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1. NanoSIMS single cell analyses reveal the contrasting nitrogen sources for small phytoplankton

   https://doi.org/10.1038/s41396-018-0285-8  

   Berthelot, Hugo ; Duhamel, Solange ; L’Helguen, Stéphane ; Maguer, Jean-Francois ; Wang, Seaver ; Cetinić, Ivona ; Cassar, Nicolas ( October 2018 , The ISME Journal)

   Nitrogen (N) is a limiting nutrient in vast regions of the world’s oceans, yet the sources of N available to various phytoplankton groups remain poorly understood. In this study, we investigated inorganic carbon (C) fixation rates and nitrate (NO₃⁻), ammonium (NH₄⁺) and urea uptake rates at the single cell level in photosynthetic pico-eukaryotes (PPE) and the cyanobacteriaProchlorococcusandSynechococcus. To that end, we used dual¹⁵N and¹³C-labeled incubation assays coupled to flow cytometry cell sorting and nanoSIMS analysis on samples collected in the North Pacific Subtropical Gyre (NPSG) and in the California Current System (CCS). Based on these analyses, we found thatmore »photosynthetic growth rates (based on C fixation) of PPE were higher in the CCS than in the NSPG, while the opposite was observed forProchlorococcus. Reduced forms of N (NH₄⁺and urea) accounted for the majority of N acquisition for all the groups studied. NO₃⁻represented a reduced fraction of total N uptake in all groups but was higher in PPE (17.4 ± 11.2% on average) than inProchlorococcusandSynechococcus(4.5 ± 6.5 and 2.9 ± 2.1% on average, respectively). This may in part explain the contrasting biogeography of these picoplankton groups. Moreover, single cell analyses reveal that cell-to-cell heterogeneity within picoplankton groups was significantly greater for NO₃⁻uptake than for C fixation and NH₄⁺uptake. We hypothesize that cellular heterogeneity in NO₃⁻uptake within groups facilitates adaptation to the fluctuating availability of NO₃⁻in the environment.

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2. Microbial community structure in the western tropical South Pacific

   https://doi.org/10.5194/bg-15-3909-2018  

   Bock, Nicholas ; Van Wambeke, France ; Dion, Moïra ; Duhamel, Solange ( January 2018 , Biogeosciences)

   Abstract. Oligotrophic regions play a central role in global biogeochemical cycles, with microbial communities in these areas representing an important term in global carbon budgets. While the general structure of microbial communities has been well documented in the global ocean, some remote regions such as the western tropical South Pacific (WTSP) remain fundamentally unexplored. Moreover, the biotic and abiotic factors constraining microbial abundances and distribution remain not well resolved. In this study, we quantified the spatial (vertical and horizontal) distribution of major microbial plankton groups along a transect through the WTSP during the austral summer of 2015, capturing important autotrophicmore »and heterotrophic assemblages including cytometrically determined abundances of non-pigmented protists (also called flagellates). Using environmental parameters (e.g., nutrients and light availability) as well as statistical analyses, we estimated the role of bottom–up and top–down controls in constraining the structure of the WTSP microbial communities in biogeochemically distinct regions. At the most general level, we found a typical tropical structure, characterized by a shallow mixed layer, a clear deep chlorophyll maximum at all sampling sites, and a deep nitracline. Prochlorococcus was especially abundant along the transect, accounting for 68±10.6% of depth-integrated phytoplankton biomass. Despite their relatively low abundances, picophytoeukaryotes (PPE) accounted for up to 26±11.6% of depth-integrated phytoplankton biomass, while Synechococcus accounted for only 6±6.9%. Our results show that the microbial community structure of the WTSP is typical of highly stratified regions, and underline the significant contribution to total biomass by PPE populations. Strong relationships between N₂ fixation rates and plankton abundances demonstrate the central role of N₂ fixation in regulating ecosystem processes in the WTSP, while comparative analyses of abundance data suggest microbial community structure to be increasingly regulated by bottom–up processes under nutrient limitation, possibly in response to shifts in abundances of high nucleic acid bacteria (HNA).

