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Mixed layer depth and phytoplankton accumulation dynamics in the North AtlanticThe NAAMES expeditions intensively measured biological, chemical, and physical properties from 4 to 7 locations, or stations, in each bloom phase during November (Winter Transition), March−April (Accumulation), May (Climax; same as Climax Transition22), and September (Decline)22. Stations spanned a broad range in latitude (~37 °N to ~55 °N, Fig. 1a), sub-regional classifications (Gulf Stream and Sargasso Sea, Subtropical, Temperate and Subpolar)24, and MLDs (tens to hundreds of meters) (Fig. 1b and Supplementary Fig. 1). MLDs were calculated using a density difference threshold of 0.03 kg m−3 from the top 10 m25. Field data and associated analyses are derived from phytoplankton 1–20 µm in diameter and their associated communities sampled within the photic zone (40, 20, 1% surface irradiance) and within the mixed layer, unless otherwise noted.Fig. 1: Mixed layer depth and phytoplankton accumulation dynamics.a Locations of sampled stations within subregions of the Northwest Atlantic during the NAAMES expeditions (color coded and shaped by the bloom phase; W. Tran = Winter Transition; Acc = Accumulation; Clim = Climax; Decl = Decline; See key in Panel B). Black rectangle represents the study area of NAAMES and this research. b Mixed layer depths within the NAAMES campaigns (black box in Fig. 1a), calculated from CTD casts at each of the station locations (colored symbols) and Bio-ARGO profiling floats that were deployed at stations and sampled continuously (small circles with separate grey lines for each float). The latter provided a history of mixed layer depths before, during, and after occupation. c Bloom phase distribution of accumulation rates for in situ phytoplankton populations sampled several times per day at 5 m. Each point represents the median accumulation rate of each station. d Bloom phase distribution of phytoplankton cell accumulation rates derived from on-deck incubations of phytoplankton populations at simulated in situ light and temperature conditions (see ‘Methods’). Each point represents a biological replicate. Data in panels (c) and (d) are based on cell concentrations and contoured with ridgeline smoothing to represent the distribution of accumulation rates across stations within a given bloom phase. The size of contour peaks is driven by frequency of observations. e Phytoplankton concentration (taken from 5 m) as a function of water column stratification (expressed as buoyancy frequency; s−1). Higher buoyancy frequencies to the right of the plot represent more stratification. A LOESS line of best fit (shaded area = 95% confidence interval) for data shows the general trend of phytoplankton concentration across all seasonal phases. Different letters denote statistically significant groups (p  0.05, Kruskal−Wallis) between populations collected from 5 m in-line sampling throughout the day (in situ) and contemporaneous incubations of the same phytoplankton populations under simulated in situ irradiance and temperature (incubations; see ‘Methods’) (Fig. 1c, d). Accumulation rates using incubations calculated via cell concentration or via biovolume were not statistically different (Supplementary Fig. 2b).Phytoplankton cell concentration and biovolume generally increased with water column stability (stratification), during the Winter Transition, Accumulation, and Climax phases (Fig. 1e and Supplementary Fig. 2c). Stratification was quantified by the buoyancy frequency averaged over the upper 300 m of the water column (see ‘Methods’). Higher values of buoyancy frequency indicate a more stratified water column where exchange with nutrient-rich water below the surface is reduced. Strongly stratified water columns (buoyancy frequencies above 2 × 10−5 s−1) during the Decline phase were associated with lower cell concentrations (Fig. 1e), consistent with enhanced phytoplankton loss or reduced accumulation. Phytoplankton biovolume and cell size distribution within 1–20 µm-sized phytoplankton cells increased during the Decline phase (Supplementary Fig. 2c–e). These higher biovolumes could have been a result of changes in community composition. They could have also been attributed to aggregation caused by virus infection20,21,28, as virus concentrations were highest during this season (discussed below), or by light stress27, as mixed layer populations were more consistently exposed to daily higher irradiance levels characteristic of shallow mixed layers (Fig. 1e).In situ phytoplankton cell concentrations increased from Winter Transition until the Climax phase, from ~1 × 106 to 2.5 × 107 cells L−1 (Fig. 2a, c, gray boxes). On-deck incubations showed similar trends but had higher