COMPUTATIONAL MODELING AND SIMULATION AS ENABLERS FOR BIOLOGICAL DISCOVERY 201
fitness ratings. (This phenomenon is known as convergent evolution, in which a given environment
might evolve several different species that are in some sense equally well adapted to that environment.)
As an example of spatial localization, Kerr et al. developed a computational model to examine the
behavior of a community consisting of three strains of E. coli,^123 based on a modification of the lattice-
based simulation of Durrett and Levin.^124 One of the strains carried a gene that created an antibiotic
called colicin. (The colicin-producing strain, C, was immune to the colicin it produced.) A second strain
was sensitive to colicin (S), while a third strain was resistant to colicin (R). Furthermore, the factors that
make the S strain sensitive also facilitate its consumption of certain nutrients, and the R strain is less able
to consume these nutrients. However, because the R strain does not have to produce colicin, it avoids a
metabolic cost incurred by the C strain. The result is that C bacteria kill S bacteria, S bacteria thrive
where R bacteria do not, and R bacteria thrive where C bacteria do not. The community thus satisfies a
“rock-paper-scissors” relationship.
The intent of the simulation was to explore the spatial scale of ecological processes in a community
of these three strains. It was found found (and confirmed experimentally) that when dispersal and
interaction were local, patches of different strains formed, and these patches chased one another over
the lattice—type C patches encroached on S patches, S patches displaced R patches and R patche
invaded C patches. Within this mosaic of patches, the local gains made by any one type were soon
enjoyed by another type; hence the diversity of the system was maintained. However, dispersal and
interaction were no longer exclusively local (i.e., in the “well-mixed” case in which all three strains are
allowed to interact freely with each other): continual redistribution of C rapidly drove S extinct, and R
then came to dominate the entire community
5.4.8.3.2 Forest Dynamics^125 To simulate the growth of northeastern forests, a stochastic and mecha-
nistic model known as SORTIE^ has been developed to follow the fates of individual trees and their
offspring. Based on species-specific information on growth rates, fecundity, mortality, and seed dis-
persal distances, as well as detailed, spatially explicit information about local light regimes, SORTIE
follows tens of thousands of trees to generate dynamic maps of distributions of nine dominant or
subdominant species of tree that look like real forests and match data observed in real forests at
appropriate levels of spatial resolution. SORTIE predicts realistic forest responses to disturbances (e.g.,
small circles within the forest boundaries within which all trees are destroyed), clear-cuts (i.e., large
disturbances), and increased tree mortality.
SORTIE consists of two units that account for local light availability and species life history for each
of nine tree species. Local light availability refers to the availability of light at each individual tree. This
is a function of all of the neighboring trees that shade the tree in question. Information on the spatial
relations among these neighboring tree crowns is combined with the movement of the sun throughout
the growing season to determine the total, seasonally averaged light expressed as a percentage of full
sun. In other words, the growth of any given tree depends on the growth of all neighboring trees.
The species life history (available for each of nine tree species) provides the relationship between
radial growth rates as a function of its local light environment and is based on empirically estimated
life-history information. Radial growth predicts height growth, canopy width, and canopy depth in
accordance with estimated allometric relations. Fecundity is estimated as an increasing power function
of tree size, and seeds are dispersed stochastically according to a relation whereby the probability of
(^123) B. Kerr, M.A. Riley, M.W. Feldman, and B.J. Bohannan, “Local Dispersal Promotes Biodiversity in a Real-life Game of Rock-
Paper-Scissors,” Nature 418(6894):171-174, 2002.
(^124) R. Durrett and S. Levin, “Allelopathy in Spatially Distributed Populations,” Journal of Theoretical Biology 185(2):165-171, 1997.
(^125) Section 5.4.8.3.2 is based largely on D.H. Deutschman, S.A. Levin, C. Devine, and L.A. Buttel, “Scaling from Trees to Forests:
Analysis of a Complex Simulation Model,” Science Online supplement to Science 277(5332), 1997, available at http://
http://www.sciencemag.org/content/vol277/issue5332. Science Online article available at http://www.sciencemag.org/feature/data/
deutschman/home.htm.