Terrain variables used for ensemble distribution modelling of vulnerable marine ecosystems indicator taxa on data-limited seamounts of Cabo Verde (NW Africa)
Data files
May 31, 2024 version files 44.95 MB
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00_Caboverde_bathy_100m_UTM26N.tif
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DataProcessing_R_code.Rmd
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README.md
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Seamounts_Mask.zip
Abstract
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.
README: 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
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
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.
Methods
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 et al., 2008). For this, the functions ‘SlpAsp’ and ‘Qfit’ of the “Multiscale DTM” library (Ilich et al., 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 et al., 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 et al., 2021), with a native horizontal resolution of 1/20° (~ 5.3 km). Bottom U and V were converted into mean bottom current speed.
References:
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