overall cell concentrations (Fig. 2a, c, white boxes). The Decline phase was characterized by a 4-fold reduction in median phytoplankton cell concentrations from the peak abundances observed during Climax phase (Fig. 2a, c). The stress markers utilized in this study provided a unique view into the physiological status of communities across these annual bloom phases (Supplementary Table 1). Our ROS and compromised cell membranes biomarkers specifically targeted eukaryotic phytoplankton, given the conditions used for flow cytometry analysis (see ‘Methods’). PCD-related proteases and lipids were extracted from biomass collected onto 1.2 and 0.2 µm diameter membrane filters, respectively. Consequently, these biomarkers could also include eukaryotic heterotrophs and bacteria in the system. Induction of caspase and metacaspase activities have been found in diverse phytoplankton, such as coccolithophores, diatoms, chlorophytes, nitrogen-fixing cyanobacteria, and dinoflagellates cells undergoing stress, senescence, and death29. They have also been reported in stressed or dying grazers30, although no marine species has been explicitly studied. TAGs are found mainly in marine eukaryotic phytoplankton31,33,33 and grazers34. The highly unsaturated fatty acids in the PC and OxPCs detected in our measurements are also indicative of eukaryotic organisms, and not marine cyanobacteria32 or heterotrophic bacteria35.Fig. 2: Seasonal phases have distinct physiological state signatures.a, c Concentration of phytoplankton cells sampled within the mixed layer at depths associated with 40, 20, or 1% surface irradiance during different seasonal phases (W.Tran = Winter Transition; Acc = Accumulation; Clim = Climax; Decl = Decline). Data are shown for in situ water (grey bars) and on-deck incubations (open bars). Population-wide levels of a, b cellular reactive oxygen species (colored by fluorescence fold change from unstained; median per population) and c, d cell death (colored by % compromised membrane). Plots (b) and (d) are contoured with ridgeline smoothing to represent the relative in situ distribution of biomarker levels within each phase. The size of contour peaks is driven by frequency of observations. e, f In situ inventories of live (e; green) and dead (f; red) cells within the mixed layer through the different phases. Individual circles denote biological replicates. Box plots in (a), (c), (e) and (f) represent the median value bounded by the upper and lower quartiles with whiskers representing median + quartile × 1.5. Different letters denote statistically significant groups (p  5 µM; PO4  > 0.4 µM). Notably, nutrient concentrations during the Climax phase were similar or higher than those observed for Accumulation phase samples, which had lower ROS signatures (Fig. 2b).Phytoplankton cells in the Decline and Winter Transition phases had a higher percentage of compromised cell membranes, reaching levels as high as 80% (Fig. 2c, d). Both late stage viral infection and PCD have been linked to high levels of compromised membranes13,29. The percentage of phytoplankton cells with compromised membranes was used to calculate concentrations of live and dead cells within the mixed layer across the bloom phases. Living phytoplankton cell concentrations generally increased from the Winter Transition through the Climax phase (Fig. 2e). The variability of dead cells was highest in the Decline phase, which also had the largest variation in total, living, and dead cell concentrations (Fig. 2c, e, f).Targeted analysis of OxPC, and TAGs in resident phytoplankton communities provided further context of changes in physiological states due to their relevance in cellular stress and loss processes. The seasonal bloom phases were characterized by distinct levels of these lipids (Fig. 3 and Supplementary Fig. 4). OxPC levels were highest in the Climax phase (Fig. 3a), where mixed layers had recently shallowed (Fig. 1b) and were concomitant with high intracellular ROS levels (Fig. 2b). Subcellular environments lacking in adequate antioxidant capacity are expected to accumulate OxPC40 particularly when a shallow mixed-layer enhances UV exposure15. Chlorophyll-normalized TAG was highest in the Decline phase (Fig. 3b), which also had the lowest accumulation rates (Fig. 1c, d). High cellular TAG levels have been observed in senescent41,42 or nutrient limited9 diatoms, and virus infected haptophytes43.Fig. 3: Seasonal phases are characterized by distinct lipid profiles and cell death-associated proteolytic activity.a Oxidized phosphatidylcholine (OxPC40:10, OxPC42:11, OxPC44:12) normalized to total phosphatidylcholine (PC40:10, PC42:11, PC44:12). b Triacylglycerol (TAG; pmol L−1), normalized to ChlA (peak area/L). c (top) The proportion of in situ samples with positive caspase activity (cleavage of IETD-AFC; color shading). (bottom) Caspase-specific activity rates (µmol substrate hydrolyzed h−1 µg protein−1) for in situ populations. d (top) The proportion of in situ samples with positive metacaspase activity (cleavage of VRPR-AMC; color shading). (bottom) Metacaspase-specific activity rates (µmol substrate hydrolyzed h−1 µg protein−1) for in situ populations. All box plots represent the median value bounded by the upper and lower quartiles, with whiskers representing median + quartile × 1.5. Different letters denote statistically significant groups (p  More

