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Get started with jSDM

jSDM package

jSDM is an R package for fitting joint species distribution models (JSDM) in a hierarchical Bayesian framework.

The Gibbs sampler is written in C++. It uses Rcpp, Armadillo and GSL to maximize computation efficiency.

Package: jSDM
Type: Package
Version: 0.2.1
Date: 2019-01-11
License: GPL-3
LazyLoad: yes

The package includes the following functions to fit various species distribution models :

function data type data format
jSDM_binomial_logit() presence-absence wide
jSDM_binomial_probit() presence-absence wide
jSDM_binomial_probit_sp_constrained() presence-absence wide
jSDM_binomial_probit_long_format() presence-absence long
jSDM_poisson_log() abundance wide
jSDM_gaussian() continuous wide

In this vignette, we illustrate the use of the jSDM R package which aims at providing user-friendly statistical functions using field observations (occurrence or abundance data) to fit jSDMs models.

Package’s functions are developed in a hierarchical Bayesian framework and use adaptive rejection Metropolis sampling algorithms or conjugate priors within Gibbs sampling to estimate model’s parameters. Using compiled C++ code for the Gibbs sampler reduce drastically the computation time. By making these new statistical tools available to the scientific community, we hope to democratize the use of more complex, but more realistic, statistical models for increasing knowledge in ecology and conserving biodiversity.

Directed Acyclic Graph (DAG)

(ref:cap-DAG) A graphical summary of the jSDM-package statistical framework. In this Directed Acyclic Graph (DAG), the orange boxes refer to data, the blue ellipses to parameters to be estimated, and the arrows to functional relationships described with the help of statistical distributions.

(ref:cap-DAG)

(ref:cap-DAG)

Model types available in jSDM R package are not limited to those described in this example. jSDM includes various model types for occurrence and abundance data, you can find more examples of use on the jSDM website.

Load librairies

We first load the jSDM library.

# Load libraries
library(jSDM)
#> ##
#> ## jSDM R package 
#> ## For joint species distribution models 
#> ## https://ecology.ghislainv.fr/jSDM 
#> ##

Bernoulli probit regression

Below, we show an example of the use of jSDM-package for fitting species distribution model to occurence data for 9 frog’s species.

Definition of the model

Referring to the models used in the articles Warton et al. (2015) and Albert & Siddhartha (1993), we define the following model :

\[ \mathrm{probit}(\theta_{ij}) =\alpha_i + \beta_{0j}+X_i.\beta_j+ W_i.\lambda_j \]

\[y_{ij}=\begin{cases} 0 & \text{ if species $j$ is absent on the site $i$}\\ 1 & \text{ if species $j$ is present on the site $i$}. \end{cases}\]

\[y_{ij}=\begin{cases} 1 & \text{if} \ z_{ij} > 0 \\ 0 & \text{otherwise.} \end{cases}\]

It can be easily shown that: \(y_{ij} \sim \mathcal{B}ernoulli(\theta_{ij})\).

Occurrence data-set

(ref:cap-frog) Litoria ewingii (Wilkinson et al. 2019).

(ref:cap-frog)

(ref:cap-frog)

This data-set is available in jSDM-package. It can be loaded with the data() command. The frogs dataset is in “wide” format: each line is a site and the occurrence data (from Species_1 to Species_9) are in columns. A site is characterized by its x-y geographical coordinates, one discrete covariate and two other continuous covariates.

