Philippa Kaur

Analytical targetsTwo species of undulation motion rays with different pectoral fin shapes bred in KAIYUKAN were analyzed: sharpnose stingray Dasyatis acutirostra and pitted stingray Dasyatis matsubarai (Fig. 1a,b). Blender 2.7925 was used to construct stingray models from pictures26,27 as accurately as possible; Blender is a free and open-source 3D creation suite used to make realistic characters for movies, etc. Detailed information on how to construct models using Blender is provided in our previous paper28. To focus on the effects of pectoral fin movements, we did not consider the body’s shape as in the previous studies12,29. The height and disk width (WD) of all models were set to 0.01 m and 0.44 m, respectively, considering the previous studies30,31. The disk length of each model was determined from WD, referring to the aspect ratio of the rays’ photographs26,27; the disk length (LD) of D. acutirostra and D. matsubarai are 0.348 m and 0.344 m, respectively.Figure 1Analytical targets and description of motion. (a) Analytical model of D. acutirostra. (b) Analytical model of D. matsubarai. (c) Description of motion, (d) the relationship between any two points on the surface before and after the deformation.Full size imageMotionThe motion was given to satisfy the following equations:$$z = left{ {begin{array}{*{20}l} {A;sin left( {omega left( {t – kTleft( {frac{{angl{text{e}}left( {x_{i} ,y_{i} } right) – 10^{{text{o}}} }}{{All;angl{text{e}}}} – theta } right)} right)h_{1} h_{2} } right.} hfill & {left( {10^{{text{o}}} le angl{text{e}}left( {x_{i} ,y_{i} } right) le 170^{{text{o}}} } right)} hfill \ {A;sin left( {omega left( {t – kTleft( {frac{{350^{{text{o}}} – left( {angl{text{e}}left( {x_{i} ,y_{i} } right) – 10^{{text{o}}} } right)}}{{All;angl{text{e}}}}} right)} right)h_{1} h_{2} } right.} hfill & {left( {190^{{text{o}}} le angl{text{e}}left( {x_{i} ,y_{i} } right) le 350^{{text{o}}} } right)} hfill \ end{array} } right.$$
(1)
$$begin{array}{c}{h}_{1}=a{r}_{i}^{3}+b{r}_{i}^{2}+c{r}_{i}end{array}$$
(2)
$$h_{2} = left{ {begin{array}{*{20}l} {dleft( {angleleft( {x_{i} ,y_{i} } right) – 10^{ circ } } right)^{2} + eleft( {angleleft( {x_{i} ,y_{i} } right) – 10^{ circ } } right)} hfill & {left( {10^{ circ } le angleleft( {x_{i} ,y_{i} } right) le 170^{ circ } } right)} hfill \ {dleft( {350^{ circ } – left( {angleleft( {x_{i} ,y_{i} } right) – 10^{ circ } } right)} right)^{2} + eleft( {350^{ circ } – left( {angleleft( {x_{i} ,y_{i} } right) – 10^{ circ } } right)} right)} hfill & {left( {190^{ circ } le angleleft( {x_{i} ,y_{i} } right) le 350^{ circ } } right)} hfill \ end{array} } right.$$
(3)
$$begin{array}{c}{left({r}_{i}-{r}_{i-1}right)}^{2}+{left({z}_{i}-{z}_{i-1}right)}^{2}={left({r}_{i}^{mathrm{^{prime}}}-{r}_{i-1}^{mathrm{^{prime}}}right)}^{2}+{left({z}_{i}^{mathrm{^{prime}}}-{z}_{i-1}^{mathrm{^{prime}}}right)}^{2}end{array}$$
(4)
$$begin{array}{c}angleleft({x}_{i},{y}_{i}right)= angleleft({x}_{i}^{^{prime}},{y}_{i}^{^{prime}}right).end{array}$$
(5)
Equation (1) represents the amount of movement of the model surface in the z-axis direction, where (A) is the amplitude of the pectoral fin tip, (omega) is the angular velocity, (t) is time, (k) is the wavenumber, (T) is the period, angle(({x}_{i},{y}_{i})) is the angle made by the line connecting the center of rotation and any point (({x}_{i},{y}_{i})) on the model surface with the x-axis, Allangle is the range where the motion is given (160°), and (theta) is the phase difference between the movements of the right and left pectoral fins (Fig. 1c). ({h}_{1}) is the weighting from the center to the radial direction: it is necessary to set the amplitude at the ray’s center to zero and increase the amplitude toward the pectoral fin tip (a = 119.786, b = -7.957, c = 0.498). ({h}_{2}) is the weighting in the circumferential direction: it is necessary to increase the amplitude from the anterior to the tip of the pectoral fin and decrease the amplitude from the tip of the pectoral fin to the posterior (d = − 1.563 × 10–4, e = 0.025). Equation (4) is the condition in which the distance between two neighboring points in the same radial direction is equal before and after the movement (Fig. 1d). (r) is the distance between the center of rotation and any point (({x}_{i},{y}_{i})), defined as (sqrt{{x}_{i}^{2}+{y}_{i}^{2}}). Equation (5) defines angle(({x}_{i},{y}_{i})) as being constant before and after the move (Fig. 1c). The variables after the move are marked with ‘. Variables used in the analysis are A = 0.089 m, T = 0.499, k = 1.270, and ω = 12.599 rad/s. Videos of the created motion from the front and the side are shown in the “Supplement” (Supplement Movies 3, 4).Analytical conditionsAnalysis cases were conducted with eight conditions: two types of pectoral fin shape (Fig. 1a,b) and four types of phase difference (0 (T), 0.25 (T), 0.5 (T), and 0.75 (T)). These conditions were set for investigating the effects of phase differences between left and right pectoral fin movements on swimming and how these effects vary with pectoral fin shape.Numerical methodsA CFD simulation of the ray models in the water flow was performed using OPENFOAM, an open-source finite volume method CFD toolbox32, to calculate the forces acting on the rays in each axial direction and the moment around each axis. The governing equations were the continuity equation and the three-dimensional incompressible Reynolds-averaged Navier–Stokes equation, expressed by:$$begin{array}{*{20}c} {nabla cdot u = 0} \ end{array}$$
(6)
$$begin{array}{*{20}c} {frac{partial u}{{partial {text{t}}}} + nabla cdot left( {uu} right) = – nabla p + nabla cdot left( {vnabla u} right) + nabla cdot left[ {nu left{ {left( {nabla u} right)^{T} – frac{1}{3}nabla cdot uI} right}} right], } \ end{array}$$
(7)
where (u) is the velocity vector, t is the time, p is the static pressure divided by the reference density, (nu) is the kinematic viscosity, and I is the unit tensor. The Reynolds number was defined regarding the previous studies3 as:$$begin{array}{c}{R}_{e}=frac{U{L}_{D}}{nu },end{array}$$
(8)
where (U) (/ms) is the given flow speed3 (1.5 × LD/ms), ({L}_{D}) (m) is the length of the ray models, and (nu) is the kinematic viscosity of water at 20 °C (1.0 × 10–6 m2/s). The Reynolds number in this study is 1.8 × 105; considering this, we used the k–ω shear stress turbulence model33,34. The k–ω shear stress turbulence model is a type of Reynolds-averaged Navier–Stokes equation (RANS) turbulence model that is widely used to calculate for the fish swimming flow35,36,37. The overset grid method was used in this study; it is a generic implementation of overset meshes. For both static and dynamic cases, cell-to-cell mapping between multiple, disconnected mesh regions is employed to generate a composite domain38,39. This method permits complex mesh motions and interactions without the penalties associated with deforming meshes. The process is described in detail by Noack40. The calculation volume was 5.4 WD in length, 5.4 WD in height, and 5.4 WD in width (Fig. 2a,b). A hexahedral volume mesh was created using the snappyHexMesh of OPENFOAM. The fluid region was divided into two parts: the overset region and the background region (Fig. 2a,b). The overset region moves and transforms to match the motion of the ray and was made with fine meshes around the analysis target and coarse meshes in the outlying areas; a one-layer boundary layer mesh was created around the analysis target. The overset region shape is an ellipsoid (Fig. 2a,b). The minimum mesh volume is 7.3 × 10–10 (m3), and the maximum mesh volume is 2.6 × 10–2 (m3). The total number of meshes was 9.0 × 105. At the outlet boundary, the average static relative pressure was set to 0 Pa. The surfaces of the fish model were formed into non-slip surfaces.Figure 2Meshes for CFD simulation and differences in force between different meshes. (a) Meshes at the coronal plane of the whole fluid region. (b) Frontal cross-section of the fluid region at the green line in (a). The red region is the overset region. (c,d) Comparison of the instantaneous drag coefficient and the moment coefficient around the y-axis of D. matsubarai between the coarse, fine, and dense mesh.Full size imageThe drag coefficient ({C}_{D}left(tright)), the lateral force coefficient ({C}_{l}left(tright)), the lift coefficient ({C}_{L}left(tright)), the moment coefficient around the x-axis ({C}_{mx}left(tright)), the moment coefficient around the y-axis ({C}_{my}left(tright)) and the moment coefficient around the z-axis ({C}_{mz}left(tright)) were calculated as:$$begin{array}{c}{C}_{D}left(tright)=frac{Dleft(tright)}{frac{1}{2}rho {U}^{2}{L}_{D}{W}_{D}}end{array}$$
(9)
$$begin{array}{c}{C}_{l}left(tright)=frac{lleft(tright)}{frac{1}{2}rho {U}^{2}{L}_{D}{W}_{D}}end{array}$$
(10)
$$begin{array}{c}{C}_{L}left(tright)=frac{Lleft(tright)}{frac{1}{2}rho {U}^{2}{L}_{D}{W}_{D}}end{array}$$
(11)
$$begin{array}{c}{C}_{mx}left(tright)=frac{{M}_{psi }left(tright)}{frac{1}{2}rho {U}^{2}{L}_{D}^{2}{W}_{D}}end{array}$$
(12)
$$begin{array}{c}{C}_{my}left(tright)=frac{{M}_{phi }left(tright)}{frac{1}{2}rho {U}^{2}{L}_{D}^{2}{W}_{D}}end{array}$$
(13)
$$begin{array}{c}{c}_{mz}left(tright)=frac{{M}_{theta }left(tright)}{frac{1}{2}rho {U}^{2}{L}_{D}^{2}{W}_{D}},end{array}$$
(14)
where (Dleft(tright)) is the calculated drag, (lleft(tright)) is the calculated lateral force, (Lleft(tright)) is the calculated lift, ({M}_{psi }left(tright)) is the calculated moment around the x-axis, ({M}_{phi }left(tright)) is the calculated moment around the y-axis, ({M}_{theta }left(tright)) is the calculated moment around the z-axis, and (rho) (kg/m3) is the density of water at 20 °C (998 kg/m3). As shown in a previous study41. the propulsive efficiency (eta) is defined as the ratio of output power ({P}_{o}) to input power ({P}_{e}) which can be written as:$$begin{array}{c}{P}_{o}left(tright)=frac{1}{T}{int }_{0}^{T}Dleft(tright)Udtend{array}$$
