Marine phytoplankton inhabit a dynamic environment where turbulence, together with nutrient and light availability, shapes species fitness, succession and selection1,2. Many species of phytoplankton are motile and undertake diel vertical migrations to gain access to nutrient-rich deeper layers at night and well-lit surface waters during the day3,4. Disruption of this migratory strategy by turbulence is considered to be an important cause of the succession between motile and non-motile species when conditions turn turbulent1,5,6. However, this classical view neglects the possibility that motile species may actively respond to turbulent cues to avoid layers of strong turbulence7. Here we report that phytoplankton, including raphidophytes and dinoflagellates, can actively diversify their migratory strategy in response to hydrodynamic cues characteristic of overturning by Kolmogorov-scale eddies. Upon experiencing repeated overturning with timescales and statistics representative of ocean turbulence, an upward-swimming population rapidly (5–60 min) splits into two subpopulations, one swimming upward and one swimming downward. Quantitative morphological analysis of the harmful-algal-bloom-forming raphidophyte Heterosigma akashiwo together with a model of cell mechanics revealed that this behaviour was accompanied by a modulation of the cells’ fore–aft asymmetry. The minute magnitude of the required modulation, sufficient to invert the preferential swimming direction of the cells, highlights the advanced level of control that phytoplankton can exert on their migratory behaviour. Together with observations of enhanced cellular stress after overturning and the typically deleterious effects of strong turbulence on motile phytoplankton5,8, these results point to an active adaptation of H. akashiwo to increase the chance of evading turbulent layers by diversifying the direction of migration within the population, in a manner suggestive of evolutionary bet-hedging. This migratory behaviour relaxes the boundaries between the fluid dynamic niches of motile and non-motile phytoplankton, and highlights that rapid responses to hydrodynamic cues are important survival strategies for phytoplankton in the ocean.
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Margalef, R. Life-forms of phytoplankton as survival alternatives in an unstable environment. Oceanol. Acta 1, 493–509 (1978)
Smayda, T. J. & Reynolds, C. S. Community assembly in marine phytoplankton: application of recent models to harmful dinoflagellate blooms. J. Plankton Res. 23, 447–461 (2001)
Bollens, S. M., Rollwagen-Bollens, G., Quenette, J. A. & Bochdansky, A. B. Cascading migrations and implications for vertical fluxes in pelagic ecosystems. J. Plankton Res. 33, 349–355 (2011)
Schuech, R. & Menden-Deuer, S. Going ballistic in the plankton: anisotropic swimming behavior of marine protists. Limnol. Oceanogr. Fluids Environ. 4, 1–16 (2014)
Sullivan, J. M., Swift, E., Donaghay, P. L. & Rines, J. E. B. Small-scale turbulence affects the division rate and morphology of two red-tide dinoflagellates. Harmful Algae 2, 183–199 (2003)
Berdalet, E. et al. Species-specific physiological response of dinoflagellates to quantified small-scale turbulence. J. Phycol. 43, 965–977 (2007)
Lozovatsky, I., Lee, J., Fernando, H. J. S., Kang, S. K. & Jinadasa, S. U. P. Turbulence in the East China Sea: the summertime stratification. J. Geophys. Res. Oceans. 120, 1856–1871 (2015)
Thomas, W. H. & Gibson, C. H. Effects of small-scale turbulence on microalgae. J. Appl. Phycol. 2, 71–77 (1990)
Sverdrup, H. On conditions for the vernal blooming of phytoplankton. J. Cons. Perm. Int. Explor. Mer 18, 287–295 (1953)
Barton, A. D., Dutkiewicz, S., Flierl, G., Bragg, J. & Follows, M. J. Patterns of diversity in marine phytoplankton. Science 327, 1509–1511 (2010)
Thomas, M. K., Kremer, C. T., Klausmeier, C. A. & Litchman, E. A global pattern of thermal adaptation in marine phytoplankton. Science 338, 1085–1088 (2012)
Brun, P. et al. Ecological niches of open ocean phytoplankton taxa. Limnol. Oceanogr. 60, 1020–1038 (2015)
Litchman, E. & Klausmeier, C. A. Trait-based community ecology of phytoplankton. Annu. Rev. Ecol. Evol. Syst. 39, 615–639 (2008)
