High Resolution Spatial Distribution for the Hexactinellid Sponges Asconema setubalense and Pheronema carpenteri in the Central Cantabrian Sea

In the present work we focus on the distribution of two species of sponges. One of these is Asconema setubalense, a sponge found in rocky substrate that was sampled with a photogrammetric vehicle through georeferenced images. The other is Pheronema carpenteri, which inhabits soft bottoms and was sampled by beam trawl. For the spatial distribution modeling of both sponges, the geomorphological variables of depth, slope, broad and fine scale bathymetric position index (BPI), aspect, and types of bottoms were used, all with a resolution of 32 m. Additionally, layers of silicates and currents near the bottom were extracted from Copernicus Marine Environment Monitoring Service (CMEMS), with a resolution of-4 and-9 km, respectively. Due to the low resolution of the layers, it was considered necessary to validate their use by model comparison, where those that included these variables turned out to be more explanatory than the others. The models were developed in a complex continental break of the Central Cantabrian Sea, which comprises several submarine canyons and a seamount (Le Danois Bank). On the one hand, a very high resolution (32 m) spatial distribution model based on A. setubalense presence was developed using the MaxEnt maximum entropy model. On the other, depending on the availability of density data, generalized additive models (GAMs) were developed for P. carpenteri distribution, although in this case the sampler only allowed a maximum resolution of almost 1 Km. For the A. setubalense, the variables that best explained their distribution were ground types and depth, and for P. carpenteri, silicates, slope, northness, and eastward seawater velocity. The final model scores obtained were an AUC of 0.98 for the MaxEnt model, and an R squared of 0.87 for the GAM model.

Automated summary

This study focuses on the distribution of two species of sponges, using geomorphological variables and oceanographic data to develop models, validating the use of low-resolution layers, and ultimately obtaining spatial distribution models for both sponges.