Dietary Profile of Rhinopithecus bieti and Its Socioecological Implications

To enhance our understanding of dietary adaptations and socioecological correlates in colobines, we conducted a 20-mo study of a wild group of Rhinopithecus bieti (Yunnan snub-nosed monkeys) in the montane Samage Forest. This forest supports a patchwork of evergreen broadleaved, evergreen coniferous, and mixed deciduous broadleaved/coniferous forest assemblages with a total of 80 tree species in 23 families. The most common plant families by basal area are the predominantly evergreen Pinaceae and Fagaceae, comprising 69% of the total tree biomass. Previous work has shown that lichens formed a consistent component in the monkeys’ diet year-round (67%), seasonally complemented with fruits and young leaves. Our study showed that although the majority of the diet was provided by 6 plant genera (Acanthopanax, Sorbus, Acer, Fargesia, Pterocarya, and Cornus), the monkeys fed on 94 plant species and on 150 specific food items. The subjects expressed high selectivity for uncommon angiosperm tree species. The average number of plant species used per month was 16. Dietary diversity varied seasonally, being lowest during the winter and rising dramatically in the spring. The monkeys consumed bamboo shoots in the summer and bamboo leaves throughout the year. The monkeys also foraged on terrestrial herbs and mushrooms, dug up tubers, and consumed the flesh of a mammal (flying squirrel). We also provide a preliminary evaluation of feeding competition in Rhinopithecus bieti and find that the high selectivity for uncommon seasonal plant food items distributed in clumped patches might create the potential for food competition. The finding is corroborated by observations that the subjects occasionally depleted leafy food patches and stayed at a greater distance from neighboring conspecifics while feeding than while resting. Key findings of this work are that Yunnan snub-nosed monkeys have a much more species-rich plant diet than was previously believed and are probably subject to moderate feeding competition

Functional brain networks develop from a "local to distributed" organization.

The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions. Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks. In this report, we combine resting state functional connectivity MRI (rs-fcMRI), graph analysis, community detection, and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies. As we have previously reported, we find, across development, a trend toward 'segregation' (a general decrease in correlation strength) between regions close in anatomical space and 'integration' (an increased correlation strength) between selected regions distant in space. The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks. Communities in children are predominantly arranged by anatomical proximity, while communities in adults predominantly reflect functional relationships, as defined from adult fMRI studies. In sum, over development, the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more "distributed" architecture in young adults. We argue that this "local to distributed" developmental characterization has important implications for understanding the development of neural systems underlying cognition. Further, graph metrics (e.g., clustering coefficients and average path lengths) are similar in child and adult graphs, with both showing "small-world"-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. These observations suggest that early school age children and adults both have relatively efficient systems that may solve similar information processing problems in divergent ways