Table 2. Significance of the nestedness of the MN matrix using alternative algorithms.

	NTC algorithm			NODF algorithm
	NNTC	Bernoulli	Probabilistic degree	NNODF	Bernoulli	Probabilistic degree
Normal analysis	0.9541	P<1e-5	P<1e-5	0.0341	P<1e-5	P=0.2336
Multi-scale analysis	0.93590.92630.8568	P<1e-5 P<1e-5 P=1	P=1 P=1 P=1	0.0062	P=1	P=1

Abbreviations: MN matrix, Moebus and Nattkemper matrix; NODF, nestedness metric based on overlap and decreasing filling; NTC, nestedness temperature calculator; The P-value denotes the fraction of random matrices that have a larger value of nestedness, N, than the observed MN matrix. In the ‘normal' analysis, the NTC algorithm and NODF algorithms are used to estimate nestedness using alternative null models (see Materials and methods). For the multi-scale analysis three values have been reported for analyzing the significance of nestedness using the NTC algorithm: (1) Modules are sorted according to the sort heuristic described in Supplementary Text S3; (2) Modules are sorted in descending order of the number of phages; (3) Modules are sorted in ascending order of the number of phages. See Supplementary Figure S6 for the details of sorting. Note that the values of nestedness can differ depending on the algorithm used, it is their relative value to the null model that determines significance.