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3. Phosphorus availability and leaching losses in annual and perennial cropping systems in an upper US Midwest landscape

   https://doi.org/10.5061/dryad.8sf7m0cpx  

   Hussain, Mir Zaman ; Hamilton, Stephen ; Robertson, G. Philip ; Basso, Bruno ( January 2021 )

   Abstract

   Excessive phosphorus (P) applications to croplands can contribute to eutrophication of surface waters through surface runoff and subsurface (leaching) losses. We analyzed leaching losses of total dissolved P (TDP) from no-till corn, hybrid poplar (Populus nigra X P. maximowiczii), switchgrass (Panicum virgatum), miscanthus (Miscanthus giganteus), native grasses, and restored prairie, all planted in 2008 on former cropland in Michigan, USA. All crops except corn (13 kg P ha−1 year−1) were grown without P fertilization. Biomass was harvested at the end of each growing season except for poplar. Soil water at 1.2 m depth was sampled weekly to biweekly for TDP determination during March–November 2009–2016 using tension lysimeters. Soil test P (0–25 cm depth) was measured every autumn. Soil water TDP concentrations were usually below levels where eutrophication of surface waters is frequently observed (> 0.02 mg L−1) but often higher than in deep groundwater or nearby streams and lakes. Rates of P leaching, estimated from measured concentrations and modeled drainage, did not differ statistically among cropping systems across years; 7-year cropping system means ranged from 0.035 to 0.072 kg P ha−1 year−1 with large interannual variation. Leached P was positively related to STP, which decreased over the 7 years in all systems. These results indicate that both P-fertilized and unfertilized cropping systems may leach legacy P from past cropland management.
   

