Modelling abundance fluctuations of species is a crucial first step for understanding and forecasting system dynamics under future conditions. But, especially in multivariate response data, this can be hampered by characteristics of the study system such as unknown complexity, differently formed spatial and temporal dependency, non-linear relationships, and observation characteristics such as zero-inflation. This study aimed to explore how such challenges can be addressed by using hierarchical Dynamic Generalized Additive Models (DGAM) for multivariate count responses in a Bayesian framework while modelling multi-site monthly captures for the Desert Pocket Mouse () over 23 years from a long-term study in Arizona, USA. By fitting models of increasing complexity and developing bespoke checking functions that captured targeted ecological aspects such as spatio-temporal dependence, we show how nonlinear dynamic models can be built to improve forecasts for multivariate count-valued time series

中文翻译：

---

使用分层动态广义加性模型模拟沙漠啮齿动物对环境变化的非线性响应

对物种丰度波动进行建模是理解和预测未来条件下系统动态的关键第一步。但是，特别是在多变量响应数据中，这可能会受到研究系统的特征的阻碍，例如未知的复杂性、不同形式的空间和时间依赖性、非线性关系以及零膨胀等观察特征。本研究旨在探索如何通过在贝叶斯框架中使用分层动态广义加性模型 (DGAM) 进行多变量计数响应来解决这些挑战，同时对沙漠袖珍鼠 (Desert Pocket Mouse) 23 年来的多站点每月捕获量进行建模。在美国亚利桑那州进行学期学习。通过拟合日益复杂的模型并开发捕获目标生态方面（例如时空依赖性）的定制检查函数，我们展示了如何构建非线性动态模型来改进多元计数值时间序列的预测