Asymmetric nonlinear modeling of temperature-dependent mycelial growth reveals distinct thermal profiles among Phytophthora isolates

Inaccurate thermal parameter estimation compromises climate-based disease forecasting for Phytophthora pathogens. We compared fourteen nonlinear regression models describing temperature-dependent mycelial growth in eleven economically significant isolates using information-theoretic criteria (AICc, Δᵢ, Akaike weights ωᵢ). The asymmetric Briere2 model provided the best fit for most isolates, while Weibull and Segmented models best described P. cactorum/P. megasperma and P. inundata, respectively. Estimated cardinal temperatures exhibited pronounced differentiation: base temperature (Tb) ranged from 5.54 °C (P. inundata) to 12.54 °C (P. capsici), optimum temperature (Topt) from 20.30 °C (P. cactorum) to 30.33 °C (P. niederhauseri), and maximum temperature (Tmax) from 30.11 °C (P. plurivora) to 39.16 °C (P. inundata). Bootstrap resampling (n = 1000) confirmed parameter stability (p < 0.001) and revealed clear segregation between cool-adapted isolates and thermophilic isolates. These findings demonstrate that asymmetric nonlinear models, particularly Briere2, provide a robust, transferable framework for improving climate-based disease forecasting and temperature-driven risk assessment in oomycete pathosystems.