P. Baker
- Courses3
- Reviews3
- School: University of Miami
- Campus:
- Department: Sociology
- Email address: Join to see
- Phone: Join to see
-
Location:
1320 S Dixie Hwy
Coral Gables, FL - 33146 - Dates at University of Miami: November 2008 - March 2009
- Office Hours: Join to see
Biography
University of Miami - Sociology
Assistant Professor at Miami University
Higher Education
Robert
Baker
Laramie, Wyoming
I am broadly interested in understanding the adaptation and evolution of genomes, phenotypes, and species as it pertains to intraspecific evolution and crop domestication/improvement. My specific research interests lie at the interface of development, evolution, and ecology. I use comparative and experimental approaches to investigate the evolution of development (evo-devo) in growth chamber, greenhouse, and field studies. A strong foundation in plant developmental morphology and careful phenotyping at the anatomic through organismal levels anchors my work at the gene, genome, and transcriptome levels.
Experience
Miami University
Assistant Professor
P. worked at Miami University as a Assistant Professor
University of Wyoming
Postdoctoral Research Associate
Agro-ecological annotation of gene function and computational analysis of developmental gene networks
University of Wyoming
NSF Postdoctoral Fellow
Genome wide associations between anatomy, crop-specific resource allocation strategies, and water use efficiency
Harvard University
Research Associate
Quantitative expression analyses of candidate genes associated with branch outgrowth
University of Colorado Boulder
Research Assistant
Website design and maintenance, grant disbursements, conference organization for microMORPH: Microevolutionary Molecular and Organismic Research in Plant History, an NSF Research Coordination Network
University of Colorado Boulder
Research Associate
The evolution and development of apomixes in Erigeron annuus
University of Colorado Boulder
Research Assistant
Website design and maintenance, grant disbursements, conference organization for MORPH: Molecular and Organismic Research in Plant History, an NSF Research Coordination Network
Education
University of Colorado
Doctor of Philosophy (PhD)
Ecology and Evolutionary Biology
Reed College
Bachelor of Arts (BA)
Biology, General
Publications
Polyploidy and the relationship between leaf structure and function: implications for correlated evolution of anatomy, morphology, and physiology in Brassica
BMC Plant Biology
Polyploidy is well studied from a genetic and genomic perspective, but the morphological, anatomical, and physiological consequences of polyploidy remain relatively uncharacterized. Whether these potential changes bear on functional integration or are idiosyncratic remains an open question. Among six Brassica species, we found significant effects of species and ploidy level for morphological, anatomical, and physiological traits. We identified three suites of intercorrelated traits in both diploid parents and allotetraploids: Morphological traits (such as leaf area and perimeter) anatomic traits (including ab- and ad-axial epidermis) and aspects of physiology. Of particular note, there were no significant correlations between morphological structure and physiological function in the diploid parents. Increased phenotypic integration in the allotetraploid hybrids may be due, in part, to increased trait ranges or simply different structure-function relationships. The trait correlations that disappear after hybridization as well as the novel trait correlations observed in allotetraploid hybrids may represent relatively evolutionarily labile associations and therefore could be ideal targets for artificial selection and crop improvement.
Polyploidy and the relationship between leaf structure and function: implications for correlated evolution of anatomy, morphology, and physiology in Brassica
BMC Plant Biology
Polyploidy is well studied from a genetic and genomic perspective, but the morphological, anatomical, and physiological consequences of polyploidy remain relatively uncharacterized. Whether these potential changes bear on functional integration or are idiosyncratic remains an open question. Among six Brassica species, we found significant effects of species and ploidy level for morphological, anatomical, and physiological traits. We identified three suites of intercorrelated traits in both diploid parents and allotetraploids: Morphological traits (such as leaf area and perimeter) anatomic traits (including ab- and ad-axial epidermis) and aspects of physiology. Of particular note, there were no significant correlations between morphological structure and physiological function in the diploid parents. Increased phenotypic integration in the allotetraploid hybrids may be due, in part, to increased trait ranges or simply different structure-function relationships. The trait correlations that disappear after hybridization as well as the novel trait correlations observed in allotetraploid hybrids may represent relatively evolutionarily labile associations and therefore could be ideal targets for artificial selection and crop improvement.
Quantifying time-series of leaf morphology using 2D and 3D photogrammetry methods for high-throughput plant phenotyping
Computers and Electronics in Agriculture
Conventional phenotyping methods impose a significant bottleneck to the characterization of genotypic and environmental effects on trait expression in plants. In particular, invasive and destructive sampling methods along with manual measurements widely used in conventional studies are labor-intensive, time-consuming, costly, and can lack consistency. These experimental features impede large-scale genetic studies of both crops and wild plant species. Here, we present a high-throughput phenotyping pipeline using photogrammetry and 3D modeling techniques in the model species, Arabidopsis thaliana. We develop novel photogrammetry and computer vision algorithms to quantify 2D and 3D leaf areas for a mapping population of 1050 Arabidopsis thaliana lines, and use 2D areas to analyze plant nastic movements and diurnal cycles. Compared to the 2D leaf areas, 3D leaf areas show an uncorrupted growth trend regardless of plant nastic movement. With optimized algorithms, our pipeline throughput is very computationally efficient for screening a large number of plants. The pipeline not only supports measurement of organ-level growth and development over time, but also enables analysis of whole-plant phenotypes and, thus, identification of genotype-specific performance. Further, the accuracy results evaluating the relationship between physical dimensions and 3D measurements indicate an R2 = 0.99, and the average 3D area processing time per plant is 0.02 s. Our algorithms provide both high accuracy and throughput in plant phenotyping, thereby, enabling progress in plant genotypic modeling.
Polyploidy and the relationship between leaf structure and function: implications for correlated evolution of anatomy, morphology, and physiology in Brassica
BMC Plant Biology
Polyploidy is well studied from a genetic and genomic perspective, but the morphological, anatomical, and physiological consequences of polyploidy remain relatively uncharacterized. Whether these potential changes bear on functional integration or are idiosyncratic remains an open question. Among six Brassica species, we found significant effects of species and ploidy level for morphological, anatomical, and physiological traits. We identified three suites of intercorrelated traits in both diploid parents and allotetraploids: Morphological traits (such as leaf area and perimeter) anatomic traits (including ab- and ad-axial epidermis) and aspects of physiology. Of particular note, there were no significant correlations between morphological structure and physiological function in the diploid parents. Increased phenotypic integration in the allotetraploid hybrids may be due, in part, to increased trait ranges or simply different structure-function relationships. The trait correlations that disappear after hybridization as well as the novel trait correlations observed in allotetraploid hybrids may represent relatively evolutionarily labile associations and therefore could be ideal targets for artificial selection and crop improvement.
