Skip to contents

Using fluxible with the Li7500

Source: vignettes/li7500.Rmd
li7500.Rmd

This vignette shows how to process ecosystem fluxes (CO2 and H2O) measured with the LI7500 tent setup presented in Halbritter et al. (2024). Because of field constraints, this setup differs from the smaller chamber in several points:

  • one file per flux
  • start and end of files do not represent start and end of measurements because setting up the tent can take more or less time
  • for similar reasons, the length of the measurements is not constant
Tent setup
Tent setup

Importing the files

We import the data with licoread::import7500.

library(licoread)
li7500_data <- import7500(
  "ex_data/li7500",
  plotinfo = c("site", "treatment", "date", "plot_id", "trial")
)

The licoread::import7500 function provides f_start and f_end, meaning we could skip fluxible::flux_match. However, f_start and f_end are based on the start and end of the files, which would require a strict data collection routine. If such a strict routine did not happen, there are two options to deal with that.

Making a new field record

If f_start and f_end cannot be used as start and end of measurement, we can recreate a record file (for example in a spreadsheet) with the real start and end datetime:

  • use start and end from licoread::import7500
  • process fluxes until visualization (flux_fitting, flux_quality and flux_plot)
  • based on the plots, create a record file with metadata identifying the fluxes (filename), start and end (note that flux_match requires datetime format)
  • rename the columns f_start, f_end and f_fluxid to avoid losing those informations (flux_match will overwrite them)
  • include flux_match before flux_fitting in your workflow, and match the fluxes with the record file you created
  • adapt the record file if necessary and rerun until flux_plot
  • once satisfied with cutting and fitting, run flux_calc as normally

That makes the processing non homogeneous but still reproducible.

One side is reliable

If the start or the end are consistent but not the other, you can change the direction of the cutting in flux_fitting. By default, flux_fitting cuts the focus window as start + start_cut to end - end_cut. The argument cut_direction allows to change that:

  • cut_direction = "from_start" means the focus window will be from start + start_cut to start + end_cut
  • cut_direction = "from_end" means the focus window will be from end - start_cut to end - end_cut

Processing with fluxible

CO2

Wet air correction:

library(fluxible)
li7500_data_co2 <- flux_drygas(li7500_data, `CO2 umol/mol`, `H2O mmol/mol`)

Fitting a linear model:

li7500_fits_co2 <- flux_fitting(li7500_data_co2,
                                f_conc = `CO2 umol/mol_dry`,
                                fit_type = "linear")

Using fluxible::flux_quality to assess the quality of the dataset.

li7500_flags_co2 <- flux_quality(li7500_fits_co2,
                                 f_conc = `CO2 umol/mol_dry`,
                                 rsquared_threshold = 0.5)
#> 
#>  Total number of measurements: 4
#> 
#>  ok   4   100 %
#>  discard      0   0 %
#>  zero     0   0 %
#>  force_discard    0   0 %
#>  start_error      0   0 %
#>  no_data      0   0 %
#>  force_ok     0   0 %
#>  force_zero   0   0 %
#>  force_lm     0   0 %
#>  no_slope     0   0 %

Plotting:

flux_plot(li7500_flags_co2,
          f_conc = `CO2 umol/mol_dry`,
          f_ylim_upper = 500,
          y_text_position = 450,
          print_plot = TRUE)
Output of `flux_plot` for CO~2~ fluxes

Output of flux_plot for CO2 fluxes

Can be exported in pdf directly (much easier to handle with a lot of data) with:

flux_plot(li7500_flags_co2,
          f_conc = `CO2 umol/mol_dry`,
          f_ylim_upper = 500,
          y_text_position = 450,
          print_plot = FALSE,
          output = "longpdf",
          f_plotname = "li7500_co2")

If other data (PAR and co) need processing, that would happen here. To “compress” environmental variables at a single value per flux, see the arguments cols_ave, cols_sum, and cols_med in fluxible::flux_calc. To keep the raw data (gas concentration or anything else) in a nested column, see the cols_nest argument.

Now let’s calculate the fluxes with fluxible::flux_calc.

li7500_fluxes_co2 <- flux_calc(li7500_flags_co2,
                               slope_col = f_slope_corr,
                               temp_air_col = Temperature,
                               setup_volume = 2197,
                               atm_pressure = pressure_atm,
                               plot_area = 1.44,
                               conc_unit = "ppm",
                               flux_unit = "umol/m2/s",
                               cols_keep = c(
                                 "site", "treatment", "date", "plot_id", "trial"
                               ))
#> Cutting data according to 'keep_arg'...
#> Averaging air temperature for each flux...
#> Creating a df with the columns from 'cols_keep' argument...
#> Calculating fluxes...
#> R constant set to 0.082057 L * atm * K^-1 * mol^-1
#> Concentration was measured in ppm
#> Fluxes are in umol/m2/s

H2O

Wet air correction:

li7500_data_h2o <- flux_drygas(li7500_data, `H2O mmol/mol`, `H2O mmol/mol`)

Fitting a linear model:

li7500_fits_h2o <- flux_fitting(li7500_data_h2o,
                                f_conc = `H2O mmol/mol_dry`,
                                fit_type = "linear",
                                start_cut = 0,
                                end_cut = 0)

