Periodic Reporting for period 1 - PhenoDim (Phenomics and evolution of sexual dimorphism and female colour polymorphism in damselflies)
Okres sprawozdawczy: 2021-09-01 do 2023-08-31
What is the problem/issue being addressed?
Historically, the study of color 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 natural selection - differential survival of different genetic variants. Yet there are only a few studies that have quantified multiple axes of correlated phenotypic variation in addition to color, which limits our current understanding of the emergence and evolutionary maintenance of genetic color polymorphisms.
Why is it important for society?
Studying color variations in nature is essential because it helps us understand how different traits evolve and are preserved in populations. This knowledge isn't just for scientists; it has real-world importance. It aids in our efforts to protect and preserve biodiversity in the face of environmental challenges like climate change. It also offers insights into broader concepts of evolution, which can be useful in fields like medicine and agriculture. In simple terms, examining why animals or plants have different colors helps us grasp how life adapts to its environment and can be a valuable tool for addressing various important issues in our world.
What are the overall objectives?
The specific objectives of this project are to investigate the emergence and evolutionary maintenance of genetic color polymorphisms in nature. By quantifying multiple axes of correlated phenotypic variation, including color, the study aims to uncover the underlying mechanisms that preserve these traits within local populations. This research will contribute to a deeper understanding of how different color variations are perpetuated in the natural world, enriching our knowledge of evolution and providing insights with potential applications in fields such as biodiversity conservation and ecology.
Historically, the study of color 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 natural selection - differential survival of different genetic variants. Yet there are only a few studies that have quantified multiple axes of correlated phenotypic variation in addition to color, which limits our current understanding of the emergence and evolutionary maintenance of genetic color polymorphisms.
Why is it important for society?
Studying color variations in nature is essential because it helps us understand how different traits evolve and are preserved in populations. This knowledge isn't just for scientists; it has real-world importance. It aids in our efforts to protect and preserve biodiversity in the face of environmental challenges like climate change. It also offers insights into broader concepts of evolution, which can be useful in fields like medicine and agriculture. In simple terms, examining why animals or plants have different colors helps us grasp how life adapts to its environment and can be a valuable tool for addressing various important issues in our world.
What are the overall objectives?
The specific objectives of this project are to investigate the emergence and evolutionary maintenance of genetic color polymorphisms in nature. By quantifying multiple axes of correlated phenotypic variation, including color, the study aims to uncover the underlying mechanisms that preserve these traits within local populations. This research will contribute to a deeper understanding of how different color variations are perpetuated in the natural world, enriching our knowledge of evolution and providing insights with potential applications in fields such as biodiversity conservation and ecology.
In this project, a series of three work packages were carried out to investigate male-likeness in androchrome morphs in the damselfly species I. elegans. In WP1, the phenotypic configuration of three morphs of I. elegans was determined through the implementation of a robust CV workflow for phenotyping. This involved processing a substantial dataset of 12,500 individuals spanning six years (2015 - 2020), resulting in the extraction of trait data and comprehensive analysis.
WP2 focused on testing for environmental effects on morph development, with an emphasis on the role of predator presence and its impact on developmental trajectories. Two attempts to set up large mesocosm experiments were made in the summers of 2021 and 2022, but both of these experiments failed. To compensate for the missing deliverable, I published the computer vision package used for the phenotyping in WP1 and WP3.
Finally, WP3 aimed to investigate male-likeness across the phylogeny by determining the phenotypic configuration in other damselfly species that exhibit color polymorphisms. This involved the collection of 2200 damselflies, scanning them, customizing the CV pipeline from WP1, and conducting a comprehensive analysis to gain insights into male-likeness in related species within the local damselfly community.
WP2 focused on testing for environmental effects on morph development, with an emphasis on the role of predator presence and its impact on developmental trajectories. Two attempts to set up large mesocosm experiments were made in the summers of 2021 and 2022, but both of these experiments failed. To compensate for the missing deliverable, I published the computer vision package used for the phenotyping in WP1 and WP3.
Finally, WP3 aimed to investigate male-likeness across the phylogeny by determining the phenotypic configuration in other damselfly species that exhibit color polymorphisms. This involved the collection of 2200 damselflies, scanning them, customizing the CV pipeline from WP1, and conducting a comprehensive analysis to gain insights into male-likeness in related species within the local damselfly community.
PhenoDim is exploring questions of significant evolutionary relevance by employing phenomic techniques in the analysis of an extensively researched model organism. This methodology proves advantageous, as the resulting phenotypic data can be integrated with extensive prior research on the ecology and evolution of both the target species and its relatives, enabling interpretations within broader frameworks. This approach promises to enhance our comprehension of the evolutionary processes governing the development of sexual dimorphism and female-limited polymorphism on a macroevolutionary scale and across extended temporal horizons.
By harnessing computer vision for large-scale image analysis, PhenoDim aims to advance high-throughput phenotyping in ecological and evolutionary research. These achievements can potentially yield broader implications, not only in the scientific realm but also in terms of societal and economic impact. By enhancing our understanding of diversification patterns and processes, the project has the potential to inform conservation strategies, agricultural practices, and ecological management, thereby contributing to a more sustainable and biodiverse future. Additionally, the development of novel phenotyping methods using high-resolution images may find applications beyond the scientific community, potentially influencing various industries and technology sectors, ultimately benefiting society at large.
By harnessing computer vision for large-scale image analysis, PhenoDim aims to advance high-throughput phenotyping in ecological and evolutionary research. These achievements can potentially yield broader implications, not only in the scientific realm but also in terms of societal and economic impact. By enhancing our understanding of diversification patterns and processes, the project has the potential to inform conservation strategies, agricultural practices, and ecological management, thereby contributing to a more sustainable and biodiverse future. Additionally, the development of novel phenotyping methods using high-resolution images may find applications beyond the scientific community, potentially influencing various industries and technology sectors, ultimately benefiting society at large.