An omics evolutionary perspective on phytophagous insect–host plant interactions in <i>Anastrepha obliqua</i>: a review
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Bibliographic record
Abstract
Abstract Phytophagous insects have a close relationship with their host plants. For this reason, their interactions can lead to important changes in insect population dynamics and evolutionary trajectories. Next generation sequencing (NGS) has provided an opportunity to analyze omics data on a large scale, facilitating the change from a classical genetics approach to a more holistic understanding of the underlying molecular mechanisms of host plant use by insects. Most studies have been carried out on model species in Holarctic and temperate zones. In tropical zones, however, the effects of use of various host plants on evolutionary insect history is less understood. In the current review, we describe how omics methodologies help us to understand phytophagous insect–host plant interactions from an evolutionary perspective, using as example the Neotropical phytophagous insect West Indian fruit fly, Anastrepha obliqua (Macquart) (Diptera: Tephritidae), an economically important fruit crop pest in the Americas. Anastrepha obliqua could adopt a generalist or a specialist lifestyle. We first review the adaptive molecular mechanisms of phytophagous insects to host plants, and then describe the main tools to study phytophagous insect–host plant interactions in the era of omics sciences. The omics approaches will advance the understanding of insect molecular mechanisms and their influence on diversification and evolution. Finally, we discuss the importance of a multidisciplinary approach that integrates the use of omics tools and other, more classical methodologies in evolutionary studies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it