# frogs data
data(frogs, package="jSDM")
head(frogs)
#>   Covariate_1 Covariate_2 Covariate_3 Species_1 Species_2 Species_3 Species_4
#> 1    3.870111           0    0.045334         1         0         0         0
#> 2    3.326950           1    0.115903         0         0         0         0
#> 3    2.856729           1    0.147034         0         0         0         0
#> 4    1.623249           1    0.124283         0         0         0         0
#> 5    4.629685           1    0.081655         0         0         0         0
#> 6    0.698970           1    0.107048         0         0         0         0
#>   Species_5 Species_6 Species_7 Species_8 Species_9        y        x
#> 1         0         0         0         0         0 66.41479 9.256424
#> 2         0         1         0         0         0 67.03841 9.025588
#> 3         0         1         0         0         0 67.03855 9.029416
#> 4         0         1         0         0         0 67.04200 9.029745
#> 5         0         1         0         0         0 67.04439 9.026514
#> 6         0         0         0         0         0 67.03894 9.023580

We rearrange the data in two data-sets: a first one for the presence-absence observations for each species (columns) at each site (rows), and a second one for the site characteristics.

We also normalize the continuous explanatory variables to facilitate MCMC convergence.

# data.obs
PA_frogs <- frogs[,4:12]

# Normalized continuous variables
Env_frogs <- cbind(scale(frogs[,1]),frogs[,2],scale(frogs[,3]))
colnames(Env_frogs) <- colnames(frogs[,1:3])

Parameter inference

We use the jSDM_binomial_probit() function to fit the jSDM (increase the number of iterations to achieve convergence).

mod_frogs_jSDM_probit <- jSDM_binomial_probit(
  # Chains
  burnin=1000, mcmc=1000, thin=1,
  # Response variable 
  presence_data = PA_frogs, 
  # Explanatory variables 
  site_formula = ~.,   
  site_data = Env_frogs,
  # Model specification 
  n_latent=2, site_effect="random",
  # Starting values
  alpha_start=0, beta_start=0,
  lambda_start=0, W_start=0,
  V_alpha=1, 
  # Priors
  shape_Valpha=0.1,
  rate_Valpha=0.1,
  mu_beta=0, V_beta=1,
  mu_lambda=0, V_lambda=1,
  # Various 
  seed=1234, verbose=1)
#> 
#> Running the Gibbs sampler. It may be long, please keep cool :)
#> 
#> **********:10.0% 
#> **********:20.0% 
#> **********:30.0% 
#> **********:40.0% 
#> **********:50.0% 
#> **********:60.0% 
#> **********:70.0% 
#> **********:80.0% 
#> **********:90.0% 
#> **********:100.0%

Analysis of the results

We visually evaluate the convergence of MCMCs by representing the trace and density a posteriori of some estimated parameters.

np <- nrow(mod_frogs_jSDM_probit$model_spec$beta_start)
oldpar <- par(no.readonly = TRUE)
## beta_j of the first two species
par(mfrow=c(2,2))
for (j in 1:2) {
  for (p in 1:np) {
    coda::traceplot(coda::as.mcmc(mod_frogs_jSDM_probit$mcmc.sp[[j]][,p]))
    coda::densplot(coda::as.mcmc(mod_frogs_jSDM_probit$mcmc.sp[[j]][,p]), 
                   main = paste(colnames(mod_frogs_jSDM_probit$mcmc.sp[[j]])[p],
                                ", species : ",j), cex.main=0.9)
  }
}


## lambda_j of the first two species
n_latent <- mod_frogs_jSDM_probit$model_spec$n_latent
par(mfrow=c(2,2))
for (j in 1:2) {
  for (l in 1:n_latent) {
    coda::traceplot(coda::as.mcmc(mod_frogs_jSDM_probit$mcmc.sp[[j]][,np+l]))
    coda::densplot(coda::as.mcmc(mod_frogs_jSDM_probit$mcmc.sp[[j]][,np+l]), 
                   main = paste(colnames(mod_frogs_jSDM_probit$mcmc.sp[[j]])
                                [np+l],", species : ",j), cex.main=0.9)
  }
}


## Latent variables W_i for the first two sites
par(mfrow=c(2,2))
for (l in 1:n_latent) {
  for (i in 1:2) {
  coda::traceplot(mod_frogs_jSDM_probit$mcmc.latent[[paste0("lv_",l)]][,i],
                  main = paste0("Latent variable W_", l, ", site ", i),
                  cex.main=0.9)
  coda::densplot(mod_frogs_jSDM_probit$mcmc.latent[[paste0("lv_",l)]][,i],
                 main = paste0("Latent variable W_", l, ", site ", i),
                 cex.main=0.9)
  }
}


