PhotoGEA: An R package for closer fitting of photosynthetic gas exchange data with non-gaussian confidence interval estimation

Authors: LOCHOCKI, EDWARD - University Of Illinois; SALESSE-SMITH, CORALIE - University Of Illinois; McGrath, Justin

Submitted to: Plant Cell and Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/15/2025
Publication Date: 3/30/2025
Citation: Lochocki, E.B., Salesse-Smith, C.E., McGrath, J.M. 2025. PhotoGEA: An R package for closer fitting of photosynthetic gas exchange data with non-gaussian confidence interval estimation. Plant Cell and Environment. https://doi.org/10.1111/pce.15501.
DOI: https://doi.org/10.1111/pce.15501

Interpretive Summary: Measuring the rate of leaf photosynthesis over a range of carbon dioxide concentrations provides a way to estimate key underlying traits of the leaf's photosynthetic machinery. For example, this provides information about the maximum activity of Rubisco, a key limiting enzyme photosynthetic rate. Obtaining these estimates from the original data requires fitting the results to a mathematical model, and numerous methods have been developed to do this easily and robustly, but current solutions have two key problems. First, they rely on non-zero derivatives existing for all parameters across ranges of carbon dioxide concentrations, which for this particular type of data is often not true. In such cases, the results produced can be misleading. Second, they do not present statistical confidence limits for the parameter estimates or they present confidence limits that assume normally distributed parameters, which is often not the case for these data sets. The approach presented here does not rely on derivatives and calculates confidence intervals that do not assume normal distributions. Compared to four other popular methods, the approach here achieves the closest fit to the data and provides confidence intervals not provided by the other methods. These analyses are very common in the field and researchers would likely find the this to be a valuable tool.

Technical Abstract: • Fitting mechanistic models, such as the Farquhar-von-Caemmerer-Berry model, to experimentally measured photosynthetic CO2 response curves (A-Ci curves) is a widely used technique for estimating the values of key leaf biochemical parameters and determining limitations to photosynthesis in vivo. • Here we present PhotoGEA, an R package with tools for C3 A-Ci, C3 Variable J, and C4 A-Ci curve fitting. In contrast to existing software, these automated tools use derivative-free optimizers to ensure close fits and they calculate nonparametric confidence limits to indicate which parameter values are most reliable. • Results from PhotoGEA's C3 A-Ci curve fitting tool are compared against other available tools, where it is found to achieve the closest fits and most reasonable parameter estimates across a range of curves with different characteristics. PhotoGEA's C3 Variable J and C4 A-Ci fitting tools are also presented, demonstrating how they can provide insights into the mesophyll conductance and the processes limiting C4 photosynthesis at high CO2 concentrations. • PhotoGEA enables users to develop data analysis pipelines for efficiently reading, processing, fitting, and analyzing photosynthetic gas exchange measurements. It includes extensive documentation and example scripts to help new users become proficient as quickly as possible.