1. LI-6800 data and recalculations
The data used in this example is a CO2 response curve
measured at low oxygen. Before loading the data files, the calibration
factors for the instrument used to acquire the data must be imported.
This is only necessary for some advanced functionality used later, and
currently only available for calibration factors stored by the LI-6800
instrument.
The code below shows how to load the data from the .txt
and .xlsx files.
exampleFiles <- system.file("extdata", package = "gasanalyzer")
# import calibration factors for the 6800 used here:
import_factory_cals(exampleFiles)
# load data
xlsxfile <- read_6800_xlsx(paste(exampleFiles, "lowo2.xlsx", sep = "/"))
txtfile <- read_6800_txt(paste(exampleFiles, "lowo2", sep = "/"))
The LI-6800 confusingly stores the results of divisions by zero as
zero in the txt file. After excluding columns containing
the results of divisions by zero and the column with equations (a list
column), there are very few differences between both files:
# exclude list columns with equations, and divisions by zero
excludeCols <- vapply(xlsxfile,
FUN=\(x) is.list(x) ||
all(is.infinite(x)) ||
all(is.nan(x)), TRUE)
# Interestingly, Leak.Fan reported in the txt file differs slightly
# from that in the xlsx. It is likely caused by rounding/averaging in the
# instrument firmware.
all.equal(txtfile[!excludeCols], xlsxfile[!excludeCols], tolerance = 1e-6)
#> [1] "Component \"Leak.Fan\": Mean relative difference: 0.0012"
Using plotting facilities available with R,
relationships in the data can be visualized. In the following example,
var2label() was used to replace the variable names in the
data with commonly-used symbols for the plot. The relation between net
photosynthesis (\(A\)) and
intercellular CO2 (\(C_i\))
is shown.
# default points:
update_geom_defaults("point", list(shape = 1, size = 3))
# convenience function to change plot labels automatically:
nice_labels <- function(plt) {
plt$labels <- var2label(plt$labels)
plt
}
#generate some descriptive stats and plotmath it with a call to var2label:
descr <- colMeans(xlsxfile[c("LeafQ.Qin", "Meas.Tleaf", "Const.Oxygen")])
dlist <- var2label(rep(names(descr), 2),
use_units = rep(c(FALSE, TRUE), each = 3),
val = c(rep(NA, 3), as.character(descr)),
group = c("", "")) |>
setNames(letters[1:6]) |> unlist() |>
substitute(a==d*","~b==e*","~c==f, env = _ ) |> as.expression()
AvsCi1 <- xlsxfile |> ggplot(aes(GasEx.Ci, GasEx.A)) + geom_point() +
labs(title = dlist)
nice_labels(AvsCi1)
#> Warning: The `scale_name` argument of `continuous_scale()` is deprecated as of ggplot2
#> 3.5.0.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
If a researcher would like to make changes to some of constants used
by the instrument for calculating derived variables, all data should be
recalculated to take the effect into account. For example, an
application note by the instrument vendor shows how to use
complimentary measurements with a porometer to determine the ratio of
stomatal conductances between both sides of the leaves. For the leaf in
this experiment, the ratio was 0.22. Such corrections typically have
only very small effects because of the high boundary layer conductance
in the leaf chambers that are commonly used for such experiments.
gswOld <- xlsxfile$GasEx.gsw
xlsxfile <- xlsxfile |>
transform(Const.K = 0.22) |>
recalculate()
#max effect:
set_units(max(1 - as.numeric(xlsxfile$GasEx.gsw / gswOld)) * 100, "%")
#> -0.76 [%]
Gas-exchange measurements can be combined with measurements of
chlorophyll fluorescence to obtain estimates of the electron transport
rate (\(J_F\)) through photosystem 2
(PSII). However, this requires knowledge of the amount of light energy
absorbed by the leaf (\(\alpha\)), and
the fraction of energy partitioned to PSII (\(\beta\)):
\[J_F = \alpha \beta \Phi_{PSII}
Q_{in}\]
where \(\Phi_{PSII}\) is the
operating quantum efficiency of PSII, and \(Q_{in}\) is the light intensity incident on
the leaf.
