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Mayfield-Jones, D.

Publications and source records attributed to Mayfield-Jones, D..

2 recordsLinked to original sources

Plasticity in stomatal behavior across a gradient of water supply is consistent among field-grown maize inbred lines with varying stomatal patterning

Stomata regulate leaf CO2 assimilation (A) and water loss. The Ball-Berry and Medlyn models predict stomatal conductance (gs) with a slope parameter (m or g1) that reflects sensitivity of gs to A, atmospheric CO2 and humidity, and is inversely related to water use efficiency (WUE). This study addressed knowledge gaps about what the values of m and g1 are in C4 crops under field conditions, as well as how they vary among genotypes and with drought stress. m and g1 were unexpectedly consistent in four inbred maize genotypes across a gradient of water supply. This was despite genotypic variation in stomatal patterning, A and gs. m and g1 were strongly correlated with soil water content, moderately correlated with pre-dawn leaf water potential ({Psi}pd), and weakly correlated with midday leaf water potential ({Psi}md). This implied that m and g1 respond to long-term water supply more than short-term drought stress. The conserved nature of m and g1 across anatomically diverse genotypes and water supplies suggests there is flexibility in structure-function relationships underpinning WUE. This evidence can guide simulation of maize gs across a range of water supply in the primary maize growing region and inform efforts to improve WUE. Summary statementParameter values for models simulating stomatal conductance were unexpectedly consistent for anatomically and physiologically diverse genotypes of the model C4 crop maize when they were grown across a range of water supplies in the field.

plant biology↗

Optical topometry and machine learning to rapidly phenotype stomatal patterning traits for QTL mapping in maize

Stomata are adjustable pores on leaf surfaces that regulate the trade-off of CO2 uptake with water vapor loss, thus having critical roles in controlling photosynthetic carbon gain and plant water use. The lack of easy, rapid methods for phenotyping epidermal cell traits have limited the use of quantitative, forward and reverse genetics to discover the genetic basis of stomatal patterning. A new high-throughput epidermal cell phenotyping pipeline is presented here and used for quantitative trait loci (QTL) mapping in field-grown maize. The locations and sizes of stomatal complexes and pavement cells on images acquired by an optical topometer from mature leaves were automatically determined. Computer estimated stomatal complex density (SCD; R2 = 0.97) and stomatal complex area (SCA; R2 = 0.71) were strongly correlated with human measurements. Leaf gas exchange traits correlated with the dimensions and proportion of stomatal complexes but, unexpectedly, did not correlate with SCD. Genetic variation in epidermal traits were consistent across two field seasons. Out of 143 QTLs in total, 36 QTLs were consistently identified for a given trait in both years. 24 hotspots of overlapping QTLs for multiple traits were identified. Orthologs of genes known to regulate stomatal patterning in Arabidopsis were located within some, but not all, of these regions. This study demonstrates how discovery of the genetic basis for stomatal patterning can be accelerated in maize, a model for C4 species where these processes are poorly understood. One sentence summaryOptical topometry and machine learning tools were developed to assess epidermal cell patterning, and applied to analyze its genetic architecture alongside leaf photosynthetic gas exchange in maize.

plant biology↗