Quantifying time-series of leaf morphology using 2D and 3D photogrammetry methods for high-throughput plant phenotyping
Computers and Electronics in Agriculture
Conventional phenotyping methods impose a significant bottleneck to the characterization of genotypic and environmental effects on trait expression in plants. In particular, invasive and destructive sampling methods along with manual measurements widely used in conventional studies are labor-intensive, time-consuming, costly, and can lack consistency. These experimental features impede large-scale genetic studies of both crops and wild plant species. Here, we present a high-throughput phenotyping pipeline using photogrammetry and 3D modeling techniques in the model species, Arabidopsis thaliana. We develop novel photogrammetry and computer vision algorithms to quantify 2D and 3D leaf areas for a mapping population of 1050 Arabidopsis thaliana lines, and use 2D areas to analyze plant nastic movements and diurnal cycles. Compared to the 2D leaf areas, 3D leaf areas show an uncorrupted growth trend regardless of plant nastic movement. With optimized algorithms, our pipeline throughput is very computationally efficient for screening a large number of plants. The pipeline not only supports measurement of organ-level growth and development over time, but also enables analysis of whole-plant phenotypes and, thus, identification of genotype-specific performance. Further, the accuracy results evaluating the relationship between physical dimensions and 3D measurements indicate an R2 = 0.99, and the average 3D area processing time per plant is 0.02 s. Our algorithms provide both high accuracy and throughput in plant phenotyping, thereby, enabling progress in plant genotypic modeling.
Mapping and predicting non-linear Brassica rapa growth phenotypes based on Bayesian and frequentist trait estimation
G3: Genes Genomes Genetics
Predicting phenotypes based on genotypes and understanding the effects of complex multi-locus traits on plant performance requires a description of the underlying developmental processes, growth trajectories, and their genomic architecture. Using data from Brassica rapa genotypes grown in multiple density settings and seasons, we applied a hierarchical Bayesian Function-Valued Trait (FVT) approach to fit logistic growth curves to leaf phenotypic data (length and width) and characterize leaf development. We found evidence of genetic variation in phenotypic plasticity of rate and duration of leaf growth to growing season. In contrast, the magnitude of the plastic response for maximum leaf size was relatively small, suggesting that growth dynamics vs. final leaf sizes have distinct patterns of environmental sensitivity. Consistent with patterns of phenotypic plasticity, several QTL-by-year interactions were significant for parameters describing leaf growth rates and durations but not leaf size. In comparison to frequentist approaches for estimating leaf FVT, Bayesian trait estimation resulted in more mapped QTL that tended to have greater average LOD scores and to explain a greater proportion of trait variance. We then constructed QTL-based predictive models for leaf growth rate and final size based on data from one treatment (uncrowded plants in one growing season). Models predicted non-linear developmental phenotypes for genotypes not used in model construction and, due to a lack of QTL × environment interactions, predicted phenotypes across sites differing in plant density.
Polyploidy and the relationship between leaf structure and function: implications for correlated evolution of anatomy, morphology, and physiology in Brassica
BMC Plant Biology
Polyploidy is well studied from a genetic and genomic perspective, but the morphological, anatomical, and physiological consequences of polyploidy remain relatively uncharacterized. Whether these potential changes bear on functional integration or are idiosyncratic remains an open question. Among six Brassica species, we found significant effects of species and ploidy level for morphological, anatomical, and physiological traits. We identified three suites of intercorrelated traits in both diploid parents and allotetraploids: Morphological traits (such as leaf area and perimeter) anatomic traits (including ab- and ad-axial epidermis) and aspects of physiology. Of particular note, there were no significant correlations between morphological structure and physiological function in the diploid parents. Increased phenotypic integration in the allotetraploid hybrids may be due, in part, to increased trait ranges or simply different structure-function relationships. The trait correlations that disappear after hybridization as well as the novel trait correlations observed in allotetraploid hybrids may represent relatively evolutionarily labile associations and therefore could be ideal targets for artificial selection and crop improvement.
Quantifying time-series of leaf morphology using 2D and 3D photogrammetry methods for high-throughput plant phenotyping
Computers and Electronics in Agriculture
Conventional phenotyping methods impose a significant bottleneck to the characterization of genotypic and environmental effects on trait expression in plants. In particular, invasive and destructive sampling methods along with manual measurements widely used in conventional studies are labor-intensive, time-consuming, costly, and can lack consistency. These experimental features impede large-scale genetic studies of both crops and wild plant species. Here, we present a high-throughput phenotyping pipeline using photogrammetry and 3D modeling techniques in the model species, Arabidopsis thaliana. We develop novel photogrammetry and computer vision algorithms to quantify 2D and 3D leaf areas for a mapping population of 1050 Arabidopsis thaliana lines, and use 2D areas to analyze plant nastic movements and diurnal cycles. Compared to the 2D leaf areas, 3D leaf areas show an uncorrupted growth trend regardless of plant nastic movement. With optimized algorithms, our pipeline throughput is very computationally efficient for screening a large number of plants. The pipeline not only supports measurement of organ-level growth and development over time, but also enables analysis of whole-plant phenotypes and, thus, identification of genotype-specific performance. Further, the accuracy results evaluating the relationship between physical dimensions and 3D measurements indicate an R2 = 0.99, and the average 3D area processing time per plant is 0.02 s. Our algorithms provide both high accuracy and throughput in plant phenotyping, thereby, enabling progress in plant genotypic modeling.
Mapping and predicting non-linear Brassica rapa growth phenotypes based on Bayesian and frequentist trait estimation
G3: Genes Genomes Genetics
Predicting phenotypes based on genotypes and understanding the effects of complex multi-locus traits on plant performance requires a description of the underlying developmental processes, growth trajectories, and their genomic architecture. Using data from Brassica rapa genotypes grown in multiple density settings and seasons, we applied a hierarchical Bayesian Function-Valued Trait (FVT) approach to fit logistic growth curves to leaf phenotypic data (length and width) and characterize leaf development. We found evidence of genetic variation in phenotypic plasticity of rate and duration of leaf growth to growing season. In contrast, the magnitude of the plastic response for maximum leaf size was relatively small, suggesting that growth dynamics vs. final leaf sizes have distinct patterns of environmental sensitivity. Consistent with patterns of phenotypic plasticity, several QTL-by-year interactions were significant for parameters describing leaf growth rates and durations but not leaf size. In comparison to frequentist approaches for estimating leaf FVT, Bayesian trait estimation resulted in more mapped QTL that tended to have greater average LOD scores and to explain a greater proportion of trait variance. We then constructed QTL-based predictive models for leaf growth rate and final size based on data from one treatment (uncrowded plants in one growing season). Models predicted non-linear developmental phenotypes for genotypes not used in model construction and, due to a lack of QTL × environment interactions, predicted phenotypes across sites differing in plant density.
Plant high-throughput phenotyping using photogrammetry and imaging techniques to measure leaf length and rosette area
Computers and Electronics in Agriculture
Plant phenotyping is central to understand causal effects of genotypes and environments on trait expression and is a critical factor in expediting plant breeding. Previously, plant phenotypic traits were quantified using invasive, time-consuming, labor-intensive, cost-inefficient, and often destructive manual sampling methods that were also prone to observer error. In this study, we introduce an automated high-throughput phenotyping pipeline using affordable imaging systems and image processing algorithms to build 2D mosaicked orthophotos. Chamber-based and ground-level field implementations are used to measure phenotypic traits such as leaf length and rosette area in 2D images. Our automated pipeline has cross-platform capabilities and a degree of instrument independence, making it suitable for various situations.
