Places and preferences: a longitudinal analysis of self-selection and contextual effects

Contextual theories of political behaviour assert that the contexts in which people live influence their political beliefs and vote choices. Most studies of political assimilation, however, rely on cross-sectional data and fail to distinguish contextual influence from self-selection of individuals into areas. This paper advances understanding of this longstanding controversy by tracking thousands of individuals over an 18-year period in England. We observe individual-level left-right position and party identification before and after residential moves across areas with different political orientations. We find evidence of both non-random selection into areas and assimilation of new entrants to the majority political orientation. However, these effects are contingent on the type of area an individual moves to and, moreover, contextual effects are weak and dominated by the larger effect of self-selection into areas.

Genetic variation of and environmental effects on inducibility of resistance in tomatoes (Solanum lycopersicum L.) to Phytophthora infestans (Mont) de Bary

Many plant strengtheners are promoted for their supposed effects on nutrient uptake and/or resistance induction (IR). In addition, many organic fertilizers are supposed to enhance plant health and several studies have shown that tomatoes grown organically are more resistant to late blight, caused by Phytophthora infestans to tomatoes grown conventionally. Much is known about the mechanisms underlying IR. In contrast, there is no systematic knowledge about genetic variation for IR. Therefore, the following questions were addressed in the presented dissertation: (i) Is there genetic variation among tomato genotypes for inducibility of resistance to P. infestans? (ii) How do different PS compare with the chemical inducer BABA in their ability to IR? (iii) Does IR interact with the inducer used and different organic fertilizers? A varietal screening showed that contrary to the commonly held belief IR in tomatoes is genotype and isolate specific. These results indicate that it should be possible to select for inducibility of resistance in tomato breeding. However, isolate specificity also suggests that there could be pathogen adaptation. The three tested PS as well as two of the three tested organic fertilisers all induced resistance in the tomatoes. Depending on PS or BABA variety and isolate effects varied. In contrast, there were no variety and isolate specific effects of the fertilisers and no interactions with the PS and fertilisers. This suggests that the different PS should work independent of the soil substrate used. In contrast the results were markedly different when isolate mixtures were used for challenge inoculations. Plants were generally less susceptible to isolate mixtures than to single isolates. In addition, the effectiveness of the PS was greater and more similar to BABA when isolate mixtures were used. The fact that the different PS and BABA differed in their ability to induce resistance in different host genotype -pathogen isolate combinations puts the usefulness of IR as a breeding goal in question. This would result in varieties depending on specific inducers. The results with the isolate mixtures are highly relevant. On the one hand they increase the effectiveness of the resistance inducers. On the other hand, measures that increase the pathogen diversity such as the use of diversified host populations will also increase the overall resistance of the hosts. For organic tomato production the results indicate that it is possible to enhance the tomato growing system with respect to plant health management by using optimal fertilisers, plant strengtheners and any measures that increase system diversity.

Changes of traditional farming systems and their effects on land degradation and socio-economic conditions in the Inle Lake region, Myanmar

