The relationship between diet quality and basal metabolic rate in endotherms: Insights from intraspecific analysis

In this article, we review intraspecific studies of basal metabolic rate (BMR) that address the correlation between diet quality and BMR. The food-habit hypothesis stands as one of the most striking and often-mentioned interspecific patterns to emerge from studies of endothermic energetics. Our main emphasis is the explicit empirical comparison of predictions derived from interspecific studies with data gathered from within-species studies in order to explore the mechanisms and functional significance of the putative adaptive responses encapsulated by the food-habit hypothesis. We suggest that, in addition to concentrating on the relationship among diet quality, internal morphology, and BMR, new studies should also attempt to unravel alternative mechanisms that shape the interaction between diet and BMR, such as enzymatic plasticity, and the use of energy-saving mechanisms, such as torpor. Another avenue for future study is the measurement of the effects of diet quality on other components of the energy budget, such as maximum thermogenic and sustainable metabolic rates. It is possible that the effects of diet quality operate on such components rather than directly on BMR, which might then push or pull along changes in these traits. Results from intraspecific studies suggest that the factors responsible for the association between diet and BMR at an ecological timescale might not be the same as those that promoted the evolution of this correlation. Further analyses should consider how much of a role the proximate and ultimate processes have played in the evolution of BMR.

Comparison between the complete Bayesian method and empirical Bayesian method for ARCH models using Brazilian financial time series

In this work we compared the estimates of the parameters of ARCH models using a complete Bayesian method and an empirical Bayesian method in which we adopted a non-informative prior distribution and informative prior distribution, respectively. We also considered a reparameterization of those models in order to map the space of the parameters into real space. This procedure permits choosing prior normal distributions for the transformed parameters. The posterior summaries were obtained using Monte Carlo Markov chain methods (MCMC). The methodology was evaluated by considering the Telebras series from the Brazilian financial market. The results show that the two methods are able to adjust ARCH models with different numbers of parameters. The empirical Bayesian method provided a more parsimonious model to the data and better adjustment than the complete Bayesian method.