Personality and job stress: a comparison of direct effects on parenting

The impact of personality and job characteristics on parental rearing styles was compared in 353 employees. Hypotheses concerning the relationships between personality and job variables were formulated in accordance with findings in past research and the Belsky’s model (1984). Structural equation nested models showed that Aggression-hostility, Sociability and job Demand were predictive of Rejection and Emotional Warmth parenting styles, providing support for some of the hypothesized relationships. The findings suggest a well-balanced association of personality variables with both parenting styles: Aggression-Hostility was positively related to Rejection and negatively to Emotional Warmth, whereas Sociability was positively related to Emotional Warmth and negatively related to Rejection. Personality dimensions explained a higher amount of variance in observed parenting styles. However, a model that considered both, personality and job dimensions as antecedent variables of parenting was the best representation of observed data, as both systems play a role in the prediction of parenting behavior.

The five and seven factors personality models: differences and similitude between the TCI-R, NEO-FFI-R and ZKPQ-50-CC

The present study tests the relationships between the three frequently used personality models evaluated by the Temperament Character Inventory-Revised (TCI-R), Neuroticism Extraversion Openness Five Factor Inventory – Revised (NEO-FFI-R) and Zuckerman-Kuhlman Personality Questionnaire-50- Cross-Cultural (ZKPQ-50-CC). The results were obtained with a sample of 928 volunteer subjects from the general population aged between 17 and 28 years old. Frequency distributions and alpha reliabilities with the three instruments were acceptable. Correlational and factorial analyses showed that several scales in the three instruments share an appreciable amount of common variance. Five factors emerged from principal components analysis. The first factor was integrated by A (Agreeableness), Co (Cooperativeness) and Agg-Host (Aggressiveness-Hostility), with secondary loadings in C (Conscientiousness) and SD (Self-directiveness) from other factors. The second factor was composed by N (Neuroticism), N-Anx (Neuroticism-Anxiety), HA (Harm Avoidance) and SD (Self-directiveness). The third factor was integrated by Sy (Sociability), E (Extraversion), RD (Reward Dependence), ImpSS (Impulsive Sensation Seeking) and NS (novelty Seeking). The fourth factor was integrated by Ps (Persistence), Act (Activity), and C, whereas the fifth and last factor was composed by O (Openness) and ST (Self- Transcendence). Confirmatory factor analyses indicate that the scales in each model are highly interrelated and define the specified latent dimension well. Similarities and differences between these three instruments are further discussed.

Genetic diversity of a brazilian wine grape germplasm collection based on morphoagronomic traits

The objectives of this study were to evaluate the performance of cultivars, to quantify the variability and to estimate the genetic distances of 66 wine grape accessions in the Grape Germplasm Bank of the EMBRAPA Semi-Arid, in Juazeiro, BA, Brazil, through the characterization of discrete and continuous phenotypic variables. Multivariate statistics, such as, principal components, Tocher's optimization procedure, and the graphic of the distance, were efficient in grouping more similar genotypes, according to their phenotypic characteristics. There was no agreement in the formation of groups between continuous and discrete morpho-agronomic traits, when Tocher's optimization procedure was used. Discrete variables allowed the separation of Vitis vinifera and hybrids in different groups. Significant positive correlations were observed between weight, length and width of bunches, and a negative correlation between titratable acidity and TSS/TTA. The major part (84.12%) of the total variation present in the original data was explained by the four principal components. The results revealed little variability between wine grape accessions in the Grape Germplasm Bank of Embrapa Semi-Arid.

Agronomic and molecular traits of the 'Italia Muscat' clone in Sao Francisco River Valley

The objective of this study was to evaluate agronomic and molecular traits of the 'Italia Muscat' clone and compare it with the cv. 'Italia', providing information to support the cultivation of 'Italia Muscat' this cultivar in the São Francisco River Valley. Agronomic characteristics of both clones were evaluated for two seasons in 2004. The characteristics were phenology, bud break (%), bud fertility (%), yield (kg) mass of bunches (g), length and width of bunches (cm), mass of berries (g), length and diameter of berries (mm), TSS (ºBrix), ATT (% titratable acidity) and TSS/TTA. Molecular analysis of seven SSR markers were carried out. The clone 'Italia Muscat' showed larger berries, mass of bunches and better TSS/TA ratio than 'Italia'. The molecular analysis resulted in the same allelic profile in both clones, highlighting the need to use a larger number of microsatellite markers or other molecular technique to allow their discrimination. Based on their morpho-agronomic characteristics, 'Italia Muscat' seems to be a good table grape cultivar alternative for grape growers of São Francisco River Valley.

Using species richness and functional traits predictions to constrain assemblage predictions from stacked species distribution models

Aim: Modelling species at the assemblage level is required to make effective forecast of global change impacts on diversity and ecosystem functioning. Community predictions may be achieved using macroecological properties of communities (MEM), or by stacking of individual species distribution models (S-SDMs). To obtain more realistic predictions of species assemblages, the SESAM framework suggests applying successive filters to the initial species source pool, by combining different modelling approaches and rules. Here we provide a first test of this framework in mountain grassland communities. Location: The western Swiss Alps. Methods: Two implementations of the SESAM framework were tested: a "Probability ranking" rule based on species richness predictions and rough probabilities from SDMs, and a "Trait range" rule that uses the predicted upper and lower bound of community-level distribution of three different functional traits (vegetative height, specific leaf area and seed mass) to constraint a pool of environmentally filtered species from binary SDMs predictions. Results: We showed that all independent constraints expectedly contributed to reduce species richness overprediction. Only the "Probability ranking" rule allowed slightly but significantly improving predictions of community composition. Main conclusion: We tested various ways to implement the SESAM framework by integrating macroecological constraints into S-SDM predictions, and report one that is able to improve compositional predictions. We discuss possible improvements, such as further improving the causality and precision of environmental predictors, using other assembly rules and testing other types of ecological or functional constraints.