Another inherent difficulty that ecological modeling must contend with is 

 the plethora and interaction of biotic and abiotic variables that control 

 ecosystem state. Physical variables such as temperature, radiation, and 

 turbulence are driving variables that control ecosystem structure and function. 

 Chemical variables ranging from nutrients to toxics can interact to alter 

 ecosystem state and response. Biotic components such as species present, 

 population densities, competition, and behavioral patterns combine to provide 

 the ecosystem modeler with a complex matrix of interacting variables which is 

 almost impossible to untangle. This problem becomes more complex when one 

 tries to integrate the synergies between all three classes of variables. It is 

 true that the wise scientist tries to minimize extraneous variables emphasizing 

 only those factors subjectively believed to be "important." However, he or she 

 must enter into that exercise with the knowledge that the underlying assumptions 

 and conceptual formulations may be incompletely defined. 



Finally, ecological data which are crucial to ecosystem modeling needs are 

 often lacking. Perhaps most important is the lack of basic physiological data 

 for many species. For example, verifiable toxico logical data are available for 

 only a handful of chemicals and species. The data that exist are for single 

 compounds tested under limited environmental or experimental conditions; 

 knowledge of synergistic effects of toxics and toxic effects over a wide range 

 of environmental conditions is virtually nonexistent. Important ecosystem rate 

 kinetics are unknown as are fluxes, routes, and reservoirs of many elements and 

 compounds in ecosystems. 



These constraints in theory, complexity, and data are facts that ecosystem 

 modelers must face before they promote their potential products to resource 

 managers and decisionmakers. Engineers can predict safe loading levels for 

 bridges, and the public expects ecologists to be able to predict safe loading 

 levels for pollutants in ecosystems. Ecologists and resource managers must be 

 eminently cognizant of the constraints to ecosystem modeling and the limitations 

 to model applications and predictions. 



State-of-the-Art 



The term "ecosystem model" encompasses a very broad range of approaches, 

 each with strengths and weaknesses, each with different data requirements for 

 development and evaluation, and thus each relevant to different aspects of 

 management and decisionmaking. None of these approaches, however, can now or 

 in the foreseeable future provide results that are conclusive and reliable 

 enough to be the sole basis for any significant management decision. Further, 

 modelers do not believe that model output should ever be the sole or primary 

 basis for a management decision. The state-of-the-art of ecosystem modeling is 

 such that it can produce information that is germane and useful to management, 

 but such information must always be closely integrated with many other forms of 

 information input: expert opinions, direct experiments, and socio-economic, 

 health, and political deliberations. 



It is important to recognize quantitative models of all types as extensions 

 of the conceptual perceptions of how ecosystems function. As such, models are 

 never more than tests of necessarily incomplete hypotheses. As these hypothesis 

 evolve with our experience, modeling inherently becomes an iterative intellectual 

 process. In this context, it is important to recognize the necessity of 

 including the modeler and the modeling process throughout the decisionmaking 



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