Abstract: Corrosion induced by the microorganisms is a phenomenon still poorly known and difficult to interpret. In this work we are interested in marine corrosion of stainless steel 254 SMO by Amazonians biofilms. The objective is to achieve an experimental laboratory to reproduce the corrosion completely controlled environment. The microbiologically influenced corrosion (MIC) of stainless steel by marine bacteria was investigated using surface analysis (scanning electron microscopy (SEM)), and electrochemical techniques (the open circuit potential, electrochemical impedance spectroscopy (EIS), and potentiodynamic polarization curves). The epifluorescence microscopy has been used to correlate the experiments. We investigated the role of some chemical parameters and the influence of the tide.

Abstract: Statisticians carry out close examination of spatialized data, such as the distribution of household income, the location of industrial or commercial establishments, the distribution of schools in cities, etc. Answers can be found through analyses of one or more predefined geographical scales such as neighbourhoods, districts or statistical blocks. However, it is tempting to preserve the individual data and to work with the exact position of the entities that are being studied. If that is the case, statisticians have to conduct analyses based on geolocation data without carrying out any geographical aggregation. Observations are taken as points in space and the objective is to characterise these point distributions. Understanding and mastering statistical methods that process this individual and spatialized information enables us to work on data that are now increasingly accessible and sought after because they provide very precise analyses of distributions studied (Ellison et al. 2010; Barlet et al. 2013). In this framework of analysis, statisticians who have sets of points to analyse are faced with several important methodological questions: how can such data with thousands or even millions of observations be represented and characterised spatially? What statistical tools exist that can be used to study these observations relating to households, employees, firms, stores, equipment or travel, for example? How can the qualitative or quantitative characteristics of the observations being studied be taken into account? How can any attractions or repulsions between points or between different types of points be highlighted? How can we assess the significance of the results obtained, etc? The purpose of this chapter is to help statisticians to provide statistically robust results from the study of spatialized data that is not based on predefined zoning. To do this, we will review the literature on the subject of statistical methods used to characterise point distributions and we will explain the associated issues. We will use simple examples to explain the advantages and disadvantages of the most frequently adopted approaches. The code provided in R will be used to reproduce the examples covered.

Abstract: Until now the altitudinal factor has not been taken into account to estimate tropical arthropod diversity. The ultimate aim of the terrestrial biodiversity
survey “Our Planet Reviewed – Papua New Guinea” was to estimate biological diversity generated by altitudinal turnover of arthropod species. It took place on Mount Wilhelm, Papua New Guinea highest peak (4509 m a.s.l.), and one of the few equatorial mountains outside the Andes left with a continuous undisturbed forest from the sea level all the way to the timber line limit. An unprecedented sampling effort was concentrated over 16 days in 2012 with a semi-simultaneous sampling at eight different elevations (every 500 m from 200 m to 3700 m a.s.l.). Arthropods were collected with various methods: flight interception traps (targeting Coleoptera), Malaise traps (targeting Hymenoptera, Diptera and Hemiptera), Steiner traps (targeting tephritid flies), beating of the understorey vegetation, and insecticide spraying on tree barks (various groups targeted). A botany survey was conducted at each elevation to characterize vegetation. An additional site, Wanang, was sampled according to the same protocol, as replicated lowland site. Our team combined international experts with local postgraduate students, para-ecologists and villagers. Arthropod samples collected during the biotic survey were pre-sorted in Papua New Guinea and forwarded to taxonomists worldwide. The current book presents the first taxonomic results of the biotic survey. Project outputs included not only species discovery, but also direct financial benefits to landowner communities, raised profile of conservation areas, training of paraecologists and postgraduate students, education programmes and, finally, crucial biodiversity information needed for ecological analyses and conservation management.