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Modelling native fish richness to evaluate the effects of hydromorphological changes and river restoration (Júcar River Basin, Spain)

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Modelling native fish richness to evaluate the effects of hydromorphological changes and river restoration (Júcar River Basin, Spain)

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Olaya Marín, EJ.; Martinez-Capel, F.; Soares Costa, RM.; Alcaraz-Hernández, JD. (2012). Modelling native fish richness to evaluate the effects of hydromorphological changes and river restoration (Júcar River Basin, Spain). Science of the Total Environment. 440:95-105. doi:10.1016/j.scitotenv.2012.07.093

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/33328

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Título: Modelling native fish richness to evaluate the effects of hydromorphological changes and river restoration (Júcar River Basin, Spain)
Autor:
Entidad UPV: Universitat Politècnica de València. Instituto de Investigación para la Gestión Integral de Zonas Costeras - Institut d'Investigació per a la Gestió Integral de Zones Costaneres
Universitat Politècnica de València. Departamento de Ingeniería Hidráulica y Medio Ambiente - Departament d'Enginyeria Hidràulica i Medi Ambient
Fecha difusión:
Resumen:
The richness of native fish is considered to be an indicator of aquatic ecosystem health, and improving richness is a key goal in the management of river ecosystems. An artificial neural network (ANN) model based on field ...[+]
Palabras clave: Artificial neural networks , Fish richness , Hydromorphology , Mitigation measures , River connectivity , River restoration , Aquatic ecosystem , Artificial neural network models , Basin scale , Cross-validation methods , Ecological restoration , Environmental variables , European Water Framework Directive , Field data , Fish communities , Fish species richness , Global change , Hidden layers , K-values , Key variables , Levenberg-Marquardt learning algorithms , Mediterranean rivers , Model training , Partial derivatives , Physical habitat , Reference sites , Reliability and robustness , River basins , River ecosystem , Water scarcity , Ecosystems , Fish , Learning algorithms , Neural networks , Restoration , Water quality , Watersheds , Rivers , Artificial neural network , Bioindicator , Ecomorphology , Ecosystem health , Ecosystem management , Habitat quality , Mitigation , Model validation , Restoration ecology , Riffle , River basin , River management , Species richness , Article , Ecosystem restoration , Environmental change , Environmental factor , Environmental impact assessment , Learning algorithm , Mathematical model , Nonhuman , Priority journal , Riparian ecosystem , Sensitivity analysis , Validation process , Jucar Basin , Spain
Derechos de uso: Cerrado
Fuente:
Science of the Total Environment. (issn: 0048-9697 )
DOI: 10.1016/j.scitotenv.2012.07.093
Editorial:
Elsevier
Versión del editor: http://dx.doi.org/10.1016/j.scitotenv.2012.07.093
Agradecimientos:
This study was partially funded by the Spanish Ministry of Economy and Competitiveness with the projects SCARCE (Consolider-Ingenio 2010 CSD2009-00065) and POTECOL "Evaluacion del Potencial Ecologico de Rios Regulados por ...[+]
Tipo: Artículo

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