spOccupancy fits single-species, multi-species, and integrated spatial occupancy models using Markov Chain Monte Carlo (MCMC). Models are fit using Pólya-Gamma data augmentation. Spatial models are fit using either Gaussian processes or Nearest Neighbor Gaussian Processes (NNGP) for large spatial datasets. The package provides functionality for data integration of multiple single-species occupancy data sets using a joint likelihood framework. For multi-species models, spOccupancy provides functions to account for residual species correlations in a joint species distribution model framework while accounting for imperfect detection. As of v0.4.0,
spOccupancy provides functions for multi-season (i.e., spatio-temporal) single-species occupancy models. Below we provide a very brief introduction to some of the package’s functionality, and illustrate just one of the model fitting functions. For more information, see the resources referenced at the bottom of this page.
You can install the released version of
spOccupancy from CRAN with:
||Single-species occupancy model|
||Single-species spatial occupancy model|
||Single-species occupancy model with multiple data sources|
||Single-species spatial occupancy model with multiple data sources|
||Multi-species occupancy model|
||Multi-species spatial occupancy model|
||Joint species distribution model without imperfect detection|
||Spatial joint species distribution model without imperfect detection|
||Multi-species occupancy model with species correlations|
||Multi-species spatial occupancy model with species correlations|
||Multi-species occupancy model with multiple data sources|
||Single-species multi-season occupancy model|
||Single-species multi-season spatio-temporal occupancy model|
||Single-species spatially-varying coefficient GLM|
||Single-species spatially-varying coefficient occupancy model|
||Single-species spatially-varying coefficient multi-season GLM|
||Single-species spatially-varying coefficient multi-season occupancy model|
||Multi-species spatially-varying coefficient occupancy model|
||Multi-species, multi-season occupancy model|
||Multi-species, multi-season spatial occupancy model|
||Multi-species, multi-season spatially-varying coefficient occupancy model|
||Fit a linear (mixed) model using estimates from a previous model fit|
||Posterior predictive check using Bayesian p-values|
||Compute Widely Applicable Information Criterion (WAIC)|
||Simulate single-species occupancy data|
||Simulate single-species multi-season occupancy data|
||Simulate detection-nondetection data with perfect detection|
||Simulate multi-season detection-nondetection data with perfect detection|
||Simulate multi-species occupancy data|
||Simulate multi-species, multi-season occupancy data|
||Simulate single-species occupancy data from multiple data sources|
||Simulate multi-species occupancy data from multiple data sources|
To get started with
spOccupancy we load the package and an example data set. We use data on twelve foliage-gleaning birds from the Hubbard Brook Experimental Forest, which is available in the
spOccupancy package as the
hbef2015 object. Here we will only work with one bird species, the Black-throated Blue Warbler (BTBW), and so we subset the
hbef2015 object to only include this species.
Below we fit a single-species spatial occupancy model to the BTBW data using a Nearest Neighbor Gaussian Process. We use the default priors and initial values for the occurrence (
beta) and detection (
alpha) coefficients, the spatial variance (
sigma.sq), the spatial range parameter (
phi), the spatial random effects (
w), and the latent occurrence values (
z). We assume occurrence is a function of linear and quadratic elevation along with a spatial random intercept. We model detection as a function of linear and quadratic day of survey and linear time of day the survey occurred.
We run the model using an Adaptive MCMC sampler with a target acceptance rate of 0.43. We run 3 chains of the model each for 10,000 iterations split into 400 batches each of length 25. For each chain, we discard the first 2000 iterations as burn-in and use a thinning rate of 4 for a resulting 6000 samples from the joint posterior. We fit the model using 5 nearest neighbors and an exponential correlation function. We also specify the
k.fold argument to perform 2-fold cross-validation after fitting the full model. Run
?spPGOcc for more detailed information on all function arguments.
# Run the model out <- spPGOcc(occ.formula = btbw.occ.formula, det.formula = btbw.det.formula, data = btbwHBEF, n.batch = 400, batch.length = 25, accept.rate = 0.43, cov.model = "exponential", NNGP = TRUE, n.neighbors = 5, n.burn = 2000, n.thin = 4, n.chains = 3, verbose = FALSE, k.fold = 2)
This will produce a large output object, and you can use
str(out) to get an overview of what’s in there. Here we use the
summary() function to print a concise but informative summary of the model fit.
