RESUMEN
One of the more challenging tasks in Marine Spatial Planning (MSP) is identifying critical areas for management and conservation of fish stocks. However, this objective is difficult to achieve in data-poor situations with different sources of uncertainty. In the present study we propose a combination of hierarchical Bayesian spatial models and remotely sensed estimates of environmental variables to be used as flexible and reliable statistical tools to identify and map fish species richness and abundance hot-spots. Results show higher species aggregates in areas with higher sea floor rugosity and habitat complexity, and identify clear richness hot-spots. Our findings identify sensitive habitats through essential and easy-to-use interpretation tools, such as predictive maps, which can contribute to improving management and operability of the studied data-poor situations.
