Speaker: Soraia Pereira (CEAUL).
Abstract: 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.
Bio: Soraia Pereira obtained her PhD degree from the Faculty of Sciences, University of Lisbon, Portugal. She has coauthored of 5 publications including Spatial Statistics and REVSTAT. Soraia is a postdoctoral fellow at Centre of Statistics and its Applications (CEAUL). Her current research focuses on spatial modeling and extremes.