Beta Diversity
To deal with unequal sequence coverage and compositional variation within the data, we used Cumulative Sum Scaling (CSS) normalization on the filtered dataset using the R package metagenomeseq (Paulson et al., 2013) following the methods in Maraci et al., (2021). We then log (x + 0.0001) transformed the data and later corrected the transformed values by subtracting the log of the pseudo count (Thorsen et al., 2016). Dissimilarity matrices were computed based on Bray–Curtis (Bray & Curtis, 1957), unweighted UniFrac (C. Lozupone & Knight, 2005), and weighted UniFrac (Lozupone et al., 2007). To visualize the dissimilarities between nestlings based on parasite treatment and urbanization, we used a principal coordinate analysis (PCoA) using the ordinate function in the phyloseq package (McMurdie & Holmes, 2013). To determine the effect of parasitism and urbanization on beta diversity metrics, we used PERMANOVAs with parasite treatment, location, year (2018, 2019), and the interaction between parasite treatment and location as fixed effects and beta diversity metrics as response variables. Nest identification was included as a random effect in all models. We used the adonis2 function with thevegan package for these analyses (Oksanen et al., 2022).