Model fitting
I fitted the models to the lag time data in a Bayesian framework in R version 4.0.3 (R Core Team 2017) using a Metropolis-Hastings algorithm (modified from the R package MHadaptive; Chivers 2012) to estimate the posterior distributions of the parameters. Being a Bayesian model, I specified a prior distribution for all parameters using relatively uninformative priors: a normal distribution with mean zero and standard deviation 10. I ran the models in an adaptive phase for 20000 iterations, using the final 2000 iterations to determine the parameters for the proposal distribution and initial parameter starting values. I then ran three chains for 10000 iterations using the proposal distribution and initial parameter values from the adaptive phase, and checked the resulting parameters for convergence using the Gelman-Rubin statistic (Gelman & Rubin 1992), which was less than 1.1 for all parameters indicating adequate convergence. I identified the best performing model using the approximate leave-one-out cross-validation (LOO) criteria (Vehtari et al. 2017), which estimates the predictive accuracy of each model. LOO is considered an improvement on other information-criterion based model selection measures such as AIC, WAIC and DIC that are widely used to compare model performance (see Vehtari et al. 2017 for details). I used Pareto smoothed importance sampling to estimate LOO (PSIS-LOO) and compared models by calculating the difference in expected predictive accuracy (ΔPSIS-LOO) between each model and the best-performing model on the deviance scale using the loo package in R (Vehtari et al. 2019), with smaller PSIS-LOO values implying a model with better predictive accuracy.