National Institute for Mathematical and Biological Synthesis, United States
Abstract: The origin of allometric scaling patterns that are multiples of ΒΌ has long fascinated biologists. While not universal, quarter-power scaling relationships are common and have been described in all major clades. Several models have been advanced to explain the origin of such patterns, but questions regarding the discordance between model predictions and empirical data have limited their widespread acceptance. Notable among these is a fractal branching model which predicts power law scaling of both metabolism and physical dimensions. While a power law is a useful first approximation to some datasets, non-linear data compilations suggest the possibility of alternative mechanisms. Here, we show that quarter power scaling can be derived using only the preservation of volume flow rate and velocity as model constraints. Applying our model to land plants, we show that incorporating biomechanical principles and allowing different parts of plant branching networks to be optimized to serve different functions predicts non-linearity in allometric relationships and helps explain why interspecific scaling exponents covary along a fractal continuum and tend to fall within a confined range. We also demonstrate that while branching may be a stochastic process, due to the conservation of volume, data may still be consistent with the expectations for a fractal network when one examines subtrees within a tree. Data from numerous sources at the level of petioles, stems, saplings, and entire trees show strong agreement with our model predictions. This novel theoretical framework provides an easily testable alternative to current general models of plant metabolic allometry.
