Ensemble prediction distribution maps of macroalgae for current conditions and four climate change scenarios and high resultion bathymetry for Potter Cove, WAP, Antarctica

Species distribution models (SDM) predict species occurrence based on statistical relationships with environmental conditions. The R-package biomod2 which includes 10 different SDM techniques and 10 different evaluation methods was used in this study. Macroalgae are the main biomass producers in Potter Cove, King George Island (Isla 25 de Mayo), Antarctica, and they are sensitive to climate change factors such as suspended particulate matter (SPM). Macroalgae presence and absence data were used to test SDMs suitability and, simultaneously, to assess the environmental response of macroalgae as well as to model four scenarios of distribution shifts by varying SPM conditions due to climate change. According to the averaged evaluation scores of Relative Operating Characteristics (ROC) and True scale statistics (TSS) by models, those methods based on a multitude of decision trees such as Random Forest and Classification Tree Analysis, reached the highest predictive power followed by generalized boosted models (GBM) and maximum-entropy approaches (Maxent). The final ensemble model used 135 of 200 calculated models (TSS > 0.7) and identified hard substrate and SPM as the most influencing parameters followed by distance to glacier, total organic carbon (TOC), bathymetry and slope. The climate change scenarios show an invasive reaction of the macroalgae in case of less SPM and a retreat of the macroalgae in case of higher assumed SPM values.

Ecologia e história natural de população Hemidactylus agrius (Squamata: Gekkonidae) em área de Caatinga, com avaliação da distribuição das espécies nativas do gênero no nordeste do Brasil

The genus Hemidactylus Oken, 1817 has cosmopolite distribution, with three species occurring in Brazil, two of them native, H. brasilianus and H. agrius, and one exotic, H. mabouia. Considering the studies about ecology of lizards conducted in the Ecological Station of the Seridó, from 2001 to 2011, this study aimed (1) to re-evaluate the occurrence of the species of Hemidactylus in this ESEC; (2) to analyze ecological and biological aspects of the H. agrius population; and (3) to investigate the current and potential distribution of the native species of the genus in northeastern Brazil, analyzing the suitability of ESEC to this taxon. For the first two objectives, a sampling area consisting of five transects of 200 x 20 m, was inspected in alternating daily shifts for three consecutive days, from August 2012 to August 2013. For the latter objective, occurrence points of H. agrius and H. brasilianus from literature and from the database of Herpetological Collections of the UFRN and the UNICAMP were consulted to build predictive maps via the Maximum Entropy algorithm (MaxEnt). In ESEC Seridó, 62 H. agrius individuals were collected (25 females, 18 males and 19 juveniles), and two neonates were obtained from a communal nest incubated in the laboratory. No record was made for the other two species of the genus. Hemidactylus agrius demonstrated to be a nocturnal species specialized in habitats with rocky outcrops; but this species is generalist regarding microhabitat use. In the population studied, females had an average body length greater than males, and showed higher frequencies of caudal autotomy. Regarding diet, H. agrius is a moderately generalist species that consumes arthropods, especially insect larvae, Isoptera and Araneae; and vertebrates, with a case of cannibalism registered in the population. With respect to seasonal differences, only the number of food items ingested differed between seasons. The diet was similar between sexes, but ontogenetic differences were recorded for the total volume and maximum length of the food items. Significant relationships were found between lizard body/head size measurements and the maximum length of prey consumed. Cases of polydactyly and tail bifurcation were recorded in the population, with frequencies of 1.6% and 3.1%, respectively. In relation xv to the occurrence points of the native species, 27 were identified, 14 for H. agrius and 13 for H. brasilianus. The first species presented restricted distribution, while the second showed a wide distribution. In both models generated, the ESEC Seridó area showed medium to high suitability. The results of this study confirm the absence of H. brasilianus and H. mabouia this ESEC, and reveal H. agrius as a dietary opportunist and cannibal species. Further, the results confirm the distribution patterns shown by native species of Hemidactylus, and point ESEC Seridó as an area of probable occurrence for the species of the genus, the establishing of H. brasilianus and H. mabouia are probably limited by biotic factors, a fact yet little understood

