Bayesian Inference for a Complex Ecological System: Viability and Reproduction under Finite Resource Constraints with Incomplete Observations

We address the problem of MCMC analysis of a complex ecological system using a Bayesian inferential approach. We describe a complete likelihood framework for the life history of the wavyleaf thistle, including missing stages and density dependence. We indicate how, in order to make inference on life history transitions involving both missing information and density dependence, the stochastic models underlying each component can be combined to obtain expressions that can be directly sampled. Our approach allows otherwise unobtainable inference on interactions between demographic rates. This innovation and the principles described could be extended to other species featuring such missing stage information, with potential for improving inference relating to a range of ecological or evolutionary questions.