   Methods

   Experimental details The Biofuel Cropping System Experiment (BCSE) is located at the W.K. Kellogg Biological Station (KBS) (42.3956° N, 85.3749° W; elevation 288 m asl) in southwestern Michigan, USA. This site is a part of the Great Lakes Bioenergy Research Center (www.glbrc.org) and is a Long-term Ecological Research site (www.lter.kbs.msu.edu). Soils are mesic Typic Hapludalfs developed on glacial outwash54 with high sand content (76% in the upper 150 cm) intermixed with silt-rich loess in the upper 50 cm55. The water table lies approximately 12–14 m below the surface. The climate is humid temperate with a mean annual air temperature of 9.1 °C and annual precipitation of 1005 mm, 511 mm of which falls between May and September (1981–2010)56,57. The BCSE was established as a randomized complete block design in 2008 on preexisting farmland. Prior to BCSE establishment, the field was used for grain crop and alfalfa (Medicago sativa L.) production for several decades. Between 2003 and 2007, the field received a total of ~ 300 kg P ha−1 as manure, and the southern half, which contains one of four replicate plots, received an additional 206 kg P ha−1 as inorganic fertilizer. The experimental design consists of five randomized blocks each containing one replicate plot (28 by 40 m) of 10 cropping systems (treatments) (Supplementary Fig. S1; also see Sanford et al.58). Block 5 is not included in the present study. Details on experimental design and site history are provided in Robertson and Hamilton57 and Gelfand et al.59. Leaching of P is analyzed in six of the cropping systems: (i) continuous no-till corn, (ii) switchgrass, (iii) miscanthus, (iv) a mixture of five species of native grasses, (v) a restored native prairie containing 18 plant species (Supplementary Table S1), and (vi) hybrid poplar. Agronomic management Phenological cameras and field observations indicated that the perennial herbaceous crops emerged each year between mid-April and mid-May. Corn was planted each year in early May. Herbaceous crops were harvested at the end of each growing season with the timing depending on weather: between October and November for corn and between November and December for herbaceous perennial crops. Corn stover was harvested shortly after corn grain, leaving approximately 10 cm height of stubble above the ground. The poplar was harvested only once, as the culmination of a 6-year rotation, in the winter of 2013–2014. Leaf emergence and senescence based on daily phenological images indicated the beginning and end of the poplar growing season, respectively, in each year. Application of inorganic fertilizers to the different crops followed a management approach typical for the region (Table 1). Corn was fertilized with 13 kg P ha−1 year−1 as starter fertilizer (N-P-K of 19-17-0) at the time of planting and an additional 33 kg P ha−1 year−1 was added as superphosphate in spring 2015. Corn also received N fertilizer around the time of planting and in mid-June at typical rates for the region (Table 1). No P fertilizer was applied to the perennial grassland or poplar systems (Table 1). All perennial grasses (except restored prairie) were provided 56 kg N ha−1 year−1 of N fertilizer in early summer between 2010 and 2016; an additional 77 kg N ha−1 was applied to miscanthus in 2009. Poplar was fertilized once with 157 kg N ha−1 in 2010 after the canopy had closed. Sampling of subsurface soil water and soil for P determination Subsurface soil water samples were collected beneath the root zone (1.2 m depth) using samplers installed at approximately 20 cm into the unconsolidated sand of 2Bt2 and 2E/Bt horizons (soils at the site are described in Crum and Collins54). Soil water was collected from two kinds of samplers: Prenart samplers constructed of Teflon and silica (http://www.prenart.dk/soil-water-samplers/) in replicate blocks 1 and 2 and Eijkelkamp ceramic samplers (http://www.eijkelkamp.com) in blocks 3 and 4 (Supplementary Fig. S1). The samplers were installed in 2008 at an angle using a hydraulic corer, with the sampling tubes buried underground within the plots and the sampler located about 9 m from the plot edge. There were no consistent differences in TDP concentrations between the two sampler types. Beginning in the 2009 growing season, subsurface soil water was sampled at weekly to biweekly intervals during non-frozen periods (April–November) by applying 50 kPa of vacuum to each sampler for 24 h, during which the extracted water was collected in glass bottles. Samples were filtered using different filter types (all 0.45 µm pore