CheneBelgiqueV2, Main, Exploration, bibRecordById, Pascal:12-0212878

Predicting forest site productivity in temperate lowland from forest floor, soil and litterfall characteristics using boosted regression trees

Identifieur interne : 000399 ( Main/Exploration ); précédent : 000398; suivant : 000400

Predicting forest site productivity in temperate lowland from forest floor, soil and litterfall characteristics using boosted regression trees

Auteurs : Wim Aertsen [Belgique] ; Vincent Kint [Belgique] ; Bruno De Vos [Belgique] ; Jozef Deckers [Belgique] ; Jos Van Orshoven [Belgique] ; Bart Muys [Belgique]

Source :

Plant and soil [ 0032-079X ] ; 2012.

RBID : Pascal:12-0212878

Descripteurs français

Pascal (Inist)
- Prédiction, Station forestière, Productivité primaire, Forêt, Zone basse, Litière, Accumulation, Caractéristiques, Concentration chimique, Fagus sylvatica, Pinus sylvestris, Quercus robur, Azote, Sol forestier, Nutriment, Production nette, Dépôt.
Wicri :
- topic : Forêt, Azote.

English descriptors

KwdEn :
- Accumulation, Characteristics, Chemical concentration, Deposition, Fagus sylvatica, Forest site, Forest soil, Forests, Litter, Lowland, Net production, Nitrogen, Nutrient, Pinus sylvestris, Prediction, Primary productivity, Quercus robur.

Abstract

Aims The aim of this study is on the one hand to identify the most determining variables predicting the site productivity of pedunculate oak, common beech and Scots pine in temperate lowland forests of Flanders; and on the other hand to test whether the accuracy of site productivity models based exclusively on soil or forest floor predictor variables is similar to the accuracy achieved by full ecosystem models, combining all soil, vegetation, humus and litterfall composition related variables. Methods Boosted Regression Trees (BRT) were used to model in a climatically homogeneous region the relationship between environmental variables and site productivity. A distinction was made between soil (soil physical and chemical), forest floor (vegetation and humus) and ecosystem (soil, forest floor and litterfall composition jointly) predictors. Results Our results have illustrated the strength of BRT to model the non-linear behaviour of ecological processes. The ecosystem models, based on all collected variables, explained most of the variability and were more accurate than those limited to either soil or forest floor variables. Nevertheless, both the soil and forest floor models can serve as good predictive models for many forest management practices. Conclusions Soil granulometric fractions and litterfall nitrogen concentrations were the most effective predictors of forest site productivity in Flanders. Although many studies revealed a fertilising effect of increased nitrogen deposition, nitrogen saturation seemed to reduce species' productivity in this region.

Affiliations:

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Le document en format XML

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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Accumulation</term>
<term>Characteristics</term>
<term>Chemical concentration</term>
<term>Deposition</term>
<term>Fagus sylvatica</term>
<term>Forest site</term>
<term>Forest soil</term>
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<term>Litter</term>
<term>Lowland</term>
<term>Net production</term>
<term>Nitrogen</term>
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<term>Pinus sylvestris</term>
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<keywords scheme="Pascal" xml:lang="fr"><term>Prédiction</term>
<term>Station forestière</term>
<term>Productivité primaire</term>
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<term>Litière</term>
<term>Accumulation</term>
<term>Caractéristiques</term>
<term>Concentration chimique</term>
<term>Fagus sylvatica</term>
<term>Pinus sylvestris</term>
<term>Quercus robur</term>
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<term>Nutriment</term>
<term>Production nette</term>
<term>Dépôt</term>
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<front><div type="abstract" xml:lang="en">Aims The aim of this study is on the one hand to identify the most determining variables predicting the site productivity of pedunculate oak, common beech and Scots pine in temperate lowland forests of Flanders; and on the other hand to test whether the accuracy of site productivity models based exclusively on soil or forest floor predictor variables is similar to the accuracy achieved by full ecosystem models, combining all soil, vegetation, humus and litterfall composition related variables. Methods Boosted Regression Trees (BRT) were used to model in a climatically homogeneous region the relationship between environmental variables and site productivity. A distinction was made between soil (soil physical and chemical), forest floor (vegetation and humus) and ecosystem (soil, forest floor and litterfall composition jointly) predictors. Results Our results have illustrated the strength of BRT to model the non-linear behaviour of ecological processes. The ecosystem models, based on all collected variables, explained most of the variability and were more accurate than those limited to either soil or forest floor variables. Nevertheless, both the soil and forest floor models can serve as good predictive models for many forest management practices. Conclusions Soil granulometric fractions and litterfall nitrogen concentrations were the most effective predictors of forest site productivity in Flanders. Although many studies revealed a fertilising effect of increased nitrogen deposition, nitrogen saturation seemed to reduce species' productivity in this region.</div>
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Predicting forest site productivity in temperate lowland from forest floor, soil and litterfall characteristics using boosted regression trees

Predicting forest site productivity in temperate lowland from forest floor, soil and litterfall characteristics using boosted regression trees

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri