Probability distribution of allometric coefficients and Bayesian estimation of aboveground tree biomass

Allometric biomass equations are widely used to predict aboveground biomass in forest ecosystems. A major limitation of these equations is that they need to be developed for specific local conditions and species to minimize bias and prediction errors. This variability in allometries across sites and species contradicts universal scaling rules that predicts constant coefficients for plants. A large number of biomass equations have been developed over the years, which provides an opportunity to synthesize parameter values and estimate their probability distributions. These distributions can be used as a priori probabilities to develop new equations for other species or sites. Here we found the distribution of the parameters a and b of the allometry between aboveground biomass (M) and diameter at breast height (D), ln (M) = a + bln(D), well approximated by a bivariate normal. We propose a method to develop new biomass equations based on prior information of parameter distributions and apply it to a dataset of tropical trees. The method we propose outperforms the classical statistical approach of least-square regression at small sample sizes. With this method it is possible to obtain similar significant values in the estimation of parameters using a sample size of 6 trees rather than 40-60 trees in the classical approach. Further, the Bayesian approach suggests that allometric scaling coefficients should be studied in the framework of probability distributions rather than fixed parameter values.

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Main Authors: 133104 Zapata-Cuartas, Mauricio Research Center on Forest and Global Change Carbono & Bosques, Medellín, Colombia, 118799 Sierra, Carlos A. (autor/a) Max Planck Institute for Biogeochemistry, Jena, Germany, 41510 Alleman, Lauren (autor/a) University of Louisiana, Lafayette, USA
Format: Texto biblioteca
Language:eng
Published: Forest Ecology and Management 2012
Subjects:ANATOMIA DE LA PLANTA, CRECIMIENTO, MEDICION, BIOMASA, ALOMETRIA, DESARROLLO BIOLOGICO, MODELOS ALOMETRICOS, ECUACIONES ALOMETRICAS,
Online Access:https://www.sciencedirect.com/science/article/abs/pii/S0378112712002484
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spelling KOHA-OAI-BVE:1366182022-03-16T23:27:58ZProbability distribution of allometric coefficients and Bayesian estimation of aboveground tree biomass 133104 Zapata-Cuartas, Mauricio Research Center on Forest and Global Change Carbono & Bosques, Medellín, Colombia 118799 Sierra, Carlos A. (autor/a) Max Planck Institute for Biogeochemistry, Jena, Germany 41510 Alleman, Lauren (autor/a) University of Louisiana, Lafayette, USA textForest Ecology and Management2012engpdfAllometric biomass equations are widely used to predict aboveground biomass in forest ecosystems. A major limitation of these equations is that they need to be developed for specific local conditions and species to minimize bias and prediction errors. This variability in allometries across sites and species contradicts universal scaling rules that predicts constant coefficients for plants. A large number of biomass equations have been developed over the years, which provides an opportunity to synthesize parameter values and estimate their probability distributions. These distributions can be used as a priori probabilities to develop new equations for other species or sites. Here we found the distribution of the parameters a and b of the allometry between aboveground biomass (M) and diameter at breast height (D), ln (M) = a + bln(D), well approximated by a bivariate normal. We propose a method to develop new biomass equations based on prior information of parameter distributions and apply it to a dataset of tropical trees. The method we propose outperforms the classical statistical approach of least-square regression at small sample sizes. With this method it is possible to obtain similar significant values in the estimation of parameters using a sample size of 6 trees rather than 40-60 trees in the classical approach. Further, the Bayesian approach suggests that allometric scaling coefficients should be studied in the framework of probability distributions rather than fixed parameter values.26 referencias bibliográficasAllometric biomass equations are widely used to predict aboveground biomass in forest ecosystems. A major limitation of these equations is that they need to be developed for specific local conditions and species to minimize bias and prediction errors. This variability in allometries across sites and species contradicts universal scaling rules that predicts constant coefficients for plants. A large number of biomass equations have been developed over the years, which provides an opportunity to synthesize parameter values and estimate their probability distributions. These distributions can be used as a priori probabilities to develop new equations for other species or sites. Here we found the distribution of the parameters a and b of the allometry between aboveground biomass (M) and diameter at breast height (D), ln (M) = a + bln(D), well approximated by a bivariate