Calculations implemented by the vendor typically assume \(\beta\) is 0.5 and \(\alpha\) is around 0.85 (possibly adjusted
for the spectrum of the light source used). An interesting application
of this electron transport estimate is to to back-calculate the
CO2 concentration at the site of carboxylation, and by
extension estimate the mesophyll conductance (\(g_m\)).
However, leaves vary in the amount of light absorbed. Moreover, not
all of the absorbed light is used for photosynthetic carbon reduction.
To address such concerns, an additional estimate for the electron
transport rate (\(J_C\)) may be
obtained by measuring gas exchange under non-photorespiratory conditions
(i.e. at low oxygen). In this case, it follows from the C3
biochemical model that:
\[J_C \approx 4(A + R_L) \to A \approx
J_C/4 - R_L\]
where \(R_{L}\) represents the dark
respiration rate in the light. Assuming that alternative electron flows
are minimal, the following relationship can be derived (Yin et
al. 2009):
\[A \approx J/4 - R_L = \alpha \beta
\Phi_{PSII} Q_{in} / 4 - R_L\]
However, applying this assumptions on the data used here shows that
either this model is too simple, or the assumed values for \(\alpha\) and/or \(\beta\) are incorrect:
#confirm that the slope of A vs ETR is not 1:
xlsxfile |>
lm(GasEx.A ~ I(FLR.ETR/4), data = _) |> coef() |> _[[2]]
#> [1] 1.1
If the goal is to estimate \(g_m\),
it has been recommended (e.g. Yin et al. 2009) to not rely on the \(\alpha \beta\) outputted by the instrument
(0.42 in this case), but instead use a linear regression to find an
“effective” value:
# a linear regression:
lm1 <-
xlsxfile |> transform(FLR.PhiQin_4 = FLR.phiPS2 * LeafQ.Qin / 4) |>
lm(GasEx.A ~ FLR.PhiQin_4, data = _)
alphabeta <- lm1$coef[[2]]
The following plots the result of the regression:
AvsPhiQin1 <- ggplot(lm1$model, aes(x = .data[[names(lm1$model)[2]]],
y = .data[[names(lm1$model)[1]]])) +
geom_point() +
stat_smooth(method = "lm", formula = y ~ x) +
labs(subtitle = bquote(alpha ~ beta == .(lm1$coef[[2]]) ~~~
R[L] == .(-lm1$coef[[1]])))
nice_labels(AvsPhiQin1)
The fitted value for the slope can now be used to adjust the constant
value used by the instrument. The default equations used by the LI-6800
recalculate \(\alpha\) based on the
spectral composition of the light source. These equations are stored as
R calls in the gasanalyzer.Equations column.
They can also be manually extracted from a given xlsx file
using extract_6800_equations() and edited using
modify_equations():
# a long equation estimating the light absorption:
xlsxEqs <- xlsxfile$gasanalyzer.Equations[[1]]
print(xlsxEqs$LeafQ.alpha)
#> (QConst.fQambIn * LQConst.AbsAmbient + QConst.fQambOut * LQConst.AbsAmbient +
#> QConst.fQflr * ((FlrLS.Qred + FlrLS.Qmodavg)/pmax(FlrLS.Qred +
#> FlrLS.Qmodavg + FlrLS.Qblue, 0.1) * LQConst.RedAbsFlr +
#> FlrLS.Qblue/pmax(FlrLS.Qred + FlrLS.Qmodavg + FlrLS.Qblue,
#> 0.1) * LQConst.BlueAbsFlr))/(QConst.fQambIn + QConst.fQambOut +
#> QConst.fQflr)
# add our custom alpha by dividing it through the used beta:
xlsxfile$Custom.alpha <- alphabeta / xlsxfile$Const.fPS2
# and change the equation to use the new value:
xlsxEqs <- modify_equations(xlsxEqs, LeafQ.alpha = \() {Custom.alpha})
# make a copy of the data and apply the new equations:
xlsxfileCopy <- xlsxfile |>
transform(gasanalyzer.Equations = list(xlsxEqs)) |>
recalculate()
# confirm that the slope of A vs ETR is now 1:
xlsxfileCopy |>
lm(GasEx.A ~ I(FLR.ETR / 4), data = _) |> coef() |> _[[2]]
#> [1] 1
The xlsx equations imported from the file are typically
sufficient for simple recalculations, but limited to the specific set of
equations defined in the file. Moreover, in case only text-based data
files are available, an alternative way to recalculate data is required.
