Practical guidelines and the EMLN R package for handling ecological multilayer networks

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Noa Frydman, Shirly Freilikhman, Itamar Talpaz, Shai Pilosof 


Network analysis provides a powerful framework to study the complexity underlying the structure, dynamics, stability, and function of ecological systems. By now, analysing single-layered networks is a common practice with clear guidelines and well-established computational tools. Nevertheless, ecological communities are multilayered because they vary over space and time and contain multiple types of interactions. In recent years, the analysis of ecological multilayer networks (EMLNs) has allowed researchers to include such multilayered complexity. However, there is a paucity of practical guidelines and standardised tools to handle EMLN data, even before downstream analysis. In this article, we accomplish three objectives: (1) We provide practical guidelines for handling EMLN data. (2) We developed the EMLN R package to standardise the workflow of creating, storing, and working with EMLN objects. The package also enables conversion between multiple formats for downstream analysis with other standard packages. (3) We provide EMLN data sets for research and training purposes. A dedicated website with explanations, detailed examples, and code accompanies our paper. This website is a gateway for novice and experienced network ecologists who want to include EMLNs in their research. By simplifying the analysis of multilayer networks and promoting standardised approaches we facilitate the analysis of EMLNs. This paves the road towards gaining deeper insights into functioning and resilience of natural ecosystems.



Ecology and Evolutionary Biology, Life Sciences


data structures, network analysis, network ecology, network science, open source, workflow


Published: 2023-07-25 04:29

Last Updated: 2023-11-21 10:08

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CC-By Attribution-NonCommercial-NoDerivatives 4.0 International

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Conflict of interest statement:
No conflict of interest

Data and Code Availability Statement:
The R package published with this paper uses already published data. Each data set included in the package is referenced to its original paper and data sources. Details: