Biotic and abiotic variables show little redundancy in explaining tree species distributions

Abiotic factors such as climate and soil determine the species fundamental niche, which is further constrained by biotic interactions such as interspecific competition. To parameterize this realized niche, species distribution models (SDMs) most often relate species occurrence data to abiotic variables, but few SDM studies include biotic predictors to help explain species distributions. Therefore, most predictions of species distributions under future climates assume implicitly that biotic interactions remain constant or exert only minor influence on large-scale spatial distributions, which is also largely expected for species with high competitive ability. We examined the extent to which variance explained by SDMs can be attributed to abiotic or biotic predictors and how this depends on species traits. We fit generalized linear models for 11 common tree species in Switzerland using three different sets of predictor variables: biotic, abiotic, and the combination of both sets. We used variance partitioning to estimate the proportion of the variance explained by biotic and abiotic predictors, jointly and independently. Inclusion of biotic predictors improved the SDMs substantially. The joint contribution of biotic and abiotic predictors to explained deviance was relatively small (similar to 9%) compared to the contribution of each predictor set individually (similar to 20% each), indicating that the additional information on the realized niche brought by adding other species as predictors was largely independent of the abiotic (topo-climatic) predictors. The influence of biotic predictors was relatively high for species preferably growing under low disturbance and low abiotic stress, species with long seed dispersal distances, species with high shade tolerance as juveniles and adults, and species that occur frequently and are dominant across the landscape. The influence of biotic variables on SDM performance indicates that community composition and other local biotic factors or abiotic processes not included in the abiotic predictors strongly influence prediction of species distributions. Improved prediction of species' potential distributions in future climates and communities may assist strategies for sustainable forest management.

Adherence to nutritional recommendations and sociodemographic variables in patients with diabetes mellitus

OBJECTIVETo determine if there is a relationship between adherence to nutritional recommendations and sociodemographic variables in Brazilian patients with type 2 diabetes mellitus.METHODSCross-sectional observational study using a stratified random sample of 423 individuals. The Food Frequency Questionnaire (FFQ) was used, and the Fisher's exact test was applied with 95% confidence interval (p<0.05).RESULTSOf the 423 subjects, 66.7% were women, mean age of 62.4 years (SD = 11.8), 4.3 years of schooling on average (SD = 3.6) and family income of less than two minimum wages. There was association between the female gender and adherence to diet with adequate cholesterol content (OR: 2.03; CI: 1.23; 3.34), between four and more years of education and adherence to fractionation of meals (OR: 1 92 CI: 1.19; 3.10), and income of less than two minimum wages and adherence to diet with adequate cholesterol content (OR: 1.74; CI: 1.03, 2.95).CONCLUSIONAdherence to nutritional recommendations was associated with the female gender, more than four years of education and family income of less than two minimum wages.

Fusion of data sets in multivariate linear regression with errors-in-variables

We consider the application of normal theory methods to the estimation and testing of a general type of multivariate regressionmodels with errors--in--variables, in the case where various data setsare merged into a single analysis and the observable variables deviatepossibly from normality. The various samples to be merged can differ on the set of observable variables available. We show that there is a convenient way to parameterize the model so that, despite the possiblenon--normality of the data, normal--theory methods yield correct inferencesfor the parameters of interest and for the goodness--of--fit test. Thetheory described encompasses both the functional and structural modelcases, and can be implemented using standard software for structuralequations models, such as LISREL, EQS, LISCOMP, among others. An illustration with Monte Carlo data is presented.

Productivity and the welfare of nations

We show that the welfare of a representative consumer can be related to observable aggregatedata. To a first order, the change in welfare is summarized by (the present value of) the Solowproductivity residual and by the growth rate of the capital stock per capita. We also show thatproductivity and the capital stock suffice to calculate differences in welfare across countries, withboth variables computed as log level deviations from a reference country. These results hold forarbitrary production technology, regardless of the degree of product market competition, and applyto open economies as well if TFP is constructed using absorption rather than GDP as the measureof output. They require that TFP be constructed using prices and quantities as perceived byconsumers. Thus, factor shares need to be calculated using after-tax wages and rental rates, andwill typically sum to less than one. We apply these results to calculate welfare gaps and growthrates in a sample of developed countries for which high-quality TFP and capital data are available.We find that under realistic scenarios the United Kingdom and Spain had the highest growth ratesof welfare over our sample period of 1985-2005, but the United States had the highest level ofwelfare.

Evaluating predictive densities of U.S. output growth and inflation in a large macroeconomic data set

We evaluate conditional predictive densities for U.S. output growth and inflationusing a number of commonly used forecasting models that rely on a large number ofmacroeconomic predictors. More specifically, we evaluate how well conditional predictive densities based on the commonly used normality assumption fit actual realizationsout-of-sample. Our focus on predictive densities acknowledges the possibility that, although some predictors can improve or deteriorate point forecasts, they might have theopposite effect on higher moments. We find that normality is rejected for most modelsin some dimension according to at least one of the tests we use. Interestingly, however,combinations of predictive densities appear to be correctly approximated by a normaldensity: the simple, equal average when predicting output growth and Bayesian modelaverage when predicting inflation.

