What, when and where: making biodiversity data accessible
Ecosystems around the world are changing at an alarming rate, facing threats from climate change, pollution and unsustainable resource exploitation. To combat these threats and protect biodiversity, accurate and reliable environmental observations are crucial. These observations come from a variety of sources, including citizen scientists, museums, research organisations and automated sensors. Coordinating biodiversity information in the face of rapid environmental change is essential for implementing effective environmental policies and safeguarding the natural world. Using the Essential Biodiversity Variables (EVB) framework, the EU-funded B3 project employs the concept of data cubes to improve access and coordinate data.
Data cubes guide policy
The EVB framework offers a fresh approach to data management, focusing on integrating and standardising information. By leveraging the concept of data cubes, it helps organisations reduce inefficiency and fragmentation in their data monitoring efforts, ensuring adaptability and sustainability. A biodiversity data cube coordinates information related to time, space and a biological component. The coordination of these factors allows scientists and other stakeholders to make informed predictions about specific concerns and enact effective environmental policies. Ensuring that such knowledge is widely available is a primary focus of B3. Project partners developed software tools designed for a cloud computing environment with FAIR principles in mind. Asked about the most promising results of B3, project coordinator Quentin Groom says: “Using SQL to access the Global Biodiversity Information Facility (GBIF) is a game changer in the creation of automated workflows from biodiversity data. It is so versatile it could provide daily indicators from GBIF and rapid repeatable modelling.” Data cubes are multi-dimensional arrays of data that allow for efficient data storage, retrieval, and analysis. They enhance modelling capabilities by integrating diverse datasets, leading to more accurate environmental forecasts and better-informed policy decisions. The B-3 data cubes aim to facilitate the complex queries and aggregations needed for improved modelling capabilities and better environmental forecasting.
Scalability and case studies
Four case studies are designed to test the application and usefulness of B3’s algorithms and software tools. These studies test a range of situations, both local and global, to address important features of the B3 solution. Two case studies focus on regional concerns. In Flanders, Belgium, an area with ample biodiversity data, project workflows will generate indicators and models for biodiversity. In contrast, South Africa presents a very different regional scenario: abundant biodiversity, but relatively little in the way of observational data. Both studies provide context for a particular focus of B3: reducing the impact of invasive species. One case study at a broader scale explores significant disparities in biodiversity observation worldwide. The fourth case study focuses on the European continent, specifically the species covered by the EU’s Habitat Directive. A stakeholder-driven study, it will support monitoring and reporting requirements. The pace of environmental change and its negative impact on biodiversity are alarming, but society has the capacity to respond effectively. As Groom notes: "B3 envisages a future where primary biodiversity data are seamlessly integrated into monitoring and forecasting such that policy and management can proactively respond to problems, reducing the negative impacts of biodiversity change."
Keywords
B3, biodiversity, EVB framework, data cubes, Global Biodiversity Information Facility, Habitat Directive
