Tree architecture, growth, and mortality change with increasing tree size and associated light conditions. To date, few studies have quantified how size-dependent changes in growth and mortality rates co-vary with architectural traits, and how such size-dependent changes differ across species and possible light capture strategies. We applied a hierarchical Bayesian model to quantify size-dependent changes in demographic rates and correlated demographic rates and architectural traits for 145 co-occurring Malaysian rain-forest tree species covering a wide range of tree sizes. Demographic rates were estimated using relative growth rate in stem diameter (RGR) and mortality rate as a function of stem diameter. Architectural traits examined were adult stature measured as the 95-percentile of the maximum stem diameter (upper diameter), wood density, and three tree architectural variables: tree height, foliage height, and crown width. Correlations between demographic rates and architectural traits were examined for stem diameters ranging from 1 to 47 cm. As a result, RGR and mortality varied significantly with increasing stem diameter across species. At smaller stem diameters, RGR was higher for tall trees with wide crowns, large upper diameter, and low wood density. Increased mortality was associated with low wood density at small diameters, and associated with small upper diameter and wide crowns over a wide range of stem diameters. Positive correlations between RGR and mortality were found over the whole range of stem diameters, but they were significant only at small stem diameters. Associations between architectural traits and demographic rates were strongest at small stem diameters. In the dark understory of tropical rain forests, the limiting amount of light is likely to make the interspecific difference in the effects of functional traits on demography more clear. Demographic performance is therefore tightly linked with architectural traits such as adult stature, wood density, and capacity for horizontal crown expansion. The enhancement of a demographic trade-off due to interspecific variation in functional traits in the understory helps to explain species coexistence in diverse rain forests.
Accurate estimation of tree biomass is necessary to provide realistic values of the carbon stored in the terrestrial biosphere. A recognized source of errors in tree aboveground biomass (AGB) estimation is introduced when individual tree height values (H) are not directly measured but estimated from diameter at breast height (DBH) using allometric equations. In this paper, we evaluate the performance of 12 alternative DBH : H equations and compare their effects on AGB estimation for three tropical forests that occur in contrasting climatic and altitudinal zones. We found that fitting a three-parameter Weibull function using data collected locally generated the lowest errors and bias in H estimation, and that equations fitted to these data were more accurate than equations with parameters derived from the literature. For computing AGB, the introduced error values differed notably among DBH : H allometric equations, and in most cases showed a clear bias that resulted in either over- or under-estimation of AGB. Fitting the three-parameter Weibull function minimized errors in AGB estimates in our study and we recommend its widespread adoption for carbon stock estimation. We conclude that many previous studies are likely to present biased estimates of AGB due to the method of H estimation.
Remote sensing is revolutionizing the way we study forests, and recent technological advances mean we are now able - for the first time - to identify and measure the crown dimensions of individual trees from airborne imagery. Yet to make full use of these data for quantifying forest carbon stocks and dynamics, a new generation of allometric tools which have tree height and crown size at their centre are needed. Here, we compile a global database of 108753 trees for which stem diameter, height and crown diameter have all been measured, including 2395 trees harvested to measure aboveground biomass. Using this database, we develop general allometric models for estimating both the diameter and aboveground biomass of trees from attributes which can be remotely sensed - specifically height and crown diameter. We show that tree height and crown diameter jointly quantify the aboveground biomass of individual trees and find that a single equation predicts stem diameter from these two variables across the world's forests. These new allometric models provide an intuitive way of integrating remote sensing imagery into large-scale forest monitoring programmes and will be of key importance for parameterizing the next generation of dynamic vegetation models.