size) depending on the volume of leachate collected: 33-mm dia. cellulose acetate membrane filters when volumes were less than 50 mL; and 47-mm dia. Supor 450 polyethersulfone membrane filters for larger volumes. Total dissolved phosphorus (TDP) in water samples was analyzed by persulfate digestion of filtered samples to convert all phosphorus forms to soluble reactive phosphorus, followed by colorimetric analysis by long-pathlength spectrophotometry (UV-1800 Shimadzu, Japan) using the molybdate blue method60, for which the method detection limit was ~ 0.005 mg P L−1. Between 2009 and 2016, soil samples (0–25 cm depth) were collected each autumn from all plots for determination of soil test P (STP) by the Bray-1 method61, using as an extractant a dilute hydrochloric acid and ammonium fluoride solution, as is recommended for neutral to slightly acidic soils. The measured STP concentration in mg P kg−1 was converted to kg P ha−1 based on soil sampling depth and soil bulk density (mean, 1.5 g cm−3). Sampling of water samples from lakes, streams and wells for P determination In addition to chemistry of soil and subsurface soil water in the BCSE, waters from lakes, streams, and residential water supply wells were also sampled during 2009–2016 for TDP analysis using Supor 450 membrane filters and the same analytical method as for soil water. These water bodies are within 15 km of the study site, within a landscape mosaic of row crops, grasslands, deciduous forest, and wetlands, with some residential development (Supplementary Fig. S2, Supplementary Table S2). Details of land use and cover change in the vicinity of KBS are given in Hamilton et al.48, and patterns in nutrient concentrations in local surface waters are further discussed in Hamilton62. Leaching estimates, modeled drainage, and data analysis Leaching was estimated at daily time steps and summarized as total leaching on a crop-year basis, defined from the date of planting or leaf emergence in a given year to the day prior to planting or emergence in the following year. TDP concentrations (mg L−1) of subsurface soil water were linearly interpolated between sampling dates during non-freezing periods (April–November) and over non-sampling periods (December–March) based on the preceding November and subsequent April samples. Daily rates of TDP leaching (kg ha−1) were calculated by multiplying concentration (mg L−1) by drainage rates (m3 ha−1 day−1) modeled by the Systems Approach for Land Use Sustainability (SALUS) model, a crop growth model that is well calibrated for KBS soil and environmental conditions. SALUS simulates yield and environmental outcomes in response to weather, soil, management (planting dates, plant population, irrigation, N fertilizer application, and tillage), and genetics63. The SALUS water balance sub-model simulates surface runoff, saturated and unsaturated water flow, drainage, root water uptake, and evapotranspiration during growing and non-growing seasons63. The SALUS model has been used in studies of evapotranspiration48,51,64 and nutrient leaching20,65,66,67 from KBS soils, and its predictions of growing-season evapotranspiration are consistent with independent measurements based on growing-season soil water drawdown53 and evapotranspiration measured by eddy covariance68. Phosphorus leaching was assumed insignificant on days when SALUS predicted no drainage. Volume-weighted mean TDP concentrations in leachate for each crop-year and for the entire 7-year study period were calculated as the total dissolved P leaching flux (kg ha−1) divided by the total drainage (m3 ha−1). One-way ANOVA with time (crop-year) as the fixed factor was conducted to compare total annual drainage rates, P leaching rates, volume-weighted mean TDP concentrations, and maximum aboveground biomass among the cropping systems over all seven crop-years as well as with TDP concentrations from local lakes, streams, and groundwater wells. When a significant (α = 0.05) difference was detected among the groups, we used the Tukey honest significant difference (HSD) post-hoc test to make pairwise comparisons among the groups. In the case of maximum aboveground biomass, we used the Tukey–Kramer method to make pairwise comparisons among the groups because the absence of poplar data after the 2013 harvest resulted in unequal sample sizes. We also used the Tukey–Kramer method to compare the frequency distributions of TDP concentrations in all of the soil leachate samples with concentrations in lakes, streams, and groundwater wells, since each sample category had very different numbers of measurements.
   