To handle calculations more comprehensively for all types of data,
gasanalyzer includes a several sets of equations related to
gas exchange, chlorophyll fluorescence and carbon isotope
discrimination. To ensure greater reliability, units of the used
quantities in these equations are automatically enforced by the
units package. In the code below, the data is
recalculated using the default set, with additional instrument specific
computations for the LI-6800. In addition, a several equations are added
to take the oxygen concentration stored in the data into account when
recalculating the gas mole fractions. Finally, \(\alpha\) is set to the previously fitted
value.
RecalEqs <- create_equations(c("li6800", "default", "O2_correction"),
LeafQ.alpha = \() {Custom.alpha})
xlsxfileRecal <- recalculate(xlsxfile, RecalEqs)
all.equal(xlsxfileRecal[names(xlsxfile)], xlsxfile, tol = 0.001)
#> [1] "Component \"GasEx.NetTherm\": Mean relative difference: 0.0015"
#> [2] "Component \"LeafQ.Qabs\": Mean relative difference: 0.096"
#> [3] "Component \"LeafQ.alpha\": Mean relative difference: 0.096"
#> [4] "Component \"FLR.QabsFs\": Mean relative difference: 0.096"
#> [5] "Component \"FLR.ETR\": Mean relative difference: 0.096"
#> [6] "Component \"FLR.phiCO2\": Mean relative difference: 0.11"
#> [7] "Component \"gasanalyzer.UseEqUnits\": 15 element mismatches"
Small differences are found in variables as a result of the new \(\alpha\) and in a value related to the
energy balance. The latter is a result of a more precise conversion from
Celsius to Kelvin units. Equations can be checked and modified before
use, and custom equations can be added as R functions:
# providing a bad name will show a warning with valid names:
try <- create_equations("help")
#> Warning in create_equations("help"):
#> Valid flags are: default, li6400, li6800, gfs3000, ciras4,
#> cuticular_conductance, GoffGratch1946, Buck1981, Buck1996,
#> gfs3000_light_bot, gm_fluorescence, d13C, d13C_e_Busch2020, d13C_dis,
#> match, raw, O2_correction
#> Warning in create_equations("help"): No valid equations found!
# an equation set for gm, with a custom equation for resistance:
create_equations("gm_fluorescence", FLR.rm = \(x) {1 / FLR.gm})
#> $FLR.Cc
#> {
#> GP <- GasEx.A + g0(Const.RL, NA_real_, "µmol*m^-2*s^-1")
#> JF <- g0(FLR.ETR, NA_real_, "µmol*m^-2*s^-1")
#> g0(Const.GammaStar, NA_real_) * (JF + 8 * GP)/(JF - 4 * GP)
#> }
#>
#> $FLR.gm
#> {
#> GasEx.A/(GasEx.Ci - FLR.Cc)
#> }
#>
#> $FLR.rm
#> {
#> 1/FLR.gm
#> }
#>
#> $gasanalyzer.UseFlags
#> [1] "gm_fluorescence"
The calibration of infra-red gas analyzers is sensitive to the
gas-mixture used for the measurements. Thus, when doing measurements are
done under low oxygen, this has to be taken into account. Analyzers
produced by LI-COR Inc. or Heinz Walz GmbH allow an oxygen concentration
to be specified or measured and adjust the reported concentrations based
on several calibration factors. These equations are unfortunately not
included in the stored data files, but are included when either the
O2_correction or raw flags are passed to
create_equations() as done above. The latter allows for
recalculation from raw absorptance values but requires such data to be
stored during the measurement, which is not done by default. The
O2_correction flag back-calculates such raw values based on
the original O2 concentration specified in the data, and then
recalculates gas mole fractions based on a potentially updated
O2 concentration. It must be added that since the data
reported by the instrument may be averaged before storing, recalculating
the data at different oxygen levels may not always be exact.