## alpha_i of the first two sites
plot(coda::as.mcmc(mod_frogs_jSDM_probit$mcmc.alpha[,1:2]))


## V_alpha
par(mfrow=c(2,2))
coda::traceplot(mod_frogs_jSDM_probit$mcmc.V_alpha)
coda::densplot(mod_frogs_jSDM_probit$mcmc.V_alpha)
## Deviance
coda::traceplot(mod_frogs_jSDM_probit$mcmc.Deviance)
coda::densplot(mod_frogs_jSDM_probit$mcmc.Deviance)


## probit_theta
par (mfrow=c(1,2))
hist(mod_frogs_jSDM_probit$probit_theta_latent,
     main = "Predicted probit theta",
     xlab ="predicted probit theta")
hist(mod_frogs_jSDM_probit$theta_latent,
     main = "Predicted theta", 
     xlab ="predicted theta")

par(oldpar)

Overall, the traces and the densities of the parameters indicate the convergence of the algorithm. Indeed, we observe on the traces that the values oscillate around averages without showing an upward or downward trend and we see that the densities are quite smooth and for the most part of Gaussian form.

Matrice of correlations

After fitting the jSDM with latent variables, the full species residual correlation matrix \(R=(R_{ij})^{i=1,\ldots, n_{species}}_{j=1,\ldots, n_{species}}\) can be derived from the covariance in the latent variables such as : \[\Sigma_{ij} = \lambda_i^T .\lambda_j \], then we compute correlations from covariances : \[R_{i,j} = \frac{\Sigma_{ij}}{\sqrt{\Sigma _{ii}\Sigma _{jj}}}\].

We use the function plot_residual_cor() to compute and display the residual correlation matrix between species :

plot_residual_cor(mod_frogs_jSDM_probit)

Predictions

We use the predict.jSDM() S3 method on the mod_frogs_jSDM_probit object of class jSDM to compute the mean (or expectation) of the posterior distributions obtained and get the expected values of model’s parameters.

# Sites and species concerned by predictions :
## 50 sites among the 104
Id_sites <- sample.int(nrow(PA_frogs), 50)
## All species 
Id_species <- colnames(PA_frogs)
# Simulate new observations of covariates on those sites 
simdata <- matrix(nrow=50, ncol = ncol(mod_frogs_jSDM_probit$model_spec$site_data))
colnames(simdata) <- colnames(mod_frogs_jSDM_probit$model_spec$site_data)
rownames(simdata) <- Id_sites
simdata <- as.data.frame(simdata)
simdata$Covariate_1 <- rnorm(50)
simdata$Covariate_3 <- rnorm(50)
simdata$Covariate_2 <- rbinom(50,1,0.5)

# Predictions 
theta_pred <- predict(mod_frogs_jSDM_probit, newdata=simdata, Id_species=Id_species,
                      Id_sites=Id_sites, type="mean")
hist(theta_pred, main="Predicted theta with simulated data", xlab="predicted theta")

References

Albert, J.H. & Siddhartha, C. (1993) Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association, 88, 669–679.
Gelman, A. & Rubin, D.B. (1992) Inference from Iterative Simulation Using Multiple Sequences. Statistical Science, 7, 457–472.
Warton, D.I., Blanchet, F.G., O’Hara, R.B., Ovaskainen, O., Taskinen, S., Walker, S.C. & Hui, F.K.C. (2015) So many variables: Joint modeling in community ecology. Trends in Ecology & Evolution, 30, 766–779.
Wilkinson, D.P., Golding, N., Guillera-Arroita, G., Tingley, R. & McCarthy, M.A. (2019) A comparison of joint species distribution models for presence-absence data. Methods in Ecology and Evolution, 10, 198–211.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.