Quantifying Shape: Streamlining the Process of Outline Morphometrics

Beyond learning the nuances of shape theory, initial inroads into morphometrics are often accompanied by frustration with the procedural difficulties of capturing and analyzing shape data, which typically involves a convoluted series of computer programs. With the development of digital photography and independently-written freeware, such as NIH programs or the IMP series by David Sheets, the procedural side of quantifying shape has improved significantly. But there is still a long way to go towards developing an efficient, user-friendly, open source process. Studies of morphological disparity can be hampered by inadequate sample sizes, resulting from poor preservation and/or sampling, but also from the time intensive task of collecting shape data for large collections of specimens. Here, I present methodological improvements using outline morphometrics geared towards both novice and experienced researchers, with particular emphasis placed on automating multiple procedural steps to cut time, increase sample size, and decrease error around mean values.

Using the bivalve genus Cucullaea as an exemplar, I present a streamlined workflow for outline morphometrics, from initial photography through to the generation of a morphological framework for identifying differences among individuals or groups in morphospace. While bivalves are particularly well suited for such analyses, this procedure can be tailored by individual researchers to quantify anatomical shapes for a variety of other taxonomic groups. Overall the procedure uses the following steps: 1) Adobe Photoshop batch processing for image enhancement, autotracing and outline exporting; 2) the NIH program ImageJ for automating the collection of XY coordinates and scale factors for each trace; 3) HANGLE programs (Haines and Crampton, 1996) for calculating Fourier coefficients, normalization and reconstruction of traces from synthetic coefficient data; and 4) the programming language R for constructing the backing grid of synthetic outline shapes for morphospace, and for calculating distance, variance and range metrics.