Villareal, T. A. & Carpenter, E. J. Buoyancy regulation and the potential for vertical migration in the oceanic cyanobacterium Trichodesmium. Microb. Ecol. 45, 1–10 (2003)
Guasto, J. S., Rusconi, R. & Stocker, R. Fluid mechanics of planktonic microorganisms. Annu. Rev. Fluid Mech. 44, 373–400 (2012)
Franks, P. J. S. Has Sverdrup’s critical depth hypothesis been tested? Mixed layers vs. turbulence layers. J. Mar. Sci. 72, 1–11 (2015)
Zirbel, M. J., Veron, F. & Latz, M. I. The reversible effect of flow on the morphology of Ceratocorys horrida. J. Phycol. 58, 46–58 (2000)
Roberts, A. M. Geotaxis in motile micro-organisms. J. Exp. Biol. 53, 687–699 (1970)
Durham, W. M., Kessler, J. O. & Stocker, R. Disruption of vertical motility by shear triggers formation of thin phytoplankton layers. Science 323, 1067–1070 (2009)
Durham, W. M. et al. Turbulence drives microscale patches of motile phytoplankton. Nature Commun. 4, 2148 (2013)
Yamasaki, Y. et al. Extracellular polysaccharide–protein complexes of a harmful alga mediate the allelopathic control it exerts within the phytoplankton community. ISME J. 3, 808–817 (2009)
Thorpe, S. A. An Introduction to Ocean Turbulence (Cambridge Univ. Press, 2007)
Hara, Y. & Chihara, M. Morphology, ultrastructure and taxonomy of the raphidophycean alga Heterosigma akashiwo. Bot. Mag. 100, 151–163 (1987)
Smayda, T. J. Adaptations and selection of harmful and other dinoflagellate species in upwelling systems.1. Morphology and adaptive polymorphism. Prog. Oceanogr. 85, 71–91 (2010)
Hemmersbach, R., Volkmann, D. & Hader, D. P. Graviorientation in protists and plants. J. Plant Physiol. 154, 1–15 (1999)
Bulmer, M. G. Selection for iteroparity in a variable environment. Am. Nat. 126, 63–71 (1985)
Moxon, E. R., Rainey, P. B., Nowak, M. A. & Lenski, R. E. Adaptive evolution of highly mutable loci in pathogenic bacteria. Curr. Biol. 4, 24–33 (1994)
Ackermann, M. A functional perspective on phenotypic heterogeneity in microorganisms. Nature Rev. Microbiol. 13, 497–508 (2015)
Harvey, E. L., Menden-Deuer, S. & Rynearson, T. A. Persistent intra-specific variation in genetic and behavioral traits in the raphidophyte, Heterosigma akashiwo. Front. Microbiol. 6, 1277 (2015)
Foster, K. W. & Smyth, R. D. Light Antennas in phototactic algae. Microbiol. Rev. 44, 572–630 (1980)
Ouellette, N. T., Xu, H. & Bodenschatz, E. A quantitative study of three-dimensional Lagrangian particle tracking algorithms. Exp. Fluids 40, 301–313 (2006)
Gurarie, E., Grünbaum, D. & Nishizaki, M. T. Estimating 3D movements from 2D observations using a continuous model of helical swimming. Bull. Math. Biol. 73, 1358–1377 (2011)
Roberts, A. M. & Deacon, F. M. Gravitaxis in motile micro-organisms: the role of fore–aft body asymmetry. J. Fluid Mech. 452, 405–423 (2002)
Wada, M., Miyazaki, A. & Fujii, T. On the mechanisms of diurnal vertical migration behavior of Heterosigma akashiwo (Raphidophyceae). Plant Cell Physiol. 26, 431–436 (1985)
Milo, R. & Phillips, R. Cell Biology by the Numbers (Garland Science, Taylor & Francis Group, 2016)
Happel, J. & Brenner, H. Low Reynolds Number Hydrodynamics: With Special Application to Particulate Media (Mechanics of Fluids and Transport Processes) Vol. 1 (Englewood Cliffs, 1983)
Koenig, S. H. Brownian motion of an ellipsoid. A correction to Perrin’s results. Biopolymers 14, 2421–2423 (1975)
Bouchard, J. N. & Yamasaki, H. Heat stress stimulates nitric oxide production in Symbiodinium microadriaticum: a possible linkage between nitric oxide and the coral bleaching phenomenon. Plant Cell Physiol. 49, 641–652 (2008)
Vardi, A. et al. A diatom gene regulating nitric-oxide signaling and susceptibility to diatom-derived aldehydes. Curr. Biol. 18, 895–899 (2008)
We thank M. Ackermann for input on bet-hedging, G. Boffetta and M. Cencini for sharing the direct numerical simulations data, S. Menden-Deuer for providing H. akashiwo CCMP3107 and for discussions, E. Berdalet, A. Vardi, D. Anderson and D. J. McGillicuddy for suggestions, and M. Barry for initial development of the flip chamber. This work was partially supported by a Human Frontier Science Program Cross Disciplinary Fellowship (LT000993/2014-C to A.S.), a Swiss National Science Foundation Early Postdoc Mobility Fellowship (to F.C.), and a Gordon and Betty Moore Marine Microbial Initiative Investigator Award (GBMF 3783 to R.S.).
