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In the face of ubiquitous threats from parasites, hosts often evolve strategies to resist infection or to altogether avoid contact with parasites. At the microbial scale, bacteria frequently encounter viral parasites, bacteriophages. While bacteria are known to utilize a number of strategies to resist infection by phages, and can physically navigate their environment using complex motility behaviors, it is unknown whether bacteria evolve avoidance of phages. In order to answer this question, we combined experimental evolution and mathematical modeling. Experimental evolution of the bacteriumPseudomonas fluorescensin environments with differing spatial distributions of the phage Phi2 revealed that the host bacteria evolved resistance depending on parasite distribution and infectivity, but did not evolve dispersal to avoid parasite infection. Simulations using parameterized mathematical models of bacterial growth and swimming motility showed that this is a general finding: while increased dispersal is adaptive in the absence of parasites, in the presence of parasites that fitness benefit disappears and resistance becomes adaptive, regardless of the spatial distribution of parasites. Together, these experiments suggest that parasites should rarely, if ever, drive the evolution of bacterial avoidance via dispersal.

Characterization of microbial growth is of both fundamental and applied interest. Modern platforms can automate collection of high-throughput microbial growth curves, necessitating the development of computational tools to handle and analyze these data to produce insights. However, existing tools are limited. Many use parametric analyses that require mathematical assumptions about the microbial growth characteristics. Those that use non-parametric or model-free analyses often can only quantify a few traits of interest, and none are capable of importing and reshaping all known growth curve data formats. To address this gap, here I present a newly-developed R package: gcplyr. gcplyr can flexibly import growth curve data in every known format, and reshape it under a flexible and extendable framework so that users can design custom analyses or plot data with popular visualization packages. gcplyr can also incorporate metadata and generate or import experimental designs to merge with data. Finally, gcplyr carries out model-free and non-parametric analyses, extracting a broad range of clinically and ecologically important traits, including initial density, lag time, growth rate and doubling time, carrying capacity, diauxie, area under the curve, extinction time, and more. In sum, gcplyr makes scripted analysis of growth curve data in R straightforward, streamlines common data wrangling and analysis steps, and easily integrates with common visualization and statistical analyses.

Bacteria-phage symbioses are ubiquitous in nature and serve as valuable biological models. Historically, the ecology and evolution of bacteria-phage systems have been studied in either very simple or very complex communities. Although both approaches provide insight, their shortcomings limit our understanding of bacteria and phages in multispecies contexts. To address this gap, here we synthesize the emerging body of bacteria-phage experiments in medium-complexity communities, specifically those that manipulate bacterial community presence. Generally, community presence suppresses both focal bacterial (phage host) and phage densities, while sometimes altering bacteria-phage ecological interactions in diverse ways. Simultaneously, community presence can have an array of evolutionary effects. Sometimes community presence has no effect on the coevolutionary dynamics of bacteria and their associated phages, whereas other times the presence of additional bacterial species constrains bacteria-phage coevolution. At the same time, community context can alter mechanisms of adaptation and interact with the pleiotropic consequences of (co)evolution. Ultimately, these experiments show that community context can have important ecological and evolutionary effects on bacteria-phage systems, but many questions still remain unanswered and ripe for additional investigation.