computing individual TP using the tRophicPosition

[Alexandre Garcia]

Dear Quezada-Romegialli,

Hi there. My name is Alexandre Miranda Garcia and I’m a researcher at Universidade Federal de Rio Grande (FURG) in south Brazil. My colleagues and I are using the tRophicPosition to estimate the TP of juveniles of the Goliath grouper (Epinephelus itajara), which is the largest grouper in the Atlantic Ocean and is under threat of extinction.

We need to estimate individual values of TP for the species, but I noticed that the tRophicPosition doesn’t have a specific script to model individual TP. Do you have any plans to provide this function in the near future?

Meanwhile, I made an attempt to estimate individual values using the current script available (see below). First, I run the script as recommended to obtain an average (mode) value.  Secondly, I run the same script considering each individual as it was a different species to obtain TP estimates for each individual. The values are very similar in both cases (please see values marked in red in the attached excel file). In the absence of a specific formulation to compute individual value, do you think it is reasonable to use these individual TP values in our work?

I’ll be looking forward to your response and thank you in advance.

Sincerely,

Alexandre Miranda Garcia

https://www.researchgate.net/profile/Alexandre_Garcia9

Universidade Federal do Rio Grande (FURG)

Instituto de Oceanografia
Rio Grande, RS, Brasil

** This is the script we use:

######## One baseline one isotope (N) #############

Model1 <- read.csv2("mero_IT15.csv")

MeroList <- extractIsotopeData(Model1, b1 = "Base1",
                                  baselineColumn = "Baseline", consumersColumn = "SP.",
                                  d15N = "d15N",
                                  deltaN = 3.54,sd.DeltaN=0.30)

#Simulação TP
Mero_models1 <- multiSpeciesTP(MeroList, model = "oneBaseline",
                                 n.adapt = 10000, n.iter = 10000,
                                 burnin = 10000, n.chains = 5, print = FALSE)

# To get the mode
getPosteriorMode(Mero_models1$"TPs")