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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Isaac Ankamah-Yeboah; Claire W. Armstrong; Stephen Hynes; Bui Bich Xuan; +1 Authors

    ecent events around the world have revealed varying degrees of public support for climate change and environmental regulation. Applying a latent class logit model, this study investigates Norwegian and Scottish public’s economic support for proposed deep sea management policies for novel attributes, identifying the presence of preference heterogeneity. Marine litter and health of fish stocks were the attributes with the highest values in absolute terms. This was followed by the size of the protected area coverage, whilst the creation of jobs was the least valued. The results highlight public support for the further collective action required by the EU in moving beyond the 2020 objective of achieving good environmental status of Europe’s seas, despite the low WTP values of the minority classes in each country. This is an Accepted Manuscript of an article published by Taylor & Francis in the Journal of Environmental Economics and Policy on 18 May 2021, available online: https://doi.org/10.1080/21606544.2021.1924286.

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    Authors: Carlson, Daniel;

    This dataset consists of a digital elevation model (DEM), orthomosaics, and 3D models of a grounded iceberg off the northern coast of Qeqertarsuaq Island in Northwest Greenland (71.669N, 53.433W) on 26 July 2017. 127 images low-altitude aerial images acquired from an unoccupied aerial vehicle (DJI Phantom 3 Standard) were processed using Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). The iceberg aerial survey was conducted as part of the Mission Arctic sailing expedition to Western Greenland in 2017 (http://www.missionarctic.com/). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were also used. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. The dense point cloud was then computed using the ‘High’ setting, followed by the DEM, orthomosaic, and textured mesh. The resolution of the orthomosaic is 7.67 mm/pixel. The mesh model was exported in .obj and .pdf formats. A complete file list is provided in the README file that accompanies this dataset. Ref: Carlson et al. The Mission Arctic citizen science sailing expedition to western Greenland and Baffin Bay. Manuscript in preparation for Frontiers in Climate.

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    Article . 2020
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    Article . 2020
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      Article . 2020
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      Article . 2020
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    Authors: Schaap, Dick M.A.; Thijsse, Peter;

    This report D2.6 describes the Blue Cloud architecture as it is known at Month 9. It is the first release of the architecture as it is expected that the actual development and implementation of the Blue Cloud system will provide further challenges and elaborated details. For that purpose, two more releases of the architecture document are planned, namely report D2.7 in Month 15 and report D2.8 in Month 27. The technical framework of the pilot Blue-Cloud will feature: the Blue Cloud Data Discovery and Access service component to serve federated discovery and access to blue data infrastructures the Blue Cloud Virtual Research Environment (VRE) component to provide a Blue Cloud VRE as a federation of computing platforms and analytical services. In this report, the architecture and functionalities of each of these components are described in detail as well as the roles of partners that will develop and host modules. The Blue Cloud Data Discovery and Access service architecture is based upon a combination of the GeoDab metadata broker service of CNR-IIA, and the SeaDataNet CDI service modules as developed by MARIS, IFREMER, and EUDAT in the framework of the EU SeaDataCloud project. For the Blue-Cloud Discovery and Access service and its modules, additional developments will be needed such as adapting and upgrading of existing services, adding new services, testing modules, integrating modules, and testing the integrated service, in order to achieve the planned functionality. The Blue Cloud VRE will be based upon the existing D4Science e-infrastructure as developed and managed by CNR-ISTI. This e-infrastructure already hosts multiple Virtual Labs and offers a variety of services, which can be adopted for the Blue Cloud. The D4Science e-infrastructure also has proven solutions for connecting to external computing platforms and means for orchestrating distributed services, which will be instrumental for smart connections to the other e-infrastructures in the Blue-Cloud system. The Blue Cloud demonstrators will be developed as Virtual Labs embedded in the D4Science VRE e-infrastructure and supported by data input from the Blue Cloud Data Discovery and Access service and other data resources, and possible additional computing services. The demonstrators are being worked out in a cooperation between WP3 and WP4, analysing their scientific workflows and technical set-up, and considering the present D4Science VRE infrastructure and services that will provide the basis platform. However, at this time of development, there is no insight yet whether the demonstrators will require upgrading of existing functionality or development of additional services. Therefore, the described architecture of the VRE concerns the current D4Science e-infrastructure. In addition, consideration is given to integration aspects, such as two-way linking between the 2 Blue Cloud components, and expanding the VRE with additional platforms for computing and D2.6Blue Cloud Architecture (Release 1) algorithms, and where needed for specific demonstrators, direct access to data infrastructures. Moreover, aspects of authentication and monitoring are considered on full Blue Cloud scale. The initial Blue Cloud architecture as described in this report, is designed to be scalable and sustainable for near-future expansions, such as connecting additional blue data infrastructures, implementing more and advanced blue analytical services, configuring more dedicated Virtual Labs, and targeting more (groups of) users.

