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Estimating fish community diversity from environmental features in the Tagus estuary (Portugal): Multiple Linear Regression and Artificial Neural Network approaches

2008; Wiley; Volume: 24; Issue: 2 Linguagem: Inglês

10.1111/j.1439-0426.2007.01039.x

1439-0426

Juan Carlos Gutiérrez‐Estrada, Rita P. Vasconcelos, María José Costa,

Hydrological Forecasting Using AI

Journal of Applied IchthyologyVolume 24, Issue 2 p. 150-162 Estimating fish community diversity from environmental features in the Tagus estuary (Portugal): Multiple Linear Regression and Artificial Neural Network approaches J. C. Gutiérrez-Estrada, J. C. Gutiérrez-Estrada Departamento de Ciencias Agroforestales, Escuela Politécnica Superior, Campus Universitario de La Rábida, Universidad de Huelva, Palos de la Frontera, Huelva, SpainSearch for more papers by this authorR. Vasconcelos, R. Vasconcelos Instituto de Oceanografia/Departamento de Biologia Animal, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, Lisboa, PortugalSearch for more papers by this authorM. J. Costa, M. J. Costa Instituto de Oceanografia/Departamento de Biologia Animal, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, Lisboa, PortugalSearch for more papers by this author J. C. Gutiérrez-Estrada, J. C. Gutiérrez-Estrada Departamento de Ciencias Agroforestales, Escuela Politécnica Superior, Campus Universitario de La Rábida, Universidad de Huelva, Palos de la Frontera, Huelva, SpainSearch for more papers by this authorR. Vasconcelos, R. Vasconcelos Instituto de Oceanografia/Departamento de Biologia Animal, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, Lisboa, PortugalSearch for more papers by this authorM. J. Costa, M. J. Costa Instituto de Oceanografia/Departamento de Biologia Animal, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, Lisboa, PortugalSearch for more papers by this author First published: 04 February 2008 https://doi.org/10.1111/j.1439-0426.2007.01039.xCitations: 31Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Summary Relationships between environmental variables and diversity (Shannon-Weaver index) of the fish communities in the Tagus estuary and adjacent coastal areas were analyzed. The focus was on the linearity or nonlinearity of these abiotic/biotic characteristics, with the aim to obtain an accurate short–medium term time-scale diversity prediction from habitat variables alone. Multiple Linear Regressions (MLR) were used for the linear approach and Artificial Neural Networks (ANNs) for the nonlinear approach. MLR results in the external validation phase indicated a lack of model accuracy (R2 = 0.0710; %SEP = 47.5868; E = −0.0217; ARV = 1.0217; N = 43). Results of the best of the Artificial Neural Networks used in this study (12-15-15-1 architecture) in the external validation phase (ANN: R2 = 0.9736; %SEP = 7.8499; E = 0.9722; ARV = 0.0278; N = 43) were more accurate than those obtained with MLR. This indicates a clear nonlinear relationship between variables. In the best ANN model, nitrate concentration, depth, dissolved oxygen and temperature were the most important predictors of fish diversity in the Tagus estuary. 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