Improved Mass Accuracy and Isotope Confirmation through Alignment of Ultrahigh-Resolution Mass Spectra of Complex Natural Mixtures

Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is one of the state-of-the-art methods to analyze complex natural organic mixtures. The precision of detected masses is crucial for molecular formula attribution. Random errors can be reduced by averaging multiple measurements of the same mass, but because of limited availability of ultrahigh-resolution mass spectrometers, most studies cannot afford analyzing each sample multiple times. Here we show that random errors can be eliminated also by averaging mass spectral data from independent environmental samples. By averaging the spectra of 30 samples analyzed on our 15 T instrument we reach a mass precision comparable to a single spectrum of a 21 T instrument. We also show that it is possible to accurately and reproducibly determine isotope ratios with FT-ICR-MS. Intensity ratios of isotopologues were improved to a degree that measured deviations were within the range of natural isotope fractionation effects. In analogy to δ13C in environmental studies, we propose Δ13C as an analytical measure for isotope ratio deviances instead of widely employed C deviances. In conclusion, here we present a simple tool, extensible to Orbitrap-based mass spectrometers, for postdetection data processing that significantly improves mass accuracy and the precision of intensity ratios of isotopologues at no extra cost

Nitrogen deposition causes eutrophication in bryophyte communities in central and northern European forests

Key message Our results indicate that nitrogen deposition is likely to adversely affect forest bryophyte communities, having negative impacts in terms of increased dominance of nitrophilic species at the expense of N-sensitive species and a decrease in evenness. Context Elevated atmospheric deposition of nitrogen (N) has long been recognised as a threat to biodiversity and, despite declines in European emission levels, will remain a threat in the future. Aims It has proven difficult to show clear large-scale impacts of N deposition on vascular forest understorey species, and few studies have looked at impacts on forest bryophytes. Here, we assess the impact of nitrogen deposition on forest bryophyte communities. Methods We used data from 187 plots included in European monitoring schemes to analyse the relationship between levels of throughfall nitrogen deposition and bryophyte taxonomic and functional diversity and community nitrogen preference. Results We found that nitrogen deposition is significantly associated with increased bryophyte community nitrogen preference and decreases in species evenness. Conclusion Our results indicate that nitrogen deposition is likely to adversely affect forest bryophyte communities, having negative impacts in terms of increased dominance of nitrophilic species at the expense of N-sensitive species and a decrease in species evenness

Geographic redistribution of microcystin hotspots in response to climate warming

High concentrations of cyanobacterial toxins such as microcystin represent a global challenge to water quality in lakes, threatening health, economies and ecosystem stability. Lakes are sentinels of climate change but how warming will affect microcystin concentrations is still unclear. Here we examine how warming impacts the probability of exceeding microcystin water quality thresholds across 2,804 lakes in the United States and show how future warming will alter these probabilities. We find that higher temperatures consistently increase the likelihood of microcystin occurrence but that the probability of microcystin concentrations above water quality thresholds is highest for water temperatures between 20 and 25 °C. Regions with temperatures that promote microcystin will shift to higher latitudes in the coming decades, leading to relative changes in exceedance probabilities of more than 50% in many basins of the United States. High nitrogen concentrations amplify the impact of rising temperatures, calling for increased awareness of a substantial hazard to ecosystems and human health under global warming

A novel algorithm for ocean chlorophyll-a concentration using MODIS Aqua data

The ability to infer ocean chlorophyll-a concentrations (Chla) from spaceborne instruments is key to assessments of global ocean productivity and monitoring of water quality. Here, we present a novel parametric algorithm, OCG, trained on a set of global in situ high-performance liquid chromatography (HPLC) data that leverages Level- 3 remote sensing reflectance (Rrs) products from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua satellite. The OCG algorithm leverages more bands than existing algorithms and also provides pixel-wise uncertainty assessments that enable the calculation of the probability of exceeding specific Chla thresholds. This feature has significant implications for water quality management, particularly in monitoring harmful algal blooms. The OCG surpasses existing algorithms in bias and accuracy without overfitting, especially in coastal areas, where it outperforms the current standard product (CI OC3) by 20 % in median symmetric accuracy. Moreover, the OCG reduces the signed symmetric percentage bias (SSPB) in coastal regions from 41 % (CI OC3) to below 5 %. Globally, the OCG algorithm yields lower Chla in coastal regions, the Southern Ocean and the Mediterranean Sea, and higher values in the open ocean, particularly in ocean gyres and polar regions. For the Chesapeake Bay and the Baltic Sea, for example, daily OCG estimates for 2002 to 2021 are, on average, 2.9 g/ L and 3.7 g/L lower than CI OC3 estimates, respectively. The presented approach also shows great potential for other existing and upcoming sensors, enabling widespread application in remote sensing

