Principles of resilient coding for plant ecophysiologists

Abstract

SummaryPlant physiological ecology 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 most 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 outline principles and standards for coding in plant ecophysiology to develop: 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; and 7) extensive tutorials for biologists new to coding to rapidly become proficient with software.We illustrate these principles using a new R package, {photosynthesis}, designed to provide a set of analytical tools for plant ecophysiology.Our goal with these principles is to future-proof coding efforts to ensure new advances and analytical tools can be rapidly incorporated into the field, while ensuring software maintenance across scientific generations.

Physiological impacts of beech leaf disease across a gradient of symptom severity among understory American beech

“…All statistical analysis and data visualizations were performed in R studio (version 4.0.2, R Foundation for Statistical Computing, Vienna, Austria). Light response curves were fit with a nonrectangular hyperbola model (Marshall and Biscoe, 1980) using the package "photosynthesis" (Stinziano et al, 2020). From these curve fits, estimates of light-saturated CO 2 assimilation (A max ), dark respiration rate (R d ), light compensation point ( i), and apparent quantum yield (α) were derived.…”

Identification of quantitative trait loci for dynamic and steady-state photosynthetic traits in a barley mapping population

“…In the past, technical limitations may have prevented such an approach, but new gas exchange instruments have the computational power, storage capacity and environmental control to establish this as common practice. New data analysis software, including the R packages {Plantecophys} ( Duursma 2015 ) and {Photosynthesis} ( Stinziano et al 2020 ), that allow large gas exchange data sets to be quickly processed will further enable this change.…”

Inference of photosynthetic capacity parameters from chlorophyll a fluorescence is affected by redox state of PSII reaction centers

“…We did not employ the theoretical formulations (Equations and ) to directly calculate V cmax and J max in this study, as the actual values of the input parameters (e.g., $${\unicode{x00413}}^{*}$$, K co, k DF , θ , and σ) are currently unknown although they have been considered as constants (note the validity of these equations were still validated in Supporting Information Note ). Instead, to estimate V cmax and J max , we adopted the traditional approach, that is, fitting CO 2 response curves based upon the FvCB model (Farquhar et al, 1980; Sharkey, 1985) using the photosynthesis R package (Stinziano et al, 2020). This approach adopts the fitting strategy of Gu et al (2010), which iterates all possible C i transitional points to automate the determination of the carboxylation limitation state, removal of inadmissible curves fits, and selection of the best fit by minimizing the cost function.…”