Population dynamics can be highly variable in the face of environmental heterogeneity, and understanding this variation is central in the study of ecology. Robust management decisions require that we understand how populations respond to management at a range of scales, and under a broad suite of conditions. Population models are potentially valuable tools in addressing this challenge. However, without adequate data, models can fail to produce useful results. Populations of arable weeds are particularly problematic in this respect, as they are widespread and their dynamics are extremely variable. Owing to the inherent cost of collecting data, most studies of plant population dynamics are derived from localized experiments under a small range of environmental conditions, limiting the extent to which variance in population dynamics can be measured. Density-structured models provide a route to rapid, large-scale analysis of population dynamics, and can expand the scale of ecological models that are directly tied to data. Here we extend previous density-structured models to include environmental heterogeneity, variation in management, and to account for inter-population variation. We develop, parameterize, and test hierarchical density-structured models for a common agricultural weed, black-grass (Alopecurus myosuroides). We model the dynamics of this species in response to crop management, using survey data gathered over 4 yr from 364 fields across a network of 45 UK farms. We show that hierarchical density-structured models provide a substantial improvement over their nonhierarchical counterparts. Using these models, we demonstrate that several alternative crop rotations are effective in reducing weed densities. Rotations with high wheat prevalence exhibit the most severe infestations, and diverse rotations generally have lower weed densities. However, a key outcome is that in many cases the effect of crop rotation is small compared to the high variability arising from spatiotemporal heterogeneity. This result highlights the need to monitor and model population dynamics across large spatial and temporal scales in order to account for variation in the drivers of plant dynamics. Our framework for data collection and modeling provides a means to achieve this.

中文翻译：

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开发层次密度结构模型来研究可耕地杂草的全国性动态

面对环境异质性，人口动态变化很大，了解这种变化是生态学研究的核心。稳健的管理决策要求我们了解种群如何在各种规模和广泛的条件下对管理做出反应。人口模型是解决这一挑战的潜在有价值的工具。但是，如果没有足够的数据，模型可能无法产生有用的结果。耕地杂草的数量在这方面尤其成问题，因为它们分布广泛且动态变化极大。由于收集数据的内在成本，大多数植物种群动态研究来自小范围环境条件下的局部实验，限制了可以测量种群动态变化的程度。密度结构模型为快速、大规模地分析种群动态提供了一条途径，并且可以扩大与数据直接相关的生态模型的规模。在这里，我们扩展了以前的密度结构模型，以包括环境异质性、管理变化和人口间变化。我们为一种常见的农业杂草黑草开发、参数化和测试分层密度结构模型（看麦娘）。我们使用从 45 个英国农场网络中的 364 个田地收集超过 4 年的调查数据，模拟该物种响应作物管理的动态。我们表明，分层密度结构模型比非分层模型提供了实质性的改进。使用这些模型，我们证明了几种替代作物轮作可有效降低杂草密度。小麦流行率高的轮作表现出最严重的侵染，不同的轮作通常杂草密度较低。然而，一个关键的结果是，在许多情况下，与时空异质性引起的高变异性相比，轮作的影响很小。这一结果凸显了在大空间和时间尺度上监测和模拟种群动态的必要性，以解释植物动态驱动因素的变化。我们的数据收集和建模框架提供了实现这一目标的方法。