Iron Marines V1.5.8 Build 325 APK OBB [Latest] Free Download
Sibyl Piccuillo
Figure 1 Block-shaped steel weights of a bottom-lander (above) and sediment sampling along a gradient with decreasing iron concentrations in the vicinity of corroding bottom-weights (below) using push-corers (PC1-8) handled by a Remotely Operated Vehicle QUEST 4000 ( MARUM).
Figure 2 Sediment characteristics (upper panel) and densities of the small benthic biota (lower panel) along the push-corer transect. Fe: iron concentrations (no data for PC1), H2O: water contents (porosity), AFDW: organic matter contents, CPE: chloroplastic pigment equivalent concentrations, BN: bacterial densities, FORAM: Foraminifera, NEMA: Nematoda, and OTHERS: all other metazoan meiofauna. Grey shading: visually iron-impacted sediments.
Figure 3 Gradients in iron concentrations (Fe; top left), water contents (H2O; top right) and organic matter contents (AFDW: bottom left; CPE: bottom right) in surface sediments along the transect with increasing distance from the corroding bottom-weights (no data for iron concentrations at PC1).
Figure 5 Non-metric multidimensional scaling (NMDS) based on similarity matrix (square-root transformed data, Bray-Curtis similarity) of bacteria (A) and nematode genera (B) abundance in iron-enriched (PC1-4) and unaffected (PC5-8) sediments.
Table 2 Bacterial community composition at genus resolution. Heatmap of the dominant community members along the sampled transect. Displayed are average values (%) per push-core, integrating the surface sediment from 0-5 cm depth. Only genera with a minimum relative abundance of 0.1% are shown (all values below 0.1% but above 0.01% still show pale colours). Asterisks indicate significant variance between means of iron-impacted (PC1-4) and unaffected (PC5-8) sediment communities (FDR adjusted p-values: *p>0.05, **p>0.01, ***p>0.001).
Table 4 Results of the environmental quality assessment within the uppermost centimeter of ferrous (PC1-4) and non-ferrous sediments (PC5-8) according to different nematode community descriptors as quality indicators.
Table 5 DistLM (distance-based linear model) results showing the relationship between environmental (iron content, food availability and sedimentporosity) parameters on variation in nematode and bacteria community structure (Bray-Curtis similarity of square-root transformed abundance ofnematode and bacteria taxa).
Figure 6 Distance-based redundancy analysis ordination to investigate relationship between the environmental variables and bacteria communities in ferrous and non-ferrous sediments. CHL: chlorophyll a; AFDW: total organic matter measured as ash-free dry-weight of the sediments; H2O: sediment porosity indicated by the water content of the sediments.
Figure 7 Distance-based redundancy analysis ordination to investigate relationship between the environmental variables and nematode communities in ferrous and non-ferrous sediments. CHL: chlorophyll a; BN: bacterial numbers; AFDW: total organic matter measured as ash-free dry-weight of the sediments; H2O: sediment porosity indicated by the water content of the sediments.
Citation: Soltwedel T, Rapp JZ and Hasemann C (2023) Impact of local iron enrichment on the small benthic biota in the deep Arctic Ocean. Front. Mar. Sci. 10:1118431. doi: 10.3389/fmars.2023.1118431
Microbial denitrification proceeds via several metabolic steps, which can be followed by the activity of the respective enzymes. The only known microbially mediated reduction of N2O is the microbial reduction to N2 via (a)typical nosZ-encoded N2O reductases18,19. Nitrate reduction can be mediated by microorganisms that couple Fe(II) oxidation to nitrate reduction (NRFeOx)20. Several cultures of NRFeOx have been isolated from various environments and have been shown to be involved in the emission of high levels of N2O21. Only recently it has been proven that the oxidation of Fe(II) during NRFeOx is an abiotic process stimulated by nitrite and Fe(II)22. This abiotic process is triggered by the biotic production of reactive nitrogen species during denitrification22. The rapid abiotic reduction of nitrite by Fe(II) is an important N2O source in nature and termed chemodenitrification23,24.
The RNA-based 16S rRNA sequence analyses showed that the amendment of Fe(II) and nitrate followed by N2O formation through (chemo)denitrification led to the enrichment of active Defluviicoccus (Rhodospirialles) (1.3%), Sulfurimonas (Campylobacterales) (21.1%), and Arcobacter (Campylobacterales) (13.7%) (Fig. S2). Defluviicoccus sp. is a glycogen-accumulating organism and typically active in enhanced biological phosphorus removal-activated sludge systems32. While Sulfurimonas sp. are known for their ability of catalyzing chemolithotrophic reactions with ferrous iron and pyrite and the reduction of nitrate and nitrite33, Arcobacter sp. are known for their activity in Fe-rich habitats34, their ability to catalyze Fe(III) and Mn(IV) reduction35, their use of Fe(III) citrate as electron acceptor36, and their nitrogen-fixation ability37.
