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Traits in wild relatives of crop species can help breed sustainable crop varieties that produce more food with fewer resources. To make use of this variation, we need to find the genetic regions that allow wild species to use water and nutrients more efficiently. Leaf anatomy has a major effect on photosynthesis by determining rates of carbon gain and water loss. However, finding the genetic regions underlying leaf anatomical evolution has been limited by low-throughput and low-resolution trait measurements. 3D imaging using X-ray microcomputed tomography (μCT) may overcome these obstacles by providing high-throughput, high-resolution data on leaf anatomy. Compared to traditional 2D methods for leaf anatomy, 3D imaging captures physiologically important volumetric traits, is less biased, and encompasses a larger leaf area. We used synchrotron μCT to measure leaf anatomy on two tomato species Solanum lycopersicum (cultivated tomato) and S. pennellii (wild, drought-tolerant species), and four introgression lines containing loci that alter leaf anatomy. We measured stomatal density, size, and 3D arrangement, as well as leaf thickness and mesophyll porosity. Preliminary analyses show that synchrotron μCT can identify previously described quantitative trait loci for stomatal traits and leaf thickness and show how those traits are related to 3D leaf anatomy. We will use finite element models to show how these anatomical differences may contribute to genetic variation leaf CO2 and water vapour exchange. 

Abstract Plant ecophysiology is founded on a rich body of physical and chemical theory, but it is challenging to connect theory with data in unambiguous, analytically rigorous and reproducible ways. Custom scripts written in computer programming languages (coding) enable plant ecophysiologists to model plant processes and fit models to data reproducibly using advanced statistical techniques. Since many ecophysiologists lack formal programming education, we have yet to adopt a unified set of coding principles and standards that could make coding easier to learn, use and modify. We identify eight principles to help in plant ecophysiologists without much programming experience to write resilient code: (i) standardized nomenclature, (ii) consistency in style, (iii) increased modularity/extensibility for easier editing and understanding, (iv) code scalability for application to large data sets, (v) documented contingencies for code maintenance, (vi) documentation to facilitate user understanding; (vii) extensive tutorials and (viii) unit testing and benchmarking. We illustrate these principles using a new R package, {photosynthesis}, which provides a set of analytical and simulation tools for plant ecophysiology. Our goal with these principles is to advance scientific discovery in plant ecophysiology by making it easier to use code for simulation and data analysis, reproduce results and rapidly incorporate new biological understanding and analytical tools.