Understanding the distribution of animals over space, as well as how that distribution is influenced by environmental covariates, is a fundamental requirement for the effective management of animal populations. This is especially the case for populations which are harvested. The sardine is one of the most important fisheries species, both for its economic, sociologic, antropologic and cultural values.
Here we intend to understand the spatial distribution of the average number of sardine eggs by m3. Our main objectives are to identify the environmental variables that better explain the spatial variation in sardine eggs density and to make predictions in spatial points that were not observed.
The data structure presents an excess of zeros and extreme values. To deal with this, we propose a point-referenced zero-inflated model to model the probability of presence together with the positive sardine eggs density and a point-referenced generalized Pareto model for the extremes. Finally, we combine the results of these two models to get the spatial predictions of the variable of interest. We follow a Bayesian approach and the inference is made using the package R-INLA in the software R.