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This paper is concerned with a contemporary Bayesian approach to the effectof temperature on developmental rates. We develop statistical methods usingrecent computational tools to model four commonly used ecological non-linearmathematical curves that describe arthropods' developmental rates. Such modelsaddress the effect of temperature fluctuations on the developmental rate ofarthropods. In addition to the widely used Gaussian distributional assumption,we also explore Inverse Gamma--based alternatives, which naturally accommodateadaptive variance fluctuation with temperature. Moreover, to overcome theassociated parameter indeterminacy in the case of no development, we suggestthe Zero Inflated Inverse Gamma model. The ecological models are comparedgraphically via posterior predictive plots and quantitatively via Marginallikelihood estimates and Information criteria values. Inference is performedusing the Stan software and we investigate the statistical and computationalefficiency of its Hamiltonian Monte Carlo and Variational Inference methods. Weexplore model uncertainty and use Bayesian Model Averaging framework for robustestimation of the key ecological parameters

Inference and model determination for Temperature-Driven non-linear Ecological Models