The authors declare no competing financial interests.
Reviewer Information Nature thanks M. J. Behrenfeld, S. Menden-Deuer and T. Pedley for their contribution to the peer review of this work.
Extended data figures and tables
a–d, Vertical distribution is reported for a population grown under a diel light cycle (a), for regrown cells (b), for a monoclonal population (c) and for a starved population (d). a, Upward bias for exponential-phase HA452 cells grown under a diel light cycle (14 h light: 10 h dark), showing the characteristic split after 100 flips (blue, n = 844 cells; red curve is control, n = 910). A similar split was observed for cells cultured under constant illumination (Fig. 1g). b, Cells regrown from those collected from the bottom of the chamber after 100 flips. Although cells collected from the bottom were positively gravitactic (swimming downwards; blue curve in a), cells regrown from these are negatively gravitactic (swimming upwards; pink curve in b, n = 411). Upon exposure to 100 flips, these regrown cells again exhibited the population split (cyan curve, n = 391). a, b, The solid lines represent the mean of the equilibrium vertical distribution over time (a: mean of 92 frames; b: mean of 78 frames), and the shaded regions represent ± 1 s.d. from the mean. In the insets, the error bars of the upward bias, r, are extracted from the cells’ vertical distribution. c, The population split also occurs in a monoclonal population of HA452. The inset shows the upward bias (mean ± s.d. of three replicates) calculated from the relative distribution of the cells at equilibrium, after being exposed to 100 flips (blue, total number of cells n = 2,985) and for the control (red, n = 2,490). The star indicates statistical significance in the difference between treatment and control (two-sided t-test, t4 = 4.79, P = 0.009). d, A nutrient-starved HA452 population does not split upon flipping. Nutrient-starved cells were harvested at stationary phase (350 h after inoculation). The inset shows the upward bias (mean ± s.d. of three replicates, two-sided t-test, t4 = 0.91, P = 0.42) of the cells after being exposed to 100 flips (blue, n = 743) and for the control (red, n = 1,011). c, d, The solid lines represent the mean of the equilibrium vertical distribution over three replicates, and the shaded regions represent ± 1 s.d. from the mean. Source data
a, The relative distribution of swimming speeds, obtained by image analysis of cells in the flipping chamber, showing no difference in the absolute swimming speed of the two subpopulations, HA452(↑): v = 74.5 ± 42.4 μm s−1 (n = 1,780 cells); and HA452(↓): v = 73.8 ± 46.2 μm s−1 (n = 992). b, Distribution of the vertical component of the swimming velocity in HA452(↑) and HA452(↓), showing distinct peaks in opposite directions, at approximately ±50 μm s−1, and corresponding to upward and downward swimming, respectively. c, Distribution of the horizontal component of the swimming velocity in HA452(↑) and HA452(↓), showing no appreciable difference between the two subpopulations. In all panels, speeds were obtained by tracking cells for 15 s just after a single additional flip following the 100 flips. Here, trajectories in the top 1 mm of the chamber were assigned to HA452(↑) and trajectories in the bottom 1 mm were assigned to HA452(↓). For each subpopulation, velocities were averaged over all trajectories. d, e, The joint distribution of the swimming velocity in the vertical and horizontal directions for HA452(↑) (d) and HA452(↓) (e). The colour scale indicates the relative distribution of cell trajectories counted over the 15 s movie normalized by the maximum in each subpopulation. Source data
Extended Data Figure 3 Additional flipping experiments with a range of raphidophyte and dinoflagellate species revealed that rapid behavioural responses in phytoplankton to flipping are not restricted to HA452.
