A Standardized Workflow Based on the STAVIRO Unbaited Underwater Video System for Monitoring Fish and Habitat Essential Biodiversity Variables in Coastal Areas

Essential Biodiversity Variables (EBV) related to benthic habitats and high trophic levels such as fish communities must be measured at fine scale but monitored and assessed at spatial scales that are relevant for policy and management actions. Local scales are important for assessing anthropogenic impacts, and conservation-related and fisheries management actions, while reporting on the conservation status of biodiversity to formulate national and international policies requires much broader scales. Measurements must account for the fact that coastal habitats and fish communities are heterogeneously distributed locally and at larger scales. Assessments based on in situ monitoring generally suffer from poor spatial replication and limited geographical coverage, which is challenging for area-wide assessments. Requirements for appropriate monitoring comprise cost-efficient and standardized observation protocols and data formats, spatially scalable and versatile data workflows, data that comply with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles, while minimizing the environmental impact of measurements. This paper describes a standardized workflow based on remote underwater video that aims to assess fishes (at species and community levels) and habitat-related EBVs in coastal areas. This panoramic unbaited video technique was developed in 2007 to survey both fishes and benthic habitats in a cost-efficient manner, and with minimal effect on biodiversity. It can be deployed in areas where low underwater visibility is not a permanent or major limitation. The technique was consolidated and standardized and has been successfully used in varied settings over the last 12 years. We operationalized the EBV workflow by documenting the field protocol, survey design, image post-processing, EBV production and data curation. Applications of the workflow are illustrated here based on some 4,500 observations (fishes and benthic habitats) in the Pacific, Indian and Atlantic Oceans, and Mediterranean Sea. The STAVIRO’s proven track-record of utility and cost-effectiveness indicates that it should be considered by other researchers for future applications.publishedVersio

Galaxy Training: A powerful framework for teaching!

There is an ongoing explosion of scientific datasets being generated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analysis, and stewardship are still rarely taught in life science educational programs, resulting in a skills gap in many of the researchers tasked with analysing these big datasets. In order to address this skills gap and empower researchers to perform their own data analyses, the Galaxy Training Network (GTN) has previously developed the Galaxy Training Platform (https://training.galaxyproject.org), an open access, community-driven framework for the collection of FAIR (Findable, Accessible, Interoperable, Reusable) training materials for data analysis utilizing the user-friendly Galaxy framework as its primary data analysis platform. Since its inception, this training platform has thrived, with the number of tutorials and contributors growing rapidly, and the range of topics extending beyond life sciences to include topics such as climatology, cheminformatics, and machine learning. While initially aimed at supporting researchers directly, the GTN framework has proven to be an invaluable resource for educators as well. We have focused our efforts in recent years on adding increased support for this growing community of instructors. New features have been added to facilitate the use of the materials in a classroom setting, simplifying the contribution flow for new materials, and have added a set of train-the-trainer lessons. Here, we present the latest developments in the GTN project, aimed at facilitating the use of the Galaxy Training materials by educators, and its usage in different learning environments

Open Science for Better FAIRness: A biodiversity virtual research environment point of view

"FAIR (Findable, Accessible, Interoperable, Reusable) principles" (Wilkinson et al. 2016) and "open science" are two complementary movements in biodiversity science. Although we need to transition to making scientific data and associated material more FAIR, this does not necessarily imply open data or open source algorithms. Here, based on the experience of the French Biodiversity Data Hub ("Pôle national de données de Biodiversité" - PNDB), which is an e-infrastructure for and by researchers, we want to showcase how focusing on openness can be a strategy to efficiently reach greater FAIRness. Following DataOne guidance, we can build a complete data/metadata ecosystem allowing us to structure heterogeneous environmental information systems. Using the Galaxy analysis platform and its related initiatives (Galaxy training network, European Erasmus+ Gallantries project, bioconda, bioContainer), we can thus illustrate how we can create transparent, peer-reviewed and accessible tools and workflows and collaborative training material. The Galaxy platform also facilitates use of high performance computing infrastructure through notably the European Open Science Cloud marketplace. Finally, through our experiences contributing to open source projects like EML (Ecological Metadata Language (Michener et al. 1997)) Assembly Line, EDI (Environmental Data Initiative, or PAMPA (Indicators of Marine Protected Areas performance for managing coastal ecosystems, resources and their uses), a French platform to help protected areas managers to standardize and analyse their data, we also show how building open source "doors" through the R Shiny programming language to these environments can be beneficial for all

From Biodiversity Observation Networks to Datasets and Workflows Supporting Biodiversity Indicators, a French Biodiversity Observation Network (BON) Essential Biodiversity Variables (EBV) Operationalization Pilot using Galaxy and Ecological Metadata Language