(15)
$$begin{array}{c}{P}_{e}left(tright)=frac{1}{T}{int }_{0}^{T}left[Dleft(tright)dot{x}left(tright)+lleft(tright)dot{y}left(tright)+Lleft(tright)dot{z}left(tright)right]dtend{array}$$
(16)
$$begin{array}{c}eta =frac{{P}_{o}}{{P}_{e}}.end{array}$$
(17)
An in-house program calculated the forces acting on rays in each axial direction and the moment around each axis. The numerical method’s validity and reliability were verified by comparing previous experimental and numerical analytical studies of heaving and pitching on airfoil naca001341. A high degree of similarity to previous studies was confirmed; the mean difference in the propulsive efficiency from the previous study of analysis was 5%, and the difference from the previous study of the experiment was 9%. Detailed information such as mesh, length, and velocity, of this analysis method’s verification is provided in the “Supplement”.A grid sensitivity study was conducted using three meshes: coarse, fine, and dense. The coarse mesh has 8.1 × 105 elements, the fine mesh has 9.0 × 105 elements, and the dense mesh has 9.9 × 105 elements. The analysis was conducted using a condition with no phase difference of D. matsubarai. As shown in Fig. 2c,d, the drag coefficient and the moment coefficient around the y-axis are almost the same when the mesh is fine and when the mesh is dense. The mean drag and propulsive efficiency error of fine and coarse meshes are 2.7% and 3.5%, respectively. The fine mesh was used in all simulation cases considering accuracy. We used the same meshes for all cases. More

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Isolation and cultivationIn total, 39 mixotrophic flagellates were investigated (Table 1). Thirty-three isolates were enriched and isolated from euphotic zone samples at Station ALOHA (22° 45′ N, 158° 00′ W) in February and May 2019. To select for mixotrophic grazers of Prochlorococcus, whole seawater was amended with K medium [28] (1/20 final concentration), and live Procholorococcus (MIT9301) was added as prey (~5 × 106 cells mL−1 final concentration). Enriched seawater samples were incubated under ~70 µmol photons m−2 s−1 irradiance on a 12 h:12 h light:dark cycle and monitored by microscopy daily up to five days. Each day, samples were serially diluted to extinction (9 dilution steps, 12 replicates per dilution) in 96-well plates in nutrient-reduced K medium (1/20 concentration) with a constant background of Prochlorococcus cells. Wells at the highest dilution showing growth of putative grazers were subjected to 3–6 further rounds of dilution to extinction. Four additional mixotrophs were isolated in full K medium using water from earlier cruises, and two (dictyochophyte strains UHM3021 described in [24] and UHM3050) were enriched in K minus nitrogen medium (K-N) without Prochlorococcus enrichment. All isolates were rendered unialgal, but not axenic, and maintained at 24 °C in K-N medium (~0.2 µM N) amended with Prochlorococcus prey, under the same light conditions as above. Dense Prochlorococcus cells grown in Pro99 medium [29], were harvested and concentrated through gentle centrifugation at 2000 RCF for 5 minutes and resuspended in fresh K-N medium to minimize nutrient carryover. To ensure their long-term accessibility, the isolates used in this study are being transferred to the National Center for Marine Algae and Microbiota at the Bigelow Laboratory for Ocean Science, East Boothbay, ME, USA (ncma.bigelow.org).Table 1 Mixotrophic flagellates investigated in this study.Full size table18S rRNA gene sequencing and phylogenetic analysisCells were harvested by centrifuging 25–50 mL dense cultures at 3000 RCF for 10 min at 4 °C. Genomic DNA was extracted from the pellets using the ZymoBIOMICS DNA Kit (Zymo Research, Irvine, CA, USA). A near-full-length section of the eukaryotic small-subunit ribosomal RNA (18S rRNA) gene was amplified by PCR with the Roche Expand High Fidelity PCR System (Sigma-Aldrich, St. Louis, MO, USA) using either forward primer 5′-ACCTGGTTGATCCTGCCAG-3′ and reverse primer 5′-TGATCCTTCYGCAGGTTCAC-3′ [30], or Euk63F 5′-ACGCTTGTCTCAAAGATTA-3 and Euk1818R 5′-ACGGAAACCTTGTTACGA-3′ [31]. Amplicons were purified using spin columns (DNA Clean & Concentrator-25; Zymo Research, Irvine, CA, USA) and sequenced (Sanger) using the same PCR amplification primers and an additional reverse primer 1174R, 5′-CCCGTGTTGAGTCAAA-3′ [32], when necessary to connect two ends. For phylogenetic analyses, similar sequences were retrieved from the PR2 database [33] based on BLAST similarity, and two environmental homologs (GenBank Acc. FJ537342 and FJ537336) were retrieved from NCBI GenBank for the undescribed haptophyte taxon, which was not affiliated with any reference sequence from the PR2 database. Sequence alignments including 39 isolates, 29 reference and 2 outgroup taxa were created with MAFFT v7.450 using the G-INS-i algorithm [34] in Geneious R11.1.5 (http://www.geneious.com) [35]. Terminal sites that lacked data for any of the sequences were trimmed and any sites with greater than 25% gaps were removed from the alignment, which generated a total sequence length of 1617 bases. Phylogenetic analysis was performed using MrBayes v3.2.6 in Geneious R11.1.5 [36] with two runs of four chains for 1,000,000 generations, subsampling every 200 generations with burn-in length 100,000, under the GTR substitution model. The Bayesian majority consensus tree was further edited within iTOL v5 [37]. All 18 S rRNA gene sequences were deposited in GenBank with accession numbers MZ611704–MZ611740; MN615710–MN615711.Microscopic observationThe average diameter of flagellates in the exponential growth phase (n = 20 cells per strain) was measured by transmitted light microscopy using image analysis software (NIS-Elements AR, Nikon, Minato City, Tokyo, Japan) calibrated with a stage micrometer. Equivalent spherical diameter (ESD) and biovolumes were calculated assuming spherical cells. Chloroplasts were visualized by autofluorescence under epifluorescence microscopy. An average ESD of 0.64 µm was used for Prochlorococcus prey [24].Visual evidence of phagocytosis was obtained by adding fluorescent beads (0.5 μm YG Fluoresbrite Microspheres; Polysciences) to each culture. Samples post incubation (~2 h) were fixed with an equal volume of 4% ice-cold glutaraldehyde, and subsamples (20 µL) were mounted on a glass slide under a coverslip. Paired images captured using epifluorescence and transmitted light microscopy (Olympus BX51 with Leica DFC 7000 T color digital camera) were overlain to identify cells with ingested beads.Grazing experimentsLong-term grazing experiments were conducted for all 39 grazers, and 31 were used to quantify grazing rates based on rates of disappearance of Prochlorococcus cells, which persist but do not readily grow in K-N medium [24]. Rates were not calculated for eight isolates (seven Florenciella and one DictyX) because they were sampled at a lower frequency. Fifteen isolates representing all genera (or approximately genus-level clades) were examined in more detail by replicating grazing experiments two times (marked in bold in Supplementary Table S1), while the remaining sixteen isolates were tested once to survey within- and across- genus variation. Prior to the experiments, all grazer cultures were maintained/acclimated in the experimental medium (K-N with prey). Experiments were initialized by inoculating late-exponential-phase grazers into fresh K-N medium at a final concentration of ~103 flagellates mL−1, and adding live, unstained prey at a final concentration of 2–3 × 106 Prochlorococcus mL−1. Grazers were incubated for 3–8 days in total, depending on how fast prey were ingested. To minimize carryover of dissolved nutrients and prey growth in the grazing experiments, Prochlorococcus were grown to stationary phase in Pro99 medium, then pelleted (2000 RCF for 3–5 min) and resuspended in fresh K-N medium prior to addition to control and experimental cultures. Control cultures of grazer without added prey and prey without grazers were included during each grazing experiment to confirm that grazer growth and prey removal were attributable to grazing. Cell concentrations of prey and grazers were measured every 12–24 h by flow cytometry of glutaraldehyde-fixed samples at final concentration of 0.5% (Attune NxT; Thermo Fisher Scientific, Waltham, MA, USA, and CytoFLEX; Beckman Coulter Life Sciences, Indianapolis, IN, USA). Populations were distinguished based on light scatter and pigment autofluorescence and occasionally confirmed with DNA fluorescence (stained post-sampling with DNA stain SYBR Green I). Ambient bacteria concentrations, monitored using DNA stains, were ≤10% of the added Prochlorococcus at the start and during most periods of all grazing experiments.Grazing rates and biovolume conversion efficiencyWe calculated ingestion rates, I (prey grazer−1 h−1) for each grazer as:$$I = frac{{P_t – P_{t + 1}}}{{G_{{{{{rm{avg}}}}}}(T_{t + 1} – T_t)}}$$
(1)
