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Casamatta D.,University of North Florida | Tanic D.S.,Florida International University | Tanic D.S.,Institute for Biodiversity and Ecosystem Dynamics IBED | Gantar M.,Florida International University | Richardson L.L.,Florida International University

Black band disease (BBD) is a pathogenic microbial consortium dominated in terms of biomass by phycoerythrin-rich, gliding, filamentous cyanobacteria. Studies of BBD using molecular methods have shown that the 16S rRNA gene sequence of 'Oscillatoria' sp. is consistently found in BBD samples worldwide. The purpose of this study is to erect a new genus, Roseofilum gen. et sp. nov., encompassing these and other previously reported strains. Using a polyphasic approach we characterized two strains of BBD Oscillatoria isolated from BBD infected corals in the Caribbean. These strains have the ability to tolerate sulphide, anoxia, and pH values in the range from 4.5 to 10, with optimal growth at pH 6 to 8. Growth occurs by photoautotrophy, including sulphide-insensitive oxygenic photosynthesis, and mixotrophy but not by heterotrophy under dark aerobic or anaerobic conditions. Both strains synthesize microcystin-LR. Results of infectivity experiments carried out under controlled laboratory conditions showed that both strains are capable of initiating an infection on healthy coral that resembles in situ BBD. 16S rRNA gene sequence data place these strains into a highly supported clade with other strains identified as Oscillatoria sensu lato, yet clearly genetically distinct from the type, Oscillatoria princeps. Further, while the BBD strains share more morphological similarity with members of Phormidium, they are also distinct from this genus based on sequence data. Based on morphology, ecology, physiology, and phylogenetic distinctiveness, we propose the novel epithet Roseofilum reptotaenium. Source

Diks C.G.H.,University of Amsterdam | Vrugt J.A.,Los Alamos National Laboratory | Vrugt J.A.,Institute for Biodiversity and Ecosystem Dynamics IBED | Vrugt J.A.,University of California at Irvine
Stochastic Environmental Research and Risk Assessment

Multi-model averaging is currently receiving a surge of attention in the atmospheric, hydrologic, and statistical literature to explicitly handle conceptual model uncertainty in the analysis of environmental systems and derive predictive distributions of model output. Such density forecasts are necessary to help analyze which parts of the model are well resolved, and which parts are subject to considerable uncertainty. Yet, accurate point predictors are still desired in many practical applications. In this paper, we compare a suite of different model averaging techniques by their ability to improve forecast accuracy of environmental systems. We compare equal weights averaging (EWA), Bates-Granger model averaging (BGA), averaging using Akaike's information criterion (AICA), and Bayes' Information Criterion (BICA), Bayesian model averaging (BMA), Mallows model averaging (MMA), and Granger-Ramanathan averaging (GRA) for two different hydrologic systems involving water flow through a 1950 km 2 watershed and 5 m deep vadose zone. Averaging methods with weights restricted to the multi-dimensional simplex (positive weights summing up to one) are shown to have considerably larger forecast errors than approaches with unconstrained weights. Whereas various sophisticated model averaging approaches have recently emerged in the literature, our results convincingly demonstrate the advantages of GRA for hydrologic applications. This method achieves similar performance as MMA and BMA, but is much simpler to implement and use, and computationally much less demanding. © 2010 The Author(s). Source

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