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Site informationWe collected radioactivity data of wild mushrooms and wild edible plants from inspection results of specimens brought in by residents in Kawauchi Village, which is located 12–30 km away from the FDNPP (Fig. 1). Kawauchi Village is considered small, with an area of 197.4 km2, and a population of about 2500 (2820 in 2010 and 2518 in 2021)48. It is located in the middle of the Abukuma Highlands, where the elevation ranges from 270 to 1,192 m above the sea level. It has a forest coverage of 89.0%, which is higher than the average for Fukushima Prefecture (71%) and Japan as a whole (69%)49. 137Cs deposition in the village ranged from 42 to 960 kBq/m2 in 2011, estimated from an aircraft monitoring28. Before the accident, its residents were accustomed to gathering wild foods, such as wild edible mushrooms, plants, mammals, and wild honey50; many have been brought in for inspection. Information on collection areas of sub-village levels, called “Ko-aza” in Japanese, is also recorded. For these reasons, we thought that the data of the brought in inspection in Kawauchi Village would possess high value as data for inter-species and inter-region analysis on the wild mushrooms and edible plants’ radioactivity concentrations.Radioactivity data of mushrooms and wild plantsFukushima Prefecture sets up a system for each municipality to inspect radioactivity in vegetables and mushrooms consumed by residents, and Kawauchi Village started its inspection program in May 2012. Simple inspection machines are set up at public facilities, and inspections are conducted upon application by residents. In Kawauchi Village, the location of samples inspected was requested at the sub-village level. The inspection results were regularly reported in the village newsletter, along with the inspection date, inspected food, and collection location. The data compiled from May 2012 to March 2020 was provided to us through the village officials. Orita et al. analyzed the same inspection data of agricultural products in Kawauchi Village24. They used 7668 food data from April 2013 to December 2014, including 1986 wild plants and mushrooms data for internal radiation exposure assessment. Some of their data overlap with the data used in our analyses.System of monitoring radioactivity in Kawauchi VillageKawauchi Village started the brought in inspection in May 2012, and there is a maximum of eight inspection stations and currently three stations managed by residents. In the inspection sites, there are four types of NaI (Tl) or CsI (Tl) scintillation detectors. The machine names are Triathler Becquerel Finder (Hidex, Oy, Finland), Captus-3000A (Capintec, NJ), CAN-OSP-NAI (Hitachi Aloka, Tokyo, Japan), and FD-08Cs1000-1 (X-Ray Technology, Osaka, Japan). Table S4 shows the specifications of the machines51,52,53. All instruments have been confirmed to meet the radiocesium screening method requirements for food53. Among these machines, FD-08Cs1000-1 can measure radioactivity non-destructively, and the others conduct destructive measurements. The sample weight is approximately 500 g, and the counting time is 30 min. FD-08Cs1000-1 outputs the summed concentration of the two radiocesium nuclides (134Cs and 137Cs), and its detection limit is 10 Bq/kg (for total 134Cs + 137Cs). Each of the other three machines separately outputs the concentrations of 134Cs and 137Cs, and the detection limit is 10 Bq/kg for each radionuclide. Energy calibrations and background checks were performed daily, and the accuracy was periodically verified with brown rice whose radiocesium concentration was verified by calibrated high-purity Germanium (HPGe) detectors installed in the Fukushima Nuclear Center49. Table S4 shows the results of quality control using brown rice.Data preparation of radioactivity of samplesFrom the radioactivity data of wild mushrooms and plants, we picked up data that met the following criteria;

Data have information of sampling location at sub-village levels

Items that are not confirmed to be cooked products such as “boiled” or “dried.”

Species with more than ten samples in which radiocesium was detected.