At many locations in Myanmar, ongoing changes in land use have negative environmental impacts and threaten natural ecosystems at local, regional and national scales. In particular, the watershed area of Inle Lake in eastern Myanmar is strongly affected by the environmental effects of deforestation and soil erosion caused by agricultural intensification and expansion of agricultural land, which are exacerbated by the increasing population pressure and the growing number of tourists. This thesis, therefore, focuses on land use changes in traditional farming systems and their effects on socio-economic and biophysical factors to improve our understanding of sustainable natural resource management of this wetland ecosystem. The main objectives of this research were to: (1) assess the noticeable land transformations in space and time, (2) identify the typical farming systems as well as the divergent livelihood strategies, and finally, (3) estimate soil erosion risk in the different agro-ecological zones surrounding the Inle Lake watershed area. GIS and remote sensing techniques allowed to identify the dynamic land use and land cover changes (LUCC) during the past 40 years based on historical Corona images (1968) and Landsat images (1989, 2000 and 2009). In this study, 12 land cover classes were identified and a supervised classification was used for the Landsat datasets, whereas a visual interpretation approach was conducted for the Corona images. Within the past 40 years, the main landscape transformation processes were deforestation (- 49%), urbanization (+ 203%), agricultural expansion (+ 34%) with a notably increase of floating gardens (+ 390%), land abandonment (+ 167%), and marshlands losses in wetland area (- 83%) and water bodies (- 16%). The main driving forces of LUCC appeared to be high population growth, urbanization and settlements, a lack of sustainable land use and environmental management policies, wide-spread rural poverty, an open market economy and changes in market prices and access. To identify the diverse livelihood strategies in the Inle Lake watershed area and the diversity of income generating activities, household surveys were conducted (total: 301 households) using a stratified random sampling design in three different agro-ecological zones: floating gardens (FG), lowland cultivation (LL) and upland cultivation (UP). A cluster and discriminant analysis revealed that livelihood strategies and socio-economic situations of local communities differed significantly in the different zones. For all three zones, different livelihood strategies were identified which differed mainly in the amount of on-farm and off-farm income, and the level of income diversification. The gross margin for each household from agricultural production in the floating garden, lowland and upland cultivation was US$ 2108, 892 and 619 ha-1 respectively. Among the typical farming systems in these zones, tomato (Lycopersicon esculentum L.) plantation in the floating gardens yielded the highest net benefits, but caused negative environmental impacts given the overuse of inorganic fertilizers and pesticides. The Revised Universal Soil Loss Equation (RUSLE) and spatial analysis within GIS were applied to estimate soil erosion risk in the different agricultural zones and for the main cropping systems of the study region. The results revealed that the average soil losses in year 1989, 2000 and 2009 amounted to 20, 10 and 26 t ha-1, respectively and barren land along the steep slopes had the highest soil erosion risk with 85% of the total soil losses in the study area. Yearly fluctuations were mainly caused by changes in the amount of annual precipitation and the dynamics of LUCC such as deforestation and agriculture extension with inappropriate land use and unsustainable cropping systems. Among the typical cropping systems, upland rainfed rice (Oryza sativa L.) cultivation had the highest rate of soil erosion (20 t ha-1yr-1) followed by sebesten (Cordia dichotoma) and turmeric (Curcuma longa) plantation in the UP zone. This study indicated that the hotspot region of soil erosion risk were upland mountain areas, especially in the western part of the Inle lake. Soil conservation practices are thus urgently needed to control soil erosion and lake sedimentation and to conserve the wetland ecosystem. Most farmers have not yet implemented soil conservation measures to reduce soil erosion impacts such as land degradation, sedimentation and water pollution in Inle Lake, which is partly due to the low economic development and poverty in the region. Key challenges of agriculture in the hilly landscapes can be summarized as follows: fostering the sustainable land use of farming systems for the maintenance of ecosystem services and functions while improving the social and economic well-being of the population, integrated natural resources management policies and increasing the diversification of income opportunities to reduce pressure on forest and natural resources.

Predicting random effects with an expanded finite population mixed model

Prediction of random effects is an important problem with expanding applications. In the simplest context, the problem corresponds to prediction of the latent value (the mean) of a realized cluster selected via two-stage sampling. Recently, Stanek and Singer [Predicting random effects from finite population clustered samples with response error. J. Amer. Statist. Assoc. 99, 119-130] developed best linear unbiased predictors (BLUP) under a finite population mixed model that outperform BLUPs from mixed models and superpopulation models. Their setup, however, does not allow for unequally sized clusters. To overcome this drawback, we consider an expanded finite population mixed model based on a larger set of random variables that span a higher dimensional space than those typically applied to such problems. We show that BLUPs for linear combinations of the realized cluster means derived under such a model have considerably smaller mean squared error (MSE) than those obtained from mixed models, superpopulation models, and finite population mixed models. We motivate our general approach by an example developed for two-stage cluster sampling and show that it faithfully captures the stochastic aspects of sampling in the problem. We also consider simulation studies to illustrate the increased accuracy of the BLUP obtained under the expanded finite population mixed model. (C) 2007 Elsevier B.V. All rights reserved.

Undersampled Critical Branching Processes on Small-World and Random Networks Fail to Reproduce the Statistics of Spike Avalanches

The power-law size distributions obtained experimentally for neuronal avalanches are an important evidence of criticality in the brain. This evidence is supported by the fact that a critical branching process exhibits the same exponent t~3=2. Models at criticality have been employed to mimic avalanche propagation and explain the statistics observed experimentally. However, a crucial aspect of neuronal recordings has been almost completely neglected in the models: undersampling. While in a typical multielectrode array hundreds of neurons are recorded, in the same area of neuronal tissue tens of thousands of neurons can be found. Here we investigate the consequences of undersampling in models with three different topologies (two-dimensional, small-world and random network) and three different dynamical regimes (subcritical, critical and supercritical). We found that undersampling modifies avalanche size distributions, extinguishing the power laws observed in critical systems. Distributions from subcritical systems are also modified, but the shape of the undersampled distributions is more similar to that of a fully sampled system. Undersampled supercritical systems can recover the general characteristics of the fully sampled version, provided that enough neurons are measured. Undersampling in two-dimensional and small-world networks leads to similar effects, while the random network is insensitive to sampling density due to the lack of a well-defined neighborhood. We conjecture that neuronal avalanches recorded from local field potentials avoid undersampling effects due to the nature of this signal, but the same does not hold for spike avalanches. We conclude that undersampled branching-process-like models in these topologies fail to reproduce the statistics of spike avalanches.