where (P_t) and (P_{t + 1}) are the prey abundance at sampling interval t and t + 1 (cells mL−1), Gavg is the arithmetic mean grazer abundance (cells mL−1) over the time interval, and ((T_{t + 1} – T_t)) is the time (h) between two sampling intervals. Clearance rates (C, nL grazer−1 h−1) were calculated by dividing the ingestion rate by average prey concentration over the same interval, and specific clearance rate (body volume grazer−1 h−1) was calculated by dividing the clearance rate by cellular biovolume (µm3) of each grazer. Equation (1) uses a linear approximation of prey and grazer trajectories over the sampling interval, which was appropriate for our data where change could be relatively linear, concave-up, or concave-down. Other commonly used ingestion rate calculations assume exponential prey decline [38] and/or exponential grazer growth [39].Clearance rates over time in each experiment were assessed visually to obtain a representative series of rates that minimized potential influence of modest prey growth/decline observed in control cultures (Supplementary Fig. S1), as well as potential slowing of ingestion as the grazer neared carrying capacity or depleted prey to a low concentration. For each experiment a contiguous set of relatively constant rates were used to calculate a mean clearance rate. This assessment sometimes excluded the first 12–24 h, but not when removal rates were particularly fast. Intervals when the grazer neared carrying capacity were also often excluded, if grazing rates slowed down. To assess whether clearance rates increased as prey were depleted we plotted clearance rate and ingestion rate as a function of prey concentration (Supplementary Figs. S2 and S3). In general, there was no relationship between clearance rate and prey concentration, and ingestion increased linearly with prey concentration. These patterns imply that the prey concentrations in this experiment did not saturate the ingestion rates of these grazers. Under non-saturating prey concentrations the average clearance rate over an experiment should be a good estimate of the maximum clearance rate (Cmax). Consistent with this interpretation, functional responses fit to these experiments yielded Cmax estimates that were similar to the reported average clearance rates. Because these experiments were not designed with a sufficient range of prey density to esimate functional responses, we do not report the Cmax estimates, but we note here that our reported average clearance rates may be useful as approximate Cmax numbers in future work.Six grazers representing three classes (three dictyochophytes, two haptophytes, and one chrysophyte) were further investigated to determine functional grazing responses using a wide range of initial prey densities (105–107 cells mL−1). Functional responses were modeled using the Holling type II curve, (I = frac{{I_{{{{{rm{max}}}}}}P}}{{P + frac{{I_{{{{{rm{max}}}}}}}}{{C_{{{{{rm{max}}}}}}}}}}), where I is the ingestion rate over a sampling interval (Eq. 1), Imax is the maximum ingestion rate, Cmax is the maximum clearance rate and P is the arithmetic mean prey density between two sampling points. This curve was fit to ingestion rate data using maximum likelihood with R package bbmle [40].For 31 isolates we calculated the amount of grazer biovolume created per prey biovolume consumed, using data from the same grazing experiments used to calculate grazing rates. It was calculated based on the following formula:$$E = frac{{(F_{{{{{rm{f}}}}}} – F_{{{{{rm{i}}}}}})}}{{(P_{{{{{rm{i}}}}}} – P_{{{{{rm{f}}}}}})}}frac{{B_{{{{{rm{F}}}}}}}}{{B_{{{{{rm{p}}}}}}}}$$
(2)
where Ff and Fi are the final and initial flagellate concentrations, Pi and Pf are initial and final prey concentrations in each culture, and BF and BP are the cellular biovolume of prey and grazer. We refer to the quantity E as the biovolume conversion efficiency, and we use it as an indicator of physiological differences among diverse mixotrophs. Note that biovolume conversion efficiency can be greater than 1, if prey have greater nutrient:biovolume than the grazer.Quantitative PCRReal-time, quantitative PCR (qPCR) was performed to quantify the 18S rRNA gene abundances of representative mixotroph groups discriminated at approximately the genus level, including Florenciella, Rhizochromulina and another undescribed clade within the class Dictyochophyceae; Chrysochromulina and another undescribed clade within the division Haptophyta; clade H in the class Chrysophyceae; and Triparma eleuthera and Triparma mediterranea in the class Bolidophyceae. Primers (Supplementary Table S2) were designed to target a short region (95–176 bases) of the 18S rRNA gene and meet basic criteria (≤2 °C difference in melting temperature between members of a pair, %G + C content between 45 and 65%, ≤1 degenerate position per primer, no predicted primer dimers). Sequences considered targets for a given primer set had ≤1 mismatch across both primers, which included all or most known members within the corresponding targeted clade. Members in the nearest non-targeted clade had ≥3 mismatches distributed across both primers. Efficiency and specificity of the synthesized primers (IDT Inc., Coralville, IA, USA) was tested by ensuring there was specific amplification (qPCR followed by melting curve analysis and gel electrophoresis) when using DNA from cultures within the targeted group and no amplification when using DNA from cultures close to, but outside of the targeted group (Supplementary Table S3). Empirical observations of amplification success using control cultures were used to infer whether species known only by environmental sequences were likely to amplify with a given primer set (Supplementary Fig. S4).In situ gene abundances were quantified in water samples collected from Station ALOHA at 5, 25, 45, 75, 100, 125, 150, and 175 m, during HOT cruise numbers 259 (Jan), 262 (Apr), 264 (Jul), and 266 (Oct) of 2014. Seawater (ca. 2 L) was filtered through 0.02 μm pore-size, aluminum oxide filters (Whatman Anotop, Sigma-Aldrich, Saint Louis, MO, USA) and stored at −80 °C. Genomic DNA of both grazer cultures and environmental samples was extracted (MasterPure Complete DNA and RNA Purification Kit; Epicentre) as described elsewhere [41]. Four replicated PCR reactions (10 μL) were carried out for each sample except for Triparma (duplicates) and consisted of 5 μl of 2× PowerTrack SYBR Green Master Mix (Thermo Fisher Scientific, USA), 10 ng environmental DNA, 500 nM of each primer, and nuclease-free water. Reactions were run on an Eppendorf Mastercycler epgradient S realplex2 real-time PCR instrument. Each run contained fresh serial dilutions (1–6 log gene copies) of target-specific, 750-bp synthetic standards (gBlocks, IDT) prepared in triplicate. The cycling program included an initial denaturation step of 95 °C for 2 min, followed by 40 cycles of 95 °C for 5 s and 55 °C for 30 s. Specificity of amplification was checked with a melting curve run immediately after the PCR program and occasionally, by gel electrophoresis. Amplification efficiencies ranged from 95% to 106% for all the primers.To convert gene copies to cell numbers, 18S rRNA gene copy number per cell−1 was determined for representative isolates in the seven targeted genera/clades. Known quantities of cultured cells (106–107 cells) from each isolate with 2–8 replicates were pelleted at 4000 RCF for 15 min at 4 °C. DNA was extracted from the pelleted cells (MasterPure Complete DNA and RNA Purification Kit, Lucigen), quantified by fluorometry (Qubit, Invitrogen) and the extract volume adjusted to achieve a DNA concentration of 10 ng µL−1. The expected number of eukaryotic cells µL−1 of extract was calculated as the difference between the total cells in the sample prior to centrifugation and in the supernatant afterward (as determined by flow cytometry) divided by the final extract volume. Copy number of the 18S rRNA genes µL−1 of extract was determined by qPCR with the appropriate group-specific primers. The resulting value of gene copies µL−1 was divided by the equivalent number of eukaryotic cells µL−1 in the extract (assuming 100% extraction efficiency) to derive minimum estimates of gene copies cell−1. An average value for representatives within each genus/clade (1–5 isolates) was used to calculate in situ cell concentrations for the genus. These derived in situ abundances were compared to flow cytometric counts of total photosynthetic picoeukaryotes at Station ALOHA obtained from the Hawai’i Ocean Time-series Data Organization and Graphical System (https://hahana.soest.hawaii.edu/hot/hot-dogs/).Global distribution revealed through Tara Oceans 18S rRNA metabarcodesTo estimate the relative abundance of the OTUs closely related to our diverse isolates on a broader geographic scale, we searched the 18S rRNA-V9 sequence data from the 0.8–5 µm fraction of surface water sampled at 40 stations by the Tara Oceans project (http://taraoceans.sb-roscoff.fr/EukDiv/). Reads for ‘Tara lineages’ with highest similarity (E-value < 10−15) to each of our targeted clades (Supplementary Table S1) were expressed as a fraction of total reads excluding dinoflagellates but included all other Tara Oceans phytoplankton ‘taxogroups’: Bacillariophyta, Bolidophyceae, Chlorarachnea, Chlorophyceae, Chrysophyceae/Synurophyceae, Cryptophyta, Dictyochophyceae, Euglenida, Glaucocystophyta, Haptophyta, Mamiellophyceae, Other Archaeplastida, Other Chlorophyta, Pelagophyceae, Phaeophyceae, Pinguiophyceae, Prasino-Clade-7, Pyramimonadales, Raphidophyceae, Rhodophyta and Trebouxiophyceae. Dinoflagellates were excluded because of the difficulty in assigning phototrophic vs. heterotrophic status to all taxa, and because nearly all dinoflagellate reads were from a single, poorly annotated OTU that was also highly abundant in larger size fractions. More

