School of Biology, University of St Andrews, St Andrews, Fife KY16 9TH, United Kingdom.
Ecology. 2010 Jul;91(7):1892-9. doi: 10.1890/09-0731.1.
Understanding functional relationships within ecological networks can help reveal keys to ecosystem stability or fragility. Revealing these relationships is complicated by the difficulties of isolating variables or performing experimental manipulations within a natural ecosystem, and thus inferences are often made by matching models to observational data. Such models, however, require assumptions-or detailed measurements-of parameters such as birth and death rate, encounter frequency, territorial exclusion, and predation success. Here, we evaluate the use of a Bayesian network inference algorithm, which can reveal ecological networks based upon species and habitat abundance alone. We test the algorithm's performance and applicability on observational data of avian communities and habitat in the Peak District National Park, United Kingdom. The resulting networks correctly reveal known relationships among habitat types and known interspecific relationships. In addition, the networks produced novel insights into ecosystem structure and identified key species with high connectivity. Thus, Bayesian networks show potential for becoming a valuable tool in ecosystem analysis.
理解生态网络中的功能关系有助于揭示生态系统稳定性或脆弱性的关键。然而，由于在自然生态系统中难以隔离变量或进行实验操作，因此通常通过将模型与观测数据进行匹配来得出这些关系的推论。然而，这些模型需要对诸如出生率和死亡率、遭遇频率、领地排斥和捕食成功率等参数进行假设或详细测量。在这里，我们评估了一种贝叶斯网络推理算法的使用，该算法仅基于物种和栖息地丰度就可以揭示生态网络。我们在英国峰区国家公园的鸟类群落和栖息地观测数据上测试了该算法的性能和适用性。得到的网络正确地揭示了已知的栖息地类型之间的关系以及已知的种间关系。此外，这些网络还为生态系统结构提供了新的见解，并确定了具有高连通性的关键物种。因此，贝叶斯网络在生态系统分析中具有成为一种有价值的工具的潜力。
