For the developmental process, equation 1 is numerically integrated starting from uniform initial concentrations in space for all gene products (Pi(x) = 0.1) except for P0 x).
We then predicted biomass at the selected DBH class using equations from each available category and determined that equations with developmental DBH ranges distant from the target DBH classes produced wider ranges of estimates, with midpoints shifted from those produced by the equations that spanned the given DBH class.
It seems reasonable to suppose that equations with developmental DBH ranges that lie far from a target DBH class will be worse predictors than those with developmental DBH ranges that span the target DBH class or classes of interest.
Even then, there are unavoidably some trees in the sample that are larger than the developmental ranges of the equations used for a given species and location.
We explored this by selecting three DBH classes (20, 60, and 100 cm), then finding equations with developmental DBH ranges that (1) spanned the DBH class, (2) ended at half the DBH class, or (3) started at twice the DBH class.
On the other hand, the equation governing the developmental process is varied as a result of mutation.
Conceptually, at the centre of the model lies the regulation of WUS via two separable feedback operated reaction-diffusion systems, a commonly used type of differential equation models for developmental processes in biology [22].
In this section, the 2D and 3D FE numerical results are discussed, and the developmental procedure of the interpolation equations is described.
Figure 6B demonstrates how production costs accumulate for these developmental trajectories, calculated according to equations (2) and (7), resulting in greater costs for fast developers, and increased costs for the expression of compensatory development.
This is likely due to a dearth of root equations with large developmental DBH ranges.
