Understanding ecological processes and predicting long-term dynamics are ongoing challenges in ecology. To address these challenges, we suggest an approach combining mathematical analyses and Bayesian hierarchical statistical modeling with diverse data sources. Novel mathematical analysis of ecological dynamics permits a process-based understanding of conditions under which systems approach equilibrium, experience large oscillations, or persist in transient states. This understanding is improved by combining ecological models with empirical observations from a variety of sources. Bayesian hierarchical models explicitly couple process-based models and data, yielding probabilistic quantification of model parameters, system characteristics, and associated uncertainties. We outline relevant tools from dynamical analysis and hierarchical modeling and argue for their integration, demonstrating the value of this synthetic approach through a simple predator–prey example.

了解生态过程并预测长期动态是生态学中的持续挑战。为了应对这些挑战，我们建议将数学分析和贝叶斯层次统计模型与各种数据源结合起来的方法。新颖的生态动力学数学分析使您可以基于过程来了解系统达到平衡，经历大的振荡或持续处于瞬态的条件。通过将生态模型与来自各种来源的经验观察相结合，可以提高这种理解。贝叶斯层次模型将基于过程的模型和数据明确地结合在一起，从而对模型参数，系统特性和相关的不确定性进行概率量化。