Integration of biological data with different ecological scales is complex! The biodiversity community (scientists, policy makers, managers, citizen, NGOs) needs to build a framework of harmonized and interoperable data from raw, heterogeneous and scattered datasets. Such a framework will help observation, measurement and understanding of the spatio-temporal dynamic of biodiversity from local to global scales. One of the most relevant approaches to reach that aim is the concept of Essential Biodiversity Variables (EBV). As we can potentially extract a lot of information from raw datasets sampled at different ecological scales, the EBV concept represents a useful leverage for identifying appropriate data to be collated as well as associated analytical workflows for processing these data. Thanks to FAIR data and source code implementation (Findable, Accessible, Interoperability, Reusable), it is possible to make a transparent assessment of biodiversity by generating operational biodiversity indicators (that can be reused / declined) through the EBV framework, and help designing or improving biodiversity monitoring at various scales. Through the BiodiFAIRse GO FAIR implementation network, we established how ecological and environmental sciences can benefit from existing open standards, tools and platforms used by European, Australian and United States infrastructures, particularly regarding the Galaxy platform for code sources accessiblility and the DataOne network of data catalogs and the Ecological Metadata Language standard for data management. We propose that these implementation choices can help fight the biodiversity crisis by supporting the important mission of GEO BON (Group on Earth Observation Biodiversity Observation Network): “Improve the acquisition, coordination and delivery of biodiversity observations and related services to users including decision makers and the scientific community” (GEO BON 2022)

French Biodiversity Data Hub: Linking local to global biodiversity through international initiatives and open science clouds

The French national biodiversity data hub (“Pôle National de Données de Biodiversité” - PNDB) is a national e-infrastructure created in 2018 and led by the National Museum of Natural History, contributing to the Open Science policy of the Ministry of Higher Education, Research and Innovation (MESRI).PNDB contributes to building an integrative framework taking into account biodiversity over the long term (from the origins of life to future models), at all biological scales (from the molecule to the socio-ecosystem), and in all its interactions, by providing tools and services for the description, access, validation, analysis and reuse of biodiversity data.With the diversity and complementary type of research biodiversity data (information systems, institutional data repositories, research infrastructures as observatories, experimental devices, natural history collections, etc.), but also from public policy data, the missions of the PNDB are deeply based on the FAIR approach (making data Findable, Accessible, Interoperable, Reusable).Thanks to its nomination in 2022 as a thematic reference center of the MESRI, PNDB will contribute to promoting the FAIR approach, will increase the skills (e.g., by training, good practices) of the scientific communities around open science, and stimulate interactions between producers and users of biodiversity data.PNDB has led the French participation to GEO BON (Group on Earth Observations Biodiversity Observation Network) since 2018 and recently shared the lead with public policies information system coordination. Thanks to this co-lead, this national BON proposes an innovative coordination of all biodiversity monitoring programs, from expertise to research around an innovative Essential Biodiversity Variable (EBV) operationalization pilot. This pilot is made of open practical solutions providing a particular high degree of FAIRNess of biodiversity research objects, from data to source codes. PNDB is also a major European point of contact for the DataOne network, who, in combination with the strong link between PNDB and French Global Biodiversity Information Facility (GBIF) node colleagues, allows the dissemination of all types of data through the world in the best manner

KOSMOS: An Open Source Underwater Video Lander for Monitoring Coastal Fishes and Habitats

Background: Monitoring the ecological status of coastal ecosystems is essential to track the consequences of anthropogenic pressures and assess conservation actions. Monitoring requires periodic measurements collected in situ, replicated over large areas and able to capture their spatial distribution over time. This means developing tools and protocols that are cost-effective and provide consistent and high-quality data, which is a major challenge. A new tool and protocol with these capabilities for non-extractively assessing the status of fishes and benthic habitats is presented here: the KOSMOS 3.0 underwater video system. Methods: The KOSMOS 3.0 was conceived based on the pre-existing and successful STAVIRO lander, and developed within a digital fabrication laboratory where collective intelligence was contributed mostly voluntarily within a managed project. Our suite of mechanical, electrical, and software engineering skills were combined with ecological knowledge and field work experience. Results: Pool and aquarium tests of the KOSMOS 3.0 satisfied all the required technical specifications and operational testing. The prototype demonstrated high optical performance and high consistency with image data from the STAVIRO. The project’s outcomes are shared under a Creative Commons Attribution CC-BY-SA license. The low cost of a KOSMOS unit (~1400 €) makes multiple units affordable to modest research or monitoring budgets

The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2022 update

Abstract Galaxy is a mature, browser accessible workbench for scientific computing. It enables scientists to share, analyze and visualize their own data, with minimal technical impediments. A thriving global community continues to use, maintain and contribute to the project, with support from multiple national infrastructure providers that enable freely accessible analysis and training services. The Galaxy Training Network supports free, self-directed, virtual training with >230 integrated tutorials. Project engagement metrics have continued to grow over the last 2 years, including source code contributions, publications, software packages wrapped as tools, registered users and their daily analysis jobs, and new independent specialized servers. Key Galaxy technical developments include an improved user interface for launching large-scale analyses with many files, interactive tools for exploratory data analysis, and a complete suite of machine learning tools. Important scientific developments enabled by Galaxy include Vertebrate Genome Project (VGP) assembly workflows and global SARS-CoV-2 collaborations