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Intense upper ocean mixing due to large aggregations of spawning fish

Abstract

Small-scale turbulent mixing plays a pivotal role in shaping ocean circulation and a broad range of physical and biogeochemical processes. Despite advances in our understanding of the geophysical processes responsible for this mixing, the nature and importance of biomixing—turbulent mixing caused by marine biota—are still debated. A major source of uncertainty pertains to the efficiency of biomixing (the fraction of the turbulent energy produced through swimming that is spent in mixing the ocean vertically), which the few in situ observations available suggest to be much lower than that of geophysical turbulence. Here we shed light on this problem by analysing 14 days of continuous measurements of centimetre-scale turbulence in an area of coastal upwelling. We show that turbulent dissipation is elevated 10- to 100-fold (reaching 10−6–10−5 W kg−1) every night of the survey due to the swimming activity of large aggregations of anchovies that gather regularly over the spawning season. Turbulent mixing is invigorated concurrently with dissipation, and occurs with an efficiency comparable to that of geophysical turbulence. Our results demonstrate that biologically driven turbulence can be a highly effective mixing agent, and call for a re-examination of its impacts on productive upper ocean regions.

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Fig. 1: Hydrography, turbulence and mixing during the REMEDIOS survey.
Fig. 2: Anchovy egg concentrations.
Fig. 3: Mixing efficiency.
Fig. 4: Schematic of the onset of efficient biomixing.

Data availability

The data that support the findings of this study are available via Zenodo at https://doi.org/10.5281/zenodo.5559023.

Code availability

The scripts used for microstructure data processing are freely available via GitHub at https://github.com/bieitofernandez/MSS_processing.

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Acknowledgements

Funding for this work was provided by the Spanish Ministry of Economy and Innovation under the research project REMEDIOS (grant number CTM2016-75451-C2-1-R) to B.M.-C. B.F.C. was supported by the Spanish Ministry of Economy and Innovation through a Juan de La Cierva-Formación postdoctoral fellowship (grant number FJCI-641 2015-25712) and by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement number 834330 (SO-CUP). E.B. was supported by a postgraduate fellowship (grant number ED481A-2019/288) from Xunta de Galicia, co-funded by FSE Galicia. A.C. was supported by a postgraduate fellowship FPI (grant number BES-2017-080935) from the Spanish Ministry of Economy and Competitiveness. A.C.N.G. acknowledges the support of the Royal Society and the Wolfson Foundation. We thank all the participants in the REMEDIOS cruise, particularly the crew of the RV Ramón Margalef for their support, and P. Rial, I. Ramilo and M. Villamaña for their contribution to data collection. We are also thankful to G. Casas for his assistance in counting and staging anchovy eggs. We are especially grateful to P. Chouciño for her logistical support during the cruise and assistance with microstructure data processing. S. Piccoloraz and Ó. Sepúlveda Steiner contributed to the development of the microstructure processing functions. C. Ofelio designed the illustration in Fig. 4.

Author information

Authors and Affiliations

Authors

Contributions

B.F.C. and B.M.-C. conceived the study. B.M.-C. led the cruise. B.F.C., E.N., M.G., E.B., A.C. and B.M.-C. participated in the data collection. B.F.C., M.P., E.N. and M.G. analysed the data. All the authors contributed to the scientific discussions and interpretation of the results. B.F.C., B.M.-C. and A.C.N.G. wrote the manuscript with contributions from all the co-authors.

Corresponding author

Correspondence to Bieito Fernández Castro.

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Nature Geoscience thanks Jonathan Nash, Hidekatsu Yamazaki and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Simon Harold, Kyle Frischkorn and James Super, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Location of survey.

Map of the location of the REMEDIOS sampling station P2-Bueu (red star, 42.357°N, 8.773°W, mean depth 30 m) in the Ría de Pontevedra (off the Galician coast, NW Iberian Peninsula). The location of the closest Meteogalicia (www.meteogalicia.gal) meteorological station (yellow dot, Cape Udra, 42.340°N, -8.884°E) is also shown.

Extended Data Fig. 2 Hydrographic setting.

Hourly mean time series of a salinity, b de-tided eastward velocity (u), c squared buoyancy frequency (N2), and d squared vertical shear of horizontal velocity (sh2) during the three sampling periods (I01, I02 and I03). Gray shading indicates night-time periods of enhanced biophysical turbulence. These periods were determined by inspection of the turbulent dissipation rate and volume backscattering strength records. De-tided residual velocity was calculated with a 24/25/24 h Godin filter. Positive eastward velocity imports offshore waters into the Ría, and negative westward velocity exports onshore waters out of the Ría. Note the use of logarithmic color scale in panels c and d.

Extended Data Fig. 3 Sources of turbulence.

Depth-averaged (10–25 m) ε vs. a depth-averaged Rig and b 38 KHz volume backscattering strength (Sv). ε median values in bins of Rig and Sv38kHz are indicated as larger circles. Linear fits in logarithmic scale and Spearman correlation coefficients are shown. The dot color scale represents Sv38kHz and Rig in panels a and b, respectively.

Extended Data Fig. 4 Acoustic backscatter frequency response.

Three examples of night-time echograms at 18 KHz, recorded during sampling periods I01 (a, 4 July), I02 (b, 8 July) and I03 (c, 12 July). Panels d-f show the mean frequency response (Sv at each frequency minus Sv at 38 kHz) for the region enclosed by the orange rectangles in panels a-c.

Extended Data Fig. 5 Microstructure spectra.

Randomly selected wavenumber (kz, units: cycles per meter, cpm) spectra of vertical shear (a-d) and temperature gradient (e, f) microstructure between 10 and 25 m depth, during the third sampling period (I03). Periods dominated by geophysical turbulence are shown in the left column, and those dominated by biophysical turbulence (gray shading in Figure 1), in the right column. The corresponding universal spectra are indicated by dotted colored lines, and the computed dissipation rates of turbulent kinetic energy (ε) and thermal variance (χ) are reported. Spectra recorded with the two shear sensors over the same portion of the water column are shown a, b and c, d, respectively. Empirical spectra of thermistor noise are represented by the gray dotted line e,f.

Extended Data Fig. 6 Turbulence and mixing parameters.

Time series of hourly mean a rate of dissipation of thermal variance (χ), b Thorpe scale (LT), c buoyancy Reynolds number (Reb), d turbulent Reynolds number (ReT), d turbulent Froude number (FrT), and f flux Richardson number (Rf, a proxi for mixing efficiency) during the three sampling periods (I01, I02 and I03). Gray shading indicates night-time periods of enhanced biophysical turbulence. Note the use of a logarithmic color scale in all panels.

Extended Data Fig. 7 ADCP backscatter.

Time series of volume backscattering strength (Sv, dB) measured with a 300 KHz bottom-moored ADCP. Nights and biomixing events during the sampling periods (I01, I02 and I03) are indicated with black and gray shading, respectively. The y-axis coordinate is meters above bottom (mab).

Extended Data Table 1 Mean turbulent properties
Extended Data Table 2 Anchovy development stages

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Supplementary Figs. 1–3.

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Fernández Castro, B., Peña, M., Nogueira, E. et al. Intense upper ocean mixing due to large aggregations of spawning fish. Nat. Geosci. 15, 287–292 (2022). https://doi.org/10.1038/s41561-022-00916-3

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