Supporting biodiversity studies with the EUBrazilOpenBio Hybrid Data Infrastructure

[EN] EUBrazilOpenBio is a collaborative initiative addressing strategic barriers in biodiversity research by integrating open access data and user-friendly tools widely available in Brazil and Europe. The project deploys the EU-Brazil Hybrid Data Infrastructure that allows the sharing of hardware, software and data on-demand. This infrastructure provides access to several integrated services and resources to seamlessly aggregate taxonomic, biodiversity and climate data, used by processing services implementing checklist cross-mapping and ecological niche modelling. A Virtual Research Environment was created to provide users with a single entry point to processing and data resources. This article describes the architecture, demonstration use cases and some experimental results and validation.EUBrazilOpenBio - Open Data and Cloud Computing e-Infrastructure for Biodiversity (2011-2013) is a Small or medium-scale focused research project (STREP) funded by the European Commission under the Cooperation Programme, Framework Programme Seven (FP7) Objective FP7-ICT-2011- EU-Brazil Research and Development cooperation, and the National Council for Scientific and Technological Development of Brazil (CNPq) of the Brazilian Ministry of Science, Technology and Innovation (MCTI) under the corresponding matching Brazilian Call for proposals MCT/CNPq 066/2010. BSC authors also acknowledge the support of the grant SEV-2011-00067 of Severo Ochoa Program, awarded by the Spanish Government and the Spanish Ministry of Science and Innovation under contract TIN2012-34557 and the Generalitat de Catalunya (contract 2009-SGR-980).Amaral, R.; Badia, RM.; Blanquer Espert, I.; Braga-Neto, R.; Candela, L.; Castelli, D.; Flann, C.... (2015). Supporting biodiversity studies with the EUBrazilOpenBio Hybrid Data Infrastructure. Concurrency and Computation: Practice and Experience. 27(2):376-394. https://doi.org/10.1002/cpe.3238S376394272EUBrazilOpenBio Consortium EU-Brazil Open Data and Cloud Computing e-Infrastructure for Biodiversity http://www.eubrazilopenbio.eu/Triebel, D., Hagedorn, G., & Rambold, G. (2012). An appraisal of megascience platforms for biodiversity information. MycoKeys, 5, 45-63. doi:10.3897/mycokeys.5.4302Edwards, J. L. (2000). Interoperability of Biodiversity Databases: Biodiversity Information on Every Desktop. 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The Hierarchic treatment of marine ecological information from spatial networks of benthic platforms

Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.Peer ReviewedPostprint (published version

COMP Superscalar, an interoperable programming framework

COMPSs is a programming framework that aims to facilitate the parallelization of existing applications written in Java, C/C++ and Python scripts. For that purpose, it offers a simple programming model based on sequential development in which the user is mainly responsible for identifying the functions to be executed as asynchronous parallel tasks and annotating them with annotations or standard Python decorators. A runtime system is in charge of exploiting the inherent concurrency of the code, automatically detecting and enforcing the data dependencies between tasks and spawning these tasks to the available resources, which can be nodes in a cluster, clouds or grids. In cloud environments, COMPSs provides scalability and elasticity features allowing the dynamic provision of resources.This work has been supported by the following institutions: the Spanish Government with grant SEV-2011-00067 of the Severo Ochoa Program and contract Computacion de Altas Prestaciones VI (TIN2012-34557); by the SGR programme (2014-SGR-1051) of the Catalan Government; by the project The Human Brain Project, funded by the European Commission under contract 604102; by the ASCETiC project funded by the European Commission under contract 610874; by the EUBrazilCloudConnect project funded by the European Commission under contract 614048; and by the Intel-BSC Exascale Lab collaboration.Peer ReviewedPostprint (published version

OMWS: A Web Service Interface for Ecological Niche Modelling

[EN] Ecological niche modelling (ENM) experiments often involve a high number of tasks to be performed. Such tasks may consume a significant amount of computing resources and take a long time to complete, especially when using personal computers. OMWS is a Web service interface that allows more powerful computing back-ends to be remotely exploited by other applications to carry out ENM tasks. Its latest version includes a new operation that can be used to specify complex workflows in a single request, adding the possibility of using workflow management systems on parallel computing back-end. In this paper we describe the OMWS protocol and compare its most recent version with the previous one by running the same ENM experiment using two functionally equivalent clients, each designed for one of the OMWS interface versions. Different back-end configurations were used to investigate how the performance scales for each protocol version when more processing power is made available. Results show that the new version outperforms (in a factor of 2) the previous one when more computing resources are used.The latest version of OMWS contains improvements coming from different sets of requirements originated from two projects that funded their corresponding implementation: EUBrazilOpenBio14, with grants from the European Commission and the National Council for Scientific and Technological Development of Brazil (CNPq) of the Brazilian Ministry of Science and Technology (MCT), and BioVeL, with grants from the European Commission. Server infrastructure was operated through a provisioning system developed in the frame of the Spanish project CLUVIEM (TIN2013-44390-R) funded by the "Ministerio de Economía y Competitividad".Giovanni, RD.; Torres Serrano, E.; Amaral, RB.; Blanquer Espert, I.; Rebello, V.; Canhos, VP. (2015). OMWS: A Web Service Interface for Ecological Niche Modelling. Biodiversity Informatics. 10:35-44. https://doi.org/10.17161/bi.v10i0.4853S35441

