Exploring nest destruction and bird mortality in mown Mediterranean dry grasslands: an increasing threat to grassland bird conservation

In recent years, haying has extended to Iberian Mediterranean dry grasslands potentially impacting on grassland bird ecology. We evaluated the impact of haying on a grassland bird community of South Portugal. Our main goals were: (1) to investigate the exposure of different species to haying, (2) to investigate potential removal of nests and dead birds from hayed fields by haying machinery using the ratio (REC) between the expected number of records and the number of records collected and (3) to link clutch destruction and bird mortality with haying management practices. Hayed fields were surveyed for signs of breeding and birds censused prior to mowing. Linear models were computed, linking the REC with haying machinery and sward properties. GLMs and model averaging were used to obtain models linking clutch destruction, bird mortality and haying management variables. Only 4 % of records evidenced successful nesting attempts (N = 177). REC evaluation suggested high nest or dead bird removal by the machinery, particularly in fields with lower vegetation biomass prior to cutting. Sickle bar mowers and one-rotor rotary rakes returned higher REC but lower probability of found nests removed from the original nesting sites comparatively to discs mowers and wheel rakes. Higher probabilities of mortality events were found in fields mown earlier (but not in all years). On the other hand, lower mortality was found in fields raked with two-rotor rotary rakes. Delayed haying, silage production in temporary crops and the use haying machinery enabling simultaneously mowing and gathering hay in lines are discussed as management alternatives.

Between and within-year effects of haying on grassland bird

In recent years, haying has extended to Iberian Mediterranean dry grasslands potentially threatening grassland birds. We evaluate the between and within-year effects of haying on grassland birds in Alentejo region, Portugal. Our main goals were: (1) to investigate variations on bird abundance and species richness in the fields hayed, with respect to past haying events occurred in a field and its surroundings and (2) to investigate the shifts in bird abundance, species richness and spatial dynamics resulting from haying a field and its surrounding area in a given year. We conducted grassland bird censuses during the breeding season through point counts from 2012 to 2015. The relationship between bird abundance/richness and past haying events was investigated using Generalized Linear Models whereas within-year effects of haying were analysed using Generalized Additive Models. Bird abundance in a field was positively related with the surface hayed in the vicinity of that field in the previous year. However, contrasting yearly effects were found for non passerines. Also, some species prefer fields with less haying events or surface hayed, whereas others occur mostly in fields frequently managed for haying. Haying a field leads, in the short term, to its abandonment by birds, and thus to a decrease in bird abundance and, for some species, to spatial concentration in surrounding fields offering suitable habitat. We conclude that within-year effects of haying have higher impact on grassland birds than between-year effects. Maintaining haying at low levels by rotating haying yearly through the different fields in each farm and using partial haying may be an adequate way to ensure an effective management of grassland bird populations.

