Indirect effects drive coevolution in mutualistic networks

Guimarães, Paulo R.; Pires, Mathias M.; Jordano, Pedro; Bascompte, Jordi; Thompson, John N.

doi:10.1038/nature24273

Cited by 251 publications

(347 citation statements)

References 29 publications

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“…The features we identified as most predictive for microbial biodiversity differed greatly between Eukaryotes and Prokaryotes, supporting the notion that microorganisms from these domains have different ecological roles [21, 24, 27, 28]. In contrast to this, the most predictive features for the different alpha diversity indices calculated from Prokaryotic sequences show a high degree of similarity.…”

Section: Discussionsupporting

confidence: 69%

SEDE-GPS: socio-economic data enrichment based on GPS information

Sperlea¹,

Füser²,

Boenigk³

et al. 2018

BMC Bioinformatics

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BackgroundMicrobes are essentail components of all ecosystems because they drive many biochemical processes and act as primary producers. In freshwater ecosystems, the biodiversity in and the composition of microbial communities can be used as indicators for environmental quality. Recently, some environmental features have been identified that influence microbial ecosystems. However, the impact of human action on lake microbiomes is not well understood. This is, in part, due to the fact that environmental data is, albeit theoretically accessible, not easily available.ResultsIn this work, we present SEDE-GPS, a tool that gathers data that are relevant to the environment of an user-provided GPS coordinate. To this end, it accesses a list of public and corporate databases and aggregates the information in a single file, which can be used for further analysis. To showcase the use of SEDE-GPS, we enriched a lake microbial ecology sequencing dataset with around 18,000 socio-economic, climate, and geographic features. The sources of SEDE-GPS are public databases such as Eurostat, the Climate Data Center, and OpenStreetMap, as well as corporate sources such as Twitter. Using machine learning and feature selection methods, we were able to identify features in the data provided by SEDE-GPS that can be used to predict lake microbiome alpha diversity.ConclusionThe results presented in this study show that SEDE-GPS is a handy and easy-to-use tool for comprehensive data enrichment for studies of ecology and other processes that are affected by environmental features. Furthermore, we present lists of environmental, socio-economic, and climate features that are predictive for microbial biodiversity in lake ecosystems. These lists indicate that human action has a major impact on lake microbiomes. SEDE-GPS and its source code is available for download at http://SEDE-GPS.heiderlab.deElectronic supplementary materialThe online version of this article (10.1186/s12859-018-2419-4) contains supplementary material, which is available to authorized users.

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Section: Discussionsupporting

confidence: 69%

SEDE-GPS: socio-economic data enrichment based on GPS information

Sperlea¹,

Füser²,

Boenigk³

et al. 2018

BMC Bioinformatics

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“…Rather, they enter complex webs, or networks, of interactions (Thompson, ). The structure of these networks is expected to drive co‐evolution (Guimarães, Pires, Jordano, Bascompte, & Thompson, ), populations’ stability (May, ; Melian & Bascompte, ; Pimm, ), species coexistence (Bastolla et al, ) and community productivity (Poisot, Mouquet, & Gravel, ). A major frontier in community ecology is thus to elucidate the drivers of the complex and repeatable patterns, or motifs, that we observe in ecological networks.…”

Section: Introductionmentioning

confidence: 99%

Species diversification and phylogenetically constrained symbiont switching generated high modularity in the lichen genus Peltigera

2019

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Ecological interactions range from purely specialized to extremely generalized in nature. Recent research has showed very high levels of specialization in the cyanolichens involving Peltigera (mycobionts) and their Nostoc photosynthetic partners (cyanobionts). Yet, little is known about the mechanisms contributing to the establishment and maintenance of such high specialization levels. Here, we characterized interactions between Peltigera and Nostoc partners at a global scale, using more than one thousand thalli. We used tools from network theory, community phylogenetics and biogeographical history reconstruction to evaluate how these symbiotic interactions may have evolved. After splitting the interaction matrix into modules of preferentially interacting partners, we evaluated how module membership might have evolved along the mycobionts’ phylogeny. We also teased apart the contributions of geographical overlap vs phylogeny in driving interaction establishment between Peltigera and Nostoc taxa. Module affiliation rarely evolves through the splitting of large ancestral modules. Instead, new modules appear to emerge independently, which is often associated with a fungal speciation event. We also found strong phylogenetic signal in these interactions, which suggests that partner switching is constrained by conserved traits. Therefore, it seems that a high rate of fungal diversification following a switch to a new cyanobiont can lead to the formation of large modules, with cyanobionts associating with multiple closely retated Peltigera species. Finally, when restricting our analyses to Peltigera sister species, the latter differed more through partner acquisition/loss than replacement (i.e., switching). This pattern vanishes as we look at sister species that have diverged longer ago. This suggests that fungal speciation may be accompanied by a stepwise process of (a) novel partner acquisition and (b) loss of the ancestral partner. This could explain the maintenance of high specialization levels in this symbiotic system where the transmission of the cyanobiont to the next generation is assumed to be predominantly horizontal. Synthesis. Overall, our study suggests that oscillation between generalization and ancestral partner loss may maintain high specialization within the lichen genus Peltigera, and that partner selection is not only driven by partners’ geographical overlap, but also by their phylogenetically conserved traits.

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“…The main limitation of the IHS model is that it only simulates the evolution of consumer species. In nature, consumption is likely to be a selective force that also drives resource species evolution (Thompson 1994, Guimarães et al 2017. We must admit that this simplification strongly reduces the realism of our model, especially when consumption has a strong effect on resource species fitness.…”

Section: Limitations Of the Modelmentioning

confidence: 99%

A new model explaining the origin of different topologies in interaction networks

Pinheiro

Félix

Dormann

et al. 2019

Ecology

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Nestedness and modularity have been recurrently observed in species interaction networks. Some studies argue that those topologies result from selection against unstable networks, and others propose that they likely emerge from processes driving the interactions between pairs of species. Here we present a model that simulates the evolution of consumer species using resource species following simple rules derived from the integrative hypothesis of specialization (IHS). Without any selection on stability, our model reproduced all commonly observed network topologies. Our simulations demonstrate that resource heterogeneity drives network topology. On the one hand, systems containing only homogeneous resources form generalized nested networks, in which generalist consumers have higher performance on each resource than specialists. On the other hand, heterogeneous systems tend to have a compound topology: modular with internally nested modules, in which generalists that divide their interactions between modules have low performance. Our results demonstrate that all real‐world topologies likely emerge through processes driving interactions between pairs of species. Additionally, our simulations suggest that networks containing similar species differ from heterogeneous networks and that modules may not present the topology of entire networks.

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Indirect effects drive coevolution in mutualistic networks

Cited by 251 publications

References 29 publications

SEDE-GPS: socio-economic data enrichment based on GPS information

SEDE-GPS: socio-economic data enrichment based on GPS information

Species diversification and phylogenetically constrained symbiont switching generated high modularity in the lichen genus Peltigera

A new model explaining the origin of different topologies in interaction networks

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