Genetic differentiation of three populations of the fall armyworm, <i>Spodoptera frugiperda</i> (Lepidoptera: Noctuidae), in Mexico
Bibliographic record
Abstract
Abstract The fall armyworm, Spodoptera frugiperda (J. E. Smith) (Lepidoptera: Noctuidae), is an important insect pest of maize and numerous other crops throughout the world. In this study, the genetic diversity and structure of three Mexican populations of S. frugiperda , collected from three maize-producing areas in the states of Sinaloa (Sf-SIN), Michoacán (Sf-MICH), and Chiapas (Sf-CHI), were evaluated using the mitochondrial cytochrome oxidase (COI) gene. Neighbor-joining analysis showed that the S. frugiperda sequences of our study were grouped, with 99 % branch support, with reference sequences from Canada, United States of America, and Mexico. Sf-SIN and Sf-CHI sequences were closely related to other Mexican reference sequences, while Sf-MICH sequences formed a well-supported separate clade. AMOVA analysis showed that most of the genetic variability was within populations. The highest correlation between genetic distance and haplotypical frequency was observed between Sf-CHI and Sf-MICH populations. Ten haplotypes were detected considering the three areas sampled and the haplotype diversity was higher in Sf-MICH and Sf-SIN populations. The haplotypic network indicates that two and one individuals from Sf-CHI and Sf-SIN populations, respectively, belonged to the same group. We concluded that the genetic diversity among S. frugiperda populations was more influenced by the variability within individuals of the same population than individuals of different populations. In addition, the presence of shared haplotypes between northwest (Sf-SIN) and southeast (Sf-CHI) individuals possibly indicate a moderate genetic exchange between populations. This diversity is essential for their ability to survive and adapt to environmental changes, which can influence how pest populations respond to control methods.
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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 | 0.001 |
| 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) | 0.000 | 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".