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We compared the repeatability, reproducibility (intra- and inter-measurer similarity), separative power and subjectivity (measurer effect on results) of four morphometric methods frequently used in ichthyological research, the “traditional” caliper-based (TRA) and truss-network (TRU) distance methods and two geometric methods that compare landmark coordinates on the body (GMB) and scales (GMS). In each case, measurements were performed three times by three measurers on the same specimen of three common cyprinid species (roach Rutilus rutilus (Linnaeus, 1758), bleak Alburnus alburnus (Linnaeus, 1758) and Prussian carp Carassius gibelio (Bloch, 1782)) collected from three closely-situated sites in the Lake Balaton catchment (Hungary) in 2014. TRA measurements were made on conserved specimens using a digital caliper, while TRU, GMB and GMS measurements were undertaken on digital images of the bodies and scales. In most cases, intra-measurer repeatability was similar. While all four methods were able to differentiate the source populations, significant differences were observed in their repeatability, reproducibility and subjectivity. GMB displayed highest overall repeatability and reproducibility and was least burdened by measurer effect. While GMS showed similar repeatability to GMB when fish scales had a characteristic shape, it showed significantly lower reproducability (compared with its repeatability) for each species than the other methods. TRU showed similar repeatability as the GMS. TRA was the least applicable method as measurements were obtained from the fish itself, resulting in poor repeatability and reproducibility. Although all four methods showed some degree of subjectivity, TRA was the only method where population-level detachment was entirely overwritten by measurer effect. Based on these results, we recommend a) avoidance of aggregating different measurer’s datasets when using TRA and GMS methods; and b) use of image-based methods for morphometric surveys. Automation of the morphometric workflow would also reduce any measurer effect and eliminate measurement and data-input errors.

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