Polyploidy and the relationship between leaf structure and function: implications for correlated evolution of anatomy, morphology, and physiology in Brassica
BMC Plant Biology
Polyploidy is well studied from a genetic and genomic perspective, but the morphological, anatomical, and physiological consequences of polyploidy remain relatively uncharacterized. Whether these potential changes bear on functional integration or are idiosyncratic remains an open question. Among six Brassica species, we found significant effects of species and ploidy level for morphological, anatomical, and physiological traits. We identified three suites of intercorrelated traits in both diploid parents and allotetraploids: Morphological traits (such as leaf area and perimeter) anatomic traits (including ab- and ad-axial epidermis) and aspects of physiology. Of particular note, there were no significant correlations between morphological structure and physiological function in the diploid parents. Increased phenotypic integration in the allotetraploid hybrids may be due, in part, to increased trait ranges or simply different structure-function relationships. The trait correlations that disappear after hybridization as well as the novel trait correlations observed in allotetraploid hybrids may represent relatively evolutionarily labile associations and therefore could be ideal targets for artificial selection and crop improvement.
Quantifying time-series of leaf morphology using 2D and 3D photogrammetry methods for high-throughput plant phenotyping
Computers and Electronics in Agriculture
Conventional phenotyping methods impose a significant bottleneck to the characterization of genotypic and environmental effects on trait expression in plants. In particular, invasive and destructive sampling methods along with manual measurements widely used in conventional studies are labor-intensive, time-consuming, costly, and can lack consistency. These experimental features impede large-scale genetic studies of both crops and wild plant species. Here, we present a high-throughput phenotyping pipeline using photogrammetry and 3D modeling techniques in the model species, Arabidopsis thaliana. We develop novel photogrammetry and computer vision algorithms to quantify 2D and 3D leaf areas for a mapping population of 1050 Arabidopsis thaliana lines, and use 2D areas to analyze plant nastic movements and diurnal cycles. Compared to the 2D leaf areas, 3D leaf areas show an uncorrupted growth trend regardless of plant nastic movement. With optimized algorithms, our pipeline throughput is very computationally efficient for screening a large number of plants. The pipeline not only supports measurement of organ-level growth and development over time, but also enables analysis of whole-plant phenotypes and, thus, identification of genotype-specific performance. Further, the accuracy results evaluating the relationship between physical dimensions and 3D measurements indicate an R2 = 0.99, and the average 3D area processing time per plant is 0.02 s. Our algorithms provide both high accuracy and throughput in plant phenotyping, thereby, enabling progress in plant genotypic modeling.
Mapping and predicting non-linear Brassica rapa growth phenotypes based on Bayesian and frequentist trait estimation
G3: Genes Genomes Genetics
Predicting phenotypes based on genotypes and understanding the effects of complex multi-locus traits on plant performance requires a description of the underlying developmental processes, growth trajectories, and their genomic architecture. Using data from Brassica rapa genotypes grown in multiple density settings and seasons, we applied a hierarchical Bayesian Function-Valued Trait (FVT) approach to fit logistic growth curves to leaf phenotypic data (length and width) and characterize leaf development. We found evidence of genetic variation in phenotypic plasticity of rate and duration of leaf growth to growing season. In contrast, the magnitude of the plastic response for maximum leaf size was relatively small, suggesting that growth dynamics vs. final leaf sizes have distinct patterns of environmental sensitivity. Consistent with patterns of phenotypic plasticity, several QTL-by-year interactions were significant for parameters describing leaf growth rates and durations but not leaf size. In comparison to frequentist approaches for estimating leaf FVT, Bayesian trait estimation resulted in more mapped QTL that tended to have greater average LOD scores and to explain a greater proportion of trait variance. We then constructed QTL-based predictive models for leaf growth rate and final size based on data from one treatment (uncrowded plants in one growing season). Models predicted non-linear developmental phenotypes for genotypes not used in model construction and, due to a lack of QTL × environment interactions, predicted phenotypes across sites differing in plant density.
Plant high-throughput phenotyping using photogrammetry and imaging techniques to measure leaf length and rosette area
Computers and Electronics in Agriculture
Plant phenotyping is central to understand causal effects of genotypes and environments on trait expression and is a critical factor in expediting plant breeding. Previously, plant phenotypic traits were quantified using invasive, time-consuming, labor-intensive, cost-inefficient, and often destructive manual sampling methods that were also prone to observer error. In this study, we introduce an automated high-throughput phenotyping pipeline using affordable imaging systems and image processing algorithms to build 2D mosaicked orthophotos. Chamber-based and ground-level field implementations are used to measure phenotypic traits such as leaf length and rosette area in 2D images. Our automated pipeline has cross-platform capabilities and a degree of instrument independence, making it suitable for various situations.
Modeling development and quantitative trait mapping reveal independent genetic modules for leaf size and shape
New Phytologist
We found genetic trade-offs between leaf size and growth rate Function Value Traits (FVT) and uncovered differences in genotypic and QTL correlations involving FVT vs Single-Time-Points (STPs). We identified leaf shape (allometry) as a genetic module independent of length and width and identified selection on FVT parameters of development.
Polyploidy and the relationship between leaf structure and function: implications for correlated evolution of anatomy, morphology, and physiology in Brassica
BMC Plant Biology
Polyploidy is well studied from a genetic and genomic perspective, but the morphological, anatomical, and physiological consequences of polyploidy remain relatively uncharacterized. Whether these potential changes bear on functional integration or are idiosyncratic remains an open question. Among six Brassica species, we found significant effects of species and ploidy level for morphological, anatomical, and physiological traits. We identified three suites of intercorrelated traits in both diploid parents and allotetraploids: Morphological traits (such as leaf area and perimeter) anatomic traits (including ab- and ad-axial epidermis) and aspects of physiology. Of particular note, there were no significant correlations between morphological structure and physiological function in the diploid parents. Increased phenotypic integration in the allotetraploid hybrids may be due, in part, to increased trait ranges or simply different structure-function relationships. The trait correlations that disappear after hybridization as well as the novel trait correlations observed in allotetraploid hybrids may represent relatively evolutionarily labile associations and therefore could be ideal targets for artificial selection and crop improvement.
Quantifying time-series of leaf morphology using 2D and 3D photogrammetry methods for high-throughput plant phenotyping
Computers and Electronics in Agriculture
Conventional phenotyping methods impose a significant bottleneck to the characterization of genotypic and environmental effects on trait expression in plants. In particular, invasive and destructive sampling methods along with manual measurements widely used in conventional studies are labor-intensive, time-consuming, costly, and can lack consistency. These experimental features impede large-scale genetic studies of both crops and wild plant species. Here, we present a high-throughput phenotyping pipeline using photogrammetry and 3D modeling techniques in the model species, Arabidopsis thaliana. We develop novel photogrammetry and computer vision algorithms to quantify 2D and 3D leaf areas for a mapping population of 1050 Arabidopsis thaliana lines, and use 2D areas to analyze plant nastic movements and diurnal cycles. Compared to the 2D leaf areas, 3D leaf areas show an uncorrupted growth trend regardless of plant nastic movement. With optimized algorithms, our pipeline throughput is very computationally efficient for screening a large number of plants. The pipeline not only supports measurement of organ-level growth and development over time, but also enables analysis of whole-plant phenotypes and, thus, identification of genotype-specific performance. Further, the accuracy results evaluating the relationship between physical dimensions and 3D measurements indicate an R2 = 0.99, and the average 3D area processing time per plant is 0.02 s. Our algorithms provide both high accuracy and throughput in plant phenotyping, thereby, enabling progress in plant genotypic modeling.
