title: Mechanisms of multi-strain coexistence in host-phage systems with nested infection networks.
abstract: Bacteria and their viruses (bacteriophages) coexist in natural environments forming complex infection networks. Recent empirical findings suggest that phage-bacteria infection networks often possess a nested structure such that there is a hierarchical relationship among who can infect whom. Here we consider how nested infection networks may affect phage and bacteria dynamics using a multi-type Lotka-Volterra framework with cross-infection. Analysis of similar models has, in the past, assumed simpler interaction structures as a first step towards tractability. We solve the proposed model, finding trade-off conditions on the life-history traits of both bacteria and viruses that allow coexistence in communities with nested infection networks. First, we find that bacterial growth rate should decrease with increasing defense against infection. Second, we find that the efficiency of viral infection should decrease with host range. Next, we establish a relationship between relative densities and the curvature of life history trade-offs. We compare and contrast the current findings to the "Kill-the-Winner" model of multi-species phage-bacteria communities. Finally, we discuss a suite of testable hypotheses stemming from the current model concerning relationships between infection range, life history traits and coexistence in complex phage-bacteria communities.
authors.affiliation: School of Physics, Georgia Institute of Technology, Atlanta, GA, USA. ljover3@gatech.edu
authors.forename: Luis F
authors.initials: LF
authors.lastname: Jover
authors.forename: Michael H
authors.initials: MH
authors.lastname: Cortez
authors.forename: Joshua S
authors.initials: JS
authors.lastname: Weitz
mesh_terms: D001419:Bacteria / Q000821:virology*; D001435:Bacteriophages / Q000502:physiology*; D054884:Host-Pathogen Interactions / Q000502:physiology*; D008954:Models, Biological
keywords: Bacterial viruses; Bipartite networks; Complex networks; Microbial ecology; Nonlinear dynamics