   Other

   Individual spreadsheets in “data table_leaching_dissolved organic carbon and nitrogen.xls” 1.    annual precip_drainage 2.    biomass_corn, perennial grasses 3.    biomass_poplar 4.    annual N leaching _vol-wtd conc 5.    Summary_N leached 6.    annual DOC leachin_vol-wtd conc 7.    growing season length 8.    correlation_nh4 VS no3 9.    correlations_don VS no3_doc VS don Each spreadsheet is described below along with an explanation of variates. Note that ‘nan’ indicate data are missing or not available. First row indicates header; second row indicates units 1. Spreadsheet: annual precip_drainage Description: Precipitation measured from nearby Kellogg Biological Station (KBS) Long Term Ecological Research (LTER) Weather station, over 2009-2016 study period. Data shown in Figure 1; original data source for precipitation (https://lter.kbs.msu.edu/datatables/7). Drainage estimated from SALUS crop model. Note that drainage is percolation out of the root zone (0-125 cm). Annual precipitation and drainage values shown here are calculated for growing and non-growing crop periods. Variate    Description year    year of the observation crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” precip_G    precipitation during growing period (milliMeter) precip_NG    precipitation during non-growing period (milliMeter) drainage_G    drainage during growing period (milliMeter) drainage_NG    drainage during non-growing period (milliMeter)      2. Spreadsheet: biomass_corn, perennial grasses Description: Maximum aboveground biomass measurements from corn, switchgrass, miscanthus, native grass and restored prairie plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Data shown in Figure 2.   Variate    Description year    year of the observation date    day of the observation (mm/dd/yyyy) crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” replicate    each crop has four replicated plots, R1, R2, R3 and R4 station    stations (S1, S2 and S3) of samplings within the plot. For more details, refer to link (https://data.sustainability.glbrc.org/protocols/156) species    plant species that are rooted within the quadrat during the time of maximum biomass harvest. See protocol for more information, refer to link (http://lter.kbs.msu.edu/datatables/36) For maize biomass, grain and whole biomass reported in the paper (weed biomass or surface litter are excluded). Surface litter biomass not included in any crops; weed biomass not included in switchgrass and miscanthus, but included in grass mixture and prairie. fraction    Fraction of biomass biomass_plot    biomass per plot on dry-weight basis (Grams_Per_SquareMeter) biomass_ha    biomass (megaGrams_Per_Hectare) by multiplying column biomass per plot with 0.01 3. Spreadsheet: biomass_poplar Description: Maximum aboveground biomass measurements from poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Data shown in Figure 2. Note that poplar biomass was estimated from crop growth curves until the poplar was harvested in the winter of 2013-14. Variate    Description year    year of the observation method    methods of poplar biomass sampling date    day of the observation (mm/dd/yyyy) replicate    each crop has four replicated plots, R1, R2, R3 and R4 diameter_at_ground    poplar diameter (milliMeter) at the ground diameter_at_15cm    poplar diameter (milliMeter) at 15 cm height biomass_tree    biomass per plot (Grams_Per_Tree) biomass_ha    biomass (megaGrams_Per_Hectare) by multiplying biomass per tree with 0.01 4. Spreadsheet: annual N leaching_vol-wtd conc Description: Annual leaching rate (kiloGrams_N_Per_Hectare) and volume-weighted mean N concentrations (milliGrams_N_Per_Liter) of nitrate (no3) and dissolved organic nitrogen (don) in the leachate samples collected from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for nitrogen leached and volume-wtd mean N concentration shown in Figure 3a and Figure 3b, respectively. Note that ammonium (nh4) concentration were much lower and often undetectable (<0.07 milliGrams_N_Per_Liter). Also note that in 2009 and 2010 crop-years, data from some replicates are missing.    Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” crop-year    year of the observation replicate    each crop has four replicated plots, R1, R2, R3 and R4 no3 leached    annual leaching rates of nitrate (kiloGrams_N_Per_Hectare) don leached    annual leaching rates of don (kiloGrams_N_Per_Hectare) vol-wtd no3 conc.    Volume-weighted mean no3 concentration (milliGrams_N_Per_Liter) vol-wtd don conc.    Volume-weighted mean don concentration (milliGrams_N_Per_Liter) 5. Spreadsheet: summary_N leached Description: Summary of total amount and forms of N leached (kiloGrams_N_Per_Hectare) and the percent of applied N lost to leaching over the seven years for corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for nitrogen amount leached shown in Figure 4a and percent of applied N lost shown in Figure 4b. Note the fraction of unleached N includes in harvest, accumulation in root biomass, soil organic matter or gaseous N emissions were not measured in the study. Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” no3 leached    annual leaching rates of nitrate (kiloGrams_N_Per_Hectare) don leached    annual leaching rates of don (kiloGrams_N_Per_Hectare) N unleached    N unleached (kiloGrams_N_Per_Hectare) in other sources are not studied % of N applied N lost to leaching    % of N applied N lost to leaching 6. Spreadsheet: annual DOC leachin_vol-wtd conc Description: Annual leaching rate (kiloGrams_Per_Hectare) and volume-weighted mean N concentrations (milliGrams_Per_Liter) of dissolved organic carbon (DOC) in the leachate samples collected from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for DOC leached and volume-wtd mean DOC concentration shown in Figure 5a and Figure 5b, respectively. Note that in 2009 and 2010 crop-years, water samples were not available for DOC measurements.     Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” crop-year    year of the observation replicate    each crop has four replicated plots, R1, R2, R3 and R4 doc leached    annual leaching rates of nitrate (kiloGrams_Per_Hectare) vol-wtd doc conc.    volume-weighted mean doc concentration (milliGrams_Per_Liter) 7. Spreadsheet: growing season length Description: Growing season length (days) of corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in the Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Date shown in Figure S2. Note that growing season is from the date of planting or emergence to the date of harvest (or leaf senescence in case of poplar).   Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” year    year of the observation growing season length    growing season length (days) 8. Spreadsheet: correlation_nh4 VS no3 Description: Correlation of ammonium (nh4+) and nitrate (no3-) concentrations (milliGrams_N_Per_Liter) in the leachate samples from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2013-2015. Data shown in Figure S3. Note that nh4+ concentration in the leachates was very low compared to no3- and don concentration and often undetectable in three crop-years (2013-2015) when measurements are available. Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” date    date of the observation (mm/dd/yyyy) replicate    each crop has four replicated plots, R1, R2, R3 and R4 nh4 conc    nh4 concentration (milliGrams_N_Per_Liter) no3 conc    no3 concentration (milliGrams_N_Per_Liter)   9. Spreadsheet: correlations_don VS no3_doc VS don Description: Correlations of don and nitrate concentrations (milliGrams_N_Per_Liter); and doc (milliGrams_Per_Liter) and don concentrations (milliGrams_N_Per_Liter) in the leachate samples of corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2013-2015. Data of correlation of don and nitrate concentrations shown in Figure S4 a and doc and don concentrations shown in Figure S4 b. Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” year    year of the observation don    don concentration (milliGrams_N_Per_Liter) no3     no3 concentration (milliGrams_N_Per_Liter) doc    doc concentration (milliGrams_Per_Liter)
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4. Diazotrophic <i>Trichodesmium</i> impact on UV–Vis radiance and pigment composition in the western tropical South Pacific

