200 CATALYZING INQUIRY
modest efficiency and fidelity could evolve a succession of ever-larger genomes and ever-higher repli-
cation efficiencies.
5.4.8.3 Examples from Ecology^122
Simulation-based study of an ecosystem considers the dynamic behavior of systems of individual
organisms as they respond to each other and to environmental stimuli and pressures (e.g., climate) and
examines the behavior of the ecosystem in aggregate terms. However, no individual run of such a
simulation can be expected to predict the detailed behavior of each individual organism within an
ecosystem. Rather, the appropriate test of a simulation’s fidelity is the extent to which it can, through a
process of judicious averaging of many runs, predict features that are associated with aggregation at
many levels of spatial and/or temporal detail. These more qualitative features provide the basis for
descriptions of ecosystem dynamics that are robust across a variety of dynamical scenarios that are
different at a detailed level and also provide high-level descriptions that can be more readily interpreted
by researchers.
Because of the general applicability of the approach described above, simulations of dynamical
behavior can be developed for aggregations of any organisms as long as they can be informed by
adequate understandings of individual-level behavior and the implications of such behavior for interac-
tions with other individuals and with the environment.
Note also the key role played by ecosystem heterogeneity. Spatial heterogeneity is one obvious way
in which nonuniform distributions play a role. But in biodiversity, functional heterogeneity is also
important. In particular, essential ecosystem functions such as the maintenance of fluxes of certain
nutrients and pollutants, the mediation of climate and weather, and the stabilization of coastlines may
depend not on the behavior of all species within the ecosystem but rather on a limited subset of these
species. If biodiversity is to be maintained, the most fragile and functionally critical subsets species must
be identified and understood.
The mathematical and computational challenges range from techniques for representing and ac-
cessing datasets, to algorithms for simulation of large-scale spatially stochastic, multivariate systems, to
the development and analysis of simplified description. Novel data acquisition tools (e.g., a satellite-
based geographic information system that records changes for insertion in the simulations) would be
welcome in a field that is relatively data poor.
5.4.8.3.1 Impact of Spatial Distribution in EcosystemsAn important dimension of ecological environ-
ments is how organisms interact with each other. One often-made computationally simple assumption is
that an organism is equally likely to interact with every other organism in the environment. Although this
is a pragmatic assumption, actual ecosystems are physical and organisms interact only with a very small
number of other organisms—namely, the ones that are nearby in a spatial sense. Moreover, localized
selection—in which a fitness evaluation is undertaken only under nearest neighbors—is also operative.
Introducing these notions increases the speciation rate tremendously, and the speculation is that in
a nonlocalized environment, the pressures on the population tend toward population uniformity—
everything looks similar, because each entity faces selection pressure from every other entity. When
localization occurs, different species emerge in different spatial areas. Further, the individuals that are
evolving will start to look quite different from each other, even though they have (comparably) high
(^122) Section 5.4.8.3 is based largely on material taken from S.A. Levin, B. Grenfell, A. Hastings, and A.S. Perelson, “Mathematical
and Computational Challenges in Population Biology and Ecosystems Science,” Science 275(5298):334-343, 1997.