Network analysis
To understand bacterial interactions and community structure (i.e., complexity) across sites, land cover types, and the four sampling times (00:00, 06:00, 12:00, 18:00), we evaluated co-occurrence association networks of bacterial ASVs. We constructed networks at the phylum taxonomic level to examine broad complexity and the genus level to visualise higher resolution complexity, recognising that the accuracy of species-level associations would be low due to the high similarity between the 16S rRNA gene from closely related species. In the evaluated networks, vertices (also known as ‘nodes’) represent ASVs and edges (also known as ‘links’) connect a pair of ASVs if their frequencies are significantly associated (absolute abundance >0.75, p  = ≤0.01 for phylum and >0.95,p = ≤0.01 for genus). The type of association, whether positive (representing a mutualistic interaction) or negative (representing an antagonistic interaction), was denoted with blue and red edges, respectively. To account for compositional bias associated with ASV data, we used SparCC (Friedman & Alm, 2012) to define associations. Only ASVs with sequence counts >10 were included for phylum and >100 for genus to improve visualisation, selection rigour and computational processing. Randomly permuted (n  = 100) data were used to estimate the statistical significance of associations. We used the R package Matrix (Bates et al., 2023) to create a matrix from the given set of values andigraph (Csárdi et al., 2023) to visualise and evaluate the plots.