Meeting Abstract

S7-11  Sunday, Jan. 6 14:30 - 15:00  The fish shapes project. Harnessing the power of data science, museum collections and undergraduate researchers to quantify body shape evolution across teleost fishes. PRICE, SA*; CORN, KA; FRIEDMAN, ST; LAROUCHE, O; MARTINEZ, CM; ZAPFE, K; WAINWRIGHT, PC; Clemson University; Univ. of California, Davis; Univ. of California, Davis; Clemson University; Univ. of California, Davis; Clemson University; Univ. of California, Davis

Teleosts account for 96% of all fish species, nearly half of extant vertebrate diversity, and exhibit a spectacular variety of body forms from deep-bodied moonfish to elongate eels. However, attempts to comprehensively explore general patterns in the relationship between body shape, functional morphology and ecology across teleosts have been limited by data availability. We present a morphological dataset of eight functionally relevant size and shape variables, combining length, depth and width measurements as well as seven fin spine length measurements on more than 13000 specimens from 6000+ species in the Smithsonian National Museum of Natural History collections. The resulting morphospace spans the phylogenetic diversity of teleosts and encompasses 90% of extant families and 96% of living orders. Integral to the data collection were our 30+ undergraduate researchers, who spent 18 months immersed in a coordinated research experience. When analyzed in a phylogenetic framework our data enables us to identify the primary axes of shape diversification across teleosts, describe trends in shape diversity over time and identify combinations of shape and ecological, environmental and functionally relevant biological traits that are common, rare or not found in nature. Our initial analyses indicate that the primary axes of variation and the effect of specific ecological traits on rates of morphological evolution are highly dependent on the taxonomic scale of the analyses. This highlights the difficulty of inferring global macroevolutionary patterns from smaller scale analyses and vice versa.