Mapping and predicting non-linear Brassica rapa growth phenotypes based on Bayesian and frequentist trait estimation
G3: Genes Genomes Genetics
Predicting phenotypes based on genotypes and understanding the effects of complex multi-locus traits on plant performance requires a description of the underlying developmental processes, growth trajectories, and their genomic architecture. Using data from Brassica rapa genotypes grown in multiple density settings and seasons, we applied a hierarchical Bayesian Function-Valued Trait (FVT) approach to fit logistic growth curves to leaf phenotypic data (length and width) and characterize leaf development. We found evidence of genetic variation in phenotypic plasticity of rate and duration of leaf growth to growing season. In contrast, the magnitude of the plastic response for maximum leaf size was relatively small, suggesting that growth dynamics vs. final leaf sizes have distinct patterns of environmental sensitivity. Consistent with patterns of phenotypic plasticity, several QTL-by-year interactions were significant for parameters describing leaf growth rates and durations but not leaf size. In comparison to frequentist approaches for estimating leaf FVT, Bayesian trait estimation resulted in more mapped QTL that tended to have greater average LOD scores and to explain a greater proportion of trait variance. We then constructed QTL-based predictive models for leaf growth rate and final size based on data from one treatment (uncrowded plants in one growing season). Models predicted non-linear developmental phenotypes for genotypes not used in model construction and, due to a lack of QTL × environment interactions, predicted phenotypes across sites differing in plant density.
Plant high-throughput phenotyping using photogrammetry and imaging techniques to measure leaf length and rosette area
Computers and Electronics in Agriculture
Plant phenotyping is central to understand causal effects of genotypes and environments on trait expression and is a critical factor in expediting plant breeding. Previously, plant phenotypic traits were quantified using invasive, time-consuming, labor-intensive, cost-inefficient, and often destructive manual sampling methods that were also prone to observer error. In this study, we introduce an automated high-throughput phenotyping pipeline using affordable imaging systems and image processing algorithms to build 2D mosaicked orthophotos. Chamber-based and ground-level field implementations are used to measure phenotypic traits such as leaf length and rosette area in 2D images. Our automated pipeline has cross-platform capabilities and a degree of instrument independence, making it suitable for various situations.
Modeling development and quantitative trait mapping reveal independent genetic modules for leaf size and shape
New Phytologist
We found genetic trade-offs between leaf size and growth rate Function Value Traits (FVT) and uncovered differences in genotypic and QTL correlations involving FVT vs Single-Time-Points (STPs). We identified leaf shape (allometry) as a genetic module independent of length and width and identified selection on FVT parameters of development.
Patterns of shoot architecture in locally adapted populations are linked to intraspecific differences in gene regulation
New Phytologist
Shoot architecture, including the number and location of branches, is a crucial aspect of plant function, morphological diversification, life history evolution, and crop domestication. We identify axillary meristem outgrowth as a primary driver of divergent branch number and life histories in two locally adapted populations of the monkeyflower, Mimulus guttatus. Furthermore, we show that MORE AXILLARY GROWTH (MAX) gene expression strongly correlates with natural variation in branch outgrowth in this species, linking modification of the MAX-dependent pathway to the evolutionary diversification of shoot architecture.
Polyploidy and the relationship between leaf structure and function: implications for correlated evolution of anatomy, morphology, and physiology in Brassica
BMC Plant Biology
Polyploidy is well studied from a genetic and genomic perspective, but the morphological, anatomical, and physiological consequences of polyploidy remain relatively uncharacterized. Whether these potential changes bear on functional integration or are idiosyncratic remains an open question. Among six Brassica species, we found significant effects of species and ploidy level for morphological, anatomical, and physiological traits. We identified three suites of intercorrelated traits in both diploid parents and allotetraploids: Morphological traits (such as leaf area and perimeter) anatomic traits (including ab- and ad-axial epidermis) and aspects of physiology. Of particular note, there were no significant correlations between morphological structure and physiological function in the diploid parents. Increased phenotypic integration in the allotetraploid hybrids may be due, in part, to increased trait ranges or simply different structure-function relationships. The trait correlations that disappear after hybridization as well as the novel trait correlations observed in allotetraploid hybrids may represent relatively evolutionarily labile associations and therefore could be ideal targets for artificial selection and crop improvement.
Quantifying time-series of leaf morphology using 2D and 3D photogrammetry methods for high-throughput plant phenotyping
Computers and Electronics in Agriculture
Conventional phenotyping methods impose a significant bottleneck to the characterization of genotypic and environmental effects on trait expression in plants. In particular, invasive and destructive sampling methods along with manual measurements widely used in conventional studies are labor-intensive, time-consuming, costly, and can lack consistency. These experimental features impede large-scale genetic studies of both crops and wild plant species. Here, we present a high-throughput phenotyping pipeline using photogrammetry and 3D modeling techniques in the model species, Arabidopsis thaliana. We develop novel photogrammetry and computer vision algorithms to quantify 2D and 3D leaf areas for a mapping population of 1050 Arabidopsis thaliana lines, and use 2D areas to analyze plant nastic movements and diurnal cycles. Compared to the 2D leaf areas, 3D leaf areas show an uncorrupted growth trend regardless of plant nastic movement. With optimized algorithms, our pipeline throughput is very computationally efficient for screening a large number of plants. The pipeline not only supports measurement of organ-level growth and development over time, but also enables analysis of whole-plant phenotypes and, thus, identification of genotype-specific performance. Further, the accuracy results evaluating the relationship between physical dimensions and 3D measurements indicate an R2 = 0.99, and the average 3D area processing time per plant is 0.02 s. Our algorithms provide both high accuracy and throughput in plant phenotyping, thereby, enabling progress in plant genotypic modeling.
