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首页> 外文期刊>BMC Ecology >Enhanced inference of ecological networks by parameterizing ensembles of population dynamics models constrained with prior knowledge
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Enhanced inference of ecological networks by parameterizing ensembles of population dynamics models constrained with prior knowledge

机译：通过参数化与先前知识的人口动态模型的参数增强了生态网络的推理

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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. 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. 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.
机译：可以使用精确的网络型号的物种交互模型来预测人口动态，并应用于管理现实世界生态系统。然而，大多数相关模型是非线性的，然而，来自现实世界生态系统可获得的数据过于嘈杂，并略微对共同推理方法进行采样。在这里，我们通过使用新的优化算法来限制具有先前知识和基于扰动的集合方法的参数标志来改进广义Lotka-Volterra（GLV）生态网络的推断。我们将新推论从美国伊利诺伊州伊利诺伊州河流淡水鱼界的长期物种丰富数据应用于美国。我们构建了一个668的集合，平均解释了79％的数据。所示模型（70％的置信度）从祖母绿鼻耳（Notropis atherinoides）到通道鲶鱼（Ictalurus punctatus）的强阳性相互作用，我们可以使用附近观察网站的数据进行验证，并预测相对丰富大多数鱼类将继续在不久的将来逐时地波动，并在不久的将来一定地波举。该网络表明，侵入性银鲤（次噬藻米什莫里斯）对本地捕食者的影响力大得多，而不是在猎物上，支持侵略者通过替代捕食者的饮食来扰乱本地食物链的观念。由先前知识约束的集合方法可以改善从嘈杂和稀疏采样的时间序列数据的推理和生产网络，以填补现实世界生态系统的知识差距。这种网络模型可以帮助努力保护伊利诺伊河等生态系统，这受到了银鲤的入侵威胁。

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