Alicia A. Johnson

Alpha male chimpanzee grooming patterns: Implications for dominance "style"

In social primates, individuals use various tactics to compete for dominance rank. Grooming, displays and contact aggression are common components of a male chimpanzees dominance repertoire. The optimal combination of these behaviors is likely to differ among males with individuals exhibiting a dominance “style” that reflects their tendency to use cooperative and/or agonistic dominance tactics. Here, we examine the grooming behavior of three alpha male chimpanzees at Gombe National Park, Tanzania. We found that (1) these males differed significantly in their tendency to groom with other males; (2) each males grooming patterns remained consistent before, during and after his tenure as alpha, and (3) the three males tended to groom with high‐ middle‐ and low‐ranking partners equally. We suggest that body mass may be one possible determinant of differences in grooming behavior. The largest male exhibited the lowest overall grooming rates, whereas the smallest male spent the most time grooming others. This is probably because large males are more effective at physically intimidating subordinates. To achieve alpha status, a small male may need to compensate for reduced size by investing more time and energy in grooming, thereby ensuring coalitionary support from others. Rates of contact aggression and charging displays conformed to this prediction, suggesting that each male exhibited a different dominance “style.” Am. J. Primatol. 71:136–144, 2009.

Estimating Distribution Functions from Survey Data Using Nonparametric Regression

Auxiliary information is often used to improve the precision of estimators of the finite population cumulative distribution function through the use of superpopulation models. A variety of approaches are available to construct such estimators, including design-based, model-based and model-assisted methods. The superpopulation modeling framework can be either parametric or nonparametric, and the estimators can be constructed as either linear or nonlinear functions of the observations. In this article, we argue that model-assisted estimators based on a nonparametric model are a good overall choice for distribution function estimators, because they have good efficiency properties and are robust against model misspecification. When such estimators are constructed as linear functions of the data, they are also easily incorporated into the existing survey estimation paradigm through the use of survey weights. Theoretical properties of nonparametric distribution function estimators based on local linear regression are derived, and their practical behavior is evaluated in a simulation study.

Comment: Gibbs Sampling, Exponential Families, and Orthogonal Polynomials

Itis ourpleasure to congratulate theauthors(here-after DKSC) on an interesting paper that was a de-light to read. While DKSC provide a remarkablecollection of connections between different represen-tations of the Markov chains in their paper, we willfocus on the “running time analysis” portion. Thisis a familiar problem to statisticians; given a tar-get population, how can we obtain a representativesample? In the context of Markov chain Monte Carlo(MCMC) the problem can be stated as follows. LetΦ = {X