Fisheries stock assessments often assume a two-parameter functional form (e.g. Beverton-Holt, Ricker, or Logistic) for the expected recruitment produced by a given level of spawning output. Mangel et. al. (2013) and others have shown that biological reference points (RP) such as Fmsy/M and Bmsy/B0 are largely determined by a single parameter (steepness) when using two-parameter relationships. These functions introduce strong correlations between RPs that are pre-determined by the functional form, rather than a biological characteristic of the stock. Mangel et. al. note that use of a three-parameter stock-recruitment relationship allows for independent estimation of these RPs. Built around these ideas, a novel simulation framework is developed to investigate the nature of biases in RP estimates that results from fitting a two-parameter functional form when the true relationship is more complicated. First methods for generating space-filling simulation designs in the RP space of three-parameter models are developed. By simulating misspecified RP inference under common two-parameter models over these simulation designs a Gaussian Process metamodel of two-parameter RP inference is developed to control for a spectrum of common ways that two-parameter models are misspecified. This analysis demonstrates the useful limits of commonly used population dynamics models, informs the utility of reducing RP bias, and suggests mechanisms for understanding how, and when, the most common two-parameter models fail to estimate RPs. The studied models vary in complexity from the Schaefer model to delay differential models including dynamics of individual growth and lagged maturity. Additionally, the methods presented can easily be extended to further include age-based frameworks.