Genetic parameters and environmental effects on temperament score and reproductive traits of Nellore cattle

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Genetic and environmental effects on age at first farrowing in sows in southeastern Brazil

We evaluated genetic and environmental factors affecting age at first farrowing of sows in the Brazilian southeast. For this purpose, 466 observations regarding the age at first farrowing were made for Dalland-C40 (c) animals belonging to two herds. The effects of the environmental factors on this trait were assessed by means of a model that included, as random effects, the influence of the sow's father and mother and, as fixed effects, the influence the year of birth, the herd and the birth season, along with the covariable litter size at birth. The variance components were estimated using the derivative-free restricted maximum likelihood method. The estimated mean was 354.8 +/- 25.87 days, with a coefficient of variation of 7.29%. Significant effects on the trait were observed for the herd, the year and the season of birth; but a linear effect of litter size at birth on the age at first farrowing was not observed. The boar did not significantly contribute to the variation occurring among the sows, whereas the sow's mother caused significant variation. The heritability estimate for the age at first farrowing was 0.44 +/- 0.15, which is considered high. We concluded that herd effect and year and season of birth should be taken into consideration for an accurate genetic comparison; consequently, the animals should be joined into contemporary groups.

Structural and Electronic Effects of Incorporating Mn in TiO2 Films Grown by Sputtering: Anatase versus Rutile

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

Molecular systematic and historical biogeography of the armored Neotropical catfishes Hypoptopomatinae and Neoplecostominae (Siluriformes: Loricariidae)

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Multi-trait and random regression mature weight heritability and breeding value estimates in Nelore cattle

Mature weight breeding values were estimated using a multi-trait animal model (MM) and a random regression animal model (RRM). Data consisted of 82 064 weight records from 8 145 animals, recorded from birth to eight years of age. Weights at standard ages were considered in the MM. All models included contemporary groups as fixed effects, and age of dam (linear and quadratic effects) and animal age as covariates. In the RRM, mean trends were modelled through a cubic regression on orthogonal polynomials of animal age and genetic maternal and direct and maternal permanent environmental effects were also included as random. Legendre polynomials of orders 4, 3, 6 and 3 were used for animal and maternal genetic and permanent environmental effects, respectively, considering five classes of residual variances. Mature weight (five years) direct heritability estimates were 0.35 (MM) and 0.38 (RRM). Rank correlation between sires' breeding values estimated by MM and RRM was 0.82. However, selecting the top 2% (12) or 10% (62) of the young sires based on the MM predicted breeding values, respectively 71% and 80% of the same sires would be selected if RRM estimates were used instead. The RRM modelled the changes in the (co)variances with age adequately and larger breeding value accuracies can be expected using this model. © South African Society for Animal Science.

Genetic evaluation using multi-trait and random regression models in Simmental beef cattle

The Brazilian Association of Simmental and Simbrasil Cattle Farmers provided 29,510 records from 10,659 Simmental beef cattle; these were used to estimate (co)variance components and genetic parameters for weights in the growth trajectory, based on multi-trait (MTM) and random regression models (RRM). The (co)variance components and genetic parameters were estimated by restricted maximum likelihood. In the MTM analysis, the likelihood ratio test was used to determine the significance of random effects included in the model and to define the most appropriate model. All random effects were significant and included in the final model. In the RRM analysis, different adjustments of polynomial orders were compared for 5 different criteria to choose the best fit model. An RRM of third order for the direct additive genetic, direct permanent environmental, maternal additive genetic, and maternal permanent environment effects was sufficient to model variance structures in the growth trajectory of the animals. The (co)variance components were generally similar in MTM and RRM. Direct heritabilities of MTM were slightly lower than RRM and varied from 0.04 to 0.42 and 0.16 to 0.45, respectively. Additive direct correlations were mostly positive and of high magnitude, being highest at closest ages. Considering the results and that pre-adjustment of the weights to standard ages is not required, RRM is recommended for genetic evaluation of Simmental beef cattle in Brazil. ©FUNPEC-RP.

Posterior Simulation in the Generalized Linear Model with Semiparmetric Random Effects

Generalized linear mixed models with semiparametric random effects are useful in a wide variety of Bayesian applications. When the random effects arise from a mixture of Dirichlet process (MDP) model, normal base measures and Gibbs sampling procedures based on the Pólya urn scheme are often used to simulate posterior draws. These algorithms are applicable in the conjugate case when (for a normal base measure) the likelihood is normal. In the non-conjugate case, the algorithms proposed by MacEachern and Müller (1998) and Neal (2000) are often applied to generate posterior samples. Some common problems associated with simulation algorithms for non-conjugate MDP models include convergence and mixing difficulties. This paper proposes an algorithm based on the Pólya urn scheme that extends the Gibbs sampling algorithms to non-conjugate models with normal base measures and exponential family likelihoods. The algorithm proceeds by making Laplace approximations to the likelihood function, thereby reducing the procedure to that of conjugate normal MDP models. To ensure the validity of the stationary distribution in the non-conjugate case, the proposals are accepted or rejected by a Metropolis-Hastings step. In the special case where the data are normally distributed, the algorithm is identical to the Gibbs sampler.