Unveiling ecological dynamics through simulation and visualization of biodiversity data cubes

Ward Langeraert, Wissam Barhdadi, Dimitri Brosens, Rocìo Cortès, Peter Desmet, Michele Di Musciano, Chandra Earl, Sanne Govaert, Pieter Huybrechts and Matilde Martini
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Peter Desmet: Research Institute for Nature and Forest (INBO)

No vcyr7_v1, BioHackrXiv from Center for Open Science

Abstract: 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.

Date: 2024-10-28
New Economics Papers: this item is included in nep-env
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Persistent link: https://EconPapers.repec.org/RePEc:osf:biohac:vcyr7_v1

DOI: 10.31219/osf.io/vcyr7_v1

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