The construction of a network capturing the topological structure linked to the interactions among species and the analysis of its properties constitutes a clarifying way to understand the functioning of an ecosystem at different scales of analysis. Here, we present a novel systematic procedure to profit from the enhanced information derived from considering its multiple levels and apply it to analyse the presence of keystone species.

The proposed method presents a way to unveil the information stored in a network by comparing it to some randomised modification of itself. The randomising of the original network is done by swapping a controlled number of links while preserving the degree of the nodes. Then, we compare the modularity value of the original network with the randomised counterparts, which gives us a measure of the amount of relevant information stored in the first one. Once we have verified that the modularity value is meaningful, we use it to perform a community analysis and a characterisation of other topological properties in order to identify keystone species.

We applied this method to a pollinator–plant–herbivore trophic network as a case study and we found that (a) the comparison between the modularity of the original and the randomised networks is a suitable tool to detect relevant information; and (b) identifying keystone species yields different results in bipartite networks from the ones obtained in networks of more than two trophic levels. We also analysed the effect of eliminating selected species from the system on the cohesion of the network. The selection of these species was made according to the centralities values, such as degree and betweenness, of the corresponding nodes.

Our findings show that our analysis, mainly based on the measure of modularity is a reliable tool to characterise ecological networks. Additionally, we argue that since degree and betweenness are not always correlated, it is more reliable to measure both in an attempt to detect keystone species. The methodology proposed here to identify keystone species can be applied to other ecological networks currently available in the literature.

捕捉与物种之间相互作用相关的拓扑结构的网络的构建及其属性分析构成了在不同分析尺度上理解生态系统功能的清晰方式。在这里，我们提出了一种新的系统程序，以从考虑其多层次而获得的增强信息中获利，并将其应用于分析关键物种的存在。

所提出的方法提出了一种通过将存储在网络中的信息与自身的一些随机修改进行比较来揭示存储在网络中的信息的方法。原始网络的随机化是通过交换受控数量的链接同时保留节点的度数来完成的。然后，我们将原始网络的模块化值与随机对应物进行比较，这使我们可以衡量存储在第一个网络中的相关信息量。一旦我们验证了模块化值是有意义的，我们就使用它来执行群落分析和其他拓扑特性的表征，以识别关键物种。

我们将此方法应用于传粉媒介-植物-食草动物营养网络作为案例研究，我们发现（a）原始网络和随机网络的模块性之间的比较是检测相关信息的合适工具；(b) 识别关键物种在二分网络中产生的结果与在两个以上营养级别的网络中获得的结果不同。我们还分析了从系统中消除选定物种对网络凝聚力的影响。这些物种的选择是根据相应节点的中心性值（例如度和介数）进行的。

我们的研究结果表明，我们的分析主要基于模块化的度量，是表征生态网络的可靠工具。此外，我们认为，由于程度和介数并不总是相关的，因此在尝试检测关键物种时测量两者更为可靠。这里提出的识别关键物种的方法可以应用于文献中目前可用的其他生态网络。