DOI: 10.5061/dryad.0vt4b8h5g
title: Terrain variables used for ensemble distribution modelling of vulnerable marine ecosystems indicator taxa on data-limited seamounts of Cabo Verde (NW Africa)

description: Aim: Seamounts are conspicuous geological features with an important
 ecological role and can be considered Vulnerable Marine Ecosystems (VMEs).
 Since many deep-sea regions remain largely unexplored, investigating the
 occurrence of VME taxa on seamounts is challenging. Our study aimed to
 predict the distribution of four cold-water coral (CWC) taxa, indicators
 for VMEs, in a region where occurrence data is scarce. Location: Seamounts
 around the Cabo Verde Archipelago (NW Africa). Methods: We used species
 presence-absence data obtained from Remotely Operated Vehicle (ROV)
 footage collected during two research expeditions. Terrain variables
 calculated using a multiscale approach from a 100 m resolution bathymetry
 grid, as well as physical oceanographical data from the VIKING20X model,
 at a native resolution of 1/20°, were used as environmental predictors.
 Two modelling techniques (Generalized Additive Model (GAM) and Random
 Forest (RF)) were employed and single-model predictions were combined into
 a final weighted-average ensemble model. Model performance was validated
 using different metrics through cross-validation. Results: Terrain
 orientation, at broad-scale, presented one of the highest relative
 variable contributions to the distribution models of all CWC taxa,
 suggesting that hydrodynamic-topographic interactions on the seamounts
 could benefit CWCs by maximizing food supply. However, changes at finer
 scales in terrain morphology and bottom salinity were important for
 driving differences in the distribution of specific CWCs. The ensemble
 model predicted the presence of VME taxa on all seamounts and consistently
 achieved the highest performance metrics, outperforming individual models.
 Nonetheless, model extrapolation and uncertainty, measured as the
 coefficient of variation, were high, particularly, in least surveyed areas
 across seamounts, highlighting the need to collect more data in future
 surveys. Main conclusions: Our study shows how data-poor areas may be
 assessed for the likelihood of VMEs and provides important information to
 guide future research in Cabo Verde, which is fundamental to advise
 ongoing conservation planning.
description: Terrain variables were derived from a 100 m resolution bathymetry
 grid, created from a compilation of all available bathymetry data
 collected by multibeam echosounder (MBES) in the Cabo Verde region. We
 used an analytical multiscale approach to calculate terrain variables by
 considering, when possible, different neighbourhood sizes (i.e., number of
 grid-cells (n)) for calculations. In this study, slope, aspect (converted
 to eastness and northness), and three types of terrain curvature (plan,
 profile and mean) were calculated following a Fibonacci sequence of four
 increasing n values (n = 3, 9, 17, 33) (Dolan <em>et al.</em>,
 2008). For this, the functions ‘SlpAsp’ and ‘Qfit’ of the “Multiscale DTM”
 library (Ilich <em>et al.</em>, 2023) were used in R Studio.
 Topographic Position Index (TPI) and Vector Ruggedness Measure (VRM) were
 calculated at two scales, both fine- and broad-scales (n = 3, 33), using
 the ‘tpi’ and ‘vrm’ functions, respectively, of the “spatialEco” R Package
 (Evans and Ram, 2021). Roughness and Terrain Ruggedness Index (TRI) were
 calculated using the ‘terrain’ function from the “raster” R package
 (Hijmans <em>et al.</em>, 2015), using the default n = 3.
 Final terrain variables and scales considered in the models were chosen
 after investigating collinearity between variables (see next section on
 initial variable selection). The monthly averages of
 bottom temperature, bottom salinity and bottom zonal (U) and meridional
 (V) velocity components for the period of 2009 to 2019 were obtained from
 a hindcast simulation in the high-resolution VIKING20X ocean general
 circulation model (VIKING20X-JRA-OMIP described in Biastoch <em>et
 al.</em>, 2021), with a native horizontal resolution of 1/20° (~ 5.3
 km). Bottom U and V were converted into mean bottom current
 speed.
