Function Examples
Calculate leaf intrinsic water use efficiency from leaf δ13C:
library(isocalcR) #Load the package
#Calculate iWUE from leaf organic material with a δ13C signature of -27 ‰ for the year 2015,
#300 meters above sea level at 25°C.
d13C.to.iWUE(d13C.plant = -27,
year = 2015,
elevation = 300,
temp = 25)
#> [1] 87.58236
#Use custom.calc to calculate iWUE from the same leaf sample as above.
custom.calc(d13C.plant = -27,
d13C.atm = -8.44,
outvar = "iWUE",
Ca = 399.62,
elevation = 300,
temp = 25)
#> [1] 87.58236
#Calculate the ratio of leaf intercellular to atmospheric CO2 (Ci/Ca) using the simple
#formulation for leaf and wood. Internally updates apparent fractionation by Rubisco, b,
#according to Cernusak and Ubierna 2022.
d13C.to.CiCa(d13C.plant = -27,
year = 2015,
elevation = 300,
temp = 25,
tissue = "leaf")
#> [1] 0.6493374
d13C.to.CiCa(d13C.plant = -27,
year = 2015,
elevation = 300,
temp = 25,
tissue = "wood")
#> [1] 0.6954988
#Calculate iWUE using the "simple", "photorespiration", and "mesophyll" formulations.
d13C.to.iWUE(d13C.plant = -28,
year = 2015,
elevation = 300,
temp = 15,
method = "simple")
#> [1] 75.99569
d13C.to.iWUE(d13C.plant = -28,
year = 2015,
elevation = 300,
temp = 15,
method = "photorespiration")
#> [1] 75.35815
d13C.to.iWUE(d13C.plant = -28,
year = 2015,
elevation = 300,
temp = 15,
method = "mesophyll")
#> [1] 44.00744
#Calculate iWUE from tree ring (wholewood) d13C from Mathias and Thomas (2018)
#using previously loaded piru13C data
#First drop years where there are no data
piru13C <- piru13C %>%
drop_na()
#Calculate iWUE for each case using 'mapply'
piru13C$iWUE_simple <- mapply(d13C.to.iWUE, #Call the function
d13C.plant = piru13C$wood.d13C, #Assign the plant d13C value
year = piru13C$Year, #Assign the year to match atmospheric CO2 and atmospheric d13CO2
elevation = piru13C$Elevation_m, #Assign the elevation
temp = piru13C$MGT_C, #Assign the temperature
method = "simple", #Specify the method
tissue = "wood") #Specify which tissue the sample is from
piru13C$iWUE_photorespiration <- mapply(d13C.to.iWUE, #Call the function
d13C.plant = piru13C$wood.d13C, #Assign the plant d13C value
year = piru13C$Year, #Assign the year to match atmospheric CO2 and atmospheric d13CO2
elevation = piru13C$Elevation_m, #Specify elevation
temp = piru13C$MGT_C, #Specify the temperature during tissue formation
method = "photorespiration", #Specify the iWUE calculation formulation
frac = piru13C$frac) #Specify any post-photosynthetic fractionations. In this case 2 permille to account for leaf to wood.
piru13C$iWUE_mesophyll <- mapply(d13C.to.iWUE, #Call the function
d13C.plant = piru13C$wood.d13C, #Assign the plant d13C value
year = piru13C$Year, #Assign the year to match atmospheric CO2 and atmospheric d13CO2
elevation = piru13C$Elevation_m, #Specify elevation
temp = piru13C$MGT_C, #Specify the temperature during tissue formation
method = "mesophyll", #Specify the iWUE calculation formulation
frac = piru13C$frac) #Specify any post-photosynthetic fractionations. In this case 2 permille to account for leaf to wood.
#Create dataframe for visualizing differences in computed iWUE among the three formulations
piru13C_long <- piru13C %>%
select(Year, Site, iWUE_simple, iWUE_photorespiration, iWUE_mesophyll) %>% #Select only columns of interest
rename(Simple = iWUE_simple,
Photorespiration = iWUE_photorespiration,
Mesophyll = iWUE_mesophyll) %>%
pivot_longer(names_to = "Formulation", values_to = "iWUE", -c(Year, Site))
#Visually examine differences in iWUE based on the formulation used for each study location
ggplot(data = piru13C_long, aes(x = Year, y = iWUE, color = Formulation)) +
geom_point(alpha = 0.5) +
geom_smooth(aes(group = Formulation), color = "gray30") +
theme_classic() +
facet_wrap(~Site) +
ylab(expression("iWUE (µmol mol"^{-1}*")"))
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'