Dynamical Models as Paths to Evidence in Ecology

Mark L. Taper and Subhash R. Lele

in The Nature of Scientific Evidence

Published by University of Chicago Press

Published in print October 2004 | ISBN: 9780226789552
Published online February 2013 | e-ISBN: 9780226789583 | DOI:
Dynamical Models as Paths to Evidence in Ecology

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Natural scientists like to understand how nature works. Usually this quest begins with exploration of empirical patterns in nature. This search may involve a visual exploration of dependencies of variables using computer programs and high-speed graphics that allow rotation of data in three dimensions and sometimes animation to simulate the fourth dimension. The associations between variables that are seen can be tested for statistical significance using various techniques such as the Monte Carlo randomization procedures. These associations, although suggestive, do not necessarily reveal causality. Many statistical techniques and models concentrate on association rather than causation. It is important that we move from exploration and description to explanation. Dynamical models are useful for incorporating explicitly causal pathways in the statistical models. Consequently, dynamical models help in the design of experiments to differentiate among causal pathways. This chapter explores the use of dynamical models, deterministic or stochastic, as paths to evidence in ecology. The main idea is that dynamical models are more likely to lead to the understanding of causation than simple statistical association models.

Keywords: nature; ecology; dynamical models; evidence; causation; statistical models; exploration; explanation; experiments

Chapter.  9224 words.  Illustrated.

Subjects: Animal Pathology and Diseases

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