In PaulESantos/co2fluxtent: Tools for NEE and ET Fitting from CO2 Flux

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co2fluxtent

Welcome to the official repository for the co2fluxtent package – an R tool designed to facilitate the extraction of Net Ecosystem Exchange, Transpiration, and Evapotranspiration Flux Measurements from LiCOR 7500 data.

About `co2fluxtent`

co2fluxtent is a comprehensive R package that simplifies the process of analyzing data from LiCOR 7500 gas analyzers. This package provides a complete suite of tools for theoretical modeling, curve-fitting, and data analysis. It's ideal for researchers, ecologists, and environmental scientists looking to unlock insights into the exchange of CO2 and H2O in ecosystem flux data.

Introduction

This repository serves as your gateway to understanding and utilizing co2fluxtent. Our goal is to equip you with the knowledge and resources needed to perform accurate flux measurements with ease. The package implements both linear and non-linear curve-fitting methodologies, allowing you to calculate net ecosystem exchange of CO2 in µmol/m²/s and the exchange of H2O due to transpiration and evapotranspiration in mmol/m²/s. Furthermore, it provides Akaike Information Criterion (AIC) scores to help you choose the most suitable fit.

Installation

You can install the development version from GitHub with:

# install.packages("pak")
pak::pak("PaulESantos/co2fluxtent")

#or

devtools::install_github("PaulESantos/co2fluxtent")

LiCOR Filename Formatting

Given that LiCOR measurements are often conducted multiple times, our code is designed to efficiently batch analyze individual LiCOR runs. It is currently expected that all data to be processed adheres to the following naming format:

./co2fluxtent/extdata/06172020_almont_night_3a.txt

The measurementtype term plays a crucial role in identifying the type of measurement, and the code assumes specific default patterns to recognize the measurement type. It is important to note that these patterns should be the last term in the filename. Here's what each pattern signifies:

The letter a denotes an ambient measurement when the tent was not applied.
The term photo represents a measurement conducted when the tent was applied.
The term resp signifies a measurement conducted when the tent was applied and shaded.

By adhering to this standardized naming convention, our code streamlines the process of batch analyzing LiCOR data, making it more efficient and convenient for users.

Usage

The read_files function serves as a crucial component in our data analysis workflow. It is designed to streamline the process of gathering and organizing LiCOR data for analysis. By providing a path to the data files and specifying patterns to identify different types of measurements (ambient, photo, resp), the function simplifies the data retrieval process.

Here's how to use it:

Set your working directory to the folder containing your LiCOR data files.
Call the read_files function, specifying the path to the data files and the patterns for ambient, photo, and resp measurements.
The function will automatically identify and retrieve the relevant data files based on your specified patterns.
It returns a structured list of LiCOR data files for further analysis.

This makes it easier to work with multiple LiCOR data files, ensuring that you can quickly and efficiently access the data you need for your analysis. The read_files function is a valuable tool for anyone working with LiCOR data, simplifying the initial data preparation steps in your workflow.

library(co2fluxtent)

# These data files are provided to help you get started quickly and understand the data processing workflow

licor_files <- co2fluxtent::read_files(fs::path_package(package = "co2fluxtent",
                 "extdata"))

No matching photo files found.

> print(licor_files)

$photo_names
character(0)

$ambient_names
[1] "./co2fluxtent/extdata/06172020_almont_night_1a.txt"
[2] "./co2fluxtent/extdata/06172020_almont_night_3a.txt"

$resp_names
[1] "./co2fluxtent/extdata/06172020_almont_night_1resp.txt"
[2] "./co2fluxtent/extdata/06172020_almont_night_3resp.txt"

The next step is to utilize the flux_calc() function, a valuable tool for analyzing LiCOR data. This function takes the results obtained from the read_files() function as its input, seamlessly integrating data processing into your analysis workflow. With flux_calc(), you can perform both linear and non-linear fitting to assess Net Ecosystem Exchange (NEE) or Evapotranspiration (ET) data, providing invaluable insights into carbon dioxide and water vapor flux dynamics. To select the parameter for calculation, you can modify the param argument, with the default set to "et". The skip argument allows you to skip the first 'n' rows of data, and you can specify the volume of the chamber using the vol argument. Additionally, the area argument represents the chamber's area in square meters.

licor_data <- licor_files |> 
  co2fluxtent::flux_calc(param = "nee", 
                         skip = 9,
                         vol = 2.197, 
                         area = 1.69)

data |> 
  dplyr::mutate(filename = basename(filename))

While the flux_calc() function is in operation, it will prompt the user to specify the start and end times for conducting the curve fitting. This interactive feature empowers the user to exclude potentially transient data patterns detected during the data collection process. After the user selects the desired time range, both fitting procedures are executed automatically, and the results are promptly displayed in the console window. The output of the flux_calc() function is a tibble with the following columns:

filename: The name of the file
tstart: The start time of the measurement
tfinish: The end time of the measurement
camb: The ambient CO2 concentration
tav: The ambient air temperature
pav: The ambient air pressure
nee_lm: The linear model fit of the NEE data
nee_exp: The non-linear model fit of the NEE data
lm_rsqd: The R-squared value of the linear model fit
non_linear_sigma: The sigma value of the non-linear model fit
aic_lm: The AIC score of the linear model fit
aic_nlm: The AIC score of the non-linear model fit

# A tibble: 2 × 12
  filename                         tstart tfinish  camb   tav   pav  nee_lm nee_exp lm_rsqd non_linear_sigma aic_lm aic_nlm
  <chr>                             <int>   <int> <dbl> <dbl> <dbl>   <dbl>   <dbl>   <dbl>        <dbl>      <dbl>   <dbl>
1 06172020_almont_night_1resp.txt    20      60    308. -65.7  75.7  -25.5   -12.0    0.952         3.23       137.    211. 
2 06172020_almont_night_3resp.txt    10      80    305. -65.7  75.7  -0.194  -0.157   0.0369        0.364      60.9    61.2