Molecular Mechanisms Underlying Social Learning in the Butterfly \u3ci\u3eBicyclus anynana\u3c/i\u3e

Abstract

Learning plays a critical role in shaping behavior, such as influencing survival and reproduction. One particularly important form of learning is mate preference learning, which allows individuals to alter their mate preference based on prior social experience. Among the different types of mate preference learning, imprinting, where early exposure to specific mating cues influences later mate choice, has been widely studied. This process is important in the context of reproductive isolation and speciation, as learned preferences can reinforce or alter eventual mate choice, potentially leading to genetic divergence between populations. Despite its importance, the molecular mechanisms underlying mate preference learning remain largely unexplored. In this dissertation, I explore the genetic and neurogenomic basis of mate preference learning using Bicyclus anynana, a butterfly species that exhibits the ability to learn mate preferences based on early social exposure. I address four key questions: (1) How does the duration of social exposure influence mate preference learning and associated gene expression? (2) What are the molecular pathways underlying positive and negative valence attribution in learned mate preferences? (3) Does the pigmentation gene yellow, known for its pleiotropic role in coloration and courtship, also modulate mate preference learning? (4) How do temporal patterns of gene expression in antennae influence female antennal receptivity? To address these questions, I used behavioral assays, transcriptomic analyses, and genome editing. This integrative approach allows me to identify candidate genes and molecular pathways involved in mate preference learning. By elucidating the genetic architecture of mate preference learning, this research advances our understanding of how learned mate preferences can contribute to reproductive isolation and speciation at a molecular level

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Last time updated on 18/07/2025

This paper was published in ScholarWorks@UARK.

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