Ensemble modelling to predict the distribution of vulnerable marine ecosystems indicator taxa on data-limited seamounts of Cabo Verde (NW Africa)

The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1111/ddi.13896.

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 are 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 and random forest) 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.

We are thankful to Herculano Dinis and Jacob González-Solís for sharing knowledge on the marine biodiversity and conservation of the seamounts of Cabo Verde. B. V. would like to thank the EuroMarine Young Scientist Fellowship Award for supporting her training period on the use of species distribution models and POR Puglia FESR FSE 2014-2020 for funding her PhD fellowship. B.V., C. O. and V. A. I. H. enjoyed a fellowship at the Hanse-Wissenschaftskolleg Institute for Advanced Study for the data preparation and concept development of this manuscript. We would like to express our sincere gratitude to the crew, UTM and scientific team aboard the RV Sarmiento de Gamboa for their onboard assistance as well as during the preparation of the iMirabilis2 expedition. We are grateful to António Calado, Andreia Afonso, Renato Bettencourt, Bruno Ramos and Miguel Souto from the ROV Luso Team. The ship time has been provided by the Spanish Ministry of Science and Innovation. The research included in this manuscript received funding from the European Union's Horizon 2020 iAtlantic project (Grant Agreement No. 818123). This manuscript reflects the authors' view alone, and the European Union cannot be held responsible for any use that may be made of the information contained herein.

Peer reviewed