B-Cubed Hackathon, Brussels, Belgium, 2024
B-Cubed’s hackathon was a 4-day event, bringing together biodiversity informaticians, researchers, and practitioners who are passionate about leveraging biodiversity data for impactful solutions. Our common goal was to standardise biodiversity data in order to enhance efficiency and accessibility. The main idea was to experiment with data cubes and channel creativity into innovative solutions for a variety of biodiversity challenges.
Source: https://b-cubed.eu/b-cubed-hackathon
Preprints
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Unveiling ecological dynamics through simulation and visualization of biodiversity data cubes
The gcube R package, developed during the B-Cubed hackathon (Hacking Biodiversity Data Cubes for Policy), provides a flexible framework for generating biodiversity data cubes using minimal input. The package assumes three consecutive steps (1) the occurrence process, (2) the detection process, and (3) the grid designation process, accompanied by three main functions respectively: simulate_occurrences(), sample_observations(), and grid_designation(). It allows for customisable spatial and temporal patterns, detection probabilities, and sampling biases. During the hackathon, collaboration was highly efficient due to thorough preparation, task division, and the use of a scrum board. Fourteen participants contributed 209 commits, resulting in a functional package with a pkgdown website, 67 % code coverage, and successful CMD checks. However, certain limitations were identified, such as the lack of spatiotemporal autocorrelation in the occurrence simulations, which affects the model’s realism. Future development will focus on improving spatiotemporal dynamics, adding comprehensive documentation and testing, and expanding functionality to support multi-species simulations. The package also aims to incorporate a virtual species workflow, linking the virtualspecies package to the gcube processes. Despite these challenges, gcube strikes a balance between usability and complexity, offering researchers a valuable tool for simulating biodiversity data cubes to assess research questions under different parameter settings, such as the effect of spatial clustering on the occurrence-to-grid designation and the effect of different patterns of missingness on data quality and robustness of derived biodiversity indicators. -
An analysis of sex ratios using a biodiversity data cube
This investigation uses biodiversity data cubes derived from the datasets mobilised by the Global Biodiversity Information Facility (GBIF), to conduct an analysis of sex ratios of ducks across Europe. Encompassing over 4 million occurrences extracted from nearly 5000 datasets, this study elucidates sex distribution patterns across various species, focussing on temporal and spatial dynamics. The aim of this study is to highlight the availability of open sex data and its potential usefulness in research and monitoring of sex ratios of wild organisms, particularly in sexual dimorphic species. -
Phenological Diversity Trends with Remote Sensing Datacubes
During the 2024 B-Cubed Hackathon, we extended the R package “rasterdiv” by incorporating Time-Weighted Dynamic Time Warping (TWDTW) to the package’s pre-existing paRao() function for the calculation of parametric Rao’s Quadratic Diversity (Rao’s Q) index. This expands the user’s ability to biodiversity trends when using time series of Earth Observations. Biodiversity indices like Shannon’s H do not consider spatio-temporal dynamics, and others (e.g. Rao’s Q) only incorporate geographic distance between observations, often leaving phenological variation overlooked.Through integrating TWDTW into the paRao() function, users can assess different facets of an ecosystem’s biodiversity by incorporating phenological differences among its plant communities. This is also valuable to distinguish between natural habitats that follow a seasonal phenological trend and artificial land cover types, which may lack phenological changes. Previous studies have also found that the time weighting ability of TWDTW enables the discernment of different floral community types which could otherwise be misclassified as the same with traditional Dynamic Time Warping (DTW).To evaluate the efficacy of TWDTW within the paRao() function, we compared the ability of TWDTW Rao’s Q index against other biodiversity indices at classifying the different plant communities in a disturbed grassland in Calabria, Italy. Our study used a Plant Phenological Index (PPI) time series from the Sentinel-2 satellite network. The results indicated that accounting for phenological cycles can filter out artefacts and better distinguish habitats with differing plant species diversity. This improves the ability to assess ecosystem changes through space and time, providing a more comprehensive understanding of biodiversity dynamics, and the ability to gauge the resilience of different vegetation patches.We conclude that the inclusion of plant phenology in biodiversity assessment is necessary, and that our modifications to paRao() will be valuable to facilitate the accurate detection and description of ecosystem trends in response to our changing environment.