 Regarding (1), detailed predictions regarding individual trajectories fail rather rapidly for chaotic models when there is any error in specification of the initial state. So, says Kellert, instead we predict global behaviors of models and have an account of limited predictability in chaotic models. But many of these behaviors can be precisely predicted (e.g., control parameter  values[9]  at which various bifurcations occur, the onset of chaos, the return of n-periodic orbits). (1) amounts to important, but limited insight. On this view we are able to predict when to expect qualitative features of the nonlinear dynamics to undergo a sudden change, but chaos models do not yield precise values of system variables. We get the latter values by running full-blow computer simulations on the full nonlinear model equations, provided the degrees of freedom are reasonable. In this sense, chaos explanations are complimentary to the full model simulation because the former can tell us when/where to expect dynamical changes such as the onset of complicated dynamics in the latter. 