Models to predict the distribution and abundance of breeding ducks in Canada

Detailed knowledge of waterfowl abundance and distribution across Canada is lacking, which limits our ability to effectively conserve and manage their populations. We used 15 years of data from an aerial transect survey to model the abundance of 17 species or species groups of ducks within southern and boreal Canada. We included 78 climatic, hydrological, and landscape variables in Boosted Regression Tree models, allowing flexible response curves and multiway interactions among variables. We assessed predictive performance of the models using four metrics and calculated uncertainty as the coefficient of variation of predictions across 20 replicate models. Maps of predicted relative abundance were generated from resulting models, and they largely match spatial patterns evident in the transect data. We observed two main distribution patterns: a concentrated prairie-parkland distribution and a more dispersed pan-Canadian distribution. These patterns were congruent with the relative importance of predictor variables and model evaluation statistics among the two groups of distributions. Most species had a hydrological variable as the most important predictor, although the specific hydrological variable differed somewhat among species. In some cases, important variables had clear ecological interpretations, but in some instances, e.g., topographic roughness, they may simply reflect chance correlations between species distributions and environmental variables identified by the model-building process. Given the performance of our models, we suggest that the resulting prediction maps can be used in future research and to guide conservation activities, particularly within the bounds of the survey area.

Threshold detection: matching statistical methodology to ecological questions and conservation planning objectives

Two types of ecological thresholds are now being widely used to develop conservation targets: breakpoint-based thresholds represent tipping points where system properties change dramatically, whereas classification thresholds identify groups of data points with contrasting properties. Both breakpoint-based and classification thresholds are useful tools in evidence-based conservation. However, it is critical that the type of threshold to be estimated corresponds with the question of interest and that appropriate statistical procedures are used to determine its location. On the basis of their statistical properties, we recommend using piecewise regression methods to identify breakpoint-based thresholds and discriminant analysis or classification and regression trees to identify classification thresholds.

Tree-structured smooth transition models

The goal of this paper is to introduce a class of tree-structured models that combines aspects of regression trees and smooth transition regression models. The model is called the Smooth Transition Regression Tree (STR-Tree). The main idea relies on specifying a multiple-regime parametric model through a tree-growing procedure with smooth transitions among different regimes. Decisions about splits are entirely based on a sequence of Lagrange Multiplier (LM) tests of hypotheses.

Evidence for production of single top quarks

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Effects of basic fibroblast growth factor on density and morphology of fibroblasts grown on root surfaces with or without conditioning with tetracycline or EDTA.

A study was conducted to evaluate in vitro the effect of root surface conditioning with basic fibroblast growth factor (b-FGF) on morphology and proliferation of fibroblasts. Three experimental groups were used: non-treated, and treated with 50 microg or 125 microg b-FGF/ml. The dentin samples in each group were divided into subgroups according to the chemical treatment received before application of b-FGF: none, or conditioned with tetracycline-HCl or EDTA. After contact with b-FGF for 5 min, the samples were incubated for 24 h with 1 ml of culture medium containing 1 x 10(5) cells/ml plus 1 ml of culture medium alone. The samples were then subjected to routine preparation for SEM, and random fields were photographed. Three calibrated and blind examiners performed the assessment of morphology and density according to two index systems. Classification and regression trees indicated that the root surfaces treated with 125 microg b-FGF and previously conditioned with tetracycline-HCl or EDTA presented a morphology more suggestive of cellular adhesion and viability (P = 0.004). The density of fibroblasts on samples previously conditioned with EDTA, regardless of treatment with b-FGF, was significantly higher than in the other groups (P < 0.001). The present findings suggest that topical application of b-FGF has a positive influence on both the density and morphology of fibroblasts.

