Edward B Lochocki, Coralie E Salesse-Smith, Justin M McGrath
{"title":"PhotoGEA: An R Package for Closer Fitting of Photosynthetic Gas Exchange Data With Non-Gaussian Confidence Interval Estimation.","authors":"Edward B Lochocki, Coralie E Salesse-Smith, Justin M McGrath","doi":"10.1111/pce.15501","DOIUrl":null,"url":null,"abstract":"<p><p>Fitting mechanistic models, such as the Farquhar-von-Caemmerer-Berry model, to experimentally measured photosynthetic CO<sub>2</sub> response curves (A-C<sub>i</sub> 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 C<sub>3</sub> A-C<sub>i</sub>, C<sub>3</sub> Variable J and C<sub>4</sub> A-C<sub>i</sub> curve fitting. In contrast to existing software, these automated tools use derivative-free optimizers to ensure close fits and they calculate non-Gaussian confidence intervals to indicate which parameter values are most reliable. Results from PhotoGEA's C<sub>3</sub> A-C<sub>i</sub> 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 C<sub>3</sub> Variable J and C<sub>4</sub> A-C<sub>i</sub> fitting tools are also presented, demonstrating how they can provide insights into mesophyll conductance and the processes limiting C<sub>4</sub> photosynthesis at high CO<sub>2</sub> concentrations. PhotoGEA enables users to develop data analysis pipelines for efficiently reading, processing, fitting and analysing photosynthetic gas exchange measurements. It includes extensive documentation and example scripts to help new users become proficient as quickly as possible.</p>","PeriodicalId":222,"journal":{"name":"Plant, Cell & Environment","volume":" ","pages":""},"PeriodicalIF":6.0000,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Plant, Cell & Environment","FirstCategoryId":"2","ListUrlMain":"https://doi.org/10.1111/pce.15501","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PLANT SCIENCES","Score":null,"Total":0}
引用次数: 0
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 non-Gaussian confidence intervals 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 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 analysing photosynthetic gas exchange measurements. It includes extensive documentation and example scripts to help new users become proficient as quickly as possible.
期刊介绍:
Plant, Cell & Environment is a premier plant science journal, offering valuable insights into plant responses to their environment. Committed to publishing high-quality theoretical and experimental research, the journal covers a broad spectrum of factors, spanning from molecular to community levels. Researchers exploring various aspects of plant biology, physiology, and ecology contribute to the journal's comprehensive understanding of plant-environment interactions.