Modelling the vertical distribution of canopy fuel load using national forest inventory and low-density airbone laser scanning data

The fuel complex variables canopy bulk density and canopy base height are often used to predict crown fire initiation and spread. Direct measurement of these variables is impractical, and they are usually estimated indirectly by modelling. Recent advances in predicting crown fire behaviour require accurate estimates of the complete vertical distribution of canopy fuels. The objectives of the present study were to model the vertical profile of available canopy fuel in pine stands by using data from the Spanish national forest inventory plus lowdensity airborne laser scanning (ALS) metrics. In a first step, the vertical distribution of the canopy fuel load was modelled using the Weibull probability density function. In a second step, two different systems of models were fitted to estimate the canopy variables defining the vertical distributions; the first system related these variables to stand variables obtained in a field inventory, and the second system related the canopy variables to airborne laser scanning metrics. The models of each system were fitted simultaneously to compensate the effects of the inherent cross-model correlation between the canopy variables. Heteroscedasticity was also analyzed, but no correction in the fitting process was necessary. The estimated canopy fuel load profiles from field variables explained 84% and 86% of the variation in canopy fuel load for maritime pine and radiata pine respectively; whereas the estimated canopy fuel load profiles from ALS metrics explained 52% and 49% of the variation for the same species. The proposed models can be used to assess the effectiveness of different forest management alternatives for reducing crown fire hazardWe are grateful to the Galician Government and European Social Fund (Official Journal of Galicia—DOG n° 52, 17/03/2014, p. 11343, exp: POS-A/2013/049) for financing the postdoctoral research stays of Dr Eduardo González-Ferreiro at different institutions. Copyright of LiDAR data, Instituto Geográfico Nacional-Xunta de GaliciaS

Compatible System for Predicting Total and Merchantable Stem Volume over and under Bark, Branch Volume and Whole-Tree Volume of Pine Species

Accurate quantification of branch volume in trees is important for sustainable forest management, especially as these fractions are increasingly used for bioenergy, and for precise forest CO2 quantification. Whereas a large focus has been placed on the compatible estimation of tree taper and bole volume with and without bark, little effort has been made to develop models that allow a simultaneous prediction of these variables together with tree branch volume. In this study, 595 Pinus cooperi trees and 700 Pinus durangensis trees were sampled in pine-oak forests in the Sierra Madre Occidental, Mexico. A compatible system for predicting two segmented taper functions, over and under bark; the corresponding merchantable volumes; coarse branch volume and whole-tree volume was fitted using a modified continuous autoregressive structure to account for autocorrelation. The proposed compatible equations explained more than 97% of the observed variability in diameter over and under bark, volume over and under bark, and total tree volume and more than 64% of the observed variability in branch volume in both species. The method described can theoretically be replicated for any tree species, thus providing a better understanding of the patterns of volume distribution by components, potentially improving carbon accounting system and forest bioenergy planning

Co-limitation towards lower latitudes shapes global forest diversity gradients

The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0.025 degrees x 0.025 degrees) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from similar to 1.3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB). However, MTB underestimated LDG in the tropics, where high species richness was also moderated by topographic, soil and anthropogenic factors operating at local scales. Given that local landscape variables operate synergistically with bioclimatic factors in shaping the global LDG pattern, we suggest that MTB be extended to account for co-limitation by subordinate drivers