Introduction to fluxfinder"

In fluxfinder: Parsing, Computation, and Diagnostics for Greenhouse Gas Measurements

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

The fluxfinder package provides functions to parse static-chamber greenhouse gas measurement files generated by a variety of instruments; compute flux rates using multi-observation metadata; and generate diagnostic metrics and plots. It's designed to be easy to integrate into scientific workflows.

Load sample data

library(fluxfinder)

# Data from a LI-7810
f <- system.file("extdata/TG10-01087.data", package = "fluxfinder")
dat <- ffi_read_LI7810(f)

# Note that the fluxfinder read functions print some info after reading
# Set "options(fluxfinder.quiet = TRUE)" to suppress such messages

# Look at a subset of the data; the full data frame has 500+ rows and 25 columns
dat[1:6, 1:9]

The data frame returned by ffi_read_LI7810 is all data from the raw LI-7810 file, except that TIMESTAMP, TZ (time zone of the timestamps), SN (serial number), and MODEL columns have been added.

The analyzer data is basically a stream of measured greenhouse gas concentrations:

library(ggplot2)
ggplot(dat, aes(TIMESTAMP, CO2)) + geom_point()

Match with metadata

For these data to be useful, we need to associate them with metadata about the measurements: when they were started, how long they lasted, plot/treatment/collar information, etc.

# Accompanying metadata
md <- system.file("extdata/TG10-01087-metadata.csv", package = "fluxfinder")
metadat <- read.csv(md)

print(metadat)

Important note: in this sample metadata, our measurement identified is labeled Plot, but this could be named, and refer to, anything: bottle, sample, collar, etc. It's simply an identifier for this measurement, i.e. this row.

The ffi_metadata_match function matches up the data with metadata, using the TIMESTAMP column that ffi_read_LI7810 helpfully created when it read the data file.

dat$metadat_row <- ffi_metadata_match(
  data_timestamps = dat$TIMESTAMP,
  start_dates = metadat$Date,
  start_times = metadat$Start_time,
  obs_lengths = metadat$Obs_length + 10) # 10 is expected dead band length

# Note that ffi_metadata_match() warns us that one metadata row didn't match any data

# Based on the row match information, add a "Plot" column to the data
dat$Plot <- metadat$Plot[dat$metadat_row]
metadat$metadat_row <- seq_len(nrow(metadat))

# ...and plot
p <- ggplot(dat, aes(TIMESTAMP, CO2, color = Plot)) + geom_point()
print(p)

Some of these are clearly not correct--the measurement time seems to be shorter then 60 seconds for the C, D, and E plots:

library(lubridate)
p + annotate("rect", ymin = 455, ymax = 476, 
             xmin = ymd_hms("2022-10-27 10:39:30", tz = "EST"), 
             xmax = ymd_hms("2022-10-27 10:43:30", tz = "EST"), 
             color = "red", fill = NA, linewidth = 1.5)

In real life we'd want to correct the faulty metadata at its source. Here, we'll just change the values programmatically and re-match:

metadat$Obs_length[3:5] <- c(30, 45, 45)
dat$metadat_row <- ffi_metadata_match(
  data_timestamps = dat$TIMESTAMP,
  start_dates = metadat$Date,
  start_times = metadat$Start_time,
  obs_lengths = metadat$Obs_length + 10)
dat$Plot <- metadat$Plot[dat$metadat_row]

p %+% dat

That looks better!

Unit conversion

We'd like our final units to be in µmol/m2/s, and so need to do some unit conversion. (This can happen either before or after flux computation, below.) The package provides ffi_ppm_to_umol() and ffi_ppb_to_nmol() functions that perform this conversion using the Ideal Gas Law.

dat$CO2_umol <- ffi_ppm_to_umol(dat$CO2, 
                                volume = 0.1, # m3
                                temp = 24)    # degrees C

# See the message: because we didn't provide the 'atm' parameter, 
# ffi_ppm_to_umol assumed standard pressure.

# Also normalize by ground area (0.16 m2 in this example)
dat$CO2_umol_m2 <- dat$CO2_umol / 0.16

Note that in the example above we're using a constant system volume and measurement ground area. If that's not the case, there should be a column in the metadata providing the changing values (e.g. giving volume in m³) for each measurement. Then after calling ffi_metadata_match(), merge the data and metadata and pass the appropriate column to ffi_ppm_to_umol(). Here's an example:

# Let's say volume varies by measurement; this can happen if the chamber
# height changes depending on the ground vegetation in each plot
metadat$Volume <- c(0.1, 0.2, 0.1, 0.1, 0.3, 0.1, 0.1)

# Merge the data and metadata
dat_changing_vol <- merge(dat, metadat[c("Plot", "Volume")], by = "Plot", all.x = TRUE)

# Unit conversion as above, but using the changing volume information:
dat_changing_vol$CO2_umol <- ffi_ppm_to_umol(dat_changing_vol$CO2, 
                                             volume = dat_changing_vol$Volume,
                                             temp = 24)
# We still have constant ground area in this example
dat_changing_vol$CO2_umol_m2 <- dat_changing_vol$CO2_umol / 0.16

# Relative to the previous constant-volume example, our area-normalized
# amounts (µmol) have now increased for plots B and E because
# of their larger volumes:
aggregate(CO2_umol_m2 ~ Plot, data = dat, FUN = mean)
aggregate(CO2_umol_m2 ~ Plot, data = dat_changing_vol, FUN = mean)

Compute fluxes

The ffi_compute_fluxes function provides a general-purpose tool for computing fluxes from concentration time series, as well as associated QA/QC information. It returns statistics for four types of models: linear, robust linear, polynomial, and HM81, an exponential model derived from diffusion theory, following Hutchinson and Mosier (1981).

