Exploring stand and tree variability in mixed Nothofagus second-growth forests through multivariate analyses

SUMMARY: Second-growth forests of Nothofagus obliqua (roble), N. alpina (raulí) and N. dombeyi (coihue), known locally as RO-RA-CO forest type, are among the most important natural mixed forest types of Chile. Several studies have identified a wide range of factors that could influence both stand an...

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Autores principales:	Moreno,Paulo C, Gezan,Salvador A, Palmas,Sebastian, Escobedo,Francisco J, Cropper Jr.,Wendell P
Lenguaje:	English
Publicado:	Universidad Austral de Chile, Facultad de Ciencias Forestales 2018
Materias:	RO-RA-CO forest type PCA NMDS PCoA K-means cluster
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-92002018000300397
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spelling	oai:scielo:S0717-920020180003003972019-10-24Exploring stand and tree variability in mixed Nothofagus second-growth forests through multivariate analysesMoreno,Paulo CGezan,Salvador APalmas,SebastianEscobedo,Francisco JCropper Jr.,Wendell P RO-RA-CO forest type PCA NMDS PCoA K-means cluster SUMMARY: Second-growth forests of Nothofagus obliqua (roble), N. alpina (raulí) and N. dombeyi (coihue), known locally as RO-RA-CO forest type, are among the most important natural mixed forest types of Chile. Several studies have identified a wide range of factors that could influence both stand and tree variability found in these forests. To better characterize potential tree- and stand-level factors that are associated with RO-RA-CO variability, and that are available in typical forest inventories, several unsupervised multivariate statistical methods were evaluated: 1) non-metric multidimensional scaling (NMDS); 2) principal coordinates analysis (PCoA); and 3) principal component analysis (PCA). The data used in this study originated from a sample of 158 plots consisting of two plot networks that covered the full geographic area of the RO-RA-CO forest type in Chile. We found that site productivity and growth zones did not explain the differences within the sampled population. However, stand development stages, tree-to-tree competition, and tree-size attributes were critical variables with a high percentage of variance explained using PCA, ranging from 61 % to 67 %. In addition, for the PCoA analysis, the variable stand density is important, with ~78 % variance explained.info:eu-repo/semantics/openAccessUniversidad Austral de Chile, Facultad de Ciencias ForestalesBosque (Valdivia) v.39 n.3 20182018-01-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-92002018000300397en10.4067/S0717-92002018000300397
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	RO-RA-CO forest type PCA NMDS PCoA K-means cluster
spellingShingle	RO-RA-CO forest type PCA NMDS PCoA K-means cluster Moreno,Paulo C Gezan,Salvador A Palmas,Sebastian Escobedo,Francisco J Cropper Jr.,Wendell P Exploring stand and tree variability in mixed Nothofagus second-growth forests through multivariate analyses
description	SUMMARY: Second-growth forests of Nothofagus obliqua (roble), N. alpina (raulí) and N. dombeyi (coihue), known locally as RO-RA-CO forest type, are among the most important natural mixed forest types of Chile. Several studies have identified a wide range of factors that could influence both stand and tree variability found in these forests. To better characterize potential tree- and stand-level factors that are associated with RO-RA-CO variability, and that are available in typical forest inventories, several unsupervised multivariate statistical methods were evaluated: 1) non-metric multidimensional scaling (NMDS); 2) principal coordinates analysis (PCoA); and 3) principal component analysis (PCA). The data used in this study originated from a sample of 158 plots consisting of two plot networks that covered the full geographic area of the RO-RA-CO forest type in Chile. We found that site productivity and growth zones did not explain the differences within the sampled population. However, stand development stages, tree-to-tree competition, and tree-size attributes were critical variables with a high percentage of variance explained using PCA, ranging from 61 % to 67 %. In addition, for the PCoA analysis, the variable stand density is important, with ~78 % variance explained.
author	Moreno,Paulo C Gezan,Salvador A Palmas,Sebastian Escobedo,Francisco J Cropper Jr.,Wendell P
author_facet	Moreno,Paulo C Gezan,Salvador A Palmas,Sebastian Escobedo,Francisco J Cropper Jr.,Wendell P
author_sort	Moreno,Paulo C
title	Exploring stand and tree variability in mixed Nothofagus second-growth forests through multivariate analyses
title_short	Exploring stand and tree variability in mixed Nothofagus second-growth forests through multivariate analyses
title_full	Exploring stand and tree variability in mixed Nothofagus second-growth forests through multivariate analyses
title_fullStr	Exploring stand and tree variability in mixed Nothofagus second-growth forests through multivariate analyses
title_full_unstemmed	Exploring stand and tree variability in mixed Nothofagus second-growth forests through multivariate analyses
title_sort	exploring stand and tree variability in mixed nothofagus second-growth forests through multivariate analyses
publisher	Universidad Austral de Chile, Facultad de Ciencias Forestales
publishDate	2018
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-92002018000300397
work_keys_str_mv	AT morenopauloc exploringstandandtreevariabilityinmixednothofagussecondgrowthforeststhroughmultivariateanalyses AT gezansalvadora exploringstandandtreevariabilityinmixednothofagussecondgrowthforeststhroughmultivariateanalyses AT palmassebastian exploringstandandtreevariabilityinmixednothofagussecondgrowthforeststhroughmultivariateanalyses AT escobedofranciscoj exploringstandandtreevariabilityinmixednothofagussecondgrowthforeststhroughmultivariateanalyses AT cropperjrwendellp exploringstandandtreevariabilityinmixednothofagussecondgrowthforeststhroughmultivariateanalyses
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Exploring stand and tree variability in mixed Nothofagus second-growth forests through multivariate analyses

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