Clustering species using a model of population dynamics and aggregation theory
The high species diversity of some ecosystems like tropical rainforests goes in pair with the scarcity of data for most species. This hinders the development of models that require enough data for fitting. The solution commonly adopted by modellers consists in grouping species to form more sizeable data sets. Classical methods for grouping species such as hierarchical cluster analysis do not take account of the variability of the species characteristics used for clustering. In this study a clustering method based on aggregation theory is presented. It takes account of the variability of species characteristics by searching for the grouping that minimizes the quadratic error (square bias plus variance) of some model's prediction. This method allows one to check whether the gain in variance brought by data pooling compensate for the bias that it introduces. This method was applied to a data set on 94 tree species in a tropical rainforest in French Guiana, using a Usher matrix model to predict species dynamics. An optimal trade-off between bias and variance was found when grouping species. Grouping species appeared to decrease the quadratic error, except when the number of groups was very small. This clustering method yielded species groups similar to those of the hierarchical cluster analysis using Ward's method when variance was small, that is when the number of groups was small.
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Subjects: | U10 - Informatique, mathématiques et statistiques, K01 - Foresterie - Considérations générales, F40 - Écologie végétale, forêt tropicale humide, espèce, classification, modèle mathématique, peuplement forestier, dynamique des populations, http://aims.fao.org/aos/agrovoc/c_7976, http://aims.fao.org/aos/agrovoc/c_7280, http://aims.fao.org/aos/agrovoc/c_1653, http://aims.fao.org/aos/agrovoc/c_24199, http://aims.fao.org/aos/agrovoc/c_28080, http://aims.fao.org/aos/agrovoc/c_6111, http://aims.fao.org/aos/agrovoc/c_3093, http://aims.fao.org/aos/agrovoc/c_3081, |
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dig-cirad-fr-5524152024-01-28T17:49:52Z http://agritrop.cirad.fr/552415/ http://agritrop.cirad.fr/552415/ Clustering species using a model of population dynamics and aggregation theory. Picard Nicolas, Mortier Frédéric, Rossi Vivien, Gourlet-Fleury Sylvie. 2010. Ecological Modelling, 221 (2) : 152-160.https://doi.org/10.1016/j.ecolmodel.2009.10.013 <https://doi.org/10.1016/j.ecolmodel.2009.10.013> Clustering species using a model of population dynamics and aggregation theory Picard, Nicolas Mortier, Frédéric Rossi, Vivien Gourlet-Fleury, Sylvie eng 2010 Ecological Modelling U10 - Informatique, mathématiques et statistiques K01 - Foresterie - Considérations générales F40 - Écologie végétale forêt tropicale humide espèce classification modèle mathématique peuplement forestier dynamique des populations http://aims.fao.org/aos/agrovoc/c_7976 http://aims.fao.org/aos/agrovoc/c_7280 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_28080 http://aims.fao.org/aos/agrovoc/c_6111 Guyane française France http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 The high species diversity of some ecosystems like tropical rainforests goes in pair with the scarcity of data for most species. This hinders the development of models that require enough data for fitting. The solution commonly adopted by modellers consists in grouping species to form more sizeable data sets. Classical methods for grouping species such as hierarchical cluster analysis do not take account of the variability of the species characteristics used for clustering. In this study a clustering method based on aggregation theory is presented. It takes account of the variability of species characteristics by searching for the grouping that minimizes the quadratic error (square bias plus variance) of some model's prediction. This method allows one to check whether the gain in variance brought by data pooling compensate for the bias that it introduces. This method was applied to a data set on 94 tree species in a tropical rainforest in French Guiana, using a Usher matrix model to predict species dynamics. An optimal trade-off between bias and variance was found when grouping species. Grouping species appeared to decrease the quadratic error, except when the number of groups was very small. This clustering method yielded species groups similar to those of the hierarchical cluster analysis using Ward's method when variance was small, that is when the number of groups was small. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/552415/1/552415.pdf text Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1016/j.ecolmodel.2009.10.013 10.1016/j.ecolmodel.2009.10.013 http://catalogue-bibliotheques.cirad.fr/cgi-bin/koha/opac-detail.pl?biblionumber=206675 info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ecolmodel.2009.10.013 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1016/j.ecolmodel.2009.10.013 |