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    Other literature type . 2020
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    Project deliverable . 2020
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      Other literature type . 2020
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      Project deliverable . 2020
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    Authors: L. H. De Clippele; Lorenzo Rovelli; Berta Ramiro-Sánchez; Georgios Kazanidis; +4 Authors

    AbstractThis study presents a novel approach resulting in the first cold-water coral reef biomass maps, used to assess associated ecosystem functions, such as carbon (C) stock and turnover. We focussed on two dominant ecosystem engineers at the Mingulay Reef Complex, the coral Lophelia pertusa (rubble, live and dead framework) and the sponge Spongosorites coralliophaga. Firstly, from combining biological (high-definition video, collected specimens), environmental (extracted from multibeam bathymetry) and ecosystem function (oxygen consumption rate values) data, we calculated biomass, C stock and turnover which can feed into assessments of C budgets. Secondly, using those values, we employed random forest modelling to predictively map whole-reef live coral and sponge biomass. The whole-reef mean biomass of S. coralliophaga was estimated to be 304 T (range 168–440 T biomass), containing 10 T C (range 5–18 T C) stock. The mean skeletal mass of the coral colonies (live and dead framework) was estimated to be 3874 T (range 507–9352 T skeletal mass), containing a mean of 209 T of biomass (range 26–515 T biomass) and a mean of 465 T C (range 60–1122 T C) stock. These estimates were used to calculate the C turnover rates, using respiration data available in the literature. These calculations revealed that the epi- and microbial fauna associated with coral rubble were the largest contributor towards C turnover in the area with a mean of 163 T C year−1 (range 149–176 T C year−1). The live and dead framework of L. pertusa were estimated to overturn a mean of 32 T C year−1 (range 4–93 T C year−1) and 44 T C year−1 (range 6–139 T C year−1), respectively. Our calculations showed that the Mingulay Reef overturned three to seven (with a mean of four) times more C than a soft-sediment area at a similar depth. As proof of concept, the supply of C needed from surface water primary productivity to the reef was inferred. Since 65–124 T C year−1 is supplied by natural deposition and our study suggested that a mean of 241 T C year−1 (range 160–400 T C year−1), was turned over by the reef, a mean of 117–176 T C year−1 (range 36–335 T C year−1) of the reef would therefore be supplied by tidal downwelling and/or deep-water advection. Our results indicate that monitoring and/or managing surface primary productivity would be a key consideration for any efforts towards the conservation of cold-water coral reef ecosystems.

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    Coral Reefs
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      Coral Reefs
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    Authors: Stephen Hynes; Claire W. Armstrong; Bui Bich Xuan; Isaac Ankamah-Yeboah; +3 Authors

    This study tests the stability of environmental preferences and willingness to pay (WTP) values using a discrete choice experiment (DCE) across three countries pre and post the peak of the first wave of the Covid-19 pandemic. A DCE eamining the public’s preferences for alternative environmental management plans on the high seas, in the area of the Flemish Cap, was carried out in Canada, Scotland and Norway in late 2019 and was rerun in early May 2020 shortly after the Covid-19 pandemic had officially peaked in the three countries. The same choice set sequence is tested across the two periods, using different but nationally representative samples in each case. Entropy balancing, a multivariate reweighting method, is used to achieve covariate balance between the pre and post Covid samples in the analysis. The results suggest that both preferences and WTP remain relatively stable in the face of a major public health crisis and economic upheaval. SiriR

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    ZENODO; Ecological Economics
    Other literature type . Article . 2021 . Peer-reviewed
    License: Elsevier TDM
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      ZENODO; Ecological Economics
      Other literature type . Article . 2021 . Peer-reviewed
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    Authors: Sciacca, Eva; Kakaletris, George; Ntouskos, Makis;

