Résumé
Parasites are key components of ecosystems, yet their distribution and diversity across landscapes remain poorly understood. Parasite diversity hot- and cold-spots arise from complex interactions between host populations, ecosystem variables, and environmental changes. To understand coexistence of parasites, we need to understand coexistence of specialist to generalist strategies that is driven by niche breadth evolution in parasites. A key challenge is linking ecological and evolutionary dynamics to the emergence of different properties of modularity or nestedness in host-parasite interaction networks. In this study, we develop a spatially explicit, individual-based simulation model to explore how selection regimes, dispersal, and landscape connectivity shape the emergence and maintenance of parasite diversity and network structure.Our model considers a gene-for-gene (GFG) framework for infection, incorporating fitness trade-offs associated with resistance and infectivity, as well as virulence-transmission trade-offs. Presence of trade-offs would lead to fluctuating selection dynamics, and without costs would lead to arms race dynamics. We simulate two landscape structures: Optimal Channel Networks (OCNs), representing aquatic ecosystems, and random geometric graphs (RGGs), representing terrestrial ecosystems. We predict that under fluctuating selection, spatial heterogeneity in adaptation maintains high genetic diversity and drives the formation of modular infection networks, particularly in peripheral or poorly connected patches. In contrast, arms race dynamics lead to directional selection, reduced diversity, and the evolution of nested network structures dominated by generalist parasite genotypes. Landscape topology and the evolution of dispersal behaviors strongly influence these outcomes, modulating the spatial scale of coevolution. Dispersal evolves within a spatial network, allowing us to explore the coevolution of movement strategies and local adaptation. Our results offer mechanistic insights into how coevolutionary dynamics and landscape structure interact to shape host-parasite networks. By identifying the conditions under which modularity or nestedness arises, this work provides a theoretical foundation for predicting parasite diversity patterns and managing disease risk in complex, changing ecosystems.