Please see 'EI-Calculations.pdf' in the 'man/ei_calcs' folder for a step-by-step description of the Isotope Mass Balance used to calculate E:I.
Briefly, this function calculates E:I ratios, based on δ18O-H\~2\~O data. Environmental conditions (i.e. evaporation rate, humidity, temperatures, etc.) are set for the sub-arctic around Yellowknife, NT.
The function is based on a table with the following input parameters per sample:
remotes::install_github("paukes/eee2eye")
Load the package
library(eee2eye)
Add E:I ratios to the data.frame
of field data:
# create example database ei_input <- data.frame(dL_permille = c(-11.77, -15.67, -18.23), dI_permille = c(-20.7, -18.2, -20.2), dP_permille = c(-23, -28, -32), temp_C = c(14.3, 12.1, 8.9), h_dec = c(0.68, 0.71, 0.58), k = c(0.7, 0.72, 0.65)) # add calculated E:I values ei_input <- eee2eye(ei_input, 'dL_permille', 'dI_permille', 'dP_permille', 'temp_C', 'h_dec', 'k')
Add E:I ratios to a data.frame
of field data when not all input values are known or estimated for each field site so common values can be specified:
ei_input <- eee2eye(ei_input, 'dL_permille', -20.7, 'dP_permille', 14.3, 0.68, 0.7)
See the vignette for more information.
# create example database ei_input <- data.frame(E.I = c(0.2042, 0.3138, 0.1838), e_myr = c(0.3965, 0.3965, 0.3965), SA_m2 = c(315900, 300825, 589950), V_m3 = c(2466000, 3004064, 5712829)) # add calculated WRT values ei_input <- eee2eye_WRT(ei_input, 'E.I', 'e_myr', 'SA_m2', 'V_m3')
k
):k
is a very difficult parameter to quantify in that we don't know much about it. For this reason we created a small function that you could approximate a k
value based on the decimal latitude of your sampling site:
k_season <- function(x) { k <- (((90 - x) / 90 ) * 0.5) + 0.5 return(k) }
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