Caracterización de señales sísmicas del Volcán Cotopaxi utilizando estimadores espectrales clásicos y de máxima entropía

Se presenta un estudio de detección y caracterización de eventos sísmicos del tipo volcano tectónicos y largo periodo de registros sísmicos generados por el volcán Cotopaxi. La estructura secuencial de detección propuesta permite en un registro sísmico maximizar la probabilidad de presencia de un evento y minimizar la ausencia de este. La detección se la realiza en el dominio del tiempo en cuasi tiempo real manteniendo una tasa constante de falsa alarma para posteriormente realizar un estudio del contenido espectral de los eventos mediante el uso de estimadores espectrales clásicos como el periodograma y paramétricos como el método de máxima entropía de Burg, logrando así, categorizar a los eventos detectados como volcano tectónicos, largo periodo y otros cuando no poseen características pertenecientes a los otros dos tipos como son los rayos.

Active Learning of Very-High Resolution Optical Imagery with SVM: Entropy vs Margin Sampling

An active learning method is proposed for the semi-automatic selection of training sets in remote sensing image classification. The method adds iteratively to the current training set the unlabeled pixels for which the prediction of an ensemble of classifiers based on bagged training sets show maximum entropy. This way, the algorithm selects the pixels that are the most uncertain and that will improve the model if added in the training set. The user is asked to label such pixels at each iteration. Experiments using support vector machines (SVM) on an 8 classes QuickBird image show the excellent performances of the methods, that equals accuracies of both a model trained with ten times more pixels and a model whose training set has been built using a state-of-the-art SVM specific active learning method

Analysis of System Signals Based on Cross Recurrence Method for System Dynamics Characterization

Natural systems are inherently non linear. Recurrent behaviours are typical of natural systems. Recurrence is a fundamental property of non linear dynamical systems which can be exploited to characterize the system behaviour effectively. Cross recurrence based analysis of sensor signals from non linear dynamical system is presented in this thesis. The mutual dependency among relatively independent components of a system is referred as coupling. The analysis is done for a mechanically coupled system specifically designed for conducting experiment. Further, cross recurrence method is extended to the actual machining process in a lathe to characterize the chatter during turning. The result is verified by permutation entropy method. Conventional linear methods or models are incapable of capturing the critical and strange behaviours associated with the dynamical process. Hence any effective feature extraction methodologies should invariably gather information thorough nonlinear time series analysis. The sensor signals from the dynamical system normally contain noise and non stationarity. In an effort to get over these two issues to the maximum possible extent, this work adopts the cross recurrence quantification analysis (CRQA) methodology since it is found to be robust against noise and stationarity in the signals. The study reveals that the CRQA is capable of characterizing even weak coupling among system signals. It also divulges the dependence of certain CRQA variables like percent determinism, percent recurrence and entropy to chatter unambiguously. The surrogate data test shows that the results obtained by CRQA are the true properties of the temporal evolution of the dynamics and contain a degree of deterministic structure. The results are verified using permutation entropy (PE) to detect the onset of chatter from the time series. The present study ascertains that this CRP based methodology is capable of recognizing the transition from regular cutting to the chatter cutting irrespective of the machining parameters or work piece material. The results establish this methodology to be feasible for detection of chatter in metal cutting operation in a lathe.

Heavy tails, importance sampling and cross-entropy

We consider the problem of estimating P(Yi + (...) + Y-n > x) by importance sampling when the Yi are i.i.d. and heavy-tailed. The idea is to exploit the cross-entropy method as a toot for choosing good parameters in the importance sampling distribution; in doing so, we use the asymptotic description that given P(Y-1 + (...) + Y-n > x), n - 1 of the Yi have distribution F and one the conditional distribution of Y given Y > x. We show in some specific parametric examples (Pareto and Weibull) how this leads to precise answers which, as demonstrated numerically, are close to being variance minimal within the parametric class under consideration. Related problems for M/G/l and GI/G/l queues are also discussed.

Two Bayesian treatments of the n-tuple recognition method

Two probabilistic interpretations of the n-tuple recognition method are put forward in order to allow this technique to be analysed with the same Bayesian methods used in connection with other neural network models. Elementary demonstrations are then given of the use of maximum likelihood and maximum entropy methods for tuning the model parameters and assisting their interpretation. One of the models can be used to illustrate the significance of overlapping n-tuple samples with respect to correlations in the patterns.