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Epithemia isolation and cultureThe Epithemia cells were isolated from 0.5 L of seawater collected from depths of 25, 75, and 100 m in the North Pacific Subtropical Gyre (22°45′ N, 158°00′ W). Seawater was collected during the near-monthly Hawaii Ocean Time-series (HOT) expeditions to the long-term monitoring site Station ALOHA (water depth ca. 4800 m) in October 2014 (HOT cruise #266) and February–July 2019 (HOT cruises #310–313). Serial dilution (unialgal strains UHM3202, UHM3203, UHM3204) or micropipette isolation of single cells (clonal strains UHM3200, UHM3201, UHM3210, UHM3211) were used to establish the Epithemia cultures, which were grown in a seawater-based, low-nitrogen medium. Filtered (0.2 µm) and autoclaved, undiluted Station ALOHA seawater was amended with 2 μM EDTA, 50 nM ferric ammonium citrate, 7.5 μM phosphoric acid, trace metals (100 nM MnSO4, 10 nM ZnCl2, 10 nM Na2MoO4, 1 nM CoCl2, 1 nM NiCl2, 1 nM Na2SeO3), vitamins (50 μg/L inositol, 10 μg/L calcium pantothenate, 10 μg/L thiamin, 5 μg/L pyridoxine HCl, 5 μg/L nicotinic acid, 0.5 μg/L para-aminobenzoic acid, 0.1 μg/L folic acid, 0.05 μg/L biotin, 0.05 μg/L vitamin B12), and 106 μM Na2SiO3. Although not tested here, simpler formulations of diazotroph media such as PMP40 or RMP41 may also be suitable for growing Epithemia, when made with 100% seawater and adding Na2SiO3. The cultures were subsequently incubated at 24 °C on a 12:12 h light:dark cycle with 50–100 μmol quanta m−2 s−1 using cool white fluorescent bulbs. All E. pelagica and E. catenata symbioses were stable under these medium and incubation conditions. E. pelagica was successfully isolated from at least one of the three depths that were targeted during each sampling occasion.Morphological observationsEpithemia living and fixed cells were imaged by light and epifluorescence microscopy using a Nikon Eclipse 90i microscope at 40×–60× magnification. Diatom cell sizes were determined using >60 live, exponentially growing cells, imaged in either valve view (E. pelagica) or girdle view (E. catenata). Endosymbiont (spheroid body) cell sizes were averaged from DNA-stained cells for E. pelagica UHM3200 (n = 78) and E. catenata UHM3210 (n = 91), imaged by epifluorescence microscopy after preparing samples as follows: Epithemia cells were fixed in 4% glutaraldehyde for 30 min, pelleted at 1000 × g for 1 min, the supernatant was exchanged with 0.5% Triton X-100 (in autoclaved filtered seawater), samples were incubated for 10 min with gentle agitation, cells were then pelleted at 4000 × g for 1 min, supernatant was exchanged with autoclaved filtered seawater and fixed in 4% glutaraldehyde, and samples were stained with 1× final concentration of SYBR Gold nucleic acid stain (Invitrogen, cat. # S11494) for 2 h. For routine observations of endosymbionts (e.g., determining presence/absence and number per host cell), osmotic shock was used to disrupt the cell contents of diatom host cells and improve visualization of the endosymbionts. This was achieved by gently pelleting cells and exchanging the medium with either ultrapure water or 2–3 M NaCl solution, followed by immediate observation. While this is a simple technique for detecting and visualizing endosymbionts (Fig. 1c, f), it does not accurately represent the natural location of endosymbionts within the host cells, as seen when compared to fixed cell preparations for epifluorescence microscopy (Fig. 1n, o). To assess the presence of fluorescent photopigments in endosymbiont cells, live host cells were pelleted at 4000 × g for 5 min and crushed using a microcentrifuge tube pestle (SP Bel-Art, cat. # F19923-0000) to release the endosymbionts. The crushed pellet was resuspended in 75% glycerol containing live Synechococcus WH7803 cells (positive control for fluorescence), and samples were observed by epifluorescence microscopy using filter cubes appropriate for observing phycoerythrin (EX: 551/10, BS: 560, EM: 595/30) and chlorophyll (EX: 480/30, BS: 505, EM: 600LP).The loss of endosymbionts from Epithemia cultures (UHM3200 and UHM3210) was observed after propagating cells for four months in nitrogen-replete medium (K)18, where approximately 5–10% of the culture was transferred to fresh medium about every two weeks. Observations were only made at the end of the four-month period. Endosymbionts were not observed growing freely in these cultures, and the absence of endosymbionts within host cells was confirmed by the failure to observe spheroid bodies by light microscopy after osmotic shock of the diatoms, as well as a failure to amplify the endosymbiont SSU (16S rRNA) and nifH genes from cellular DNA extracts. PCR reactions were performed in parallel with DNA extracts from control cultures (grown in low-nitrogen medium), using the same template DNA amount (10 ng) and PCR conditions (see methods for Marker gene sequencing and phylogenetics).Ultrastructural observations by electron microscopy (EM) were conducted for E. pelagica UHM3200 and E. catenata UHM3210. EM preparations of diatoms typically involve the oxidative removal of organic matter to uncover the fine details of frustule ultrastructure. However, in the case of E. catenata, oxidatively cleaned cells lacked structural integrity, leading to collapsed frustules when dried and viewed by scanning EM (SEM). For this reason, both species were prepared for SEM with and without (Fig. 1a, d) the oxidative removal of organic matter, and cleaned E. catenata frustules were further analyzed by transmission EM (TEM). To remove organic matter, 100 mL of exponentially growing culture was pelleted by centrifugation at 1000 × g for 10 min and resuspended in 30% H2O2. Cells were boiled in H2O2 for 1–2 h, followed by rinsing cells six times in ultrapure water by sequential centrifugation at 1000 × g for 10 min and resuspension of cell pellets. Suspensions of the cleaned cells were dried on aluminum foil and mounted on aluminum stubs with double-sided copper tape. For some E. catenata SEM preparations, the cleaned frustules were dehydrated in an ethanol dilution series and exchanged into hexamethyldisilazane (HMDS) prior to drying on aluminum foil; this was to minimize the collapse of frustules resulting from drying. To prepare cells with organic matter intact, 25 mL of exponentially growing culture was mixed with an equal volume of fixative solution (5% glutaraldehyde, 0.2 M sodium cacodylate pH 7.2, 0.35 M sucrose, 10 mM CaCl2) and incubated overnight at 4 °C. Cells were gently filtered onto a 13 mm diameter 1.2 μm pore size polycarbonate membrane filter (Isopore, Millipore Sigma), washed with 0.1 M sodium cacodylate buffer (pH 7.4, 0.35 M sucrose), fixed with 1% osmium tetroxide in 0.1 M sodium cacodylate (pH 7.4), dehydrated in a graded ethanol series, and critical point dried. Filters were mounted on aluminum stubs with double-sided conductive carbon tape. All SEM stubs were sputter coated with Au/Pd, prior to observing on a Hitachi S-4800 field emission scanning electron microscope at the University of Hawai’i at Mānoa (UHM) Biological Electron Microscope Facility (BEMF). Cleaned E. catenata cells were prepared for TEM by drying a drop of sample on a formvar/carbon-coated grid and observing on a Hitachi HT7700 transmission electron microscope at UHM BEMF.Additional light microscopy of hydrogen-peroxide cleaned frustules was conducted for E. pelagica UHM3201 and E. catenata UHM3210. Samples were mounted in Naphrax (PhycoTech, Inc., cat. # P-Naphrax200) and observed at 100× using an Olympus BX41 Photomicroscope (Olympus America Inc., Center Valley, Pennsylvania) with differential interference contrast optics and an Olympus SC30 Digital Camera at California State University San Marcos.A key to the strains used in each micrograph is provided in Supplementary Table 2.Marker gene sequencing and phylogeneticsFor each Epithemia strain, 25–50 mL of culture was pelleted at 4000 × g for 10 min, and DNA was extracted from the pellet using the ZymoBIOMICS DNA Miniprep Kit (Zymo Research, cat. # D4300). Marker genes were amplified with the Expand High Fidelity PCR System (Roche, cat. # 4743733001), using conditions previously described for genes SSU encoding 18S rRNA (Euk328f/Euk329r)42, LSU encoding 28S rRNA (D1R/D2C)43, rbcL (rbcL66+/dp7−)44,45, psbC (psbC+/psbC−)44, and cob (Cob1f/Cob2r)21. For the endosymbionts, a partial sequence for the SSU (16S rRNA) gene was amplified using a primer set targeting unicellular cyanobacterial diazotrophs, CYA359F/Nitro821R46,47, and the nifH gene was amplified using new primers specific to the nifH of Cyanothece-like organisms, ESB-nifH-F (5′-TACGGAAAAGGCGGTATCGG-3′) and ESB-nifH-R (5′-CACCACCAAGRATACCGAAGTC-3′), with a 55 °C annealing temperature and 75 s extension time. All primers were synthesized by Integrated DNA Technologies (IDT). Amplified products were cloned and transformed into E. coli using the TOPO TA Cloning Kit for Sequencing (Invitrogen, cat. # K457501), and plated colonies were picked and grown in Circlegrow medium (MP Biomedicals, cat. # 113000132). Plasmids were extracted with the Zyppy Plasmid Miniprep kit (Zymo Research, cat. # D4019) and sequenced from the M13 vector primers using Sanger technology at GENEWIZ (South Plainfield, NJ). For the diatom SSU (18S rRNA) gene, sequencing reactions were also performed using the 502f and 1174r primers48.Phylogenetic trees (Fig. 2) were inferred using concatenated alignments for both diatom host genes (SSU encoding 18S rRNA, psbC, rbcL) and endosymbiont genes (SSU encoding 16S rRNA, nifH). For each gene, nucleotide sequences were aligned using MAFFT v7.45349 (L-INS-i method), and sites with gaps or missing data were removed. An appropriate nucleotide substitution model was selected for each gene alignment using jModelTest v2.1.1050. Bayesian majority consensus trees were inferred from the concatenated alignments using MrBayes v3.2.751 with two runs of 4–8 chains, until the average standard deviation of split frequencies dropped below 0.01. Maximum likelihood bootstrap values were generated for the Bayesian tree using RAxML v8.2.1252, implemented with 1000 iterations of rapid bootstrapping. To further analyze the phylogenetic position of the new Epithemia species in the broader context of Surirellales and Rhopalodiales