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

Elective Cancer Surgery in COVID-19-Free Surgical Pathways During the SARS-CoV-2 Pandemic: An International, Multicenter, Comparative Cohort Study.

PURPOSE: As cancer surgery restarts after the first COVID-19 wave, health care providers urgently require data to determine where elective surgery is best performed. This study aimed to determine whether COVID-19-free surgical pathways were associated with lower postoperative pulmonary complication rates compared with hospitals with no defined pathway. PATIENTS AND METHODS: This international, multicenter cohort study included patients who underwent elective surgery for 10 solid cancer types without preoperative suspicion of SARS-CoV-2. Participating hospitals included patients from local emergence of SARS-CoV-2 until April 19, 2020. At the time of surgery, hospitals were defined as having a COVID-19-free surgical pathway (complete segregation of the operating theater, critical care, and inpatient ward areas) or no defined pathway (incomplete or no segregation, areas shared with patients with COVID-19). The primary outcome was 30-day postoperative pulmonary complications (pneumonia, acute respiratory distress syndrome, unexpected ventilation). RESULTS: Of 9,171 patients from 447 hospitals in 55 countries, 2,481 were operated on in COVID-19-free surgical pathways. Patients who underwent surgery within COVID-19-free surgical pathways were younger with fewer comorbidities than those in hospitals with no defined pathway but with similar proportions of major surgery. After adjustment, pulmonary complication rates were lower with COVID-19-free surgical pathways (2.2% v 4.9%; adjusted odds ratio [aOR], 0.62; 95% CI, 0.44 to 0.86). This was consistent in sensitivity analyses for low-risk patients (American Society of Anesthesiologists grade 1/2), propensity score-matched models, and patients with negative SARS-CoV-2 preoperative tests. The postoperative SARS-CoV-2 infection rate was also lower in COVID-19-free surgical pathways (2.1% v 3.6%; aOR, 0.53; 95% CI, 0.36 to 0.76). CONCLUSION: Within available resources, dedicated COVID-19-free surgical pathways should be established to provide safe elective cancer surgery during current and before future SARS-CoV-2 outbreaks

Elective cancer surgery in COVID-19-free surgical pathways during the SARS-CoV-2 pandemic: An international, multicenter, comparative cohort study

PURPOSE As cancer surgery restarts after the first COVID-19 wave, health care providers urgently require data to determine where elective surgery is best performed. This study aimed to determine whether COVID-19–free surgical pathways were associated with lower postoperative pulmonary complication rates compared with hospitals with no defined pathway. PATIENTS AND METHODS This international, multicenter cohort study included patients who underwent elective surgery for 10 solid cancer types without preoperative suspicion of SARS-CoV-2. Participating hospitals included patients from local emergence of SARS-CoV-2 until April 19, 2020. At the time of surgery, hospitals were defined as having a COVID-19–free surgical pathway (complete segregation of the operating theater, critical care, and inpatient ward areas) or no defined pathway (incomplete or no segregation, areas shared with patients with COVID-19). The primary outcome was 30-day postoperative pulmonary complications (pneumonia, acute respiratory distress syndrome, unexpected ventilation). RESULTS Of 9,171 patients from 447 hospitals in 55 countries, 2,481 were operated on in COVID-19–free surgical pathways. Patients who underwent surgery within COVID-19–free surgical pathways were younger with fewer comorbidities than those in hospitals with no defined pathway but with similar proportions of major surgery. After adjustment, pulmonary complication rates were lower with COVID-19–free surgical pathways (2.2% v 4.9%; adjusted odds ratio [aOR], 0.62; 95% CI, 0.44 to 0.86). This was consistent in sensitivity analyses for low-risk patients (American Society of Anesthesiologists grade 1/2), propensity score–matched models, and patients with negative SARS-CoV-2 preoperative tests. The postoperative SARS-CoV-2 infection rate was also lower in COVID-19–free surgical pathways (2.1% v 3.6%; aOR, 0.53; 95% CI, 0.36 to 0.76). CONCLUSION Within available resources, dedicated COVID-19–free surgical pathways should be established to provide safe elective cancer surgery during current and before future SARS-CoV-2 outbreaks

