Spectral mixture kernels are covariance kernels for methods such as Gaussian process regression that are capable of theoretically learning any stationary covariance structure by representing the inverse Fourier transform of the kernel as a Gaussian mixture model. Ergodic search likewise relies on Fourier decomposition to compute the loss metric used in its optimization procedure, typically relying on a pre-determined set of frequencies and knowledge of the information distribution over which search is to occur. This project seeks to use the Fourier decompositions present in both methods to link the two such that as a spatial distribution is learned via Gaussian process regression with spectral mixture kernels, the ergodic search algorithm is adapted to use the frequencies found to be important for describing the distribution.