diatoms, individual gene trees (SSU encoding 18S rRNA, LSU, rbcL, psbC, and cob; Supplementary Figs. 13–19) were constructed from sequences aligned using MAFFT (automatic detection method) and trimmed using trimAl v1.253 (gappyout method). rRNA gene phylogenies were also inferred using sequences aligned according to the global SILVA alignment for SSU and LSU genes using SINA54, which were either left untrimmed in the case of the LSU gene or trimmed to remove highly variable positions (SINA’s “012345” positional variability filter) and gappy positions (trimAL v1.2, gappyout method) in the case of the SSU gene. These trimming strategies were selected based on their ability to maximize the monophyly of the previously described Rhopalodiales clade and minimize the separation of known conspecific strains, such as the strains of E. pelagica described here. All gene phylogenies were inferred using the Bayesian methods described above. To investigate the level of support for constrained tree topologies placing E. catenata within or outside of the genus Epithemia and family Rhopalodiaceae, SH55 and AU56 statistical tests were performed in IQ-TREE 257 (implementing ModelFinder58) using all alignments from the individual gene trees (Supplementary Table 3).Given E. catenata’s unusual morphology, test trees were inferred with the inclusion of diatom sequences from orders Bacillariales (Nitzschia, Pseudo-nitzschia), Cymbellales (Didymosphenia), Naviculales (Amphiprora, Navicula, Pinnularia), and Thalassiophysales (Amphora, Halamphora, Thalassiophysa); however, E. catenata was consistently placed within Rhopalodiales, and these trees were not pursued further.An additional nifH phylogeny was constructed using all environmental sequences from NCBI’s non-redundant nucleotide (nt) database >300 bp and sharing >95% nucleotide sequence identity with EpSB and EcSB nifH sequences (Supplementary Fig. 23), including 51 environmental sequences from prior studies investigating marine diazotrophs34,59,60,61,62,63,64,65,66. Environmental nifH sequences were aligned to the previously generated nifH sequence alignment using MAFFT (automatic method detection and addfragments options), and the best-scoring maximum likelihood phylogeny was inferred using RAxML with 1000 iterations of rapid bootstrapping. NCBI accession numbers for all tree sequences are in the Source Data file.Analysis of Epithemia endosymbiont nifH sequences in environmental datasetsNucleotide sequences for EpSB and EcSB nifH were queried against NCBI’s non-redundant nucleotide (nt) database using webBLAST67 (megablast; https://blast.ncbi.nlm.nih.gov/) and SRA databases for nifH amplicon sequencing projects from the marine environment using the SRA Toolkit68 (dc-megablast, with database validation using vdb-validate; https://github.com/ncbi/sra-tools). Database hits with 98–100% nucleotide identity over an alignment of the entire subject sequence (BLAST alignment length = subject sequence length) were identified, and the associated sample’s latitude and longitude coordinates (where available) were mapped. Coordinates were also mapped for metagenome and metatranscriptome samples containing matches to unigene MATOU-v1_93255274 from the Marine Atlas of Tara Oceans Unigenes69, a unigene that shares 100% identity over the entire length of the EpSB UHM3202 nifH sequence and >99.4% identity with all other EpSB nifH sequences.The presence of EpSB and EcSB nifH sequences was examined in metagenomes prepared from sinking particles collected at 4000 m depth at Station ALOHA27,28. The sinking particles were collected during intervals of 12, 10, and 8 days during 2014, 2015, and 2016, respectively, using a McLane sediment trap equipped with a 21-sample bottle carousel. The presence of EpSB and EcSB nifH sequences in the metagenomes was assessed by blastn70, after first removing low quality bases from metagenomic reads using Trimmomatic v0.3971 (parameters: LEADING:20 TRAILING:20 MINLEN:100). For each sediment trap metagenome, the total number of reads matching EpSB or EcSB nifH nucleotide sequences with 100% identity were tallied and normalized to the total number of reads in the database. Only EpSB-matching reads were detected in this analysis.Quantitative PCRSpecific PCR primers were designed targeting a 102 bp region of E. pelagica’s LSU gene (Epel-LSU-F, 5′-GAAACCAGTGCAAGCCAAC-3′; Epel-LSU-R, 5′-AGGCCATTATCATCCCTTGTC-3′) and an 85 bp region EpSB’s nifH gene (EpSB-nifH-F, 5′-CACACTAAAGCACAAACTACC-3′; EpSB-nifH-R, 5′-CAAGTAGTACTTCGTCTAGCTC-3′) and were synthesized by IDT. Gene copy concentrations were quantified for Station ALOHA water samples (~2 L) collected by Niskin bottles at 5, 25, 45, 75, 100, 125, 150, and 175 m on January 16 and July 1 (except 5 m), 2014, during HOT cruises #259 and #264. Samples were filtered onto 25 mm diameter, 0.02 μm pore size aluminum oxide filters (Anotop; Whatman, cat. # WHA68092102) and stored at −80 °C until extracting DNA using the MasterPure Complete DNA and RNA Purification Kit (Epicentre, cat. # MC85200) according to Mueller et al.72. Briefly, a 3-mL syringe filled with 1 mL of tissue and cell lysis solution (MasterPure) containing 100 μg mL−1 proteinase K was attached to the outlet of the filter, and the filter inlet was sealed with a second 3-mL syringe. The lysis solution was pulled halfway through to saturate the filter membrane, and the entire assembly was incubated at 65 °C for 15 min while attached to a rotisserie in a hybridization oven rotating at ca. 16 rpm. The lysis buffer was then drawn fully into the inlet syringe, transferred to a microcentrifuge tube, and placed on ice. The remaining steps for protein precipitation and removal and nucleic acid precipitation were carried out following the manufacturer’s instructions. For each sample, DNA was resuspended in a final volume of 100 μL. Quantitative PCR (qPCR) was performed using the PowerTrack SYBR Green Master Mix system (Applied Biosystems, cat. # A46109) and run on an Eppendorf Mastercycler epgradient S realplex2 real-time PCR machine. Reactions (20 µL total volume) were prepared according to the manufacturer’s protocol, containing 500 nM of each primer. Sample reactions (four replicates) contained 2 μL of environmental DNA extract (24–76 ng DNA), while standards (three replicates) contained 2 μL of gBlocks Gene Fragments (IDT) that were prepared at 1, 2, 3, 4, 5, and 6 log gene copies/μL. The gBlocks Gene Fragments were 500 bp in length and encompassed the entire E. pelagica UHM3201 LSU sequence and positions 1–500 of the EpSB UHM3201 nifH sequence, respectively. The main cycling conditions consisted of an initial denaturation and enzyme activation step of 95 °C for 2 min, followed by 40 cycles of 95 °C for 5 s and 57 °C or 55 °C for 30 s for the LSU and nifH genes, respectively. Melting curves were analyzed to verify the specificity of the amplifications, and reactions containing Epithemia catenata DNA extract were included as negative controls. Reaction efficiencies were 104.23% and 95.15% for the LSU and nifH genes, respectively. The limit of detection for these assays was not empirically determined. gBlocks sequences, qPCR threshold cycle values, and conversion equations are provided in the Source Data file.Physiology experimentsThe daily patterns of N2 fixation were quantified for E. pelagica UHM3200 and E. catenata UHM3210 using two techniques: acetylene (C2H2) reduction to ethylene (C2H4) and argon induced dihydrogen (H2) production (AIHP). Both analyses were conducted using a gaseous flow-through system that quantified the relevant trace gas on the sample outlet line with a temporal resolution of 10 min73. To conduct the measurements, a 10-mL subsample of each Epithemia culture was placed in a 20-mL borosilicate vial and closed using gas-tight rubber stoppers and crimp seals. Separate bottles were used for H2 production and C2H2 reduction. During the experimental period, the temperature was maintained at 25 ± 0.2 °C using a benchtop incubator (Incu-Shaker; Benchmark Scientific) and light exposure was 200 μmol photons m−2 s−1 at wavelengths of 380–780 nm with a 12:12 h square light:dark cycle (Prime HD+; Aqua Illumination). To conduct the AIHP method, the sample vial containing the culture was flushed with a high purity gas mixture consisting of argon (makeup gas; 80%), oxygen (20%), and carbon dioxide (0.04%). In the absence of N2, all of the electrons that would have been used to reduce N2 to NH3 are diverted to H2 production, thereby providing a measure of Total Nitrogenase Activity (TNA). The C2H2 reduction assay also represents a measure of TNA. Our analytical set-up introduced C2H2 at a 1% addition (vol/vol) to the high purity air with a total flow rate (13 mL min−1) identical to the AIHP method. The gas emissions were analyzed using separate reductive trace gas analyzers that were optimized for the quantification of H2 and C2H4. To verify the observed daily patterns in N2 fixation, 15N2 assimilation measurements were conducted on triplicate samples of Epithemia cultures at targeted time points. Five milliliters of 15N-enriched seawater was added to the subsamples, which were subsequently crimp sealed and incubated for a 2 h period with the same light and temperature conditions as the daily gas measurements. At the end of the incubation, the contents of each vial were filtered onto a pre-combusted glass fiber filter. The concentration and isotopic composition (δ15N) of particulate nitrogen for incubated and non-incubated (i.e., natural abundance) samples was measured using an elemental analyzer/isotope ratio mass spectrometer (Carlo-Erba EA NC2500 coupled with a ThermoFinnigan Delta Plus XP). For each of the described analyses, cell-specific rates were calculated based on the average of triplicate cell concentration measurements, obtained from cell samples preserved at 4 °C with Lugol’s iodine solution and quantified within a week using a Sedgwick-Rafter counting chamber (Electron Microscopy Sciences, cat. # 68050-52). All rate measurement data is provided in the Source Data file.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

