Literature DB >> 27529186

An Integrated Experimental and Modeling Approach to Predict Sediment Mixing from Benthic Burrowing Behavior.

Kevin R Roche1, Antoine F Aubeneau2, Minwei Xie1, Tomás Aquino3, Diogo Bolster3, Aaron I Packman1.   

Abstract

Bioturbation is the dominant mode of sediment transport in many aquatic environments and strongly influences both sediment biogeochemistry and contaminant fate. Available bioturbation models rely on highly simplified biodiffusion formulations that inadequately capture the behavior of many benthic organisms. We present a novel experimental and modeling approach that uses time-lapse imagery to directly relate burrow formation to resulting sediment mixing. We paired white-light imaging of burrow formation with fluorescence imaging of tracer particle redistribution by the oligochaete Lumbriculus variegatus. We used the observed burrow formation statistics and organism density to parametrize a parsimonious model for sediment mixing based on fundamental random walk theory. Worms burrowed over a range of times and depths, resulting in homogenization of sediments near the sediment-water interface, rapid nonlocal transport of tracer particles to deep sediments, and large areas of unperturbed sediments. Our fundamental, parsimonious random walk model captures the central features of this highly heterogeneous sediment bioturbation, including evolution of the sediment-water interface coupled with rapid near-surface mixing and anomalous late-time mixing resulting from infrequent, deep burrowing events. This approach provides a general, transferable framework for explicitly linking sediment transport to governing biophysical processes.

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Year:  2016        PMID: 27529186     DOI: 10.1021/acs.est.6b01704

Source DB:  PubMed          Journal:  Environ Sci Technol        ISSN: 0013-936X            Impact factor:   9.028


  2 in total

1.  Long-term prediction of [Formula: see text]Cs in Lake Onuma on Mt. Akagi after the Fukushima accident using fractional diffusion model.

Authors:  Eiichi Suetomi; Yuko Hatano; Masakiyo Fujita; Yukiko Okada; Kyuma Suzuki; Shun Watanabe
Journal:  Sci Rep       Date:  2021-10-13       Impact factor: 4.379

2.  A Process-Based Model for Bioturbation-Induced Mixing.

Authors:  Tomás Aquino; Kevin R Roche; Antoine Aubeneau; Aaron I Packman; Diogo Bolster
Journal:  Sci Rep       Date:  2017-10-27       Impact factor: 4.379

  2 in total

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