knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(fig.width=8, fig.height=8,echo=TRUE, warning=FALSE, message=FALSE)
Stability is a critical but often unstandardized aspect of gas exchange measurements. One common approach is for operators to manually log measurements once a user-defined stable period in photosynthesis and/or conductance is reached. However this popular approach relies heavily on the individual operator and therefore may introduce variability across measurements and multiple operators within a study.
autoGoldy was developed to objectively select the most stable set of observations in a time-series of infrared gas exchange (IRGA) data on the LICOR 6400. Rather than manually logging, this approach uses the AutoLog program to continuously collect data over a measurement period (e.g. every 5 seconds for 2 – 7 min), which is then processed post-collection using the autoGoldy package in R to select a stable window in the time-series and then output the averaged data over that window along with the associated plot diagnostics (see Fig. 1).
Below we describe the methods for the two main parts of this objective selection: 1. How to collect the IRGA data time-series for the autoGoldy program on the LICOR 6400 and 2. How to install and run the autoGoldy package in R.
Figure 1. Plot diagnostics example. The autoGoldy program selects a stable window (green box) and then calculates averages for all variables during that window, which is output to a .csv file. Stability is determined by calculating slope and the coefficient of variation for a sliding window across the data and selecting the window with the minimum summed, wighted quantile. Users select one or more traits (e.g. photo, cond), window length and have the option to weight the traits and stability measures.
The IRGA data time-series must be collected and saved in a specific manner for use in the autoGoldy package. The AutoLog program on the LICOR 6400 is used to continuously collected data over a measurement period for each plant sample using the following steps:
autoGoldy is a data processing function, written in R that takes IRGA .rtf output data and chooses the most stable window. This likely represents the best estimate of physiological parameters for an individual leaf measurement.
autoGoldy can use up to two of the measurements in the .rtf file to determine stability. The default is to use COND and PHOTO, but these can be changed by the user under the "trait1" and "trait2" arguments.
The user can also adjust the weight. For example, if it is thought that COND is more sensitive to timing than PHOTO, the user may want to place more weight on these data. This can be specified in the "weights" vector.
The size of the window in the sliding window optimization protocol. Window refers to the number of observations and therefore window length is determined by the number of observations and logging frequency. For example window = 12 at a logging frequency of every 5 seconds equals a one-minute window. This window length will “slide” across all observations in the dataset.
To download from github, you need to have the R package "devtools" installed as well.
library(devtools) install_github("jtlovell/goldy")
Now load the goldy R package
library(goldy)
Set the input directory to the location of IRGA .rtf files
input.directory<-"/Users/John/Downloads/autoGoldy-Beta/exampledata"
Set the folder location for output graphics and summary information
output.directory<-"/Users/John/Downloads/autoGoldy-Beta/exampleoutput"
change window and weights as desired
out<-autoGoldy(filename="/Users/John/Downloads/autoGoldy-Beta/exampledata/CE071111_101S", trait1 = "Photo", trait2 = "Cond", window=6, weights=c(1,1,1,1))
Change window and weights as desired
out<-autoGoldy(dirname=input.directory, output.dir=output.directory, trait1 = "Photo", trait2 = "Cond", window=6, weights=c(1,1,1,1))
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