English   español  
Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/87549
Compartir / Impacto:
Estadísticas
Add this article to your Mendeley library MendeleyBASE
Citado 8 veces en Web of Knowledge®  |  Ver citas en Google académico
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar otros formatos: Exportar EndNote (RIS)Exportar EndNote (RIS)Exportar EndNote (RIS)
Título

A bayesian spatial approach for predicting seagrass occurrence

Autor March, David ; Alós, Josep ; Cabanellas-Reboredo, Miguel ; Infantes, Eduardo ; Jordi, Antoni ; Palmer, Miquel
Fecha de publicación 2013
EditorAcademic Press
Citación Estuarine, Coastal and Shelf Science 131: 206-212 (2013)
ResumenWe implement a Bayesian spatial approach to predict and map the probability of occurrence of seagrass Posidonia oceanica at high spatial resolution based environmental variables. We found that depth, near-bottom orbital velocities and a spectral pattern of Landsat imagery were relevant environmental variables, although there was no effect of slope or water residence time. We generated a data inventory of P.oceanica samples at Palma Bay, NW Mediterranean, from three main sources: side scan sonar, aerial imagery and a customized drop-camera system. A hierarchical Bayesian spatial model for non-Gaussian data was used to relate presence-absence data of P.oceanica with environmental variables in the presence of spatial autocorrelation (SA). A spatial dimension reduction method, the predictive process approach, was implemented to overcome computational constraints for moderately large datasets. Our results suggest that incorporating spatial random effects removes SA from the residuals and improves model fit compared to non-spatial regression models. The main products of this work were probability and uncertainty model maps, which could benefit seagrass management and the assessment of the ecological status of seagrass meadows. © 2013 Elsevier Ltd.
URI http://hdl.handle.net/10261/87549
DOI10.1016/j.ecss.2013.08.009
Identificadoresdoi: 10.1016/j.ecss.2013.08.009
issn: 0272-7714
Aparece en las colecciones: (IMEDEA) Artículos
Ficheros en este ítem:
Fichero Descripción Tamaño Formato  
accesoRestringido.pdf15,38 kBAdobe PDFVista previa
Visualizar/Abrir
Mostrar el registro completo
 



NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.