Data from: Stomatal response to VPD in C4 plants with different biochemical subpathways
Data files
Aug 20, 2024 version files 88.02 KB

Full_Aci_1.csv

README.md
Abstract
C_{4} plants are integral to many ecosystems around the world and are abundantly found in grasslands and savannas in North America, Australia, and Africa, and deserts in Central Asia among other ecosystems in and around Europe (Edwards and Still 2008; Pyankov et al. 2010; Rudov et al. 2020; Wan et al. 2001). Despite making up 20% of the global plant biomass (Ehleringer et al. 1997), our understanding of C_{4} physiology and biochemistry still has room for improvements. More specifically, the three subtypes of C_{4} plants (NADPmalic enzyme, NADmalic enzyme, and PEP carboxykinase) should be better characterized in terms of their biochemistry. To do that, we require a comprehensive characterization of species from across C_{4} families, not just model C_{4} species. However, it is often laborintensive to collect the full gamut of physiological and biochemical data for large numbers of species. Here, I describe a hierarchical Bayesian approach in parametrizing the C_{4} photosynthesis model that can estimate biochemical parameters using gas exchange data while accounting for plant, species, and subtype variation.
README: Data from: Stomatal response to VPD in C4 plants with different biochemical subpathways
https://doi.org/10.5061/dryad.vx0k6djz5
This dataset contains a file of net CO2 assimilation rate (photosynthesis rate) vs intercellular CO2 response for twelve C4 species measured using LiCor 6400XT Portable Photosynthesis System. There are five replicates for each species which were grown in a growth chamber. This dataset can be used in the Bayesian model of C4 photosynthesis coded in R using the package rJAGS (included in the files). The C4 photosynthesis process model is taken from von Caemmerer's C4 photosynthesis model. The Bayesian approach allows hierarchical parametrization of key biochemical variables and can account for error in the data, thus making parametrizations more robust. This model allows the parametrization of maximum ribulose1,5bisphosphate carboxylase/oxygenase (Rubisco the main photosynthetic enzyme) activity, maximum PEP carboxylase activity, among other important components of C4 photosynthesis with the use of experimental data of photosynthesis rate.
Description of the data and file structure
The data file containing photosynthesis response to intercellular CO2 data (Full_ACi_1.csv) is in long format, meaning that there are several rows of entry for each replicate. The column with the title "A" is net CO2 assimilation rate (photosynthesis rate) with the units of µmol m^2s^1 and "Ci" is intercellular CO2 concentration in µmol m^2s^1. Each replicate is indicated by the "Biorep" column as well as the "Plant" column. The abbreviation of the species name is given in the first column under "Species" and is as follow: Acara = Alternanthera caracasana; Ainc = Allionia incarnata; Aretro = Amaranthus retroflexus; Aros = Atriplex rosea; Bcocc = Boerhavia coccinea; Kscop = Kochia scoparia; Pami = Portulaca amilis; Pcap = Panicum capillare; Pole = Portulaca oleracea; Svir = Setaria viridis; Tport = Trianthema portulacastrum; Zpent = Zaleya pentandra. There is a column for the C4 subtype called "Subtype". The "Pairing" column refers to species that are from the same family. For example, Acara and Aretro are both from pair a because they are both from the Amaranthaceae family. "Pressure" refers to the atmospheric pressure in kilopascal (kPa) during which the measurements were taken. "Temp" column is temperature in Celsius. "Ci_p" column is the intercellular CO2 in partial pressure (kPa). "Cm" is the mesophyll CO2 concentration in µmol m^2s^1 as measured by the LiCor 6400XT. "Qobs" is the light level in photosynthetically active radiation (PAR) at which the measurements were taken.
