Coding sequences and splice sites contained significantly fewer of these indels than expected from random expectations, suggesting that selection is a factor in limiting their persistence.
The excess/deficiency of immune genes was then expressed as differences between the observed immune gene density and the expected gene density under random expectations (Fig. 1B).
In case of within-species conservation of the trait, we expect that species' artificial nodes account for significantly less variability than random expectations (significant left-tailed P-value for Sc).
Random expectations would suggest a repeat rate of 6.25%.Across the three groups, the average persistence was 5.54%; worse than tossing a coin.
Finally, hypothesis testing was done by comparing the means between AUCrandom and AUCpartial to test whether the predictive model performed better than random expectations.
The five networks (displayed in Additional file 2) were all highly connected (0.76 ± 0.10) and highly nested (0.80 ± 2.07) compared to random expectations, and all displayed similar structure.
The distribution of AUC ratios, calculated from the bootstrap values as AUCpartial/AUCrandom, was significantly greater than random expectations showing very good model consistency (Fig. 3).
Here, an AUC value of ≤ 0.5 indicates a performance that is no better than random expectations, whereas, a value of one indicates perfect discrimination (Thuiller et al. 2005).
Individual space use also explained community structure, although spatial assortment was globally weaker than random expectations, suggesting that observed associations were not an artefact of the sampling design or spatial distribution of individuals.
On the other hand, cellular networks are not generated through random processes, and we can ask how much of the deviations from random expectations are associated with the rules of network growth.
In P. monticola, spatial autocorrelation likewise identified no distance classes that deviated from random expectations.