a, b, Vertical distribution of Chattonella marina_cf CM2962 (a) and Prorocentrum minimum PM291 (b), both showing a split similar to that of HA452. Solid lines represent the mean of the equilibrium vertical distribution over four and three replicates respectively. Shaded regions represent ± 1 s.d. from the mean. The insets show the upward bias, r, after 300 flips (blue) and for the control, consisting of the same time in the chamber without flipping (red, mean ± s.d.). The star indicates statistical significance (two-sided t-test) between the two treatments (CM2962: t6 = 3.66, P = 0.01; PM291: t4 = 2.85, P = 0.04). c, d, Upward bias index, r (mean ± s.d.), for seven raphidophyte strains (c) and ten dinoflagellate strains (d). The error bars of the upward bias, r, are extracted from the cells’ vertical distribution. Full names of strains are provided in Extended Data Table 1. The number of cells analysed for each case is given in Extended Data Table 1. Many of these strains showed a moderate to strong response to flipping, as shown by the change in their upward bias between treatment and control. Source data
Extended Data Figure 4 Quantification of the shape and nucleus position of H. akashiwo cells based on single-cell microscopy, for subpopulations HA452(↑), and HA452(↓), as well as strain HA3107.
a–c, The top row in each panel shows micrographs obtained by epifluorescence microscopy (Methods), of HA452 cells harvested from the top (HA452(↑), n = 13) (a) and bottom (HA452(↓), n = 10) (b) of the millifluidic chamber after 100 flips, and of HA3107 (n = 12) (c). The cell itself was visualized using an inverted microscope (Nikon TE2000) in phase contrast, equipped with a ×20 or ×40 objective and an Andor iXon Ultra 897 camera. Prior to imaging, cells were stained with SYTO 9 (Methods) to visualize the nucleus through fluorescence microscopy (central bright spot). Image analysis was used to extract the contour of each cell and the position of its nucleus (middle rows). Experimentally obtained cell contours (black) were fitted with a three-parameter curve (equation (1); red) (bottom rows). Single-cell parameters associated with these fits are given in Extended Data Table 2 and Supplementary Table 2. d, Quantification of the axial symmetry of HA452 cells from single-cell microscopy. The top row shows micrographs obtained by epifluorescence microscopy for ten randomly chosen cells after flipping. Image analysis was used to extract the contour of each cell and the position of its nucleus (middle row). Experimentally obtained cell contours (black) were fitted with an ellipse with major and minor semi-axis bx, by (red) and a circle of radius req (blue). The degree of axial asymmetry, quantified as R = bx/by = 1.08 ± 0.06 (mean ± s.d., n = 10), was very close to that of a circle (R = 1), showing that cells were very close to axially symmetric. The offset of the position of the nucleus compared to the centre of the circle in the plane perpendicular to the major axis was LNb = 0.25 ± 0.26 μm (mean ± s.d.).
a, The graph shows cell shape variation in terms of the degree of fore–aft asymmetry and minor/major axis ratio (see equation (1)). The parameter c denotes the degree of fore–aft asymmetry, a is the semi-major axis, b is the semi-minor axis. We highlighted the average contours (see Extended Data Table 2) for the subpopulation of downward swimmers (HA452(↓), blue), the subpopulation of upward swimmers (HA452(↑), orange) and HA3107 (green). Values of a, b and c are given in Extended Data Table 2 and Supplementary Table 2. b, Epifluorescence micrograph showing the chloroplasts. c, Three-dimensional schematic of an HA452 cell used to compute the contribution of the chloroplasts to the offset of the centre of mass relative to the contribution of the nucleus. The large sphere represents the nucleus (density ρN = 1,300 kg m−3, radius sN = 2.5 μm) and the 20 small spheres represent the chloroplasts (density ρchlo = 1,150 kg m−3, radius rchlo = 0.75 μm), which for the purpose of computing the contribution to the centre of mass were taken to be randomly distributed adjacent to the cell membrane. The contribution of the chloroplasts to the offset of the centre of mass from the centre of buoyancy was found to be <4% of the contribution of the nucleus and was thus neglected in the stability analysis.
