  Numerous modeling techniques exist to estimate abundance of plant and
wildlife species. These methods seek to estimate abundance while accounting for
multiple complexities found in ecological data, such as observational biases,
spatial autocorrelation, and species correlations. There is, however, a lack of
user-friendly and computationally efficient software to implement the various
models, particularly for large data sets. We developed the spAbundance R
package for fitting spatially-explicit Bayesian single-species and
multi-species hierarchical distance sampling models, N-mixture models, and
generalized linear mixed models. The models within the package can account for
spatial autocorrelation using Nearest Neighbor Gaussian Processes and
accommodate species correlations in multi-species models using a latent factor
approach, which enables model fitting for data sets with large numbers of sites
and/or species. We provide three vignettes and three case studies that
highlight spAbundance functionality. We used spatially-explicit multi-species
distance sampling models to estimate density of 16 bird species in Florida,
USA, an N-mixture model to estimate Black-throated Blue Warbler (Setophaga
caerulescens) abundance in New Hampshire, USA, and a spatial linear mixed model
to estimate forest aboveground biomass across the continental USA. spAbundance
provides a user-friendly, formula-based interface to fit a variety of
univariate and multivariate spatially-explicit abundance models. The package
serves as a useful tool for ecologists and conservation practitioners to
generate improved inference and predictions on the spatial drivers of
populations and communities.
