<div>Fig. 2. Illustrations for morphometric characters.</div><h2 data-label="data-analysis" class="ltx_title_subsection">Data analysis –</h2><div>Distribution patterns of objects (i.e. specimens represented by 21
characters measured by the eleven different gaugers) were displayed in a
scatterplot via Principal Component Analysis (PCA; Venables &amp; Ripley,
2002) using a standardization to zero mean and the variance unit
(Legendre &amp; Gallagher, 2001). A Permutational Multivariate Analysis of
Variance (PERMANOVA) was performed using the Morosita index of
dissimilarity with 9999 iterations (Anderson, 2001).</div><div>Reliability depends on the magnitude of the error in the measurements to
the inherent variability between subjects. These measures of variability
can be expressed as standard deviations (SDs). Reliability is defined as
a quadratic term of the measured values divided by the sum of the
quadratic term of the measured plus the square standard deviation. It is
formally described by Bartlett and Frost (2008) as</div><div><i>(SD of subject’s true values)<sup>2</sup></i> (SD subjects’ true values)<sup>2</sup> + (SD measurement
error)<sup>2</sup>.</div><div>This measure of reliability is also known as intraclass correlation
(ICC). If reliability is high, measuring error is small in comparison to
the true differences between subjects, so that subjects can be
relatively well distinguished (in terms of the quantity being measured)
on the basis of the error-prone measurements (Bartlett &amp; Frost, 2008).</div><div>To estimate the within-subject SD, we applied a one-way analysis of
variance (ANOVA) to model the data containing the repeat measurements
made on subjects. In addition, we also tested the effect of the gaugers’
expertise and their equipment’s performance on the accuracy of ICC
estimation by using Spearman’s rank correlation. The analyses were
carried out in R 3.6.2 (R Core Team, 2019) by using the “Vegan”
package (version 2.5-6, Oksanen et al., 2019) for PCA and PERMANOVA and
“car package” (version 3.0-7, Fox &amp; Weisberg 2019). Repeatability was
calculated for each gauger respectively in order to assess whether the
gauger’s skills or equipment quality played major roles in measurement
consistency.</div>