Adaptability in the aperiodic Drosophila populations and evolution of the life-history traits.

Exposure of diurnal animals to constant light for extended periods dampens the clock gene circadian rhythms, which in turn affect the life history traits. However, animals are expected to maintain some form of rhythm for the body to function effectively. In this study, we used three populations of <i>Drosophila melanogaster</i> that were maintained under constant light for 312 generations. We entrained three other populations (derived from those in constant light for 312 generations) to 12L:12D cycles. The adaptive evolution under aperiodic conditions has compromised on longevity and fecundity- two most important fitness traits for an iteroparous species.

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Journal
PubMed
Published
2024-12-01
DOI
https://doi.org/10.17912/micropub.biology.001436
Primary Topic
Cognitive Computing and Networks
Type
article
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article

Adaptability in the aperiodic Drosophila populations and evolution of the life-history traits.

Mallikarjun Shakarad, Khushboo Sharma, Nalini Mishra
PubMed
Cognitive Computing and Networks
article

Adaptability in the aperiodic Drosophila populations and evolution of the life-history traits.

Mallikarjun Shakarad, Khushboo Sharma, Nalini Mishra
article en

Abstract

Exposure of diurnal animals to constant light for extended periods dampens the clock gene circadian rhythms, which in turn affect the life history traits. However, animals are expected to maintain some form of rhythm for the body to function effectively. In this study, we used three populations of <i>Drosophila melanogaster</i> that were maintained under constant light for 312 generations. We entrained three other populations (derived from those in constant light for 312 generations) to 12L:12D cycles. The adaptive evolution under aperiodic conditions has compromised on longevity and fecundity- two most important fitness traits for an iteroparous species.

PubMedVol. 2025
University of Delhi (IN), Banaras Hindu University (IN)
Openalex Percentile: Top 73%
Cognitive Computing and Networks
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