Dissolved organic compounds with synchronous dynamics share chemical properties and origin

Merder J, Röder H, Dittmar T, et al. Dissolved organic compounds with synchronous dynamics share chemical properties and origin. Limnology and Oceanography. 2021;66(11):4001-4016

Biogeochemical cycling of molybdenum and thallium during a phytoplankton summer bloom: A mesocosm study

Molybdenum (Mo) and thallium (Tl) are known as conservative-type elements in open ocean settings, despite their involvement in bio-cycling processes. In coastal oceans like the southern North Sea, however, positive and negative anomalies of dissolved Mo and Tl concentrations occur during certain time periods of the year, which are characterized by intensive organic matter cycling. The main motivation of the present study was to identify potential drivers for the non-conservative behavior of Mo and Tl. For this purpose, we conducted an indoor mesocosm experiment with natural seawater and sediment (including a natural microorganism community) and applied close to natural light and tidal (diurnal) conditions. After an incubation time of 35 days, we initialized a storm event to examine its influence on organic matter as well as nutrient and trace metal cycling. The temporal pattern of the inorganic macronutrients (N-species, dissolved phosphorous, dissolved silicate) as well as dissolved and particulate organic matter was highly dependent on the interplay of the phytoplankton and its associated bacteria bloom. Our results suggest that the redox-sensitive trace metals manganese (Mn), vanadium (V) and iron (Fe) were involved in bio-cycling processes. While temporal pattern of dissolved Mn and V were likely induced by active (macro-)nutrient assimilation rather than redox induced phase changes, dissolved Fe was present as organo-metallic complex and shielded from the flocculation as metal oxide. Our results further reveal positive Mo and negative Tl anomalies, especially during pre-storm conditions. The additional input of Mo was derived from the oxidation of reducing bottom sediments. Thereby, the degree as well as the rate of Mo-input was dependent on the composition of the background sediment. In the water column Mo was not only present in its dissolved oxidized form but was also stabilized by organic (algae-detritus, ligands) and inorganic (aluminosilicates) binding partners, preventing its (re-)deposition. Negative Tl anomalies were found to be induced by its immobilization by organic (algae-detritus, ligands) and inorganic (aluminosilicates) carrier phases in the water column prior to its deposition and potential fixation in the sulfidic bottom sediments. Particles derived from the storm event did not have any considerable effect on dissolved organic nor inorganic compounds, as they (re-)deposited before significant remineralization processes could take place

Thresholds for ecological responses to global change do not emerge from empirical data

International audienceTo understand ecosystem responses to anthropogenic global change, a prevailing framework is the definition of threshold levels of pressure, above which response magnitudes and their variances increase disproportionately. However, we lack systematic quantitative evidence as to whether empirical data allow definition of such thresholds. Here, we summarize 36 meta-analyses measuring more than 4,600 global change impacts on natural communities. We find that threshold transgressions were rarely detectable, either within or across meta-analyses. Instead, ecological responses were characterized mostly by progressively increasing magnitude and variance when pressure increased. Sensitivity analyses with modelled data revealed that minor variances in the response are sufficient to preclude the detection of thresholds from data, even if they are present. The simulations reinforced our contention that global change biology needs to abandon the general expectation that system properties allow defining thresholds as a way to manage nature under global change. Rather, highly variable responses, even under weak pressures, suggest that ‘safe-operating spaces’ are unlikely to be quantifiable

Unraveling the Linkages between Molecular Abundance and Stable Carbon Isotope Ratio in Dissolved Organic Matter Using Machine Learning

Dissolved organic matter (DOM) is a complex mixture of molecules that constitutes one of the largest reservoirs of organic matter on Earth. While stable carbon isotope values (δ13C) provide valuable insights into DOM transformations from land to ocean, it remains unclear how individual molecules respond to changes in DOM properties such as δ13C. To address this, we employed Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) to characterize the molecular composition of DOM in 510 samples from the China Coastal Environments, with 320 samples having δ13C measurements. Utilizing a machine learning model based on 5199 molecular formulas, we predicted δ13C values with a mean absolute error (MAE) of 0.30‰ on the training data set, surpassing traditional linear regression methods (MAE 0.85‰). Our findings suggest that degradation processes, microbial activities, and primary production regulate DOM from rivers to the ocean continuum. Additionally, the machine learning model accurately predicted δ13C values in samples without known δ13C values and in other published data sets, reflecting the δ13C trend along the land to ocean continuum. This study demonstrates the potential of machine learning to capture the complex relationships between DOM composition and bulk parameters, particularly with larger learning data sets and increasing molecular research in the future