 <strong>References</strong>:  
 Biastoch, A., Schwarzkopf, F. U.,
 Getzlaff, K., Rühs, S., Martin, T., Scheinert, M., Schulzki, T.,
 Handmann, P., Hummels, R., & Böning, C. W. (2021). Regional
 imprints of changes in the Atlantic Meridional Overturning Circulation in
 the eddy-rich ocean model VIKING20X. Ocean Science, 17(5),
 1177–1211. https://doi.org/10.5194/os-17-1177-2021 Dolan, M. F. J., Grehan, A. J., Guinan, J. C., & Brown, C. (2008). Modelling the local distribution of cold-water corals in relation to bathymetric variables: Adding spatial context to deep-sea video data. Deep Sea Research Part I: Oceanographic Research Papers, 55, 1564–1579. https://doi.org/10.1016/j.dsr.2008.06.010 Evans, J. S., & Ram, K. (2021). Package ‘spatialEco.’ R CRAN Project. Hansteen, T. H., Klügel, A., Kwasnitschka, T. (2014). Cape Verde Seamounts – Cruise No. M80/3 – December 4178  29, 2009 – February 1, 2010—Dakar (Senegal)—Las Palmas de Gran Canaria (Spain). METEOR-Berichte, 4179  M80/3, 42 pp., DFG-Senatskommission für Ozeanographie. https://doi.org/10.2312/cr_m80_3 Hijmans, R. J., Van Etten, J., Cheng, J., Mattiuzzi, M., Sumner, M., Greenberg, J. A., Lamigueiro, O. P., Bevan, A., Racine, E. B., & Shortridge, A. (2015). Package ‘raster.’ R Package, 734, 473. Ilich, A. R., Misiuk, B., Lecours, V., & Murawski, S. A. (2023). MultiscaleDTM: An open-source R package for multiscale geomorphometric analysis. Transactions in GIS, 27(4), 1164–1204. https://doi.org/10.1111/tgis.13067 Langenkämper, D., Zurowietz, M., Schoening, T., & Nattkemper, T. W. (2017). Biigle 2.0-browsing and annotating large marine image collections. Frontiers in Marine Science, 4, 83. https://doi.org/10.3389/fmars.2017.00083 Orejas, 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). Expedition report iMirabilis2 survey. https://doi.org/10.5281/ZENODO.6352141
description: # Terrain variables used for ensemble distribution modelling of Vulnerable
 Marine Ecosystems indicator taxa on data-limited seamounts of Cabo Verde
 (NW Africa)
 [https://doi.org/10.5061/dryad.0vt4b8h5g](https://doi.org/10.5061/dryad.0vt4b8h5g) Dataset and code to calculate terrain variables used in the manuscript entitled "Ensemble modelling to predict the distribution of Vulnerable Marine Ecosystems indicator taxa on data-limited seamounts of Cabo Verde (NW Africa)" published in "Diversity and Distributions". We have submitted a shapefile to mask the spatial extent considered in our study (“Seamounts_Mask.zip”); a raster file of a Multibeam Echosounder (MBES) bathymetry data for the Cabo Verde region, at 100 m resolution (“00_Caboverde_bathy_100m_UTM26N.tif”) and a R script (“DataProcessing_R_code.Rmd”) used to calculate terrain variables using a multi-scale approach and to prepare oceanographical data (available in Schwarzkopf, 2024) for modelling. ## Description of the data and file structure * **00_Caboverde_bathy_100m_UTM26N.tif**: A Raster file (in .tif) corresponding to a compilation of the available high-resolution Multibeam Echosounder (MBES) bathymetry data available for the Cabo Verde region (as of 2023), at 100 m resolution. * **Seamounts_Mask.zip**: A zip folder with the shapefile (in .shp) corresponding to the spatial extent used in the study, i.e. the five modelled seamounts and the considered water depth (750 to 2100 m). * **DataProcessing_R_code.Rmd:** A R Markdown file with the code used to calculate terrain variables and to prepare the oceanographical data. ## Sharing/Access information Species presence-absence and oceanographical data to fully reproduce the study are available at other sources. Species presence-absence data is available here: * Vinha, B.; Hansteen, T. H.; Huvenne, V. A. I.; Orejas, C. (2023): Presence-absence records for four cold-water coral taxa on the seamounts of Cape Verde (NW Africa). PANGAEA, [https://doi.org/10.1594/PANGAEA.963704](https://doi.org/10.1594/PANGAEA.963704) Data from the VIKING20x oceanographical model (in .nc format) of monthly averages of bottom temperature, salinity and ocean velocities (U and V) for the Cabo Verde region, with a temporal resolution from December 2009 to December 2019 is available here: * Schwarzkopf, Franziska (2024). Supplementary data to Vinha et al. (2024): Ensemble modelling to predict the distribution of Vulnerable Marine Ecosystems indicator taxa on data-limited seamounts of Cabo Verde (NW Africa) [dataset]. GEOMAR Helmholtz Centre for Ocean Research Kiel [distributor]. hdl:20.500.12085/20248b0a-49fd-4868-90bf-581c61f4b396  ## Code/software R is required to run DataProcessing_R_code.Rmd; the script was created using version 4.1.1.