Carriger John F, Fisher William S
U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Solutions and Emergency Response, Cincinnati, OH 45268, USA.
U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modeling, Gulf Breeze, FL 32561, USA.
Ecol Inform. 2024 Jun 5;82:102665. doi: 10.1016/j.ecoinf.2024.102665.
Most coral reef studies focus on scleractinian (stony) corals to indicate reef condition, but there are other prominent assemblages that play a role in ecosystem structure and function. In Puerto Rico these include fish, gorgonians, and sponges. The U.S. Environmental Protection Agency conducted unique surveys of coral reef communities across the southern coast of Puerto Rico that included simultaneous measurement of all four assemblages. Evaluating the results from a community perspective demands endpoints for all four assemblages, so patterns of community structure were explored by probabilistic clustering of measured variables with Bayesian networks. Most variables were found to have stronger associations within than between taxa, but unsupervised structure learning identified three cross-taxa relationships with potential ecological significance. Clusters for each assemblage were constructed using an expectation-maximization algorithm that created a factor node jointly characterizing the density, size, and diversity of individuals in each taxon. The clusters were characterized by the measured variables, and relationships to variables for other taxa were examined, such as stony coral clusters with fish variables. Each of the factor nodes were then used to create a set of meta-factor clusters that further summarized the aggregate monitoring variables for the four taxa. Once identified, taxon-specific and meta-clusters represent patterns of community structure that can be examined on a regional or site-specific basis to better understand risk assessment, risk management and delivery of ecosystem services.
大多数珊瑚礁研究聚焦于石珊瑚来指示珊瑚礁状况，但还有其他重要的生物群落对生态系统结构和功能也发挥着作用。在波多黎各，这些生物群落包括鱼类、柳珊瑚和海绵。美国环境保护局对波多黎各南部海岸的珊瑚礁群落进行了独特的调查，其中包括对所有这四类生物群落同时进行测量。从群落角度评估结果需要所有四类生物群落的终点指标，因此通过使用贝叶斯网络对测量变量进行概率聚类来探索群落结构模式。结果发现，大多数变量在分类单元内部的关联性比在分类单元之间更强，但无监督结构学习识别出了三种具有潜在生态意义的跨分类单元关系。使用期望最大化算法为每个生物群落构建聚类，该算法创建了一个因子节点，共同表征每个分类单元中个体的密度、大小和多样性。这些聚类由测量变量表征，并研究了与其他分类单元变量的关系，例如石珊瑚聚类与鱼类变量的关系。然后，每个因子节点被用于创建一组元因子聚类，进一步汇总这四类生物分类单元的总体监测变量。一旦确定，特定分类单元聚类和元聚类就代表了群落结构模式，可在区域或特定地点基础上进行研究，以更好地理解风险评估、风险管理和生态系统服务的提供。
