School of Biological and Chemical Sciences, Queen Mary University of London, Mile End Road, London, E1 4NS, UK.
Department of Zoology, Zoology Research and Administration Building, University of Oxford, 11a Mansfield Road, Oxford, OX1 3SZ, UK.
Ecology. 2021 Mar;102(3):e03256. doi: 10.1002/ecy.3256. Epub 2021 Feb 2.
Constructing ecological networks has become an indispensable approach in understanding how different taxa interact. However, the methods used to generate data in network research vary widely among studies, potentially limiting our ability to compare results meaningfully. In particular, methods of classifying nodes vary in their precision, likely altering the architecture of the network studied. For example, rather than being classified as Linnaean species, taxa are regularly assigned to morphospecies in observational studies, or to molecular operational taxonomic units (MOTUs) in molecular studies, with the latter defined based on an arbitrary threshold of sequence similarity. Although the use of MOTUs in ecological networks holds great potential, especially for allowing rapid construction of large data sets of interactions, it is unclear how the choice of clustering threshold can influence the conclusions obtained. To test the impact of taxonomic precision on network architecture, we obtained and analyzed 16 data sets of ecological interactions, inferred from metabarcoding and observations. Our comparisons of networks constructed under a range of sequence thresholds for assigning taxa demonstrate that even small changes in node resolution can cause wide variation in almost all key metric values. Moreover, relative values of commonly used metrics such as robustness were seen to fluctuate continuously with node resolution, thereby potentially causing error in conclusions drawn when comparing multiple networks. In observational networks, we found that changing node resolution could, in some cases, lead to substantial changes to measurements of network topology. Overall, our findings highlight the importance of classifying nodes to the greatest precision possible, and demonstrate the need for caution when comparing networks that differ with respect to node resolution, even where taxonomic groups and interaction types are similar. In such cases, we recommend that comparisons of networks should focus on relative differences rather than absolute values between the networks studied.
构建生态网络已成为理解不同分类群如何相互作用的不可或缺的方法。然而，网络研究中用于生成数据的方法在不同的研究中差异很大，这可能限制了我们有意义地比较结果的能力。特别是，节点分类方法在精度上存在差异，这可能改变所研究网络的结构。例如，在观察性研究中，分类单元通常被归类为形态种，而在分子研究中则被归类为分子操作分类单元（MOTU），后者是根据序列相似性的任意阈值定义的。虽然生态网络中 MOTU 的使用具有很大的潜力，特别是可以快速构建大量相互作用数据集，但选择聚类阈值如何影响得出的结论尚不清楚。为了测试分类精度对网络结构的影响，我们获得并分析了 16 个基于宏条形码和观察推断的生态相互作用数据集。我们比较了在为分类单元分配序列阈值的一系列范围内构建的网络，结果表明，即使节点分辨率的微小变化也会导致几乎所有关键度量值的广泛变化。此外，常用度量标准（如稳健性）的相对值被发现随着节点分辨率连续波动，从而在比较多个网络时可能导致结论错误。在观察性网络中，我们发现改变节点分辨率有时会导致网络拓扑结构测量值发生重大变化。总的来说，我们的研究结果强调了尽可能精确地对节点进行分类的重要性，并表明在比较节点分辨率不同的网络时需要谨慎，即使分类群和相互作用类型相似也是如此。在这种情况下，我们建议网络比较应侧重于所研究网络之间的相对差异，而不是绝对值之间的差异。
