Terrain variables used for ensemble distribution modelling of vulnerable marine ecosystems indicator taxa on data-limited seamounts of Cabo Verde (NW Africa)

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.