Modelação do cancro da mama na região do Alentejo

Estudos epidemiológicos são estudos estatísticos onde se procura relacionar ocorrências de eventos de saúde com uma ou várias causas específicas. A importância que os modelos epidemiológicos assumem hoje no estudo de doenças de foro oncológico, em particular no estabelecimento das suas etiologias, é incontornável. Segundo Ogden, J. (1999) o cancro é "um crescimento incontrolável de células anormais que produzem tumores chamados neoplasias". Estes tumores podem ter origem benigna (não se espalham pelo corpo) ou maligna (apresentam metastização de outros órgãos). Sendo uma doença actual, com uma elevada taxa de incidência em Portugal quando comparada com outras doenças (Instituto Nacional de Estatística- INE, 2009), aumentando esta taxa com a idade tal como refere Marques, L. (2003), podendo ocorrer o diagnóstico desta doença em qualquer idade. De acordo com INE (2000) pode dizer-se que o cancro está entre as três principais causas de morte em Portugal, registando-se um aumento progressivo do seu peso proporcional, sendo o cancro da mama o tipo de cancro mais comum entre as mulheres e uma das doenças com maior impacto na nossa sociedade. O objectivo principal deste trabalho é a estimação e modelação do risco de contrair uma doença de natureza não contagiosa e rara (neste caso, cancro da mama), usando dados da região do Alentejo. Pretende-se fazer um apanhado das metodologias mais empregues nesta área e aplicá-las na prática, com ênfase nos estudos caso-controlo e nos modelos lineares generalizados (GLM) - mais concretamente regressão logística. Os estudos caso-controlo são usados para identificar os factores que podem contribuir para uma condição médica, comparando indivíduos que têm essa condição (casos) com pacientes que não têm a condição, mas que de resto são semelhantes (controlos). Neste trabalho utilizou-se essa metodologia para estudar a associação entre o viver em ambiente rural/urbano e o cancro da mama. Tendo em conta que o objectivo principal deste estudo se prende com o estudo da relação entre variáveis, mais propriamente, análise de influência que uma ou mais variáveis (explicativas) têm sobre uma variável de interesse (resposta), para esse efeito são estudados os modelos lineares generalizados - GLM - unificados na mesma moldura teórica pela primeira vez por Nelder & Wedderburn (1972) - e, posteriormente aplicados ao conjunto de dados sobre cancro da mama na Região do Alentejo. O presente trabalho pretende assim, ser um contributo na identificação de factores de risco do cancro da mama na região do Alentejo. ABSTRACT: Epidemiological studies are statistical studies where attempts to relate occurrences of health events with one or more specific causes. The importance of epidemiological models that are far in the study of diseases of cancer forum, particularly in establishing their etiology, is inescapable. According to Ogden, J. (1999) cancer is "an incontrollable growth of abnormal cells that produce tumors called cancer". These tumors may be benign (not spread throughout the body) or malignant (show metastasis to other organs). Being a current illness with a high incidence rate in Portugal compared with the same respect to other diseases (National Statistics 1nstitute -1NE, 2009) having an increasing rate with age as mentioned Marques, L. (2003), and can possibly be diagnosed at any age. According to 1NE (2000) the cancer is among the top three causes of death in Portugal and there is a progressive increase of its proportional weight. Breast cancer is the most common form of cancer among women and the diseases with major impact in our society. The main objective of this work is to model and estimate the risk of contracting a non-contagious and rare disease (in this case, breast cancer), using data from the Alentejo region. It is intended to summarize some of the methodologies employed in this area and apply them in practice, with emphasis on case-control studies and generalized linear models (GLM) - more specifically the logistic regression. The case-control studies are used to identify factors that may contribute to a medical condition, comparing individuals who have this condition (cases) with patients who have not the condition but that are otherwise similar (controls). ln this work we used this methodology to study the association between living in a rural/urban and breast cancer. Given that the main objective of this study rather relates to the study of the relationship between variables to analyze the influence that one or more variables (explanatory) have on a variable (response), for this purpose we study the generalized linear models - GLM - first unified in the same theoretical framework by Nelder and Wedderburn (1972) and subsequently applied to the data set on breast cancer in the Alentejo region. This work intends to be a contribution in identifying risk factors for breast cancer in the Alentejo region.

Predição de ganhos genéticos via modelos mistos em progênies de café Conilon.

Objetivou-se, neste trabalho, avaliar os ganhos genéticos preditos por meio de diferentes índices de seleção pela metodologia REML/BLUP, em cinco caracteres de interesse ao programa de melhoramento do café conilon do Incaper. Foram avaliadas 8 progênies de meios-irmãos, de ciclo de maturação precoce, média de duas safras, com três repetições, o que totalizou 1368 observações, utilizados os índices de seleção clássico, multiplicativo e com base na soma de postos. Avaliaramse, na época de colheita, as características tamanho dos grãos (TG), produtividade (PRO), porte (PT), vigor vegetativo (VIG) e grau de inclinação (GI). A população foi avaliada na Fazenda Experimental de Marilândia, região Noroeste do estado do Espírito Santo. As análises genético-estatísticas foram realizadas pelo programa Selegen - REM/BLUP. Verificou-se, a partir da análise dos parâmetros genéticos, um excelente potencial seletivo entre famílias, para todas as características avaliadas. O índice Mulamba e Mock foi o que mostrou maior eficiência de seleção entre famílias de meios-irmãos de café conilon.

Different strategies to estimate genetic parameters for the rank of Santa Ines sheep in agricultural-shows.

2016

Censored Linear Regression Models For Irregularly Observed Longitudinal Data Using The Multivariate-t Distribution.

