The utility of image processing techniques for morphometric analysis and stock identification

Abstract

The development of image analysis systems has facilitated progress and diversification of morphometric methods and expands the potential for using morphometry as a tool for stock identification. Identifying intraspecific groups with different life histories according to shape variation has become more powerful, based on a review of published studies and our experiences with American lobster and Atlantic salmon. Traditional multivariate morphometrics, accounting for variation in size and shape, have successfully discriminated many fish stocks. However, traditional methods have been enhanced by image processing techniques, through better data collection, more effective descriptions of shape, and new analytical tools. Properly calibrated coordinates of morphometric locations, or 'landmarks', are generally more efficient and precise than manual distance measurements. 'Truss networks' of distances between landmark coordinates provide more comprehensive coverage of form for greater discriminating power. Image analysis systems also allow more advanced 'geometric morphometrics', which include outline methods and landmark methods. Unfortunately, few stock identification studies have applied outline methods, and landmark methods have not been used to discriminate fishery stocks. It appears that advanced geometric methods have not been commonly applied in fishery science, because ontogenetic interpretations are difficult.