FITTING PREDATOR-PREY MODELS TO TIME-SERIES WITH OBSERVATION ERRORS

Fits of nonlinear difference equations to time series with observation errors were examined by stochastic simulation and analysis of plankto n time series from two lakes. Even modest observation errors (e.g., co efficient of variation among replicate samples approximate to 10%) cau se errors in model identification and bias in parameter estimates. The latter problem can be corrected by estimation techniques that account for observation error, but model identification is difficult unless t he state variables are manipulated. Without manipulation, statistical criteria tend to favor linear models, even when data are simulated by nonlinear processes. Methods that account for observation error produc ed satisfactory fits to time series of edible algae from two lakes ove r 7 yr. In Paul Lake, which has not been manipulated, the best fitting model included linear growth, a linear functional response for grazin g loss, and an autoregressive moving average model for the errors. In manipulated Tuesday Lake, the best fitting model included linear growt h and a nonlinear functional response. Experimental manipulations, or other substantial perturbations, may be essential for detection of non linearities in ecological interactions.