Because these models are based generally on mechanistic relationships rather 

 than on strict statistical criteria, they can certainly produce reasonable 

 simulations using sets of different coefficient values. While each set of values 

 may result in indistinguishable state variable predictions, they will not likely 

 all give accurate representation of the processes that contribute to those state 

 variable trajectories. Errors in those process rate simulations may lead to 

 erroneous conclusions concerning critical controls of ecosystem dynamics. It 

 is thus important to measure simultaneously both state variables and process 

 in situ when developing or testing these models. Although many field estimates 

 of processes are difficult to obtain, measurement of even some will provide 

 checkpoints of the simulation of internal dynamics. It is less likely that 

 compensating errors in process simulations will remain if the model adequately 

 reproduces both measured states variables and certain processes. 



Finally, it must be remembered that nature is not predictable in a strictly 

 deterministic fashion given the state of our understanding of its laws. Many 

 events occur on time and space scales smaller than we can attempt to model. 

 These events will add stochastic properties to our measurements. Recognition 

 of these processes and their effects on model prediction confidence must be 

 addressed in any model of nature. 



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