Global Patterns and Predictions of Seafloor Biomass Using Random Forests

Authors

• Chih-Lin Wei, Texas A & M University - College Station
• Gilbert T. Rowe, Texas A & M University - Galveston
• Elva Escobar-Briones, Universidad Nacional Autonoma de Mexico
• Antje Boetius, Alfred Wegener Institute for Polar and Marine Research
• Thomas Soltwedel, Alfred Wegener Institute for Polar and Marine Research
• M. Julian Caley, Australian Institute of Marine Science
• Yousria Soliman, Qatar University
• Falk Huettmann, University of Alaska Fairbanks
• Fangyuan Qu, Ocean University of Qingdao
• Zishan Yu, Ocean University of Qingdao
• C. Roland Pitcher, CSIRO Marine and Atmospheric Research
• Richard L. Haedrich, Memorial University of Newfoundland
• Mary K. Wicksten, Texas A & M University - College Station
• Michael A. Rex, University of Massachusetts BostonFollow
• Jeffrey G. Baguley, University of Nevada - Reno
• Jyotsna Sharma, University of Texas at San Antonio
• Roberto Danovaro, Polytechnic University of Marche
• Ian R. MacDonald, Florida State University
• Clifton C. Nunnally, Texas A & M University - College Station
• Jody W. Deming, University of Washington - Seattle Campus
• Paul Montagna, Texas A & M University - Corpus Christi
• Mélanie Lévesque, Universite du Quebec a Rimouski
• Jan Marcin Weslawski, Institute of Oceanology, Polish Academy of Sciences
• Maria Wlodarska-Kowalczuk, Institute of Oceanology, Polish Academy of Sciences
• Baban S. Ingole, National Institute of Oceanography, India
• Brian J. Bett, National Oceanography Centre, United Kingdom
• David S. M. Billett, National Oceanography Centre, United Kingdom
• Andrew Yool, National Oceanography Centre, United Kingdom
• Bodil A. Bluhm, University of Alaska Fairbanks
• Katrin Iken, University of Alaska Fairbanks
• Bhavani E. Narayanaswamy, Scottish Marine Institute

Document Type

Article

Publication Date

12-30-2010

Abstract

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 functions of surface primary production and delivery of the particulate organic carbon (POC) flux to the seafloor. At a regional scale, the census maps illustrate that integrated biomass is highest at the poles, on continental margins associated with coastal upwelling and with broad zones associated with equatorial divergence. Lowest values are consistently encountered on the central abyssal plains of major ocean basins The shift of biomass dominance groups with depth is shown to be affected by the decrease in average body size rather than abundance, presumably due to decrease in quantity and quality of food supply. This biomass census and associated maps are vital components of mechanistic deep-sea food web models and global carbon cycling, and as such provide fundamental information that can be incorporated into evidence-based management.

Comments

Originally published in the open access journal PLoS ONE: http://www.plosone.org.

This research was supported by a grant from the Census of Marine Life (CoML) and the Sloan Foundation to G. T. Rowe and E. Escobar-Briones. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Recommended Citation

Wei C-L, Rowe GT, Escobar-Briones E, Boetius A, Soltwedel T, et al. (2010) Global Patterns and Predictions of Seafloor Biomass Using Random Forests. PLoS ONE 5(12): e15323. doi:10.1371/journal.pone.0015323

Publisher

Public Library of Science (PLoS)

Rights

© 2010 Wei et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.