School of Biological and Chemical Sciences, Queen Mary University of London, London, UK.
Centre for Biodiversity Genomics, University of Guelph, Guelph, Ontario, Canada.
Mol Ecol. 2019 Jan;28(2):503-519. doi: 10.1111/mec.14941. Epub 2018 Dec 10.
As molecular tools for assessing trophic interactions become common, research is increasingly focused on the construction of interaction networks. Here, we demonstrate three key methods for incorporating DNA data into network ecology and discuss analytical considerations using a model consisting of plants, insects, bats and their parasites from the Costa Rica dry forest. The simplest method involves the use of Sanger sequencing to acquire long sequences to validate or refine field identifications, for example of bats and their parasites, where one specimen yields one sequence and one identification. This method can be fully quantified and resolved and these data resemble traditional ecological networks. For more complex taxonomic identifications, we target multiple DNA loci, for example from a seed or fruit pulp sample in faeces. These networks are also well resolved but gene targets vary in resolution and quantification is difficult. Finally, for mixed templates such as faecal contents of insectivorous bats, we use DNA metabarcoding targeting two sequence lengths (157 and 407 bp) of one gene region and a MOTU, BLAST and BIN association approach to resolve nodes. This network type is complex to generate and analyse, and we discuss the implications of this type of resolution on network analysis. Using these data, we construct the first molecular-based network of networks containing 3,304 interactions between 762 nodes of eight trophic functions and involving parasitic, mutualistic and predatory interactions. We provide a comparison of the relative strengths and weaknesses of these data types in network ecology.
随着评估营养相互作用的分子工具变得越来越普遍，研究越来越集中于构建相互作用网络。在这里，我们展示了将 DNA 数据纳入网络生态学的三种关键方法，并使用由哥斯达黎加干燥森林中的植物、昆虫、蝙蝠及其寄生虫组成的模型讨论了分析注意事项。最简单的方法是使用 Sanger 测序获取长序列来验证或细化现场鉴定，例如蝙蝠及其寄生虫，一个标本产生一个序列和一个鉴定。这种方法可以完全量化和解决，并且这些数据类似于传统的生态网络。对于更复杂的分类鉴定，我们针对多个 DNA 基因座，例如粪便中种子或果肉样本。这些网络也很好地解决了，但基因靶标分辨率不同，定量很困难。最后，对于食虫蝙蝠等混合模板的粪便内容物，我们使用靶向一个基因区域的两个序列长度（157 和 407bp）和 MOTU、BLAST 和 BIN 关联方法的 DNA 宏条形码技术来解决节点。这种网络类型生成和分析都很复杂，我们讨论了这种分辨率对网络分析的影响。使用这些数据，我们构建了第一个包含 3304 个相互作用和 762 个 8 个营养功能节点的基于分子的网络，涉及寄生、互利和捕食相互作用。我们比较了这些数据类型在网络生态学中的相对优缺点。
