The paper presents a new recursive estimation algorithm designed expressly for the purpose of model structure identification (not for state estimation or primarily for parameter estimation) and discusses two applications thereof, one to a motivating, hypothetical example and one to data from whole-pond manipulations designed to explore sediment-nutrient-phytoplankton dynamics. The algorithm is the current culmination of a long-term technical development from state estimation using a Kalman filter, through state parameter estimation using an extended Kalman filter, through a recursive prediction error (RPE) algorithm for parameter estimation cast in the state space and recently modified for estimating time-varying model parameters, to an RPE algorithm for estimating time-varying parameters but cast in a parameter space formulation. It is concluded that the algorithm performs well, in the sense of being robust and indeed in revealing specifically where (but less so exactly how) a prior candidate model's structure may be in error.