DOI: 10.6084/m9.figshare.26780044.v2
title: Data and code to reproduce the study of Vinha et al. (2025). Characterization and mapping of bathyal benthic communities of Cabo Verde (NW Africa). Progress in Oceanography

description: Data and code used in the study of Vinha et al. (2025). Characterization and mapping of bathyal benthic communities of Cabo Verde (NW Africa). Progress in Oceanography.Here, we share:<b>“01 iMirabilis2_ROV_BIIGLE-report.csv”</b> corresponding to the raw report file with the ROV video annotations from iMirabilis2 expedition (Orejas et al., 2022) conducted using BIIGLE (Langenkämper et al., 2017)"<b>02 Community-Analysis_CaboVerde.Rmd</b>" containing the R code used to conduct community analysis to identify the different bathyal benthic communities of Cabo Verde. The following datasets are required to run this code:<b>"02-1 Spatial Coordinates_SU100.csv"</b> with the spatial coordinates of each sampling unit (SU) of 100m<sup>2</sup>, based on the ROV transects"<b>02-2 Species Matrix_SU100.csv</b>" corresponding to the matrix containing morphospecies densities per SU. Please refer to “<b>02-2 Species Codes.csv</b>” for the full name of the morphospecies code used in the matrix. For more information on morphospecies, see photocatalog used for video analysis (Vinha et al., 2022; Zenodo. https://doi.org/10.5281/zenodo.6560869)<b>"02-3 Environmental Matrix_SU100.csv" </b>corresponding to the matrix containing the values of each environmental parameters considered, for each SU. Environmental data include terrain variables, water column parameters and proportions of six different substrate type categories (volcanic rocky, boulders, soft substrate, sand with boulders, sand with pebbles and cobbles and iron-rich rocks)."<b>03 Community-Predictive-Mapping.Rmd</b>" with the R code used to run a Random Forest Classification to create a predictive community map based on the environmental variables used in the Multivariate Regression Tree Analysis. The following data are needed to run this code:<b>Bathymetry data</b> from Huvenne et al., 2023, PANGAEA https://doi.org/10.1594/PANGAEA.954018, also used to calculate terrain derivates (in the code)<b>"03-1 iMira_CommunityAnalysisMRT.shp"</b>, a shapefile with the spatial distribution of each community identified<b>"03-2 Substrate_Classification.tif"</b>, a raster file with a predictive substrate map for the explored region<b>"03-3 iMira_mask.shp"</b>, a shapefile used to mask the spatial extent of the models<b>References:</b>Huvenne, V. A. I., Orejas, C., Rodriguez, P., & Wardell, C. (2023). <i>Multibeam bathymetry processed data (Atlas Hydrosweep DS 3 echo sounder working area dataset) of RV SARMIENTO DE GAMBOA during cruise iMirabilis2, Leg.1, Cabo Verde</i> [Dataset]. PANGAEA. https://doi.org/10.1594/PANGAEA.954018Langenkämper, D., Zurowietz, M., Schoening, T., & Nattkemper, T. W. (2017). Biigle 2.0-browsing and annotating large marine image collections. <i>Frontiers in Marine Science</i>, <i>4</i>, 83. https://doi.org/10.3389/fmars.2017.00083Orejas, C., Huvenne, V., Sweetman, A. K., Vinha, B., Abella, J. C., Andrade, P., Afonso, A., Antelo, J., Austin-Berry, R., Baltasar, L., Barbosa, N., Barnhill, K. A., Barreiro, A., Bettencourt, R., Blanco, S., Buigues, A., Calado, A., Casal, I., Torre, J. de la, … Vélez-Belchí, P. (2022). <i>Expedition report iMirabilis2 survey</i>. https://doi.org/10.5281/ZENODO.6352141Vinha, B., Simon-Lledó, E., Arantes, R., Aguilar, R., Carreiro-Silva, M., Colaço, A., Piraino, S., Gori, A., Huvenne, V. A. I., & Orejas, C. (2022). Deep-sea benthic megafauna of Cabo Verde (Eastern Equatorial Atlantic Ocean). <i>Zenodo</i>. https://doi.org/10.5281/zenodo.6560869<br>