Can multilayer perceptron ensembles model the ecological niche of freshwater fish species?

Muñoz-Mas, Rafael and Martínez Capel, Francisco and Alcaraz Hernández, Juan Diego and Mouton, A.M. (2015). Can multilayer perceptron ensembles model the ecological niche of freshwater fish species?. "Ecological Modelling" (n. 309); pp. 72-81. ISSN 0304-3800. https://doi.org/10.1016/j.ecolmodel.2015.04.025.

Description

Title: Can multilayer perceptron ensembles model the ecological niche of freshwater fish species?
Author/s:
  • Muñoz-Mas, Rafael
  • Martínez Capel, Francisco
  • Alcaraz Hernández, Juan Diego
  • Mouton, A.M.
Item Type: Article
Título de Revista/Publicación: Ecological Modelling
Date: August 2015
ISSN: 0304-3800
Subjects:
Freetext Keywords: Artificial neural networks; Barbus haasi; Data mining;Species distribution modelling; Uncertainty analysis
Faculty: E.T.S.I. Montes (UPM)
Department: Otro
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

The potential of Multilayer Perceptron (MLP) Ensembles to explore the ecology of freshwater fish species was tested by applying the technique to redfin barbel (Barbus haasi Mertens, 1925), an endemic and montane species that inhabits the North-East quadrant of the Iberian Peninsula. Two different MLP Ensembles were developed. The physical habitat model considered only abiotic variables, whereas the biotic model also included the density of the accompanying fish species and several invertebrate predictors. The results showed that MLP Ensembles may outperform single MLPs. Moreover, active selection of MLP candidates to create an optimal subset of MLPs can further improve model performance. The physical habitat model confirmed the redfin barbel preference for middle-to-upper river segments whereas the importance of depth confirms that redfin barbel prefers pool-type habitats. Although the biotic model showed higher uncertainty, it suggested that redfin barbel, European eel and the considered cyprinid species have similar habitat requirements. Due to its high predictive performance and its ability to deal with model uncertainty, the MLP Ensemble is a promising tool for ecological modelling or habitat suitability prediction in environmental flow assessment.

More information

Item ID: 41387
DC Identifier: http://oa.upm.es/41387/
OAI Identifier: oai:oa.upm.es:41387
DOI: 10.1016/j.ecolmodel.2015.04.025
Official URL: https://www.sciencedirect.com/science/article/pii/S0304380015001763?via%3Dihub
Deposited by: Memoria Investigacion
Deposited on: 26 Jul 2016 12:36
Last Modified: 07 Jun 2019 14:44
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