Identifying fish diversity hot-spots in data-poor situations

• Published in: Marine environmental research Vol. 129; pp. 365 - 373
• Main Authors: Fonseca, Vinícius Prado, Pennino, Maria Grazia, de Nóbrega, Marcelo Francisco, Oliveira, Jorge Eduardo Lins, de Figueiredo Mendes, Liana
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.08.2017
• Subjects: Animals; Bayesian models; Biodiversity; Conservation of Natural Resources - methods; Data-poor situations; Fisheries - statistics & numerical data; Fishes - classification; Fishes - growth & development; INLA; Richness index; Bayesian models; INLA; Richness index; Data-poor situations
• ISSN: 0141-1136; 1879-0291
• DOI: 10.1016/j.marenvres.2017.06.017

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. •Identification richness and abundance hot-spots could be difficult in data-poor situations.•A hierarchical Bayesian spatial modeling approach using INLA is proposed here as reliable tool.•Results showed higher species aggregations on areas with higher sea floor rugosity.•Predictive maps could be easy-to-use interpretation tools for the Marine Spatial Planning.