Stochastic and deterministic processes interact in the assembly of desert microbial communities on a global scale

Extreme arid regions in the worlds' major deserts are typified by quartz pavement terrain. Cryptic hypolithic communities colonize the ventral surface of quartz rocks and this habitat is characterized by a relative lack of environmental and trophic complexity. Combined with readily identifiable major environmental stressors this provides a tractable model system for determining the relative role of stochastic and deterministic drivers in community assembly. Through analyzing an original, worldwide data set of 16S rRNA-gene defined bacterial communities from the most extreme deserts on the Earth, we show that functional assemblages within the communities were subject to different assembly influences. Null models applied to the photosynthetic assemblage revealed that stochastic processes exerted most effect on the assemblage, although the level of community dissimilarity varied between continents in a manner not always consistent with neutral models. The heterotrophic assemblages displayed signatures of niche processes across four continents, whereas in other cases they conformed to neutral predictions. Importantly, for continents where neutrality was either rejected or accepted, assembly drivers differed between the two functional groups. This study demonstrates that multi-trophic microbial systems may not be fully described by a single set of niche or neutral assembly rules and that stochasticity is likely a major determinant of such systems, with significant variation in the influence of these determinants on a global scale.

A tree augmented classifier based on Extreme Imprecise Dirichlet Model

We present TANC, a TAN classifier (tree-augmented naive) based on imprecise probabilities. TANC models prior near-ignorance via the Extreme Imprecise Dirichlet Model (EDM). A first contribution of this paper is the experimental comparison between EDM and the global Imprecise Dirichlet Model using the naive credal classifier (NCC), with the aim of showing that EDM is a sensible approximation of the global IDM. TANC is able to deal with missing data in a conservative manner by considering all possible completions (without assuming them to be missing-at-random), but avoiding an exponential increase of the computational time. By experiments on real data sets, we show that TANC is more reliable than the Bayesian TAN and that it provides better performance compared to previous TANs based on imprecise probabilities. Yet, TANC is sometimes outperformed by NCC because the learned TAN structures are too complex; this calls for novel algorithms for learning the TAN structures, better suited for an imprecise probability classifier.

A Tree Augmented Classifier Based on Extreme Imprecise Dirichlet Model

In this paper we present TANC, i.e., a tree-augmented naive credal classifier based on imprecise probabilities; it models prior near-ignorance via the Extreme Imprecise Dirichlet Model (EDM) (Cano et al., 2007) and deals conservatively with missing data in the training set, without assuming them to be missing-at-random. The EDM is an approximation of the global Imprecise Dirichlet Model (IDM), which considerably simplifies the computation of upper and lower probabilities; yet, having been only recently introduced, the quality of the provided approximation needs still to be verified. As first contribution, we extensively compare the output of the naive credal classifier (one of the few cases in which the global IDM can be exactly implemented) when learned with the EDM and the global IDM; the output of the classifier appears to be identical in the vast majority of cases, thus supporting the adoption of the EDM in real classification problems. Then, by experiments we show that TANC is more reliable than the precise TAN (learned with uniform prior), and also that it provides better performance compared to a previous (Zaffalon, 2003) TAN model based on imprecise probabilities. TANC treats missing data by considering all possible completions of the training set, but avoiding an exponential increase of the computational times; eventually, we present some preliminary results with missing data.

Extreme Loads on an Oscillating Wave Surge Converter

A major difficulty in the design of full scale Wave Energy Converters is the need to design for two conflicting design criteria. In one instance devices must be designed to couple heavily to the incident wave force resulting in the efficient extraction of energy in small sea states, however devices must also be capable of withstanding the harsh conditions encountered during extreme seas. This paper presents an initial investigation of the extreme wave loading of a generic, surface-piercing, pitching flap-type device deployed in near shore wave conditions. Slamming of the flap is selected as the extreme load event for further investigation and the experimental methodologies employed are described. Preliminary results showing both local and global loading under such events are presented for the case of a flap tested in a 3-dimensional environment. Results are presented which show flap slamming effects on the pressures experienced on the front face of the flap.

Presence and possible cause of periodicities in 20th-century extreme coastal surge: Belfast Harbour, Northern Ireland.

Identifying 20th-century periodic coastal surge variation is strategic for the 21st-century coastal surge estimates, as surge periodicities may amplify/reduce future MSL enhanced surge forecasts. Extreme coastal surge data from Belfast Harbour (UK) tide gauges are available for 1901–2010 and provide the potential for decadal-plus periodic coastal surge analysis. Annual extreme surge-elevation distributions (sampled every 10-min) are analysed using PCA and cluster analysis to decompose variation within- and between-years to assess similarity of years in terms of Surge Climate Types, and to establish significance of any transitions in Type occurrence over time using non-parametric Markov analysis. Annual extreme surge variation is shown to be periodically organised across the 20th century. Extreme surge magnitude and distribution show a number of significant cyclonic induced multi-annual (2, 3, 5 & 6 years) cycles, as well as dominant multi-decadal (15–25 years) cycles of variation superimposed on an 80 year fluctuation in atmospheric–oceanic variation across the North Atlantic (relative to NAO/AMO interaction). The top 30 extreme surge events show some relationship with NAO per se, given that 80% are associated with westerly dominant atmospheric flows (+ NAO), but there are 20% of the events associated with blocking air massess (− NAO). Although 20% of the top 30 ranked positive surges occurred within the last twenty years, there is no unequivocal evidence of recent acceleration in extreme surge magnitude related to other than the scale of natural periodic variation.