The goal of isocalcR is to provide a suite of
user-friendly, open source functions for commonly performed calculations
when working with stable isotope data. A major goal of
isocalcR is to help eliminate errors associated with data
compilation necessary for many standard calculations, as well as to
provide the scientific community with a reliable, easily accessible
resource for reproducible work. Part of this effort includes best
practices of data usage, as the user is not required to download
atmospheric CO2 or atmospheric δ13CO2
data for the workhorse calculations, but instead relies on published,
peer-reviewed, and recommended publicly available data (Belmecheri and
Lavergne, 2020). isocalcR is not meant to replace an
understanding of the underlying physiological mechanisms related to
these calculations, but instead to streamline the process. At present,
calculations for years 0 C.E. - 2021 C.E. are stable and will work with
all functions, with 2022 being added at the end of the year.
isocalcR 0.0.1 and isocalcR 0.0.2
incorporated photorespiratory processes into calculations where
Ci was computed. The current release version of
isocalcR, 0.1.0, now has the option to specify the
formulation used in calculating physiological indices where
Ci is necessary for calculations (i.e. CiCa, diffCaCi, iWUE).
Furthermore, isocalcR now includes the function
“custom.calc”, which allows the user to specify variables such as
atmospheric [CO2], that are automatically determined in other
functions.
You can install the released version of isocalcR from CRAN with:
install.packages("isocalcR")And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("justinmathias/isocalcR")isocalcR Function |
Description |
|---|---|
d13C.to.D13C |
Calculate leaf carbon isotope discrimination (∆13C) given plant tissue δ13C signature (‰) |
d13C.to.Ci |
Calculate leaf intercellular CO2 concentration (ppm) given plant tissue δ13C signature (‰) |
d13C.to.CiCa |
Calculate the ratio of leaf intercellular CO2 to atmospheric CO2 concentration (ppm) given plant tissue δ13C signature (‰) |
d13C.to.diffCaCi |
Calculate the difference between atmospheric CO2 concentration (ppm) and leaf intercellular CO2 concentration (ppm) given plant tissue δ13C signature (‰) |
d13C.to.iWUE |
Calculate leaf intrinsic water use efficiency (µmol CO2 mol H2O-1) given plant tissue δ13C signature (‰) |
custom.calc |
Calculate ∆13C, Ci, CiCa, diffCaCi, or iWUE given plant tissue δ13C signature (‰) |
Calculate leaf intrinsic water use efficiency from leaf δ13C:
library(isocalcR) #Load the package
library(tidyverse) #Load the tidyverse
#> ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
#> ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
#> ✔ tibble 3.1.7 ✔ dplyr 1.0.8
#> ✔ tidyr 1.2.0 ✔ stringr 1.4.0
#> ✔ readr 2.1.2 ✔ forcats 0.5.1
#> Warning: package 'tidyr' was built under R version 4.0.5
#> Warning: package 'readr' was built under R version 4.0.5
#> Warning: package 'dplyr' was built under R version 4.0.5
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
#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] 44.00744Data for atmospheric CO2 and atmospheric δ13CO2 for the period 0 C.E. to 2021 C.E. can be loaded and viewed. Data are from Belmecheri and Lavergne (2020).
data(CO2data) #Load CO2data into your environment
head(CO2data, 10) #View initial CO2data observations
#> yr Ca d13C.atm
#> 1 0 277.63 -6.41
#> 2 1 277.63 -6.41
#> 3 2 277.64 -6.41
#> 4 3 277.64 -6.41
#> 5 4 277.65 -6.41
#> 6 5 277.66 -6.41
#> 7 6 277.66 -6.41
#> 8 7 277.67 -6.41
#> 9 8 277.67 -6.41
#> 10 9 277.68 -6.41
tail(CO2data, 10) #View most recent CO2data observations
#> yr Ca d13C.atm
#> 2012 2011 390.65 -8.33
#> 2013 2012 392.77 -8.36
#> 2014 2013 395.16 -8.39
#> 2015 2014 397.54 -8.42
#> 2016 2015 399.62 -8.44
#> 2017 2016 402.82 -8.48
#> 2018 2017 405.13 -8.51
#> 2019 2018 407.14 -8.55
#> 2020 2019 410.04 -8.58
#> 2021 2020 413.95 -8.61Data for piru13C can loaded, viewed, and used to calculate iWUE for each formulation. Data are from Mathias and Thomas (2018).
data(piru13C)
head(piru13C)
#> # A tibble: 6 × 10
#> # Groups: Year [2]
#> Year Site wood.d13C MGT_C Elevation_m frac iWUE_simple iWUE_photorespirati…
#> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1940 CGL -23.2 17.4 1206 2 81.7 67.3
#> 2 1940 MCG -24.0 17.4 1060 2 73.4 59.9
#> 3 1940 SOR -24.5 17.4 1033 2 69.2 56.1
#> 4 1941 CGL -22.9 18.6 1206 2 84.4 69.1
#> 5 1941 MCG -23.6 18.6 1060 2 77.5 63.0
#> 6 1941 SOR -24.4 18.6 1033 2 70.4 56.5
#> # … with 2 more variables: iWUE_mesophyll <dbl>, iWUE_mesophylll <dbl>
#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'
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