Reference : An artificial neural network analysis of factors controlling ecosystem metabolism in ...
Scientific journals : Article
Life sciences : Aquatic sciences & oceanology
http://hdl.handle.net/2268/2600
An artificial neural network analysis of factors controlling ecosystem metabolism in the coastal ocean
English
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 mailto [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 [> > > >]
2007
Ecological Applications
Ecological Society of America
17
5
S185–S196
Yes (verified by ORBi)
International
1051-0761
Washington
DC
[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.
Researchers
http://hdl.handle.net/2268/2600

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