Comparison of traditional and geometric morphometrics using Lake Huron ciscoes of the Coregonus artedi complex
Here we determine how traditional morphometrics (TM) compares with geometric morphometrics (GM) in discriminating among morphologies of four forms of ciscoes of the Coregonus artedi complex collected from Lake Huron. One of the forms comprised two groups of the same deepwater cisco separated by capture depth, whereas the other three forms were shallow-water ciscoes. Our three groups of shallow-water ciscoes were better separated (3% versus 19% overlap) in Principle Component Analysis (PCA) with TM data than with GM data incorporating semilandmarks (evenly spaced nonhomologous landmarks used to bridge between widely separated homologous landmarks). Our two deepwater cisco groups, comprising a putatively single form collected from different depths, separated more in PCAs with GM data (33% overlap) than in PCAs with TM data (66% overlap), an anomaly caused by greater decompression of the swimbladder and deformation of the body wall in the group captured at greater depths. Separation of the two deepwater cisco groups captured at different depths was not affected by the removal of semilandmarks. Assignment of forms using canonical variate analysis (CVA) accurately assigned 86% of individuals using TM data, 98% of individuals using GM data incorporating semilandmarks, and 100% of individuals using GM data without semilandmarks. However, we considered assignments from the same form of deepwater cisco into separate groups as misassignments resulting from different capture depths, which reduced the accuracy of assignments with GM data to 66% with semilandmarks. Our study implies that TM will continue to have an important role in morphological discrimination within Coregonus and other fishes similarly shaped.
|Comparison of traditional and geometric morphometrics using Lake Huron ciscoes of the Coregonus artedi complex
|Transactions of the American Fisheries Society
|Great Lakes Science Center
|Canada, United States
|Google Analytic Metrics