Template-type: ReDIF-Paper 1.0
Author-Name: Carota, Cinzia
Author-Email: cinzia.carota@unito.it
Author-Name: Nava, Consuelo Rubina
Author-Email: consuelorubina.nava@unito.it
Author-Name: Soldani, Irene
Author-Email: irene.soldani@aizoon.it
Author-Name: Ghiglione, Claudio
Author-Email:claudio.ghiglione@riftia.eu
Author-Name: Schiapparelli, Stefano
Author-Email: stefano.schiaparelli@unige.it
Author-Workplace-Name: University of Turin
Author-Workplace-Homepage: http://www.est.unito.it/
Title: Statistical models for species richness in the Ross Sea.
Abstract: In recent years, a large international effort has been placed in compiling a complete list of
Antarctic mollusc distributional records based both on historical occurrences, dating back to 1899, and
on newly collected data. Such dataset is highly asymmetrical in the quality of contained information,
due to the variety of sampling gears used and the amount of information recorded at each sampling
station (e.g. sampling gear used, sieve mesh size used, etc.). This dataset stimulates to deploy all
statistical potential in terms of data representation, estimation, clusterization and prediction.
In this paper we aim at selecting an appropriate statistical model for this dataset in order to explain
species richness (i.e. the number of observed species) as a function of several covariates, such as gear
used, latitude, etc.. Given the nature of data, we preliminary implement a Poisson regression model
and we extend it with a Negative Binomial regression to manage over-dispersion. Generalized linear
mixed models (GLMM) and generalized additive models (GAM) are also explored to capture a possible
extra explicative power of the covariates. However, preliminary results under them suggest that more
sophisticated models are needed. Therefore, we introduce a hierarchical Bayesian model, involving a
nonparametric approach through the assumption of random effects with a Dirichlet Process prior.
Length: 6 pages
Creation-Date: 2015-07
File-URL: http://www.est.unito.it/do/home.pl/Download?doc=/allegati/wp2015dip/wp_31_2015.pdf
File-Format: Application/PDF
Handle: RePEc:uto:dipeco:201531