    This report summarizes NEANIAS activities and actions towards comprehensive Data Management which is aligned to EC guidelines and FAIR Data principles [1]. In particular, D1.5 describes NEANIAS data management plan and discusses the data management lifecycle to answer questions regarding both Ethical and Research data management. NEANIAS cloud services will be able to support the data life cycle management of relatively large and heterogeneous datasets, will be based on best practices in coherence with the developments of the current and next generation EOSC frameworks. NEANIAS will secure openness and accessibility of the science results, data publishing in a useful manner and FAIR principles that are considered as baseline. By making NEANIAS data Findable, Accessible, Interoperable and Reusable new ways of interdisciplinary collaborations will be enabled and complex and global challenges could be addressed through EOSC. Deliverable 1.5 besides addressing which data, data types and data sources will be considered in the context of NEANIAS (Section 2) answers questions regarding the overall Research Data Management plan (Section 3), including: - how can data be employed and exploited by NEANIAS services - how long will datasets and products be kept - which methodology and standards will be applied - whether data will be shared/ made open access - how data will be curated and preserved (including after the end of the project). Further questions related to ethical data management are addressed in Section 4 includes: - the employed privacy policies - use and process of (end-) user data for research purposes - ethical vs. compliance aspects of privacy - any ethical implications of algorithms used - lawful bases for data processing

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    Authors: Lemenkova, Polina;

    Technical report. Contribution to the bathymetric mapping of the Antarctic.

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    Authors: Ankamah-Yeboah I; Xuan BB; Hynes S; Armstrong C;

    Knowledge of people’s understanding of environmental problems is vital for the effective implementation of the ecosystem approach to marine management. This is especially relevant when conservation goals are aimed at ecosystems in the deep-sea that are remote to the consciousness of most people. This study explores public perceptions of the deep-sea environment among the Scottish and Norwegian public. It further analyses the relationships between respondents’ pro-environmental concerns towards the marine environment and personal characteristics using a multiple indicators multiple causes model. The results show that public knowledge of the deep-sea environment is low for Scottish and moderate for Norwegians. Awareness of cold-water corals was high for the Lofoten case study area amongst the Norwegian public and low for the Mingulay reef complex in the Scottish case. These differences might arise because Norway is known to host the world’s largest cold-water corals in the Lofoten area; a fact that has been well publicised. We find that most people think changes in the deep-sea have at least some effect on them. On average, the public perceive the deep-sea condition to be at most ‘fairly good’ but are dissatisfied with the management of it with approximately only one third or less thinking it is well-managed. Generally, the public perception from both countries show ecocentric attitudes towards the marine environment implying that they recognize the value of ecosystem services, the current ecological crisis and the need for sustainable management.

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    DOAJ
    Article . 2020
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    Frontiers in Marine Science
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      Frontiers in Marine Science
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    Authors: Eleanor Frajka-Williams; Isabelle J. Ansorge; Johanna Baehr; Harry L. Bryden; +38 Authors

    The Atlantic Meridional Overturning Circulation (AMOC) extends from the Southern Ocean to the northern North Atlantic, transporting heat northwards throughout the South and North Atlantic, and sinking carbon and nutrients into the deep ocean. Climate models indicate that changes to the AMOC both herald and drive climate shifts. Intensive trans-basin AMOC observational systems have been put in place to continuously monitor meridional volume transport variability, and in some cases, heat, freshwater and carbon transport. These observational programs have been used to diagnose the magnitude and origins of transport variability, and to investigate impacts of variability on essential climate variables such as sea surface temperature, ocean heat content and coastal sea level. AMOC observing approaches vary between the different systems, ranging from trans-basin arrays (OSNAP, RAPID 26 degrees N, 11 degrees S, SAMBA 34.5 degrees S) to arrays concentrating on western boundaries (e.g., RAPID WAVE, MOVE 16 degrees N). In this paper, we outline the different approaches (aims, strengths and limitations) and summarize the key results to date. We also discuss alternate approaches for capturing AMOC variability including direct estimates (e.g., using sea level, bottom pressure, and hydrography from autonomous profiling floats), indirect estimates applying budgetary approaches, state estimates or ocean reanalyses, and proxies. Based on the existing observations and their results, and the potential of new observational and formal synthesis approaches, we make suggestions as to how to evaluate a comprehensive, future-proof observational network of the AMOC to deepen our understanding of the AMOC and its role in global climate.

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    OceanRep
    Article . 2019 . Peer-reviewed
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    Frontiers in Marine Science
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    Article . 2019
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      Frontiers in Marine Science
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      Article . 2019
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    Authors: García-Alegre, Ana; Durán Muñoz, Pablo; Sacau Cuadrado, Mar;

    This presentation was given during the 4th ATLAS General Assembly.

