Diversity and competition influence tree allometric relationships – developing functions for mixed‐species forests

Abstract

Summary Promoting mixed‐species forests is an important strategy for adaptation and risk reduction in the face of global change. Concurrently, a main challenge in ecology is to quantify the effects of species diversity on ecosystem functioning. In forests, the effects of individual tree species on ecosystem functions depend largely on their dimensions, which are commonly predicted using allometric equations. However, little is known about how diversity influences allometry or how to incorporate this effect into allometric equations. Ignoring the effects of interspecific interactions on allometric relationships may result in severely biased predictions. This study examined the effects of tree‐species diversity, competition and tree social status on crown‐projection area (cpa), height (h) and live‐crown length (lcl) of trees using a European‐wide data set containing 17 target species and 12 939 trees. The cpa, h and lcl were predicted as functions of stem diameter at 1·3 m, tree‐species diversity, tree height relative to the stand mean height (rh) and a competition index (CI) that accounted for stand density and interspecific differences in competitive ability based on species‐specific wood density or shade tolerance. Averaged across species, diameter had the greatest effect on cpa and lcl, followed by the competition index, while rh had the greatest effect on lcl. Tree‐species diversity had the smallest effect on cpa, h and lcl. Interspecific variability in cpa, h or lcl responses to diversity, CI, or rh was sometimes related to wood density or shade tolerance. Synthesis. This study shows the strong influence of stand structure and species composition on allometric relationships. These influences can be quantified using measures of competition, tree‐species diversity and relative tree height so that general equations can be developed for a given species to be applied to a wide range of species compositions and stand structures. This new approach will greatly improve predictions of biomass and carbon stocks in structurally and compositionally diverse forests.