1 2 Predicting marine species distributions: complementarity of food-web and Bayesian 3 hierarchical modelling approaches 4 5 M. Coll*l,2, M. Grazia Pennino*3,4,5, J. Steenbeek1,2, J. Sole1, J.M. Bellido5,6 6 *Authors share first co-authorship. 7 8 1 Institut de Ciències del Mar (CMIMA-CSIC), P. Marítim de la Barceloneta, 37-49, 08003 Barcelona, Spain (current 9 address). 10 2 Ecopath International Initiative Research Association, Barcelona, Spain. 11 3 Fishing Ecology Management and Economics (FEME) - Universidade Federal do Rio Grande do Norte – UFRN. Depto. 12 de Ecologia, Natal (RN), Brazil. 13 4 Instituto Español de Oceanografía (IEO), Centro Oceanográfico de Vigo, Subida a Radio Faro 50-52, 14 36390 Vigo, Pontevedra, Spain. 15 5 Statistical Modeling Ecology Group (SMEG). Departament d'Estadística i Investigació Operativa, Universitat de 16 València. C/Dr. Moliner 50, Burjassot. 46100 Valencia, Spain. 17 6 Instituto Español de Oceanografía, Centro Oceanográfico de Murcia. C/Varadero 1, San Pedro del Pinatar. 30740 Murcia, 18 Spain. 19 Corresponding author: Marta Coll. E-mail:
[email protected];
[email protected] 20 21 Keywords: spatial ecology, species distribution models, Bayesian model, food-web model, 22 Ecospace, commercial species, Mediterranean Sea. 23 24 25 1 26 Abstract 27 The spatial prediction of species distributions from survey data is a significant component of spatial 28 planning and the ecosystem-based management approach to marine resources. Statistical analysis of 29 species occurrences and their relationships with associated environmental factors is used to predict 30 how likely a species is to occur in unsampled locations as well as future conditions.