Infant loss during and after male replacement in gibbons

According to the sexual selection hypothesis, infanticide during resident male replacement is an adaptive strategy that has evolved because the killing of unweaned offspring sired by previous males shortens the inter‐birth intervals of the mothers whose infants are targeted and thereby increases the reproductive fitness of the perpetrator. To test this hypothesis, we describe previously unreported cases of primary male replacement for two gibbon species (Hylobates lar and Nomascus nasutus), and review all other reported cases of primary male replacement in gibbons. Overall, infants were present in nearly half of all cases (16/33, 48%) and of the 18 infants present during replacement, 50% (N = 9) disappeared within 2 months of the event. In four of the five cases where there was sufficient demographic information to identify the likely sire of the subsequent offspring of females that lost infants, the new male was believed to be the sire. Infants were also less likely to die or disappear if the new male and original resident male were possible kin. However, there was no significant difference in the age of infants between those that died or disappeared following replacement and those that survived to weaning (p = .630). Our review of takeover‐related infant loss in gibbons confirms that periods of male instability are risky for unweaned infants and that replacing males benefit from infant loss. Nevertheless, variability in the context of infant loss and difficulties related to data collection in the field make it difficult to test competing hypotheses concerning the mechanisms and functions of infanticide in the small apes.     

Mapping Interdisciplinary Fields: Efficiencies,Gaps and Redundancies in HIV/AIDS Research

While interdisciplinarity continues to increase in popularity among funders and other scientific organizations, its potential to promote scientific advances remains under-examined. For HIV/AIDS research, we examine the dynamics of disciplinary integration (or lack thereof) providing insight into a field''s knowledge base and those questions that remain unresolved. Drawing on the complete histories of two interdisciplinary journals, we construct bibliographic coupling networks based on overlapping citations to identify segregation into research clusters and estimate topic models of research content. We then compare how readily those bibliographic coupling clusters account for the structuring of topics covered within the field as it evolves over two decades. These comparisons challenge one-dimensional and/or cross-sectional approaches to interdisciplinarity. Some topics are increasingly coordinated across disciplinary boundaries (e.g., vaccine development); others remain relatively segmented into disconnected disciplinary domains for the full period (e.g., drug resistance). This divergence indicates heterogeneity in interdisciplinarity and emphasizes the need for critical approaches to studying the organization of science.     

Use of the EM algorithm to detect QTL affecting multiple-traits in an across half-sib family analysis

QTL detection experiments in livestock species commonly use the half-sib design. Each male is mated to a number of females, each female producing a limited number of progeny. Analysis consists of attempting to detect associations between phenotype and genotype measured on the progeny. When family sizes are limiting experimenters may wish to incorporate as much information as possible into a single analysis. However, combining information across sires is problematic because of incomplete linkage disequilibrium between the markers and the QTL in the population. This study describes formulæ for obtaining MLEs via the expectation maximization (EM) algorithm for use in a multiple-trait, multiple-family analysis. A model specifying a QTL with only two alleles, and a common within sire error variance is assumed. Compared to single-family analyses, power can be improved up to fourfold with multi-family analyses. The accuracy and precision of QTL location estimates are also substantially improved. With small family sizes, the multi-family, multi-trait analyses reduce substantially, but not totally remove, biases in QTL effect estimates. In situations where multiple QTL alleles are segregating the multi-family analysis will average out the effects of the different QTL alleles.