BIRDIE ABU: Model summary

Introduction

The species abundance module (ABU) of the BIRDIE pipeline has four main steps: data preparation, model fitting, model diagnostics and model summary. See the BIRDIE: basics and BIRDIE: species abundance vignettes for general details about BIRDIE and about the ABU module, respectively. In this vignette, we will go through the different tasks that are performed during the first step of the ABU module: model summary.

The main function used for summarising a model fit is ppl_summarise_ssm(). This is a ppl_ function, and therefore it doesn’t do much processing itself (see BIRDIE: basics if this is confusing), but it does call the right functions to do the work.

Model predictions for latent population states (abundance and rates of change) are prepared by JAGS during model fitting. Therefore, the only task that needs to be performed here is preparing these predictions as a dataframe and get them ready for plotting. As side products we will produce .png images for each species and site that will help in model assessment.

The function that performs these tasks is plotJagsSsm2ss(), which is found in R/utils-jags.R. For more details on how this function works see ?plotJagsSsm2ss. The output of this function give us a dataframe with predictions on the estimated abundance and rate of change of the summer and winter populations for each species. Each dataframe contains all the suitable sites (see BIRDIE ABU: data preparation) the species was counted at. We are currently predicting abundance in the linear scale (the scale of the data), but there is also the option to produce these estimates in the log scale. This is a better option to assess model fit.