@misc{9901, abstract = {Clusters of Orthologous Genes (COGs) and corresponding functional categories assigned to OGs. (CSV 117 kb)}, author = {Sigalova, Olga M. and Chaplin, Andrei V. and Bochkareva, Olga and Shelyakin, Pavel V. and Filaretov, Vsevolod A. and Akkuratov, Evgeny E. and Burskaia, Valentina and Gelfand, Mikhail S.}, publisher = {Springer Nature}, title = {{Additional file 9 of Chlamydia pan-genomic analysis reveals balance between host adaptation and selective pressure to genome reduction}}, doi = {10.6084/m9.figshare.9808907.v1}, year = {2019}, } @misc{9899, abstract = {Summary of orthologous groups (OGs) for 227 genomes of genus Chlamydia. (CSV 362 kb)}, author = {Sigalova, Olga M. and Chaplin, Andrei V. and Bochkareva, Olga and Shelyakin, Pavel V. and Filaretov, Vsevolod A. and Akkuratov, Evgeny E. and Burskaia, Valentina and Gelfand, Mikhail S.}, publisher = {Springer Nature}, title = {{Additional file 2 of Chlamydia pan-genomic analysis reveals balance between host adaptation and selective pressure to genome reduction}}, doi = {10.6084/m9.figshare.9808865.v1}, year = {2019}, } @misc{9900, abstract = {Pan-genome statistics by species. (CSV 3 kb)}, author = {Sigalova, Olga M. and Chaplin, Andrei V. and Bochkareva, Olga and Shelyakin, Pavel V. and Filaretov, Vsevolod A. and Akkuratov, Evgeny E. and Burskaia, Valentina and Gelfand, Mikhail S.}, publisher = {Springer Nature}, title = {{Additional file 5 of Chlamydia pan-genomic analysis reveals balance between host adaptation and selective pressure to genome reduction}}, doi = {10.6084/m9.figshare.9808886.v1}, year = {2019}, } @article{6936, abstract = {A key challenge for community ecology is to understand to what extent observational data can be used to infer the underlying community assembly processes. As different processes can lead to similar or even identical patterns, statistical analyses of non‐manipulative observational data never yield undisputable causal inference on the underlying processes. Still, most empirical studies in community ecology are based on observational data, and hence understanding under which circumstances such data can shed light on assembly processes is a central concern for community ecologists. We simulated a spatial agent‐based model that generates variation in metacommunity dynamics across multiple axes, including the four classic metacommunity paradigms as special cases. We further simulated a virtual ecologist who analysed snapshot data sampled from the simulations using eighteen output metrics derived from beta‐diversity and habitat variation indices, variation partitioning and joint species distribution modelling. Our results indicated two main axes of variation in the output metrics. The first axis of variation described whether the landscape has patchy or continuous variation, and thus was essentially independent of the properties of the species community. The second axis of variation related to the level of predictability of the metacommunity. The most predictable communities were niche‐based metacommunities inhabiting static landscapes with marked environmental heterogeneity, such as metacommunities following the species sorting paradigm or the mass effects paradigm. The most unpredictable communities were neutral‐based metacommunities inhabiting dynamics landscapes with little spatial heterogeneity, such as metacommunities following the neutral or patch sorting paradigms. The output metrics from joint species distribution modelling yielded generally the highest resolution to disentangle among the simulated scenarios. Yet, the different types of statistical approaches utilized in this study carried complementary information, and thus our results suggest that the most comprehensive evaluation of metacommunity structure can be obtained by combining them. }, author = {Ovaskainen, Otso and Rybicki, Joel and Abrego, Nerea}, issn = {1600-0587}, journal = {Ecography}, number = {11}, pages = {1877--1886}, publisher = {Wiley}, title = {{What can observational data reveal about metacommunity processes?}}, doi = {10.1111/ecog.04444}, volume = {42}, year = {2019}, } @article{6857, abstract = {Gene Drives are regarded as future tools with a high potential for population control. Due to their inherent ability to overcome the rules of Mendelian inheritance, gene drives (GD) may spread genes rapidly through populations of sexually reproducing organisms. A release of organisms carrying a GD would constitute a paradigm shift in the handling of genetically modified organisms because gene drive organisms (GDO) are designed to drive their transgenes into wild populations and thereby increase the number of GDOs. The rapid development in this field and its focus on wild populations demand a prospective risk assessment with a focus on exposure related aspects. Presently, it is unclear how adequate risk management could be guaranteed to limit the spread of GDs in time and space, in order to avoid potential adverse effects in socio‐ecological systems. The recent workshop on the “Evaluation of Spatial and Temporal Control of Gene Drives” hosted by the Institute of Safety/Security and Risk Sciences (ISR) in Vienna aimed at gaining some insight into the potential population dynamic behavior of GDs and appropriate measures of control. Scientists from France, Germany, England, and the USA discussed both topics in this meeting on April 4–5, 2019. This article summarizes results of the workshop.}, author = {Giese, B and Friess, J L and Schetelig, M F and Barton, Nicholas H and Messer, Philip and Debarre, Florence and Meimberg, H and Windbichler, N and Boete, C}, issn = {1521-1878}, journal = {BioEssays}, number = {11}, publisher = {Wiley}, title = {{Gene Drives: Dynamics and regulatory matters – A report from the workshop “Evaluation of spatial and temporal control of Gene Drives”, 4 – 5 April 2019, Vienna}}, doi = {10.1002/bies.201900151}, volume = {41}, year = {2019}, }