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.
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The role of visualization in understanding a complex forest simulation model
Deutschman, Douglas H.; Devine, Catherine; Buttel, Linda A.
Follow ACM SIGGRAPH Computer Graphics
, Volume 34 (1)
Association for Computing Machinery – Feb 1, 2000
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