Linking social and pathogen transmission networks using microbial genetics in giraffe (Giraffa camelopardalis)

• Published in: The Journal of animal ecology 2014-03, Vol.83 (2), p.406-414
• Main Authors: VanderWaal, Kimberly L, Atwill, Edward R, Isbell, Lynne. A, McCowan, Brenda, Altizer, Sonia
• Format: Article
• Language: English
• Subjects: Animal and plant ecology; Animal ecology; Animal populations; Animal, plant and microbial ecology; Animals; bacterial genotyping; Biological and medical sciences; Data transmission; disease ecology; Disease transmission; Ecological genetics; ecology; Epidemiology; Escherichia coli; Escherichia coli - genetics; Escherichia coli - physiology; Escherichia coli Infections - microbiology; Escherichia coli Infections - transmission; Escherichia coli Infections - veterinary; Female; Fundamental and applied biological sciences. Psychology; General aspects; Giraffa camelopardalis; Giraffes; Homing Behavior; infection dynamics; Kenya; Male; Mammalia; microbial genetics; Microbiology; microorganisms; Parasite and disease ecology; pathogen occurrence; pathogens; Pathology; Population genetics; Social Behavior; social network analysis; Social networks; social structure; space‐use patterns; Vertebrates: general zoology, morphology, phylogeny, systematics, cytogenetics, geographical distribution; wildlife; wildlife disease; Wildlife ecology
• ISSN: 0021-8790; 1365-2656
• DOI: 10.1111/1365-2656.12137

Although network analysis has drawn considerable attention as a promising tool for disease ecology, empirical research has been hindered by limitations in detecting the occurrence of pathogen transmission (who transmitted to whom) within social networks. Using a novel approach, we utilize the genetics of a diverse microbe, Escherichia coli, to infer where direct or indirect transmission has occurred and use these data to construct transmission networks for a wild giraffe population (Giraffe camelopardalis). Individuals were considered to be a part of the same transmission chain and were interlinked in the transmission network if they shared genetic subtypes of E. coli. By using microbial genetics to quantify who transmits to whom independently from the behavioural data on who is in contact with whom, we were able to directly investigate how the structure of contact networks influences the structure of the transmission network. To distinguish between the effects of social and environmental contact on transmission dynamics, the transmission network was compared with two separate contact networks defined from the behavioural data: a social network based on association patterns, and a spatial network based on patterns of home‐range overlap among individuals. We found that links in the transmission network were more likely to occur between individuals that were strongly linked in the social network. Furthermore, individuals that had more numerous connections or that occupied ‘bottleneck’ positions in the social network tended to occupy similar positions in the transmission network. No similar correlations were observed between the spatial and transmission networks. This indicates that an individual's social network position is predictive of transmission network position, which has implications for identifying individuals that function as super‐spreaders or transmission bottlenecks in the population. These results emphasize the importance of association patterns in understanding transmission dynamics, even for environmentally transmitted microbes like E. coli. This study is the first to use microbial genetics to construct and analyse transmission networks in a wildlife population and highlights the potential utility of an approach integrating microbial genetics with network analysis.