Individual-tree diameter growth models for mixed Nothofagus second growth forests in southern Chile
Second growth forests of Nothofagus obliqua (roble), N. alpina (raulí), and N. dombeyi (coihue), known locally as RORACO, are among the most important native mixed forests in Chile. To improve the sustainable management of these forests, managers need adequate information and models regarding not on...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2017
- Institución:
- Universidad del Rosario
- Repositorio:
- Repositorio EdocUR - U. Rosario
- Idioma:
- eng
- OAI Identifier:
- oai:repository.urosario.edu.co:10336/19152
- Acceso en línea:
- http://repository.urosario.edu.co/handle/10336/19152
- Palabra clave:
- Ecosystems
Error Statistics
Geographical Distribution
Mean Square Error
Cross Validation
Forest Type
Lasso
Tree Competition
Variable Selection
Forestry
Competition (Ecology)
Diameter
Forest Management
Geographical Distribution
Growth Rate
Modeling
Shrub
Silviculture
Sustainability
Chile
Nothofagus
Nothofagus Alpina
Nothofagus Dombeyi
Nothofagus Obliqua
Biología
Ecosistemas
Árboles
Coihue
- Rights
- License
- Abierto (Texto Completo)
Summary: | Second growth forests of Nothofagus obliqua (roble), N. alpina (raulí), and N. dombeyi (coihue), known locally as RORACO, are among the most important native mixed forests in Chile. To improve the sustainable management of these forests, managers need adequate information and models regarding not only existing forest conditions, but their future states with varying alternative silvicultural activities. In this study, an individual-tree diameter growth model was developed for the full geographical distribution of the RORACO forest type. This was achieved by fitting a complete model by comparing two variable selection procedures: cross-validation (CV), and least absolute shrinkage and selection operator (LASSO) regression. A small set of predictors successfully explained a large portion of the annual increment in diameter at breast height (DBH) growth, particularly variables associated with competition at both the tree- and stand-level. Goodness-of-fit statistics for this final model showed an empirical coefficient of correlation (R2emp) of 0.56, relative root mean square error of 44.49% and relative bias of -1.96% for annual DBH growth predictions, and R2emp of 0.98 and 0.97 for DBH projection at 6 and 12 years, respectively. This model constitutes a simple and useful tool to support management plans for these forest ecosystems. © 2017 by the authors. |
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