Our data show that nitrite-induced chemodenitrification has substantial consequences for the active N- and Fe-cycling microbial community (based on 16S rRNA gene sequences) (Figs 4 and S2, S3). Community members that carry the potential to trigger heterotrophic Fe(III) and nitrate reduction were enriched and active (Fig. S3), which is also supported by the slight decrease of DOC in nitrate and Fe(II) amended microcosms (Fig. 1). The stimulating effects of chemodenitrification on active Fe-cycling microorganisms (based on 16S rRNA gene sequences) were obvious by an increase of potential Fe(III)-reducers (Arcobacter, Sulfospirillum, and Shewanella in nitrate-amended setups, and Psychrilyobacter in nitrite-amended setups). In addition, an increase in the relative abundance of active Fe(II)-oxidizing bacteria [Sulfurimonas (with nitrate amendment), and Marinobacter and Pseudomonas (with nitrite amendment)] has been confirmed by 16S rRNA gene sequence analysis (Fig. S3)). Several Marinobacter species have been found in Fe-rich habitats55, and their metabolic potential to utilize nitrate as terminal electron acceptors and iron (i.e. FeS2 and CuFeS2) as an electron donor has been demonstrated previously40,56. Potential Fe(III)-reducers (e.g. Desulfobulbus, Desulfomusa, Sulfospirillum, Shewanella) were more abundant and active than bacteria that might be enrolled in Fe(II) oxidation. These results are in line with previous studies that quantified significantly more Fe(III)-reducing bacteria (up to 2.8%) compared to Fe(II)-oxidizing bacteria (in particular nitrate-reducing Fe(II)-oxidizers with 0.3%) in the same sediment57,58. Potential denitrifying bacteria with typical nosZ such as Shewanella spp. (which are metabolically flexible, i.e. they can use either Fe(III), nitrate or nitrite, and were detected in nitrate-amended microcosms), Pseudomonas spp. and Marinobacter spp. (in nitrite-amended microcosms) were stimulated by nitrate/nitrite addition and chemodenitrification reactions (Fig. S3, selection of typical nosZ bacteria from Norsminde Fjord sediment).
Iron is represented in biogeochemical ocean models by a variety of structurally different approaches employing generally poorly constrained empirical parameterizations. Increasing the structural complexity of iron modules also increases computational costs and introduces additional uncertainties, with as yet unclear benefits. In order to demonstrate the benefits of explicitly representing iron, we calibrate a hierarchy of iron modules and evaluate the remaining model-data misfit. The first module includes a complex iron cycle with major processes resolved explicitly, the second module applies iron limitation in primary production using prescribed monthly iron concentration fields, and the third module does not explicitly include iron effects at all. All three modules are embedded into the same circulation model. Models are calibrated against global data sets of NO3, PO4 and O2 applying a state-of-the-art multi-variable constraint parameter optimization. The model with fully resolved iron cycle is marginally (up to 4.8%) better at representing global distributions of NO3, PO4 and O2 compared to models with implicit or absent parameterizations of iron. We also found a slow down of global surface nutrient cycling by about 30% and a shift of productivity from the tropics to temperate regions for the explicit iron module. The explicit iron model also reduces the otherwise overestimated volume of suboxic waters, yielding results closer to observations.
Nickelsen et al (2015) introduced a dynamic iron module into an earth system model of intermediate complexity and hand-tuned model parameters against surface macro-nutrient observations and sparse observations of iron concentrations. The module resolved major components of the marine iron cycle, e.g. iron sources including aerosol deposition, detrital remineralization and sedimentary release, and iron sinks including biological uptake, iron scavenging and colloid formation. The focus of this earlier effort was on exploring the impact of an explicit iron cycle on model sensitivities to environmental change, while the hand-tuning ensured that overall model performance for present-day ocean state was not affected negatively by the addition of the iron module.
In this study we present calibrations of three variants of a global model of ocean biogeochemical cycles (dynamic iron cycle, Nickelsen et al 2015; prescribed iron mask, Keller et al 2012; without iron, disabled iron limitation in Keller et al 2012). Model variants share the same physical circulation but differ in their representation of the micro-nutrient iron. We calibrate against oceanic observations of NO3, PO4 and O2, using a recently developed framework (Kriest et al 2017). Our aim was to assess whether, and to what extent, the incorporation of Fe-related processes in the model improves the model skill of simulating NO3, PO4 and O2 as well as global indicators of biogeochemical cycles (described below). While it is generally assumed that the inclusion of an explicit iron cycle improves the capability of marine biogeochemical models to simulate distributions and fluxes of biogeochemical tracers also other than iron, this has, to our knowledge, not yet been demonstrated in a quantitative manner.
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