Butterflies of European islands: the implications of the geography and ecology of rarity and endemicity for conservation

Depending on their faunal content islands can function as important ‘vehicles’ for conservation. In this study, we examine data on 440 butterfly species over 564 European islands in 10 island groups. To determine the status of the butterfly fauna, we have adopted two approaches, island-focused and species-focused, examined using principal components analysis and regression modelling. In the former, we relate species richness, rarity and endemicity to island geography (area, elevation, isolation and location in latitude and longitude); in the latter, species occurrence on islands is examined in relation to distribution, range, range boundaries, and altitudinal limits on the continent as well as species’ ecology (number of host plants) and morphology (wing expanse). Species on islands are also assessed for their status on the continental mainland, their distributional dynamics (extinctions, distribution changes) and conservation status (Red Data Book, European Habitat Directive, Species of European Conservation Concern and Bern Convention listing. Unexpectedly, we find that a large fraction of the European butterfly species is found on the islands (63.4%; 59% on small islands) comprising some 6.2% of the land area of Europe. Although species occurring on the islands tend, on the whole, to have lower conservation status and are not declining over Europe, 45 species are endemics restricted to the islands. Species richness shows only a weak locational pattern and is related as expected to isolation from the continental source and island area; but, both rarity and endemicity have distinctive geographical bias to southern Europe, on islands now under increasing pressure from climate change and increasingly intensive human exploitation. The vulnerability of species on islands is emphasised in the relationship of island occurrence (% occurrence and presence/absence of species on any island) with continental distributions. A large proportion of the variation (84%) is accounted by continental distribution, the southern range limit and lower altitudinal limit. Most species (69%) occur on very few islands (<5%). In view of ongoing species dynamics on islands, migrations and extinctions of species, island repositories of species depend in large part on conservation of butterflies at continental sources. The unique faunas and rare species on islands also depend on appropriate concern being given to the island faunas. Conservation of European islands is thus a two-way process, sustaining sources and conserving island refuges. Residuals from the regressions (islands with more or fewer species, rare and endemic species; species occurring more or less frequently than expected on islands) provide warning signals of regions and islands deserving immediate attention.     

Relationship between assemblages of mycorrhizal fungi and bacteria on grass roots

Soils support an enormous microbial diversity, but the ecological drivers of this diversity are poorly understood. Interactions between the roots of individual grass species and the arbuscular mycorrhizal (AM) fungi and bacteria in their rhizoplane were studied in a grazed, unimproved upland pasture. Individual root fragments were isolated from soil cores, DNA extracted and used to identify plant species and assess rhizoplane bacterial and AM fungal assemblages, by amplifying part of the small-subunit ribosomal RNA gene, followed by terminal restriction fragment length polymorphism analysis. For the first time we showed that AM fungal and bacterial assemblages are related in situ and that this relationship occurred at the community level. Principal coordinate analyses of the data show that the AM fungi were a major factor determining the bacterial assemblage on grass roots. We also report a strong influence of the composition of the plant community on AM fungal assemblage. The bacterial assemblage was also influenced by soil pH and was spatially structured, whereas AM fungi were influenced neither by the bacteria nor by soil pH. Our study shows that linkages between plant roots and their microbial communities exist in a complex web of interactions that act at individual and at community levels, with AM fungi influencing the bacterial assemblage, but not the other way round.     

Nonlinear growth generates age changes in the moments of the frequency distribution: the example of height in puberty

Higher moments of the frequency distribution of child height and weight change with age, particularly during puberty, though why is not known. Our aims were to confirm that height skewness and kurtosis change with age during puberty, to devise a model to explain why, and to test the model by analyzing the data longitudinally. Heights of 3245 Christ's Hospital School boys born during 1927-1956 were measured twice termly from 9 to 20 years (n=129508). Treating the data as independent, the mean, standard deviation (SD), skewness, and kurtosis were calculated in 40 age groups and plotted as functions of age t. The data were also analyzed longitudinally using the nonlinear random-effects growth model H(t)=h(t-epsilon )+alpha, with H(t) the cross-sectional data, h(t) the individual mean curve, and epsilon and alpha subject-specific random effects reflecting variability in age and height at peak height velocity (PHV). Mean height increased monotonically with age, while the SD, skewness, and kurtosis changed cyclically with, respectively, 1, 2, and 3 turning points. Surprisingly, their age curves corresponded closely in shape to the first, second, and third derivatives of the mean height curve. The growth model expanded as a Taylor series in epsilon predicted such a pattern, and the longitudinal analysis showed that adjusting for age at PHV on a multiplicative scale largely removed the trends in the higher moments. A nonlinear growth process where subjects grow at different rates, such as in puberty, generates cyclical changes in the higher moments of the frequency distribution.     

Probe signal correction for differential methylation hybridization experiments

Background  

Non-biological signal (or noise) has been the bane of microarray analysis. Hybridization effects related to probe-sequence composition and DNA dye-probe interactions have been observed in differential methylation hybridization (DMH) microarray experiments as well as other effects inherent to the DMH protocol.     

A Poisson mixture model to identify changes in RNA polymerase II binding quantity using high-throughput sequencing technology

We present a mixture model-based analysis for identifying differences in the distribution of RNA polymerase II (Pol II) in transcribed regions, measured using ChIP-seq (chromatin immunoprecipitation following massively parallel sequencing technology). The statistical model assumes that the number of Pol II-targeted sequences contained within each genomic region follows a Poisson distribution. A Poisson mixture model was then developed to distinguish Pol II binding changes in transcribed region using an empirical approach and an expectation-maximization (EM) algorithm developed for estimation and inference. In order to achieve a global maximum in the M-step, a particle swarm optimization (PSO) was implemented. We applied this model to Pol II binding data generated from hormone-dependent MCF7 breast cancer cells and antiestrogen-resistant MCF7 breast cancer cells before and after treatment with 17beta-estradiol (E2). We determined that in the hormone-dependent cells, approximately 9.9% (2527) genes showed significant changes in Pol II binding after E2 treatment. However, only approximately 0.7% (172) genes displayed significant Pol II binding changes in E2-treated antiestrogen-resistant cells. These results show that a Poisson mixture model can be used to analyze ChIP-seq data.     

BioVenn - a web application for the comparison and visualization of biological lists using area-proportional Venn diagrams

Background

Recently in a number of studies, it has been demonstrated that the innate immune system doesn't merely acts as the first line of defense but provides critical signals for the development of specific adaptive immune response. Innate immune system employs a set of receptors called pattern recognition receptors (PRRs) that recognize evolutionarily conserved patterns from pathogens called pathogen associated molecular patterns (PAMPs). In order to assist scientific community, a database PRRDB has been developed that provides extensive information about pattern recognition receptors and their ligands.

Results

The current version of database contains around 500 patterns recognizing receptors from 77 distinct organisms ranging from insects to human. This includes 177 Toll-like receptors, 124 are Scavenger receptors and 67 are Nucleotide Binding Site-Leucine repeats rich receptors. The database also provides information about 266 ligands that includes carbohydrates, proteins, nucleic acids, glycolipids, glycoproteins, lipopeptides. A number of web tools have been integrated in PRRDB in order to provide following services: i) searching on any field; ii) database browsing; and iii) BLAST search against the pattern-recognition receptors. PRRDB also provides external links to standard databases like Swiss-Prot and Pubmed.

Conclusion

PRRDB is a unique database of its kind, which provides comprehensive information about innate immunity. This database will be very useful in designing effective adjuvant for subunit vaccine and in understanding role of innate immunity. The database is available from the URL's in the Availabiltiy and requirements section.