Density, climate and varying return points: an analysis of long-term population fluctuations in the threatened European tree frog.

Experimental research has identified many putative agents of amphibian decline, yet the population-level consequences of these agents remain unknown, owing to lack of information on compensatory density dependence in natural populations. Here, we investigate the relative importance of intrinsic (density-dependent) and extrinsic (climatic) factors impacting the dynamics of a tree frog (Hyla arborea) population over 22 years. A combination of log-linear density dependence and rainfall (with a 2-year time lag corresponding to development time) explain 75% of the variance in the rate of increase. Such fluctuations around a variable return point might be responsible for the seemingly erratic demography and disequilibrium dynamics of many amphibian populations.

Genetic analysis of the breeding system of an invasive subterranean termite, Reticulitermes santonensis, in urban and natural habitats.

Reticulitermes santonensis is a subterranean termite that invades urban areas in France and elsewhere where it causes damage to human-built structures. We investigated the breeding system, colony and population genetic structure, and mode of dispersal of two French populations of R. santonensis. Termite workers were sampled from 43 and 31 collection points, respectively, from a natural population in west-central France (in and around the island of Oleron) and an urban population (Paris). Ten to 20 workers per collection point were genotyped at nine variable microsatellite loci to determine colony identity and to infer colony breeding structure. There was a total of 26 colonies, some of which were spatially expansive, extending up to 320 linear metres. Altogether, the analysis of genotype distribution, F-statistics and relatedness coefficients suggested that all colonies were extended families headed by numerous neotenics (nonwinged precocious reproductives) probably descended from pairs of primary (winged) reproductives. Isolation by distance among collection points within two large colonies from both populations suggested spatially separated reproductive centres with restricted movement of workers and neotenics. There was a moderate level of genetic differentiation (F(ST) = 0.10) between the Oleron and Paris populations, and the number of alleles was significantly higher in Oleron than in Paris, as expected if the Paris population went through bottlenecks when it was introduced from western France. We hypothesize that the diverse and flexible breeding systems found in subterranean termites pre-adapt them to invade new or marginal habitats. Considering that R. santonensis may be an introduced population of the North American species R. flavipes, a breeding system consisting primarily of extended family colonies containing many neotenic reproductives may facilitate human-mediated spread and establishment of R. santonensis in urban areas with harsh climates.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Forest fires cluster detection with space-time scan statistics

Forest fires are defined as uncontrolled fires often occurring in wildland areas, but that can also affect houses or agricultural resources. Causes are both natural (e.g.,lightning phenomena) and anthropogenic (human negligence or arsons).Major environmental factors influencing the fire ignition and propagation are climate and vegetation. Wildfires are most common and severe during drought period and on windy days. Moreover, under water-stress conditions, which occur after a long hot and dry period, the vegetation is more vulnerable to fire. These conditions are common in the United State and Canada, where forest fires represent a big problem. We focused our analysis on the state of Florida, for which a big dataset on forest fires detection is readily available. USDA Forest Service Remote Sensing Application Center, in collaboration with NASA-Goddard Space Flight Center and the University of Maryland, has compiled daily MODIS Thermal Anomalies (fires and biomass burning images) produced by NASA using a contextual algorithm that exploits the strong emission of mid-infrared radiation from fires. Fire classes were converted in GIS format: daily MODIS fire detections are provided as the centroids of the 1 kilometer pixels and compiled into daily Arc/INFO point coverage.

Statistics in science and in society: From a state-of-the-art to a new research agenda

Statistics occupies a prominent role in science and citizens' daily life. This article provides a state-of-the-art of the problems associated with statistics in science and in society, structured along the three paradigms defined by Bauer, Allum and Miller (2007). It explores in more detail medicine and public understanding of science on the one hand, and risks and surveys on the other. Statistics has received a good deal of attention; however, very often handled in terms of deficit - either of scientists or of citizens. Many tools have been proposed to improve statistical literacy, the image of and trust in statistics, but with little understanding of their roots, with little coordination among stakeholders and with few assessments of impacts. These deficiencies represent as many new and promising directions in which the PUS research agenda could be expanded.

Conditioning of multiple-point statistics facies simulations to tomographic images

Geophysical tomography captures the spatial distribution of the underlying geophysical property at a relatively high resolution, but the tomographic images tend to be blurred representations of reality and generally fail to reproduce sharp interfaces. Such models may cause significant bias when taken as a basis for predictive flow and transport modeling and are unsuitable for uncertainty assessment. We present a methodology in which tomograms are used to condition multiple-point statistics (MPS) simulations. A large set of geologically reasonable facies realizations and their corresponding synthetically calculated cross-hole radar tomograms are used as a training image. The training image is scanned with a direct sampling algorithm for patterns in the conditioning tomogram, while accounting for the spatially varying resolution of the tomograms. In a post-processing step, only those conditional simulations that predicted the radar traveltimes within the expected data error levels are accepted. The methodology is demonstrated on a two-facies example featuring channels and an aquifer analog of alluvial sedimentary structures with five facies. For both cases, MPS simulations exhibit the sharp interfaces and the geological patterns found in the training image. Compared to unconditioned MPS simulations, the uncertainty in transport predictions is markedly decreased for simulations conditioned to tomograms. As an improvement to other approaches relying on classical smoothness-constrained geophysical tomography, the proposed method allows for: (1) reproduction of sharp interfaces, (2) incorporation of realistic geological constraints and (3) generation of multiple realizations that enables uncertainty assessment.

Vestibular Schwannoma: The Evolution of Hearing and Tumor Size in Natural Course and after Treatment by LINAC Stereotactic Radiosurgery.

OBJECTIVE: To review the natural course of tumor size and hearing during conservative management of 151 patients with unilateral vestibular schwannoma (VS), and to evaluate the same parameters for the part of the group (n = 84) who were treated by LINAC stereotactic radiosurgery (SRS). METHODS: In prospectively collected data, patients underwent MRI and complete audiovestibular tests at inclusion, during the conservative management period and after SRS. Hearing was graded according to the Gardner-Robertson (GR) scale and tumor size according to Koos. Statistics were performed using Kaplan-Meier survival analysis and multivariate analyses including linear and logistic regression. Specific insight was given to patients with serviceable hearing. RESULTS: During the conservative management period (mean follow-up time: 24 months, range: 6-96), the annual risk of GR class degradation was 6% for GRI and 15% for GR II patients. Hearing loss as an initial symptom was highly predictive of further hearing loss (p = 0.003). Tumor growth reached 25%. For SRS patients, functional hearing preservation was 51% at 1 year and 36% at 3 years. Tumor control was 94 and 91%, respectively. CONCLUSION: In VS patients, hearing loss at the time of diagnosis is a predictor of poorer hearing outcome. LINAC SRS is efficient for tumor control. Patients who preserved their pretreatment hearing presented less hearing loss per year after SRS than before treatment, suggesting a protective effect of SRS when cochlear function can be preserved.