RIVERINE DISSOLVED ORGANIC MATTER DECOMPOSITION AND DYNAMICS

Aquatic and terrestrial ecosystems are intimately linked through the transfer of energy and materials. A common example of ecosystem linkage is the input of terrestrial dissolved organic matter (DOM) to rivers and streams. DOM can play a variety of roles in stream ecosystem function by fueling local food webs, influencing trophic state, and affecting the dissolved nutrient availability. Microorganisms utilize, transform, and produce DOM during microbial metabolism, a relationship that links microbes to DOM quality and quantity. Chemical and physical properties are known to vary with DOM source, and thus the type of terrestrial input may dictate how DOM is processed in a stream.  Using laboratory microcosms, and added terrestrial organic matter substrates, we carried out a leaching experiment over forty-five days. We employed a suite of complementary techniques to determine the effect of leaching DOM sources on microorganisms, DOM processing, and ecosystem function.  Microbial community composition changed from the original stream water inoculum and depended on DOM source. Cell abundances for all DOM sources spiked after two days, after which abundances dropped and remained relatively steady until the end of the experiment.  DOM concentrations decreased exponentially with the maximum amount of carbon utilization taking place within the first five days.  The DOM fluorescent signature, initially influenced by amino acid-like fluorescence shifts to more humic-like character over the course of the experiment, indicating DOM humification over time. Our results showcase the advantages of interdisciplinary tools to elucidate the connection of microbial processing, DOM chemistry, and ecosystem function

Machine-learning based approach to examine ecological processes influencing the diversity of riverine dissolved organic matter composition

Dissolved organic matter (DOM) assemblages in freshwater rivers are formed from mixtures of simple to complex compounds that are highly variable across time and space. These mixtures largely form due to the environmental heterogeneity of river networks and the contribution of diverse allochthonous and autochthonous DOM sources. Most studies are, however, confined to local and regional scales, which precludes an understanding of how these mixtures arise at large, e.g., continental, spatial scales. The processes contributing to these mixtures are also difficult to study because of the complex interactions between various environmental factors and DOM. Here we propose the use of machine learning (ML) approaches to identify ecological processes contributing toward mixtures of DOM at a continental-scale. We related a dataset that characterized the molecular composition of DOM from river water and sediment with Fourier-transform ion cyclotron resonance mass spectrometry to explanatory physicochemical variables such as nutrient concentrations and stable water isotopes (2H and 18O). Using unsupervised ML, distinctive clusters for sediment and water samples were identified, with unique molecular compositions influenced by environmental factors like terrestrial input and microbial activity. Sediment clusters showed a higher proportion of protein-like and unclassified compounds than water clusters, while water clusters exhibited a more diversified chemical composition. We then applied a supervised ML approach, involving a two-stage use of SHapley Additive exPlanations (SHAP) values. In the first stage, SHAP values were obtained and used to identify key physicochemical variables. These parameters were employed to train models using both the default and subsequently tuned hyperparameters of the Histogram-based Gradient Boosting (HGB) algorithm. The supervised ML approach, using HGB and SHAP values, highlighted complex relationships between environmental factors and DOM diversity, in particular the existence of dams upstream, precipitation events, and other watershed characteristics were important in predicting higher chemical diversity in DOM. Our data-driven approach can now be used more generally to reveal the interplay between physical, chemical, and biological factors in determining the diversity of DOM in other ecosystems

Machine-learning based approach to examine ecological processes influencing the diversity of riverine dissolved organic matter composition

Dissolved organic matter (DOM) assemblages in freshwater rivers are formed from mixtures of simple to complex compounds that are highly variable across time and space. These mixtures largely form due to the environmental heterogeneity of river networks and the contribution of diverse allochthonous and autochthonous DOM sources. Most studies are, however, confined to local and regional scales, which precludes an understanding of how these mixtures arise at large, e.g., continental, spatial scales. The processes contributing to these mixtures are also difficult to study because of the complex interactions between various environmental factors and DOM. Here we propose the use of machine learning (ML) approaches to identify ecological processes contributing toward mixtures of DOM at a continental-scale. We related a dataset that characterized the molecular composition of DOM from river water and sediment with Fourier-transform ion cyclotron resonance mass spectrometry to explanatory physicochemical variables such as nutrient concentrations and stable water isotopes (2H and 18O). Using unsupervised ML, distinctive clusters for sediment and water samples were identified, with unique molecular compositions influenced by environmental factors like terrestrial input and microbial activity. Sediment clusters showed a higher proportion of protein-like and unclassified compounds than water clusters, while water clusters exhibited a more diversified chemical composition. We then applied a supervised ML approach, involving a two-stage use of SHapley Additive exPlanations (SHAP) values. In the first stage, SHAP values were obtained and used to identify key physicochemical variables. These parameters were employed to train models using both the default and subsequently tuned hyperparameters of the Histogram-based Gradient Boosting (HGB) algorithm. The supervised ML approach, using HGB and SHAP values, highlighted complex relationships between environmental factors and DOM diversity, in particular the existence of dams upstream, precipitation events, and other watershed characteristics were important in predicting higher chemical diversity in DOM. Our data-driven approach can now be used more generally to reveal the interplay between physical, chemical, and biological factors in determining the diversity of DOM in other ecosystems

