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The simulated growth of plants is a significant task in of systems biology and mathematical biology, which seeks to reproduce plant morphology with computer software. Electronic trees (e-trees) usually use L-systems to simulate growth. L-systems are very important in the field of complexity science and A-life.
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Agronomic studies often focus on the above-ground part of plant biomass, and consider crop growth rates rather than individual plant growth rates. Nonetheless there is a strong corollary between the two approaches. More specifically, the ULR as discussed above shows up in crop growth analysis as well, as: = . = .
This model can be generalized to any number of species competing against each other. One can think of the populations and growth rates as vectors, α 's as a matrix.Then the equation for any species i becomes = (=) or, if the carrying capacity is pulled into the interaction matrix (this doesn't actually change the equations, only how the interaction matrix is defined), = (=) where N is the ...
DGVMs generally combine biogeochemistry, biogeography, and disturbance submodels.Disturbance is often limited to wildfires, but in principle could include any of: forest/land management decisions, windthrow, insect damage, ozone damage etc. DGVMs usually "spin up" their simulations from bare ground to equilibrium vegetation (e.g. climax community) to establish realistic initial values for ...
The rate at which a population increases in size if there are no density-dependent forces regulating the population is known as the intrinsic rate of increase.It is = where the derivative / is the rate of increase of the population, N is the population size, and r is the intrinsic rate of increase.
Structure and function of growth models vary: some are purely empirical, based on the reproduction of past observations, while others explicitly mimic specific processes relative to tree ecophysiology, stand dynamics, etc. Typically, growth models use forest inventory data and site characteristics, such as soil type, drainage class, average ...
As mentioned above, the logistic map can be used as a model to consider the fluctuation of population size. In this case, the variable x of the logistic map is the number of individuals of an organism divided by the maximum population size, so the possible values of x are limited to 0 ≤ x ≤ 1.