Human and climatic change impact modelling on the habitat suitability for the chimpanzee (Pan troglodytes ellioti) – Case study: The proposed Mount Cameroon National Park

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies

Predicting the geographic distribution of Lutzomyia longipalpis (Diptera: Psychodidae) and visceral leishmaniasis in the state of Mato Grosso do Sul, Brazil

To understand the geographic distribution of visceral leishmaniasis (VL) in the state of Mato Grosso do Sul (MS), Brazil, both the climatic niches of Lutzomyia longipalpis and VL cases were analysed. Distributional data were obtained from 55 of the 79 counties of MS between 2003-2012. Ecological niche models (ENM) of Lu. longipalpis and VL cases were produced using the maximum entropy algorithm based on eight climatic variables. Lu. longipalpis showed a wide distribution in MS. The highest climatic suitability for Lu. longipalpis was observed in southern MS. Temperature seasonality and annual mean precipitation were the variables that most influenced these models. Two areas of high climatic suitability for the occurrence of VL cases were predicted: one near Aquidauana and another encompassing several municipalities in the southeast region of MS. As expected, a large overlap between the models for Lu. longipalpis and VL cases was detected. Northern and northwestern areas of MS were suitable for the occurrence of cases, but did not show high climatic suitability for Lu. longipalpis . ENM of vectors and human cases provided a greater understanding of the geographic distribution of VL in MS, which can be applied to the development of future surveillance strategies.

BME-based uncertainty assessment of the Chernobyl fallout

The vast territories that have been radioactively contaminated during the 1986 Chernobyl accident provide a substantial data set of radioactive monitoring data, which can be used for the verification and testing of the different spatial estimation (prediction) methods involved in risk assessment studies. Using the Chernobyl data set for such a purpose is motivated by its heterogeneous spatial structure (the data are characterized by large-scale correlations, short-scale variability, spotty features, etc.). The present work is concerned with the application of the Bayesian Maximum Entropy (BME) method to estimate the extent and the magnitude of the radioactive soil contamination by 137Cs due to the Chernobyl fallout. The powerful BME method allows rigorous incorporation of a wide variety of knowledge bases into the spatial estimation procedure leading to informative contamination maps. Exact measurements (?hard? data) are combined with secondary information on local uncertainties (treated as ?soft? data) to generate science-based uncertainty assessment of soil contamination estimates at unsampled locations. BME describes uncertainty in terms of the posterior probability distributions generated across space, whereas no assumption about the underlying distribution is made and non-linear estimators are automatically incorporated. Traditional estimation variances based on the assumption of an underlying Gaussian distribution (analogous, e.g., to the kriging variance) can be derived as a special case of the BME uncertainty analysis. The BME estimates obtained using hard and soft data are compared with the BME estimates obtained using only hard data. The comparison involves both the accuracy of the estimation maps using the exact data and the assessment of the associated uncertainty using repeated measurements. Furthermore, a comparison of the spatial estimation accuracy obtained by the two methods was carried out using a validation data set of hard data. Finally, a separate uncertainty analysis was conducted that evaluated the ability of the posterior probabilities to reproduce the distribution of the raw repeated measurements available in certain populated sites. The analysis provides an illustration of the improvement in mapping accuracy obtained by adding soft data to the existing hard data and, in general, demonstrates that the BME method performs well both in terms of estimation accuracy as well as in terms estimation error assessment, which are both useful features for the Chernobyl fallout study.

Is Tsallis thermodynamics nonextensive?

Within the Tsallis thermodynamics framework, and using scaling properties of the entropy, we derive a generalization of the Gibbs-Duhem equation. The analysis suggests a transformation of variables that allows standard thermodynamics to be recovered. Moreover, we also generalize Einsteins formula for the probability of a fluctuation to occur by means of the maximum statistical entropy method. The use of the proposed transformation of variables also shows that fluctuations within Tsallis statistics can be mapped to those of standard statistical mechanics.

