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dc.contributor.author
Pascual Garcia, Alberto
dc.contributor.author
Bell, Thomas
dc.date.accessioned
2020-07-08T09:28:32Z
dc.date.available
2020-04-15T11:07:19Z
dc.date.available
2020-04-17T07:41:47Z
dc.date.available
2020-07-08T09:28:32Z
dc.date.issued
2020-07
dc.identifier.issn
2041-210X
dc.identifier.issn
2041-2096
dc.identifier.other
10.1111/2041-210X.13377
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/409791
dc.description.abstract
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.
en_US
dc.language.iso
en
en_US
dc.publisher
Wiley
en_US
dc.subject
Community detection
en_US
dc.subject
Functional groups
en_US
dc.subject
Guilds
en_US
dc.subject
Microbial networks
en_US
dc.subject
Modules
en_US
dc.subject
Multiplex networks
en_US
dc.subject
Mutualistic networks
en_US
dc.subject
Trophic networks
en_US
dc.title
functionInk: An efficient method to detect functional groups in multidimensional networks reveals the hidden structure of ecological communities
en_US
dc.type
Journal Article
dc.date.published
2020-02-14
ethz.journal.title
Methods in Ecology and Evolution
ethz.journal.volume
11
en_US
ethz.journal.issue
7
en_US
ethz.journal.abbreviated
Methods Ecol. Evol.
ethz.pages.start
804
en_US
ethz.pages.end
817
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Oxford
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02350 - Dep. Umweltsystemwissenschaften / Dep. of Environmental Systems Science::02720 - Institut für Integrative Biologie / Institute of Integrative Biology::03584 - Bonhoeffer, Sebastian / Bonhoeffer, Sebastian
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02350 - Dep. Umweltsystemwissenschaften / Dep. of Environmental Systems Science::02720 - Institut für Integrative Biologie / Institute of Integrative Biology::03584 - Bonhoeffer, Sebastian / Bonhoeffer, Sebastian
ethz.date.deposited
2020-04-15T11:07:24Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2020-07-08T09:28:44Z
ethz.rosetta.lastUpdated
2021-02-15T15:21:19Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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