A workflow for the calculation of CSI is presented in Fig. 1c. For all the analyses, we used the R v. 4.0.3 software environment20 implemented in RStudio v. 1.4.1103. The scripts used for each methodological step are available at the Figshare repository21. After data download from primary sources (PaleoClim and WorldClim), specifically for the CSI-future map set we performed an initial step aimed to obtain individual bioclimatic variables for each future time period for the four SSPs (Fig. 1b). To achieve this, the median values of nine GCMs were calculated in functions compiled in raster R package22 for each individual bioclimatic variable (see a few exceptions of number of GCMs used in Table 2).Table 2 General circulation models (GCM) used to construct the future map sets.Full size tableThe standard deviation (SD) was estimated as a measure of the amount of variation or dispersion along time series, from which the resulting output maps showed the places where climate conditions remained constant or variable across the temporal periods considered (Fig. 1a,b). The SD, as a way to identify stable/unstable climatic areas, was previously used in other climatic or evolutionary studies4,14. To compute the SD output rasters, we applied the mosaic function setting “fun = sd” from raster R package, calculating the SD for each pixel in the 12 time period rasters for CSI-past and five times for CSI-future, independently for each variable. The mosaic function was also used for the range calculation, with “fun = min” and “fun = max” to obtain the minimum and maximum values of input rasters, respectively, with a further step for subtracting maximum to minimum values.Specifically, for CSI-past, as it includes several time periods with sea-level dropping below the present level (T1, T3, T5, T6, T7, T8, T9; Fig. 1a), we applied a mask of the current land surface, i.e. taking the T12 (Anthropocene) as a template. With this additional step, we were able to remove those pixels (grid cells) currently under the sea but that were once emerged. Most of these pixels, however, were only emerged during the LGM (ca. 21 ka), thus having values for bioclimatic variables for just a single time period (instead of the 12 routinely used for the variability estimation). The inclusion of these areas would result in highly climatically stable regions (low SD values; Supplementary Fig. 1), but this would be an obviously biased result. In contrast, we did not remove those areas affected by the sea-level rising periods, as only three periods contained “NoData” values (T2, T4, T10; Fig. 1a). However, to take this fact into consideration, we created a raster file in which these areas submerged during warm periods are indicated (see Supplementary Fig. 1). Finally, for both CSI-past and CSI-future, the resulting SD values were normalized to values between 0 and 1, with 0 representing completely stable areas and 1 the most unstable ones.The next step was focused on the selection of a relatively uncorrelated set of variables for each map set. We used the removeCollinearity function from virtualspecies R package23 that estimates the correlation value among pairs of variables from a given number of random sample points (10,000 in present case) according to a given method (Pearson for the present case) and a threshold of statistic selected (r  > 0.8 as a cut-off value). The function removeCollinearity returns a list of uncorrelated variables according to the settings specified, randomly selecting just one variable from groups of correlated ones (see Table 1 for a complete list of variables used for each map set). As we compiled estimates of variability independently for each variable and map set (e.g. SD bio1 past, SD bio2 past, etc.), each user can define his own CSI, selecting the more interesting variables according to the case of study.The final CSI maps were obtained by summing the SD values of the variables selected and the subsequent outputs normalized (0 to 1) (Figs. 2–4). Histogram plots were represented with ggplot2 R package24 and maps were exported with ArcGIS v.10.2.2 (Esri, Redlands, California, USA 2014). The histograms were computed for these final CSI maps, which represent the frequency and distribution of CSI values. We presented the final CSI maps with two different colour ramp schemes with ArcGIS. The first consisted of defining equal interval breaks from 0 to 1. The second was based on defining 32 categories with different value breaks for past and future map sets according to the value frequency shown by the histogram plot, i.e. the category with the highest CSI values (no. 32) was 0.71–1 in the past map set and 0.356–1 in the future map set.Fig. 2Maps of Climate Stability Index (CSI) values for the past map set from Pliocene (3.3 Ma) to present (1979–2013), at 2.5 arc-min grid resolution. Colours range from blue for low standard deviation (SD) values, which represents areas with low climatic fluctuations (i.e, low values of CSI) during the period Pliocene–present, to red for high SD values, which shows areas where high climatic fluctuations would have taken place (i.e., high values of CSI). On the upper map, the colour ramp shows equal interval breaks. The histogram with frequency and distribution of CSI values is also shown. On the lower map, the colour ramp has been manually adjusted to a defined set of break values (see details in the text).Full size imageFig. 3Maps of Climate Stability Index (CSI) values for the future conditions (Shared Socioeconomic Pathways: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) from present (1970–2000) to future (2100), at 2.5 arc-min grid resolution. Colours range from blue for low standard deviation (SD) values, which represents areas with low climatic fluctuations (i.e, low values of CSI) from present to future, to red for high SD values, which shows areas where high climatic fluctuations would have taken place (i.e., high values of CSI). The colour ramp shows equal interval breaks. The histogram with frequency and distribution of CSI values is also shown for each future scenario.Full size imageFig. 4Maps of Climate Stability Index (CSI) values for the future conditions (Shared Socioeconomic Pathways: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) from present (1970–2000) to future (2100), at 2.5 arc-min grid resolution. Colours range from blue for low standard deviation (SD) values, which represents areas with low climatic fluctuations (i.e, low values of CSI) from present to future, to red for high SD values, which shows areas where high climatic fluctuations would have taken place (i.e., high values of CSI). The colour ramp has been manually adjusted to a defined set of break values (see details in the text).Full size image More

Study siteAn open field small-scale release of a GM strain of Anopheles mosquitoes was carried-out in July 2019 in the village of Bana in Western Burkina Faso (see Supplementary Fig. 5). The study was granted regulatory authorisation from the National Biosafety Agency (NBA) (order No. 2018-453/MESRSI/SG/ANB of 10 August 2018 authorising the controlled release of genetically modified sterile male mosquitoes) and institutional ethical permission from the Institutional Ethics Committee for Research in Health Sciences: CEIRES (No. A-003/2019-CEIRES granted on January 9th 2019) and a programme of engagement established community acceptance. Details of the extensive stakeholder and communication processes and activities that were conducted in preparation of this release will be published elsewhere. The village of Bana is located in Western Burkina Faso (12°36′00″N, 3°28′59″W), 23 km west of the city of Bobo-Dioulasso.Bana has two main inhabited agglomerations of similar size: Bana Centre (administrative area) and Bana Market (economic area), separated by a 1.5 km unpopulated land band, crossed by a small semi-permanent river and a forest (see Supplementary Fig. 5). In its entirety, the village comprises about 130 compounds for about 759 inhabitants (local census, IRSS 2014). This region is characterised by two seasons: a wet season from June to September and a dry season from November to April. The mean annual rainfall in the village is about 800 mm and the mean temperature is about 27 °C (22–32 °C)52.Study designThe study design followed the format of an MRR experiment with an intensive period of recaptures followed by several months of monitoring to confirm the disappearance of the transgene. Both the period (July) and design (MRR-like experiment) were informed by previous baseline entomological studies and MRR experiments conducted in the same village41,52. Given the low population size expected in July and to avoid over-sampling, a lower recapture effort (reduction of daily swarm sampling number) was implemented than in previous MRR studies performed in the same area.41 The month of July corresponds to the start of the rainy season, when regular rains and cooler weather promote mosquito survival, and the target population of A. coluzzii is at a much lower level than later in the rainy season41,52. In July, plant coverage is still sparse and males tend to seek refuge inside houses and can be captured in good numbers via indoor sampling52.GM sterile strain maintenanceThe mosquito strain used in the experiment was the genetically modified mosquito Anopheles coluzzii sterile male strain referred to as Ac(DSM)2 (for Anopheles coluzzii Dominant Sterile