Using fluxible::flux_quality to assess the quality of the dataset:

li7500_flags_h2o <- flux_quality(li7500_fits_h2o,
                                 f_conc = `H2O mmol/mol_dry`,
                                 rsquared_threshold = 0.5,
                                 ambient_conc = 10, # the default is for CO2
                                 error = 2)
#> 
#>  Total number of measurements: 4
#> 
#>  ok   4   100 %
#>  discard      0   0 %
#>  zero     0   0 %
#>  force_discard    0   0 %
#>  start_error      0   0 %
#>  no_data      0   0 %
#>  force_ok     0   0 %
#>  force_zero   0   0 %
#>  force_lm     0   0 %
#>  no_slope     0   0 %

Plotting:


flux_plot(li7500_flags_h2o,
          f_conc = `H2O mmol/mol_dry`,
          print_plot = TRUE,
          f_ylim_lower = 5,
          f_ylim_upper = 15,
          y_text_position = 12)
Output of `flux_plot` for H~2~O fluxes

Output of flux_plot for H2O fluxes

Or directly exported as a pdf:

flux_plot(li7500_flags_h2o,
          f_conc = `H2O mmol/mol_dry`,
          print_plot = FALSE,
          output = "longpdf",
          f_plotname = "li7500_h2o",
          f_ylim_lower = 5,
          f_ylim_upper = 15,
          y_text_position = 12)

Flux calculations:

li7500_fluxes_h2o <- flux_calc(li7500_flags_h2o,
                               slope_col = f_slope_corr,
                               temp_air_col = Temperature,
                               setup_volume = 2197,
                               atm_pressure = pressure_atm,
                               plot_area = 1.44,
                               conc_unit = "mmol/mol",
                               flux_unit = "mmol/m2/s",
                               cols_keep = c(
                                 "site", "treatment", "date", "plot_id", "trial"
                               ))
#> Cutting data according to 'keep_arg'...
#> Averaging air temperature for each flux...
#> Creating a df with the columns from 'cols_keep' argument...
#> Calculating fluxes...
#> R constant set to 0.082057 L * atm * K^-1 * mol^-1
#> Concentration was measured in mmol/mol
#> Fluxes are in mmol/m2/s

Calculating Gross Primary Production and Transpiration

I’m glad you asked! The function fluxible::flux_diff is precisely made for that.

li7500_fluxes_co2 <- flux_diff(li7500_fluxes_co2, type_col = trial,
                               id_cols = c(
                                 "site", "treatment", "date", "plot_id"
                               ), type_a = "p", type_b = "r",
                               diff_name = "GPP", cols_keep = "all")
li7500_fluxes_h2o <- flux_diff(li7500_fluxes_h2o, type_col = trial,
                               id_cols = c(
                                 "site", "treatment", "date", "plot_id"
                               ), type_a = "p", type_b = "r",
                               diff_name = "T", cols_keep = "all")

Further calculations

In order to calculate carbon and water use efficiencies, we need both H2O and CO2 fluxes in the same dataset.

library(tidyverse)
# to avoid confusion, we add a gas column
# this might be implemented in flux_calc in the future

li7500_fluxes_co2 <- li7500_fluxes_co2 |>
  mutate(gas = "co2")

li7500_fluxes_h2o <- li7500_fluxes_h2o |>
  mutate(gas = "h2o")

li7500_fluxes <- bind_rows(li7500_fluxes_co2, li7500_fluxes_h2o)

# Now the data are in a single long df
#> tibble [12 × 13] (S3: tbl_df/tbl/data.frame)
#>  $ trial             : chr [1:12] "GPP" "p" "r" "GPP" ...
#>  $ f_flux            : num [1:12] -5.59 -3.84 1.75 -6.57 -6.02 ...
#>  $ f_fluxid          : chr [1:12] "ACJ_C_10apr2019_plot1_p.txt" "AC"..
#>  $ f_slope_corr      : num [1:12] -0.0928 -0.0928 0.0423 -0.1452 -0...
#>  $ f_temp_air_ave    : num [1:12] 25.6 25.6 25 24.8 24.8 ...
#>  $ f_atm_pressure_ave: num [1:12] 0.664 0.664 0.665 0.664 0.664 ...
#>  $ f_datetime        : POSIXct[1:12], format: "2019-04-10 15:45:11" ..
#>  $ f_model           : chr [1:12] "linear" "linear" "linear" "linea"..
#>  $ site              : chr [1:12] "ACJ" "ACJ" "ACJ" "ACJ" ...
#>  $ treatment         : chr [1:12] "C" "C" "C" "C" ...
#>  $ date              : chr [1:12] "10apr2019" "10apr2019" "10apr201"..
#>  $ plot_id           : chr [1:12] "plot1" "plot1" "plot1" "plot2" ...
#>  $ gas               : chr [1:12] "co2" "co2" "co2" "co2" ...

References

Halbritter, A.H., Vandvik, V., Cotner, S.H., Farfan-Rios, W., Maitner, B.S., Michaletz, S.T., Oliveras Menor, I., et al. (2024), “Plant trait and vegetation data along a 1314 m elevation gradient with fire history in Puna grasslands, Perú”, Scientific Data, Nature Publishing Group, Vol. 11 No. 1, p. 225.