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U10 - Informatique, mathématiques et statistiques K01 - Foresterie - Considérations générales F40 - Écologie végétale forêt tropicale humide espèce classification modèle mathématique peuplement forestier dynamique des populations http://aims.fao.org/aos/agrovoc/c_7976 http://aims.fao.org/aos/agrovoc/c_7280 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_28080 http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 U10 - Informatique, mathématiques et statistiques K01 - Foresterie - Considérations générales F40 - Écologie végétale forêt tropicale humide espèce classification modèle mathématique peuplement forestier dynamique des populations http://aims.fao.org/aos/agrovoc/c_7976 http://aims.fao.org/aos/agrovoc/c_7280 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_28080 http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 |
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U10 - Informatique, mathématiques et statistiques K01 - Foresterie - Considérations générales F40 - Écologie végétale forêt tropicale humide espèce classification modèle mathématique peuplement forestier dynamique des populations http://aims.fao.org/aos/agrovoc/c_7976 http://aims.fao.org/aos/agrovoc/c_7280 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_28080 http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 U10 - Informatique, mathématiques et statistiques K01 - Foresterie - Considérations générales F40 - Écologie végétale forêt tropicale humide espèce classification modèle mathématique peuplement forestier dynamique des populations http://aims.fao.org/aos/agrovoc/c_7976 http://aims.fao.org/aos/agrovoc/c_7280 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_28080 http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 Picard, Nicolas Mortier, Frédéric Rossi, Vivien Gourlet-Fleury, Sylvie Clustering species using a model of population dynamics and aggregation theory |
description |
The high species diversity of some ecosystems like tropical rainforests goes in pair with the scarcity of data for most species. This hinders the development of models that require enough data for fitting. The solution commonly adopted by modellers consists in grouping species to form more sizeable data sets. Classical methods for grouping species such as hierarchical cluster analysis do not take account of the variability of the species characteristics used for clustering. In this study a clustering method based on aggregation theory is presented. It takes account of the variability of species characteristics by searching for the grouping that minimizes the quadratic error (square bias plus variance) of some model's prediction. This method allows one to check whether the gain in variance brought by data pooling compensate for the bias that it introduces. This method was applied to a data set on 94 tree species in a tropical rainforest in French Guiana, using a Usher matrix model to predict species dynamics. An optimal trade-off between bias and variance was found when grouping species. Grouping species appeared to decrease the quadratic error, except when the number of groups was very small. This clustering method yielded species groups similar to those of the hierarchical cluster analysis using Ward's method when variance was small, that is when the number of groups was small. |
format |
article |
topic_facet |
U10 - Informatique, mathématiques et statistiques K01 - Foresterie - Considérations générales F40 - Écologie végétale forêt tropicale humide espèce classification modèle mathématique peuplement forestier dynamique des populations http://aims.fao.org/aos/agrovoc/c_7976 http://aims.fao.org/aos/agrovoc/c_7280 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_28080 http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 |
author |
Picard, Nicolas Mortier, Frédéric Rossi, Vivien Gourlet-Fleury, Sylvie |
author_facet |
Picard, Nicolas Mortier, Frédéric Rossi, Vivien Gourlet-Fleury, Sylvie |
author_sort |
Picard, Nicolas |
title |
Clustering species using a model of population dynamics and aggregation theory |
title_short |
Clustering species using a model of population dynamics and aggregation theory |
title_full |
Clustering species using a model of population dynamics and aggregation theory |
title_fullStr |
Clustering species using a model of population dynamics and aggregation theory |
title_full_unstemmed |
Clustering species using a model of population dynamics and aggregation theory |
title_sort |
clustering species using a model of population dynamics and aggregation theory |
url |
http://agritrop.cirad.fr/552415/ http://agritrop.cirad.fr/552415/1/552415.pdf |
work_keys_str_mv |
AT picardnicolas clusteringspeciesusingamodelofpopulationdynamicsandaggregationtheory AT mortierfrederic clusteringspeciesusingamodelofpopulationdynamicsandaggregationtheory AT rossivivien clusteringspeciesusingamodelofpopulationdynamicsandaggregationtheory AT gourletfleurysylvie clusteringspeciesusingamodelofpopulationdynamicsandaggregationtheory |
_version_ |
1792497426302500864 |