SummaryUnderstanding biological temperature responses is crucial to predicting global carbon fluxes. The current approach to modelling temperature responses of photosynthetic capacity in large scale modelling efforts uses a modified Arrhenius equation.We rederived the modified Arrhenius equation from the source publication from 1942 and uncovered a missing term that was dropped by 2002. We compare fitted temperature response parameters between the correct and incorrect derivation of the modified Arrhenius equation.We find that most parameters are minimally affected, though activation energy is impacted quite substantially. We then scaled the impact of these small errors to whole plant carbon balance and found that the impact of the rederivation of the modified Arrhenius equation on modelled daily carbon gain causes a meaningful deviation of ~18% day−1.This suggests that the error in the derivation of the modified Arrhenius equation has impacted the accuracy of predictions of carbon fluxes at larger scales since >40% of Earth System Models contain the erroneous derivation. We recommend that the derivation error be corrected in modelling efforts moving forward.

Carbon-concentration and carbon-climate feedbacks in CMIP6 models, and their comparison to CMIP5 models. Biogeosciences

Climate warming is increasing the frequency of climate-induced tree mortality events. While drought combined with heat is considered the primary cause of this mortality, little is known about whether moderately, high temperatures alone can induce mortality, or whether rising CO2 would prevent mortality at high growth temperatures. We grew tamarack (Larix laricina) under ambient (400 ppm) and elevated (750 ppm) CO2 concentrations combined with ambient, ambient +4°C, and ambient +8°C growth temperatures to investigate whether high growth temperatures lead to carbon limitations and mortality. Growth at +8°C led to 40% mortality in the ambient CO2 (8TAC) treatment, but no mortality in the elevated CO2 treatment. Thermal acclimation of respiration led to similar leaf carbon balances across the warming treatments, despite a lack of photosynthetic acclimation. Photosynthesis was stimulated under elevated CO2, increasing seedling growth, but not leaf carbon concentrations. However, growth and foliar carbon concentrations were lowest in the +8°C treatments, even with elevated CO2. Dying 8TAC seedlings had lower needle carbon concentrations and lower ratios of photosynthesis to respiration than healthy 8TAC seedlings, indicating that carbon limitations were likely the cause of seedling mortality under high growth temperatures.

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.

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.