Characterizing the complexity of ecological systems
Université de Montréal
Last modified: August 1, 2007
While most scientists would agree that ecosystems are complex, quantifying this complexity is often fraught with difficulties due to problems with data quality or quantity, as well as a lack of appropriate measures. This presentation will overview a number of recent techniques that have been developed in the field of ecological complexity in order to characterize the complexity of ecosystems. While examples and applications will be ecological, the methods presented are appropriate for the analysis of data from all types of natural systems.
Measures of complexity may be used to classify the spatial, temporal or structural signatures of a system. Complex systems have signatures that lie between the two extremes of order (equivalent to a uniform spatial pattern or temporal equilibrium) and disorder (equivalent to a random spatial distribution or white noise), exhibiting a balance between underlying regularity and complete unpredictability (chaos). The characterization of this class of structures and dynamics represents one of the key challenges in complexity research.
A number of techniques based on non-linear analysis or on information theory have been proposed as measures of complexity. Most of these methods have rarely, if ever, been applied to ecological data and many are not applicable to the type of data available in ecology (typically involving short time series, missing values, non-stationarity). We will discuss how some of these techniques can be adapted for data from natural systems and will present new methods that have been developed specifically for the characterization of the complex, spatiotemporal dynamics of ecosystems.
Note that this presentation might be suitable for a pedagogical session and/or can be adapted as necessary to suit different talk types.