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Sample collection and hydrographySampling was conducted aboard the University of Victoria’s MSV John Strickland either weekly, biweekly or monthly between 11 March 2010 and 15 November 2017 in Saanich Inlet at 48.59°N, 123.50°W (Fig. 1). To standardize measurements and due to biological significance, seawater was collected from the euphotic zone. Sampling depths corresponded to approximately 100, 50, 15, and 1% of the photosynthetically active radiation (PAR) at the surface (Io). These “light” depths were either determined using a CTD-mounted PAR sensor or a Secchi disk. CTD profiles were performed prior to each seawater cast to measure depth, temperature and conductivity of the water column, and PAR, fluorescence, and dissolved oxygen (when available).Seawater from each light depth was collected using Niskin or GO-FLO bottles on either a rosette sampler or an oceanographic wire. When possible, individual samples were collected directly from the Niskin or GO-FLO bottles. When time was not sufficient to allow direct sampling, bulk samples of seawater from each depth were collected into acid-washed polyethylene carboys, kept cold in the dark, and homogenized before sub-sampling for the individual measurements.Dissolved nutrientsFor the measurements of nitrite (NO2−), nitrate and nitrite (NO3− + NO2−), phosphate (PO43−) and Si(OH)4, seawater samples from each light depth were syringe-filtered through a combusted 0.7 µm (nominal porosity) glass fibre filter into acid-washed 30-mL polypropylene bottles and immediately frozen. All nutrient samples were stored at −20 °C until analysis. Concentrations of NO2−, NO3− + NO2−, PO43−, and Si(OH)4 were determined using an Astoria Nutrient Autoanalyzer (Astoria-Pacific, OR, USA) following the methodology of Barwell-Clarke and Whitney22. During 2014 and 2015, samples for the measurement of Si(OH)4 were collected separately from those for the other nutrients, filtered with a 0.6 µm polycarbonate membrane filter and stored at 4 °C. During this period, Si(OH)4 concentrations were determined manually using the molybdate blue colorimetric methodology23. Replicate (2 or 3) nutrient samples were taken at each depth; average data are presented in the published dataset and the figures (Fig. 2).Fig. 2Dissolved macronutrient concentrations in the euphotic zone of Saanich Inlet from March 11, 2010 to November 15, 2017. Left panels show depth-integrated concentrations (black bars on top) and time-series profiles (filled contour/scatter plots on bottom) for (A) nitrate plus nitrite (NO3− + NO2−), (B) phosphate (PO43−) and (C) silicic acid (Si(OH)4). In the time-series profiles, 2012–2013 data are not interpolated due to single-depth sampling. Grey shaded regions in top panels indicate the phytoplankton growing seasons considered for this study (March 1st – October 30th). Right panels show monthly-averaged depth profiles for the entire 8-year period, illustrating euphotic zone seasonality for each nutrient. The color scale bars on the far right apply to both the time-series vertical profiles and the 8-year seasonal plots. Sampling depths are indicated by round symbols. The year labels are positioned under the tick marks corresponding to January.Full size imageSuspended particulate matterTotal chlorophyll-aChlorophyll-a (Chl-a) was used as a proxy for phytoplankton biomass (Fig. 3A). For total Chl-a analysis, seawater samples (0.25–1 L) were gently vacuum filtered onto 0.7 µm (nominal porosity) glass fiber filters, which were then stored at −20 °C until analysis. Chl-a concentrations were determined using the acetone extraction and acidification method24,25. Acidification of samples decreased the likelihood of overestimation of Chl-a concentrations due to the presence of chlorophyll degradation products26. Filters were submerged in 10 mL of 90% acetone, sonicated for 10 minutes in an ice bath, and left to extract at −20 °C for 22 h. Following the extraction period, samples were allowed to equilibrate to room temperature (~2 h). Fluorescence of the acetone solution containing the extracted Chl-a was measured before and after acidification with 1.2 N hydrochloric acid using a Turner 10-AU fluorometer. The final concentrations of total Chl-a were calculated from measurements made before (Fo) and after (Fa) acidification using Eq. (1)25. The coefficient (τ) of Eq. (1), adapted from Strickland and Parsons25, was derived from a calibration of the Turner 10-AU fluorometer with known pure chlorophyll standards (Table 2).