O efeito do fator de crescimento de fibroblastos básico aplicado em superfícies radiculares condicionadas com cloridrato de tetraciclina ou EDTA na morfologia e densidade de fibroblastos. Estudo in vitro

Pós-graduação em Odontologia - FOAR

Biodiversidade dos parasitas de peixes provenientes do rio Sapucaí-Mirim, Estado de São Paulo, Brasil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Biodiversidade de parasitas de peixes da família Loricariidae (Teleostei: Siluriformes) procedentes do rio Sapucaí-Mirim, Brasil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Comparative analysis of shell occupation by two southern populations of the hermit crab Loxopagurus loxochelis (Decapoda, Diogenidae)

The present study aimed to comparatively verify the relation between the hermit crabs and the shells they use in two populations of Loxopagurus loxochelis. Samples were collected monthly from July 2002 to June 2003, at Caraguatatuba and Ubatuba Bay, Sao Paulo, Brazil. The animals sampled had their sex identified, were weighed and measured; their shells were identified, measured and weighed, and their internal volume determined. To relate the hermit crab's characteristics and the shells' variables, principal component analysis (PCA) and a regression tree were used. According to the PCA analysis, the three gastropod shells most frequently used by L. loxochelis varied in size. The regression tree successfully explained the relationship between the hermit crab's characteristics and the internal volume of the inhabited shell. It can be inferred that the relationship between the morphometry of an individual hermit crab and its shell is not straightforward and it is impossible to explain only on the basis of direct correlations between the body's and the shell's attributes. Several factors (such as the morphometry and the availability of the shell, environmental conditions and inter- and intraspecific competition) interact and seem to be taken into consideration by the hermit crabs when they choose a shell, resulting in the diversified pattern of shell occupancy shown here and elsewhere.

Estudio epidemiológico de las patologías corneales en la especie canina en el Hospital Docente Universitario durante el periodo 2001-2011

Programa de doctorado: Clínica e investigación terapéutica.

Use of hydroclimatic forecasts for improved water management in central Texas

Accurate seasonal to interannual streamflow forecasts based on climate information are critical for optimal management and operation of water resources systems. Considering most water supply systems are multipurpose, operating these systems to meet increasing demand under the growing stresses of climate variability and climate change, population and economic growth, and environmental concerns could be very challenging. This study was to investigate improvement in water resources systems management through the use of seasonal climate forecasts. Hydrological persistence (streamflow and precipitation) and large-scale recurrent oceanic-atmospheric patterns such as the El Niño/Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), the Atlantic Multidecadal Oscillation (AMO), the Pacific North American (PNA), and customized sea surface temperature (SST) indices were investigated for their potential to improve streamflow forecast accuracy and increase forecast lead-time in a river basin in central Texas. First, an ordinal polytomous logistic regression approach is proposed as a means of incorporating multiple predictor variables into a probabilistic forecast model. Forecast performance is assessed through a cross-validation procedure, using distributions-oriented metrics, and implications for decision making are discussed. Results indicate that, of the predictors evaluated, only hydrologic persistence and Pacific Ocean sea surface temperature patterns associated with ENSO and PDO provide forecasts which are statistically better than climatology. Secondly, a class of data mining techniques, known as tree-structured models, is investigated to address the nonlinear dynamics of climate teleconnections and screen promising probabilistic streamflow forecast models for river-reservoir systems. Results show that the tree-structured models can effectively capture the nonlinear features hidden in the data. Skill scores of probabilistic forecasts generated by both classification trees and logistic regression trees indicate that seasonal inflows throughout the system can be predicted with sufficient accuracy to improve water management, especially in the winter and spring seasons in central Texas. Lastly, a simplified two-stage stochastic economic-optimization model was proposed to investigate improvement in water use efficiency and the potential value of using seasonal forecasts, under the assumption of optimal decision making under uncertainty. Model results demonstrate that incorporating the probabilistic inflow forecasts into the optimization model can provide a significant improvement in seasonal water contract benefits over climatology, with lower average deficits (increased reliability) for a given average contract amount, or improved mean contract benefits for a given level of reliability compared to climatology. The results also illustrate the trade-off between the expected contract amount and reliability, i.e., larger contracts can be signed at greater risk.

