Designing permanent sample plots by using a spatially hierarchical matrix population model
Designing permanent sample plots is a key issue in forestry where long-term data are required to assess the sustainability of forest logging. The data that are collected in permanent sample plots are used to set parameters in a population dynamics matrix model which in turn is used to predict stock recovery rates for each species. The sampling plan for permanent plots can be designed to estimate stock recovery rates with a required accuracy at a given confidence level, while minimizing installation costs. This can be formulated as a constrained optimization problem (one constraint for each species). The question then is to quantify sampling variability, i.e. the variability of model predictions that are generated by the distribution of parameter estimators. In this study, we address the question of sampling variability for a size-classified population matrix model in a hierarchical context where sample size is itself random and driven by a multivariate spatial point process. An approximate expression is given for the accuracy of the stock recovery rate estimator. This expression is the limit of the accuracy as the expectation of sample size tends to co. We extend this expression to the multispecies case. To a first approximation, interactions between species do not affect the accuracy of the stock recovery rate for each species. A sampling plan is designed using the data for three species in a tropical rainforest in French Guiana. The optimal sampling plan appears to be determined by the most constrained species.
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Subjects: | U10 - Informatique, mathématiques et statistiques, F40 - Écologie végétale, K01 - Foresterie - Considérations générales, forêt tropicale, modèle mathématique, dynamique des populations, peuplement forestier, échantillonnage, Eperua falcata, Annonaceae, Caesalpinioideae, http://aims.fao.org/aos/agrovoc/c_24904, http://aims.fao.org/aos/agrovoc/c_24199, http://aims.fao.org/aos/agrovoc/c_6111, http://aims.fao.org/aos/agrovoc/c_28080, http://aims.fao.org/aos/agrovoc/c_6774, http://aims.fao.org/aos/agrovoc/c_34614, http://aims.fao.org/aos/agrovoc/c_456, http://aims.fao.org/aos/agrovoc/c_1180, http://aims.fao.org/aos/agrovoc/c_3093, http://aims.fao.org/aos/agrovoc/c_3081, |
Online Access: | http://agritrop.cirad.fr/549400/ http://agritrop.cirad.fr/549400/1/document_549400.pdf |
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dig-cirad-fr-5494002024-01-28T17:05:57Z http://agritrop.cirad.fr/549400/ http://agritrop.cirad.fr/549400/ Designing permanent sample plots by using a spatially hierarchical matrix population model. Chagneau Pierrette, Mortier Frédéric, Picard Nicolas. 2009. Applied Statistics, 58 (3) : 1-23.https://doi.org/10.1111/j.1467-9876.2008.00657.x <https://doi.org/10.1111/j.1467-9876.2008.00657.x> Designing permanent sample plots by using a spatially hierarchical matrix population model Chagneau, Pierrette Mortier, Frédéric Picard, Nicolas eng 2009 Applied Statistics U10 - Informatique, mathématiques et statistiques F40 - Écologie végétale K01 - Foresterie - Considérations générales forêt tropicale modèle mathématique dynamique des populations peuplement forestier échantillonnage Eperua falcata Annonaceae Caesalpinioideae http://aims.fao.org/aos/agrovoc/c_24904 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_28080 http://aims.fao.org/aos/agrovoc/c_6774 http://aims.fao.org/aos/agrovoc/c_34614 http://aims.fao.org/aos/agrovoc/c_456 http://aims.fao.org/aos/agrovoc/c_1180 Guyane française France http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 Designing permanent sample plots is a key issue in forestry where long-term data are required to assess the sustainability of forest logging. The data that are collected in permanent sample plots are used to set parameters in a population dynamics matrix model which in turn is used to predict stock recovery rates for each species. The sampling plan for permanent plots can be designed to estimate stock recovery rates with a required accuracy at a given confidence level, while minimizing installation costs. This can be formulated as a constrained optimization problem (one constraint for each species). The question then is to quantify sampling variability, i.e. the variability of model predictions that are generated by the distribution of parameter estimators. In this study, we address the question of sampling variability for a size-classified population matrix model in a hierarchical context where sample size is itself random and driven by a multivariate spatial point process. An approximate expression is given for the accuracy of the stock recovery rate estimator. This expression is the limit of the accuracy as the expectation of sample size tends to co. We extend this expression to the multispecies case. To a first approximation, interactions between species do not affect the accuracy of the stock recovery rate for each species. A sampling plan is designed using the data for three species in a tropical rainforest in French Guiana. The optimal sampling plan appears to be determined by the most constrained species. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/549400/1/document_549400.pdf application/pdf Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1111/j.1467-9876.2008.00657.x 10.1111/j.1467-9876.2008.00657.x http://catalogue-bibliotheques.cirad.fr/cgi-bin/koha/opac-detail.pl?biblionumber=205134 info:eu-repo/semantics/altIdentifier/doi/10.1111/j.1467-9876.2008.00657.x info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1111/j.1467-9876.2008.00657.x |