Two physical features—summarized by two morphological length scales—determine cell stability: the asymmetry in shape, quantified by LH/a, and the mass distribution, quantified by LW/a, where a is the semi-major axis, LH quantifies the distance between the centre of buoyancy and the centre of hydrodynamic stress, and LW the distance between the centre of buoyancy and the centre of mass (Fig. 3). Colours denote the cell rotation rate ω following an orientational perturbation (equation (4): ω > 0 denotes negatively gravitactic cells (stable upward), ω < 0 denotes positively gravitactic cells (stable downward), and ω = 0 (white dashed line) denotes neutrally stable cells. Sample asymmetry configurations corresponding to different locations on the regime diagram are illustrated by the black-and-white schematics. Filled circles denote experimental data (see Extended Data Table 2). The morphological adaptation of HA452 cells in response to overturning causes the population stability to switch (red arrow crossing the white dashed line). The original upward swimming population splits into a subpopulation swimming downward HA452(↓) and a subpopulation swimming upward, HA452(↑). Source data
akashiwo . a, Rotation rate, ω, of HA452 cells before the overturning treatment, as a function of the direction, θ, of the instantaneous swimming velocity, v, relative to the vertical. The rotation rate of the cells (n = 2,257) was quantified by tracking them in the time intervals 0–5 s (grey) and 5–15 s (magenta) directly after a single flip, and averaged over all the cells as a function of θ. The difference between the two curves denotes the presence of cells that reorient more rapidly and others that reorient more slowly. Dashed lines are sinusoidal fits to the experimental data, used to obtain the reorientation timescale B. Solid lines (colour-coded) denote the arithmetic mean over all cell trajectories. The shaded region denotes ± 1 s.e.m. The reorientation timescales obtained from these data are B = 7.2 s for the first 5 s and B = 12.2 s for the following 10 s, denoting a nearly twofold higher stability for cells that were observed reorienting in the first 5 s. b, Rotation rate, ω, of HA3107 cells before the overturning treatment, as a function of the swimming direction, θ. The rotation rate was quantified by tracking cells for 15 s directly after a single flip and averaged over all cells as a function of θ (n = 1,283). The dashed line is a sinusoidal fit to the data used to obtain B. The solid line denotes the arithmetic mean over all cell trajectories. The shaded region denotes ± 1 s.e.m. The reorientation timescale obtained for HA3017 from these data was B = 4.9 s. c, Distribution of swimming orientation of HA452 cells before the overturning treatment (same data as in a). The distribution was quantified by tracking cells in the time intervals 0–5 s (black), 5–10 s (green), 10–15 s (cyan), and 15–20 s (blue) directly after a single flip, and averaged over all cells as a function of θ. Note that after 15 s the distribution does not appreciably change. d, Time series of the vertical distribution of HA452 following a 100-flip treatment (period Q = 18 s). The cell distribution inside the chamber was tracked after the end of the overturning treatment, with time zero corresponding to the termination of the treatment (between 461 and 592 cells are included in each vertical profile). At t = 1 s (blue) the cell distribution is homogeneous because the cells have been continuously flipped for 30 min and 1 s is not long enough to allow cells to reach their equilibrium profile. To traverse the chamber (4 mm), it would take 80 s for cells swimming with a vertical speed of 50 μm s−1 (Extended Data Fig. 2). In fact, it takes 90 s (orange) to establish the bimodal distribution at equilibrium, corresponding to the population split induced by overturning. The population split is then maintained for at least 7 h (black). The upward bias shown in Figs 1 and 2 and in Extended Data Figs 1 and 3 is always computed 30 min after the overturning ceases. e, Effect of the torque generated by the offset LNb of the nucleus within the equatorial plane, obtained from the cell mechanics model, shown in terms of its effect on the dependence of the rotation rate on the body axis angle for the upward-swimming subpopulation HA452(↑). The dashed red line denotes the case without offset (LNb = 0), the purple and pink lines represent the cases in which the nucleus is offset by LNb = +0.25 