B-Cubed aims to improve the existing policy evidence base and contribute to better alert systems by providing fast access to pre-aggregated and modelled biodiversity data and standardised biodiversity indicators responsive to the addition of new data.
A data cube is a multidimensional representation of data that allows for efficient storage, retrieval, and analysis of information along multiple dimensions. In the context of biodiversity data, a data cube can integrate various dimensions such as taxonomic (what), temporal (when) and spatial (where), enabling researchers to explore complex ecological patterns and relationships more effectively.
D2.1 Software specificationsB-Cubed is creating a species occurrence cube format for a range of analyses to indicate trends and predictively model the future of biodiversity under different scenarios. It is documenting the format’s properties and the algorithms to generate it, ensuring that it is interoperable with other data cubes, particularly cubes of environmental variables.
D4.1 Documentation on modelled data cubesTo deliver maps of habitat suitability and potential distribution for user-specified species, B-Cubed is creating repeatable workflows for predictive habitat suitability modelling. It feeds on data from the species occurrence cube, georeferenced environmental data, and future change scenarios. These workflows also provide insights into current and future species distribution based on existing data.
D4.2 Deep learningTo evaluate, estimate and map species turnover based on differences in composition over multiple sites, B-Cubed is developing a Dissimilarity cube. It uses the occurrence data cube to generate a species-by-site matrix that provides the necessary information to express compositional biodiversity for a focal area. The Dissimilarity cube will further identify ‘bioregions’ based on community boundaries and provide predictions of future patterns of compositional turnover using recognised global change scenarios.
D4.3 Data qualityTo calculate and map hotspots of invasions and identify trait features that could experience high invasiveness, B-Cubed is developing a network invasibility cube. It feeds on the species occurrence cube, a list of invasive alien species of an area and species’ functional traits. The cube also contains the interaction strength matrix and provides predictions of community dynamics under biological invasions (i.e. short-term relative rate of population change for each species in the network).
B-Cubed will translate the complex multidimensional information contained in its aggregated or modelled data cubes into biodiversity indicators. These meaningful measures help policymakers and other stakeholders understand biodiversity status and trends. The project focuses on establishing common and accepted indicators for assessing biodiversity changes across different policy contexts and scales.
D5.1 Biodiversity indicatorsB-Cubed is creating workflows to calculate existing indicators of biodiversity change based on the provided biodiversity cubes.
D5.2 Phylogenetic diversityB-Cubed is developing indicators of phylogenetic diversity created from data cubes combined with phylogenetic trees in automated workflows. This is a potentially better metric for biodiversity in relation to policy than simple species counts and can be easily scaled to millions of areas and species using the scalability of cloud computing and the data cubes method.
D5.3 Invasive Alien SpeciesB-Cubed is integrating existing databases and classification systems for impacts of alien taxa with biodiversity data cubes to provide estimates of current and potential future impacts of biological invasions. It is also providing estimates of invasion debt and will forecast potential impacts spatially and temporally.
D5.4 Robustness assessmentThe indicators of robustness will provide a measure for the applicability of a biodiversity indicator for a given species, time period and area. The robustness will be calculated on its own alongside the biodiversity indicator values and uncertainties.