Biological effects 26 years after simulated deep-sea mining

The potential for imminent abyssal polymetallic nodule exploitation has raised considerable scientific attention. The interface between the targeted nodule resource and sediment in this unusual mosaic habitat promotes the development of some of the most biologically diverse communities in the abyss. However, the ecology of these remote ecosystems is still poorly understood, so it is unclear to what extent and timescale these ecosystems will be affected by, and could recover from, mining disturbance. Using data inferred from seafloor photo-mosaics, we show that the effects of simulated mining impacts, induced during the “DISturbance and reCOLonization experiment” (DISCOL) conducted in 1989, were still evident in the megabenthos of the Peru Basin after 26 years. Suspension-feeder presence remained significantly reduced in disturbed areas, while deposit-feeders showed no diminished presence in disturbed areas, for the first time since the experiment began. Nevertheless, we found significantly lower heterogeneity diversity in disturbed areas and markedly distinct faunal compositions along different disturbance levels. If the results of this experiment at DISCOL can be extrapolated to the Clarion-Clipperton Zone, the impacts of polymetallic nodule mining there may be greater than expected, and could potentially lead to an irreversible loss of some ecosystem functions, especially in directly disturbed areas

The future of zoonotic risk prediction

In the light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programmes will identify hundreds of novel viruses that might someday pose a threat to humans. To support the extensive task of laboratory characterization, scientists may increasingly rely on data-driven rubrics or machine learning models that learn from known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions. What are the prerequisites, in terms of open data, equity and interdisciplinary collaboration, to the development and application of those tools? What effect could the technology have on global health? Who would control that technology, who would have access to it and who would benefit from it? Would it improve pandemic prevention? Could it create new challenges? This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.Peer reviewe

The future of zoonotic risk prediction

In the light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programmes will identify hundreds of novel viruses that might someday pose a threat to humans. To support the extensive task of laboratory characterization, scientists may increasingly rely on data-driven rubrics or machine learning models that learn from known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions. What are the prerequisites, in terms of open data, equity and interdisciplinary collaboration, to the development and application of those tools? What effect could the technology have on global health? Who would control that technology, who would have access to it and who would benefit from it? Would it improve pandemic prevention? Could it create new challenges? This article is part of the theme issue ‘Infectious disease macroecology: parasite diversity and dynamics across the globe’.NSF BII 2021909; the University of Toronto EEB Fellowship; the Wellcome Trust; the National Institute of Allergy and Infectious Diseases of the National Institutes of Health and the Defense Threat Reduction Agency.http://rstb.royalsocietypublishing.orgam2022Medical Virolog

Engaging Research with Policy and Action: What are the Challenges of Responding to Zoonotic Disease in Africa?

Zoonotic diseases will maintain a high level of public policy attention in the coming decades. From the spectre of a global pandemic to anxieties over agricultural change, urbanization, social inequality and threats to natural ecosystems, effectively preparing and responding to endemic and emerging diseases will require technological, institutional and social innovation. Much current discussion emphasizes the need for a ‘One Health’ approach: bridging disciplines and sectors to tackle these complex dynamics. However, as attention has increased, so too has an appreciation of the practical challenges in linking multi-disciplinary, multi-sectoral research with policy, action and impact. In this commentary paper, we reflect on these issues with particular reference to the African sub-continent. We structure the themes of our analysis on the existing literature, expert opinion and 11 interviews with leading One Health scholars and practitioners, conducted at an international symposium in 2016. We highlight a variety of challenges in research and knowledge production, in the difficult terrain of implementation and outreach, and in the politicized nature of decision-making and priority setting. We then turn our attention to a number of strategies that might help reconfigure current pathways and accepted norms of practice. These include: (i) challenging scientific expertise; (ii) strengthening national multi-sectoral coordination; (iii) building on what works; and (iv) re-framing policy narratives. We argue that bridging the research-policy-action interface in Africa, and better connecting zoonoses, ecosystems and well-being in the twenty-first century, will ultimately require greater attention to the democratization of science and public policy. This article is part of the themed issue ‘One Health for a changing world: zoonoses, ecosystems and human well-being’

