Since its discovery1,2, the deep-sea glass sponge Euplectella aspergillum has attracted interest in its mechanical properties and beauty. Its skeletal system is composed of amorphous hydrated silica and is arranged in a highly regular and hierarchical cylindrical lattice that begets exceptional flexibility and resilience to damage3,4,5,6. Structural analyses dominate the literature, but hydrodynamic fields that surround and penetrate the sponge have remained largely unexplored. Here we address an unanswered question: whether, besides improving its mechanical properties, the skeletal motifs of E. aspergillum underlie the optimization of the flow physics within and beyond its body cavity. We use extreme flow simulations based on the ‘lattice Boltzmann’ method7, featuring over fifty billion grid points and spanning four spatial decades. These in silico experiments reproduce the hydrodynamic conditions on the deep-sea floor where E. aspergillum lives8,9,10. Our results indicate that the skeletal motifs reduce the overall hydrodynamic stress and support coherent internal recirculation patterns at low flow velocity. These patterns are arguably beneficial to the organism for selective filter feeding and sexual reproduction11,12. The present study reveals mechanisms of extraordinary adaptation to live in the abyss, paving the way towards further studies of this type at the intersection between fluid mechanics, organism biology and functional ecology.
Your institute does not have access to this article
Open Access articles citing this article.
Scientific Reports Open Access 17 May 2022
Subscribe to Nature+
Get immediate online access to the entire Nature family of 50+ journals
Subscribe to Journal
Get full journal access for 1 year
only $3.90 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
STL files for all of the models, raw data for the plots, and scripts to reproduce the figures are available on GitHub at https://github.com/giacomofalcucci/Euplectella_HPC. Additional data that support the findings of this study are available from the corresponding author on request.
All codes necessary to reproduce results in main paper are available on GitHub at https://github.com/giacomofalcucci/Euplectella_HPC.
Owen, R. Description of a new genus and species of sponge (Euplectella aspergillum, O.). Trans. Zool. Soc. Lond. 3, 203–215 (1849).
Report on the Scientific Results of the Voyage of H.M.S. Challenger During the Years 1873–76 — Under the Command of Captain George S. Nares, R.N., F.R.S. and Captain Frank Turle Thomson, R.N. Vol. XXI, Zoology, Plates (Neill, 1887); https://archive.org/details/reportonscientif21grea/page/n13/mode/2up (2008).
Weaver, J. C. et al. Hierarchical assembly of the siliceous skeletal lattice of the hexactinellid sponge Euplectella aspergillum. J. Struct. Biol. 158, 93–106 (2007).
Aizenberg, J. et al. Skeleton of Euplectella sp.: structural hierarchy from the nanoscale to the macroscale. Science 309, 275–278 (2005).
Monn, M. A., Weaver, J. C., Zhang, T., Aizenberg, J. & Kesari, H. New functional insights into the internal architecture of the laminated anchor spicules of Euplectella aspergillum. Proc. Natl Acad. Sci. USA 112, 4976–4981 (2015).
Fernandes, M. C., Aizenberg, J., Weaver, J. C. & Bertoldi, K. Mechanically robust lattices inspired by deep-sea sponges. Nat. Mater. 20, 237–241 (2020).
Succi, S. The Lattice Boltzmann Equation: For Complex States of Flowing Matter (Oxford Univ. Press, 2018).
Kanari, S.-I., Kobayashi, C. & Ishikawa, T. An estimate of the velocity and stress in the deep ocean bottom boundary layer. J. Fac. Sci. Hokkaido Univ. Ser. 7 Geophys. 9, 1–16 (1991).
Doron, P., Bertuccioli, L., Katz, J. & Osborn, T. R. Turbulence characteristics and dissipation estimates in the coastal ocean bottom boundary layer from PIV data. J. Phys. Oceanogr. 31, 2108–2134 (2001).
26th ITTC Specialist Committee on Uncertainty Analysis (eds). In Proc. International Towing Tank Conf. Paper 7.5-02-01-03 https://ittc.info/media/4048/75-02-01-03.pdf (ITTC, 2011).
Yahel, G., Eerkes-Medrano, D. I. & Leys, S. P. Size independent selective filtration of ultraplankton by hexactinellid glass sponges. Aquat. Microb. Ecol. 45, 181–194 (2006).
Schulze, F. E. XXIV.—On the structure and arrangement of the soft parts in Euplectella aspergillum. Earth Environ. Sci. Trans. R. Soc. Edinb. 29, 661–673 (1880).
Kitano, H. Computational systems biology. Nature 420, 206–210 (2002).
Coveney, P. V., Boon, J. P. & Succi, S. Bridging the gaps at the physics-chemistry-biology interface. Phil. Trans. R. Soc. A 374, 20160335 (2016).
Succi, S. et al. Towards exascale lattice Boltzmann computing. Comput. Fluids 181, 107–115 (2019).
Fung, Y. C. Biomechanics: Mechanical Properties of Living Tissues (Springer Science and Business Media, 2013).
