ALBERT

Description: The rocky and indented coast of NW Iberia is characterized by the presence of highly valuable and vulnerable, small and shallow barrier–lagoon systems structurally controlled. The case study was selected to analyse barrier–lagoon evolution based on detailed sedimentary architecture, chronology, geochemical and biological proxies. The main objective is to test the hypothesis of structural control and the significance at regional scale of any high-energy event recorded. This work is also aimed at identifying general patterns and conceptualizing the formation and evolution of this type of coastal systems. The results allowed us to establish a conceptual model of Holocene evolution that applies to rock-bounded barrier–lagoon systems. The initial stage (early Holocene) is characterized by freshwater peat sedimentation and ended by marine flooding. The timing of the marine flooding depends on the relation between the elevation of the basin and the relative mean sea-level position; the lower the topography, the earlier the marine inundation. Thus, the age of basin inundation ranged from 8 to 4 ka BP supporting significant structural differences. Once marine inundation occurred, all systems followed similar evolutionary patterns characterized by a phase of landward barrier migration and aeolian sedimentation towards the back-barrier (i.e. retrogradation) that extended circa 3.5 ka BP. The later phases of evolution are characterized by a general trend to the stabilization of the barriers and the infilling of the lagoons. This stabilization may be temporally interrupted by episodes of enhanced storminess or sediment scarcity. In this regard, washover deposits identified within the sedimentary architecture of the case study explored here suggest pervasive high-energy events coeval with some of the cooling events identified in the North Atlantic during the mid- to late Holocene.

Description: Predictive habitat mapping has shown great promise to improve the understanding of the spatial distribution of benthic habitats. However, although they surely represent an important step forward in process-based ecosystem management, their predictive efficiency is not always tested by independent groundtruthing data. This is particularly true for the deep-sea environment, where sample data are always limited compared to the large extent of the areas to be mapped. The aim of this study is to apply and test different spatial models to statistically predict the distribution of three Cold-Water Coral (CWC) species (Madrepora oculata, Lophelia pertusa and Dendrophyllia cornigera) in the Cap de Creus Canyon (NW Mediterranean), based on high-resolution swath-bathymetry data and video observations from the submersible JAGO (IFM-GEOMAR). Submarine canyons act as specific hosting areas for CWCs, owing to their favourable environmental conditions, which provide habitat and shelter for a wide range of species, including commercially viable fish. Maximum Entropy (MaxEnt), General Additive Model (GAM) and decision tree model (Random Forest) were independently applied to represent non-linear species-environment relationships using terrain variables derived from multibeam bathymetry (slope, geomorphologic category, rugosity, aspect, backscatter). Relevant differences between the three models were observed. Nonetheless, the predicted areas where CWCs should be found with higher probabilities coincided for the three methods when a lower spatial scale was considered. According to the models, CWCs are most likely to be found on the medium to steeply sloping, rough walls of the southern flank of the canyon, aligning with the known CWC ecology acquired from previous studies in the area. As a final step, a probabilistic predictive ensemble has been produced merging the outcomes of the three models considered, providing a more robust prediction for the three species. The main insight is that important discrepancies can arise in using different species distribution models, especially when high spatial resolutions are considered. This could in part be the result of the different statistical assumptions behind each of the models. We suggest that a more reliable prediction could be obtained by merging models into spatial ensembles, able to reduce differences and associated uncertainties, showing hence a strong potential as an objective approach in the planning and management of natural resources.