Decline of Leach’s Storm Petrels Hydrobates leucorhous at the largest colonies in the northeast Atlantic

Leach’s Storm Petrel Hydrobates leucorhous has undergone substantial population declines at North Atlantic colonies over recent decades, but censusing the species is challenging because it nests in burrows and is only active at colonies at night. Acoustic playback surveys allow birds present in nest sites to be detected when they respond to recordings of vocalisations. However, not all birds respond to playback on every occasion, response rate is likely to decline with increasing distance between the bird and the playback location, and the observer may not detect all responses. As a result, various analysis methods have been developed to measure and correct for these imperfect response and detection probabilities. We applied two classes of methods (calibration plot and hierarchical distance sampling) to acoustic survey data from the two largest colonies of breeding Leach’s Storm Petrels in the northeast Atlantic: the St Kilda archipelago off the coast of northwest Scotland, and the island of Elliðaey in the Vestmannaeyjar archipelago off the southwest of Iceland. Our results indicate an overall decline of 68% for the St Kilda archipelago between 2000 and 2019, with a current best estimate of ~8,900 (95% CI: 7,800–10,100) pairs. The population on Elliðaey appears to have declined by 40 –49% between 1991 and 2018, with a current best estimate of ~5,400 (95% CI: 4,300–6,700) pairs. We also discuss the relative efficiency and precision of the two survey methods

Decline of Leach’s Storm Petrels Hydrobates leucorhous at the largest colonies in the northeast Atlantic

Leach’s Storm Petrel Hydrobates leucorhous has undergone substantial population declines at North Atlantic colonies over recent decades, but censusing the species is challenging because it nests in burrows and is only active at colonies at night. Acoustic playback surveys allow birds present in nest sites to be detected when they respond to recordings of vocalisations. However, not all birds respond to playback on every occasion, response rate is likely to decline with increasing distance between the bird and the playback location, and the observer may not detect all responses. As a result, various analysis methods have been developed to measure and correct for these imperfect response and detection probabilities. We applied two classes of methods (calibration plot and hierarchical distance sampling) to acoustic survey data from the two largest colonies of breeding Leach’s Storm Petrels in the northeast Atlantic: the St Kilda archipelago off the coast of northwest Scotland, and the island of Elliðaey in the Vestmannaeyjar archipelago off the southwest of Iceland. Our results indicate an overall decline of 68% for the St Kilda archipelago between 2000 and 2019, with a current best estimate of ~8,900 (95% CI: 7,800–10,100) pairs. The population on Elliðaey appears to have declined by 40 –49% between 1991 and 2018, with a current best estimate of ~5,400 (95% CI: 4,300–6,700) pairs. We also discuss the relative efficiency and precision of the two survey methods

Extreme stoichiometric gradients and B-vitamin effects on phytoplankton communities.

Eutrophication of natural waters is a major environmental detriment, leading to increased occurrence of harmful algal blooms and harmful cyanobacterial blooms. In addition to the nutrients nitrogen and phosphorous, micronutrients such as B vitamins can drive phytoplankton growth. To better understand the complex interactions between nutrient stoichiometry and B vitamin biogeochemistry in phytoplankton communities, a B vitamin bottle bioassay experiment was conducted in subsequent years with samples from a long-term shallow-lake mesocosm experiment set up with an extreme nutrient stoichiometry gradient. Vitamin additions did not have a significant effect on the phytoplankton community or their nutrient stoichiometry but halting N additions to shallow-lake mesocosms in 2023 led to an increase in community fraction and photosynthetic efficiency of green algae. The legacy of nutrient supply played a major role in the phytoplankton community and continued nutrient stoichiometry. Therefore, it is important to evaluate present and legacy loading in evaluating eutrophic systems

Tackling the global expansion of CyanoHABs along the freshwater to marine continuum: The need for a dual nutrient (N and P) control strategy

