Life Sciences Commons^™

Global Patterns And Predictions Of Seafloor Biomass Using Random Forests, Chih-Lin Wei, Gilbert T. Rowe, Elva Escobar-Briones, Antje Boetius, Thomas Soltwedel, M. Julian Caley, Yousria Soliman, Falk Huettmann, Fangyuan Qu, Zishan Yu, C. Roland Pitcher, Richard L. Haedrich, Mary K. Wicksten, Michael A. Rex, Jeffrey G. Baguley, Jyotsna Sharma, Roberto Danovaro, Ian R. Macdonald, Clifton C. Nunnally, Jody W. Deming, Paul Montagna, Mélanie Lévesque, Jan Marcin Weslawski, Maria Wlodarska-Kowalczuk, Baban S. Ingole, Brian J. Bett, David S. M. Billett, Andrew Yool, Bodil A. Bluhm, Katrin Iken, Bhavani E. Narayanaswamy

Biology Faculty Publication Series

A comprehensive seafloor biomass and abundance database has been constructed from 24 oceanographic institutions worldwide within the Census of Marine Life (CoML) field projects. The machine-learning algorithm, Random Forests, was employed to model and predict seafloor standing stocks from surface primary production, water-column integrated and export particulate organic matter (POM), seafloor relief, and bottom water properties. The predictive models explain 63% to 88% of stock variance among the major size groups. Individual and composite maps of predicted global seafloor biomass and abundance are generated for bacteria, meiofauna, macrofauna, and megafauna (invertebrates and fishes). Patterns of benthic standing stocks were positive …