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U10 - Informatique, mathématiques et statistiques F40 - Écologie végétale K01 - Foresterie - Considérations générales forêt tropicale modèle mathématique dynamique des populations peuplement forestier échantillonnage Eperua falcata Annonaceae Caesalpinioideae http://aims.fao.org/aos/agrovoc/c_24904 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_28080 http://aims.fao.org/aos/agrovoc/c_6774 http://aims.fao.org/aos/agrovoc/c_34614 http://aims.fao.org/aos/agrovoc/c_456 http://aims.fao.org/aos/agrovoc/c_1180 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 U10 - Informatique, mathématiques et statistiques F40 - Écologie végétale K01 - Foresterie - Considérations générales forêt tropicale modèle mathématique dynamique des populations peuplement forestier échantillonnage Eperua falcata Annonaceae Caesalpinioideae http://aims.fao.org/aos/agrovoc/c_24904 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_28080 http://aims.fao.org/aos/agrovoc/c_6774 http://aims.fao.org/aos/agrovoc/c_34614 http://aims.fao.org/aos/agrovoc/c_456 http://aims.fao.org/aos/agrovoc/c_1180 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 F40 - Écologie végétale K01 - Foresterie - Considérations générales forêt tropicale modèle mathématique dynamique des populations peuplement forestier échantillonnage Eperua falcata Annonaceae Caesalpinioideae http://aims.fao.org/aos/agrovoc/c_24904 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_28080 http://aims.fao.org/aos/agrovoc/c_6774 http://aims.fao.org/aos/agrovoc/c_34614 http://aims.fao.org/aos/agrovoc/c_456 http://aims.fao.org/aos/agrovoc/c_1180 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 U10 - Informatique, mathématiques et statistiques F40 - Écologie végétale K01 - Foresterie - Considérations générales forêt tropicale modèle mathématique dynamique des populations peuplement forestier échantillonnage Eperua falcata Annonaceae Caesalpinioideae http://aims.fao.org/aos/agrovoc/c_24904 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_28080 http://aims.fao.org/aos/agrovoc/c_6774 http://aims.fao.org/aos/agrovoc/c_34614 http://aims.fao.org/aos/agrovoc/c_456 http://aims.fao.org/aos/agrovoc/c_1180 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 Chagneau, Pierrette Mortier, Frédéric Picard, Nicolas Designing permanent sample plots by using a spatially hierarchical matrix population model |
description |
Designing permanent sample plots is a key issue in forestry where long-term data are required to assess the sustainability of forest logging. The data that are collected in permanent sample plots are used to set parameters in a population dynamics matrix model which in turn is used to predict stock recovery rates for each species. The sampling plan for permanent plots can be designed to estimate stock recovery rates with a required accuracy at a given confidence level, while minimizing installation costs. This can be formulated as a constrained optimization problem (one constraint for each species). The question then is to quantify sampling variability, i.e. the variability of model predictions that are generated by the distribution of parameter estimators. In this study, we address the question of sampling variability for a size-classified population matrix model in a hierarchical context where sample size is itself random and driven by a multivariate spatial point process. An approximate expression is given for the accuracy of the stock recovery rate estimator. This expression is the limit of the accuracy as the expectation of sample size tends to co. We extend this expression to the multispecies case. To a first approximation, interactions between species do not affect the accuracy of the stock recovery rate for each species. A sampling plan is designed using the data for three species in a tropical rainforest in French Guiana. The optimal sampling plan appears to be determined by the most constrained species. |
format |
article |
topic_facet |
U10 - Informatique, mathématiques et statistiques F40 - Écologie végétale K01 - Foresterie - Considérations générales forêt tropicale modèle mathématique dynamique des populations peuplement forestier échantillonnage Eperua falcata Annonaceae Caesalpinioideae http://aims.fao.org/aos/agrovoc/c_24904 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_28080 http://aims.fao.org/aos/agrovoc/c_6774 http://aims.fao.org/aos/agrovoc/c_34614 http://aims.fao.org/aos/agrovoc/c_456 http://aims.fao.org/aos/agrovoc/c_1180 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 |
author |
Chagneau, Pierrette Mortier, Frédéric Picard, Nicolas |
author_facet |
Chagneau, Pierrette Mortier, Frédéric Picard, Nicolas |
author_sort |
Chagneau, Pierrette |
title |
Designing permanent sample plots by using a spatially hierarchical matrix population model |
title_short |
Designing permanent sample plots by using a spatially hierarchical matrix population model |
title_full |
Designing permanent sample plots by using a spatially hierarchical matrix population model |
title_fullStr |
Designing permanent sample plots by using a spatially hierarchical matrix population model |
title_full_unstemmed |
Designing permanent sample plots by using a spatially hierarchical matrix population model |
title_sort |
designing permanent sample plots by using a spatially hierarchical matrix population model |
url |
http://agritrop.cirad.fr/549400/ http://agritrop.cirad.fr/549400/1/document_549400.pdf |
work_keys_str_mv |
AT chagneaupierrette designingpermanentsampleplotsbyusingaspatiallyhierarchicalmatrixpopulationmodel AT mortierfrederic designingpermanentsampleplotsbyusingaspatiallyhierarchicalmatrixpopulationmodel AT picardnicolas designingpermanentsampleplotsbyusingaspatiallyhierarchicalmatrixpopulationmodel |
_version_ |
1792497212899459072 |