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    Presentation . 2019
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      Presentation . 2019
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    Authors: Isaac Ankamah-Yeboah; Claire W. Armstrong; Stephen Hynes; Bui Bich Xuan; +1 Authors

    ecent events around the world have revealed varying degrees of public support for climate change and environmental regulation. Applying a latent class logit model, this study investigates Norwegian and Scottish public’s economic support for proposed deep sea management policies for novel attributes, identifying the presence of preference heterogeneity. Marine litter and health of fish stocks were the attributes with the highest values in absolute terms. This was followed by the size of the protected area coverage, whilst the creation of jobs was the least valued. The results highlight public support for the further collective action required by the EU in moving beyond the 2020 objective of achieving good environmental status of Europe’s seas, despite the low WTP values of the minority classes in each country. This is an Accepted Manuscript of an article published by Taylor & Francis in the Journal of Environmental Economics and Policy on 18 May 2021, available online: https://doi.org/10.1080/21606544.2021.1924286.

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    Authors: Carlson, Daniel;

    This dataset consists of a digital elevation model (DEM), orthomosaics, and 3D models of a grounded iceberg off the northern coast of Qeqertarsuaq Island in Northwest Greenland (71.669N, 53.433W) on 26 July 2017. 127 images low-altitude aerial images acquired from an unoccupied aerial vehicle (DJI Phantom 3 Standard) were processed using Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). The iceberg aerial survey was conducted as part of the Mission Arctic sailing expedition to Western Greenland in 2017 (http://www.missionarctic.com/). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were also used. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. The dense point cloud was then computed using the ‘High’ setting, followed by the DEM, orthomosaic, and textured mesh. The resolution of the orthomosaic is 7.67 mm/pixel. The mesh model was exported in .obj and .pdf formats. A complete file list is provided in the README file that accompanies this dataset. Ref: Carlson et al. The Mission Arctic citizen science sailing expedition to western Greenland and Baffin Bay. Manuscript in preparation for Frontiers in Climate.

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    Authors: Schaap, Dick M.A.; Thijsse, Peter;

    This report D2.6 describes the Blue Cloud architecture as it is known at Month 9. It is the first release of the architecture as it is expected that the actual development and implementation of the Blue Cloud system will provide further challenges and elaborated details. For that purpose, two more releases of the architecture document are planned, namely report D2.7 in Month 15 and report D2.8 in Month 27. The technical framework of the pilot Blue-Cloud will feature: the Blue Cloud Data Discovery and Access service component to serve federated discovery and access to blue data infrastructures the Blue Cloud Virtual Research Environment (VRE) component to provide a Blue Cloud VRE as a federation of computing platforms and analytical services. In this report, the architecture and functionalities of each of these components are described in detail as well as the roles of partners that will develop and host modules. The Blue Cloud Data Discovery and Access service architecture is based upon a combination of the GeoDab metadata broker service of CNR-IIA, and the SeaDataNet CDI service modules as developed by MARIS, IFREMER, and EUDAT in the framework of the EU SeaDataCloud project. For the Blue-Cloud Discovery and Access service and its modules, additional developments will be needed such as adapting and upgrading of existing services, adding new services, testing modules, integrating modules, and testing the integrated service, in order to achieve the planned functionality. The Blue Cloud VRE will be based upon the existing D4Science e-infrastructure as developed and managed by CNR-ISTI. This e-infrastructure already hosts multiple Virtual Labs and offers a variety of services, which can be adopted for the Blue Cloud. The D4Science e-infrastructure also has proven solutions for connecting to external computing platforms and means for orchestrating distributed services, which will be instrumental for smart connections to the other e-infrastructures in the Blue-Cloud system. The Blue Cloud demonstrators will be developed as Virtual Labs embedded in the D4Science VRE e-infrastructure and supported by data input from the Blue Cloud Data Discovery and Access service and other data resources, and possible additional computing services. The demonstrators are being worked out in a cooperation between WP3 and WP4, analysing their scientific workflows and technical set-up, and considering the present D4Science VRE infrastructure and services that will provide the basis platform. However, at this time of development, there is no insight yet whether the demonstrators will require upgrading of existing functionality or development of additional services. Therefore, the described architecture of the VRE concerns the current D4Science e-infrastructure. In addition, consideration is given to integration aspects, such as two-way linking between the 2 Blue Cloud components, and expanding the VRE with additional platforms for computing and D2.6Blue Cloud Architecture (Release 1) algorithms, and where needed for specific demonstrators, direct access to data infrastructures. Moreover, aspects of authentication and monitoring are considered on full Blue Cloud scale. The initial Blue Cloud architecture as described in this report, is designed to be scalable and sustainable for near-future expansions, such as connecting additional blue data infrastructures, implementing more and advanced blue analytical services, configuring more dedicated Virtual Labs, and targeting more (groups of) users.

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