male strain 2). This strain is the product of local introgression (series of backcrosses) of the original Ag(DSM)2 (dominant sterile male on Anopheles gambiae G3 mosquitoes strain 2) with a local A. coluzzii wild-type (WT) colony (female DSM-carrier crossed with male WT)34. The importation of Ag(DSM)2 in Burkina Faso, introgression with local wild type background and maintenance were conducted under regulatory authorisation from the National Biosafety Agency (N°000002/MRSI/SG/ANB of October 21th 2016). The wild-type A. coluzzii strain used for introgression and maintenance of Ac(DSM)2 was colonised in July 2014 from gravid female adults collected in village 7 of the Kou valley (VK7) in western Burkina Faso. Both colonies were maintained in a dedicated ACL2 (Arthropod Containment Level 2) insectary located within the IRSS main campus at Bobo-Dioulasso, Burkina Faso.For general stock-keeping purposes, Ac(DSM)2 was reared in a dedicated and highly secured climate-controlled room at a temperature fixed at 27.4 °C (±0.2, 95% Confidence intervals) and a relative humidity of 76.3% (±3.2, 95% CIs). Rearing rooms have natural light via windows and were supplemented with an artificial lighting regime of LD 12/12 h photoperiod, including dusk (1 h) and dawn (1 h). Larvae were reared in plastic trays (20 × 30 cm) with 1 l of deionized water and fed with an optimised larvae diet regime53. When mosquito larvae reached their level 3 instar (L3) larvae stage they were sorted manually between transgenic and non-transgenic mosquito larvae using a fluorescent stereomicroscope (Olympus SZX7, 2-8 Honduras street, London, United Kingdom) and put in separated trays to continue their development till pupation. At the pupal stage a second round of sorting occurred to separate male and female (sexing) from both strains. The sexing was done under a basic stereomicroscope (Olympus SZX7 basic, 2–8 Honduras street, London, United Kingdom) using a thin soft brush. Pupae from each strain and sex were placed in small plastic cups inside separate fresh adult cages to emerge. Adults were kept in 30 × 30 × 30 cm insect cages (produced locally) and continuously supplied with 10% (w/v) glucose solution (made with deionized water). Each generation, adult female transgenic mosquitoes were mated with male mosquitoes from the wild-type colony and blood-fed with fresh rabbit’s blood, using a membrane feeder (Hemotek® feeder, Hemotek Ltd, Blackburn United Kingdom). Gravid females were allowed to oviposit in plastic Petri dishes containing a wet sponge covered with filter paper. Eggs were collected and hatched in plastic trays. First instar larvae (L1) were then redistributed into several trays to keep similar larvae abundance (about 250 L1 larvae per tray).In accordance with Mendelian inheritance, stock-maintenance crosses between Ac(DSM)2 females and wild type colony males are expected to generate ~50% hemizygous transgenic male and female progeny referred to as Ac(DSM)2 and 50% non-transgenic sibling with a wild-type phenotype referred to as WT-Ac(DSM)2. That the actual phenotypic proportions matched the expected ratio was checked at each generation a part of standard procedures of colony maintenance.Production, sexing, marking and transport of release mosquitoesReleased males were derived from the 41st backcross generation from strain importation. Assuming Mendelian inheritance, the proportion of residual non-local genetic background after so many generations would be negligible (= 0.541).In rearing the release mosquito cohort, some changes were made in the stock-keeping procedure to maximise the fitness of male mosquitoes to be released. These changes aimed to minimise male mosquito handling during the entire process (rearing, sorting, marking and transport). Crucially, no transgenic versus non-transgenic sorting was done at larval stage resulting in a mix of transgenic and non-transgenic sibling males in the release generation. Additionally, to minimise the number of transgenic female mosquitoes released during the study, male versus female sexing was done at both pupae (initial) and adult (complementary) stages leading to a very high sorting accuracy (over 99.5%). Pupae sexing followed the procedure described for stock maintenance. Next, adult sexing focused on removing the few females resulting from errors in pupal sexing. It consisted of removing those rare females from male mosquito cages through inspection by eye of cages and in using a heat source to attract females. Once spotted, these were removed from male release cages using a mouth aspirator.After pupae sexing, male pupae were placed in 25 × 25 × 25 cm emergence cages (made locally and specially designed to fit dimensions of the secured coolboxes used for secure transportation) at a density of ~1400 pupae per cage. Following adult emergence, and over the following days, the cages were inspected by eye daily to check for and remove any females that had not been detected during the pupae sexing process. This procedure led to a total of 15,384 male mosquitoes aged 3–7 days have emerged in 15 cages and ready for marking and release purposes. Screening of ~50 males randomly picked from each emergence cage was conducted in the ACL2 insectary and revealed a slight bias in favour of WT-Ac(DSM)2 sibling males while Ac(DSM)2 male represented 43.3% (39.7–46.9, 95% CIs) of all emerged males. Based on this genotypic ratio, it was estimated that the male release cohort was equivalent to about 6659 transgenic male mosquitoes Ac(DSM)2 and 8725 non-transgenic sibling mosquitoes called WT-Ac(DSM)2 sibling. All males were kept untouched and in the same cages throughout the whole process until being released.The marking process was performed inside the ACL2 insectary facility, and was carried out the day before field release to allow enough time for mosquito recovery, rest and feeding. The environmental conditions were similar to those used during mosquito production. The mosquitoes were marked directly in their cages by using a cloud dye dusting technique. Aside from being fast, this highly efficient marking procedure (100% of mosquitoes successfully marked) was developed to allow the dust-marking of males in their original emergence cages, thereby avoiding male handling and damage during the marking process. This marking technique consisted of injecting pressurised red fluorescent colour powder (Bioquip® Gladwick Rancho Dominguez, CA 90220, USA; Ref: 1162R) into the cages by using a 5 ml syringe and needle to create a cloud of powder. The cages were wrapped with aluminium foil on all sides to prevent the dust from escaping through the meshed walls. Forceful injection of small amounts of powder from different sides of the cages through the aluminium cover and side netting created a dense cloud of fluorescent powder inside the cages to mark all the mosquitoes. Following marking, sugar-water was available ad-libitum to all marked mosquitoes until field release.About 2 h before the release time, the marked mosquitoes within the mosquito cages were transferred from the IRSS insectary to the release site in Bana village. Before leaving the IRSS insectary, the mosquito cages were covered by a second layer of mosquito net for security purposes. The cages were then wrapped with damp towels and placed in lockable cool boxes dedicated to their transport into the field. After having been secured, the cool boxes containing marked mosquitoes were transported to the release site. The entire process complied carefully with all regulatory requirements related to the permissions received for maintenance, handling and the release of these genetically modified organisms in Burkina Faso.Release phaseAll marked mosquitoes were released on the same day at around 5 pm (about one hour before swarming) in the centre of Bana village by opening the travel cages and allowing free exodus. Mosquitoes that did not leave were counted and subtracted from the released total (n = 534, 3.5%). Taking into account mortality and based on the ratio of Ac(DSM)2 and their siblings previously established, a total of 14,850 male mosquitoes were effectively released, with estimated numbers of 6428 hemizygous transgenic male A. coluzzii mosquitoes Ac(DSM)2 and 8422 non-transgenic WT-Ac(DSM)2 siblings.Recapture phaseMosquito recapture activities started the same day of release (about 2 h after mosquito release) and took place daily for a period of 20 days after release. Two different recapture methods were used: swarm collections using sweep nets (SWN) and pesticides spray catches (PSC) inside houses.Swarm sampling started on the evening of the release day using a well-established sweep net collection method47,54. Previous surveys in the same village41 had allowed mapping of swarm location or natural markers where swarming repeatedly occurs. To ensure sampling across the whole study area, a stratified randomised sampling procedure was used to select and sample 15 mosquito swarms daily at dusk using the sweep net collection method. The area of Bana village and Bana Marché were divided in six and four zones, respectively. Zone 1 and 2 in Bana Village are areas of high swarm abundance and the design ensured that these were not over-represented in swarm collections. Each evening, the teams of capturers set-out to collect up to five swarms per zones depending on swarm availability (swarms are fewer and smaller in early July than later in the month). All mosquitoes captured in the swarms were transported in their sweep nets to the field laboratory and frozen until the next morning for processing. At this stage, a random sample of 15 swarms each day was picked for dust screening and genetic analyses.Pyrethroid spray catches started the morning following the release and continued for 19 