Quantifying surface topography of biological systems from 3D scans

Understanding the three-dimensional (3D) surface complexity of biological systems can yield fundamental insights into how organisms interact with their environments. The wealth current imaging technologies permits detailed 3D visualization surfaces on macro-, micro- and nanoscale. Analysis reconstructed images, however, remains a challenging proposition. Here, we present QuSTo, versatile, open-source program developed in Python to quantify topography from profiles obtained scans. calculates metrics that roughness size (i.e. height length) shape convexity constant (CC), skewness (Sk) kurtosis (Ku)) structures. We demonstrate applicability our by quantifying snake skin based newly collected data white light scans ventrum dorsum 32 species. To illustrate utility QuSTo for evolutionary ecological research, test whether species occur different habitats differ structure using phylogenetic comparative analyses. application is free, open-source, user-friendly easily adapted specific analysis requirements (available GitHub, github.com/GMLatUCDavis/QuSTo) compatible scanning techniques, example, laser scanning, photogrammetry, gel-based stereo-profilometry. Scientists various disciplines use examine properties an array animal plant both applied bioinspired research.