Mapping and predicting non-linear Brassica rapa growth phenotypes based on Bayesian and frequentist trait estimation
G3: Genes Genomes Genetics
Predicting phenotypes based on genotypes and understanding the effects of complex multi-locus traits on plant performance requires a description of the underlying developmental processes, growth trajectories, and their genomic architecture. Using data from Brassica rapa genotypes grown in multiple density settings and seasons, we applied a hierarchical Bayesian Function-Valued Trait (FVT) approach to fit logistic growth curves to leaf phenotypic data (length and width) and characterize leaf development. We found evidence of genetic variation in phenotypic plasticity of rate and duration of leaf growth to growing season. In contrast, the magnitude of the plastic response for maximum leaf size was relatively small, suggesting that growth dynamics vs. final leaf sizes have distinct patterns of environmental sensitivity. Consistent with patterns of phenotypic plasticity, several QTL-by-year interactions were significant for parameters describing leaf growth rates and durations but not leaf size. In comparison to frequentist approaches for estimating leaf FVT, Bayesian trait estimation resulted in more mapped QTL that tended to have greater average LOD scores and to explain a greater proportion of trait variance. We then constructed QTL-based predictive models for leaf growth rate and final size based on data from one treatment (uncrowded plants in one growing season). Models predicted non-linear developmental phenotypes for genotypes not used in model construction and, due to a lack of QTL × environment interactions, predicted phenotypes across sites differing in plant density.
Plant high-throughput phenotyping using photogrammetry and imaging techniques to measure leaf length and rosette area
Computers and Electronics in Agriculture
Plant phenotyping is central to understand causal effects of genotypes and environments on trait expression and is a critical factor in expediting plant breeding. Previously, plant phenotypic traits were quantified using invasive, time-consuming, labor-intensive, cost-inefficient, and often destructive manual sampling methods that were also prone to observer error. In this study, we introduce an automated high-throughput phenotyping pipeline using affordable imaging systems and image processing algorithms to build 2D mosaicked orthophotos. Chamber-based and ground-level field implementations are used to measure phenotypic traits such as leaf length and rosette area in 2D images. Our automated pipeline has cross-platform capabilities and a degree of instrument independence, making it suitable for various situations.
Modeling development and quantitative trait mapping reveal independent genetic modules for leaf size and shape
New Phytologist
We found genetic trade-offs between leaf size and growth rate Function Value Traits (FVT) and uncovered differences in genotypic and QTL correlations involving FVT vs Single-Time-Points (STPs). We identified leaf shape (allometry) as a genetic module independent of length and width and identified selection on FVT parameters of development.
Patterns of shoot architecture in locally adapted populations are linked to intraspecific differences in gene regulation
New Phytologist
Shoot architecture, including the number and location of branches, is a crucial aspect of plant function, morphological diversification, life history evolution, and crop domestication. We identify axillary meristem outgrowth as a primary driver of divergent branch number and life histories in two locally adapted populations of the monkeyflower, Mimulus guttatus. Furthermore, we show that MORE AXILLARY GROWTH (MAX) gene expression strongly correlates with natural variation in branch outgrowth in this species, linking modification of the MAX-dependent pathway to the evolutionary diversification of shoot architecture.
Selection during crop diversification involves correlated evolution of the circadian clock and ecophysiological traits in Brassica rapa
New Phytologist
Crop selection often leads to dramatic morphological diversification, in which allocation to the harvestable component increases. Shifts in allocation are predicted to impact (as well as rely on) physiological traits; yet, little is known about the evolution of gas exchange and related anatomical features during crop diversification. In Brassica rapa, we tested for physiological differentiation among three crop morphotypes (leaf, turnip, and oilseed) and for correlated evolution of circadian, gas exchange, and phenological traits. We also examined internal and surficial leaf anatomical features and biochemical limits to photosynthesis.
Polyploidy and the relationship between leaf structure and function: implications for correlated evolution of anatomy, morphology, and physiology in Brassica
BMC Plant Biology
Polyploidy is well studied from a genetic and genomic perspective, but the morphological, anatomical, and physiological consequences of polyploidy remain relatively uncharacterized. Whether these potential changes bear on functional integration or are idiosyncratic remains an open question. Among six Brassica species, we found significant effects of species and ploidy level for morphological, anatomical, and physiological traits. We identified three suites of intercorrelated traits in both diploid parents and allotetraploids: Morphological traits (such as leaf area and perimeter) anatomic traits (including ab- and ad-axial epidermis) and aspects of physiology. Of particular note, there were no significant correlations between morphological structure and physiological function in the diploid parents. Increased phenotypic integration in the allotetraploid hybrids may be due, in part, to increased trait ranges or simply different structure-function relationships. The trait correlations that disappear after hybridization as well as the novel trait correlations observed in allotetraploid hybrids may represent relatively evolutionarily labile associations and therefore could be ideal targets for artificial selection and crop improvement.
Quantifying time-series of leaf morphology using 2D and 3D photogrammetry methods for high-throughput plant phenotyping
Computers and Electronics in Agriculture
Conventional phenotyping methods impose a significant bottleneck to the characterization of genotypic and environmental effects on trait expression in plants. In particular, invasive and destructive sampling methods along with manual measurements widely used in conventional studies are labor-intensive, time-consuming, costly, and can lack consistency. These experimental features impede large-scale genetic studies of both crops and wild plant species. Here, we present a high-throughput phenotyping pipeline using photogrammetry and 3D modeling techniques in the model species, Arabidopsis thaliana. We develop novel photogrammetry and computer vision algorithms to quantify 2D and 3D leaf areas for a mapping population of 1050 Arabidopsis thaliana lines, and use 2D areas to analyze plant nastic movements and diurnal cycles. Compared to the 2D leaf areas, 3D leaf areas show an uncorrupted growth trend regardless of plant nastic movement. With optimized algorithms, our pipeline throughput is very computationally efficient for screening a large number of plants. The pipeline not only supports measurement of organ-level growth and development over time, but also enables analysis of whole-plant phenotypes and, thus, identification of genotype-specific performance. Further, the accuracy results evaluating the relationship between physical dimensions and 3D measurements indicate an R2 = 0.99, and the average 3D area processing time per plant is 0.02 s. Our algorithms provide both high accuracy and throughput in plant phenotyping, thereby, enabling progress in plant genotypic modeling.
Mapping and predicting non-linear Brassica rapa growth phenotypes based on Bayesian and frequentist trait estimation
G3: Genes Genomes Genetics
Predicting phenotypes based on genotypes and understanding the effects of complex multi-locus traits on plant performance requires a description of the underlying developmental processes, growth trajectories, and their genomic architecture. Using data from Brassica rapa genotypes grown in multiple density settings and seasons, we applied a hierarchical Bayesian Function-Valued Trait (FVT) approach to fit logistic growth curves to leaf phenotypic data (length and width) and characterize leaf development. We found evidence of genetic variation in phenotypic plasticity of rate and duration of leaf growth to growing season. In contrast, the magnitude of the plastic response for maximum leaf size was relatively small, suggesting that growth dynamics vs. final leaf sizes have distinct patterns of environmental sensitivity. Consistent with patterns of phenotypic plasticity, several QTL-by-year interactions were significant for parameters describing leaf growth rates and durations but not leaf size. In comparison to frequentist approaches for estimating leaf FVT, Bayesian trait estimation resulted in more mapped QTL that tended to have greater average LOD scores and to explain a greater proportion of trait variance. We then constructed QTL-based predictive models for leaf growth rate and final size based on data from one treatment (uncrowded plants in one growing season). Models predicted non-linear developmental phenotypes for genotypes not used in model construction and, due to a lack of QTL × environment interactions, predicted phenotypes across sites differing in plant density.