The equations depend on calibration values that depend on the
instrument type, and it may be a good idea to verify the calibration
factors. For the current example, the calibration factors for the used
instrument need to be imported before reading the data (as was mentioned
at the top of this section).
To investigate how much the results would differ when a user forgot
to enter the correct O2 concentration during the measurement,
I have recalculated the data using an oxygen concentration of 21 %:
xlsxfile21 <- xlsxfileRecal |>
transform(Const.Oxygen = set_units(21, "%")) |>
recalculate(RecalEqs)
And plot the results:
AvsCi21 <- xlsxfile21 |> ggplot() +
geom_point(aes(GasEx.Ci, GasEx.A, shape = "21% O2")) +
geom_point(aes(xlsxfileRecal$GasEx.Ci,
xlsxfileRecal$GasEx.A, shape = "1% O2")) +
scale_shape_manual(name = "Calculated at:",
values = c("1% O2" = 1, "21% O2" = 2)) +
theme(legend.position = "inside", legend.position.inside = c(0.7, 0.3))
AvsPhiQin21 <-
xlsxfile21 |>
lm(GasEx.A ~ FLR.PhiQin_4, data = _) |> (\(x) {
ggplot(x$model, aes(x = .data[[names(x$model)[2]]],
y = .data[[names(x$model)[1]]])) +
geom_point() +
stat_smooth(method = "lm", formula = y ~ x) +
labs(subtitle = bquote(alpha ~ beta == .(x$coef[[2]]) ~~~
R[L] == .(-x$coef[[1]]))) +
geom_point(data = xlsxfileRecal, shape = 2)
})()
grid.arrange(nice_labels(AvsCi21), nice_labels(AvsPhiQin21), ncol = 2)
The figure confirms that since the correction mainly affects water
concentrations, variables such as \(g_{sw}\) and therefore also \(C_i\) are most affected. However the effect
on \(C_i\) remained below 5%. It is
noteworthy that the effect of the oxygen correction is negligible for
the assimilation rate, and therefore also does not affect the relation
between \(A\) and \(J_F\):
xlsxfileRecal |>
lm(GasEx.A ~ I(FLR.ETR / 4), data = _) |> coef()
#> (Intercept) I(FLR.ETR/4)
#> -2.6 1.0
xlsxfile21 |>
lm(GasEx.A ~ I(FLR.ETR / 4), data = _) |> coef()
#> (Intercept) I(FLR.ETR/4)
#> -2.6 1.0
2. GFS-3000, loading multiple files and doing a sensitivity
analysis
Using base R functions, multiple files can easily be concatenated and
used with gasanalyzer:
aciFiles <- list.files(exampleFiles, "aci\\d\\.csv", full.names = TRUE)
ACi <- lapply(aciFiles, \(x) {print(basename(x)); read_gfs(x, tz = "CEST")}) |>
do.call("rbind", args = _)
#> [1] "aci1.csv"
#> [1] "aci2.csv"
#> [1] "aci3.csv"
data.frame(aggregate(list(RH = ACi$GasEx.RHcham),
list(file = ACi$SysObs.Filename), mean),
RH.SD = aggregate(list(ACi$GasEx.RHcham),
list(ACi$SysObs.Filename), sd)[, 2]) |>
knitr::kable()
| aci1 |
75 [%] |
0.64 |
| aci2 |
74 [%] |
1.30 |
| aci3 |
74 [%] |
0.66 |
The \(C_i\) estimates provided by
the GFS-3000 instrument do not take boundary layer conductance into
account. gasanalyzer provides the possibility to
recalculate the data using predefined sets of equations. The boundary
layer conductance (\(g_{bw}\)) of the
default chamber at different fan speeds was provided by the instrument
vendor (Heinz Walz GmbH). However, the effect on \(C_i\) is small when the stomatal
conductance (\(g_s\)) is much lower
than \(g_{bw}\):
gfsEqs <- create_equations(c("default", "gfs3000"))
ACi2 <- recalculate(ACi, gfsEqs)
all.equal(ACi$GasEx.Ci, ACi2$GasEx.Ci)
#> [1] "Mean relative difference: 0.0042"
The relationship between \(A_n\) and
\(C_i\) can be used to obtain in
