AI Article Synopsis

  • - The study focuses on understanding how insect pests evolve in agricultural environments to better predict pest-related issues like outbreaks and control failures.
  • - Researchers used mitochondrial sequencing and a genotyping approach to explore genetic diversity and adaptation, revealing low genetic diversity but signs of recent population growth and initial differentiation based on host plants like soybean and cotton.
  • - Findings suggest that agricultural landscapes impact pest evolution, with specific markers linked to positive selection tied to the pests’ ability to digest different host plants.

Article Abstract

Evolutionary studies of insect pests improve our ability to anticipate problems in agricultural ecosystems, such as pest outbreaks, control failures, or expansions of the host range. Here, we investigated the mechanisms underlying the evolutionary processes behind the recent census size expansion and local adaptation of . First, we sequenced mitochondrial markers to conduct a phylogeographic investigation of historical processes. Then, we combined a de novo genotyping-by-sequencing approach with a study of agricultural landscapes to uncover recent processes of adaptation. Primarily, we found low genetic diversity across all markers and clear indications of a recent demographic expansion. We also found a lack of significant isolation by distance (IBD), and weak or absent genetic structure considering geographic locations. However, we did find initial signs of population differentiation that were associated with host plant types (i.e., soybean and cotton). Agricultural landscape attributes, including soybean crops, were significantly associated with putative markers under positive selection. Moreover, positive selection associated with host differentiation was putatively linked to digestive enzymes. This study showed how landscape composition and host plants can affect the evolutionary process of agricultural pest insects such as .

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7463353PMC
http://dx.doi.org/10.1111/eva.12966DOI Listing

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