μm and by LNb = −0.25 μm, respectively (the average offset measured experimentally; see Extended Data Fig. 4d), and the dark green and light green lines represent the cases in which the offset corresponds to mean + s.d. of the experimentally measured values, that is, LNb = +(0.25 + 0.26) = +0.51 μm and LNb = −(0.25 + 0.26) = −0.51 μm. Note that the overall upward stability of the cells remains unchanged when one accounts for the effect of LNb, since the stable points for all the cases (coloured dots) always occur for a swimming orientation θ that is smaller than ± π/2 (dashed vertical lines), which separates upward and downward swimming (θ = ±28° for LNb = ±0.25 μm; θ = ±35° for LNb = ±0.51 μm. Note that the results are symmetric around the vertical direction, θ = 0°). f, Stability analysis demonstrating that two assumptions made in our calculations have negligible consequences, in particular the assumptions that (1) the angle α between the body axis and the direction of motion is zero (compare orange and red lines), and (2) the drag on the fore–aft asymmetric upward swimmers can be approximated by the drag on a spheroid (compare red and pink lines). Shown is the rotation rate as a function of body axis angle for three cases: a spheroidal cell in which the major axis is aligned with the direction of motion (α = 0, orange), a spheroidal cell in which the misalignment between major axis and direction of motion is accounted for (α = 4°, red), and a fore–aft asymmetric cell in which the misalignment between major axis and direction of motion is accounted for (α = 4°, pink). Parameters were taken from Extended Data Table 2 (first row), for the upward-swimming cells. The fore–aft asymmetric case was simulated with Comsol Multiphysics. Note that the cell stability is the same in all three cases, as evidenced from the fact that the curve has a stable point at a swimming angle of θ = 0 and a negative minimum at θ = π/2, which together imply upward stability. Throughout our analyses, we have thus adopted the spheroidal approximation for the calculation of the drag, and taken into account the contribution to the cell stability by the angle α. Source data
Extended Data Figure 8 The growth curve of HA452 and the upward bias, r, of cells over the course of a day.
a, To obtain the growth curve, we sampled cells from the original culture (see Methods) at the specified time points (t = 0, 36, 68, 90, 114, 126, 140, 164, 288, 360 h). Cells were counted by imaging them inside the flip chamber, in the middle plane. Red dots represent the mean number of cells over time (between 76 and 93 frames were analysed for each data point). Error bars represent ± 1 s.d. from the mean. The growth curve of HA452 is shown in linear and semi-log scale (inset). The population density at carrying capacity was 3 × 105 cells ml−1, reached after ~2 weeks of incubation (= 360 h). The population’s intrinsic growth rate was found to be 0.4 day−1, as measured by fitting a logistic curve to the data (black line). In the inset, the shaded orange region shows the growth stage at which cells were harvested for experiments with exponential-phase cells (most experiments), while the shaded magenta region denotes the growth stage at which cells were harvested for experiments with starved cells. b, Upward bias, r, of HA452 cells over the course of a day, with time measured from midnight. For each data point the equilibrium vertical distribution was measured 30 min after termination of the overturning treatment, for both 10 flips (n = 560 cells) and 100 flips (n = 723) (for the control: 30 min after introduction of cells in the flipping chamber, n = 674). A positive upward bias denotes negatively gravitactic cells (that is, preferentially up-swimming). Gravitaxis can be seen to follow a diel cycle, even though the culture was kept under constant illumination. Flipping experiments consistently show a population split, leading to a reduction in the upward bias of the 10 flips and 100 flips treatments compared to the control treatment. The experiments were all conducted between 09:00 and 12:00, where the upward bias measured for the control cells presents the maximum stability. Source data
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Sengupta, A., Carrara, F. & Stocker, R. Phytoplankton can actively diversify their migration strategy in response to turbulent cues. Nature 543, 555–558 (2017). https://doi.org/10.1038/nature21415
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