isocalcR-vignette
In isocalcR: Isotope Calculations in R

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 6,
  fig.height = 2.25
)

library(isocalcR)
library(tidyr)
library(dplyr)
library(ggplot2)

Functionality

|isocalcR Function | Description | |-------------------- |----------------------------------------------------------------------------------------------------------------| |d13C.to.D13C | Calculate leaf carbon isotope discrimination (∆^13^C) given plant tissue δ^13^C signature (‰) | |d13C.to.Ci | Calculate leaf intercellular CO~2~ concentration (ppm) given plant tissue δ^13^C signature (‰) | |d13C.to.CiCa | Calculate the ratio of leaf intercellular CO~2~ to atmospheric CO~2~ concentration (ppm) given plant tissue δ^13^C signature (‰) | |d13C.to.diffCaCi | Calculate the difference between atmospheric CO~2~ concentration (ppm) and leaf intercellular CO~2~ concentration (ppm) given plant tissue δ^13^C signature (‰) | |d13C.to.iWUE | Calculate leaf intrinsic water use efficiency (µmol CO~2~ mol H~2~O^-1^) given plant tissue δ^13^C signature (‰) | |custom.calc | Calculate ∆^13^C, Ci, CiCa, diffCaCi, or iWUE given plant tissue δ^13^C signature (‰) |

Data for atmospheric CO~2~ and atmospheric δ^13^CO~2~ 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

tail(CO2data, 10) #View most recent CO2data observations

Data for piru13C can loaded and viewed.

data(piru13C)
head(piru13C)

Function Examples

Calculate leaf intrinsic water use efficiency from leaf δ^13^C:

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) 

#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)

#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")

d13C.to.CiCa(d13C.plant = -27,
             year = 2015,
             elevation = 300,
             temp = 25,
             tissue = "wood")

#Calculate iWUE using the "simple", "photorespiration", and "mesophyll" formulations.
d13C.to.iWUE(d13C.plant = -28,
             year = 2015,
             elevation = 300,
             temp = 15,
             method = "simple")

d13C.to.iWUE(d13C.plant = -28,
             year = 2015,
             elevation = 300,
             temp = 15,
             method = "photorespiration")

d13C.to.iWUE(d13C.plant = -28,
             year = 2015,
             elevation = 300,
             temp = 15,
             method = "mesophyll")




#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}*")"))

Literature cited

Badeck, F.-W., Tcherkez, G., Nogués, S., Piel, C. & Ghashghaie, J. (2005). Post-photosynthetic fractionation of stable carbon isotopes between plant organs—a widespread phenomenon. Rapid Commun. Mass Spectrom., 19, 1381–1391.

Belmecheri, S. & Lavergne, A. (2020). Compiled records of atmospheric CO~2~ concentrations and stable carbon isotopes to reconstruct climate and derive plant ecophysiological indices from tree rings. Dendrochronologia, 63, 125748.

Bernacchi, C.J., Singsaas, E.L., Pimentel, C., Portis Jr, A.R. & Long, S.P. (2001). Improved temperature response functions for models of Rubisco-limited photosynthesis. Plant, Cell Environ., 24, 253–259.

Craig, H. (1953). The geochemistry of the stable carbon isotopes. Geochim. Cosmochim. Acta, 3, 53–92.

Cernusak, L. A. & Ubierna, N. Carbon Isotope Effects in Relation to CO~2~ Assimilation by Tree Canopies. in Stable Isotopes in Tree Rings: inferring physiological, climatic, and environmental responses 291–310 (2022).

Davies, J.A. & Allen, C.D. (1973). Equilibrium, Potential and Actual Evaporation from Cropped Surfaces in Southern Ontario. J. Appl. Meteorol., 12, 649–657.

Farquhar, G., O’Leary, M. & Berry, J. (1982). On the relationship between carbon isotope discrimination and the intercellular carbon dioxide concentration in leaves. Aust. J. Plant Physiol., 9, 121–137.

Frank, D.C., Poulter, B., Saurer, M., Esper, J., Huntingford, C., Helle, G., et al. (2015). Water-use efficiency and transpiration across European forests during the Anthropocene. Nat. Clim. Chang., 5, 579–583.

Gong, X. Y. et al. Overestimated gains in water‐use efficiency by global forests. Glob. Chang. Biol. 1–12 (2022)

Lavergne, A. et al. Global decadal variability of plant carbon isotope discrimination and its link to gross primary production. Glob. Chang. Biol. 28, 524–541 (2022).

Ma, W. T. et al. Accounting for mesophyll conductance substantially improves ^13^C-based estimates of intrinsic water-use efficiency. New Phytol. 229, 1326–1338 (2021).

Mathias, J. M. & Thomas, R. B. Disentangling the effects of acidic air pollution, atmospheric CO2 , and climate change on recent growth of red spruce trees in the Central Appalachian Mountains. Glob. Chang. Biol. 24, 3938–3953 (2018).

Tsilingiris, P.T. (2008). Thermophysical and transport properties of humid air at temperature range between 0 and 100°C. Energy Convers. Manag., 49, 1098–1110.

Ubierna, N. & Farquhar, G.D. (2014). Advances in measurements and models of photosynthetic carbon isotope discrimination in C~3~ plants. Plant. Cell Environ., 37, 1494–1498.