  The identification of complex periodic windows in the two-dimensional
parameter space of certain dynamical systems has recently attracted
considerable interest. While for discrete systems, a discrimination between
periodic and chaotic windows can be easily made based on the maximum Lyapunov
exponent of the system, this remains a challenging task for continuous systems,
especially if only short time series are available (e.g., in case of
experimental data). In this work, we demonstrate that nonlinear measures based
on recurrence plots obtained from such trajectories provide a practicable
alternative for numerically detecting shrimps. Traditional diagonal line-based
measures of recurrence quantification analysis (RQA) as well as measures from
complex network theory are shown to allow an excellent classification of
periodic and chaotic behavior in parameter space. Using the well-studied
R\"ossler system as a benchmark example, we find that the average path length
and the clustering coefficient of the resulting recurrence networks (RNs) are
particularly powerful discriminatory statistics for the identification of
complex periodic windows.