Plant high-throughput phenotyping using photogrammetry and imaging techniques to measure leaf length and rosette area
Computers and Electronics in Agriculture
Plant phenotyping is central to understand causal effects of genotypes and environments on trait expression and is a critical factor in expediting plant breeding. Previously, plant phenotypic traits were quantified using invasive, time-consuming, labor-intensive, cost-inefficient, and often destructive manual sampling methods that were also prone to observer error. In this study, we introduce an automated high-throughput phenotyping pipeline using affordable imaging systems and image processing algorithms to build 2D mosaicked orthophotos. Chamber-based and ground-level field implementations are used to measure phenotypic traits such as leaf length and rosette area in 2D images. Our automated pipeline has cross-platform capabilities and a degree of instrument independence, making it suitable for various situations.
Modeling development and quantitative trait mapping reveal independent genetic modules for leaf size and shape
New Phytologist
We found genetic trade-offs between leaf size and growth rate Function Value Traits (FVT) and uncovered differences in genotypic and QTL correlations involving FVT vs Single-Time-Points (STPs). We identified leaf shape (allometry) as a genetic module independent of length and width and identified selection on FVT parameters of development.
Patterns of shoot architecture in locally adapted populations are linked to intraspecific differences in gene regulation
New Phytologist
Shoot architecture, including the number and location of branches, is a crucial aspect of plant function, morphological diversification, life history evolution, and crop domestication. We identify axillary meristem outgrowth as a primary driver of divergent branch number and life histories in two locally adapted populations of the monkeyflower, Mimulus guttatus. Furthermore, we show that MORE AXILLARY GROWTH (MAX) gene expression strongly correlates with natural variation in branch outgrowth in this species, linking modification of the MAX-dependent pathway to the evolutionary diversification of shoot architecture.
Selection during crop diversification involves correlated evolution of the circadian clock and ecophysiological traits in Brassica rapa
New Phytologist
Crop selection often leads to dramatic morphological diversification, in which allocation to the harvestable component increases. Shifts in allocation are predicted to impact (as well as rely on) physiological traits; yet, little is known about the evolution of gas exchange and related anatomical features during crop diversification. In Brassica rapa, we tested for physiological differentiation among three crop morphotypes (leaf, turnip, and oilseed) and for correlated evolution of circadian, gas exchange, and phenological traits. We also examined internal and surficial leaf anatomical features and biochemical limits to photosynthesis.
Circadian rhythms are associated with shoot architecture and plant performance
New Phytologist
Circadian rhythms are key regulators of diverse biological processes under controlled settings. Yet, the phenotypic and fitness consequences of quantitative variation in circadian rhythms remain largely unexplored in the field. As with other pathways, phenotypic characterization of circadian outputs in the field may reveal novel clock functions. Across consecutive growing seasons, we test for associations between clock variation and flowering phenology, plant size, shoot architecture, and fruit set in clock mutants and segregating progenies of Arabidopsis thaliana expressing quantitative variation in circadian rhythms. Using structural equation modeling, we find that genotypic variation in circadian rhythms within a growing season is associated directly with branching, which in turn affects fruit production. Consistent with direct associations between the clock and branching in segregating progenies, cauline branch number is lower and rosette branch number higher in a short‐period mutant relative to wild‐type and long‐period genotypes, independent of flowering time. Differences in branching arise from variation in meristem fate as well as leaf production rate before flowering and attendant increases in meristem number. Our results suggest that clock variation directly affects shoot architecture in the field, suggesting a novel clock function and means by which the clock affects performance.
Polyploidy and the relationship between leaf structure and function: implications for correlated evolution of anatomy, morphology, and physiology in Brassica
BMC Plant Biology
Polyploidy is well studied from a genetic and genomic perspective, but the morphological, anatomical, and physiological consequences of polyploidy remain relatively uncharacterized. Whether these potential changes bear on functional integration or are idiosyncratic remains an open question. Among six Brassica species, we found significant effects of species and ploidy level for morphological, anatomical, and physiological traits. We identified three suites of intercorrelated traits in both diploid parents and allotetraploids: Morphological traits (such as leaf area and perimeter) anatomic traits (including ab- and ad-axial epidermis) and aspects of physiology. Of particular note, there were no significant correlations between morphological structure and physiological function in the diploid parents. Increased phenotypic integration in the allotetraploid hybrids may be due, in part, to increased trait ranges or simply different structure-function relationships. The trait correlations that disappear after hybridization as well as the novel trait correlations observed in allotetraploid hybrids may represent relatively evolutionarily labile associations and therefore could be ideal targets for artificial selection and crop improvement.
Quantifying time-series of leaf morphology using 2D and 3D photogrammetry methods for high-throughput plant phenotyping
Computers and Electronics in Agriculture
Conventional phenotyping methods impose a significant bottleneck to the characterization of genotypic and environmental effects on trait expression in plants. In particular, invasive and destructive sampling methods along with manual measurements widely used in conventional studies are labor-intensive, time-consuming, costly, and can lack consistency. These experimental features impede large-scale genetic studies of both crops and wild plant species. Here, we present a high-throughput phenotyping pipeline using photogrammetry and 3D modeling techniques in the model species, Arabidopsis thaliana. We develop novel photogrammetry and computer vision algorithms to quantify 2D and 3D leaf areas for a mapping population of 1050 Arabidopsis thaliana lines, and use 2D areas to analyze plant nastic movements and diurnal cycles. Compared to the 2D leaf areas, 3D leaf areas show an uncorrupted growth trend regardless of plant nastic movement. With optimized algorithms, our pipeline throughput is very computationally efficient for screening a large number of plants. The pipeline not only supports measurement of organ-level growth and development over time, but also enables analysis of whole-plant phenotypes and, thus, identification of genotype-specific performance. Further, the accuracy results evaluating the relationship between physical dimensions and 3D measurements indicate an R2 = 0.99, and the average 3D area processing time per plant is 0.02 s. Our algorithms provide both high accuracy and throughput in plant phenotyping, thereby, enabling progress in plant genotypic modeling.
Mapping and predicting non-linear Brassica rapa growth phenotypes based on Bayesian and frequentist trait estimation
G3: Genes Genomes Genetics
Predicting phenotypes based on genotypes and understanding the effects of complex multi-locus traits on plant performance requires a description of the underlying developmental processes, growth trajectories, and their genomic architecture. Using data from Brassica rapa genotypes grown in multiple density settings and seasons, we applied a hierarchical Bayesian Function-Valued Trait (FVT) approach to fit logistic growth curves to leaf phenotypic data (length and width) and characterize leaf development. We found evidence of genetic variation in phenotypic plasticity of rate and duration of leaf growth to growing season. In contrast, the magnitude of the plastic response for maximum leaf size was relatively small, suggesting that growth dynamics vs. final leaf sizes have distinct patterns of environmental sensitivity. Consistent with patterns of phenotypic plasticity, several QTL-by-year interactions were significant for parameters describing leaf growth rates and durations but not leaf size. In comparison to frequentist approaches for estimating leaf FVT, Bayesian trait estimation resulted in more mapped QTL that tended to have greater average LOD scores and to explain a greater proportion of trait variance. We then constructed QTL-based predictive models for leaf growth rate and final size based on data from one treatment (uncrowded plants in one growing season). Models predicted non-linear developmental phenotypes for genotypes not used in model construction and, due to a lack of QTL × environment interactions, predicted phenotypes across sites differing in plant density.
