Welcome to the BCSS tutorial on using JAGS and R to analyse wildlife data. We are just starting to put this together so please bear with us where it is incomplete.
The key reason for adopting Bayesian analysis for wildlife data analysis is that it provides useful information for management of wildlife and protected areas. Bayesian posterior probabilities can be used directly in formal decision-making methods. In contrast, frequentist methods based on p-values and null hypothesis significance testing (NHST) can give misleading information.
See Wade (2000) Bayesian methods in conservation biology. Conservation Biology, 14, 1308-1316 for an application to management of beluga whales in Alaska.
JAGS – together with WinBUGS, OpenBUGS and
nimble – uses the BUGS language to code models. This allows biologists to write model code in a way which reflects their understanding of the mechanisms involved.
JAGS works across platforms – Windows, Mac and Linux – and is open source (written in C) and actively maintained. WinBUGS and OpenBUGS do not work on all platforms. They are built on proprietary software which is no longer maintained.
nimble is a relatively new development; we do not include it here (yet!) as we have little experience using it.
Stan is another new package for Bayesian analysis, but it does not use the BUGS language and cannot easily handle the binary latent variables which are so common in wildlife models (eg, present/absent, dead/alive, captured/not captured).
More information? Maybe a summary of the material in this tutorial.