Ecological Research · 2013 · 26 citations · 62 references
Natural ResourcesEcologyForestryForest ConservationForest EcologyForest ManagementAllometric ModelingForest GrowthForest Stand DynamicsForest SuccessionTree EstablishmentTropical Forest EcologyForest Mortality DynamicsLocal Returns To ScaleResource Allocation In TreesStand Mortality RiskTree Mortality DynamicsTree Mortality RiskTree Survival Analysis
Abstract Tree survival is a critical driver of stand dynamics, influencing forest structure and composition. Many local‐scale drivers (tree size, abiotic and biotic factors) have been proposed as being important in explaining patterns of tree survival, but their contributions are still unknown. We examined the relative importance of these local drivers on tree survival using generalized linear mixed models in an old‐growth sub‐tropical forest in south China at three levels (community, guild, and species). Among the variables tested, tree size was typically the most important driver of tree survival, followed by abiotic and then biotic variables. Tree size has a strongly positive effect on tree survival for small trees (10–30 cm dbh) and shade‐tolerant tree species. Of the abiotic factors tested, elevation tended to be more important in affecting tree survival than other topographic variables. Abiotic factors generally influenced survival of species with relatively high abundances, for individuals in smaller size classes and for mid‐tolerant species. Among biotic factors, we found that the mortality of tree species was not driven by density‐ and frequency‐dependent effects in this sub‐tropical forest, as indicated by the results of both total basal area of neighbors and the proportion of conspecific neighbors in our study. We conclude that the relative importance of variables driving patterns of tree survival varied greatly among tree size classes, species guilds, shade tolerance, density, and abundance classes in this sub‐tropical forest. These results also provide critical information for future studies of forest dynamics and offer insight into forest management in this region.
62
R: A Language and Environment for Statistical Computing
R Core Team · 2000 · 353K citations · Full text
Natural Sciences, Computer Science, Statistical Computing +10
Mixed-Effects Models in S and S-PLUS
Technometrics · 2001 · 9.4K citations · Full text
TOWARD A METABOLIC THEORY OF ECOLOGY
James H. Brown, James F. Gillooly, Andrew P. Allen et al. · Ecology · 2004 · 7.8K citations
Life Sciences, Biology, Ecology +15
Linear Mixed-Effects Models using 'Eigen' and S4
Douglas M. Bates, Martin Maechler, Benjamin M. Bolker et al. · 2015 · 6K citations
A note on a general definition of the coefficient of determination
N. J. D. Nagelkerke · Biometrika · 1991 · 5.7K citations