five commercial orchards across three municipalities in Jalisco. Of 80 fungal isolates recovered, 64 were identified as Colletotrichum spp. with the phylogenetic analysis confirming these isolates as C. siamense (62 isolates) and C. asianum (2 isolates). Pathogenicity tests on mango fruits and leaves reproduced the characteristic anthracnose symptoms. To investigate intraspecific genetic diversity, 62 C. siamense isolates were analyzed using ISSR markers, revealing 93 DNA bands and 59 multilocus genotypes, with 58.87% polymorphism and an expected heterozygosity of 0.319. This is the first report of C. asianum and C. siamense causing preharvest anthracnose on mangoes in Jalisco. The findings also demonstrate substantial genetic diversity within C. siamense, which can be grouped into two distinct clusters. These results provide valuable insights for mango producers and extension services in developing targeted strategies to manage anthracnose.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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".