We evaluate the spatiotemporal changes in the density of a particular species of crustacean known as deep-water rose shrimp, Parapenaeus longirostris, based on biological sample data collected during trawl surveys carried out from 1995 to 2006 as part of the international project MEDITS (MEDiterranean International Trawl Surveys). As is the case for many biological variables, density data are continuous and characterized by unusually large amounts of zeros, accompanied by a skewed distribution of the remaining values. Here we analyze the normalized density data by a Bayesian delta-normal semiparametric additive model including the effects of covariates, using penalized regression with low-rank thin-plate splines for nonlinear spatial and temporal effects. Modeling the zero and nonzero values by two joint processes, as we propose in this work, allows to obtain great flexibility and easily handling of complex likelihood functions, avoiding inaccurate statistical inferences due to misclassification of the high proportion of exact zeros in the model. Bayesian model estimation is obtained by Markov chain Monte Carlo simulations, suitably specifying the complex likelihood function of the zero-inflated density data. The study highlights relevant nonlinear spatial and temporal effects and the influence of the annual Mediterranean oscillations index and of the sea surface temperature on the distribution of the deep-water rose shrimp density.

Nell’articolo vengono esaminate le tecniche di campionamento per popolazioni difficult-to-sample più frequentemente impiegate nelle indagini statistiche attuali. Tenuto conto che è difficile prescindere dalla considerazione generale per cui, l’elemento su cui puntare per ottenere dei significativi miglioramenti in termini di affidabilità delle stime, in mancanza di informazioni campionarie certe, è la conoscenza a priori (extra-campionaria) in merito al fenomeno analizzato che, il ricercatore deve essere in grado di sfruttare opportunamente. L’implementazione di nuovi strumenti inferenziali che permettano di sfruttare al meglio l’informazione extra-campionaria di cui si deve necessariamente disporre in tali contesti, dunque, appare una delle vie percorribili per ottenere risultati soddisfacenti in tale ambito di ricerca.

Il lavoro è un capitolo del testo a carattere didattico. Pertanto, la finalità è quella di presentare il Partial Least Squares sia sotto l'aspetto metodologico che applicativo nell'ambito della valutazione dei servizi. Entrambi gli aspetti sono stati presentati tenendo conto che l'utilizzatore finale è lo studente.

Questo volume raccoglie, al fine di diffondere la cultura statistica, i risultati di temi approfonditi in numerose scuole secondarie di II grado del territorio pugliese sotto la guida di un team composto da docenti del Dipartimento di Scienze Economiche e Metodi Matematici, dottori di ricerca e ricercatori ISTAT. Dette attività si sono svolte a inizio 2016 nell'ambito del Piano Nazionale Lauree Scientifiche 2015-2018 (progetto in collaborazione fra il Ministero dell'Università e dell'Istruzione, la Conferenza Nazionale dei Presidi di Scienze e Tecnologie e la Confindustria).

In order to evaluate the spatio-temporal fluctuations of an aquatic population in relation to anthropogenic and environmental factors, we consider density, biomass and size of a crustacean species particularly diffused in the North-Western Ionian Sea: Parapenaeus longirostris (Lucas, 1846). Data from twelve trawl surveys (1995-2006) were analyzed by two different spatio-temporal statistical models accounting for a complex region comprised between the coastline and a specified depth contour. First generalized additive models (GAM’s) were used with soap film smoothers (Wood et al., 2008). While conventional smoothing performs badly when used over complicated regions, soap film smoothing avoids errors across boundaries, using a set of basis functions for the interior region and another one for the boundary. These smoothers, that can be represented in terms of a low rank basis and one or two quadratic penalties, employ a global tuning parameter and one for each boundary and are estimated by penalized likelihood maximization with smoothing degree given by generalized cross validation minimization. Covariates that proved to be significant within the GAM’s were used in a Bayesian implementation combining the Stochastic Partial Differential Equation (SPDE) representation of a Gaussian process with the Integrated Nested Laplace Approximation (INLA). The SPDE approach approximates a Gaussian Markov Random Field substituting the spatio-temporal covariance function and the corresponding dense covariance matrix with a sparse precision matrix for a neighbourhood structure. With respect to MCMC methods INLA provides more accurate deterministic approximations to posterior marginal distributions and a more computational efficient algorithm for Bayesian inference.

In the ecological field, the sampling of abundance data is often characterized by the zero inflation of population distributions. Constrained zero-inflated GAM’s (COZIGAM) are obtained assuming that the probability of non-zero inflation and the mean non-zero-inflated population abundance are linearly related. Models of this class have been applied to a spatio-temporal case study concerning the deep-water rose shrimp, Parapenaeus longirostris (Lucas, 1846). Abundance data were collected during 16 experimental trawl surveys conducted from 1995 to 2010 in the Ionian Sea. The sampling design adopted was random-stratified by depth, with proportional allocation of hauls to the area of each depth range and geographical sector. Density index (N/km2) and length (mm) were considered for each haul identified by time, depth, geographic coordinates and geographical sector.

In the ecological field, the sampling of abundance data is often characterized by the zero inflation of population distributions. Constrained zero-inflated GAM’s (COZIGAM) are obtained assuming that the probability of non-zero inflation and the mean non-zero-inflated population abundance are linearly related. Models of this class have been applied to a spatio-temporal case study concerning the deep-water rose shrimp, Parapenaeus longirostris (Lucas, 1846). Abundance data were collected during 16 experimental trawl surveys conducted from 1995 to 2010 in the Ionian Sea. The sampling design adopted was random-stratified by depth, with proportional allocation of hauls to the area of each depth range and geographical sector. Density index (N/km2) and length (mm) were considered for each haul identified by time, depth, geographic coordinates and geographical sector.