Summary
During over 100 years of ecological and evolutionary research, the study of colour polymorphisms has helped to improve our understanding of phenotypic diversification in nature. Polymorphisms are typically expected to be maintained within local populations by evolutionary processes, such as negative frequency-dependent selection. Yet there are only few studies that have quantified multiple axes of correlated phenotypic variation in addition to colour, which limits our current understanding of the emergence and evolutionary maintenance of genetic colour polymorphisms, for example, via correlational selection, linkage or pleiotropic effects. To address this long standing issue, I here propose a series of research projects that use computer-driven high throughput phenotyping to conduct comprehensive phenotypic analyses (phenomics) of a sexual dimorphism and female-limited colour polymorphisms in damselflies (Odonata; Zygoptera). The overarching goal of these projects is to investigate if and how much female damselfly colour morphs that mimic male colouration to increase their fitness (androchrome females) also vary in other, potentially correlated phenotypic traits, such as morphology, behavior and development. First, I will analyze an existing image data set of evolutionary time series data of Swedish damselfly populations, using a combination of classic computer vision and deep learning algorithms to quantify axes of phenotypic variation that have not yet been investigated. Second, I will conduct a mesocosm experiment using individuals from the same local Swedish damselfly populations to investigate whether androchrome females show alternative developmental trajectories. Third, I will conduct in-depth phenotyping of an existing image dataset of different damselfly species to investigate the prevalence of androchromatism and male mimicry in other traits across a large damselfly phylogeny.
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| Web resources: | https://cordis.europa.eu/project/id/898932 |
| Start date: | 01-09-2021 |
| End date: | 31-08-2023 |
| Total budget - Public funding: | 191 852,16 Euro - 191 852,00 Euro |
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Original description
During over 100 years of ecological and evolutionary research, the study of colour polymorphisms has helped to improve our understanding of phenotypic diversification in nature. Polymorphisms are typically expected to be maintained within local populations by evolutionary processes, such as negative frequency-dependent selection. Yet there are only few studies that have quantified multiple axes of correlated phenotypic variation in addition to colour, which limits our current understanding of the emergence and evolutionary maintenance of genetic colour polymorphisms, for example, via correlational selection, linkage or pleiotropic effects. To address this long standing issue, I here propose a series of research projects that use computer-driven high throughput phenotyping to conduct comprehensive phenotypic analyses (phenomics) of a sexual dimorphism and female-limited colour polymorphisms in damselflies (Odonata; Zygoptera). The overarching goal of these projects is to investigate if and how much female damselfly colour morphs that mimic male colouration to increase their fitness (androchrome females) also vary in other, potentially correlated phenotypic traits, such as morphology, behavior and development. First, I will analyze an existing image data set of evolutionary time series data of Swedish damselfly populations, using a combination of classic computer vision and deep learning algorithms to quantify axes of phenotypic variation that have not yet been investigated. Second, I will conduct a mesocosm experiment using individuals from the same local Swedish damselfly populations to investigate whether androchrome females show alternative developmental trajectories. Third, I will conduct in-depth phenotyping of an existing image dataset of different damselfly species to investigate the prevalence of androchromatism and male mimicry in other traits across a large damselfly phylogeny.Status
CLOSEDCall topic
MSCA-IF-2019Update Date
28-04-2024
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