The VVDS type-1 AGN sample: The faint end of the luminosity function

In a previous paper (Gavignaud et al. 2006), we presented the type-1 Active Galactic Nuclei (AGN) sample obtained from the first epoch data of the VIMOS-VLT Deep Survey (VVDS). The sample consists of 130 faint, broad-line AGN with redshift up to z=5 and 17.5< I <24.0, selected on the basis of their spectra. In this paper we present the measurement of the Optical Luminosity Function up to z=3.6 derived from this sample, we compare our results with previous results from brighter samples both at low and at high redshift. Our data, more than one magnitude fainter than previous optical surveys, allow us to constrain the faint part of the luminosity function up to high redshift. By combining our faint VVDS sample with the large sample of bright AGN extracted from the SDSS DR3 (Richards et al., 2006b) and testing a number of different evolutionary models, we find that the model which better represents the combined luminosity functions, over a wide range of redshift and luminosity, is a luminosity dependent density evolution (LDDE) model, similar to those derived from the major X-surveys. Such a parameterization allows the redshift of the AGN space density peak to change as a function of luminosity and explains the excess of faint AGN that we find at 1.0< z <1.5. On the basis of this model we find, for the first time from the analysis of optically selected samples, that the peak of the AGN space density shifts significantly towards lower redshift going to lower luminosity objects. This result, already found in a number of X-ray selected samples of AGN, is consistent with a scenario of "AGN cosmic downsizing", in which the density of more luminous AGN, possibly associated to more massive black holes, peaks earlier in the history of the Universe, than that of low luminosity ones.Comment: 13 pages, 10 figures, submitted to A&

Rise and fall of island butterfly diversity : understanding genetic differentiation and extinction in a highly diverse archipelago

Aim. We describe fine-scale diversity patterns of the entire butterfly fauna occurring on the Tuscan Archipelago. By assessing the traits associated with population diversification, haplotype uniqueness and extinction, we aim to identify the factors determining the origin and maintenance of genetic diversity, and population vulnerability to environmental changes. Location. Tuscan Archipelago, Sardinia, Tuscany (Italy) and Corsica (France). Methods. We built a mtDNA dataset (1,303 COI sequences) for the 52 butterfly species reported in the Archipelago, also including specimens from neighbouring areas, and compiled data on 12 species traits and on the apparent extinction of species from the main islands. We calculated indices that measure genetic differentiation, and using phylogenetic regressions we evaluated the relationships between these indices and species traits. Finally, we inferred which traits are associated with disappearance of species on individual islands using phylogenetic regression. Results. The overall spatial pattern of genetic diversity corresponded with the proximity of the areas, but strong contrasts were also identified between geographically close areas. Together with the island endemics, several common and widespread species had a high genetic diversification among islands and mainland. Phylogenetic regressions revealed that smaller-sized, more specialized species, with a preference for drier regions, displayed greater genetic structure and/or haplotype uniqueness. Species that disappeared from islands had a higher population diversification. Capraia has experienced a notable loss of diversity, which significantly affected species with shorter flight periods. Main conclusions. Tuscan island butterflies are characterized by strong genetic contrasts and species differ in their contribution to the overall genetic diversity. By ranking the species for their contribution to genetic diversity and identifying the traits linked to the emergence and maintenance of diversity, we have developed a valuable tool for prioritizing populations as targets for monitoring and conservation action. The dataset constructed also represents a valuable resource for testing biogeographical hypotheses