vivo estimates for parameters related to biochemical processes
underlying photosynthesis. The R package photosynthesis implements
methods to obtain such estimates. Recently it has been suggested that the
\(C_i\) values calculated using
equations implemented in most gas-exchange instruments are biased
because the underlying theory did not account for gas fluxes through the
cuticula. Since estimates for cuticular conductance are not widely
available, it can be useful to perform a sensitivity analysis on
gas-exchange data to investigate the possible consequences of limiting
cuticular conductance. A first assessment of the importance of including
cuticular conductance was recently published. Here, an example is
shown that includes a similar analysis, but also test for the effect of
potential bias in assumed values for the ratio of stomatal conductances
between both sides of the leaf (Const.K), cuticular
conductance (Const.gcw), assumed relative humidity in the
stomatal cavities (Const.RHi), and the measured values for
the leaf temperature (Meas.Tleaf) and H2O mol
fraction reported by the instrument (Meas.H2Or). The
permutate() method was used to create a range of values for
these quantities while keeping all other measured and assumed variables
constant. Subsequently all derived variables are recomputed:
# we define some constants that specify the relative value of the variables
# on which we will do a sensitivity analysis:
SAset <- c("Const.calk", "Const.calg", "Const.calrhi", "Const.calt",
"Const.calh")
# symbols for the plot header:
SAlabs <- c("italic(K)", "italic(g)[cw]", "RH[i]",
"italic(T)[leaf]", "'['*H[2]*O*']'[r]")
# set defaults to 1 (100%) and define sequences over which to permutate
ACi[SAset] <- 1
# a 5% range:
seq5 <- seq(-0.05, 0.05, length.out = 11)
seq5 <- seq5[seq5 != 0]
# make equations allowing to vary the variables by a relative value:
MSFeqs <- create_equations(c("default", "gfs3000", "cuticular_conductance"),
Const.K = function() { Const.calk * Const.K },
Meas.Tleaf = function() { Const.calt * Meas.Tleaf },
Const.gcw = function() { Const.calg * Const.gcw },
Const.gcc = function() { Const.gcw / 20 },
Const.RHi = function() { Const.calrhi * Const.RHi },
Meas.H2Or = function() { Const.calh * Meas.H2Or },
Meas.H2Os = function() { Const.calh * Meas.H2Os })
# set gcw and use steady-state equations, then use the sequences
ACi[["Const.gcw"]] <- set_units(25, "mmol*m^-2*s^-1")
ACi[["Const.RHi"]] <- set_units(95, "%")
ACi[["SysConst.UseDynamic"]] <- FALSE
ACi.SA <- rbind(permutate(ACi, Const.calk = c(1, 1 + 20 * seq5)),
permutate(ACi, Const.calg = 1 + 20 * seq5),
permutate(ACi, Const.calrhi = 1 + seq5),
permutate(ACi, Const.calt = 1 + seq5),
permutate(ACi, Const.calh = 1 + seq5)) |>
recalculate(MSFeqs)
In the next step, the photosynthesis package was used to
obtain estimates for the carboxylation capacity (\(V_{cmax}\)), electron transport rate (\(J\)), triose-phosphate limitation (\(T_p\)) and the respiration rate in the
light (\(R_L\)). To facilitate analysis
of the results, the following function takes care of some intermediate
steps with the curve-fitting and provides convenient tabular output:
library(photosynthesis)
#> Loading required package: minpack.lm
aPars <- c("V_cmax", "J_max", "V_TPU", "R_d")
fitaci_per_group <- function(df, aPars, g1, ...) {
grp <- c(g1, ...)
