Ecological systems are open systems characterized by a great number of interactions within and between levels of organization and by complex exchanges with other neighboring systems. Their inherent complexity makes their study, prediction, and management very difficult.
The mathematical modeling and statistical tools that have been traditionally used in ecological research allowed significant advances in ecological knowledge, but they were mainly aimed at a reductionistic approach, which can only be successful in case very simple systems are studied.
Real ecosystems, however, are always very complex (and more complex than they appear) in their structure and dynamics. The combination of many parallel and/or sequential nonlinear interactions often induce unexpected responses, which sometimes reveal chaotic dynamics, making prediction of ecosystem behavior impossible.
Another aspect in ecology that we are dealing with is nonequilibrium systems. Many models that have been used are based on equilibrium, therefore making weak assumptions about reality. Modeling is about simplifying to get a tight description of a structure and its response to a certain stimulus or its dynamics in space or time. It is almost certain that we will never get a 'perfect model' unless we are able to reproduce the system itself. Any simplification will stay short in the model representation ability.
Another problem facing researchers is that our knowledge in one ecological system is not completely transferable to another ecosystem; it is not reducible as classical physical systems are. This again is the burden of all the above.
While our understanding of ecosystem functioning is only partial, the amount of available ecological data keeps growing, and it grows much faster than our ability to turn new data into new insights into ecological processes.
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