days. A set of 20 compounds were sampled each day. The sampling design followed that established in baseline studies leading to the release and in previous MRR studies41. Ten of the compounds were selected completely randomly and the other ten are a fixed set of compounds distributed regularly across the whole village. For each compound selected, a single room (1 sleeping room) within one of the house of compound was chosen for sampling. Although some compounds were selected more than once during the recapture period days, a different room (from a different house inside the same compound when applicable) was selected and no room was sampled twice during the survey period.Pyrethroid spray catches started the morning following the release and continued for 19 days. A set of 20 compounds were selected randomly each day. For each compound selected, a single room (sleeping room) was chosen for sampling. Although some compounds were selected more than once during the seven days, a different room (from a different house inside the same compound when applicable) was selected and no room was sampled twice during the survey period.Captured mosquitoes were identified morphologically in the field using adult anopheline morphological identification keys developed by Holstein55 and a field stereomicroscope (Perfex Sciences® Zoom Pro, Reference: S0852Z5 Toulouse, France). All An. gambiae s.l. mosquitoes were counted, checked for fluorescent dust marking using a Biofinder portable ultraviolet illuminator (Vansky, Shenzhen, China) and preserved in 80% ethanol. The identification of each marked mosquito was confirmed independently by two well-trained members of the staff before conservation in individual 1.5 ml storage microtubes for further analysis. The non-dusted wild Anopheles mosquitoes were pooled (10 individuals per tube) and stored in similar conditions. The location of each collection was recorded and mapped using a GPS (Garmin GPS) device, series GPSMAP®62.2.3. For all recaptured mosquitoes, we calculated the straight line distance from the release point to the recapture location using a Euclidean dispersal distance56. In the present case, the space was assimilated to a two-dimensional orthogonal axis system where xl and yl represent the coordinates of the release point and xr and yr represent the coordinates of the recapture point56. Calculation of the estimated flight distance of the mosquitoes then used the following formula:$${EFD}=sqrt{{left({x}_{r}-{x}_{l}right)}^{2}+{left({y}_{r}-{y}_{l}right)}^{2}}$$
(1)
Ac(DSM)2 male identificationMolecular analysis of recaptured marked mosquitoes was performed by PCR, to identify the Ac(DSM)2 strain and distinguish them from their non-transgenic WT-Ac(DSM)2 siblings. This PCR analysis consisted of detecting the integration of the eGFP::I-PpoI of the DSM transgene which characterised the transgenic mosquito strain Ac(DSM)2. In addition, a molecular species-diagnostic was performed concomitantly using the PCR technique based on the detection of SINE 200× locus57 and this PCR served as a control for DNA integrity. Each mosquito was split into two parts (abdomen and thorax) using forceps. The abdomen was used for the PCR and processed for DNA extraction using ‘squish’ buffer (PCR reaction buffer). The thorax was stored in 80% ethanol at −20 °C. For each mosquito analysed, the same DNA extract was used for both eGFP::I-PpoI transgene detection (identification of Ac(DSM)2 transgenic mosquito) and SINE 200X locus detection (for specie identification and DNA quality control). The Ac(DSM)2 construct was detected using the primers: pBacR-fwd [ATCGGTCTGTATATCGAGGTTTATT] and pBacR-Rev [CTCTAATATTTTGCCAAATGAAGTGCC] targeting the piggyBacR region required for insertion of the transgene. PCR reactions used the Gotaq® PCR kit (GoTaq® G2 Flexi DNA Polymerase, reference: M829B, Promega Corporation, 2800 Woods Hollow Road·Madison, WI 53711-5399, USA).Monitoring of Ac(DSM)2 non-persistenceMonthly mosquito collections were carried out using PSC and swarm sampling to confirm the disappearance of the Ac(DSM)2 transgene from the release site. Monitoring collections were conducted monthly for seven months. This period of monitoring was justified by the regulatory requirement of describing the Ac(DSM)2 disappearance through failure to detect the Ac(DSM)2 transgene for a minimum period of three consecutive months and with high statistical power. During each month of survey, a randomised selection of 20 houses (one room per house) and 20 swarms was sampled. All collected mosquitoes were identified morphologically using identification keys and a field stereomicroscope. Mosquitoes from A. gambiae complex were counted and preserved in 80% (v/v) ethanol for subsequent molecular identification. Each month, a representative sample of collected mosquitoes (up to 300 when available, from both PSC and swarm sampling) was analysed using the Ac(DSM)2-specific and species-specific PCR diagnostics described above to detect whether any A. gambiae s.l. mosquitoes were carrying the DSM transgene.Bayesian inference of mosquito survival, movement and population sizeWe fitted the recapture data to a diffusion model to further investigate dispersal and survival of the marked Ac(DSM)2 and their sibling males, and also to estimate the number of mosquitoes in the background population. This model assumes that the released mosquitoes tend to move in a random manner, meaning they repeatedly take short randomly directed flights that are independent of one another and of the environment. As described below, however, our estimation procedure does also allow for small additional movements where mosquitoes are attracted into nearby swarms at swarming time (dusk), or nearby houses for resting behaviour.We write the diffusion equation as$${partial }_{t}u=D{partial }_{x}^{2}u,$$
(2)
where (u(x,t)) is the probability density of the location of a single marked mosquito at location (x) and time (t), conditional on the individual being alive, and (D) is the diffusion coefficient. Assuming a point release at time (t=0), the above equation has solution$$uleft(r,tright)=frac{{e}^{-frac{{r}^{2}}{4Dt}}}{4,pi D,t}$$
(3)
where (r) is the distance from the release point. We next assume that the released mosquitoes have a constant survival probability of (s) per day, so that the expected number of extant released mosquitoes on day (d) is (R{s}^{d}) where (R) is the number that were released. The expected number of released mosquitoes in a small area ({dA}) is then given by$$qleft(r,dright)=R{s}^{d}frac{{e}^{-frac{{r}^{2}}{4Dd}}}{4,pi D,d}{dA}.$$
(4)
We take three further steps to convert this equation for (q(r,t)) into a likelihood function for the spatio-temporal distribution of recaptures of either Ac(DSM)2 or their sibling males. First, we pool the recaptures on a given day, and made by a given method (either swarm sampling or PSC), by partitioning the study area into annuli centred on the release location. These annuli are the recapture regions, and the expected number of extant marked mosquitoes in a given annulus is the integral of (q(r,d)) over that annulus. This step, therefore, averages out the expected number of marked mosquitoes from the inner to the outer radius of each annulus, and the annulus widths set the scale at which small movements towards swarms or houses, where mosquitoes may be recaptured, are assumed to occur in addition to random movements that underpin the diffusion model. We set the width of each annulus to 50 m, based on our judgement that this distance balances the capacity to separate recaptures at different distances (this capacity reduces with width), with the confidence that movements towards swarms or houses will largely remain within annuli (this confidence increases with width).Second, we assume the observation probability of mosquitoes in a given sample (representing an annulus, capture method, and day), is the number of unmarked mosquitoes in the sample divided by the (unknown) unmarked population size in that annulus. The unmarked population is assumed to have a uniform density, that we will infer alongside the mobility and survival parameters. Finally, we assume the number of marked mosquitoes in a given sample is Poisson-distributed around the expected number.For the data from each recapture method, we used the likelihood function to sample a posterior distribution for the diffusion coefficients and survival rates of the two types of released male mosquitoes, and the density of the unmarked population. We assumed uniform priors with respect to all five parameters and used a Markov chain Monte Carlo algorithm based on Metropolis-Hastings sampling to sample the posterior distribution directly from the log-likelihood. For each analysis (swarm or PSC), we sampled for 100,000 iterations, of which we discarded the initial 20,000 as a transient and thinned the remainder by 100, giving 800 samples in total.Statistical analysisData were analysed using the software JMP 14 (SAS Institute, Inc.). All data were checked for deviations from normality and heterogeneity, and analyses were conducted using parametric and non-parametric methods as appropriate. General linear modelling with Poisson distribution was used to describe male recaptures as a function of genotype and time. Kruskall-Wallis and Mann-Whitney test was used to describe respectively male participation in swarm and Euclidian dispersal distances. General linear modelling with Poisson distribution was used to describe male recaptures as a function of genotype and time. Estimates of population size, survival, and mobility were calculated using a Bayesian approach as described above.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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