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Many endangered wildlife populations are vulnerable to infectious diseases for which vaccines exist; yet, pragmatic considerations often preclude large-scale vaccination efforts. These barriers could be reduced by focusing on individuals with the highest contact rates. However, the question then becomes whether targeted vaccination is sufficient to prevent large outbreaks. To evaluate the efficacy of targeted wildlife vaccinations, we simulate pathogen transmission and control on monthly association networks informed by behavioural data from a wild chimpanzee community (Kanyawara N = 37, Kibale National Park, Uganda). Despite considerable variation across monthly networks, our simulations indicate that targeting the most connected individuals can prevent large outbreaks with up to 35% fewer vaccines than random vaccination. Transmission heterogeneities might be attributed to biological differences among individuals (e.g. sex, age, dominance and family size). Thus, we also evaluate the effectiveness of a trait-based vaccination strategy, as trait data are often easier to collect than interaction data. Our simulations indicate that a trait-based strategy can prevent large outbreaks with up to 18% fewer vaccines than random vaccination, demonstrating that individual traits can serve as effective estimates of connectivity. Overall, these results suggest that fine-scale behavioural data can help optimize pathogen control efforts for endangered wildlife., (© 2014 The Author(s) Published by the Royal Society. All rights reserved.)

1. Heterogeneity in host association patterns can alter pathogen transmission and strategies for control. Great apes are highly social and endangered animals that have experienced substantial population declines from directly transmitted pathogens; as such, network approaches to quantify contact heterogeneity could be crucially important for predicting infection probability and outbreak size following pathogen introduction, especially owing to challenges in collecting real-time infection data for endangered wildlife. 2. We present here the first study using network analysis to quantify contact heterogeneity in wild apes, with applications for predicting community-wide infectious disease risk. Specifically, within a wild chimpanzee community, we ask how associations between individuals vary over time, and we identify traits of highly connected individuals that might contribute disproportionately to pathogen spread. 3. We used field observations of behavioural encounters in a habituated wild chimpanzee community in Kibale National Park, Uganda to construct monthly party level (i.e. subgroup) and close-contact (i.e. ≤ 5 m) association networks over a 9-month period. 4. Network analysis revealed that networks were highly dynamic over time. In particular, oestrous events significantly increased pairwise party associations, suggesting that community-wide disease outbreaks should be more likely to occur when many females are in oestrus. 5. Bayesian models and permutation tests identified traits of chimpanzees that were highly connected within the network. Individuals with large families (i.e. mothers and their juveniles) that range in the core of the community territory and to a lesser extent high-ranking males were central to association networks, and thus represent the most important individuals to target for disease intervention strategies. 6. Overall, we show striking temporal variation in network structure and traits that predict association patterns in a wild chimpanzee community. These empirically-derived networks can inform dynamic models of pathogen transmission and have practical applications for infectious disease management of endangered wildlife species., (© 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society.)

Complex sex-biased dispersal patterns often characterize social-group-living species and may ultimately drive patterns of cooperation and competition within and among groups. This study investigates whether observational data or genetic data alone can elucidate the potentially complex dispersal patterns of social-group-living black and white colobus monkeys (Colobus guereza, "guerezas"), or whether combining both data types provides novel insights. We employed long-term observation of eight neighbouring guereza groups in Kibale National Park, Uganda, as well as microsatellite genotyping of these and two other neighbouring groups. We created a statistical model to examine the observational data and used dyadic relatedness values within and among groups to analyse the genetic data. Analyses of observational and genetic data both supported the conclusion that males typically disperse from their natal groups and often transfer into nearby groups and probably beyond. Both data types also supported the conclusion that females are more philopatric than males but provided somewhat conflicting evidence about the extent of female philopatry. Observational data suggested that female dispersal is rare or nonexistent and transfers into neighbouring groups do not occur, but genetic data revealed numerous pairs of closely related adult females among neighbouring groups. Only by combining both data types were we able to understand the complexity of sex-biased dispersal patterns in guerezas and the processes that could explain our seemingly conflicting results. We suggest that the data are compatible with a scenario of group dissolution prior to the start of this study, followed by female transfers into different neighbouring groups.