Principles of resilient coding for plant ecophysiologists

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: 1) standardized nomenclature, 2) consistency in style, 3) increased modularity/extensibility for easier editing and understanding, 4) code scalability for application to large datasets, 5) documented contingencies for code maintenance, 6) documentation to facilitate user understanding; 7) extensive tutorials, and 8) unit testing. 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.

Activation of CO2 assimilation during photosynthetic induction is slower in C4 than in C3 photosynthesis in three phylogenetically controlled experiments

“…To analyse steady state responses, a non-rectangular hyperbola was fitted to the light response curves ( Stinziano et al., 2021 ). The quantum yield of assimilation (α) was derived from the initial slope, and the light-saturated photosynthetic rate (A max ) from the asymptote of the curve.…”

Pear (Pyrus communisL. cv. Conference) has shade-tolerant features allowing for consistent agrivoltaic crop yield

“…Light response curves were made by measuring CO 2 uptake rate at increasing light intensity up to 2175 µmol⋅m -2 ⋅s -1 , waiting until equilibrium was reached between each increased step. The light response curves were fitted using a nonrectangular hyperbola- based model following Marshall and Biscoe (1980) using the “Photosynthesis” package (Stinziano et al ., 2021) in R version 4.3.1 - “Beagle Scouts” and R Studio 2023.06.0 Build 421 “Mountain Hydrangea” (RStudio Team, 2020), and the dark respiration rate, the light compensation point and the carbon assimilation rate at saturating light levels were calculated. From the individual fitted curves, the light compensation point (I max ) was determined using the tool developed by de Lobo et al (2013) following Prioul and Chartier (1977).…”

Field-grown ictB tobacco transformants show no difference in photosynthetic efficiency for biomass relative to the wild type

“…The gas exchange systems were matched before each curve, and F s and F m ʹ were recorded at each PPFD. The A/Q curves were fitted for quantum efficiency (Φ PSII ), leaf CO 2 uptake in saturated light ( A sat , µmol m –2 s –1 ), and light compensation point utilizing the {photosynthesis} R-package ( R Core Team, 2020 ; Stinziano et al , 2021 ), which uses the Marshall and Biscoe (1980) non-rectangular hyperbola model.…”