jtdm - Joint traitdistribution modeling

About the method

The package jtdm implements the method described in Poggiato etal. (2023)doi:10.1111/geb.13706. The code for producing theresults of the paper is available in the subfolder publications in thisrepo.

Installing the R package

## CRAN#install.packages('jtdm', repos = "http://cran.us.r-project.org")## Github#library(devtools)#install_github("giopogg/jtdm")library(jtdm)

Fit a jtdm to data

The package implements jtdm by sampling from the posteriordistribution of the parameters, which has been analytically determined.Therefore, there is no need for classical MCMC convergence checks.

Fitting a JTDM the case study dataset of Poggiato et al. In prep.

library(ggplot2)set.seed(1712)data(Y)data(X)# Short MCMC to obtain a fast example: results are unreliable !m=jtdm_fit(Y = Y,X = X,formula =as.formula("~GDD+FDD+forest"),sample =1000)# Inferred parametersgetB(m)$Bmeanget_sigma(m)$Smean

Show inferred model

We can have a first look to regression coefficients using thesummary function

summary(m)

And we can plot the regression coefficients and the residualcovariance matrix

plot(m)

###Trait-environment relationships

Single-trait trait-environment relationships

partial_response(m,indexGradient="GDD",indexTrait="SLA",FixX=list(GDD=NULL,FDD=NULL,forest=1))$p

Joint trait-environmentrelationships

Partial response curve of the most suitable community-level strategyand envelop of possible community-level strategies of SLA and LNC alongthe GDD gradient.

ellipse_plot(m,indexTrait =c("SLA","LNC"),indexGradient ="GDD")

Joint probabilities

Computes joint probabilities of both SLA and LNC to be greater than20 in a high altitude site. This measures the relative suitability ofcommunities where both SLA and LNC are higher than 20 in a high altitudesite.

joint_trait_prob(m,indexTrait =c("SLA","LNC"),Xnew = X["VCHA_2940",],bounds =list(c(20,Inf),c(20,Inf)),FullPost =TRUE)$PROBmean##          1## 0.09809922

Unsurprisingly, the probability is low. Then, we compute how thisprobability varies along the GDD gradient.

joint=joint_trait_prob_gradient(m,indexTrait=c("SLA","LNC"),indexGradient="GDD",bounds=list(c(mean(Y[,"SLA"]),Inf),c(mean(Y[,"SLA"]),Inf)),FullPost =TRUE)

And we plot it.

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.## ℹ Please use `linewidth` instead.## This warning is displayed once every 8 hours.## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was## generated.

As climatic conditions become more favorable (i.e. GDD increases),the probability of having high values of both traits increases.

Author

This package is currently developed by Giovanni Poggiato fromLaboratoire d’Ecologie Alpine. It is supported by the ANR GAMBAS. Theframework implemented in this package is described in: Joint modelingand predictions of community traits. Poggiato Giovanni, Gaüzere Pierre,Martinez Almoyna Camille, Deschamps Gabrielle, Renaud Julien, ViolleCyrille, Münkemüller Tamara, Thuiller Wilfried. In preparation.