$${rm{C}}{rm{h}}{rm{l}} mbox{-} {rm{a}}(mu g,{L}^{-1})=frac{tau }{tau -1}ast ({rm{F}}{rm{o}}-{rm{F}}{rm{a}})ast 0.814ast left(frac{{rm{V}}{rm{o}}{rm{l}}.{rm{A}}{rm{c}}{rm{e}}{rm{t}}{rm{o}}{rm{n}}{rm{e}},{rm{e}}{rm{x}}{rm{t}}{rm{r}}{rm{a}}{rm{c}}{rm{t}}{rm{e}}{rm{d}}}{{rm{V}}{rm{o}}{rm{l}}.{rm{S}}{rm{e}}{rm{a}}{rm{w}}{rm{a}}{rm{t}}{rm{e}}{rm{r}},{rm{f}}{rm{i}}{rm{l}}{rm{t}}{rm{e}}{rm{r}}{rm{e}}{rm{d}}}right)$$
(1)
Fig. 3Biological particulate concentrations in the euphotic zone of Saanich Inlet from March 11, 2010 – November 15, 2017. Left panels show time-series profiles (filled contour/scatter plots) of (A) total chlorophyll-a (Total Chl-a), (B) particulate carbon (PC), (C) particulate nitrogen (PN), and (D) particulate biogenic silica (bSiO2). The 2012–2013 data are not interpolated due to single-depth sampling. In A, the bar plot in the top panel shows percent contribution of different size fractions to total Chl-a. In (B–D), black bars in top panels show depth-integrated concentrations. Grey shaded regions in bar plots indicate phytoplankton growing seasons considered for this study (March 1st – October 30th). Right panels show monthly-averaged depth profiles for the entire 8-year period, illustrating euphotic zone seasonality for each particulate. The color scale bars on the far right apply to both the time-series vertical profiles and the 8-year seasonal plots. Sampling depths are indicated by round symbols. The year labels are positioned under the tick marks corresponding to January.Full size imageSize fractionated chlorophyll-aTo determine the percent contributions of “pico” (0.7–2 µm), “small nano” (2–5 µm), “large nano” (5–20 µm) and “micro” ( >20 µm) phytoplankton to total Chl-a, seawater samples (0.25–1 L) separate from those used for total Chl-a) were consecutively filtered through 20, 5 and 2 µm polycarbonate membrane filters and 0.7 µm (nominal porosity) glass fiber filters. Between 2013 and 2017, the “pico” and “small nano” size classes were collected as one fraction (0.7–5 µm). Analysis of Chl-a concentrations for each size fraction followed the same procedure outlined for total Chl-a.Particulate carbon and nitrogenParticulate C and N measurements were obtained from seawater samples incubated for carbon (ρC) and nitrate uptake (ρNO3) rates (see section on “Uptake rates of carbon and nitrate” for methodology) (Fig. 3B,C). PC and PN measurements presented in this dataset were taken at the end of ρC and ρNO3 incubations; however, original (‘ambient’) values can be back calculated by subtracting the amount of C and N taken up during the incubation period from the final PC and PN values. The differences between after-incubation PC and PN data and back-calculated ambient values were not significantly different than the measurement error.Particulate biogenic silicaParticulate biogenic silica was used as a proxy for siliceous phytoplankton biomass (Fig. 3D). Seawater samples (0.5–1 L) from each depth were gently vacuum filtered through 0.6 µm polycarbonate membrane filters. Filters were folded and placed in polypropylene centrifuge tubes, dried for 48 h at 60 °C, and then stored in a desiccator at room temperature until analysis. Filters were digested with 4 mL of 0.2 M NaOH for 30–45 min in a water bath at 95 °C27. After digestion, samples were neutralized with 0.1 N HCl and cooled rapidly in an ice bath. Samples were centrifuged to separate out the undissolved lithogenic silica, and colorimetric analysis was performed on the supernatant. The transmittance of the samples, standards, and reverse-order reagent blanks were read at 820 nm using a Beckman DU 530 ultraviolet-visible (UV/Vis) spectrophotometer27,28.Uptake rates of carbon and nitrateSeawater samples (~0.5–1 L) were