Identifying a common backbone of interactions underlying food webs from different ecosystems

Mora, Bernat Bramon; Gravel, Dominique; Gilarranz, Luis J.; Poisot, Timothée; Stouffer, Daniel B.

doi:10.1038/s41467-018-05056-0

Cited by 38 publications

(29 citation statements)

References 68 publications

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“…Third, we defined species’ positions purely based on the structure of plant-pollinator communities. Nevertheless, these positions could easily also account for other species’ properties such as species’ ecological traits and evolutionary histories 29 . This would allow us, for example, to study network dynamics from a functional diversity or evolutionary perspective, potentially untangling the eco-evolutionary mechanisms governing complex community dynamics.…”

Section: Discussionmentioning

confidence: 99%

“…To analyse the dynamics of these network time series, we used the alignment technique introduced by Bramon Mora et al 29 . This technique provides us with a way to map two ecological networks on top of each other.…”

Section: Methodsmentioning

confidence: 99%

“…We employ an approach here to study the complete seasonal dynamics of plant-pollinator communities using the technique of network alignment 29 . Conceptually, aligning any two ecological networks proceeds by pairing up the species that play similar structural roles in each community 21 , 30 , 31 .…”

Section: Introductionmentioning

confidence: 99%

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Untangling the seasonal dynamics of plant-pollinator communities

et al. 2020

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Ecological communities often show changes in populations and their interactions over time. To date, however, it has been challenging to effectively untangle the mechanisms shaping such dynamics. One approach that has yet to be fully explored is to treat the varying structure of empirical communities-i.e. their network of interactions-as time series. Here, we follow this approach by applying a network-comparison technique to study the seasonal dynamics of plant-pollinator networks. We find that the structure of these networks is extremely variable, where species constantly change how they interact with each other within seasons. Most importantly, we find the holistic dynamic of plants and pollinators to be remarkably coherent across years, allowing us to reveal general rules by which species first enter, then change their roles, and finally leave the networks. Overall, our results disentangle key aspects of species' interaction turnover, phenology, and seasonal assembly/disassembly processes in empirical plant-pollinator communities.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Untangling the seasonal dynamics of plant-pollinator communities

et al. 2020

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“…Examining how species’ functional guild influences species’ roles could provide incredibly useful in predicting local realizations of regional food webs especially if ecological networks truly have a common structural backbone (Bramon Mora et al. ).…”

Section: Discussionmentioning

confidence: 99%

Effects of species traits, motif profiles, and environment on spatial variation in multi‐trophic antagonistic networks

2020

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Understanding drivers of antagonistic interactions across temporal and spatial scales is important for predicting community structure. In particular, studies examining spatial variation in ecological networks are critical for anticipating community responses to anthropogenic change. Most studies examining spatial interaction turnover focus on bipartite networks begging the question of whether the results are also reflected in unipartite, multi‐trophic networks. To examine the spatial turnover in food web interactions, the environmental and ecological drivers of this, and the influence of interaction turnover on the preservation of individual species’ roles, we used a spatially expansive multi‐trophic antagonistic ecological network data set of 129 lakes spanning over 1000 kms. We used β‐diversity metrics to quantify spatial turnover in interactions and calculated the relative contributions of interaction rewiring and turnover in top, intermediate, and basal species to network turnover. We then investigated the relative and combined role of multiple ecological drivers (e.g., abundance, thermal tolerance) and environmental drivers (e.g., latitude, total phosphorus) on internal network structure. Finally, we used a motif analysis to measure the effect of spatial interaction turnover on the variation in individual species’ roles. We observed high interaction turnover across lakes, driven primarily by turnover in basal species but also the rewiring of interactions among shared species, driven, in part by underlying environmental gradients (e.g., species richness). Contrary to previous food web models applied to single sites, none of the ecological drivers we considered were effective predictors of lake‐specific interactions perhaps indicating an important distinction between network model accuracy at regional and local extents. Finally, despite high spatial turnover in interactions, species’ roles were highly conserved across the study lakes demonstrating the potential of species’ roles for predicting community structure. These findings demonstrate how integrating species’ fundamental roles into trait‐based approaches may improve our predictions of ecological networks at local scales.

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“…To this end, there are some computational tools that could provide valuable insights (see Pilosof et al, for a list of metrics). In particular, the study of network motifs—the suite of n ‐node interaction patterns within networks—has already proven to be a useful tool in characterising the structural roles that species play in networks (Bramon Mora, Gravel, Gilarranz, Poisot, & Stouffer, ) and could provide an interesting perspective on how interaction patterns are linked across layers (Figure a). Studying which motifs are linked to which across layers could shed light on the dynamics of the interaction patterns and the consistency of species’ roles (as defined by motif membership) across layers.…”

Section: Putting Emns To Work In Community Ecologymentioning

confidence: 99%

Seeing the forest for the trees: Putting multilayer networks to work for community ecology

et al. 2018

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A framework for the description and analysis of multilayer networks is established in statistical physics, and calls are increasing for their adoption by community ecologists. Multilayer networks in community ecology will allow space, time and multiple interaction types to be incorporated into species interaction networks. While the multilayer network framework is applicable to ecological questions, it is one thing to be able to describe ecological communities as multilayer networks and another for multilayer networks to actually prove useful for answering ecological questions. Importantly, documenting multilayer network structure requires substantially greater empirical investment than standard ecological networks. In response, we argue that this additional effort is worthwhile and describe a series of research lines where we expect multilayer networks will generate the greatest impact. Inter‐layer edges are the key component that differentiate multilayer networks from standard ecological networks. Inter‐layer edges join different networks—termed layers—together and represent ecological processes central to the species interactions studied (e.g., inter‐layer edges representing movement for networks separated in space). Inter‐layer edges may take a variety of forms, be species‐ or network‐specific, and be measured with a large suite of empirical techniques. Additionally, the sheer size of ecological multilayer networks also requires some changes to empirical data collection around interaction quantification, collaborative efforts and collation in public databases. Network ecology has already touched on a wide swath of ecology and evolutionary biology. Because network stability and patterns of species linkage are the most developed areas of network ecology, they are a natural starting place for multilayer investigations. However, multilayer networks will also provide novel insights to niche partitioning, the connection between traits and species’ interactions, and even the geographic mosaic of co‐evolution. Synthesis. Multilayer networks provide a formal way to bring together the study of species interaction networks and the processes that influence them. However, describing inter‐layer edges and the increasing amounts of data required represent challenges. The pay‐off for added investment will be ecological networks that describe the composition and capture the dynamics of ecological communities more completely and, consequently, have greater power for understanding the patterns and processes that underpin diversity in ecological communities. A http://onlinelibrary.wiley.com/doi/10.1111/1365-2435.13237/suppinfo is available for this article.

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Identifying a common backbone of interactions underlying food webs from different ecosystems

Cited by 38 publications

References 68 publications

Untangling the seasonal dynamics of plant-pollinator communities

Untangling the seasonal dynamics of plant-pollinator communities

Effects of species traits, motif profiles, and environment on spatial variation in multi‐trophic antagonistic networks

Seeing the forest for the trees: Putting multilayer networks to work for community ecology

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