Species–area relationships and nestedness patterns were studied in three groups of small terrestrial vertebrates (mammals, reptiles, amphibians) on 14 landbridge islands of the eastern Adriatic. Islands ranged in surface area between 15 and 410 km2 and contained from eight to 36 species from a total species pool of 48. Reptiles were the most species rich group (S = 28), and had more species than mammals (S = 13) and amphibians (S = 7) combined. Island surface area predicted species richness best in reptiles (r2 = 0.79) and most poorly in amphibians (r2 = 0.52). Mammals showed a significantly lower slope of the species–area curve than amphibians and reptiles, and thus accumulated species counts with increase in area at the lowest rate. Nestedness patterns in all groups were significantly more organised than expected by chance. Amphibian nested structure points to extinction dominated and well insularised populations with no subsequent recolonisations. Frequent unexpected presences and absences in the nestedness patterns of mammals and reptiles suggest complex biogeographic histories for these two groups, with several factors putatively in operation: heterogeneity in habitats and the original source fauna, post- isolation immigrations and differential extinction rate due to human-caused habitat degradation.
The tenebrionid beetles on 25 circum-Sicilian islands were studied to determine the influence of island geographical and landscape features on three main intercorrelated biogeographical patterns: (1) species richness, studied using species-area and species environment relationships, (2) species assemblage composition, investigated using Canonical Correspondence Analysis (CCA), and (3) inter-site faunal similarity, investigated using Canonical Correlation Analysis (CANCOR) applied to multidimensional scaling of inter-island faunal dissimilarities. Species richness was mostly influenced by island area and landscape heterogeneity (expressed using various indices of diversity based on land cover categories). When species identities were considered in the CCA, no substantial effect of landscape was detected. Current island isolation did not have a strong influence on species richness, but has a distinct effect in determining species assortments on the remotest islands. Historical influences of Pleistocene landbridge connections were not detectable in species richness relationships using geographical variables in species richness analyses or in assemblage gradients in the CCA, but emerged distinctly from inter-island similarities in the CANCOR. and Simone Fattorini.
To date, thousands of microRNAs (miRNAs) and their precursors (pre-miRNAs) have been identified in insects and their nucleotide sequences deposited in the miRBase database. In the present work, we have systematically analyzed, utilizing bioinformatics tools, the featural differences between human and insect pre-miRNAs, as well as differences across 24 insect species. Results showed that the nucleotide composition, sequence length, nucleotides preference and secondary structure features between human and insects were different. Subsequently, with the aid of three available SVM-based prediction programs, pre-miRNA sequences were evaluated and given corresponding scores. Thus it was found that of 2633 sequences from the 24 chosen insect species, 2229 (84.7%) were successfully recognized by the Mirident classifier, higher than Triplet-SVM (72.5%) and PMirP (72.6%). In contrast, four species, including the domesticated silkworm, Bombyx mori L., the fruit fly, Drosophila melanogaster Meigen, the honeybee, Apis mellifera L. and the red flour beetle, Tribolium castaneum (Herbst), were found to be largely responsible for the poor performance of some sequence matching. Compared with other species, B. mori especially showed the worst performance with the lowest average MFE index (0.73). Collectively these results pave the way for understanding specificity and diversity of miRNA precursors in insects, and lay the foundation for the further development of more suitable algorisms for insects., Li, Jisheng ... [et al.]., and Obsahuje seznam literatury