Fig. 2. Illustrations for morphometric characters.

Data analysis –

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 & Ripley, 2002) using a standardization to zero mean and the variance unit (Legendre & Gallagher, 2001). A Permutational Multivariate Analysis of Variance (PERMANOVA) was performed using the Morosita index of dissimilarity with 9999 iterations (Anderson, 2001).
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
(SD of subject’s true values)2 (SD subjects’ true values)2 + (SD measurement error)2.
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 & Frost, 2008).
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 & 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.