The tree of life (cladogram) for unicellular eukaryotes encompassing the phyla of organisms analyzed in the present study. Aquatic organisms (living in marine, brackish, or freshwater environments) have their branches drawn in blue while parasitic organisms have their branches drawn in red. Ciliates are indicated by an asterisk after their names. For each phylum marked in bold font, a representative organism has been sketched next to its name. Phylogenetic data from Hinchliff et al. (2015).

Contexts in source publication

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... have collected swimming data on 189 unicellular eukaryotic microorganisms ( = 112 agellates and = 77 ciliates) (see Appendix 1 and Appendix 1 -Source Data File 1). Figure 1 shows a tree encompassing the phyla of organisms studied and sketches of a representative organism from each phylum. A large morphological variation is clearly visible. ...

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... to the morphological and size differences between ciliates and agellates, we investigate separately the statistical properties of each. Figure 2 shows the two swimming speed histograms superimposed, based on the raw distributions shown in Figure 2- Figure Supplement 1, where bin widths have been adjusted to their respective samples using the Freedman-Diaconis rule (see Materials and Methods). Ciliates span a much larger range of speeds, up to 7 mm/s, whereas generally smaller agellates remain in the sub-mm/s range. ...

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... both the integrated absolute difference between the distributions (0.028) and the Kullback-Leibler divergence (0.0016) are very small (see Materials and Methods), demonstrating the close similarity of the two distributions. This similarity is robust to the choice of characteristic speed, as shown in Figure 4- Figure Supplement 1, where the arithmetic mean is used in place of the median. In living cells, the sources for intrinsic variability within organisms are well characterized on the molecular and cellular level (Kirkwood et al., 2005) but less is known about variability within taxonomic groups. ...

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... estimated and , we can compute the higher order moments from Eq. (5) and compare to those calculated directly from the data, as shown in Figure 3- Figure Supplement 1. To t the data, we have used both the MATLAB tting routines and the Python scipy.stats ...

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... that, there is general agreement that a sum of independent log-normal random variables can be well approximated by another log-normal random variable. It has been proven by Szyszkowicz and Yanikomeroglu (2009) that the sum of identically distributed equally and positively correlated joint log-normal distributions converges to a log-normal distribution of known characteristics but for uncorrelated variables only estimations are available (Beaulieu et al., 1995). We use these results to conclude that our distributions contain enough data to be unbiased and seen in full. ...