Modeling non-catastrophic individual-tree mortality for Pinus radiata plantations in northwestern Spain

• Published in: Forest ecology and management Vol. 257; no. 6; pp. 1542 - 1550
• Main Authors: Crecente-Campo, Felipe, Marshall, Peter, Rodríguez-Soalleiro, Roque
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier B.V 10.03.2009; [Amsterdam]: Elsevier Science; Elsevier
• Subjects: Animal and plant ecology; Animal, plant and microbial ecology; basal area; Biological and medical sciences; Competition; cut-off value; equations; estimation; forest growth; forest inventory; forest plantations; forest yields; Forestry; Fundamental and applied biological sciences. Psychology; Logistic regression; Pinus radiata; plant competition; prediction; Radiata pine; regression analysis; stand density; statistical models; Survival; Synecology; Terrestrial ecosystems; tree age; tree mortality; trees; vigor; Spain; Southern Europe; Europe; Competition; Logistic regression; Radiata pine; Survival; Vegetals; Mortality; Forest ecology; Forest tree; Woody plant; Pinus radiata; Temperate zone; Modeling; Statistical method; Statistical regression; Northwest; Geographical division; Applied mathematics; Individual; Softwood forest tree; Gymnospermae; Forestry; Coniferales; Spermatophyta; Models
• ISSN: 0378-1127; 1872-7042
• DOI: 10.1016/j.foreco.2009.01.007

An individual-tree mortality model for radiata pine ( Pinus radiata D. Don) plantations in northwestern Spain was developed. The model relies on data from three inventories of a network of 130 permanent plots. The equation that predicts the probability of complete survival is a logistic function, which depends on tree size and competition (diameter, mean-squared diameter, stand basal area, dominant height and BALMOD competition index). Several methods of implementing the mortality model were tested (deterministic versus stochastic threshold selection and the use of expansion factors). The best results were obtained when using a fixed cut-off value to decide which trees would survive to the next growing period. When a cut-off value of 0.55 was applied, the model correctly classified 99% of the live trees and 25% of the dead trees. A cut-off of 0.71 provided an estimated mortality rate that was closest to the observed rate. With this cut-off value, the model correctly classified 95% of the live trees and 47% of the dead trees. This cut-off value is recommended when using the model to predict radiata pine individual-tree mortality in the study area.