Tracking infectious disease spread in wild animals
Individual animals form social networks(opens in new window), which can help them survive. Understanding the dynamics of these networks can tell us a great deal about the long-term success of a particular species, and even why some species act as reservoirs of disease that can spill over to other species. “Animal behaviour studies often focus on how different social patterns influence survival rates and reproductive success,” explains NETDEM project fellow Matthew Silk(opens in new window) from the University of Edinburgh(opens in new window) in the United Kingdom. “Changes in population size though can alter these social networks, which in turn can impact evolutionary processes and the spread of infectious diseases.”
Animal social behaviour and population dynamics
The NETDEM project, which was supported by the Marie Skłodowska-Curie Actions(opens in new window) programme and coordinated by the National Centre for Scientific Research(opens in new window) (CNRS) in France, sought to bring the study of animal social behaviour and population dynamics closer together. “There is a bit of a knowledge gap here, because it is difficult to fully capture data from wild animals,” says Silk. “We can’t watch or monitor every individual from a group all the time, so there is an element of uncertainty when it comes to social networks.” In terms of tracking population dynamics, one commonly used technique is called capture-recapture. This might involve capturing an animal in the summer, and then recapturing it in winter, to confirm its survival. Again, this is an imperfect method, as many animals might easily evade recapture. To address this, Silk developed statistical models that combine social network data with capture-recapture data. Working with his supervisor Olivier Gimenez from the CNRS, Silk was able to bring together cutting-edge modelling approaches with expertise in mathematical ecology, animal demography and conservation.
Tracking spread of infectious disease
A key outcome of this work is a statistical software package(opens in new window). The hope is that this can be used by researchers to combine different data sets, to build up a clearer picture of population dynamics within a particular species that might be hard to track. “One application I’m very excited about here is in the study of disease ecology and the spread of infectious disease,” adds Silk. “We’ve all seen from the past few years how social behaviour, population dynamics and disease spread go hand in hand.” Silk and Gimenez also organised a workshop in Montpellier, France. This brought together experts in both social network analysis and population ecology. “Getting these people together in the same place helped us to form a plan to take forward,” notes Silk. “If we can get our approach working, it will be a big step forward in linking the social structure and dynamics of wildlife populations to population dynamics.”
Understanding ecological and evolutionary processes
Silk believes that the NETDEM project is a step forward in answering some critical questions, including how behaviour and population dynamics can impact disease dynamics. He hopes to apply the methodologies developed to specific animal populations, such as European badgers. Silk is especially interested in understanding why some species are important reservoirs of disease that might spill over into humans, livestock or other species. This field of work is at the centre of his current work at the University of Edinburgh, which builds on the success of NETDEM. “We are interested in the variations in social structures found in animal populations and understanding their consequences for ecological and evolutionary processes,” he says.
