  This paper is concerned with a contemporary Bayesian approach to the effect
of temperature on developmental rates. We develop statistical methods using
recent computational tools to model four commonly used ecological non-linear
mathematical curves that describe arthropods' developmental rates. Such models
address the effect of temperature fluctuations on the developmental rate of
arthropods. In addition to the widely used Gaussian distributional assumption,
we also explore Inverse Gamma--based alternatives, which naturally accommodate
adaptive variance fluctuation with temperature. Moreover, to overcome the
associated parameter indeterminacy in the case of no development, we suggest
the Zero Inflated Inverse Gamma model. The ecological models are compared
graphically via posterior predictive plots and quantitatively via Marginal
likelihood estimates and Information criteria values. Inference is performed
using the Stan software and we investigate the statistical and computational
efficiency of its Hamiltonian Monte Carlo and Variational Inference methods. We
explore model uncertainty and use Bayesian Model Averaging framework for robust
estimation of the key ecological parameters