Effects of sample size on the performance of species distribution models

A wide range of modelling algorithms is used by ecologists, conservation practitioners, and others to predict species ranges from point locality data. Unfortunately, the amount of data available is limited for many taxa and regions, making it essential to quantify the sensitivity of these algorithms to sample size. This is the first study to address this need by rigorously evaluating a broad suite of algorithms with independent presence-absence data from multiple species and regions. We evaluated predictions from 12 algorithms for 46 species (from six different regions of the world) at three sample sizes (100, 30, and 10 records). We used data from natural history collections to run the models, and evaluated the quality of model predictions with area under the receiver operating characteristic curve (AUC). With decreasing sample size, model accuracy decreased and variability increased across species and between models. Novel modelling methods that incorporate both interactions between predictor variables and complex response shapes (i.e. GBM, MARS-INT, BRUTO) performed better than most methods at large sample sizes but not at the smallest sample sizes. Other algorithms were much less sensitive to sample size, including an algorithm based on maximum entropy (MAXENT) that had among the best predictive power across all sample sizes. Relative to other algorithms, a distance metric algorithm (DOMAIN) and a genetic algorithm (OM-GARP) had intermediate performance at the largest sample size and among the best performance at the lowest sample size. No algorithm predicted consistently well with small sample size (n < 30) and this should encourage highly conservative use of predictions based on small sample size and restrict their use to exploratory modelling.

Novel method to estimate the phenotypic variation explained by genome-wide association studies reveals large fraction of the missing heritability.

Genome-wide association studies (GWAS) are conducted with the promise to discover novel genetic variants associated with diverse traits. For most traits, associated markers individually explain just a modest fraction of the phenotypic variation, but their number can well be in the hundreds. We developed a maximum likelihood method that allows us to infer the distribution of associated variants even when many of them were missed by chance. Compared to previous approaches, the novelty of our method is that it (a) does not require having an independent (unbiased) estimate of the effect sizes; (b) makes use of the complete distribution of P-values while allowing for the false discovery rate; (c) takes into account allelic heterogeneity and the SNP pruning strategy. We applied our method to the latest GWAS meta-analysis results of the GIANT consortium. It revealed that while the explained variance of genome-wide (GW) significant SNPs is around 1% for waist-hip ratio (WHR), the observed P-values provide evidence for the existence of variants explaining 10% (CI=[8.5-11.5%]) of the phenotypic variance in total. Similarly, the total explained variance likely to exist for height is estimated to be 29% (CI=[28-30%]), three times higher than what the observed GW significant SNPs give rise to. This methodology also enables us to predict the benefit of future GWA studies that aim to reveal more associated genetic markers via increased sample size.

A Method for Sky-Condition Classification from Ground-Based Solar Radiation Measurements

Identification of clouds from satellite images is now a routine task. Observation of clouds from the ground, however, is still needed to acquire a complete description of cloud conditions. Among the standard meteorologicalvariables, solar radiation is the most affected by cloud cover. In this note, a method for using global and diffuse solar radiation data to classify sky conditions into several classes is suggested. A classical maximum-likelihood method is applied for clustering data. The method is applied to a series of four years of solar radiation data and human cloud observations at a site in Catalonia, Spain. With these data, the accuracy of the solar radiation method as compared with human observations is 45% when nine classes of sky conditions are to be distinguished, and it grows significantly to almost 60% when samples are classified in only five different classes. Most errors are explained by limitations in the database; therefore, further work is under way with a more suitable database

Neutron tomography using projection data obtained by Monte Carlo simulation for nondestructive evaluation

This work present the application of a computer package for generating of projection data for neutron computerized tomography, and in second part, discusses an application of neutron tomography, using the projection data obtained by Monte Carlo technique, for the detection and localization of light materials such as those containing hydrogen, concealed by heavy materials such as iron and lead. For tomographic reconstructions of the samples simulated use was made of only six equal projection angles distributed between 0º and 180º, with reconstruction making use of an algorithm (ARIEM), based on the principle of maximum entropy. With the neutron tomography it was possible to detect and locate polyethylene and water hidden by lead and iron (with 1cm-thick). Thus, it is demonstrated that thermal neutrons tomography is a viable test method which can provide important interior information about test components, so, extremely useful in routine industrial applications.