gently collected into clear polycarbonate bottles. One additional sample was collected from the 100% light depth, into a dark polycarbonate bottle, which did not allow light penetration. After the addition of the isotopic tracers (see below), bottles were placed into an acrylic incubator with constant seawater flow to maintain surface seawater temperature. Three acrylic tubes wrapped in colored and neutral density photo-film (to obtain 50, 15, and 1% of surface PAR) were used to incubate sample bottles under the same in-situ light conditions from which samples were collected. Samples from the 100% light level were placed inside the same acrylic incubator, but outside of the film-covered tubes. A LI-COR® LI-190 Quantum sensor was installed next to the incubator and continuously recorded incoming PAR for the entire incubation period. During sampling in 2010 and 2011, all experiments were performed using a shipboard incubator. For sampling from 2012 onwards, all experiments were done using an incubator on land (University of Victoria Aquatic Facility), which was connected to a seawater system maintained at local surface seawater temperature (approximately 9–12 °C depending on the time of year).Rates of C (ρC) and NO3 (ρNO3) uptake were determined using a stable isotope tracer-technique29,30 (Fig. 4). A single seawater sample from each light depth received a dual spike, with NaH13CO3 (99% 13C purity, Cambridge Isotope Laboratories) for the determination of ρC and Na15NO3 (98 + % 15N purity, Cambridge Isotopes Laboratories) for the determination of ρNO3. Isotope additions were made at approximately 10% of ambient dissolved inorganic carbon (DIC) and NO3− concentrations.Fig. 4Carbon and nitrate uptake rates in the euphotic zone of Saanich Inlet from March 11, 2010 – November 15, 2017. Left panels show depth-integrated rates (black-bars on top) and time-series profiles (filled contour/scatter plots below) of (A) carbon (ρC) and (B) nitrate (ρNO3) uptake rates. In the time-series profiles, 2012–2013 data are not interpolated due to single-depth sampling. Grey shaded regions in depth-integrated plots indicate phytoplankton growing seasons for this study (March 1st – October 30th). Right panels show monthly-averaged depth profiles for the entire 8-year period, illustrating euphotic zone seasonality for carbon and nitrate uptake. The color scale bars on the far right apply to both the total time-series vertical profiles and the 8-year seasonal plots. Sampling depths are indicated by round symbols. The year labels are positioned under the tick marks corresponding to January.Full size imageSpiked seawater samples were incubated for 24 h, except from 2010 to 2013 when the incubation period was 4 to 6 hr. After incubation, the entire sample was gently vacuum filtered onto a combusted 0.7 µm (nominal porosity) glass fibre filter. Filters were dried for 48 h at 60 °C and kept in a desiccator at room temperature until analysis. Filters were packed into pellets and sent to the Stable Isotope Facility at the University of California (UC) Davis for analysis of 13C and 15N enrichment, and total C and N content by continuous flow isotope ratio mass spectrometry and elemental analysis, respectively. For these measurements, UC Davis uses either an Elementar Vario EL Cube or Micro Cube elemental analyzer (Elementar Analysensysteme GmbH, Hanau, Germany) interfaced to either a PDZ Europa 20–20 isotope ratio mass spectrometer (Sercon Ltd., Cheshire, UK) or an Isoprime VisION IRMS (Elementar UK Ltd, Cheadle, UK).Carbon and NO3− uptake rates were calculated using Eq. 3 of Hama et al.29, and Eq. 3 and 6 of Dugdale and Wilkerson30, respectively.For samples incubated for less than 24 h, the daily C or NO3− uptake rates (ρX) were calculated using a PAR extrapolation method shown in Eq. (2):$$rho X(mu mol,{L}^{-1}da{y}^{-1})=left(rho X(mu mol,{L}^{-1}h{r}^{-1})divleft(frac{PAR,during,incubation}{Total,Daily,PAR}right)right)ast 24$$
(2)