In acquired immunodeficiency syndrome (AIDS) studies it is quite common to observe viral load measurements collected irregularly over time. Moreover, these measurements can be subjected to some upper and/or lower detection limits depending on the quantification assays. A complication arises when these continuous repeated measures have a heavy-tailed behavior. For such data structures, we propose a robust structure for a censored linear model based on the multivariate Student's t-distribution. To compensate for the autocorrelation existing among irregularly observed measures, a damped exponential correlation structure is employed. An efficient expectation maximization type algorithm is developed for computing the maximum likelihood estimates, obtaining as a by-product the standard errors of the fixed effects and the log-likelihood function. The proposed algorithm uses closed-form expressions at the E-step that rely on formulas for the mean and variance of a truncated multivariate Student's t-distribution. The methodology is illustrated through an application to an Human Immunodeficiency Virus-AIDS (HIV-AIDS) study and several simulation studies.

Residuals for log-Burr XII regression models in survival analysis

In this paper, we compare three residuals to assess departures from the error assumptions as well as to detect outlying observations in log-Burr XII regression models with censored observations. These residuals can also be used for the log-logistic regression model, which is a special case of the log-Burr XII regression model. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and the empirical distribution of each residual is displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended to the modified martingale-type residual in log-Burr XII regression models with censored data.

Log-modified Weibull regression models with censored data: Sensitivity and residual analysis

This paper proposes a regression model considering the modified Weibull distribution. This distribution can be used to model bathtub-shaped failure rate functions. Assuming censored data, we consider maximum likelihood and Jackknife estimators for the parameters of the model. We derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and we also present some ways to perform global influence. Besides, for different parameter settings, sample sizes and censoring percentages, various simulations are performed and the empirical distribution of the modified deviance residual is displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended for a martingale-type residual in log-modified Weibull regression models with censored data. Finally, we analyze a real data set under log-modified Weibull regression models. A diagnostic analysis and a model checking based on the modified deviance residual are performed to select appropriate models. (c) 2008 Elsevier B.V. All rights reserved.

Deviance residuals in generalised log-gamma regression models with censored observations

In this article, we compare three residuals based on the deviance component in generalised log-gamma regression models with censored observations. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and the empirical distribution of each residual is displayed and compared with the standard normal distribution. For all cases studied, the empirical distributions of the proposed residuals are in general symmetric around zero, but only a martingale-type residual presented negligible kurtosis for the majority of the cases studied. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended for the martingale-type residual in generalised log-gamma regression models with censored data. A lifetime data set is analysed under log-gamma regression models and a model checking based on the martingale-type residual is performed.

Local Influence Analysis for Skew-Normal Linear Mixed Models

In this article, we consider local influence analysis for the skew-normal linear mixed model (SN-LMM). As the observed data log-likelihood associated with the SN-LMM is intractable, Cook`s well-known approach cannot be applied to obtain measures of local influence. Instead, we develop local influence measures following the approach of Zhu and Lee (2001). This approach is based on the use of an EM-type algorithm and is measurement invariant under reparametrizations. Four specific perturbation schemes are discussed. Results obtained for a simulated data set and a real data set are reported, illustrating the usefulness of the proposed methodology.

Stochastic search for semiparametric linear regression models

This paper introduces and analyzes a stochastic search method for parameter estimation in linear regression models in the spirit of Beran and Millar [Ann. Statist. 15(3) (1987) 1131–1154]. The idea is to generate a random finite subset of a parameter space which will automatically contain points which are very close to an unknown true parameter. The motivation for this procedure comes from recent work of Dümbgen et al. [Ann. Statist. 39(2) (2011) 702–730] on regression models with log-concave error distributions.

Development, validation and uncertainty analysis of quantitative structure and activity relationship models for Log P of disinfection by -products