Marconi100, the new accelerated system. https://www.hpc.cineca.it/hardware/marconi100 (SuperComputing Applications and Innovation, 2020).
Reitner, J. & Mehl, D. Early Paleozoic diversification of sponges; new data and evidences. Geol.-paläontol. Mitt. Innsbruck 20, 335–347 (1995).
Moore, T. J. XXVIII.—On the habitat of the Regadera (watering-pot) or Venus’s flower-basket (Euplectella aspergillum, Owen). J. Nat. Hist. 3, 196–199 (1869).
Leys, S. P., Mackie, G. O. & Reiswig, H. M. The biology of glass sponges. Adv. Mar. Biol. 52, 1–145 (2007).
Gray, J. E. LXIV.—Venus’s flower-basket (Euplectella speciosa). Ann. Mag. Nat. Hist. 18, 487–490 (1866).
Saito, T., Uchida, I. & Takeda, M. Skeletal growth of the deep-sea hexactinellid sponge Euplectella oweni, and host selection by the symbiotic shrimp Spongicola japonica (Crustacea: Decapoda: Spongicolidae). J. Zool. 258, 521–529 (2002).
Chree, C. Recent advances in our knowledge of silicon and of its relations to organised structures. Nature 81, 206–208 (1909).
Woesz, A. et al. Micromechanical properties of biological silica in skeletons of deep-sea sponges. J. Mater. Res. 21, 2068–2078 (2006).
Monn, M. A., Vijaykumar, K., Kochiyama, S. & Kesari, H. Lamellar architectures in stiff biomaterials may not always be templates for enhancing toughness in composites. Nat. Commun. 11, 373 (2020).
Nayar, K. G., Sharqawy, M. H. & Banchik, L. D. Thermophysical properties of seawater: a review and new correlations that include pressure dependence. Desalination 390, 1–24 (2016).
Vogel, S. Current-induced flow through living sponges in nature. Proc. Natl Acad. Sci. USA 74, 2069–2071 (1977).
Prandtl, L. & Tietjens, O. G. Applied Hydro- and Aeromechanics (transl. Den Hartog, J. P.) (Dover Publications, 1957).
Tritton, D. J. Physical Fluid Dynamics 2nd edn Ch. 21 (Clarendon, 1988).
Gualtieri, P., Casciola, C. M., Benzi, R., Amati, G. & Piva, R. Scaling laws and intermittency in homogeneous shear flow. Phys. Fluids 14, 583–596 (2002).
Koehl, M. A. R. How do benthic organisms withstand moving water? Am. Zool. 24, 57–70 (1984).
Yahel, G., Whitney, F., Reiswig, H. M., Eerkes-Medrano, D. I. & Leys, S. P. In situ feeding and metabolism of glass sponges (Hexactinellida, Porifera) studied in a deep temperate fjord with a remotely operated submersible. Limnol. Oceanogr. 52, 428–440 (2007).
Hunt, J. C. R., Wray, A. & Moin, P. Eddies, Stream, and Convergence Zones in Turbulent Flows. Report CTR-S88 (Center for Turbulence Research, 1988).
Haller, G. An objective definition of a vortex. J. Fluid Mech. 525, 1–26 (2005).
Krastev, V. K., Amati, G., Succi, S. & Falcucci, G. On the effects of surface corrugation on the hydrodynamic performance of cylindrical rigid structures. Eur. Phys. J. E 41, 95 (2018).
Kawamura, T., Takami, H. & Kuwahara, K. Computation of high Reynolds number flow around a circular cylinder with surface roughness. Fluid Dyn. Res. 1, 145 (1986).
Hanchi, S., Askovic, R. & Ta Phuoc, L. Numerical simulation of a flow around an impulsively started radially deforming circular cylinder. Int. J. Numer. Methods Fluids 29, 555–573 (1999).
Sahin, M. & Owens, R. G. A numerical investigation of wall effects up to high blockage ratios on two-dimensional flow past a confined circular cylinder. Phys. Fluids 16, 1305–1320 (2004).
Fujisawa, N., Tanahashi, S. & Srinivas, K. Evaluation of pressure field and fluid forces on a circular cylinder with and without rotational oscillation using velocity data from PIV measurement. Meas. Sci. Technol. 16, 989–996 (2005).
Henderson, R. D. Detail of the drag curve near the onset of vortex shedding. Phys. Fluids 7, 2102–2104 (1995).
Posdziech, O. & Grundmann, R. Numerical simulation of the flow around an infinitely long circular cylinder in the transition regime. Theor. Comput. Fluid Dyn. 15, 121–141 (2001).
Succi, S. The Lattice Boltzmann Equation: For Fluid Dynamics and Beyond (Oxford Univ. Press, 2001).
Norberg, C. An experimental investigation of the flow around a circular cylinder: influence of aspect ratio. J. Fluid Mech. 258, 287–316 (1994).
Krüger, T. et al. The Lattice Boltzmann Method (Springer International, 2017).