Plant high-throughput phenotyping using photogrammetry and imaging techniques to measure leaf length and rosette area
Computers and Electronics in Agriculture
Plant phenotyping is central to understand causal effects of genotypes and environments on trait expression and is a critical factor in expediting plant breeding. Previously, plant phenotypic traits were quantified using invasive, time-consuming, labor-intensive, cost-inefficient, and often destructive manual sampling methods that were also prone to observer error. In this study, we introduce an automated high-throughput phenotyping pipeline using affordable imaging systems and image processing algorithms to build 2D mosaicked orthophotos. Chamber-based and ground-level field implementations are used to measure phenotypic traits such as leaf length and rosette area in 2D images. Our automated pipeline has cross-platform capabilities and a degree of instrument independence, making it suitable for various situations.
Modeling development and quantitative trait mapping reveal independent genetic modules for leaf size and shape
New Phytologist
We found genetic trade-offs between leaf size and growth rate Function Value Traits (FVT) and uncovered differences in genotypic and QTL correlations involving FVT vs Single-Time-Points (STPs). We identified leaf shape (allometry) as a genetic module independent of length and width and identified selection on FVT parameters of development.
Patterns of shoot architecture in locally adapted populations are linked to intraspecific differences in gene regulation
New Phytologist
Shoot architecture, including the number and location of branches, is a crucial aspect of plant function, morphological diversification, life history evolution, and crop domestication. We identify axillary meristem outgrowth as a primary driver of divergent branch number and life histories in two locally adapted populations of the monkeyflower, Mimulus guttatus. Furthermore, we show that MORE AXILLARY GROWTH (MAX) gene expression strongly correlates with natural variation in branch outgrowth in this species, linking modification of the MAX-dependent pathway to the evolutionary diversification of shoot architecture.
Selection during crop diversification involves correlated evolution of the circadian clock and ecophysiological traits in Brassica rapa
New Phytologist
Crop selection often leads to dramatic morphological diversification, in which allocation to the harvestable component increases. Shifts in allocation are predicted to impact (as well as rely on) physiological traits; yet, little is known about the evolution of gas exchange and related anatomical features during crop diversification. In Brassica rapa, we tested for physiological differentiation among three crop morphotypes (leaf, turnip, and oilseed) and for correlated evolution of circadian, gas exchange, and phenological traits. We also examined internal and surficial leaf anatomical features and biochemical limits to photosynthesis.
Circadian rhythms are associated with shoot architecture and plant performance
New Phytologist
Circadian rhythms are key regulators of diverse biological processes under controlled settings. Yet, the phenotypic and fitness consequences of quantitative variation in circadian rhythms remain largely unexplored in the field. As with other pathways, phenotypic characterization of circadian outputs in the field may reveal novel clock functions. Across consecutive growing seasons, we test for associations between clock variation and flowering phenology, plant size, shoot architecture, and fruit set in clock mutants and segregating progenies of Arabidopsis thaliana expressing quantitative variation in circadian rhythms. Using structural equation modeling, we find that genotypic variation in circadian rhythms within a growing season is associated directly with branching, which in turn affects fruit production. Consistent with direct associations between the clock and branching in segregating progenies, cauline branch number is lower and rosette branch number higher in a short‐period mutant relative to wild‐type and long‐period genotypes, independent of flowering time. Differences in branching arise from variation in meristem fate as well as leaf production rate before flowering and attendant increases in meristem number. Our results suggest that clock variation directly affects shoot architecture in the field, suggesting a novel clock function and means by which the clock affects performance.
Developmental plasticity of shoot architecture: Morphological expression and ecologically relevant onset in locally adapted populations of Mimulus guttatus
International Journal of Plant Sciences
Shoot architecture profoundly affects vegetative and reproductive features of plants. Our ontogenetic study demonstrates that plastic responses in M. guttatus architecture are expressed at ecologically relevant times during ontogeny. In particular, IM plants behave like aggressive annuals while DUN plants exhibit more conservative growth rates and delayed plastic responses. Plastic responses in the IM population included increased flower production, indicating a potential adaptive role for phenotypic plasticity in this population.
Polyploidy and the relationship between leaf structure and function: implications for correlated evolution of anatomy, morphology, and physiology in Brassica
BMC Plant Biology
Polyploidy is well studied from a genetic and genomic perspective, but the morphological, anatomical, and physiological consequences of polyploidy remain relatively uncharacterized. Whether these potential changes bear on functional integration or are idiosyncratic remains an open question. Among six Brassica species, we found significant effects of species and ploidy level for morphological, anatomical, and physiological traits. We identified three suites of intercorrelated traits in both diploid parents and allotetraploids: Morphological traits (such as leaf area and perimeter) anatomic traits (including ab- and ad-axial epidermis) and aspects of physiology. Of particular note, there were no significant correlations between morphological structure and physiological function in the diploid parents. Increased phenotypic integration in the allotetraploid hybrids may be due, in part, to increased trait ranges or simply different structure-function relationships. The trait correlations that disappear after hybridization as well as the novel trait correlations observed in allotetraploid hybrids may represent relatively evolutionarily labile associations and therefore could be ideal targets for artificial selection and crop improvement.
Quantifying time-series of leaf morphology using 2D and 3D photogrammetry methods for high-throughput plant phenotyping
Computers and Electronics in Agriculture
Conventional phenotyping methods impose a significant bottleneck to the characterization of genotypic and environmental effects on trait expression in plants. In particular, invasive and destructive sampling methods along with manual measurements widely used in conventional studies are labor-intensive, time-consuming, costly, and can lack consistency. These experimental features impede large-scale genetic studies of both crops and wild plant species. Here, we present a high-throughput phenotyping pipeline using photogrammetry and 3D modeling techniques in the model species, Arabidopsis thaliana. We develop novel photogrammetry and computer vision algorithms to quantify 2D and 3D leaf areas for a mapping population of 1050 Arabidopsis thaliana lines, and use 2D areas to analyze plant nastic movements and diurnal cycles. Compared to the 2D leaf areas, 3D leaf areas show an uncorrupted growth trend regardless of plant nastic movement. With optimized algorithms, our pipeline throughput is very computationally efficient for screening a large number of plants. The pipeline not only supports measurement of organ-level growth and development over time, but also enables analysis of whole-plant phenotypes and, thus, identification of genotype-specific performance. Further, the accuracy results evaluating the relationship between physical dimensions and 3D measurements indicate an R2 = 0.99, and the average 3D area processing time per plant is 0.02 s. Our algorithms provide both high accuracy and throughput in plant phenotyping, thereby, enabling progress in plant genotypic modeling.