summary(out) #> #> Call: #> spPGOcc(occ.formula = btbw.occ.formula, det.formula = btbw.det.formula, #> data = btbwHBEF, cov.model = "exponential", NNGP = TRUE, #> n.neighbors = 5, n.batch = 400, batch.length = 25, accept.rate = 0.43, #> verbose = FALSE, n.burn = 2000, n.thin = 4, n.chains = 3, #> k.fold = 2) #> #> Samples per Chain: 10000 #> Burn-in: 2000 #> Thinning Rate: 4 #> Number of Chains: 3 #> Total Posterior Samples: 6000 #> Run Time (min): 1.6487 #> #> Occurrence (logit scale): #> Mean SD 2.5% 50% 97.5% Rhat ESS #> (Intercept) 4.0762 0.6183 3.0681 4.0071 5.4855 1.0019 261 #> scale(Elevation) -0.5203 0.2249 -0.9772 -0.5132 -0.0825 1.0039 984 #> I(scale(Elevation)^2) -1.1806 0.2257 -1.6958 -1.1593 -0.7969 1.0033 244 #> #> Detection (logit scale): #> Mean SD 2.5% 50% 97.5% Rhat ESS #> (Intercept) 0.6647 0.1155 0.4398 0.6623 0.8955 1.0012 5605 #> scale(day) 0.2899 0.0709 0.1507 0.2900 0.4273 1.0020 6000 #> scale(tod) -0.0319 0.0697 -0.1680 -0.0327 0.1055 0.9999 6000 #> I(scale(day)^2) -0.0756 0.0867 -0.2476 -0.0752 0.0926 1.0000 6000 #> #> Spatial Covariance: #> Mean SD 2.5% 50% 97.5% Rhat ESS #> sigma.sq 1.2610 1.0218 0.2063 0.9694 4.0863 1.0130 101 #> phi 0.0093 0.0085 0.0009 0.0056 0.0294 1.0763 45
ppcOcc performs a posterior predictive check on the resulting list from the call to
spPGOcc. For binary data, we need to perform Goodness of Fit assessments on some binned form of the data rather than the raw binary data. Below we perform a posterior predictive check on the data grouped by site with a Freeman-Tukey fit statistic, and then use the
summary function to summarize the check with a Bayesian p-value.
ppc.out <- ppcOcc(out, fit.stat = 'freeman-tukey', group = 1) summary(ppc.out) #> #> Call: #> ppcOcc(object = out, fit.stat = "freeman-tukey", group = 1) #> #> Samples per Chain: 10000 #> Burn-in: 2000 #> Thinning Rate: 4 #> Number of Chains: 3 #> Total Posterior Samples: 6000 #> #> Bayesian p-value: 0.4828 #> Fit statistic: freeman-tukey
waicOcc function computes the Widely Applicable Information Criterion (WAIC) for use in model selection and assessment (note that due to Monte Carlo error your results will differ slightly).
waicOcc(out) #> elpd pD WAIC #> -679.88774 22.96844 1405.71235
Alternatively, we can perform k-fold cross-validation (CV) directly in our call to
spPGOcc using the
k.fold argument and compare models using a deviance scoring rule. We fit the model with
k.fold = 2 and so below we access the deviance scoring rule from the 2-fold cross-validation. If we have additional candidate models to compare this model with, then we might select for inference the one with the lowest value of this CV score.
out$k.fold.deviance #>  1414.417
Prediction is possible using the
predict function, a set of occurrence covariates at the new locations, and the spatial coordinates of the new locations. The object
hbefElev contains elevation data across the entire Hubbard Brook Experimental Forest. Below we predict BTBW occurrence across the forest, which are stored in the
# First standardize elevation using mean and sd from fitted model elev.pred <- (hbefElev$val - mean(btbwHBEF$occ.covs[, 1])) / sd(btbwHBEF$occ.covs[, 1]) coords.0 <- as.matrix(hbefElev[, c('Easting', 'Northing')]) X.0 <- cbind(1, elev.pred, elev.pred^2) out.pred <- predict(out, X.0, coords.0, verbose = FALSE)
vignette("modelFitting") provides a more detailed description and tutorial of the core functions in
spOccupancy. For full statistical details on the MCMC samplers for core functions in
vignette("mcmcSamplers"). In addition, see the introductory spOccupancy paper that describes the package in more detail (Doser et al. 2022). For a detailed description and tutorial of joint species distribution models in
spOccupancy that account for residual species correlations, see
vignette("mcmcFactorModels"), and our open-access paper (Doser et al. 2023). For a description and tutorial of multi-season (spatio-temporal) occupancy models in
vignette("spaceTimeModels"). For a tutorial on spatially-varying coefficient models in
Doser, J. W., Finley, A. O., Kery, M., and Zipkin, E. F. (2022a). spOccupancy: An R package for single-species, multi-species, and integrated spatial occupancy models. Methods in Ecology and Evolution. 13(8) 1670-1678. https://doi.org/10.1111/2041-210X.13897.
Doser, J. W., Finley, A. O., and Banerjee, S. (2023). Joint species distribution models with imperfect detection for high-dimensional spatial data. Ecology. https://doi.org/10.1002/ecy.4137.