Tertiary active transport of amino acids reconstituted by coexpression of System A and L transporters in Xenopus oocytes

The System L transporter facilitates cellular import of large neutral amino acids (AAs) such as Leu, a potent activator of the intracellular target of rapamycin (TOR) pathway, which signals for cell growth. System L is an AA exchanger, proposed to accumulate certain AAs by coupling to dissipation of concentration gradient(s) of exchange substrates generated by secondary active AA transporters such as System A (SNAT2). We addressed the hypothesis that this type of coupling (termed tertiary active transport) acts as an indirect mechanism to extend the range of AA stimulating TOR to those transported by both Systems A and L (e.g., Gln) through downstream enhancement of Leu accumulation. System A overexpression enabled Xenopus oocytes to accumulate substrate AAs (notably Ser, Gln, Ala, Pro, Met; totaling 2.6 nmol/oocyte) from medium containing a physiological AA mixture at plasma concentrations. Net accumulation of System L (4F2hc-xLAT1) substrates from this medium by System L-overexpressing oocytes was increased by 90% (from 0.7 to 1.35 nmol/oocyte; mainly Leu, Ile) when Systems A and L were coexpressed, coincident with a decline in accumulation of specific System A substrates (Gln, Ser, Met), as expected if the latter were also System L substrates and functional coupling of the transport Systems occurred. AA flux coupling was confirmed as trans-stimulation of Leu influx in System L-expressing oocytes by Gln injection (0.5 nmol/oocyte). The observed changes in Leu accumulation are sufficient to activate the TOR pathway in oocytes, although intracellular AA metabolism limits the potential for AA accumulation by tertiary active transport in this system

Biological effects 26 years after simulated deep-sea mining

The potential for imminent abyssal polymetallic nodule exploitation has raised considerable scientific attention. The interface between the targeted nodule resource and sediment in this unusual mosaic habitat promotes the development of some of the most biologically diverse communities in the abyss. However, the ecology of these remote ecosystems is still poorly understood, so it is unclear to what extent and timescale these ecosystems will be affected by, and could recover from, mining disturbance. Using data inferred from seafloor photo-mosaics, we show that the effects of simulated mining impacts, induced during the “DISturbance and reCOLonization experiment” (DISCOL) conducted in 1989, were still evident in the megabenthos of the Peru Basin after 26 years. Suspension-feeder presence remained significantly reduced in disturbed areas, while deposit-feeders showed no diminished presence in disturbed areas, for the first time since the experiment began. Nevertheless, we found significantly lower heterogeneity diversity in disturbed areas and markedly distinct faunal compositions along different disturbance levels. If the results of this experiment at DISCOL can be extrapolated to the Clarion-Clipperton Zone, the impacts of polymetallic nodule mining there may be greater than expected, and could potentially lead to an irreversible loss of some ecosystem functions, especially in directly disturbed areas.</p

An evaluation of benthic community structure in and around the Sackville Spur closed area (Northwest Atlantic) in relation to the protection of vulnerable marine ecosystems

Publicado

BioTIME:a database of biodiversity time series for the Anthropocene

Abstract Motivation: The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community‐led open‐source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene. Main types of variables included: The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record. Spatial location and grain: BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km² (158 cm²) to 100 km² (1,000,000,000,000 cm²). Time period and grain: BioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year. Major taxa and level of measurement: BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates. Software format: .csv and .SQL

BioTIME:a database of biodiversity time series for the Anthropocene

Motivation: The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community led open-source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene.Main types of variables included: The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of two, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology andcontextual information about each record.Spatial location and grain: BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km2 (158 cm2) to 100 km2 (1 000 000 000 000 cm2).Time period and grain: BioTIME records span from 1874 to 2016. The minimum temporal grain across all datasets in BioTIME is year.Major taxa and level of measurement: BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton, and terrestrial invertebrates to small and large vertebrates.Software format: .csv and .SQ