Survival analysis of loblolly pine trees with spatially correlated random effects
Loblolly pine, a native pine species of the southeastern United States, is the most-planted
species for commercial timber. Predicting survival of loblolly pine following planting is of …
species for commercial timber. Predicting survival of loblolly pine following planting is of …
Modeling clustered survival times of loblolly pine with time-dependent covariates and shared frailties
Tree mortality is an important component of forest tree and stand growth models, which
provide decision support for forest managers. Mortality patterns, however, are highly …
provide decision support for forest managers. Mortality patterns, however, are highly …
Modeling mortality of second-rotation loblolly pine plantations in the Piedmont/Upper Coastal Plain and Lower Coastal Plain of the southern United States
Remeasurement data from permanent plots of six loblolly pine studies were used to develop
mortality models for second-rotation loblolly pine plantations growing in the Piedmont/Upper …
mortality models for second-rotation loblolly pine plantations growing in the Piedmont/Upper …
Applying survival analysis to managed even-aged stands of ponderosa pine for assessment of tree mortality in the western United States
FCC Uzoh, SR Mori - Forest Ecology and Management, 2012 - Elsevier
A critical component of a growth and yield simulator is an estimate of mortality rates. The
mortality models presented here are developed from long-term permanent plots in provinces …
mortality models presented here are developed from long-term permanent plots in provinces …
Predicting mortality after thinning in old-field loblolly pine plantations
RC Lemin Jr, HE Burkhart - Southern Journal of Applied Forestry, 1983 - academic.oup.com
Four equations were compared for predicting mortality after thinning in old-field loblolly pine
(Pinus taeda L.) plantations. The model selected is of the form: N2=[N1a1+ a2 (A2a3-A1a3)] …
(Pinus taeda L.) plantations. The model selected is of the form: N2=[N1a1+ a2 (A2a3-A1a3)] …
A basal area ratio predictor of loblolly pine plantation mortality
GR Glover, JN Hool - Forest Science, 1979 - academic.oup.com
A predictor of the probability of plantation-grown loblolly pine (Pinus taeda L.) periodic
mortality is presented. The dependent mortality variable, Proportional Stem Mortality (PSM) …
mortality is presented. The dependent mortality variable, Proportional Stem Mortality (PSM) …
Constraining individual tree diameter increment and survival models for loblolly pine plantations
S Zhang, RL Amateis, HE Burkhart - Forest Science, 1997 - academic.oup.com
Among extant forest growth and yield models, individual tree growth models provide the
most detailed information, which is useful for intensive forest management. However …
most detailed information, which is useful for intensive forest management. However …
Modeling Stand-Level Mortality of Loblolly Pine (Pinus taeda L.) Using Stand, Climate, and Soil Variables
R Thapa, HE Burkhart - Forest Science, 2015 - academic.oup.com
Accurate prediction of mortality is an important component of forest growth-and-yield
systems, yet mortality remains one of the least understood components of the system …
systems, yet mortality remains one of the least understood components of the system …
Simulation of forest tree mortality
RA Monserud - Forest science, 1976 - academic.oup.com
Several methods (discriminant analysis, Probit analysis, Logit analysis) were compared for
the prediction of individual overstory tree mortality in northern hardwood stands in …
the prediction of individual overstory tree mortality in northern hardwood stands in …
A multilevel approach to individual tree survival prediction
CE Rose Jr, DB Hall, BD Shiver, ML Clutter… - Forest …, 2006 - academic.oup.com
Traditionally, modeling of permanent plot individual tree survival has not considered the
multiple sources of heterogeneity and correlation that may arise due to the multilevel data …
multiple sources of heterogeneity and correlation that may arise due to the multilevel data …