SURVIVAL PREDICTION FOR BRAIN TUMOR PATIENTS USING GENE EXPRESSION DATA

Brain tumor is one of the most aggressive types of cancer in humans, with an estimated median survival time of 12 months and only 4% of the patients surviving more than 5 years after disease diagnosis. Until recently, brain tumor prognosis has been based only on clinical information such as tumor grade and patient age, but there are reports indicating that molecular profiling of gliomas can reveal subgroups of patients with distinct survival rates. We hypothesize that coupling molecular profiling of brain tumors with clinical information might improve predictions of patient survival time and, consequently, better guide future treatment decisions. In order to evaluate this hypothesis, the general goal of this research is to build models for survival prediction of glioma patients using DNA molecular profiles (U133 Affymetrix gene expression microarrays) along with clinical information. First, a predictive Random Forest model is built for binary outcomes (i.e. short vs. long-term survival) and a small subset of genes whose expression values can be used to predict survival time is selected. Following, a new statistical methodology is developed for predicting time-to-death outcomes using Bayesian ensemble trees. Due to a large heterogeneity observed within prognostic classes obtained by the Random Forest model, prediction can be improved by relating time-to-death with gene expression profile directly. We propose a Bayesian ensemble model for survival prediction which is appropriate for high-dimensional data such as gene expression data. Our approach is based on the ensemble "sum-of-trees" model which is flexible to incorporate additive and interaction effects between genes. We specify a fully Bayesian hierarchical approach and illustrate our methodology for the CPH, Weibull, and AFT survival models. We overcome the lack of conjugacy using a latent variable formulation to model the covariate effects which decreases computation time for model fitting. Also, our proposed models provides a model-free way to select important predictive prognostic markers based on controlling false discovery rates. We compare the performance of our methods with baseline reference survival methods and apply our methodology to an unpublished data set of brain tumor survival times and gene expression data, selecting genes potentially related to the development of the disease under study. A closing discussion compares results obtained by Random Forest and Bayesian ensemble methods under the biological/clinical perspectives and highlights the statistical advantages and disadvantages of the new methodology in the context of DNA microarray data analysis.

Geospatial analysis of groundwater depth for manual well drilling suitability in the Zinder Region, Niger

Manual and low-tech well drilling techniques have potential to assist in reaching the United Nations' millennium development goal for water in sub-Saharan Africa. This study used publicly available geospatial data in a regression tree analysis to predict groundwater depth in the Zinder region of Niger to identify suitable areas for manual well drilling. Regression trees were developed and tested on a database for 3681 wells in the Zinder region. A tree with 17 terminal leaves provided a range of ground water depth estimates that were appropriate for manual drilling, though much of the tree's complexity was associated with depths that were beyond manual methods. A natural log transformation of groundwater depth was tested to see if rescaling dataset variance would result in finer distinctions for regions of shallow groundwater. The RMSE for a log-transformed tree with only 10 terminal leaves was almost half that of the untransformed 17 leaf tree for groundwater depths less than 10 m. This analysis indicated important groundwater relationships for commonly available maps of geology, soils, elevation, and enhanced vegetation index from the MODIS satellite imaging system.

Table 1: Soil chemical properties of the analysed depth profiles in the Venice lagoon

Visual traces of iron reduction and oxidation are linked to the redox status of soils and have been used to characterise the quality of agricultural soils.We tested whether this feature could also be used to explain the spatial pattern of the natural vegetation of tidal habitats. If so, an easy assessment of the effect of rising sea level on tidal ecosystems would be possible. Our study was conducted at the salt marshes of the northern lagoon of Venice, which are strongly threatened by erosion and rising sea level and are part of the world heritage 'Venice and its lagoon'. We analysed the abundance of plant species at 255 sampling points along a land-sea gradient. In addition, we surveyed the redox morphology (presence/absence of red iron oxide mottles in the greyish topsoil horizons) of the soils and the presence of disturbances. We used indicator species analysis, correlation trees and multivariate regression trees to analyse relations between soil properties and plant species distribution. Plant species with known sensitivity to anaerobic conditions (e.g. Halimione portulacoides) were identified as indicators for oxic soils (showing iron oxide mottles within a greyish soil matrix). Plant species that tolerate a low redox potential (e.g. Spartina maritima) were identified as indicators for anoxic soils (greyish matrix without oxide mottles). Correlation trees and multivariate regression trees indicate the dominant role of the redox morphology of the soils in plant species distribution. In addition, the distance from the mainland and the presence of disturbances were identified as tree-splitting variables. The small-scale variation of oxygen availability plays a key role for the biodiversity of salt marsh ecosystems. Our results suggest that the redox morphology of salt marsh soils indicates the plant availability of oxygen. Thus, the consideration of this indicator may enable an understanding of the heterogeneity of biological processes in oxygen-limited systems and may be a sensitive and easy-to-use tool to assess human impacts on salt marsh ecosystems.