Plant growth is an essential ecological process, integrating across scales from physiology to community dynamics. Many models have been proposed but most suffer from the almost inescapable tradeoffs among a model’s flexibility, its predictive capacity, and the data requirements for its parameterization. A further challenge in modeling is that plant growth rates tend to decrease with increasing size, presumably as a result of increasing allocation to non-photosynthetic tissues. Functional models of growth typically handle this slowing poorly, either by ignoring it (as in linear and exponential functions) or by exaggerating it (as in asymptotic functions such as Gompertz). Mechanistic models, in which growth is explicitly modeled as a function of carbon assimilation, can avoid such problems, but may be difficult to generalize across growth forms and experimental designs, and usually require more data for parameterization. Furthermore, mechanistic models are typically written as a set of difference (or differential) equations, and rarely have analytical solutions, precluding the use of traditional statistical approaches to parameter estimation. Fortunately, searches over the likelihood surface in parameter space are ever easier given user-friendly implementations of the Metropolis-Hastings algorithm and access to increasingly powerful computing resources.
Results/Conclusions
We survey the wide variety of functional and mechanistic models currently available, discussing their strengths, weaknesses, and applications. We stress the importance of correcting for initial size when estimating growth rates. We discuss model construction, with the objective of promoting the generation of a general modeling framework for plant growth. This framework should help illuminate congruencies in the growth of plants in disparate circumstances, and aid in the prediction of community dynamics and ecosystem services in the face of global climate change.
