BACKGROUND Accurate network models of species interaction could be used to predict population dynamics and be applied to manage real world ecosystems. Most relevant models are nonlinear, however, and data available from real world ecosystems are too noisy and sparsely sampled for common inference approaches. Here we improved the inference of generalized Lotka-Volterra (gLV) ecological networks by using a new optimization algorithm to constrain parameter signs with prior knowledge and a perturbation-based ensemble method. RESULTS We applied the new inference to long-term species abundance data from the freshwater fish community in the Illinois River, United States. We constructed an ensemble of 668 gLV models that explained 79% of the data on average. The models indicated (at a 70% level of confidence) a strong positive interaction from emerald shiner (Notropis atherinoides) to channel catfish (Ictalurus punctatus), which we could validate using data from a nearby observation site, and predicted that the relative abundances of most fish species will continue to fluctuate temporally and concordantly in the near future. The network shows that the invasive silver carp (Hypophthalmichthys molitrix) has much stronger impacts on native predators than on prey, supporting the notion that the invader perturbs the native food chain by replacing the diets of predators. CONCLUSIONS Ensemble approaches constrained by prior knowledge can improve inference and produce networks from noisy and sparsely sampled time series data to fill knowledge gaps on real world ecosystems. Such network models could aid efforts to conserve ecosystems such as the Illinois River, which is threatened by the invasion of the silver carp.

中文翻译：

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通过参数化受先验知识约束的群体动力学模型的集合，增强对生态网络的推断。

背景技术物种相互作用的精确网络模型可用于预测种群动态并被用于管理现实世界的生态系统。但是，最相关的模型是非线性的，并且对于常见的推理方法，现实世界中的生态系统中可用的数据过于嘈杂且采样稀疏。在这里，我们通过使用新的优化算法以先验知识约束参数符号和基于扰动的集成方法，改进了广义Lotka-Volterra（gLV）生态网络的推理。结果我们将新的推论应用于来自美国伊利诺伊河淡水鱼群落的长期物种丰度数据。我们构建了668个gLV模型的集合，该模型平均可以解释79％的数据。这些模型显示（在置信度为70％的情况下）从翡翠光泽（Notropis atherinoides）到channel鱼（Ictalurus punctatus）有很强的正相互作用，我们可以使用附近观察点的数据进行验证，并预测在不久的将来，大多数鱼类将继续在时间上和协调地波动。该网络表明，入侵silver鱼（Hypophthalmichthys molitrix）对本地捕食者的影响比对猎物的影响要大得多，这表明入侵者通过取代捕食者的饮食而扰乱了本地食物链。结论受先验知识约束的集成方法可以改善推理并从嘈杂和稀疏采样的时间序列数据中生成网络，以填补现实世界生态系统上的知识空白。