Multilevel linear mixed model for tree diameter increment in stone pine (Pinus pinea) A calibrating approach

Diameter increment for stone pine (Pinus pinea L.) is described using a multilevel linear mixed model, where stochastic variability is broken down among period, plot, tree and within-tree components. Covariates acting at tree and stand level, as breast height diameter, density, dominant height or site index are included in the model as fixed effects in order to explain residual random variability. The effect of competition on diameter increment is expressed by including distance independent competition indices. The entrance of regional effects within the model is tested to determine whether a single model is sufficient to explain stone pine diameter increment in Spain, or if, on the contrary, regional models are needed. Diameter increment model can be calibrated by predicting random components using data from past growth measurements taken in a complementary sample of trees. Calibration is carried out by using the best linear unbiased predictor (BLUP) theory. Both the fixed effects model and the calibrated model mean a substantial improvement when compared with the classical approach, widely used in forest management, of assuming constancy in diameter increment for a short projection period.

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Main Authors: Calama, R., Montero, G.
Format: journal article biblioteca
Language:eng
Published: 2005
Online Access:http://hdl.handle.net/20.500.12792/5530
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spelling dig-inia-es-20.500.12792-55302020-12-15T09:46:39Z Multilevel linear mixed model for tree diameter increment in stone pine (Pinus pinea) A calibrating approach Calama, R. Montero, G. Diameter increment for stone pine (Pinus pinea L.) is described using a multilevel linear mixed model, where stochastic variability is broken down among period, plot, tree and within-tree components. Covariates acting at tree and stand level, as breast height diameter, density, dominant height or site index are included in the model as fixed effects in order to explain residual random variability. The effect of competition on diameter increment is expressed by including distance independent competition indices. The entrance of regional effects within the model is tested to determine whether a single model is sufficient to explain stone pine diameter increment in Spain, or if, on the contrary, regional models are needed. Diameter increment model can be calibrated by predicting random components using data from past growth measurements taken in a complementary sample of trees. Calibration is carried out by using the best linear unbiased predictor (BLUP) theory. Both the fixed effects model and the calibrated model mean a substantial improvement when compared with the classical approach, widely used in forest management, of assuming constancy in diameter increment for a short projection period. 2020-10-22T20:23:02Z 2020-10-22T20:23:02Z 2005 journal article http://hdl.handle.net/20.500.12792/5530 eng Attribution-NonCommercial-ShareAlike 4.0 International http://creativecommons.org/licenses/by-nc-sa/4.0/ open access
institution INIA ES
collection DSpace
country España
countrycode ES
component Bibliográfico
access En linea
databasecode dig-inia-es
tag biblioteca
region Europa del Sur
libraryname Biblioteca del INIA España
language eng
description Diameter increment for stone pine (Pinus pinea L.) is described using a multilevel linear mixed model, where stochastic variability is broken down among period, plot, tree and within-tree components. Covariates acting at tree and stand level, as breast height diameter, density, dominant height or site index are included in the model as fixed effects in order to explain residual random variability. The effect of competition on diameter increment is expressed by including distance independent competition indices. The entrance of regional effects within the model is tested to determine whether a single model is sufficient to explain stone pine diameter increment in Spain, or if, on the contrary, regional models are needed. Diameter increment model can be calibrated by predicting random components using data from past growth measurements taken in a complementary sample of trees. Calibration is carried out by using the best linear unbiased predictor (BLUP) theory. Both the fixed effects model and the calibrated model mean a substantial improvement when compared with the classical approach, widely used in forest management, of assuming constancy in diameter increment for a short projection period.
format journal article
author Calama, R.
Montero, G.
spellingShingle Calama, R.
Montero, G.
Multilevel linear mixed model for tree diameter increment in stone pine (Pinus pinea) A calibrating approach
author_facet Calama, R.
Montero, G.
author_sort Calama, R.
title Multilevel linear mixed model for tree diameter increment in stone pine (Pinus pinea) A calibrating approach
title_short Multilevel linear mixed model for tree diameter increment in stone pine (Pinus pinea) A calibrating approach
title_full Multilevel linear mixed model for tree diameter increment in stone pine (Pinus pinea) A calibrating approach
title_fullStr Multilevel linear mixed model for tree diameter increment in stone pine (Pinus pinea) A calibrating approach
title_full_unstemmed Multilevel linear mixed model for tree diameter increment in stone pine (Pinus pinea) A calibrating approach
title_sort multilevel linear mixed model for tree diameter increment in stone pine (pinus pinea) a calibrating approach
publishDate 2005
url http://hdl.handle.net/20.500.12792/5530
work_keys_str_mv AT calamar multilevellinearmixedmodelfortreediameterincrementinstonepinepinuspineaacalibratingapproach
AT monterog multilevellinearmixedmodelfortreediameterincrementinstonepinepinuspineaacalibratingapproach
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