Influence of migration range and foraging ecology on mercury accumulation in Southern Ocean penguins.

In order to evaluate mercury (Hg) accumulation patterns in Southern Ocean penguins, we measured Hg concentrations and carbon (δ13C) and nitrogen (δ15N) stable isotope ratios in body feathers of adult Adélie (Pygoscelis adeliae), gentoo (Pygoscelis papua), and chinstrap (Pygoscelis antarctica) penguins living near Anvers Island, West Antarctic Peninsula (WAP) collected in the 2010/2011 austral summer. With these and data from Pygoscelis and other penguin genera (Eudyptes and Aptenodytes) throughout the Southern Ocean, we modelled Hg variation using δ13C and δ15N values. Mean concentrations of Hg in feathers of Adélie (0.09 ± 0.05 µg g-1) and gentoo (0.16 ± 0.08 µg g-1) penguins from Anvers Island were among the lowest ever reported for the Southern Ocean. However, Hg concentrations in chinstrap penguins (0.80 ± 0.20 µg g-1), which undertake relatively broad longitudinal winter migrations north of expanding sea ice, were significantly higher (P < 0.001) than those in gentoo or Adélie penguins. δ13C and δ15N values for feathers from all three Anvers Island populations were also the lowest among those previously reported for Southern Ocean penguins foraging within Antarctic and subantarctic waters. These observations, along with size distributions of WAP krill, suggest foraging during non-breeding seasons as a primary contributor to higher Hg accumulation in chinstraps relative to other sympatric Pygoscelis along the WAP. δ13C values for all Southern Ocean penguin populations, alone best explained feather Hg concentrations among possible generalized linear models (GLMs) for populations grouped by either breeding site (AICc = 36.9, wi = 0.0590) or Antarctic Frontal Zone (AICc = 36.9, wi = 0.0537). Although Hg feather concentrations can vary locally by species, there was an insignificant species-level effect (wi < 0.001) across the full latitudinal range examined. Therefore, feeding ecology at breeding locations, as tracked by δ13C, control Hg accumulation in penguin populations across the Southern Ocean.

Hematite enhances microbial autotrophic nitrate removal in carbonate and phosphate-rich environments by increasing Fe(II) activity.

Groundwater contamination by nitrates presents significant risks to both human health and the environment. In groundwater characterized as oligotrophic-low in organic carbon, but abundant in carbonate and phosphate-chemolithoautotrophic bacteria, including nitrate-reducing Fe(II)-oxidizing bacteria (NRFeOB), play a vital role in denitrification. The chemoautotrophic nitrate reduction is sensitive to environmental factors, including widespread iron oxides like hematite in nature. However, the specific mechanisms of this influence remain unclear. We examined the mechanism of how hematite impacts autotrophic nitrate reduction in a model NRFeOB community known as culture KS. We found that hematite enhances the rate of autotrophic nitrate reduction by promoting Fe(II) oxidation. Mössbauer spectroscopy detected a significant amount of adsorbed Fe(II) when hematite was present, leading to a reduction in dissolved ferrous iron. In conjunction with XRD data, it can be inferred that the formation of vivianite decreased, thereby increasing the Fe(II) activity in the reaction system. Within the culture KS bacterial consortium, hematite fosters the proliferation of autotrophic microorganisms, specifically Gallionellaceae, and amplifies the presence of denitrifying microbes, notably Rhodanobacter. This dual enhancement improves Fe(II) utilization and nitrate reduction capabilities. Our findings highlight intricate interactions between hematite and a model NRFeOB community, offering insights into groundwater nitrate removal mechanisms and the ecological strategies of autotrophic bacteria in mineral-rich environments.

Evidence of a putative CO2 delivery system to the chromatophore in the photosynthetic amoeba Paulinella.

