Gianluca Mastrantonio

The seascape of demersal fish nursery areas in the North Mediterranean Sea, a first step towards the implementation of spatial planning for trawl fisheries

The identification of nursery grounds and other essential fish habitats of exploited stocks is a key requirement for the development of spatial conservation planning aimed at reducing the adverse impact of fishing on the exploited populations and ecosystems. The reduction in juvenile mortality is particularly relevant in the Mediterranean and is considered as one of the main prerequisites for the future sustainability of trawl fisheries. The distribution of nursery areas of 11 important commercial species of demersal fish and shellfish was analysed in the European Union Mediterranean waters using time series of bottom trawl survey data with the aim of identifying the most persistent recruitment areas. A high interspecific spatial overlap between nursery areas was mainly found along the shelf break of many different sectors of the Northern Mediterranean indicating a high potential for the implementation of conservation measures. Overlap of the nursery grounds with existing spatial fisheries management measures and trawl fisheries restricted areas was also investigated. Spatial analyses revealed considerable variation depending on species and associated habitat/depth preferences with increased protection seen in coastal nurseries and minimal protection seen for deeper nurseries (e.g. Parapenaeus longirostris 6%). This is partly attributed to existing environmental policy instruments (e.g. Habitats Directive and Mediterranean Regulation EC 1967/2006) aiming at minimising impacts on coastal priority habitats such as seagrass, coralligenous and maerl beds. The new knowledge on the distribution and persistence of demersal nurseries provided in this study can support the application of spatial conservation measures, such as the designation of no-take Marine Protected Areas in EU Mediterranean waters and their inclusion in a conservation network. The establishment of no-take zones will be consistent with the objectives of the Common Fisheries Policy applying the ecosystem approach to fisheries management and with the requirements of the Marine Strategy Framework Directive to maintain or achieve seafloor integrity and good environmental status.

Bayesian hidden Markov modelling using circular-linear general projected normal distribution

We introduce a multivariate hidden Markov model to jointly cluster time-series observations with different support, that is, circular and linear. Relying on the general projected normal distribution, our approach allows for bimodal and/or skewed cluster-specific distributions for the circular variable. Furthermore, we relax the independence assumption between the circular and linear components observed at the same time. Such an assumption is generally used to alleviate the computational burden involved in the parameter estimation step, but it is hard to justify in empirical applications. We carry out a simulation study using different data-generation schemes to investigate model behavior, focusing on well recovering the hidden structure. Finally, the model is used to fit a real data example on a bivariate time series of wind speed and direction. Copyright

Spatio-temporal circular models with non-separable covariance structure

Circular data arise in many areas of application. Recently, there has been interest in looking at circular data collected separately over time and over space. Here, we extend some of this work to the spatio-temporal setting, introducing space–time dependence. We accommodate covariates, implement full kriging and forecasting, and also allow for a nugget which can be time dependent. We work within a Bayesian framework, introducing suitable latent variables to facilitate Markov chain Monte Carlo model fitting. The Bayesian framework enables us to implement full inference, obtaining predictive distributions for kriging and forecasting. We offer comparison between the less flexible but more interpretable wrapped Gaussian process and the more flexible but less interpretable projected Gaussian process. We do this illustratively using both simulated data and data from computer model output for wave directions in the Adriatic Sea off the coast of Italy.

The wrapped skew Gaussian process for analyzing spatio-temporal data

We consider modeling of angular or directional data viewed as a linear variable wrapped onto a unit circle. In particular, we focus on the spatio-temporal context, motivated by a collection of wave directions obtained as computer model output developed dynamically over a collection of spatial locations. We propose a novel wrapped skew Gaussian process which enriches the class of wrapped Gaussian process. The wrapped skew Gaussian process enables more flexible marginal distributions than the symmetric ones arising under the wrapped Gaussian process and it allows straightforward interpretation of parameters. We clarify that replication through time enables criticism of the wrapped process in favor of the wrapped skew process. We formulate a hierarchical model incorporating this process and show how to introduce appropriate latent variables in order to enable efficient fitting to dynamic spatial directional data. We also show how to implement kriging and forecasting under this model. We provide a simulation example as a proof of concept as well as a real data example. Both examples reveal consequential improvement in predictive performance for the wrapped skew Gaussian specification compared with the earlier wrapped Gaussian version.

A time-dependent extension of the projected Normal regression model for longitudinal circular data based on a hidden Markov heterogeneity structure

The modelling of animal movement is an important ecological and environmental issue. It is well-known that animals change their movement patterns over time, according to observable and unobservable factors. To trace the dynamics of behaviors, to identify factors influencing these dynamics and unobserved characteristics driving intra-subjects correlations, we introduce a time-dependent mixed effects projected normal regression model. A set of animal-specific parameters following a hidden Markov chain is introduced to deal with unobserved heterogeneity. For the maximum likelihood estimation of the model parameters, we outline an expectation–maximization algorithm. A large-scale simulation study provides evidence on model behavior. The data analysis approach based on the proposed model is finally illustrated by an application to a dataset, which derives from a population of Talitrus saltator from the beach of Castiglione della Pescaia (Italy).

Counts of unique females with cubs in the Apennine brown bear population, 2006–2014

Abstract Brown bears (Ursus arctos marsicanus) in the Apennines, central Italy, survive in a precarious conservation status but the reproductive performance of the population has never been formally assessed. Each year, from 2006 to 2014, we conducted surveys of females with cubs (FWC) to estimate the minimum number of female bears that reproduced and annual productivity in this bear population. We discriminated unique family groups based on simultaneity of sightings, presence of individually recognizable bears, and ad hoc distance-based rules developed using Global Positioning System relocations from 11 adult female bears in our study population. To estimate the true number of FWC from unique counts, we applied 2 estimators (Chao2, Capwire) known to handle heterogeneity in sighting probabilities relatively well at small sample sizes. Annually, we estimated 1–6 ( = 3.9 ± 1.5 SD) unique FWC and tallied a minimum of 3–11 ( = 7.4 ± 3.0 SD) cubs in the population. No temporal trend in FWC was observed and the mean estimate of reproductive females corresponded well with an independent estimate of total population size obtained in 2011. Although we confirmed that the population is still reproductively functional, the small number of reproducing females and their year-to-year fluctuations dramatically underlined the precarious status of Apennine bears. We concur with previous authors that counts of unique FWC are an effective means to assess reproductive output in small bear populations, although it is advisable that more in-depth demographic studies complement this technique.

Hidden Markov model for discrete circular–linear wind data time series

ABSTRACT In this work, we deal with a bivariate time series of wind speed and direction. Our observed data have peculiar features, such as informative missing values, non-reliable measures under a specific condition and interval-censored data, that we take into account in the model specification. We analyse the time series with a non-parametric Bayesian hidden Markov model, introducing a new emission distribution, suitable to model our data, based on the invariant wrapped Poisson, the Poisson and the hurdle density. The model is estimated on simulated datasets and on the real data example that motivated this work.