In cases where mushrooms and wild plants were given in dialects, we confirmed the species’ names with residents. The names of the species were determined from the Japanese names of the items, but in some cases, it was not possible to distinguish between Cortinarius salor (“Murasakiaburashimejimodoki” in Japanese) and C. iodes (“Murasakiaburashimeji”), considered to be closely related species, so the two were mixed for analysis. The leaf stalk and scape of Petasites japonicus (Japanese butterbur) are called “Fuki” and “Fukinotou” in Japanese, respectively, and are registered separately. Therefore, despite being the same species, they were distinguished in the analysis. In this data, there were not sampling date but measurement date. Therefore, the date of measurement and sample collection were assumed to be the same.The 137Cs concentration results were used in the model analysis. The reason for not using the134Cs concentration among the measured values is explained in the subsection of “Bayesian estimation”. 137Cs concentrations were decay-corrected to March 11th, 2011 for comparison with Komatsu et al. (2019). Based on the assumption that the 134Cs/137Cs ratio at the time of the accident was one54, the summed concentration of 134Cs and 137Cs concentration taken by FD08-Cs1000-1 was converted to a 137Cs concentration, which was decay-corrected to March 11th, 2011, using the following equation;$${}^{137}C{s}_{2011/03/11}=tC{s}_{mathrm{sampling}_mathrm{day}}*frac{{0.5}^{dy/30.17}}{{0.5}^{dy/2.065}+{0.5}^{dy/30.17}}$$In this equation, dy indicates the period from March 11th, 2011, to the date of measuring, and it is expressed by decimal years.Sub-village (“Ko-aza”) boundary map of Kawauchi VillageKawauchi Village comprises eight administrative communities (called “Oh-aza” in Japanese), which are further subdivided into small administrative units known as “Ko-aza”. Here, we refer to these small administrative units as sub-villages. We obtained a sub-village map from the administrative office. The printed map was originally drawn by hand and had been used for village administration. To create a polygon shapefile of the map, we digitized it by scanning, geo-rectifying, and digitizing using GIS software in TNTmips v2014 (MicroImages, Inc, NE) and ArcGIS 10.3 (Esri, Inc, CA). We used this map to associate land names with monthly radioactivity data from samples and to estimate sample collection locations.Deposition dataFor the 137Cs deposition data of this area, we used 250 m grid deposition data measured by the Ministry of Education, Culture, Sports, Science and Technology28,55 and then corrected by Kato and Onda26. We computed the geometric mean value of 137Cs deposition within each sub-village polygon. The 137Cs deposition is also decay-corrected to March 11th, 2011.Bayesian estimationWe constructed a Bayesian model partially modified from Komatsu et al.22 to estimate 137Cs concentration (137Cssample). The model is based on the Gonze and Calmon’s concept of normalized concentration (NC) as expressed by:$$NC= frac{Cs}{D}$$where D indicates the radiocesium deposition amount based on the aircraft monitoring. Then the above equation is transformed and logarithmized to yield;$$mathrm{log}Cs=mathrm{log}NC+mathrm{log}D$$In this expression of the model equation, we further assumed that the logartihm of NC encompassed the summed effects of species identity, collection date, and collection site, and that the logarithm of NC was normally distributed around the estimated mean as per the following equations;$$begin{array}{l}{text{log}}_{10}{hspace{0.17em}}^{137}C{s}_{mathrm{sample}} sim Normal({mu }_{mathrm{sample}},sigma )\ {mu }_{mathrm{sample}} ={text{log}}_{10}N{C}_{mathrm{sp}}+{lambda }_{mathrm{sp}}Y+{text{log}}_{10}{D}_{mathrm{loc}}+{r}_{mathrm{loc}}\ {text{log}}_{10}N{C}_{mathrm{sp}} sim Normal({mu }_{mathrm{sp}},{sigma }_{mathrm{sp}})\ {lambda }_{mathrm{sp}} sim Normal({mu }_{mathrm{lambda sp}},{sigma }_{mathrm{lambda sp}})\ {r}_{mathrm{loc}} sim Normal(0,{sigma }_{mathrm{loc}})end{array}$$where NCsp, λsp, Dloc and rloc indicate characteristics of concentration of species, temporal trends of species, 137Cs deposition of each sub-village area and effects of sub-village on concentration, respectively. rloc is a parameter with zero mean that represents the deviation of the concentration effect from the expected value based on the deposition (Dloc) value at the point of collection. These parameters except Dloc were obtained from hierarchically sampled from normal distribution with hierarchical parameters (μsp, σsp, μλsp, σλsp, σloc). Additionally, rloc was sampled using the Intrinsic Conditional Auto-Regressive (Intrinsic CAR) model56, which is one of the models considering spatial auto-correlation. For samples whose measured radiocesium concentrations were below the detection limit, radiocesium concentration values were estimated by a censoring distribution in which the detection limit was treated as the upper bound57. This model was defined as the “sub-village model” for this research. This model is similar to model 6 in Komatsu et al.22 but differs in that their previous model takes into account 134Cs values and differences between 134 and 137Cs values. Komatsu et al. evaluated the regional trend in the difference between134Cs and 137Cs concentrations across eastern Japan because 137Cs originating from nuclear bomb tests before the FDNPP accident was detected in wild mushrooms sampled in the northern and southern parts of eastern Japan, which are far from the FDNPP and received less deposition from the accident ( More

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