Statistical analysis
All analysis was performed in R (version 4.0.5; R Core Team 2018) and
plots were made using the R package ‘ggplot2’ (Wickham 2016). Population
density data were log-transformed and bacteria resistance data were
arcsine-transformed before analysis. Bacterial population size (density)
during the evolution experiment was analysed using mixed-effect linear
model in the ‘nlme’ package (Pinheiro et al. 2021). Treatment and
time (transfer number) were included as categorical and continuous
explanatory variables respectively, with microcosm ID as a random
effect. Significance of each explanatory variable was estimated using
the ‘Anova’ function provided by the ‘car’ package (Fox et al.2021). Pairwise multiple comparison between treatments was performed
using Tukey’s HSD with the ‘glht’ function in ‘multcomp’ package (Bretzet al. 2022). To further estimate population dynamics, separate
analyses were conducted for each treatment using linear models with time
as an explanatory variable.
One-tailed t-tests were used to analyse the growth performance (density
data) measured at 10°C and the top-down effect by phages. Wilcoxon rank
tests were used when data were not normally distributed. The effects of
treatment and transfer time on bacteria resistance were estimated using
mixed-effect linear models.