outCols <- c(grp, aPars, "group", "Plots")
splitOn <- paste("~", paste(grp, collapse = " + "))
# drop category name and Filename for shorter labels:
lbls <- vapply(strsplit(grp, ".", fixed = TRUE), \(x) rev(x), c("nm","gp")) |>
paste0(": ") |>
gsub("Filename: ", "", x = _, fixed = TRUE)
# layout:
plotAdd <- list(do.call(labs, var2label(list(y = "GasEx.A", x = "GasEx.Ci"),
TRUE)),
theme(legend.position = "none", plot.title.position = "plot",
plot.title = element_text(size = 8)))
# translate names to what the photosynthesis package uses (sadly not essdive):
out <- df[c("GasEx.A", "GasEx.Ci", "LeafQ.Qin", "Meas.Tleaf",
"Meas.Pa", "gasanalyzer.Permutate", grp)] |>
setNames(c("A_net", "C_i", "PPFD", "T_leaf", "Pa", "group", grp)) |>
drop_units() |> transform(T_leaf = T_leaf + 273.15) |>
split(as.formula(splitOn), drop = TRUE) |>
lapply(\(x) {
lbl <- paste0(lbls, x[1, grp], collapse=", ")
fit <- try(fit_aci_response(x, Pa = mean(x$Patm)))
if (!inherits(fit, "try-error"))
data.frame(x[1, c("group", grp)],
fit[["Fitted Parameters"]],
Plots = I(list(fit$Plot + ggtitle(lbl) + plotAdd))
)[outCols]
else
data.frame()
})
do.call(rbind, c(out, make.row.names = FALSE))
}
Now, we will call this function and transform the output to relative
values and average them over the 3 plants:
# call the function to fit the curves:
CO2curves.SA <- ACi.SA |>
fitaci_per_group(aPars, "SysObs.Filename", SAset)
# reference values are where the SAset variables equal 1:
refids <- rowSums(CO2curves.SA[SAset] == 1) == length(SAset)
# combine all relative values in one column based on group:
CO2curves.SA$gval <- unlist(CO2curves.SA[cbind(seq_len(nrow(CO2curves.SA)),
match(CO2curves.SA$group,
names(CO2curves.SA)))])
# value as relative change:
CO2curves.SA$gval <- 100 * (as.numeric(CO2curves.SA$gval) - 1)
# calc mean over the 3 reps:
CO2curves.SAm <-
lapply(names(CO2curves.SA[aPars]),
\(x) {
# relative change:
rval <- 100 * as.numeric((CO2curves.SA[[x]] -
CO2curves.SA[[x]][refids]) /
abs(CO2curves.SA[[x]][refids]))
# grouping:
byl <- as.list(CO2curves.SA[SAset])
with(CO2curves.SA,
data.frame(nm = x,
aggregate(cbind(gval), byl, head, n = 1),
group = aggregate(group, byl, head, n = 1)[["x"]],
val = aggregate(rval, byl, mean)[["x"]],
sd = aggregate(rval, byl, sd)[["x"]]))}) |>
do.call(rbind, args = _)
The model fits computed by the photosynthesis package
are stored in the data and can be plotted:
grid.arrange(grobs = CO2curves.SA$Plots[refids], ncol = 3)
The code below creates a combined plot showing how a relative change
in the 5 different variables affects the parameters of the C3
biochemical model:
# make lbls factors for good order and plotmath
CO2curves.SAm$group <- factor(CO2curves.SAm$group,
levels = SAset,
labels = SAlabs)
CO2curves.SAm$nm <- factor(CO2curves.SAm$nm,
levels = c("V_cmax", "J_max", "V_TPU", "R_d"),
labels = c("italic(V)[cmax]", "italic(J)[max]",
"italic(V)[TPU]", "italic(R)[L]"))
# and plot:
CO2curves.SAm |>
ggplot(aes(x = gval, y = val, group = nm)) +
geom_vline(aes(xintercept = 0), linetype = "dotdash", color = "darkgrey") +
geom_point() +
geom_ribbon(aes(ymin = val - sd, ymax = val + sd), alpha = 0.1) +
facet_grid(cols = vars(group), rows = vars(nm),
scales="free", labeller = label_parsed) +
xlab("Relative change in variable (%)") +
ylab("Relative change in parameter (%)") +
theme(plot.title = element_text(size = 14),
panel.grid.minor = element_blank(),
panel.spacing.x = unit(14, "pt"))
The results show that for these measurements, accounting for stomatal
or cuticular conductance has only very small effects on the estimates.