normal. We propose a method to develop new biomass equations based on prior information of parameter distributions and apply it to a dataset of tropical trees. The method we propose outperforms the classical statistical approach of least-square regression at small sample sizes. With this method it is possible to obtain similar significant values in the estimation of parameters using a sample size of 6 trees rather than 40-60 trees in the classical approach. Further, the Bayesian approach suggests that allometric scaling coefficients should be studied in the framework of probability distributions rather than fixed parameter values.ANATOMIA DE LA PLANTACRECIMIENTOMEDICIONBIOMASAALOMETRIADESARROLLO BIOLOGICOMODELOS ALOMETRICOSECUACIONES ALOMETRICAShttps://www.sciencedirect.com/science/article/abs/pii/S0378112712002484
institution IICA
collection Koha
country Costa Rica
countrycode CR
component Bibliográfico
access En linea
En linea
databasecode cat-sibiica
tag biblioteca
region America Central
libraryname Sistema de Bibliotecas IICA/CATIE
language eng
topic ANATOMIA DE LA PLANTA
CRECIMIENTO
MEDICION
BIOMASA
ALOMETRIA
DESARROLLO BIOLOGICO
MODELOS ALOMETRICOS
ECUACIONES ALOMETRICAS
ANATOMIA DE LA PLANTA
CRECIMIENTO
MEDICION
BIOMASA
ALOMETRIA
DESARROLLO BIOLOGICO
MODELOS ALOMETRICOS
ECUACIONES ALOMETRICAS
spellingShingle ANATOMIA DE LA PLANTA
CRECIMIENTO
MEDICION
BIOMASA
ALOMETRIA
DESARROLLO BIOLOGICO
MODELOS ALOMETRICOS
ECUACIONES ALOMETRICAS
ANATOMIA DE LA PLANTA
CRECIMIENTO
MEDICION
BIOMASA
ALOMETRIA
DESARROLLO BIOLOGICO
MODELOS ALOMETRICOS
ECUACIONES ALOMETRICAS
133104 Zapata-Cuartas, Mauricio Research Center on Forest and Global Change Carbono & Bosques, Medellín, Colombia
118799 Sierra, Carlos A. (autor/a) Max Planck Institute for Biogeochemistry, Jena, Germany
41510 Alleman, Lauren (autor/a) University of Louisiana, Lafayette, USA
Probability distribution of allometric coefficients and Bayesian estimation of aboveground tree biomass
description Allometric biomass equations are widely used to predict aboveground biomass in forest ecosystems. A major limitation of these equations is that they need to be developed for specific local conditions and species to minimize bias and prediction errors. This variability in allometries across sites and species contradicts universal scaling rules that predicts constant coefficients for plants. A large number of biomass equations have been developed over the years, which provides an opportunity to synthesize parameter values and estimate their probability distributions. These distributions can be used as a priori probabilities to develop new equations for other species or sites. Here we found the distribution of the parameters a and b of the allometry between aboveground biomass (M) and diameter at breast height (D), ln (M) = a + bln(D), well approximated by a bivariate normal. We propose a method to develop new biomass equations based on prior information of parameter distributions and apply it to a dataset of tropical trees. The method we propose outperforms the classical statistical approach of least-square regression at small sample sizes. With this method it is possible to obtain similar significant values in the estimation of parameters using a sample size of 6 trees rather than 40-60 trees in the classical approach. Further, the Bayesian approach suggests that allometric scaling coefficients should be studied in the framework of probability distributions rather than fixed parameter values.
format Texto
topic_facet ANATOMIA DE LA PLANTA
CRECIMIENTO
MEDICION
BIOMASA
ALOMETRIA
DESARROLLO BIOLOGICO
MODELOS ALOMETRICOS
ECUACIONES ALOMETRICAS
author 133104 Zapata-Cuartas, Mauricio Research Center on Forest and Global Change Carbono & Bosques, Medellín, Colombia
118799 Sierra, Carlos A. (autor/a) Max Planck Institute for Biogeochemistry, Jena, Germany
41510 Alleman, Lauren (autor/a) University of Louisiana, Lafayette, USA
author_facet 133104 Zapata-Cuartas, Mauricio Research Center on Forest and Global Change Carbono & Bosques, Medellín, Colombia
118799 Sierra, Carlos A. (autor/a) Max Planck Institute for Biogeochemistry, Jena, Germany
41510 Alleman, Lauren (autor/a) University of Louisiana, Lafayette, USA
author_sort 133104 Zapata-Cuartas, Mauricio Research Center on Forest and Global Change Carbono & Bosques, Medellín, Colombia
title Probability distribution of allometric coefficients and Bayesian estimation of aboveground tree biomass
title_short Probability distribution of allometric coefficients and Bayesian estimation of aboveground tree biomass
title_full Probability distribution of allometric coefficients and Bayesian estimation of aboveground tree biomass
title_fullStr Probability distribution of allometric coefficients and Bayesian estimation of aboveground tree biomass
title_full_unstemmed Probability distribution of allometric coefficients and Bayesian estimation of aboveground tree biomass
title_sort probability distribution of allometric coefficients and bayesian estimation of aboveground tree biomass
publisher Forest Ecology and Management
publishDate 2012
url https://www.sciencedirect.com/science/article/abs/pii/S0378112712002484
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