Abstract: Captive studies can make a unique contribution to primate socioecology by documenting species-typical social dispositions under controlled conditions. Recent theories seek to connect the dominance relationships, group cohesiveness, and feeding ecology of primates. The present study explores the first two aspects by comparing the social organization of rhesus (Macaca mulatta) and stumptail monkeys (M. arctoides). Data were collected over a period of eight years, with five different methods, on three well-established captive groups in identical environments. The groups were found to share one characteristic: a clear-cut, linear formal dominance hierarchy as expressed in teeth-baring displays. The two main study groups (one of each species) differed significantly, however, with respect to nine of eleven behavioral measures. In addition to a previously reported higher frequency of reconciliation in the stumptail group, this group showed (1) more frequent but less severe aggressive behavior, (2) greater symmetry of contests, (3) greater social tolerance, (4) more nonagonistic approaches, and (5) more allogrooming. The differences can be summarized as a contrast in dominance style, with the stumptails having a more relaxed style and placing greater emphasis on social cohesion than the rhesus monkeys. An egalitarian attitude was also reflected in approach behavior: contacts in the rhesus group were mostly initiated by dominants, whereas contacts in the stumptail group were initiated independent of rank. Comparisons with a second rhesus group, and with published reports, suggest that while some of the observed differences are probably representative of the two species, considerable intraspecific variation does exist, and a more comprehensive program of comparative studies is needed.

Abstract: Book Description
The success of the first edition of Generalized Linear Models led to the updated Second Edition, which continues to provide a definitive unified, treatment of methods for the analysis of diverse types of data. Today, it remains popular for its clarity, richness of content and direct relevance to agricultural, biological, health, engineering, and other applications. The authors focus on examining the way a response variable depends on a combination of explanatory variables, treatment, and classification variables. They give particular emphasis to the important case where the dependence occurs through some unknown, linear combination of the explanatory variables. The Second Edition includes topics added to the core of the first edition, including conditional and marginal likelihood methods, estimating equations, and models for dispersion effects and components of dispersion. The discussion of other topics-log-linear and related models, log odds-ratio regression models, multinomial response models, inverse linear and related models, quasi-likelihood functions, and model checking-was expanded and incorporates significant revisions. Comprehension of the material requires simply a knowledge of matrix theory and the basic ideas of probability theory, but for the most part, the book is self-contained. Therefore, with its worked examples, plentiful exercises, and topics of direct use to researchers in many disciplines, Generalized Linear Models serves as ideal text, self-study guide, and reference.