There are two R scripts: one in R markdown format of the interface for running the Bayesian model; one in R script format of the Bayesian model. The R markdown file (C4 model for pub.Rmd) is where the data set is loaded into the model and all the starting parameters of the model are listed. The file also contains the R code for executing the Bayesian model as well as the GelmanRubin test for convergence. Comments are provided within the file to aid with clarity and usage. The R script file (Vsimp C4 JAGS model 3.R) contains the code for the model itself, which includes all of the equations of the C4 photosynthesis model as well as the hierarchical Bayesian structure for parametrizing the model. The code includes output for the photosynthesis rate (A) as predicted by the model which can be compared to the experimental data of photosynthesis rate. "Aobs" are the photosynthesis rate values as measured experimentally, and "Apred" are the photosynthesis rate values predicted by the model. Both are assumed to follow a normal distribution. All the variable and parameter names are exactly as written in the original C4 photosynthesis model by von Caemmerer, 2000. Please refer to the publications for explanation of all constants and parameters.
Code/Software
The code is run using R version 4.3.2 in RStudio Version 2023.9.1.494 using JAGS and interfaced through the rJAGS package. Please be sure to set the working directory to where all the scripts and data file are.
Methods
To enable a comparative analysis, we compared 6 closely related C_{4} lineages that each contain an independent origin of NADME and NADPME subtypes, for a total of 12 species.
We analyzed the response of net CO_{2} assimilation rate (A) to intercellular concentration of CO_{2} (C_{i}) and we measured the response of stomatal conductance (g_{s}) to VPD. All gas exchange measurements were conducted with two Li6400 Portable Photosynthesis Systems (LiCor Biosciences, Lincoln, NE, USA) over a course of five months. We conducted measurements at a constant leaf temperature of 30 °C, PPFD of 1800 µmol m^{2}s^{1}, and CO_{2} concentration at ambient level of 400 ppm, unless otherwise noted.
We used a hierarchical Bayesian approach to model C_{4} photosynthesis to estimate maximum PEPC activity (V_{pmax}) and maximum Rubisco activity (V_{cmax}) from the A/C_{i} data. The hierarchical Bayesian approach followed methods of Patrick et al. (2009) and allowed us to estimate multiple parameters (namely V_{pmax} and V_{cmax}) simultaneously, as well as accounting for observational error in the data set while identifying uncertainty in the estimated parameters. We used a C_{4} photosynthesis model (von Caemmerer 2000) that was simplified to only estimate PEPClimited and Rubiscolimited values of A. From the model, we estimated the C_{i} where the transition from PEPClimited to Rubiscolimited A occurred. Our hierarchical model had three main components: the likelihood equation of observing the data, the deterministic model which is the C_{4} photosynthesis model (von Caemmerer 2000), and the prior distributions of the variables involved in the deterministic model. These components generated posterior distributions of V_{pmax}, V_{cmax}, and the transition C_{i} between the limitations. Because this was a hierarchical model, we were able to set parameters to vary at either the species level or the plant level. V_{pmax}, V_{cmax}, and the transition point were all set to vary at the plant level such that each plant can have its own distinct value. The MichaelisMenten constants for CO_{2} for PEPC and for O_{2} and CO_{2} for Rubisco were allowed to vary at the species level such that all the replicates within a species shared the same value. All other parameters in the model were included as constants. Because there were small variations in temperature during gas exchange measurements (28.3 – 32.1 °C) and the oxygenation and carboxylation rates of Rubisco and carboxylation rates of PEPC are temperature dependent, we used an Arrhenius function to standardize all values to 30 °C (von Caemmerer, 2000). The hierarchical Bayesian photosynthesis model was implemented using JAGS (Plummer 2003) interfaced with RStudio using the package “rJAGS” (Plummer et al. 2022). We ran three parallel Markov chain Monte Carlo (MCMC) chains for 30 000 iterations each and the chains were evaluated for convergence using the GelmanRubin diagnostic (Gelman and Rubin 1992). The burnin samples (first 5000) were discarded. The model goodnessoffit was evaluated by generating predicted A values and comparing these with observed A values. If the model was a good fit, then the predicted A and observed A should fall along the 1:1 line.