@phdthesis{Kuerschner2022,
  author    = {K{\"u}rschner, Tobias},
  title     = {Disease transmission and persistence in dynamic landscapes},
  doi       = {10.25932/publishup-56468},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-564689},
  school      = {Universit{\"a}t Potsdam},
  pages     = {120, LXXIII},
  year      = {2022},
  abstract  = {Infectious diseases are an increasing threat to biodiversity and human health. Therefore, developing a general understanding of the drivers shaping host-pathogen dynamics is of key importance in both ecological and epidemiological research. Disease dynamics are driven by a variety of interacting processes such as individual host behaviour, spatiotemporal resource availability or pathogen traits like virulence and transmission. External drivers such as global change may modify the system conditions and, thus, the disease dynamics. Despite their importance, many of these drivers are often simplified and aggregated in epidemiological models and the interactions among multiple drivers are neglected. In my thesis, I investigate disease dynamics using a mechanistic approach that includes both bottom-up effects - from landscape dynamics to individual movement behaviour - as well as top-down effects - from pathogen virulence on host density and contact rates. To this end, I extended an established spatially explicit individual-based model that simulates epidemiological and ecological processes stochastically, to incorporate a dynamic resource landscape that can be shifted away from the timing of host population-dynamics (chapter 2). I also added the evolution of pathogen virulence along a theoretical virulence-transmission trade-off (chapter 3). In chapter 2, I focus on bottom-up effects, specifically how a temporal shift of resource availability away from the timing of biological events of host-species - as expected under global change - scales up to host-pathogen interactions and disease dynamics. My results show that the formation of temporary disease hotspots in combination with directed individual movement acted as key drivers for pathogen persistence even under highly unfavourable conditions for the host. Even with drivers like global change further increasing the likelihood of unfavourable interactions between host species and their environment, pathogens can continue to persist with heir hosts. In chapter 3, I demonstrate that the top-down effect caused by pathogen-associated mortality on its host population can be mitigated by selection for lower virulent pathogen strains when host densities are reduced through mismatches between seasonal resource availability and host life-history events. I chapter 4, I combined parts of both theoretical models into a new model that includes individual host movement decisions and the evolution of pathogenic virulence to simulate pathogen outbreaks in realistic landscapes. I was able to match simulated patterns of pathogen spread to observed patterns from long-term outbreak data of classical swine fever in wild boar in Northern Germany. The observed disease course was best explained by a simulated high virulent strain, whereas sampling schemes and vaccination campaigns could explain differences in the age-distribution of infected hosts. My model helps to understand and disentangle how the combination of individual decision making and evolution of virulence can act as important drivers of pathogen spread and persistence. As I show across the chapters of this thesis, the interplay of both bottom-up and top-down processes is a key driver of disease dynamics in spatially structured host populations, as they ultimately shape host densities and contact rates among moving individuals. My findings are an important step towards a paradigm shift in disease ecology away from simplified assumptions towards the inclusion of mechanisms, such as complex multi-trophic interactions, and their feedbacks on pathogen spread and disease persistence. The mechanisms presented here should be at the core of realistic predictive and preventive epidemiological models.},
  language  = {en}
}