Mapping and predicting non-linear Brassica rapa growth phenotypes based on Bayesian and frequentist trait estimation
G3: Genes Genomes Genetics
Predicting phenotypes based on genotypes and understanding the effects of complex multi-locus traits on plant performance requires a description of the underlying developmental processes, growth trajectories, and their genomic architecture. Using data from Brassica rapa genotypes grown in multiple density settings and seasons, we applied a hierarchical Bayesian Function-Valued Trait (FVT) approach to fit logistic growth curves to leaf phenotypic data (length and width) and characterize leaf development. We found evidence of genetic variation in phenotypic plasticity of rate and duration of leaf growth to growing season. In contrast, the magnitude of the plastic response for maximum leaf size was relatively small, suggesting that growth dynamics vs. final leaf sizes have distinct patterns of environmental sensitivity. Consistent with patterns of phenotypic plasticity, several QTL-by-year interactions were significant for parameters describing leaf growth rates and durations but not leaf size. In comparison to frequentist approaches for estimating leaf FVT, Bayesian trait estimation resulted in more mapped QTL that tended to have greater average LOD scores and to explain a greater proportion of trait variance. We then constructed QTL-based predictive models for leaf growth rate and final size based on data from one treatment (uncrowded plants in one growing season). Models predicted non-linear developmental phenotypes for genotypes not used in model construction and, due to a lack of QTL × environment interactions, predicted phenotypes across sites differing in plant density.
Plant high-throughput phenotyping using photogrammetry and imaging techniques to measure leaf length and rosette area
Computers and Electronics in Agriculture
Plant phenotyping is central to understand causal effects of genotypes and environments on trait expression and is a critical factor in expediting plant breeding. Previously, plant phenotypic traits were quantified using invasive, time-consuming, labor-intensive, cost-inefficient, and often destructive manual sampling methods that were also prone to observer error. In this study, we introduce an automated high-throughput phenotyping pipeline using affordable imaging systems and image processing algorithms to build 2D mosaicked orthophotos. Chamber-based and ground-level field implementations are used to measure phenotypic traits such as leaf length and rosette area in 2D images. Our automated pipeline has cross-platform capabilities and a degree of instrument independence, making it suitable for various situations.
Modeling development and quantitative trait mapping reveal independent genetic modules for leaf size and shape
New Phytologist
We found genetic trade-offs between leaf size and growth rate Function Value Traits (FVT) and uncovered differences in genotypic and QTL correlations involving FVT vs Single-Time-Points (STPs). We identified leaf shape (allometry) as a genetic module independent of length and width and identified selection on FVT parameters of development.
Patterns of shoot architecture in locally adapted populations are linked to intraspecific differences in gene regulation
New Phytologist
Shoot architecture, including the number and location of branches, is a crucial aspect of plant function, morphological diversification, life history evolution, and crop domestication. We identify axillary meristem outgrowth as a primary driver of divergent branch number and life histories in two locally adapted populations of the monkeyflower, Mimulus guttatus. Furthermore, we show that MORE AXILLARY GROWTH (MAX) gene expression strongly correlates with natural variation in branch outgrowth in this species, linking modification of the MAX-dependent pathway to the evolutionary diversification of shoot architecture.
Selection during crop diversification involves correlated evolution of the circadian clock and ecophysiological traits in Brassica rapa
New Phytologist
Crop selection often leads to dramatic morphological diversification, in which allocation to the harvestable component increases. Shifts in allocation are predicted to impact (as well as rely on) physiological traits; yet, little is known about the evolution of gas exchange and related anatomical features during crop diversification. In Brassica rapa, we tested for physiological differentiation among three crop morphotypes (leaf, turnip, and oilseed) and for correlated evolution of circadian, gas exchange, and phenological traits. We also examined internal and surficial leaf anatomical features and biochemical limits to photosynthesis.
Circadian rhythms are associated with shoot architecture and plant performance
New Phytologist
Circadian rhythms are key regulators of diverse biological processes under controlled settings. Yet, the phenotypic and fitness consequences of quantitative variation in circadian rhythms remain largely unexplored in the field. As with other pathways, phenotypic characterization of circadian outputs in the field may reveal novel clock functions. Across consecutive growing seasons, we test for associations between clock variation and flowering phenology, plant size, shoot architecture, and fruit set in clock mutants and segregating progenies of Arabidopsis thaliana expressing quantitative variation in circadian rhythms. Using structural equation modeling, we find that genotypic variation in circadian rhythms within a growing season is associated directly with branching, which in turn affects fruit production. Consistent with direct associations between the clock and branching in segregating progenies, cauline branch number is lower and rosette branch number higher in a short‐period mutant relative to wild‐type and long‐period genotypes, independent of flowering time. Differences in branching arise from variation in meristem fate as well as leaf production rate before flowering and attendant increases in meristem number. Our results suggest that clock variation directly affects shoot architecture in the field, suggesting a novel clock function and means by which the clock affects performance.
Developmental plasticity of shoot architecture: Morphological expression and ecologically relevant onset in locally adapted populations of Mimulus guttatus
International Journal of Plant Sciences
Shoot architecture profoundly affects vegetative and reproductive features of plants. Our ontogenetic study demonstrates that plastic responses in M. guttatus architecture are expressed at ecologically relevant times during ontogeny. In particular, IM plants behave like aggressive annuals while DUN plants exhibit more conservative growth rates and delayed plastic responses. Plastic responses in the IM population included increased flower production, indicating a potential adaptive role for phenotypic plasticity in this population.
Bayesian estimation and use of high-throughput remote sensing indices for quantitative genetic analyses of leaf growth
Theoretical and Applied Genetics
We develop Bayesian function-valued trait models that mathematically isolate genetic mechanisms underlying leaf growth trajectories by factoring out genotype-specific differences in photosynthesis. Remote sensing data can be used instead of leaf-level physiological measurements. Abstract: Describing genetic programs that specifically regulate morphological traits can be complicated by genotypic differences in physiological traits. We describe the growth trajectories of leaves using novel Bayesian function-valued trait (FVT) modeling approaches in Brassica rapa lines raised in heterogeneous field settings. While frequentist approaches estimate parameter values by treating each experimental replicate discretely, Bayesian models can utilize information in the global dataset, potentially leading to more robust trait estimation. We illustrate this principle by estimating growth asymptotes in the face of missing data and comparing heritabilities of growth trajectory parameters estimated by Bayesian and frequentist approaches. Using pseudo-Bayes factors, we compare the performance of an initial Bayesian logistic growth model and a model that incorporates carbon assimilation (Amax) as a cofactor, thus statistically accounting for genotypic differences in carbon resources. We further evaluate two remotely sensed spectroradiometric indices, photochemical reflectance (pri2) and MERIS Terrestrial Chlorophyll Index (mtci) as covariates in lieu of Amax, because these two indices were genetically correlated with Amax across years and treatments yet allow much higher throughput compared to direct leaf-level gas-exchange measurements. For leaf lengths in uncrowded settings, including Amax improves model fit over the initial model. The mtci and pri2 indices also outperform direct Amax measurements. Of particular importance for evolutionary biologists and plant breeders, hierarchical Bayesian models estimating FVT parameters improve heritabilities compared to frequentist approaches.
