Global Carbon Cycling on a Heterogeneous Seafloor

Diverse biological communities mediate the transformation, transport, and storage of elements fundamental to life on Earth, including carbon, nitrogen, and oxygen. However, global biogeochemical model outcomes can vary by orders of magnitude, compromising capacity to project realistic ecosystem responses to planetary changes, including ocean productivity and climate. Here, we compare global carbon turnover rates estimated using models grounded in biological versus geochemical theory and argue that the turnover estimates based on each perspective yield divergent outcomes. Importantly, empirical studies that include sedimentary biological activity vary less than those that ignore it. Improving the relevance of model projections and reducing uncertainty associated with the anticipated consequences of global change requires reconciliation of these perspectives, enabling better societal decisions on mitigation and adaptation.Peer reviewe

Resolving fine-scale population structure and fishery exploitation using sequenced microsatellites in a northern fish

Funding Information Natural Sciences and Engineering Research Council of Canada (NSERC) Strategic Project Atlantic Canada Opportunities Agency and Department of Tourism, Culture, Industry and Innovation grants allocated to the Labrador Institute (MC) Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Genomics Research and Development Initiative (GRDI) Weston Family AwardPeer reviewedPublisher PD

Biophysical Factors Affecting the Distribution of Demersal Fish around the Head of a Submarine Canyon Off the Bonney Coast, South Australia

We sampled the demersal fish community of the Bonney Canyon, South Australia at depths (100–1,500 m) and locations that are poorly known. Seventy-eight species of demersal fish were obtained from 12 depth-stratified trawls along, and to either side, of the central canyon axis. Distributional patterns in species richness and biomass were highly correlated. Three fish assemblage groupings, characterised by small suites of species with narrow depth distributions, were identified on the shelf, upper slope and mid slope. The assemblage groupings were largely explained by depth (ρw = 0.78). Compared to the depth gradient, canyon-related effects are weak or occur at spatial or temporal scales not sampled in this study. A conceptual physical model displayed features consistent with the depth zonational patterns in fish, and also indicated that canyon upwelling can occur. The depth zonation of the fish assemblage was associated with the depth distribution of water masses in the area. Notably, the mid-slope community (1,000 m) coincided with a layer of Antarctic Intermediate Water, the upper slope community (500 m) resided within the core of the Flinders Current, and the shelf community was located in a well-mixed layer of surface water (<450 m depth)

From Sea to Sea: Canada's Three Oceans of Biodiversity

Evaluating and understanding biodiversity in marine ecosystems are both necessary and challenging for conservation. This paper compiles and summarizes current knowledge of the diversity of marine taxa in Canada's three oceans while recognizing that this compilation is incomplete and will change in the future. That Canada has the longest coastline in the world and incorporates distinctly different biogeographic provinces and ecoregions (e.g., temperate through ice-covered areas) constrains this analysis. The taxonomic groups presented here include microbes, phytoplankton, macroalgae, zooplankton, benthic infauna, fishes, and marine mammals. The minimum number of species or taxa compiled here is 15,988 for the three Canadian oceans. However, this number clearly underestimates in several ways the total number of taxa present. First, there are significant gaps in the published literature. Second, the diversity of many habitats has not been compiled for all taxonomic groups (e.g., intertidal rocky shores, deep sea), and data compilations are based on short-term, directed research programs or longer-term monitoring activities with limited spatial resolution. Third, the biodiversity of large organisms is well known, but this is not true of smaller organisms. Finally, the greatest constraint on this summary is the willingness and capacity of those who collected the data to make it available to those interested in biodiversity meta-analyses. Confirmation of identities and intercomparison of studies are also constrained by the disturbing rate of decline in the number of taxonomists and systematists specializing on marine taxa in Canada. This decline is mostly the result of retirements of current specialists and to a lack of training and employment opportunities for new ones. Considering the difficulties encountered in compiling an overview of biogeographic data and the diversity of species or taxa in Canada's three oceans, this synthesis is intended to serve as a biodiversity baseline for a new program on marine biodiversity, the Canadian Healthy Ocean Network. A major effort needs to be undertaken to establish a complete baseline of Canadian marine biodiversity of all taxonomic groups, especially if we are to understand and conserve this part of Canada's natural heritage

Four Regional Marine Biodiversity Studies: Approaches and Contributions to Ecosystem-Based Management

We compare objectives and approaches of four regional studies of marine biodiversity: Gulf of Maine Area Census of Marine Life, Baltic Sea History of Marine Animal Populations, Great Barrier Reef Seabed Biodiversity Project, and Gulf of Mexico Biodiversity Project. Each program was designed as an "ecosystem" scale but was created independently and executed differently. Each lasted 8 to 10 years, including several years to refine program objectives, raise funding, and develop research networks. All resulted in improved baseline data and in new, or revised, data systems. Each contributed to the creation or evolution of interdisciplinary teams, and to regional, national, or international science-management linkages. To date, there have been differing extents of delivery and use of scientific information to and by management, with greatest integration by the program designed around specific management questions. We evaluate each research program's relative emphasis on three principal elements of biodiversity organization: composition, structure, and function. This approach is used to analyze existing ecosystem-wide biodiversity knowledge and to assess what is known and where gaps exist. In all four of these systems and studies, there is a relative paucity of investigation on functional elements of biodiversity, when compared with compositional and structural elements. This is symptomatic of the current state of the science. Substantial investment in understanding one or more biodiversity element(s) will allow issues to be addressed in a timely and more integrative fashion. Evaluating research needs and possible approaches across specific elements of biodiversity organization can facilitate planning of future studies and lead to more effective communication between scientists, managers, and stakeholders. Building a general approach that captures how various studies have focused on different biodiversity elements can also contribute to meta-analyses of worldwide experience in scientific research to support ecosystem-based management