Adaptive learning in models with lagged variables

In this work I study the stability of the dynamics generated by adaptivelearning processes in intertemporal economies with lagged variables. Iprove that determinacy of the steady state is a necessary condition for the convergence of the learning dynamics and I show that the reciprocal is not true characterizing the economies where convergence holds. In the case of existence of cycles I show that there is not, in general, a relationship between determinacy and convergence of the learning process to the cycle. I also analyze the expectational stability of these equilibria.

Forecasting and turning point predictions in a Bayesian panel VAR model

We provide methods for forecasting variables and predicting turning points in panel Bayesian VARs. We specify a flexible model which accounts for both interdependencies in the cross section and time variations in the parameters. Posterior distributions for the parameters are obtained for a particular type of diffuse, for Minnesota-type and for hierarchical priors. Formulas for multistep, multiunit point and average forecasts are provided. An application to the problem of forecasting the growth rate of output and of predicting turning points in the G-7 illustrates the approach. A comparison with alternative forecasting methods is also provided.

Causes of subcontracting: Evidence from panel data on construction firms

This paper examines factors explaining subcontracting decisions in the construction industry. Rather than the more common cross-sectional analyses, we use panel data to evaluate the influence of all relevant variables. We design and use a new index of the closeness to small numbers situations to estimate the extent of hold-up problems. Results show that as specificity grows, firms tend to subcontract less. The opposite happens when output heterogeneity and the use of intangible assets and capabilities increase. Neither temporary shortage of capacity nor geographical dispersion of activities seem to affect the extent of subcontracting. Finally, proxies for uncertainty do not show any clear effect.

Distribución horizontal de la anchoveta peruana con relación a variables ambientales en el período 1986 - 2000

Se analizó la distribución horizontal de la anchoveta Engraulis ringens utilizando sistemas de información geográfica. Los datos fueron obtenidos de los cruceros de evaluación hidroacústica de recursos pelágicos realizados durante los veranos de 1986 al 2000. Los resulitados indican que la distribución horizontal de la anchoveta está asociada a parámetros oceanográficos superficiales del mar, como rangos de temperatura, salinidad y clorofila a; sin embargo,parece ser que el parámetro más importante es la salinidad.

On the robust effects of technology shocks on hours worked and output

We analyze the effects of neutral and investment-specific technology shockson hours and output. Long cycles in hours are captured in a variety of ways.Hours robustly fall in response to neutral shocks and robustly increase inresponse to investment specific shocks. The percentage of the variance ofhours (output) explained by neutral shocks is small (large); the opposite istrue for investment specific shocks. News shocks are uncorrelated with theestimated technology shocks.

The structural dynamics of output growth and inflation: some international evidence

We examine the dynamics of output growth and inflation in the US, Euro area and UK using a structural time varying coefficient VAR. There are important similarities in structural inflation dynamics across countries; output growth dynamics differ. Swings in the magnitude of inflation and output growth volatilities and persistences are accounted for by a combination of three structural shocks. Changes over time in the structure of the economy are limited and permanent variations largely absent. Changes in the volatilities of structural shocks matter.

Inflation and output dynamics with firm-owned capital

We model firm-owned capital in a stochastic dynamic New-Keynesian generalequilibrium model à la Calvo. We find that this structure impliesequilibrium dynamics which are quantitatively di¤erent from the onesassociated with a benchmark case where households accumulate capital andrent it to firms. Our findings therefore stress the importance ofmodeling an investment decision at the firm level in addition to ameaningful price setting decision. Along the way we argue that the problemof modeling firm-owned capital with Calvo price-setting has not been solvedin a correct way in the previous literature.

A Gestão de Stock: O Fornecimento de OutPut como factor de redução de custo

O presente trabalho tem como objectivo mostrar a importância que a Gestão económica de Stock tem no processo de redução de custo, por via da implementação de um modelo. E para isso far-se-á um estudo de caso numa empresa inserida no ramo de transformação de pescado – FRESCOMAR. A metodologia adoptada para alcançar os objectivos propostos passou pela pesquisa bibliográfica, recolha de dados e de informação relevante junto da empresa e tratamento dos dados numa folha de Excel. O modelo de gestão de Stock a ser implementado depende essencialmente da componente procura, onde que para uma empresa com procura constante utiliza-se os modelos determinísticos e para uma com procura aleatória utiliza-se os modelos estocásticos (Nível de Encomenda e Revisão Cíclica). Nos modelos determinísticos temos 4 modelos diferenciados pela forma de reposição e a admissibilidade da procura. Com as informações recolhidas junto da empresa em estudo escolhemos implementar o modelo de reposição não instantânea com ruptura não permitida e obtivemos a optimização do Stock com níveis reduzidos de produção e consequentemente houve uma diminuição do custo. Com resultados obtiveram-se ainda valores da Quantidade Óptima a ser Produzida, do Stock Máximo e do Custo Total Óptimo por unidade de tempo. Devido a diversidade de produtos propomos à empresa a utilização da análise ABC para classificar os produtos reagrupando-os em classes A, B e C, conforme o peso no consumo de Stock.