Molecular transformation and degradation of refractory dissolved organic matter in the Atlantic and Southern Ocean

More than 90% of the global ocean dissolved organic carbon (DOC) is refractory, has an average age of 4,000–6,000 years and a lifespan from months to millennia. The fraction of dissolved organic matter (DOM) that is resistant to degradation is a long-term buffer in the global carbon cycle but its chemical composition, structure, and biochemical formation and degradation mechanisms are still unresolved. We have compiled the most comprehensive molecular data set of 197 Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) analyses from solid-phase extracted marine DOM covering two major oceans, the Atlantic sector of the Southern Ocean and the East Atlantic Ocean (ranging from 50° N to 70° S). Molecular trends and radiocarbon dating of 34 DOM samples (comprising Δ14C values from -229 to -495‰) were combined to model an integrated degradation rate for bulk DOC resulting in a predicted age of >24 ka for the most persistent DOM fraction. First order kinetic degradation rates for 1,557 mass peaks indicate that numerous DOM molecules cycle on timescales much longer than the turnover of the bulk DOC pool (estimated residence times of >100 ka) and the range of validity of radiocarbon dating. Changes in elemental composition were determined by assigning molecular formulae to the detected mass peaks. The combination of residence times with molecular information enabled modelling of the average elemental composition of the slowest degrading fraction of the DOM pool. In our dataset, a group of 361 molecular formulae represented the most stable composition in the oceanic environment (“island of stability”). These most persistent compounds encompass only a narrow range of the elemental ratios H/C (average of 1.17 ± 0.13), and O/C (average of 0.52 ± 0.10) and molecular masses (360 ± 28 and 497 ± 51 Da). In the Weddell Sea DOC concentrations in the surface waters were low (46.3 ± 3.3 μM) while the organic radiocarbon was significantly more depleted than that of the East Atlantic, indicating average surface water DOM ages of 4,920 ± 180 a. These results are in accordance with a highly degraded DOM in the Weddell Sea surface water as also shown by the molecular degradation index IDEG obtained from FT-ICR MS data. Further, we identified 339 molecular formulae which probably contribute to an increased DOC concentration in the Southern Ocean and potentially reflect an accumulation or enhanced sequestration of refractory DOC in the Weddell Sea. These results will contribute to a better understanding of the persistent nature of marine DOM and its role as an oceanic carbon buffer in a changing climate

Southern Ocean carbon sink enhanced by sea-ice feedbacks at the Antarctic Cold Reversal

The Southern Ocean occupies some 14% of the planet’s surface and plays a fundamental role in the global carbon cycle and climate. It provides a direct connection to the deep ocean carbon reservoir through biogeochemical processes that include surface primary productivity, remineralisation at depth, and the upwelling of carbon-rich water masses. However, the role of these different processes in modulating past and future air-sea carbon flux remains poorly understood. A key period in this regard is the Antarctic Cold Reversal (ACR, 14.6-12.7 kyr BP), a period of mid- to high-latitude cooling that coincided with a sustained plateau in deglacial atmospheric rise in CO2 globally. Here we reconstruct high-latitude Southern Ocean surface productivity from marine derived aerosols captured in a highly-resolved horizontal ice core. Our multiproxy reconstruction reveals a coherent signal of enhanced productivity across the ACR. Transient climate modelling indicates this period coincided with maximum seasonal variability in sea-ice extent, implying that sea-ice biological feedbacks enhanced CO2 sequestration, creating a significant regional marine carbon sink that contributed to the sustained plateau in CO2 at the ACR. Our results highlights the role Antarctic sea ice plays in controlling global CO2, and demonstrates the need to incorporate such feedbacks in climate-carbon models

Lung and airway surgical procedures

Notwithstanding that some basilar technical and oncological concepts are still valid, thoracic incisions and surgical techniques to cure lung and airway diseases have been progressively modified during the years. Minimally invasive surgery is by now considered the preferred approach in early stages of lung cancer. The improvement of perioperative patient’s management and of technology has allowed performing more and more difficult and extended procedures with good results in terms of early and long-term survival. Finally, surgery represents a valid option for very selected emphysema patients