Model statistics include Akaike information criterion (AIC), R squared (r.squared), standard error of the residuals (sigma), and model p-value (p.value). For the robust linear regression only, a logical value converged is included; see the documentation for MASS::rlm().

Flux (slope) statistics estimate and std.error;

For the robust linear regression model only, a logical value converged.

fluxes <- ffi_compute_fluxes(dat,
                             group_column = "Plot", 
                             time_column = "TIMESTAMP", 
                             gas_column = "CO2_umol_m2",
                             dead_band = 10) 
#Here we use a constant dead band value, but this can also vary by group; see the documentation


# By default, ffi_compute_fluxes returns a data.frame with one row per
# grouping variable value (i.e., per measurement). The first column is the
# group label; the second is the average value of the `time_column`;
# and the rest of the columns are fit statistics for a linear fit of
# concentration as a function of time, along with information about polynomial
# and robust-linear fits. See ?ffi_compute_fluxes for more details.
names(fluxes)

# For clarity, print out only a subset of the columns 
fluxes[c("Plot", "TIMESTAMP", "lin_r.squared", "lin_flux.estimate", "HM81_flux.estimate")]

Note that the fluxes extract printed above has one row per Plot, the grouping variable; the mean TIMESTAMP of the group; model statistics such as lin_r.squared; and the flux estimate. The final column, HM81_flux.estimate is only numeric (i.e., not NA) when the data show evidence of a saturating curvature. So in this case we might want to examine more carefully the data from plots A, C, and F.

Plotting our computed fluxes:

ggplot(fluxes, aes(Plot, lin_flux.estimate, color = lin_r.squared)) +
  geom_point() +
  geom_linerange(aes(ymin = lin_flux.estimate - lin_flux.std.error,
                     ymax = lin_flux.estimate + lin_flux.std.error)) +
  ylab("CO2 flux (µmol/m2/s)")

We might want to check whether the robust-linear slope diverges from the linear fit slope, suggesting influential outliers, or whether the polynomial R² is much larger, potentially indicating curvature of the observations due to e.g. diffusion limitations.

ggplot(fluxes, aes(lin_flux.estimate, rob_flux.estimate, color = Plot)) +
  geom_point() + geom_abline() + theme(legend.position = "none")
ggplot(fluxes, aes(lin_r.squared, poly_r.squared, color = Plot)) +
  geom_point() + geom_abline() + theme(legend.position="none")

The plot C (green) data have more scatter, and thus lower R² values and higher uncertainty on the computed flux, but there's no strong evidence of nonlinearity or outlier problems (although see note above about the HM81_estimate field).

A clearly nonlinear case

In our experience, static-chamber fluxes are frequently linear (Forbrich et al. 2010) even though molecular diffusion means that chamber feedbacks will inevitably lead to curvilinear (saturating) behavior over time (Pedersen et al. 2010).

Still, how does one use fluxfinder to diagnose and work with nonlinear data?

Let's use R's built-in Puromycin dataset (simply because it exhibits saturating behavior; it's unrelated to greenhouse gases) as an example:

ggplot(Puromycin, aes(conc, rate)) + geom_point() + geom_smooth(method = "lm")

Visually, we can see that a linear model will not be appropriate in this case.

ffi_compute_fluxes(Puromycin,
                   group_column = NULL,
                   time_column = "conc",
                   gas_column = "rate")

x <- ffi_compute_fluxes(Puromycin,
                        group_column = NULL,
                        time_column = "conc",
                        gas_column = "rate")
x <- round(x, 3)

From the diagnostics returned by ffi_compute_fluxes:

• HM81_flux.estimate is not NA, which only occurs with saturating behavior;
• The lin_AIC (r x$lin_AIC) and rob_AIC (r x$rob_AIC) Akaike information criterion values are similar, so no indication of influential outliers;
• The lin_r.squared (r x$lin_r.squared) and poly_r.squared (r x$poly_r.squared) values are very different, suggesting a failure of the linear model;
• The root mean square error (RMSE) of the linear model is much higher than the other models' values;
• The HM81_r.squared (r x$HM81_r.squared) and HM81_AIC (r x$HM81_AIC) are considerably higher and lower, respectively, than the linear model.

All of these metrics point to a common conclusion: a linear model is not appropriate for these data. If these were real data, we should use the HM81_flux.estimate value as our flux estimate.

Conclusion

This vignette covered fluxfinder basics: loading data and metadata, matching the two, performing unit conversion, computing fluxes, and some basic QA/QC. The test data we worked above could be fit well by linear model, but for many reasons this might not always be true; see the vignette on integrating with the gasfluxes package for guidance on using more sophisticated model-fitting routines.

fluxfinder documentation built on Sept. 9, 2025, 5:39 p.m.