Additionally, to account for NO3− uptake occurring under no light, ρNO3− was measured in dark bottles and this rate was added to the ρNO3− of each sample incubated for less than 24 h. The ρNO3− DARK was calculated following Eq. (3):$$rho N{O}_{3}DARK(mu mol,{L}^{-1}da{y}^{-1})=left(rho N{O}_{3}DARK(mu mol,{L}^{-1}h{r}^{-1})divleft(frac{Total,Daily,PAR-PAR,during,incubation}{Total,Daily,PAR}right)right)ast 24$$
(3)
PAR data used in Eqs. (2) and (3) came from the LI-COR® LI-190 Quantum sensor that was mounted beside the incubator. The seawater DIC value for each sample was calculated using a regression equation relating water density to DIC for Saanich Inlet31. Ambient NO3− concentrations were measured as described above. More

Monitoring sites and speciesThe monitoring survey was conducted at five sites in Fukushima Prefecture (sites 1–4 and A1) and at one site in Ibaraki Prefecture (site 5), Japan (Fig. 1). Sites 1, 2, and A1 are located in Kawauchi Village, site 3 in Otama Village, site 4 in Tadami Town, and site 5 in Ishioka City. Monitoring at sites 1–5 was started in 2011 or 2012, and site A1 was additionally monitored since 2017. The tree species, age, mean diameter at breast height, initial deposition density of 137Cs, and sampling year of each sample at each site are listed in Table 1. The dominant tree species in the contaminated area, namely, Japanese cedar (Cryptomeria japonica [L.f.] D.Don), Japanese cypress (Chamaecyparis obtusa [Siebold et Zucc.] Endl.), konara oak (Quercus serrata Murray), and Japanese red pine (Pinus densiflora Siebold et Zucc.) were selected for monitoring. Japanese chestnut (Castanea crenata Siebold et Zucc.) was supplementally added in 2017. The cedar, cypress, and pine are evergreen coniferous species, and the oak and chestnut are deciduous broad-leaved species. Sites 1 and 3 each have three plots, and each plot contains a different monitoring species. Site A1 has one plot containing two different monitoring species, and the remaining sites each have one plot with one monitoring species, giving ten plots in total.Figure 1Locations of the monitoring sites and initial deposition densities of 137Cs (decay-corrected to July 2, 2011) following the Fukushima nuclear accident in Fukushima and Ibaraki Prefectures. Open circles indicate the monitoring sites and the cross mark indicates the Fukushima Dai-ichi Nuclear Power Plant. Data on the deposition density were provided by MEXT19,20 and refined by Kato et al.21. The map was created using R (version 4.1.0)22 with ggplot2 (version 3.3.5)23 and sf (version 1.0–0)24 packages.Full size imageTable 1 Description of the sampled trees and monitoring sites.Full size tableSample collection and preparationBulk sampling of bark and wood disks was conducted by felling three trees per year at all sites during 2011–20168,25 and at sites 3–5 and A1 during 2017–2020. Partial sampling from six trees per year was conducted at sites 1 and 2 during 2017–2020 (from seven trees at site 2 in 2017) to sustain the monitoring trees. All the samples were obtained from the stems around breast height. During the partial sampling, bark pieces sized approximately 3 cm × 3 cm (axial length × tangential length) were collected from four directions of the tree stem using a chisel, and 12-mm-diameter wood cores were collected from two directions of the tree stem using an automatic increment borer (Smartborer, Seiwa Works, Tsukuba, Japan) equipped with a borer bit (10–101-1046, Haglöf Sweden, Långsele, Sweden). Such partial sampling increases the observational errors in the bark and wood 137Cs concentrations in individual trees26. To mitigate this error and maintain an accurate mean value of the 137Cs concentration, we increased the number of sampled trees from three to six. The sampling was conducted mainly in July–September of each year; the exceptions were site-5 samples in 2011 and 2012, which were collected irregularly during January–February of the following year. The collected bark pieces were separated into outer and inner barks, and the wood disks and cores were split into sapwood and heartwood. The outer and inner bark samples during 2012–2016 were obtained by partial sampling of barks sized approximately 10 cm × 10 cm from 2–3 directions on 2–3 trees per year.The bulk samples of bark, sapwood, and heartwood were air-dried