  In the face of uncertain biological response to climate change and the many
critiques concerning model complexity it is increasingly important to develop
predictive mechanistic frameworks that capture the dominant features of
ecological communities and their dependencies on environmental factors. This is
particularly important for critical global processes such as biomass changes,
carbon export, and biogenic climate feedback. Past efforts have successfully
understood a broad spectrum of plant and community traits across a range of
biological diversity and body size, including tree size distributions and
maximum tree height, from mechanical, hydrodynamic, and resource constraints.
Recently it was shown that global scaling relationships for net primary
productivity are correlated with local meteorology and the overall biomass
density within a forest. Along with previous efforts, this highlights the
connection between widely observed allometric relationships and predictive
ecology. An emerging goal of ecological theory is to gain maximum predictive
power with the least number of parameters. Here we show that the explicit
dependence of such critical quantities can be systematically predicted knowing
just the size of the largest tree. This is supported by data showing that
forests converge to our predictions as they mature. Since maximum tree size can
be calculated from local meteorology this provides a general framework for
predicting the generic structure of forests from local environmental parameters
thereby addressing a range of critical Earth-system questions.
