ORBi: Rochelle Newall Emma J. - An artificial neural network analysis of factors controlling ecosystem metabolism in the coastal ocean

Reference : An artificial neural network analysis of factors controlling ecosystem metabolism in the...


	Document type :	Scientific journals : Article
	Discipline(s) :	Life sciences : Aquatic sciences & oceanology
	To cite this reference:	http://hdl.handle.net/2268/2600

Title :	An artificial neural network analysis of factors controlling ecosystem metabolism in the coastal ocean
Language :	English
Author, co-author :	Rochelle Newall, Emma J. [Université Pierre et Marie Curie-Paris 6 > Laboratoire d’Océanographie]
	Winter, Christian [Instituto Mediterraneo de Estudios Avanzados > Grupo de Oceanografıa Interdisciplinar]
	Barrón, Cristina [Université de Liège - ULg > Département d'astrophys., géophysique et océanographie (AGO) > Océanographie chimique >]
	Borges, Alberto [Instituto Mediterraneo de Estudios Avanzados > Grupo de Oceanografıa Interdisciplinar]
	Duarte, Carlos M. [University of Hull > Institute of Estuarine and Coastal Studies]
	Elliott, Mike [> > > >]
	Frankignoulle, Michel [> > > >]
	Gazeau, Frédéric [> > > >]
	Middelburg, Jack J. [> > > >]
	Pizay, Marie-Dominique [> > > >]
	Gattuso, Jean-Pierre [> > > >]
Publication date :	2007
Journal title :	Ecological Applications
Publisher :	Ecological Society of America
Volume :	17
Issue/season :	5
Pages :	S185–S196
Audience :	International
ISSN :	1051-0761
City :	Washington
Country :	DC
Abstract :	[en] Knowing the metabolic balance of an ecosystem is of utmost importance in determining whether the system is a net source or net sink of carbon dioxide to the atmosphere. However, obtaining these estimates often demands significant amounts of time and manpower. Here we present a simplified way to obtain an estimation of ecosystem metabolism. We used artificial neural networks (ANNs) to develop a mathematical model of the gross primary production to community respiration ratio (GPP:CR) based on input variables derived from three widely contrasting European coastal ecosystems (Scheldt Estuary, Randers Fjord, and Bay of Palma). Although very large gradients of nutrient concentration, light penetration, and organic-matter concentration exist across the sites, the factors that best predict the GPP:CR ratio are sampling depth, dissolved organic carbon (DOC) concentration, and temperature. We propose that, at least in coastal ecosystems, metabolic balance can be predicted relatively easily from these three predictive factors. An important conclusion of this work is that ANNs can provide a robust tool for the determination of ecosystem metabolism in coastal ecosystems.
Target :	Researchers
Permalink :	http://hdl.handle.net/2268/2600

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