(ii) Prior distribution
Bayesian approaches incorporate prior knowledge about the parameters into the model. Our choices for these distributions are summarised in (Suppl. Table 2) below for each parasite. We choose a common prior for Sigma, 1/gamma (0.0001,0.0001) and scale parameter (c), gamma (1,10), in all cases. We selected a flat prior for Tmin and Tmax within a defined range. We used a gamma distribution as both the scaling parameter and sigma are non-negative continuous positive values.
(iii) Likelihood
We choose a normal distribution with mean parameter μ given by the Briére equation (Briére et al . 1999) as the likelihood of the data and standard deviation σ. We run STAN (https://mc-stan.org/) for four chains of 1000 iterations each, discarding 500 iterations in each case for warmup. \(\hat{R}\), the convergence statistic reported by STAN, is close to 1 (< 1.05), indicating the 4 Markov chains are in close agreement with one another.