Future-proofing code: Principles of coding for plant ecophysiology with {photosynthesis} as a case study

Plant 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.

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