Rochelle-Newall, E.J. and Winter, C. and Barrón, C. and Borges, A.V. and Duarte, C.M. and Elliott, M. and Frankignoulle, M. and Gazeau, F.P.H. and Middelburg, J.J. and Pizay, M-D. and Thioulouse, J. and Gattuso, J.P. (2007) Artificial neural network analysis of factors controlling ecosystem metabolism in coastal systems. Ecological Applications, 17, S185-S196. ISSN 1051-0761.
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Official URL: http://dx.doi.org/10.1890/05-1769.1
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.
|Institutes:||Nederlands Instituut voor Ecologie (NIOO)|
|Deposited On:||31 Aug 2009 02:00|
|Last Modified:||24 Apr 2012 16:49|
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