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

Lead Research Organisation: Cardiff University
Department Name: School of Biosciences


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.

Technical Summary

Natural host populations often occur as fragmented metapopulations. Understanding how changes in population network topology affect disease transmission is an increasingly urgent priority, as humans continue to manipulate landscapes and alter connectivity between popoulations.
At ecological time scales, disease transmission in host networks is a balance, between rapid spread throughout the network and the virulence caused in individual hosts. Changes in network topology are likely to shift this balance leading to loss of some parasite species. We hypothesise that network topology will determine the distribution of parasite taxa across the metapopulation, while within-host parasite interactions will structure local parasite communities. At evolutionary time scales, each population's exposure profile - the incidence and types of parasite that occur in the population - will shape host immunogenetic adaptation resulting in differential patterns of vulnerability to infection.
We will study the dynamics of host-associated bacteria, protozoa and helminths (host-associated taxa - HAT) in three bighorn sheep metapopulations with different network topologies asking how does (1) How does network topology structure HAT communities? (2) How does variation among populations in HAT communities drive local immunogenetic adaptation? and (3) How does network topology shape infection risks and disease dynamics? We expect that differences in HAT communities across populations mediate differential selection on immunogenetic loci, resulting in variation in immune responses and local immunogenetic adaptation. Further, we hypothesize that feedbacks between the ecological and evolutionary processes drive the dynamics of HAT communities in host population networks, resulting in differential vulnerability to emerging infections across populations.

Planned Impact

General Public
Emerging diseases threaten human health, food security and conservation efforts, all of which are of interest and importance to the public. Most populations can be considered as metapopulations - i.e. networks connected by animal / human movement and we propose that the structure of these networks alters the capacity of populations to resist infection. Our work will determine how networks topology alters disease resistance and will therefore benefit the public by improving our capacity to undertand and control disease.Desert big horn sheep are an iconic species which are affected by controversial land use issues such as energy installations and open-range grazing of domestic livestock. They therefoe provided a perfect example with which to engage public interest in the research from our work. We will achieve this engagement, through the range of outreach activities explored in the Pathways to Impact Statement.

Agricultural and Public Health Policy Makers.
Emerging infectious disease is one of the most significant threats to human and animal health and welfare, with recent epidemics and pandemics (e.g. ebola, avian influenza, swine flu) highlighting this issue. The vast majority of these infectious diseases (approximately 80%) arise from wildlife reservoirs. Understanding how anthropogenic changes to wildlife metapopulation network structure impacts parasite communities and host immunogenetics is key to understanding risk of emerging infections. This study will allow us to determine what network features promote or defend against novel pathogen invasion. Specifically, we will a) determine whether disrupted wildlife metapopulations networks infer more risk as sources of infection spill-over and b) how restriction in contact networks among human and agricultural populations may influence the risk of those populations to such spill-over.

Wildlife Conservation Policy Makers.
Infectious diseases are a serious and increasing threat to the stability of established wildlife populations and to the recovery of endangered species. While pathogen transmission across networks has been studied before, our study will be the first to explore the role that network topology plays in driving the distribution and structure of whole parasite communities. This work will allow us to ascertain how these structural changes in parasite community influence the likelihood that these different metapopulations resist invasion by an emergent pathogen. This work has direct implications for the endangered study species, Big Horn sheep, which are vulnerable to spill-over of infections from domestic and other animal species (e.g. Mycoplasma from domestic sheep.) In addition, the general principles we explore and that are validated in this host species, can be applied to other vulnerable species existing in metapopulations.


10 25 50