   https://doi.org/10.5194/bg-15-5249-2018  

   Dupouy, Cécile ; Frouin, Robert ; Tedetti, Marc ; Maillard, Morgane ; Rodier, Martine ; Lombard, Fabien ; Guidi, Lionel ; Picheral, Marc ; Neveux, Jacques ; Duhamel, Solange ; et al ( January 2018 , Biogeosciences)

   Abstract. We assessed the influence of the marine diazotrophic cyanobacterium Trichodesmium on the bio-optical properties of western tropical South Pacific (WTSP) waters (18–22°S, 160°E–160°W) during the February–March 2015 OUTPACE cruise. We performed measurements of backscattering and absorption coefficients, irradiance, and radiance in the euphotic zone with a Satlantic MicroPro free-fall profiler and took Underwater Vision Profiler 5 (UPV5) pictures for counting the largest Trichodesmium spp. colonies. Pigment concentrations were determined by fluorimetry and high-performance liquid chromatography and picoplankton abundance by flow cytometry. Trichome concentration was estimated from pigment algorithms and validated by surface visual counts. The abundancemore »of large colonies counted by the UVP5 (maximum 7093coloniesm⁻³) was well correlated to the trichome concentrations (maximum 2093trichomesL⁻¹) with an aggregation factor of 600. In the Melanesian archipelago, a maximum of 4715trichomesL⁻¹ was enumerated in pump samples (3.2m) at 20°S,16730°E. High Trichodesmium abundance was always associated with absorption peaks of mycosporine-like amino acids (330, 360nm) and high particulate backscattering, but not with high Chl a fluorescence or blue particulate absorption (440nm). Along the west-to-east transect, Trichodesmium together with Prochlorococcus represented the major part of total chlorophyll concentration; the contribution of other groups were relatively small or negligible. The Trichodesmium contribution to total chlorophyll concentration was the highest in the Melanesian archipelago around New Caledonia and Vanuatu (60%), progressively decreased to the vicinity of the islands of Fiji (30%), and reached a minimum in the South Pacific Gyre where Prochlorococcus dominated chlorophyll concentration. The contribution of Trichodesmium to zeaxanthin was respectively 50, 40 and 20% for these regions. During the OUTPACE cruise, the relationship between normalized water-leaving radiance (nL_w) in the ultraviolet and visible and chlorophyll concentration was similar to that found during the BIOSOPE cruise in the eastern tropical Pacific. Principal component analysis (PCA) of OUTPACE data showed that nL_w at 305, 325, 340, 380, 412 and 440nm was strongly correlated to chlorophyll and zeaxanthin, while nL_w at 490 and 565nm exhibited lower correlations. These results, as well as differences in the PCA of BIOSOPE data, indicated that nL_w variability in the greenish blue and yellowish green during OUTPACE was influenced by other variables associated with Trichodesmium presence, such as backscattering coefficient, phycoerythrin fluorescence and/or zeaxanthin absorption, suggesting that Trichodesmium detection should involve examination of nL_w in this spectral domain.

