Vinha, Beatriz, Murillo, Francisco Javier, Schumacher, Mia, Hansteen, Thor H. , Schwarzkopf, Franziska U. , Biastoch, Arne , Kenchington, Ellen, Piraino, Stefano, Orejas, Covadonga and Huvenne, Veerle A. I. (2024) Ensemble modelling to predict the distribution of vulnerable marine ecosystems indicator taxa on data‐limited seamounts of Cabo Verde (NW Africa). Diversity and Distributions, 30 (8). e13896. DOI 10.1111/ddi.13896.

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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 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.

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