Bias in leaf temperature measurements have much larger effects.
3. Combining gas-exchange data with isotope discrimination
The ability to quick analyze the sensitivity of a result to different
constants is also valuable for calculations of mesophyll conductance
(\(g_m\)) from concurrent measurements
of gas exchange and carbon isotope discrimination. As an example, a data
file from an experiment combining gas exchange with carbon isotope
measurements was included. This file contains additional columns with
the \(\delta^{13}\textrm{C}\) value of
the reference and sample air, and of the air in the glasshouse where the
plants were grown.
The dataset can be used to estimate the mesophyll conductance. Such
calculations require several fractionation factors describing how the
abundance ratio of the carbon isotopes will change using a physiological
or physical process. Since uncertainty exist surrounding the exact value
of such factors, it is useful to analyze the data under several
different assumptions. In addition, the effect of different assumptions
in the isotopic model (see Busch et al. 2020) can be tested.
In the following example, gasanalyzer is used to analyze
the effect of the fractionation factor associated with photorespiration
(\(f\)) using both the connected and
disconnected isotopic models as described by Busch et al. 2020.
# load data
isoFile <- file.path(exampleFiles, "d13C.tsv")
# read and recalculate
isotopes <- read_gasexchange(isoFile) |>
recalculate(create_equations(c("default", "li6400")))
# vary f between 0 and 20, and use 2 sets of equations:
isotopes$d13CConst.e <- set_units(0, "permille")
isotopes$gasanalyzer.Equations <- list(NA)
isotopes <- isotopes |>
permutate(d13CConst.f = seq(0, 20, 0.5)) |>
permutate(gasanalyzer.Equations =
c(connected = list(create_equations("d13C")),
disconnected = list(create_equations(c("d13C",
"d13C_dis"))))) |>
recalculate() |>
transform(factor.f = factor(d13CConst.f))
# point method gm is already calculated, average the results:
gmEst <- aggregate(list(gm = as.numeric(isotopes$d13C.gm)),
list(d13CConst.f = isotopes$d13CConst.f,
Model = isotopes$gasanalyzer.PermutateLabel),
FUN = function(x)
c(d13C.gm = mean(x), d13C.gm.sem = sd(x)
/ sqrt(length(x))))
gmEst <- do.call(data.frame, c(gmEst[1:2], unname(gmEst[3])))
gmEst$method <- "point"
Before plotting the results, it is useful to also derive \(g_m\) estimates using the so-called slope
method (see e.g. Tazoe et al. 2009. For this approach, the relationship
between the difference between modeled and observed discrimination is
plotted against the ratio of net assimilation and the partial pressure
of CO2 in the ambient air (\(A/p_{C_a}\)):
d13Cslopes <- isotopes |>
subset(gasanalyzer.PermutateLabel == "connected" &
as.numeric(d13CConst.f) %in% c(7, 11, 16)) |>
ggplot(aes(y = d13C.DeltaiDeltao, x = d13C.A_pCa, color = factor.f)) +
geom_point() +
geom_smooth(method = "lm", formula = y ~ x, aes(fill = factor.f)) +
guides(fill = "none",
colour = guide_legend(override.aes = list(fill = NA))) +
labs(color = var2label("d13CConst.f", TRUE)[[1]]) +
theme(legend.position = "inside", legend.position.inside = c(0.75, 0.10),
legend.direction = "horizontal")
nice_labels(d13Cslopes)
The mesophyll conductance is inversely related to the slope of this
relationship. The slopes appear quite similar, suggesting that the slope
method is much less sensitive to assumptions on the value taken for
\(f\). However, a more thorough
analysis requires a nonlinear fitting approach.