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5. Selective Uptake of Pelagic Microbial Community Members by Caribbean Reef Corals

   https://doi.org/10.1128/AEM.03175-20  

   Hoadley, Kenneth D. ; Hamilton, Maria ; Poirier, Camille L. ; Choi, Chang Jae ; Yung, Cheuk-Man ; Worden, Alexandra Z. ( April 2021 , Applied and Environmental Microbiology)

   Johnson, Karyn N. (Ed.)

   ABSTRACT Coral reefs are possible sinks for microbes; however, the removal mechanisms at play are not well understood. Here, we characterize pelagic microbial groups at the CARMABI reef (Curaçao) and examine microbial consumption by three coral species: Madracis mirabilis , Porites astreoides , and Stephanocoenia intersepta . Flow cytometry analyses of water samples collected from a depth of 10 m identified 6 microbial groups: Prochlorococcus , three groups of Synechococcus , photosynthetic eukaryotes, and heterotrophic bacteria. Minimum growth rates (μ) for Prochlorococcus , all Synechococcus groups, and photosynthetic eukaryotes were 0.55, 0.29, and 0.45 μ day −1 , respectively, and suggestmore »relatively high rates of productivity despite low nutrient conditions on the reef. During a series of 5-h incubations with reef corals performed just after sunset or prior to sunrise, reductions in the abundance of photosynthetic picoeukaryotes, Prochlorococcus and Synechococcus cells, were observed. Of the three Synechococcus groups, one decreased significantly during incubations with each coral and the other two only with M. mirabilis. Removal of carbon from the water column is based on coral consumption rates of phytoplankton and averaged between 138 ng h −1 and 387 ng h −1 , depending on the coral species. A lack of coral-dependent reduction in heterotrophic bacteria, differences in Synechococcus reductions, and diurnal variation in reductions of Synechococcus and Prochlorococcus , coinciding with peak cell division, point to selective feeding by corals. Our study indicates that bentho-pelagic coupling via selective grazing of microbial groups influences carbon flow and supports heterogeneity of microbial communities overlying coral reefs. IMPORTANCE We identify interactions between coral grazing behavior and the growth rates and cell abundances of pelagic microbial groups found surrounding a Caribbean reef. During incubation experiments with three reef corals, reductions in microbial cell abundance differed according to coral species and suggest specific coral or microbial mechanisms are at play. Peaks in removal rates of Prochlorococcus and Synechococcus cyanobacteria appear highest during postsunset incubations and coincide with microbial cell division. Grazing rates and effort vary across coral species and picoplankton groups, possibly influencing overall microbial composition and abundance over coral reefs. For reef corals, use of such a numerically abundant source of nutrition may be advantageous, especially under environmentally stressful conditions when symbioses with dinoflagellate algae break down.« less