YY-EEID US-UK XXXX: Eco-Evolutionary dynamics of infectious diseases in host population networks.

点击翻译按钮获取中文摘要

Natural host populations are often fragmented, consisting of several small populations that are linked to one another by animal movement. Fragmented population structures may occur naturally, due to patchy distributions of suitable habitat, or result from human activity and transformations of the landscape. Understanding how changes in population network topology (e.g. size and degree of connectivity between populations) affects disease transmission is an urgent priority, because we are continuously, though often inadvertently, changing network topology. This is particularly important when we consider the transmission of infections across hosts in these networks. Our proposed work will combine data collected from wild desert bighorn sheep (DBH) with new theoretical approaches (e.g. network models) to investigate how infection risks change in populations with different levels of fragmentation. Further, because the kinds of infections animals have will, over evolutionary time, alter the types of infection they are able to respond to, we will also determine how network topology affects the genetic adaptation of immune defense. This is particularly important because, the immune responses in host populations will affect that populations' vulnerability to emerging infectious diseases and so animals in different networks are likely to have different abilities to resist new 'emerging' infections. We propose that the level of connectivity and animal movement between populations will change which parasites and microbes are able to persist within each network. Further, as more than one species of parasite can infect an animal and these parasite species can often interact, we propose that the structure of the parasite community in individual hosts will then be driven by these. To investigae our hypotheses, we will take faecal samples from sheep followed over extended periods, to uncover the landscape-level parasite community patterns in desert bighorns across three differently fragmented populations. Then focusing in on the well-studied network from the Mojave desert, we will combine these longitudinal observational data with experimental approaches to determine how parasite interactions structure the within-host parasite communities. We will also measure immune responses and survey immunogenetic profiles of sheep to estimate how different parasite communities may drive natural selection across 14 bighorn sheep populations. We will then use our empirical data to parameterize and test mathematical network models exploring how ecological and host evolutionary processes shape disease dynamics in bighorns in particular, and across population networks in general. The broad scope and ambitious goals of the proposed work are attainable because reasons: (i) the DBH provid replicate host populations that vary in population connectivity and parasite communities, but are otherwise similar. (ii) We can harness the power of novel molecular techniques to track communities of different groups of parasite. (iii) We will develop innovative modeling approaches, which will integrate our field data on the transmission of microbes and parasites with detailed measured of host immunity. Our modeling framework will allow us to explore both general questions (e.g. How does host population fragmentation impact which parasites persist and spread?) and more tactical concerns (e.g. How will particular changes in landscape connectivity -- e.g. highway construction / animal movement restrictions - affect infection risk?). Host population networks are everywhere - from desert bighorn sheep on mountain tops, to networks of protected areas, through to farms and cities. The proposed study would allow us to develop and test a mathematical framework for exploring ecological and evolutionary dynamics of infectious diseases in different host population networks, potentially transforming how we think about variation in exposure risks among populations over space and time.