The photosynthetic amoeba, Paulinella provides a recent (ca. 120 Mya) example of primary plastid endosymbiosis. Given the extensive data demonstrating host lineage-driven endosymbiont integration, we analysed nuclear genome and transcriptome data to investigate mechanisms that may have evolved in Paulinella micropora KR01 (hereinafter, KR01) to maintain photosynthetic function in the novel organelle, the chromatophore. The chromatophore is of α-cyanobacterial provenance and has undergone massive gene loss due to Muller's ratchet, but still retains genes that encode the ancestral α-carboxysome and the shell carbonic anhydrase, two critical components of the biophysical CO2 concentrating mechanism (CCM) in cyanobacteria. We identified KR01 nuclear genes potentially involved in the CCM that arose via duplication and divergence and are upregulated in response to high light and downregulated under elevated CO2. We speculate that these genes may comprise a novel CO2 delivery system (i.e., a biochemical CCM) to promote the turnover of the RuBisCO carboxylation reaction and counteract photorespiration. We posit that KR01 has an inefficient photorespiratory system that cannot fully recycle the C2 product of RuBisCO oxygenation back to the Calvin-Benson cycle. Nonetheless, both these systems appear to be sufficient to allow Paulinella to persist in environments dominated by faster-growing phototrophs.

Speciation and Possible Origins of Organosulfur Compounds in Rice Paddy Soils Affected by Acid Mine Drainage.

Although sulfur cycling in acid mine drainage (AMD)-contaminated rice paddy soils is critical to understanding and mitigating the environmental consequences of AMD, potential sources and transformations of organosulfur compounds in such soils are poorly understood. We used sulfur K-edge X-ray absorption near edge structure (XANES) spectroscopy to quantify organosulfur compounds in paddy soils from five AMD-contaminated sites and one AMD-uncontaminated reference site near the Dabaoshan sulfide mining area in South China. We also determined the sulfur stable isotope compositions of water-soluble sulfate (δ34SWS), adsorbed sulfate (δ34SAS), fulvic acid sulfur (δ34SFAS), and humic acid sulfur (δ34SHAS) in these samples. Organosulfate was the dominant functional group in humic acid sulfur (HAS) in both AMD-contaminated (46%) and AMD-uncontaminated paddy soils (42%). Thiol/organic monosulfide contributed a significantly lower proportion of HAS in AMD-contaminated paddy soils (8%) compared to that in AMD-uncontaminated paddy soils (21%). Within contaminated soils, the concentration of thiol/organic monosulfide was positively correlated with cation exchange capacity (CEC), moisture content (MC), and total Fe (TFe). δ34SFAS ranged from -6.3 to 2.7‰, similar to δ34SWS (-6.9 to 8.9‰), indicating that fulvic acid sulfur (FAS) was mainly derived from biogenic S-bearing organic compounds produced by assimilatory sulfate reduction. δ34SHAS (-11.0 to -1.6‰) were more negative compared to δ34SWS, indicating that dissimilatory sulfate reduction and abiotic sulfurization of organic matter were the main processes in the formation of HAS.

Prediction of Cr(VI) and As(V) adsorption on goethite using hybrid surface complexation-machine learning model.

This study aimed to develop surface complexation modeling-machine learning (SCM-ML) hybrid model for chromate and arsenate adsorption on goethite. The feasibility of two SCM-ML hybrid modeling approaches was investigated. Firstly, we attempted to utilize ML algorithms and establish the parameter model, to link factors influencing the adsorption amount of oxyanions with optimized surface complexation constants. However, the results revealed the optimized chromate or arsenate surface complexation constants might fall into local extrema, making it unable to establish a reasonable mapping relationship between adsorption conditions and surface complexation constants by ML algorithms. In contrast, species-informed models were successfully obtained, by incorporating the surface species information calculated from the unoptimized SCM with the adsorption condition as input features. Compared with the optimized SCM, the species-informed model could make more accurate predictions on pH edges, isotherms, and kinetic data for various input conditions (for chromate: root mean square error (RMSE) on test set = 5.90 %; for arsenate: RMSE on test set = 4.84 %). Furthermore, the utilization of the interpretable formula based on Local Interpretable Model-Agnostic Explanations (LIME) enabled the species-informed model to provide surface species information like SCM. The species-informed SCM-ML hybrid modeling method proposed in this study has great practicality and application potential, and is expected to become a new paradigm in surface adsorption model.