Hydrothermal Germination Models: Comparison of Two Data‐Fitting Approaches with Probit Optimization

Probit models for estimating hydrothermal germination rate yield model parameters that have been associated with specific physiological processes. The desirability of linking germination response to seed physiology must be weighed against expectations of model fit and the relative accuracy of predicted germination response. Computationally efficient empirical models have been proposed that do not require a priori assumptions about model shape parameters, but the accuracy of these models has not been compared to the more common probit‐optimization procedure. Thirteen seedlots, representing six native perennial rangeland grasses and an invasive annual weed, were germinated over the constant temperature range of 3 to 36°C and water potential range of 0 to ‐2.5 MPa. Hydrothermal germination models were generated using probit optimization, optimized regression, and statistical gridding. These models were evaluated for the pattern and magnitude of residual model error and the relative magnitude of predictive errors under field‐simulated temperature and moisture conditions. Residual model errors in predictions of germination rate were greatest for the probit optimization procedure. Statistical gridding and optimized regression produced lower predictive model error, but the latter procedure could not resolve germination response for slower‐germinating seed populations. The more computationally efficient and accurate regression and statistical‐gridding procedures may be desirable for identifying germination strategies and syndromes that are based on predicted response to simulated conditions of field temperature and moisture.