Montessori, A. & Falcucci, G. Lattice Boltzmann Modeling of Complex Flows for Engineering Applications (Morgan and Claypool, 2018).
Falcucci, G. et al. Lattice Boltzmann methods for multiphase flow simulations across scales. Commun. Comput. Phys. 9, 269–296 (2011).
Johnson, R. W. (ed.) Handbook of Fluid Dynamics (CRC, 2016).
G.F. acknowledges CINECA computational grant ISCRA-B IsB17–SPONGES, no. HP10B9ZOKQ and, partially, the support of PRIN projects CUP E82F16003010006 (principal investigator, G.F. for the Tor Vergata Research Unit) and CUP E84I19001020006 (principal investigator, G. Bella). G.P. acknowledges the support of the Forrest Research Foundation, under a postdoctoral research fellowship. M.P. acknowledges the support of the National Science Foundation under grant no. CMMI 1901697. S.S. acknowledges financial support from the European Research Council under the Horizon 2020 Programme advanced grant agreement no. 739964 (‘COPMAT’). G.F. and S.S. acknowledge K. Bertoldi, M. C. Fernandes and J. C. Weaver (Harvard University) for introducing them to E. aspergillum and for early discussions on the subject. A. L. Facci (Tuscia University) is acknowledged for discussions on graphics realization. M. Bernaschi (IAC-CNR) is acknowledged for discussions on extreme computing. V. Villani is acknowledged for his support with Japanese language and culture. E. Kaxiras (Harvard University) is acknowledged for early discussions that proved fruitful for the development of the present code.
The authors declare no competing interests.
Peer review information Nature thanks Carlo Massimo Casciola and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
The grid resolution within the small fenestrae of E. aspergillum models is 5.33 lattice spacings.
a, Tilted view of main text Fig. 1c, detailing the flow field downstream and within the body cavity of the complete model of E. aspergillum at Re = 2,000. Colour intensity indicates the helicity magnitude, and the streak lines are coloured according to the velocity magnitude. b, Stereo view of a.
a, Visualization of the vorticity magnitude, complementing Extended Data Fig. 2a, such that colour intensity indicates the helicity magnitude, and the streak lines are coloured in green, based on the vorticity magnitude. b, Stereo view of a.
a–i, Details of the nine variations of the hollow cylindrical lattice with helical ridges (P2), obtained by including random defects simulating wounds and scars. The nine morphological manipulations are identified as Mark01, Mark02, …, Mark09.
a–e, Comparison between the vorticity magnitudes (colour scale) for the plain cylinder (S1, left panels) and for the hollow cylindrical lattice with helical ridges (P2, right panels) at statistical steady states, for all Re simulated in the present work. Panels a–e show data for Re = 100, 500, 1,000, 1,500 and 2,000, respectively.
Zoomed-out view of main text Fig. 3c, with error bars identifying the range of predicted values of the drag coefficient CD due to random morphological manipulations. These variations lead to a modest decrease, from 2.5% to 3.5% in the drag coefficient with respect to the pristine model.
The model is reconstructed according to ref. 3: left, side view; right, AA and BB cross-sections from the left panel, detailing the osculum and the body cavity, respectively.
a, Front (centre panel) and side (leftmost and rightmost) views of the complete model of E. aspergillum; b, stereo views of the complete model of E. aspergillum realized with the Anaglyph algorithm.
Left, time trace at statistical steady state of the lift coefficient CL for the different models (see key at right) of E. aspergillum at Re = 2,000. The range of the oscillations in the porous models is two orders of magnitude less than that of the plain cylinder S1. Right, magnified view of boxed region.
This zipped file contains the STL geometry of the complete E. aspergullum, as well as the STL files to realize models S2, P1 and P2. The simple cylinder model S1 is not provided.
The Video shows the vorticity field generated by S1 (left) and P2 (right) models for Re=100, 500, 1,000, 1,500 and 2,000. The video highlights the formation of a nearly quiescent region downstream the porous model, as well as the vortical patterns within the body cavity.
The video shows the vorticity field generated by S1 (top left), P1 (top right), S2 (bottom left) and P2 (bottom right) at Re=2,000. The two porous models are characterised by a nearly quiescent region extending several diameters downstream the structure.
The video highlights the vorticity field downstream S1 (top) and P2 (bottom) models, as well as within the body cavity of P2.
About this article
Cite this article
Falcucci, G., Amati, G., Fanelli, P. et al. Extreme flow simulations reveal skeletal adaptations of deep-sea sponges. Nature 595, 537–541 (2021). https://doi.org/10.1038/s41586-021-03658-1
Scientific Reports (2022)
Nanosheet-coated synthetic wood with enhanced flame-retardancy by vacuum-assisted sonocoating technique
Nano Research (2022)
Planktos: An Agent-Based Modeling Framework for Small Organism Movement and Dispersal in a Fluid Environment with Immersed Structures
Bulletin of Mathematical Biology (2022)