FACILITATING AQUATIC INVASIVE SPECIES MANAGEMENT USING SATELLITE REMOTE SENSING AND MACHINE LEARNING FRAMEWORKS

The urgent decision-making needs of invasive species managers can be better met by the integration of biodiversity big data with large-domain models and environmental data products in the form of new workflows and tools that facilitate data utilization across platforms. Timely risk assessments allow for the spatial prioritization of monitoring that could streamline invasive species management paradigms and invasive species’ ability to prevent irreversible damage, such that decision makers can focus surveillance and intervention efforts where they are likely to be most effective under budgetary and resource constraints. I present a workflow that generates rapid spatial risk assessments on aquatic invasive species by combining occurrence data, spatially explicit environmental data, and an ensemble approach to species distribution modeling using five machine learning algorithms. For proof of concept and validation, I tested this workflow using extensive spatial and temporal occurrence data from Rainbow Trout (RBT; Oncorhynchus mykiss) invasion in the upper Flathead River system in northwestern Montana, USA. Due to this workflow’s high performance against cross-validated datasets (87% accuracy) and congruence with known drivers of RBT invasion, I developed a tool that generates agile risk assessments based on the above workflow and suggest that it can be generalized to broader spatial and taxonomic scales in order to provide data-driven management information for early detection of potential invaders. I then use this tool as technical input for a management framework that provides guidance for users to incorporate and synthesize the component features of the workflow and toolkit to derive actionable insight in an efficient manner

BioVeL : a virtual laboratory for data analysis and modelling in biodiversity science and ecology

Background: Making forecasts about biodiversity and giving support to policy relies increasingly on large collections of data held electronically, and on substantial computational capability and capacity to analyse, model, simulate and predict using such data. However, the physically distributed nature of data resources and of expertise in advanced analytical tools creates many challenges for the modern scientist. Across the wider biological sciences, presenting such capabilities on the Internet (as "Web services") and using scientific workflow systems to compose them for particular tasks is a practical way to carry out robust "in silico" science. However, use of this approach in biodiversity science and ecology has thus far been quite limited. Results: BioVeL is a virtual laboratory for data analysis and modelling in biodiversity science and ecology, freely accessible via the Internet. BioVeL includes functions for accessing and analysing data through curated Web services; for performing complex in silico analysis through exposure of R programs, workflows, and batch processing functions; for on- line collaboration through sharing of workflows and workflow runs; for experiment documentation through reproducibility and repeatability; and for computational support via seamless connections to supporting computing infrastructures. We developed and improved more than 60 Web services with significant potential in many different kinds of data analysis and modelling tasks. We composed reusable workflows using these Web services, also incorporating R programs. Deploying these tools into an easy-to-use and accessible 'virtual laboratory', free via the Internet, we applied the workflows in several diverse case studies. We opened the virtual laboratory for public use and through a programme of external engagement we actively encouraged scientists and third party application and tool developers to try out the services and contribute to the activity. Conclusions: Our work shows we can deliver an operational, scalable and flexible Internet-based virtual laboratory to meet new demands for data processing and analysis in biodiversity science and ecology. In particular, we have successfully integrated existing and popular tools and practices from different scientific disciplines to be used in biodiversity and ecological research.Peer reviewe

ENM2020 : A FREE ONLINE COURSE AND SET OF RESOURCES ON MODELING SPECIES NICHES AND DISTRIBUTIONS

The field of distributional ecology has seen considerable recent attention, particularly surrounding the theory, protocols, and tools for Ecological Niche Modeling (ENM) or Species Distribution Modeling (SDM). Such analyses have grown steadily over the past two decades-including a maturation of relevant theory and key concepts-but methodological consensus has yet to be reached. In response, and following an online course taught in Spanish in 2018, we designed a comprehensive English-language course covering much of the underlying theory and methods currently applied in this broad field. Here, we summarize that course, ENM2020, and provide links by which resources produced for it can be accessed into the future. ENM2020 lasted 43 weeks, with presentations from 52 instructors, who engaged with >2500 participants globally through >14,000 hours of viewing and >90,000 views of instructional video and question-and-answer sessions. Each major topic was introduced by an "Overview" talk, followed by more detailed lectures on subtopics. The hierarchical and modular format of the course permits updates, corrections, or alternative viewpoints, and generally facilitates revision and reuse, including the use of only the Overview lectures for introductory courses. All course materials are free and openly accessible (CC-BY license) to ensure these resources remain available to all interested in distributional ecology.Peer reviewe

Flock together with CReATIVE-B: A roadmap of global research data infrastructures supporting biodiversity and ecosystem science

Biodiversity research infrastructures are providing the integrated data sets and support for studying scenarios of biodiversity and ecosystem dynamics. The CReATIVE-B project - Coordination of Research e-Infrastructures Activities Toward an International Virtual Environment for Biodiversity – explored how cooperation and interoperability of large-scale Research Infrastructures across the globe could support the challenges of biodiversity and ecosystem research. A key outcome of the project is that the research infrastructures agreed to continue cooperation after the end of the project to advance scientific progress in understanding and predicting the complexity of natural systems. By working together in implementing the recommendations in this Roadmap, the data and capabilities of the cooperating research infrastructures are better served to address the grand challenges for biodiversity and ecosystem scientists