We tested the robustness of topological variables to potential errors in assessing the presence of bones and articulations by comparing the observed values to a randomly generated sample of 10,000 noisy networks for each anatomical network. We created noisy networks by randomly rewiring the links of the original network with a 0.05 probability, which results in introducing a 5% artificially generated error. Then, we compared the observed values of empirical networks to the sample of noisy networks. In each case, we tested the null hypothesis that observed values are equal to the sample mean. We rejected the null hypothesis with α = 0.05 if the observed value is in the 5% end of the distribution of simulated values (table S2; “TRUE,” cannot reject H0; “FALSE,” reject H0 with α = 0.05). We tested a total of 272 values (34 networks × 8 parameters): 268 fell within the confidence intervals and scored TRUE in the test. The exceptions were for Neoceratodus pectoral path length, Neoceratodus pelvic clustering coefficient and path length, and Didelphis pelvic parcellation. Because the anatomy of Neoceratodus and Didelphis derived from our own dissections, these few cases of rejection of the null hypothesis for these parameters can be attributed to the difficulty for a random noise process to produce realistic dissection errors (for example, by coding the femur as not articulating with the pelvis).

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