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Critical News:

Error in the non-hierarchical first-difference model

In versions 1.1.0, 1.1.1, and 1.1.2.0, the non-hierarchical variant of the first-difference model included a coding error. Estimates of trends and population trajectories from this model in these versions will be biased.

The error was fixed in version 1.1.2.1. I am so sorry folks, please reach out if you would like more information or assistance in fixing/recovering from this.

bbsBayes2

bbsBayes2 is a package for performing hierarchical Bayesian analysis of North American Breeding Bird Survey (BBS) data. ‘bbsBayes2’ will run a full model analysis for one or more species that you choose, or you can take more control and specify how the data should be stratified, prepared for Stan, or modelled.

bbsBayes2 is the successor to bbsBayes, with a major shift in functionality. The MCMC backend is now Stan instead of JAGS, the workflow has been streamlined, the syntax has changed, and there are new functions. This vignette should help you get started with the package, and there are three others that should help explain some of the new features, choices, and more advanced uses:

Installation instructions are below.

See the documentation for an overview of how to use bbsBayes2.

In addition, there are four vignettes that will help users get familiar with the package and the new functionality.

  • Getting Started vignette

  • Stratification vignette The stratification vignette explains the built-in options for spatial stratifications as well as the workflow required to apply a custom stratification.

  • Models vignette The models vignette explains the four built-in models that differ in the way the temporal components are structured, and it also covers the built-in options for error distributions and the differences among the model variants (e.g., model_variant = "spatial").

  • Advanced vignette The advanced vignette is helpful for users wanting to take the bbsBayes2 functionality further, including alternate calculations of population trend, customizing the Stan models, adding covariates, and even some experimental functions that allow for k-fold cross-validations to compare among models.

Installation

bbsBayes2 can be installed from the bbsBayes R-Universe:

install.packages("bbsBayes2",
                 repos = c(bbsbayes = 'https://bbsbayes.r-universe.dev',
                           CRAN = 'https://cloud.r-project.org'))

Alternatively you can install directly from our GitHub repository with either the pak (recommended) or remotes packages.

With pak:

{r} install.packages("pak") pak::pkg_install("bbsBayes/bbsBayes2")

With remotes:

{r} install.packages("remotes") remotes::install_github("bbsBayes/bbsBayes2")

If you want to install the developmental branch (which often includes additional options and newest updates), you can use the following. NOTE: bbsBayes2 is supported by a small team of committed researchers with limited capacity. The development branch may not be stable.

{r} pak::pkg_install("bbsBayes/bbsBayes2@dev")

Why bbsBayes2

We hope you’ll agree that the BBS is a spectacular dataset. Generations of committed and expert birders have contributed their time and expertise to carefully keeping track of local bird populations. For many BBS observers, it’s been a commitment that has lasted 20, 30, or even 40 years! Many federal, state, and provincial government agencies, as well as local and national conservation organizations have supported the coordination and curation of almost 60-years of data.

The BBS was started at the dawn of the modern North American conservation movement, inspired by changes in bird populations noticed by biologists, naturalists, farmers, and other stewards of the natural world. A continental-scale survey of birds, carefully designed to quantify changes in populations through time, in hopes that Rachel Carson’s “Silent Spring”, would never come to pass.

bbsBayes2 was created: to help researchers make use of these precious data; to better understand the spatial and temporal changes in bird populations; to make agency-derived estimates of population status more transparent; and to inspire improvements, elaborations, and critical feedback on the models provided here. We hope you enjoy the package and we hope you will contribute your ideas and code to this open software initiative.