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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Forest Ecology and Management
2012
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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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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 |
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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 |
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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 |
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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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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 |
work_keys_str_mv |
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_version_ |
1756066268348153856 |