Such an approach has an additional advantage, because it allows for
the estimation of the effective fractionation associated with
mitochondrial respiration (\(e'\)).
Respiration typically releases carbon from substrate that was
assimilated before the leaf was clamped in the gas-exchange chamber, and
since the isotopic composition of the tank air typically differs
drastically from that of the air during growth conditions, this may
result in a strong isotopic signal that needs to be accounted for. The
correction proposed by Busch et al. 2020 could not be used for this
analysis because the discrimination against \({}^{13}\textrm{C}\) under growth conditions
was not quantified for these plants. However, for the slope method, it
is possible to fit both \(e'\) and
\(g_m\) in one analysis (Tazoe et
al. 2009), making the results free of any assumptions regarding the
actual effect of respiratory fractionations.
Below we will use the isotope equations for the connected and
disconnected model provided by gasanalyzer for the
estimation of \(g_m\) using the slope
method:
# The equation for the slope method is already in the data, so this function
# just evaluates that. Its not the fastest way, because recalculate adds
# overhead and unneccessary calcs, but simple:
fitfunc <- function(gm, ep) {
# Di in the equation doesn't take gm into account, and assumes Ci=Cc
# to get the true Delta we so we modify Ci to be Cc again:
df$GasEx.Ci <- df$GasEx.Ci - df$GasEx.A /
(set_units(gm, "mol*m^-2*s^-1*bar^-1") * df$Meas.Pa)
# substitute estimated ep in data and recalc:
df$d13C.ep <- set_units(ep, "permille")
as.numeric(recalculate(df)$d13C.Deltai)
}
#this will take a while:
gmEstSlope <- isotopes |>
#we want to fit ep, so modify the equation list to not recalculate it:
transform(gasanalyzer.Equations =
lapply(gasanalyzer.Equations,
\(x) modifyList(x, list(d13C.ep = NULL)))) |>
#split by f and equation version, and call nls:
split(~ isotopes$d13CConst.f + isotopes$gasanalyzer.PermutateLabel) |>
lapply(function(dat) {
# data is moved to environment of fitfunc
environment(fitfunc) <- list2env(list(df = dat))
nlsfit <- nls(drop_units(d13C.Deltao) ~ fitfunc(gm, ep),
start = c(gm = 0.3, ep = -10), dat)
cfs <- summary(nlsfit)$coefficients
data.frame(d13CConst.f = dat$d13CConst.f[1],
Model = dat$gasanalyzer.PermutateLabel[1],
d13C.gm = cfs[1],
d13C.gm.sem = cfs[3], d13C.ep = cfs[2],
d13C.ep.sem = cfs[4])}) |>
do.call(rbind, args = _) |>
transform(method = "slope")
gmEst <- rbind(gmEst, gmEstSlope[names(gmEstSlope) %in% names(gmEst)],
make.row.names = FALSE)
#re-adds units:
units(gmEst$d13C.gm) <- deparse_unit(isotopes$d13C.gm)
units(gmEst$d13C.gm.sem) <- deparse_unit(isotopes$d13C.gm)
Plot the results of the analysis:
fsens <- ggplot(gmEst, aes(d13CConst.f, d13C.gm,
ymin = d13C.gm - d13C.gm.sem,
ymax = d13C.gm + d13C.gm.sem,
shape = method, color = Model)) +
geom_point(color = "black") +
geom_ribbon(alpha = 0.1, color = NA) + guides(color = "none") +
scale_shape_manual("Method:", values = c(1, 2)) + facet_wrap(vars(Model)) +
theme(legend.position = "inside", legend.position.inside = c(0.12, 0.85),
panel.grid.minor = element_blank())
nice_labels(fsens)
The results indicate that the disconnected model resulted in small
differences between the slope and point methods. Although all approaches
showed some increase in the \(g_m\)
estimate when higher values for photorespiratory fractionation \(f\) were used, the connected model lead to
an unusual strong increase in the estimates when higher values for \(f\) were used.