Estimating Tree Survival: A Study Based on the Estonian Forest Research Plots Network

Tree survival, as affected by tree and stand variables, was studied using the Estonian database of permanent forest research plots. The tree survival was examined on the basis of remeasurements during the period 1995–2004, covering the most common forest types and all age groups. In this study, the influence of 35 tree and stand variables on tree survival probability was analyzed using the data of 31 097 trees from 236 research plots. For estimating individual tree survival probability, a logistic model using the logit-transformation was applied. Tree relative height had the greatest effect on tree survival. However, different factors were included into the logistic model for different development stages: tree relative height, tree relative diameter, relative basal area of larger trees and relative sparsity of a stand for young stands; tree relative height, relative basal area of larger trees and stand density for middle-aged and maturing stands; and tree relative height and stand density for mature and overmature stands. The models can be used as preliminary sub-components for elaboration of a new individual tree based growth simulator.

[1]  H. Burkhart,et al.  Suggestions for choosing an appropriate level for modelling forest stands. , 2003 .

[2]  David A. Hamilton,et al.  Modeling the probability of individual tree mortality , 1976 .

[3]  S. Titus,et al.  A generalized logistic model of individual tree mortality for aspen, white spruce, and lodgepole pine in Alberta mixedwood forests , 2001 .

[4]  Probability Distributions as Models for Mortality , 1985 .

[5]  R. Monserud,et al.  Modeling individual tree mortality for Austrian forest species , 1999 .

[6]  J. W. Moser,et al.  Deriving Growth and Yield Functions for Uneven-Aged Forest Stands , 1969 .

[7]  H. Salminen,et al.  Evaluating estimation methods for logistic regression in modelling individual-tree mortality , 2003 .

[8]  N. Nagelkerke,et al.  A note on a general definition of the coefficient of determination , 1991 .

[9]  Margarida Tomé,et al.  GLOBTREE: an individual tree growth model for Eucalyptus globulus in Portugal. , 2003 .

[10]  J. Grau,et al.  Elaboración de un modelo preliminar de calidad de sitio y modelos preliminares de mortalidad y crecimiento de árbol individual para Pinus nigra Arn. en Cataluña , 2003 .

[11]  Paula Soares,et al.  Modelling Forest Systems , 2003 .

[12]  Emily M. Cain,et al.  ONE MORE TIME ABOUT R 2 MEASURES OF FIT IN LOGISTIC REGRESSION , 2002 .

[13]  Tron Eid,et al.  Models for individual tree mortality in Norway , 2001 .

[14]  Michael J. Crawley,et al.  The R book , 2022 .

[15]  Robert A. Monserud,et al.  Simulation of forest tree mortality , 1976 .

[16]  Shongming Huang,et al.  Modeling individual tree mortality for white spruce in Alberta , 2003 .

[17]  Corinna Hawkes,et al.  Woody plant mortality algorithms: description, problems and progress , 2000 .

[18]  J. Vanclay.  Mortality functions for north Queensland rainforests , 1991 .

[19]  R. Ozolins.  Forest Stand Assortment Structure Analysis Using Mathematical Modelling , 2003 .

[20]  K. Coates,et al.  Models of sapling mortality as a function of growth to characterize interspecific variation in shade tolerance of eight tree species of northwestern British Columbia , 1997 .

[21]  Hannu Hökkä,et al.  Models for predicting stand development in MELA System , 2002 .

[22]  Herman H. Shugart,et al.  A comparison of tree growth models , 1985 .

[23]  David R. Cox.  The analysis of binary data , 1970 .

[24]  Jerome K. Vanclay,et al.  Modelling Forest Growth and Yield: Applications to Mixed Tropical Forests , 1994 .

[25]  M. Palahí,et al.  Preliminary site index model and individual-tree growth and mortality models for black pine ( Pinus nigra Arn . ) in Catalonia ( Spain ) , 2003 .

[26]  M. Mandre.  Air pollution and forests in industrial areas of North-East Estonia. , 2000 .

[27]  D. Schaid,et al.  Score tests for association between traits and haplotypes when linkage phase is ambiguous. , 2002, American journal of human genetics.

[28]  Ramon C. Littell,et al.  SAS® System for Regression , 2001 .

[29]  R. G. Oderwald,et al.  Predicting mortality with a Weibull distribution. , 1980 .

[30]  K. Jõgiste.  Productivity of mixed stands of Norway spruce and birch affected by population dynamics: a model analysis , 1998 .

[31]  U. Diéguez-Aranda,et al.  Modelling mortality of Scots pine (Pinus sylvestris L.) plantations in the northwest of Spain , 2005, European Journal of Forest Research.

[32]  Jerome K. Vanclay,et al.  Growth models for tropical forests: a synthesis of models and methods , 1995 .

[33]  Eric R. Ziegel,et al.  An Introduction to Generalized Linear Models , 2002, Technometrics.

[34]  J. Miina,et al.  Individual-tree growth and mortality models for Eucalyptus grandis (Hill) Maiden plantations in Zimbabwe , 2002 .

[35]  D. R. Cox,et al.  The analysis of binary data , 1971 .

[36]  Ramon C. Littell,et al.  SAS for Linear Models , 2002 .

[37]  David A. Hamilton,et al.  A Logistic Model of Mortality in Thinned and Unthinned Mixed Conifer Stands of Northern Idaho , 1986, Forest Science.

[38]  A. Kiviste,et al.  A conceptual model of forest stand development based on permanent sample-plot data in Estonia , 2005 .

[39]  W. Leak,et al.  Seedling input, death, and growth in uneven-aged northern hardwoods , 1976 .