  Reservoir computers (RC) have proven useful as surrogate models in
forecasting and replicating systems of chaotic dynamics. The quality of
surrogate models based on RCs is crucially dependent on their optimal
implementation that involves selecting optimal reservoir topology and
hyperparameters. By systematically applying Bayesian hyperparameter
optimization and using ensembles of reservoirs of various topology we show that
connectednes of reservoirs is of significance only in forecasting and
replication of chaotic system of sufficient complexity. By applying RCs of
different topology in forecasting and replicating the Lorenz system, a coupled
Wilson-Cowan system, and the Kuramoto-Sivashinsky system, we show that simple
reservoirs of unconnected nodes (RUN) outperform reservoirs of connected nodes
for target systems whose estimated fractal dimension dimension is $d \lesssim
5.5$ and that linked reservoirs are better for systems with $d > 5.5$. This
finding is highly important for evaluation of reservoir computing methods and
on selecting a method for prediction of signals measured on nonlinear systems.
