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

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 va...

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Fecha de publicación:
2018
Institución:
Universidad del Rosario
Repositorio:
Repositorio EdocUR - U. Rosario
Idioma:
eng
OAI Identifier:
oai:repository.urosario.edu.co:10336/23600
Acceso en línea:
https://doi.org/10.4067/S0717-92002018000300397
https://repository.urosario.edu.co/handle/10336/23600
Palabra clave:
K-means cluster
NMDS
PCA
Pcoa
RO-RA-CO forest type
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spelling 9d4652ee-e9c6-4e7d-9eb9-e5ffe5a4454b-155f16361-57e6-4013-ab98-e52380f6a5ab-1bb05be6f-02e9-47e2-a353-811a4850ce01-1571091-13b36cea5-acf7-4123-a5d1-514094881f43-12020-05-26T00:03:31Z2020-05-26T00:03:31Z2018Second-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. © 2018, Universidad Austral de Chile. All rights reserved.application/pdfhttps://doi.org/10.4067/S0717-920020180003003970304879907179200https://repository.urosario.edu.co/handle/10336/23600engUniversidad Austral de Chile410No. 3397BosqueVol. 39Bosque, ISSN:03048799, 07179200, Vol.39, No.3 (2018); pp. 397-410https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061937084&doi=10.4067%2fS0717-92002018000300397&partnerID=40&md5=5c76cbb81fde235b4fe9dc9802cb6fdfAbierto (Texto Completo)http://purl.org/coar/access_right/c_abf2instname:Universidad del Rosarioreponame:Repositorio Institucional EdocURK-means clusterNMDSPCAPcoaRO-RA-CO forest typeExploring stand and tree variability in mixed Nothofagus second-growth forests through multivariate analysesExplorando la variabilidad de rodal y árbol en bosques de renovales mixtos de Nothofagus spp. Utilizando análisis multivariadosarticleArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501Moreno, Paulo CGezan, Salvador APalmas, SebastianEscobedo, Francisco JCropper, Wendell PORIGINAL0717-9200-bosque-39-03-00397.pdfapplication/pdf1980373https://repository.urosario.edu.co/bitstreams/198a977b-ec60-4c70-9e43-243c63732a7c/download2eb9af845a0e867fc4a99f5e72d60db7MD51TEXT0717-9200-bosque-39-03-00397.pdf.txt0717-9200-bosque-39-03-00397.pdf.txtExtracted texttext/plain55937https://repository.urosario.edu.co/bitstreams/525caab7-e97f-4db4-8a1c-4a607bae2dfc/downloada25571ba39a20e1cab4f86c57cc49fe6MD52THUMBNAIL0717-9200-bosque-39-03-00397.pdf.jpg0717-9200-bosque-39-03-00397.pdf.jpgGenerated Thumbnailimage/jpeg4283https://repository.urosario.edu.co/bitstreams/b93548a8-364a-4146-8270-6d21430bdf56/download0d6711981ccc8f91e21f13eba733ef64MD5310336/23600oai:repository.urosario.edu.co:10336/236002022-05-02 07:37:21.104437https://repository.urosario.edu.coRepositorio institucional EdocURedocur@urosario.edu.co
dc.title.spa.fl_str_mv Exploring stand and tree variability in mixed Nothofagus second-growth forests through multivariate analyses
dc.title.TranslatedTitle.spa.fl_str_mv Explorando la variabilidad de rodal y árbol en bosques de renovales mixtos de Nothofagus spp. Utilizando análisis multivariados
title Exploring stand and tree variability in mixed Nothofagus second-growth forests through multivariate analyses
spellingShingle Exploring stand and tree variability in mixed Nothofagus second-growth forests through multivariate analyses
K-means cluster
NMDS
PCA
Pcoa
RO-RA-CO forest type
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
dc.subject.keyword.spa.fl_str_mv K-means cluster
NMDS
PCA
Pcoa
RO-RA-CO forest type
topic K-means cluster
NMDS
PCA
Pcoa
RO-RA-CO forest type
description 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. © 2018, Universidad Austral de Chile. All rights reserved.
publishDate 2018
dc.date.created.spa.fl_str_mv 2018
dc.date.accessioned.none.fl_str_mv 2020-05-26T00:03:31Z
dc.date.available.none.fl_str_mv 2020-05-26T00:03:31Z
dc.type.eng.fl_str_mv article
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dc.identifier.issn.none.fl_str_mv 03048799
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https://repository.urosario.edu.co/handle/10336/23600
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dc.language.iso.spa.fl_str_mv eng
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dc.relation.citationEndPage.none.fl_str_mv 410
dc.relation.citationIssue.none.fl_str_mv No. 3
dc.relation.citationStartPage.none.fl_str_mv 397
dc.relation.citationTitle.none.fl_str_mv Bosque
dc.relation.citationVolume.none.fl_str_mv Vol. 39
dc.relation.ispartof.spa.fl_str_mv Bosque, ISSN:03048799, 07179200, Vol.39, No.3 (2018); pp. 397-410
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dc.publisher.spa.fl_str_mv Universidad Austral de Chile
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