Revealing ecological networks using Bayesian network inference algorithms
Revealing ecological networks using Bayesian network inference algorithms
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DOI：
10.1890/09-0731.1
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发表时间：
2010-07-01
期刊：
影响因子：
4.8
通讯作者：
Smith, V. Anne
中科院分区：
文献类型：
--
作者：
Milns, Isobel;Beale, Colin M.;Smith, V. Anne
了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。由于在自然生态系统中分离变量或进行实验操作的困难，揭示这些关系变得复杂，因此通常通过将模型与观测数据相匹配来做出推断。然而，这样的模型需要假设或详细测量参数，如出生和死亡率、遭遇频率、领土排斥和捕食成功。在这里，我们评估了贝叶斯网络推理算法的使用，该算法可以仅根据物种和栖息地的丰富度来揭示生态网络。我们在英国山顶地区国家公园的鸟类群落和栖息地的观测数据上测试了算法的性能和适用性。由此产生的网络正确地揭示了栖息地类型之间的已知关系和已知的种间关系。此外，这些网络对生态系统结构产生了新的见解，并确定了具有高度连通性的关键物种。因此，贝叶斯网络显示出成为生态系统分析中有价值的工具的潜力。
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.