Eulimnadia texana (Packard, 1871)

Hethke, Manja & Weeks, Stephen C., 2020, Fig. 25 in Fig. 20. Sesarmops mora n in Paralbunea dayriti, Zoological Studies 59 (33), pp. 1-11 : 5-6

publication ID

https://doi.org/ 10.6620/ZS.2020.59-33

persistent identifier

https://treatment.plazi.org/id/03B9FF46-E45F-A20A-DC2C-D3D0FB00FD07

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Felipe

scientific name

Eulimnadia texana
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Eulimnadia texana View in CoL

Size

PC1 and PC2 explain 82.4% and 9.9% of the variance in the log-transformed size dataset. All nine linear variables have positive loadings on PC1, which are illustrated by vectors in the biplot of figure 5A. So, all variables increase towards positive scores of PC1, which can thus be regarded as a general size axis, although log-transformation added an aspect of shape. Figure 5A View Fig also shows that specimens in cup 15 (= population density of 12.8 inds/400 ml, 14 days after hydration) mostly occupy negative scores on PC1 while single-individual cups (red) occupy positive scores on PC1. Hence, size in high-density cups is generally smaller than in low-density cups. Specimens of intermediate population densities occupy intermediate areas along PC1. Both, PC1 and PC2, are driven by Av, while PC2 is also strongly influenced by variables Arr and u.

NPMANOVA of log-transformed linear measurements indicates that differences in size are significant across different population densities ( Table 2A; p (same) = 0.0002). However, pairwise comparisons reveal that only density extremes (cups 1 and 15) yield significantly different carapace sizes compared to other population densities. Intermediate densities cannot be separated. In comparison to length ( Fig. 4 View Fig ), size (PC1 scores) by population density can also be well fitted by a linear relationship for Eulimnadia texana ( Fig. S1 View Fig ; that of Eocyzicus argillaquus is best fitted by a logarithmic relationship).

During the outlining process, we noticed that the anterior dorsal extremity (landmark D in Fig. 1 View Fig ), which highly influences values of Av, was sometimes difficult to identify. Hence, we carried out a second analysis with a reduced set of log-transformed linear measurements (H, L, Ch, u) that resulted in an overall similar plot, except that PC2 now explained a larger proportion of the variance in the dataset (17.1%). All four variables drive PC1 at almost equal proportions. H, L, and Ch increase towards positive scores of PC2 while u decreases. In general, there seems to be little difference between the analyses of nine and four linear variables. NPMANOVA yielded almost the same result except for one pairwise comparison (cups 15 and 10.1) that cannot be statistically distinguished in table 2B.

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