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functionInk: An efficient method to detect functional groups in multidimensional networks reveals the hidden structure of ecological communities
Methods in Ecology and Evolution ( IF 6.6 ) Pub Date : 2020-04-02 , DOI: 10.1111/2041-210x.13377
Alberto Pascual‐García 1 , Thomas Bell 1
Affiliation  

  1. Complex networks have been useful to link experimental data with mechanistic models, and have become widely used across many scientific disciplines. Recently, the increasing amount and complexity of data, particularly in biology, has prompted the development of multidimensional networks, where dimensions reflect the multiple qualitative properties of nodes, links or both. As a consequence, traditional quantities computed in single dimensional networks should be adapted to incorporate this new information. A particularly important problem is the detection of communities, namely sets of nodes sharing certain properties, which reduces the complexity of the networks, hence facilitating its interpretation.
  2. In this work, we propose an operative definition of ‘function’ for the nodes in multidimensional networks. We exploit this definition to show that it is possible to detect two types of communities: (a) modules, which are communities more densely connected within their members than with nodes belonging to other communities, and (b) guilds, which are sets of nodes connected with the same neighbours, even if they are not connected themselves. We provide two quantities to optimally detect both types of communities, whose relative values reflect their importance in the network.
  3. The flexibility of the method allowed us to analyse different ecological examples encompassing mutualistic, trophic and microbial networks. We showed that by considering both metrics we were able to obtain deeper ecological insights about how these different ecological communities were structured. The method mapped pools of species with properties that were known in advance, such as plants and pollinators. Other types of communities found, when contrasted with external data, turned out to be ecologically meaningful, allowing us to identify species with important functional roles or the influence of environmental variables. Furthermore, we found that the method was sensitive to community‐level topological properties like nestedness.
  4. In ecology there is often a need to identify groupings including trophic levels, guilds, functional groups or ecotypes. The method is therefore important in providing an objective means of distinguishing modules and guilds. The method we developed, functionInk (functional linkage), is computationally efficient at handling large multidimensional networks since it does not require optimization procedures or tests of robustness. The method is available at: https://github.com/apascualgarcia/functionInk.


中文翻译:

functionInk:一种检测多维网络中功能组的有效方法,揭示了生态群落的隐藏结构

  1. 复杂的网络对于将实验数据与力学模型联系起来非常有用,并且已在许多科学学科中得到广泛使用。最近,数据量的增加和复杂性的增加,尤其是生物学方面的数据的发展,促使多维网络的发展,其中维度反映了节点,链接或两者的多重定性特性。结果,在单维网络中计算的传统数量应被调整以合并该新信息。一个特别重要的问题是社区(即共享某些属性的节点集)的检测,这降低了网络的复杂性,因此有助于其解释。
  2. 在这项工作中,我们提出了多维网络中节点的“功能”的有效定义。我们利用这个定义来表明可以检测两种类型的社区:(a)模块,即与成员所属社区的节点相比,其社区内部与社区的联系更加紧密的社区;以及(b)公会,它们是节点集与同一邻居连接,即使他们自己没有连接。我们提供两个量来最佳地检测两种类型的社区,它们的相对值反映了它们在网络中的重要性。
  3. 该方法的灵活性使我们能够分析包括互惠,营养和微生物网络的不同生态实例。我们表明,通过同时考虑这两个指标,我们能够获得关于这些不同生态群落的结构的更深刻的生态见解。该方法绘制了具有事先已知属性的物种库,例如植物和传粉媒介。与外部数据相比,发现的其他类型的社区却具有生态学意义,使我们能够识别具有重要功能作用或环境变量影响的物种。此外,我们发现该方法对诸如嵌套之类的社区级拓扑属性敏感。
  4. 在生态学中,通常需要确定包括营养水平,行会,功能性群体或生态型在内的分组。因此,该方法对于提供区分模块和行会的客观手段很重要。我们开发的方法functionInk(功能链接)在处理大型多维网络方面具有很高的计算效率,因为它不需要优化过程或稳健性测试。该方法可从以下网址获得:https://github.com/apascualgarcia/functionInk。
更新日期:2020-04-02
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