First assessment on genetic structure and phylogeography of Mediterranean blue shark (prionace glauca, L. 1758) population using mitochondrial gene variation: a comparison with the Atlantic.

The blue shark, Prionace glauca, is one of the most vagile shark species worldwide distributed. The particular body shape allows blue sharks make transoceanic movements, leading to a circumglobal distribution. Due to its reproductive cycle, an extraordinarily high number of specimens is globally registered but, even if it is still a major bycatch of longline fishery rather than a commercial target, it is characterized by a high vulnerability. In this perspective it is important to increase the amount of informations regarding its population extent in the different worldwide areas, evaluating the possible phylogeographic patterns between different locations. This study, included in the "MedBlueSGen" European project, aims exactly at filling a gap in knowledges regarding the genetic population structure of the Mediterranean blue sharks, which has never been investigated before, with a comparison with the North-Eastern Atlantic blue shark population. To reach this objective, we used a dataset of samples from different Mediterranean areas implementing it with some samples from North-Eastern Atlantic. Analyzing the variability of the two mitochondrial markers control region and cytochrome b, with the design of new species-specific primer pairs, we assessed the mitochondrial genetic structure of Mediterranean and North-Eastern Atlantic samples, focusing on the analysis of their possible connectivity, and we tried to reconstruct their demographic history and population size. Data analyses highlighted the absence of a genetic structuring within the Mediterranean and among it and North-Eastern Atlantic, suggesting that the Strait of Gibraltar doesn't represent a phylogeographic barrier. These results are coherent to what has been found in similar investigations on other worldwide blue shark populations. Analysis of the historical demographic trend revealed a general stable pattern for the cytochrome-b and a slightly population expansion for the control region marker.

Modelling the size distribution of cosmic voids

The Standard Cosmological Model is generally accepted by the scientific community, there are still an amount of unresolved issues. From the observable characteristics of the structures in the Universe,it should be possible to impose constraints on the cosmological parameters. Cosmic Voids (CV) are a major component of the LSS and have been shown to possess great potential for constraining DE and testing theories of gravity. But a gap between CV observations and theory still persists. A theoretical model for void statistical distribution as a function of size exists (SvdW) However, the SvdW model has been unsuccesful in reproducing the results obtained from cosmological simulations. This undermines the possibility of using voids as cosmological probes. The goal of our thesis work is to cover the gap between theoretical predictions and measured distributions of cosmic voids. We develop an algorithm to identify voids in simulations,consistently with theory. We inspecting the possibilities offered by a recently proposed refinement of the SvdW (the Vdn model, Jennings et al., 2013). Comparing void catalogues to theory, we validate the Vdn model, finding that it is reliable over a large range of radii, at all the redshifts considered and for all the cosmological models inspected. We have then searched for a size function model for voids identified in a distribution of biased tracers. We find that, naively applying the same procedure used for the unbiased tracers to a halo mock distribution does not provide success- full results, suggesting that the Vdn model requires to be reconsidered when dealing with biased samples. Thus, we test two alternative exten- sions of the model and find that two scaling relations exist: both the Dark Matter void radii and the underlying Dark Matter density contrast scale with the halo-defined void radii. We use these findings to develop a semi-analytical model which gives promising results.