Greg Albery (Georgetown) presents “Social networks, spatial networks, parasites, and deer"10/29/2019 Illustration by Carly Rozins A few weeks ago in the Berkeley EEID seminar we had Dr. Greg Albery present some of his ongoing research. Greg is a recent addition to the Bansal Lab at Georgetown University, but this month he is visiting the Boots lab here at UC Berkeley. Greg’s seminar was on ‘Spocial Networks’. What is a spocial network you might ask? Well this is an important question and a topic often overlooked. But first we need to discuss social networks. A network is a collection of nodes (dots) and edges (lines connecting nodes). Nodes represent hosts and the edges represent interactions, ones that might lead to infectious disease transmission. Networks capture host heterogeneity and provide a rich dataset for determining the effects of sociality on disease dynamics and life history characteristics. Importantly, networks can be empirically derived from surveys, observations, pit tags and radio proximity collars that capture/log social contacts between individuals in the population. These empirical networks are classified as social networks. Therein lies the problem. Since space affects everything, how can these networks be classified as solely social? If space is ignored, it is impossible to determine whether the observed social effects on disease dynamics are actually due to social events, or whether they are truly due to environmental (spatial) constraints. The environment will shape how populations structure and population structure affects social behavior. Greg unraveled these two confounding factors. He collected both spatial and social data for a population of red deer on the Isle of Rum. With an Integrated Nested Laplace Approximation Linear Mixed Models (wha… that was a mouthful!) he determined that spatial heterogeneity is an important factor in both immunity as well as parasitism. Interestingly, he found fine scale spatial patterns in parasitism. Essentially, the best models are spocial, those that control for both spatial and social factors. However, Greg is not done yet! He is now working with an incredibly detailed ant data set on social and spatial behavior and he is on the lookout for even more data. So, if you know of any datasets that include any combination of social, spatial and disease data, Greg would like to chat with you! Summary by Carly Rozins
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