Hydrophobicity as measured by Log P is an important molecular property related to toxicity and carcinogenicity. With increasing public health concerns for the effects of Disinfection By-Products (DBPs), there are considerable benefits in developing Quantitative Structure and Activity Relationship (QSAR) models capable of accurately predicting Log P. In this research, Log P values of 173 DBP compounds in 6 functional classes were used to develop QSAR models, by applying 3 molecular descriptors, namely, Energy of the Lowest Unoccupied Molecular Orbital (ELUMO), Number of Chlorine (NCl) and Number of Carbon (NC) by Multiple Linear Regression (MLR) analysis. The QSAR models developed were validated based on the Organization for Economic Co-operation and Development (OECD) principles. The model Applicability Domain (AD) and mechanistic interpretation were explored. Considering the very complex nature of DBPs, the established QSAR models performed very well with respect to goodness-of-fit, robustness and predictability. The predicted values of Log P of DBPs by the QSAR models were found to be significant with a correlation coefficient R2 from 81% to 98%. The Leverage Approach by Williams Plot was applied to detect and remove outliers, consequently increasing R 2 by approximately 2% to 13% for different DBP classes. The developed QSAR models were statistically validated for their predictive power by the Leave-One-Out (LOO) and Leave-Many-Out (LMO) cross validation methods. Finally, Monte Carlo simulation was used to assess the variations and inherent uncertainties in the QSAR models of Log P and determine the most influential parameters in connection with Log P prediction. The developed QSAR models in this dissertation will have a broad applicability domain because the research data set covered six out of eight common DBP classes, including halogenated alkane, halogenated alkene, halogenated aromatic, halogenated aldehyde, halogenated ketone, and halogenated carboxylic acid, which have been brought to the attention of regulatory agencies in recent years. Furthermore, the QSAR models are suitable to be used for prediction of similar DBP compounds within the same applicability domain. The selection and integration of various methodologies developed in this research may also benefit future research in similar fields.

Linear dimensional changes in plaster die models using different elastomeric materials

Dental impression is an important step in the preparation of prostheses since it provides the reproduction of anatomic and surface details of teeth and adjacent structures. The objective of this study was to evaluate the linear dimensional alterations in gypsum dies obtained with different elastomeric materials, using a resin coping impression technique with individual shells. A master cast made of stainless steel with fixed prosthesis characteristics with two prepared abutment teeth was used to obtain the impressions. References points (A, B, C, D, E and F) were recorded on the occlusal and buccal surfaces of abutments to register the distances. The impressions were obtained using the following materials: polyether, mercaptan-polysulfide, addition silicone, and condensation silicone. The transfer impressions were made with custom trays and an irreversible hydrocolloid material and were poured with type IV gypsum. The distances between identified points in gypsum dies were measured using an optical microscope and the results were statistically analyzed by ANOVA (p < 0.05) and Tukey's test. The mean of the distances were registered as follows: addition silicone (AB = 13.6 µm, CD=15.0 µm, EF = 14.6 µm, GH=15.2 µm), mercaptan-polysulfide (AB = 36.0 µm, CD = 36.0 µm, EF = 39.6 µm, GH = 40.6 µm), polyether (AB = 35.2 µm, CD = 35.6 µm, EF = 39.4 µm, GH = 41.4 µm) and condensation silicone (AB = 69.2 µm, CD = 71.0 µm, EF = 80.6 µm, GH = 81.2 µm). All of the measurements found in gypsum dies were compared to those of a master cast. The results demonstrated that the addition silicone provides the best stability of the compounds tested, followed by polyether, polysulfide and condensation silicone. No statistical differences were obtained between polyether and mercaptan-polysulfide materials.

Phase diagram for a class of spin-1/2 Heisenberg models interpolating between the square-lattice, the triangular-lattice, and the linear-chain limits

We study the spin-1/2 Heisenberg models on an anisotropic two-dimensional lattice which interpolates between the square lattice at one end, a set of decoupled spin chains on the other end, and the triangular-lattice Heisenberg model in between. By series expansions around two different dimer ground states and around various commensurate and incommensurate magnetically ordered states, we establish the phase diagram for this model of a frustrated antiferromagnet. We find a particularly rich phase diagram due to the interplay of magnetic frustration, quantum fluctuations, and varying dimensionality. There is a large region of the usual two-sublattice Neel phase, a three-sublattice phase for the triangular-lattice model, a region of incommensurate magnetic order around the triangular-lattice model, and regions in parameter space where there is no magnetic order. We find that the incommensurate ordering wave vector is in general altered from its classical value by quantum fluctuations. The regime of weakly coupled chains is particularly interesting and appears to be nearly critical. [S0163-1829(99)10421-1].

Applying linear time-varying constraints to econometric models: With an application to demand systems

When linear equality constraints are invariant through time they can be incorporated into estimation by restricted least squares. If, however, the constraints are time-varying, this standard methodology cannot be applied. In this paper we show how to incorporate linear time-varying constraints into the estimation of econometric models. The method involves the augmentation of the observation equation of a state-space model prior to estimation by the Kalman filter. Numerical optimisation routines are used for the estimation. A simple example drawn from demand analysis is used to illustrate the method and its application.