# virgile-baudrot/trophicR

trophicR is an R package to facilitate model-fitting of functional responses (trophic interactions) using a Bayesian approach with Stan via the rstan package.
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Work in progress, the package isnot ready for release.

# trophicR

trophicR is an R package to facilitate model-fitting of functional responses (trophic interactions) using a Bayesian approach with Stan (via the rstan package).

The way I'm developping trophicR is deeply inspired by the rstanarm package.

## Installation

This package is not yet on the CRAN (basically, the classical command line install.packages("trophicR") won't work), so please use the development version provided on this Github repository.

To install from GitHub, first make sure that you can install the rstan package and C++ toolchain by following these instructions. Once rstan is successfully installed, you can install trophicR from GitHub using the devtools package by executing the following in R:

if (!require(devtools)) {
install.packages("devtools")
library(devtools)
}
install_github("virgile-baudrot/trophicR", args = "--preclean", build_vignettes = FALSE)

Make sure to include the args = "--preclean" and build_vignettes = FALSE arguments or the development version of package will not install properly. If installation fails, please let us know by filing an issue.

## Starting with trophicR

Once the package trophicR is installed in your environment, try to estimate a classical Holling type 2 functional response.

Holling type 2 functional response for multi species is mathematically described by the function Phi (the rate of ingestion of prey i depending on density of n prey species):

with x_1 and x_2 the densities of prey species (or any other measures of prey availability). Parameters a_1, a_2 are the attack rate toward prey 1 and 2 respectivelly, and h_1, h_2 are the handling times for both preys.

library(trophicR)
1. load the data set, and then a plot (using the library ggplot2)

The data set is totally explained in Raoul et al. (2010) and was use in our common paper Baudrot et al. (2016).

Raoul, F.; Deplazes, P.; Rieffel, D.; Lambert, J.-C. & Giraudoux, P. Predator dietary response to prey density variation and consequences for cestode transmission. Oecologia, Springer, 2010, 164, 129-139

Baudrot, V.; Perasso, A.; Fritsch, C.; Giraudoux, P. & Raoul, F. The adaptation of generalist predators diet in a multi-prey context: insights from new functional responses. Ecology, 2016, 97, 1832-1841

data("fox_Raoul2010")

# plot of the data set
library(ggplot2)
df_fox <- data.frame(density = c(fox_Raoul2010$N_avail[,1], fox_Raoul2010$N_avail[,2]),
diet = c(fox_Raoul2010$N_diet[,1], fox_Raoul2010$N_diet[,2] ),
nFaeces = rep(fox_Raoul2010$nFaeces, 2), species = c(rep("Arvicola scherman",41), rep("Microtus arvalis", 41))) ggplot(data = df_fox) + theme_light() + labs(x = "prey available", y = "proportion of prey ingested") + geom_point(aes(x = density, y = diet/nFaeces, group = species)) + facet_wrap(~ species) 1. Fit the model with the function trophicFit. Do not forget to specify the type of functional response (here holling2). fit_foxH2 <- trophicFit(data = fox_Raoul2010, trophic_model = "holling2") Eplore the object fit_foxH2. Red point in the following graph are predictions of medians from Bayesian estimates. 1. Try to predict for other densities # Extract results result_foxH2 <- extract(fit_foxH2, pars = c('a', 'h', 'phi', 'rep')) aA <- result_foxH2$a[,1]
aM <- result_foxH2$a[,2] hA <- result_foxH2$h[,1]
hM <- result_foxH2$h[,2] # Compute prediction dens <- expand.grid(A = seq(1,100,length.out = 12), M = seq(1,100,length.out = 12)) phiA <- aA %*% t(dens$A) / (1 + (aA * hA) %*% t(dens$A) + (aM * hM) %*% t(dens$M))
phiM <- aM %*% t(dens$M) / (1 + (aA * hA) %*% t(dens$A) + (aM * hM) %*% t(dens$M)) df_foxH2pred <- data.frame(densA = dens$A,
densM = dens$M, phiA_q50 = apply(phiA, 2, quantile, probs = 0.5), phiM_q50 = apply(phiM, 2, quantile, probs = 0.5), phiA_qinf95 = apply(phiA, 2, quantile, probs = 0.025), phiM_qinf95 = apply(phiM, 2, quantile, probs = 0.025), phiA_qsup95 = apply(phiA, 2, quantile, probs = 0.975), phiM_qsup95 = apply(phiM, 2, quantile, probs = 0.975)) # # Graphic in 2D while a 3D would be more relevant in that case # ggplot(data = df_foxH2pred) + theme_light() + labs(x = "prey available", y = "proportion of prey ingested", title ="Ingestion rate of Microtus arvalis depending of its density.\n Panel corresond to different density of Arvicola scherman") + geom_ribbon(aes(x = densM, ymin = phiM_qinf95, ymax = phiM_qsup95), fill="pink", alpha = 0.3) + geom_line(aes(x = densM, y = phiM_q50), color="red") + facet_wrap(~ densA, ncol=4)  ## Getting help #### trophicR has been developped using package rstantools, here are some notes: • It is important to clear the global environement of Rstudio before using the function rstan_package_skeleton. • A warning messages appears when compiling the package produce by rstan_package_skeleton: All text must be in a section line 26 in myPackage/man/myPackage-package.Rd. To solve this, simply remove this sentence, or move it between braces. • In the Read-and-delete-me file, line 16, it could be better to write: #' @useDynLib mypackage, .registration = TRUE rather than #' @useDynLib rstanarm, .registration = TRUE which can be confusing in regards with the begin of the sentence: useDynLib(mypackage, .registration = TRUE). It could be also relevant to specify that it's necessary to use #' @useDynLib mypackage, .registration = TRUE if the user generate the NAMESPACE file with roxygen2. • For the warning with cleanup and cleanup.win not executable: $ chmod +x cleanup
\$ chmod +x cleanup.win