and then chipped into flakes using a cutting mill with a 6-mm mesh sieve (UPC-140, HORAI, Higashiosaka, Japan). The pieces of the outer and inner bark were chipped into approximately 5 mm × 5 mm pieces using pruning shears, and the cores of the sapwood and heartwood were chipped into semicircles of thickness 1–2 mm. Each sample was packed into a container for radioactivity measurements and its mass was measured after oven-drying at 75 °C for at least 48 h. Multiplying this mass by the conversion factor (0.98 for bark and 0.99 for wood)8 yielded the dry mass at 105 °C.Radioactivity measurementsThe radioactivity of 137Cs in the samples was determined by γ-ray spectrometry with a high-purity Ge semiconductor detector (GEM20, GEM40, or GWL-120, ORTEC, Oak Ridge, TN). For measurements, the bulk and partial samples were placed into Marinelli containers (2.0 L or 0.7 L) and cylindrical containers (100 mL or 5 mL), respectively. The peak efficiencies of the Marinelli containers, the 100-mL container, and the 5-mL container were calibrated using standard sources of MX033MR, MX033U8PP (Japan Radioisotope Association, Tokyo, Japan), and EG-ML (Eckert & Ziegler Isotope Products, Valencia, CA), respectively. For the measurement of the 5-mL container, a well-type Ge detector (GWL-120) was used under the empirical assumption that the difference in γ-ray self-absorption between the standard source and the samples is negligible27. The measurement was continued until the counting error became less than 5% (higher counting errors were allowed for small or weakly radioactive samples). The activity concentration of 137Cs in the bark (whole) collected by partial sampling was calculated as the mass-weighted mean of the concentrations in the outer and inner barks; meanwhile, the concentration in the wood (whole) was calculated as the cross-sectional-area-weighted mean of sapwood and heartwood concentrations. The activity concentrations were decay-corrected to September 1, 2020, to exclude the decrease due to the radioactive decay.Trend analysesThe yearly representative values (true states) of 137Cs activity concentration in each stem part in each plot were estimated using a DLM, a state-space model in which the noise follows a normal distribution and the relationship between variables is linear. One basic DLM is the local linear trend model defined by the following equations:$$Y_{t} = mu _{t} + varepsilon _{t} ,quad quad quad varepsilon _{t} sim Normal left( {0,sigma _{varepsilon }^{2} } right)$$
(1)
$$mu_{t} = mu_{t – 1} + beta_{t – 1} + eta_{t} ,quad quad quad eta_{t} sim Normal left( {0,sigma_{eta }^{2} } right)$$
(2)
$$beta_{t} = beta_{t – 1} + zeta_{t} ,quad quad quad zeta_{t} sim Normal left( {0,sigma_{zeta }^{2} } right)$$
(3)
where Yt, μt, and βt are the observation values, level (true state), and slope, respectively, and εt, ηt, and ζt denote their corresponding noises. The subscript t is the time index. The noises εt, ηt, and ζt follow normal distributions with a mean of 0 and variances of ({sigma }_{varepsilon }^{2}), ({sigma }_{eta }^{2}), and ({sigma }_{zeta }^{2}), respectively. To detect relatively long-term trends, we employed the smooth local linear trend model28 (also called the smooth trend model, integrated random walk model, or second-order trend model), which is obtained by considering that μt and βt are driven by the same noise. The trend changes are assumed to be smoother in this model than in the local linear trend model28,29. Combining Eqs. (2) and (3), μt in the smooth local linear trend model is finally obtained as$$mu_{t} = 2mu_{t – 1} – mu_{t – 2} + eta_{t} ,quad quad quad eta_{t} sim Normal left( {0,sigma_{eta }^{2} } right)$$
(4)
