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BibTeX entry
@Article{Deutschman:2000:RVU,
author = "Douglas H. Deutschman and Catherine Devine and Linda
A. Buttel",
title = "The role of visualization in understanding a complex
forest simulation model",
journal = j-COMP-GRAPHICS,
volume = "34",
number = "1",
pages = "51--55",
month = feb,
year = "2000",
CODEN = "CGRADI, CPGPBZ",
DOI = "https://doi.org/10.1145/604446.604452",
ISSN = "0097-8930",
bibdate = "Wed Oct 7 09:18:19 MDT 2009",
bibsource = "http://www.math.utah.edu/pub/tex/bib/siggraph2000.bib",
abstract = "Ecological research is changing as scientists confront
the complexities of natural and human-influenced
ecosystems. Early ecological research was dominated by
the concepts of equilibrium and determinism [30].
Ecosystems were thought to be stable 'super-organisms,'
fine-tuned by thousands of years of mutual adaptation.
In such a world, ecosystems can be completely described
with static measures of equilibrium population
densities. Although this view has been challenged since
its inception, it is only in the past 25 years that it
has been displaced as the dominant paradigm in ecology.
Today, ecologists describe ecosystems as a dynamic
collection of individuals responding in different ways
to local interactions, broad-scale environmental change
and frequent accidents of history [18, 23, 30].
Although the behavior of the ecosystem is partially
understandable from population densities, ecosystem
dynamics are variable and complex. Ecologists use an
increasingly sophisticated toolbox of techniques to
characterize these complex dynamics. Field surveys and
laboratory experiments measure the responses of
individuals under varied conditions. Thus the mean
response as well as the variance in response can be
estimated. Improved statistical analyses allow
ecologists to describe spatial structure, temporal
dynamics and complex spatio-temporal patterns. Finally,
mathematical models are being developed that can
simulate the complex local interactions of thousands of
individuals in a dynamic, heterogeneous environment [9,
17, 34].Improvements in computer hardware and software
have facilitated this shift toward increasing
complexity. Today, ecologists are seldom limited by
hardware, and software to acquire, store and analyze
data has improved dramatically in the past decade. In
addition, computational models of ecological systems
are becoming common [14]. Models are tools to express
our understanding of mechanisms governing the structure
and function of ecological communities [20]. Models can
also be used to make predictions, determine the
robustness of these predictions, reveal system
properties and highlight weaknesses in our knowledge
[8, 25]. Complex ecological models have several
important drawbacks including the need for huge amounts
of input data, propagation of errors and difficulty
interpreting the often voluminous model output [10, 11,
13]. As a result, increased model complexity and detail
may not lead to increased understanding [10, 16, 22].",
acknowledgement = ack-nhfb,
fjournal = "Computer Graphics",
journal-URL = "http://portal.acm.org/browse_dl.cfm?idx=J166",
}
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