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. 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 also explore model uncertainty and employ Bayesian Model Averaging framework for robust estimation of the key ecological parameters.

本文关注的是当代贝叶斯方法对温度对发育速率的影响。我们使用最近的计算工具开发统计方法，对描述节肢动物发育速率的四种常用生态非线性数学曲线进行建模。这些模型解决了温度波动对节肢动物发育速度的影响。除了广泛使用的高斯分布假设之外，我们还探索了基于 Inverse Gamma 的替代方案，它自然地适应随温度变化的自适应方差波动。此外，为了克服在没有发展的情况下相关参数的不确定性，我们建议使用零膨胀逆伽马模型。生态模型通过后验预测图进行图形比较，并通过边际似然估计和信息标准进行定量。推理是使用 Stan 软件进行的，我们研究了其哈密顿蒙特卡罗和变分推理方法的统计和计算效率。我们还探索了模型的不确定性，并采用贝叶斯模型平均框架对关键生态参数进行稳健估计。