Does the social network structure of wild animal populations differ from that of animals in captivity?

The social behaviour of wild animals living in groups leads to social networks with structures that produce group-level effects and position individuals within them with differential consequences for an individual's fitness. Social dynamics in captivity can differ greatly from those in wild conspecifics given the different constraints on social organization in wild populations, e.g. group size, predation pressure, distribution of resources (food, mates), which are all regulated by human carers in captive populations. The social networks of animals in zoos is expected to differ from those of free-living conspecifics. While many studies have described the social networks of a wide diversity of wild and captive animals, none has directly compared the networks of multiple groups of a single species both in the wild and in captivity. Meerkats, Suricata suricatta, are an excellent species to compare the social networks of wild and captive groups. We replicated the methods of Madden et al. (2009, 2011), who studied eight groups in the wild, in fifteen captive groups. We tested how network structures and individual positions in grooming, foraging competition and dominance networks differed between wild and captive groups. Groups of wild and captive meerkats differed in various aspects of their social network structure. Differences in the network may be due to individuals occupying different network positions and the difference in the number and strength of their connections to other individuals. This distinct way of interacting and associating could be a result of group specific attributes, such as group size, and/or the attributes of the donor and recipient, including sex, status or age. Critically, the differences may be explained by the dissimilar living environment that each encounters.

Seasonal changes in metacommunity assembly mechanisms of benthic macroinvertebrates in a subtropical river basin

Unraveling the ecological factors that control variation in local community structure in space and time is fundamental to metacommunity ecology. In this scenario, environmental filtering and spatial processes are recognized as important drivers of community assembly, yet their relative importance is anticipated to vary for biological communities in different seasons, network positions and organisms with distinct dispersal modes. In this study, we used a dataset (macroinvertebrate communities and environmental variables) collected in different seasons from the Ganjiang River in China to test the above ideas. We divided the whole metacommunity in each season into mainstream communities, tributary communities, strictly aquatic dispersers and aquatic/aerial dispersers, and subsequently used variation partitioning to examine the relative contribution of environmental and spatial factors separately for the overall and decomposed components of the metacommunity. Our results showed that both environmental filtering and spatial processes were important drivers of variation in community structure, yet their explanatory powers varied considerably among seasons. Environmental filtering was the primary driver of metacommunity organization in most scenarios, while the effects of spatial processes surpassing environmental filtering occurred only sporadically. For communities in different network positions, tributary communities were structured by both strong environmental filtering and profound effects of spatial processes via dispersal limitation. However, communities in mainstream sites were mainly determined by environmental filtering, and the effects of spatial processes were almost negligible. Moreover, environmental filtering was clearly more important for aquatic/aerial dispersers, while spatial processes were more influential for strictly aquatic dispersers. We thus concluded that environmental filtering, spatial processes, network position and dispersal mode can interact to regulate metacommunity organization of riverine macroinvertebrates. Considering that the relative contribution of these factors varied among seasons, we strongly uphold the idea that community ecology research should go beyond one-season snapshot surveys in river networks.

How do new members affect the relationship between principal investigator's network position and academic output of granted funds?

The paper focuses on how to improve academic output of a granted fund when the adding of new members changes principal investigator's network position. This objective is refined by exploring how new members affect the relationship between principal investigator's network position and the academic output of granted funds, and whether this effect is similar in high-output and low-output collaboration networks. New members are divided into two groups, namely, international collaboration and inbound mobility. Using negative binomial regression on research funds supported by the National Natural Science Foundation of China, the results indicate that new members indeed have moderating effects on the relationship between principal investigator's network position and the academic output of granted funds. Moreover, we find that this moderating effect in high-output collaboration networks is larger than that in low-output collaboration networks. Our findings provide practical implications for the decision makers to design funding planning and for the principal investigators to improve academic output.

Structural Network Position and Performance of Health Leaders Within an HIV Prevention Trial

The effectiveness of peer leaders in promoting health may depend on the position they occupy within their social networks. Using sociocentric (whole network) and behavioral data from the intervention arm of a cluster-randomized HIV prevention trial in Dar es Salaam, Tanzania, we used generalized linear models with standardized predictors to examine the association between heath leaders' baseline structural network position (i.e., in-degree and betweenness centrality) and their 12-month self-reported (1) confidence in educating network members about HIV and gender-based violence (GBV) and (2) number of past-week conversations about HIV and GBV. As in-degree centrality increased, leaders reported fewer HIV-related conversations. As betweenness centrality increased, leaders reported greater number of conversations about GBV. Network position was not significantly associated with confidence in discussing either topic. Our results suggest that peer leaders who occupy spaces between sub-groups of network members may be more effective in engaging their peers in sensitive or controversial topics like GBV than more popular peer leaders.

Social networks and health: a systematic review of sociocentric network studies in low- and middle-income countries

In low- and middle-income countries (LMICs), naturally occurring social networks may be particularly vital to health outcomes as extended webs of social ties often are the principal source of various resources. Understanding how social network structure, and influential individuals within the network, may amplify the effects of interventions in LMICs, by creating, for example, cascade effects to non-targeted participants, presents an opportunity to improve the efficiency and effectiveness of public health interventions in such settings. We conducted a systematic review of PubMed, Econlit, Sociological Abstracts, and PsycINFO to identify a sample of 17 sociocentric network papers (arising from 10 studies) that specifically examined health issues in LMICs. We also separately selected to review 19 sociocentric network papers (arising from 10 other studies) on development topics related to wellbeing in LMICs. First, to provide a methodological resource, we discuss the sociocentric network study designs employed in the selected papers, and then provide a catalog of 105 name generator questions used to measure social ties across all the LMIC network papers (including both ego- and sociocentric network papers) cited in this review. Second, we show that network composition, individual network centrality, and network structure are associated with important health behaviors and health and development outcomes in different contexts across multiple levels of analysis and across distinct network types. Lastly, we highlight the opportunities for health researchers and practitioners in LMICs to 1) design effective studies and interventions in LMICs that account for the sociocentric network positions of certain individuals and overall network structure, 2) measure the spread of outcomes or intervention externalities, and 3) enhance the effectiveness and efficiency of aid based on knowledge of social structure. In summary, human health and wellbeing are connected through complex webs of dynamic social relationships. Harnessing such information may be especially important in contexts where resources are limited and people depend on their direct and indirect connections for support.