Predicting the response of biological communities to changes in the environment or management is a fundamental pursuit of community ecology. Meeting this challenge requires the integration of multiple processes: habitat filtering, niche differentiation, biotic interactions, competitive exclusion, and stochastic demographic events. Most approaches to this long-standing problem focus either on the role of the environment, using trait-based filtering approaches, or on quantifying biotic interactions with process-based community dynamics models. We introduce a novel approach that uses functional traits to parametrise a process-based model. By combining the two approaches we make use of the extensive literature on traits and community filtering as a convenient means of reducing the parametrisation requirements of a complex population dynamics model whilst retaining the power to capture the processes underlying community assembly. Using arable weed communities as a case study, we demonstrate that this approach results in predictions that show realistic distributions of traits and that trait selection predicted by our simulations is consistent with in-field observations. We demonstrate that trait-based filtering approaches can be combined with process-based models to derive the emergent distribution of traits. While initially developed to predict the impact of crop management on functional shifts in weed communities, our approach has the potential to be applied to other annual plant communities if the generality of relationships between traits and model parameters can be confirmed.

中文翻译：

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使用功能性状模拟一年生植物群落动态

预测生物群落对环境或管理变化的反应是群落生态学的基本追求。应对这一挑战需要整合多个过程：栖息地过滤、生态位分化、生物相互作用、竞争排斥和随机人口统计事件。解决这个长期存在的问题的大多数方法要么关注环境的作用，使用基于特征的过滤方法，要么关注使用基于过程的社区动态模型量化生物相互作用。我们引入了一种新方法，该方法使用功能特征来参数化基于过程的模型。通过结合这两种方法，我们利用关于特征和群落过滤的大量文献作为减少复杂种群动态模型参数化要求的便捷手段，同时保留捕获群落组装过程的能力。以耕地杂草群落作为案例研究，我们证明这种方法的预测结果显示了真实的性状分布，并且我们的模拟预测的性状选择与田间观察一致。我们证明了基于特征的过滤方法可以与基于过程的模型相结合，以推导出特征的紧急分布。虽然最初开发是为了预测作物管理对杂草群落功能转变的影响，