A multilevel analysis of social capital and self-rated health: Evidence from the British Household Panel Survey

Social capital is often described as a collective benefit engendered by generalised trust, civic participation, and mutual reciprocity. This feature of communities has been shown to associate with an assortment of health outcomes at several levels of analysis. The current study assesses the evidence for an association between area-level social capital and individual-level subjective health. Respondents participating in waves 8 (1998) and 9 (1999) of the British Household Panel Survey were identified and followed-up 5 years later in wave 13 (2003). Area social capital was measured by two aggregated survey items: social trust and civic participation. Multilevel logistic regression models were fitted to examine the association between area social capital indicators and individual poor self-rated health. Evidence for a protective association with current self-rated health was found for area social trust after controlling for individual characteristics, baseline self-rated health and individual social trust. There was no evidence for an association between area civic participation and self-rated health after adjustment. The findings of this study expand the literature on social capital and health through the use of longitudinal data and multilevel modelling techniques.UK Social capital Self-rated health Longitudinal British Household Panel Survey Multilevel modelling

Promoting positive maternal, newborn, and child health behaviors through a group-based health education and microfinance program: a prospective matched cohort study in western Kenya

Background: Chamas for Change (Chamas) is a group-based health education and microfinance program for pregnant and postpartum women that aims to address inequities contributing to high rates of maternal and infant mortality in rural western Kenya. In this prospective matched cohort study, we evaluated the association between Chamas participation and facility-based delivery. We additionally explored the effect of participation on promoting other positive maternal, newborn and child health (MNCH) behaviors. Methods: We prospectively compared outcomes between a cohort of Chamas participants and controls matched for age, parity, and prenatal care location. Between October–December 2012, government-sponsored community health volunteers (CHV) recruited pregnant women attending their first antenatal care (ANC) visits at rural health facilities in Busia County to participate in Chamas. Women enrolled in Chamas agreed to attend group-based health education and microfinance sessions for one year; controls received the standard of care. We used descriptive analyses, multivariable logistic regression models, and random effect models to compare outcomes across cohorts 12 months following enrollment, with α set to 0.05. Results: Compared to controls (n = 115), a significantly higher proportion of Chamas participants (n = 211) delivered in a health facility (84.4% vs. 50.4%, p < 0.001), attended at least four ANC visits (64.0% vs. 37.4%, p < 0·001), exclusively breastfed to six months (82.0% vs. 47.0%, p < 0·001), and received a CHV home visit within 48 h postpartum (75.8% vs. 38.3%, p < 0·001). In multivariable models, Chamas participants were over five times as likely as controls to deliver in a health facility (OR 5.49, 95% CI 3.12–9.64, p < 0.001). Though not significant, Chamas participants experienced a lower proportion of stillbirths (0.9% vs. 5.2%), miscarriages (5.2% vs. 7.8%), infant deaths (2.8% vs. 3.4%), and maternal deaths (0.9% vs. 1.7%) compared to controls. Conclusions: Chamas participation was associated with increased odds of facility-based delivery compared to the standard of care in rural western Kenya. Larger proportions of program participants also practiced other positive MNCH behaviors. Our findings demonstrate Chamas’ potential to achieve population-level MNCH benefits; however, a larger study is needed to validate this observed effect. Trial registration: ClinicalTrials.gov, NCT03188250 (retrospectively registered 31 May 2017).Medicine, Faculty ofNon UBCObstetrics and Gynaecology, Department ofReviewedFacult

Predicting adverse outcomes in pregnant patients positive for SARS-CoV-2: a machine learning approach- a retrospective cohort study

Abstract Background Pregnant people are particularly vulnerable to SARS-CoV-2 infection and to ensuing severe illness. Predicting adverse maternal and perinatal outcomes could aid clinicians in deciding on hospital admission and early initiation of treatment in affected individuals, streamlining the triaging processes. Methods An international repository of 1501 SARS-CoV-2-positive cases in pregnancy was created, consisting of demographic variables, patient comorbidities, laboratory markers, respiratory parameters, and COVID-19-related symptoms. Data were filtered, preprocessed, and feature selection methods were used to obtain the optimal feature subset for training a variety of machine learning models to predict maternal or fetal/neonatal death or critical illness. Results The Random Forest model demonstrated the best performance among the trained models, correctly identifying 83.3% of the high-risk patients and 92.5% of the low-risk patients, with an overall accuracy of 89.0%, an AUC of 0.90 (95% Confidence Interval 0.83 to 0.95), and a recall, precision, and F1 score of 0.85, 0.94, and 0.89, respectively. This was achieved using a feature subset of 25 features containing patient characteristics, symptoms, clinical signs, and laboratory markers. These included maternal BMI, gravidity, parity, existence of pre-existing conditions, nicotine exposure, anti-hypertensive medication administration, fetal malformations, antenatal corticosteroid administration, presence of dyspnea, sore throat, fever, fatigue, duration of symptom phase, existence of COVID-19-related pneumonia, need for maternal oxygen administration, disease-related inpatient treatment, and lab markers including sFLT-1/PlGF ratio, platelet count, and LDH. Conclusions We present the first COVID-19 prognostication pipeline specifically for pregnant patients while utilizing a large SARS-CoV-2 in pregnancy data repository. Our model accurately identifies those at risk of severe illness or clinical deterioration, presenting a promising tool for advancing personalized medicine in pregnant patients with COVID-19