Anthropogenic nutrient over-enrichment, coupled with rising temperatures and an increased frequency of extreme hydrologic events, has promoted the expansion of harmful cyanobacterial blooms (CyanoHABs) along the interconnected freshwater-to-marine continuum. Pressures exist to reverse this trend in ecosystems to conserve drinking and irrigation, fishery, and recreational waters. Traditionally, reducing phosphorus (P) inputs are prescribed for freshwater systems, while nitrogen (N) inputs control estuarine/coastal CyanoHAB formation. However, microcosm to whole-lake nutrient-enrichment experiments increasingly indicate that CyanoHABs are stimulated by enrichment with both P and N or sometimes N alone. The accumulation of P “legacy” loads in water bodies supports effective internal P recycling, making it difficult to stem eutrophication with P-only external reductions. In most waterbodies, biological N2 fixation does not satisfy ecosystem N needs, while N can “escape” via denitrification, leading to perpetual N limitation. Therefore, dual N and P point and non-point reductions on watershed scales are needed to protect the continuum. In addition, a more climatically extreme world will augment watershed-based nutrient management challenges. In the short-term, physical, chemical or biological manipulative controls may improve immediate beneficial uses, but they are only temporary “fixes” that should be accompanied by long-term dual nutrient management for CyanoHAB control along the continuum

Applying the core-satellite species concept: Characteristics of rare and common riverine dissolved organic matter

Peer reviewed: TrueAcknowledgements: We thank the WHONDRS consortium for facilitating generation of data used in this manuscript, including study design, crowdsourced sample collection, sample analysis, and public data publishing. We also thank the organizers and participants of the virtual crowdsourced workshop (Borton et al., 2022) where the initial scientific questions and hypotheses were developed.IntroductionDissolved organic matter (DOM) composition varies over space and time, with a multitude of factors driving the presence or absence of each compound found in the complex DOM mixture. Compounds ubiquitously present across a wide range of river systems (hereafter termed core compounds) may differ in chemical composition and reactivity from compounds present in only a few settings (hereafter termed satellite compounds). Here, we investigated the spatial patterns in DOM molecular formulae presence (occupancy) in surface water and sediments across 97 river corridors at a continental scale using the “Worldwide Hydrobiogeochemical Observation Network for Dynamic River Systems—WHONDRS” research consortium.MethodsWe used a novel data-driven approach to identify core and satellite compounds and compared their molecular properties identified with Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS).ResultsWe found that core compounds clustered around intermediate hydrogen/carbon and oxygen/carbon ratios across both sediment and surface water samples, whereas the satellite compounds varied widely in their elemental composition. Within surface water samples, core compounds were dominated by lignin-like formulae, whereas protein-like formulae dominated the core pool in sediment samples. In contrast, satellite molecular formulae were more evenly distributed between compound classes in both sediment and water molecules. Core compounds found in both sediment and water exhibited lower molecular mass, lower oxidation state, and a higher degree of aromaticity, and were inferred to be more persistent than global satellite compounds. Higher putative biochemical transformations were found in core than satellite compounds, suggesting that the core pool was more processed.DiscussionThe observed differences in chemical properties of core and satellite compounds point to potential differences in their sources and contribution to DOM processing in river corridors. Overall, our work points to the potential of data-driven approaches separating rare and common compounds to reduce some of the complexity inherent in studying riverine DOM.</jats:sec

Phenotypic assortment mediates the effect of social selection in a wild beetle population

Social interactions often have major fitness consequences, but little is known about how specific interacting phenotypes affect the strength of natural selection. Social influences on the evolutionary process can be assessed using a multilevel selection approach that partitions the effects of social partner phenotypes on fitness (referred to as social or group selection) from those of the traits of a focal individual (nonsocial or individual selection). To quantify the contribution of social selection to total selection affecting a trait, the patterns of phenotypic association among interactants must also be considered. We estimated selection gradients on male body size in a wild population of forked fungus beetles (Bolitotherus cornutus). We detected positive nonsocial selection and negative social selection on body size operating through differences in copulation success, indicating that large males with small social partners had highest fitness. In addition, we found that, in low-density demes, the phenotypes of focal individuals were negatively correlated with those of their social partners. This pattern reversed the negative effect of group selection on body size and led to stronger positive selection for body size. Our results demonstrate multilevel selection in nature and stress the importance of considering social selection whenever conspecific interactions occur nonrandomly