The parameters μt, ({sigma }_{eta }^{2}), and ({sigma }_{varepsilon }^{2}) of each stem part in each plot were determined by Bayesian estimation with a Markov chain Monte Carlo (MCMC) method. The Bayesian estimation was performed in R (version 4.1.0)22 with the rstan package (version 2.21.2)30. Uninformative prior distributions were used for μ1, μ2, ({sigma }_{eta }^{2}), and ({sigma }_{varepsilon }^{2}). The log-transformed values of the 137Cs activity concentration (decay-corrected to September 1, 2020) were given as Yt (the observed values of multiple individuals in each year were passed via the segment function of Stan). MCMC sampling was conducted for four chains of 50,000 iterations (the first 25,000 were discarded as warmup), obtaining 100,000 MCMC samples for each parameter. The MCMC was judged to have converged when the maximum value of Rhat was less than 1.05 and the divergent transitions after warmup were fewer than 1,000 (i.e., less than 1% of the MCMC sample size). On the datasets of the outer and inner barks from site-3 oaks and all stem parts from site-A1 pines and chestnuts, the MCMC converged poorly owing to the small number of monitoring years. Thus, the temporal trends in these datasets were not analyzed (the observational data at site A1 are shown in Supplementary Fig. S1 and Table S1).To detect decadal trends rather than yearly variations, we determined the temporal trends in the true state (μ) by setting 2–4 delimiting years and examining whether μ varied significantly from one delimiting year to the next. As the delimiting years, we selected the initial and final years of monitoring and the years in which the median µ was highest (µ-max year) and lowest (µ-min year). When the µ-max year and/or the µ-min year coincided with the initial year and/or final year of monitoring, the number of delimiting years reduced from four to two or three. The trend in µ between two delimiting years was determined to be increasing and decreasing when the 95% credible interval of µ2nd delimiting year − µ1st delimiting year (obtained from the MCMC samples) was higher and lower than zero, respectively. A flat trend (no significant variation) was detected when the 95% credible interval included zero. If the 3rd and 4th delimiting years existed, the trends between the 2nd and 3rd delimiting years and between the 3rd and 4th delimiting years were determined in the same manner.The 137Cs CRs of outer bark/inner bark, heartwood/sapwood, and inner bark/sapwood were also subjected to the above trend analyses. On datasets with less than five years of monitoring, the MCMC did not converge so the trend analysis was not attempted. More

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An experiment in secondary forests in the Democratic Republic of the Congo finds that calcium, an overlooked soil nutrient, is scarcer than phosphorus, and represents a potentially greater limitation on tropical forest growth.Ecology can reveal distributional patterns and dynamics in nature. One approach used is studying the elemental composition of plants, which has been linked to ecological processes such as growth, diversity or water use efficiency. More recently, elemental composition has been detected as a cofactor in governing the carbon sink capacity of plants, and therefore climate change mitigation1,2,3. This discovery has added an extra layer of urgency to the field, which now aims to better understand and predict global change. The study of nitrogen and/or phosphorus has until now received most of the attention of plant ecologists: nitrogen is the most abundant element in dry leaves after hydrogen and carbon, forms the main structure of proteins and is strongly linked to photosynthesis4. Phosphorus represents around one-tenth of nitrogen abundance in leaves and is key in energy storage and nucleic acids. However, although these represent only two of the many chemical elements that are in flux throughout ecosystems, whether others may have a dominant role in ecosystem dynamics is an open question. Writing in Nature Ecology & Evolution, Bauters et al.5 share some evidence to motivate broadening out from the dominant focus on nitrogen and phosphorus in terrestrial ecology, by revealing a crucial limiting role of calcium in the dynamics of tropical forest succession. More

Sabkha soil analysisSoil biochemical analysis shows higher concentration of salt in the middle (average of 200 ppt) which decreases to 20 ppt outside of the sabkha (Fig. 1B). Soil conductivity and total dissolved solids (TDS) were positively correlated with salinity (r = 0.8, p  More