Including rewiring in the estimation of the robustness of mutualistic networks

Vizentin‐Bugoni, Jeferson; Debastiani, Vanderlei J.; Bastazini, Vinicius A. G.; Maruyama, Pietro Kiyoshi; Sperry, Jinelle H.

doi:10.1111/2041-210x.13306

Cited by 54 publications

(72 citation statements)

References 57 publications

(131 reference statements)

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“…In contrast, when the most‐linked frugivore displayed unique behaviours, its loss led to stronger declines in plant richness than those predicted by TCMs. These results highlight that allowing switches in interaction partners improves our ability to predict the consequences of dispersers’ loss (Costa et al ; Vizentin‐Bugoni et al ). In JFM, rewiring is driven by frugivores’ behaviour, and hence, new and unobserved interactions can occur.…”

Section: Discussionmentioning

confidence: 71%

“…On the other hand, we usually capture snapshots of plant–animal interactions that cannot predict rearrangements in plant–animal assemblages after environmental changes or species loss (Ramos‐Jiliberto et al ). The capacity of mutualistic networks to form new pairwise interactions (rewiring) can be a key aspect of their resilience against extinctions (Kaiser‐Bunbury et al, ; Vizentin‐Bugoni et al, ). However, this process is hard to predict (Olito & Forx, ).…”

Section: Introductionmentioning

confidence: 99%

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Can network metrics predict vulnerability and species roles in bird‐dispersed plant communities? Not without behaviour

et al. 2019

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Network metrics are widely used to infer the roles of mutualistic animals in plant communities and to predict the effect of species' loss. However, their empirical validation is scarce. Here we parameterized a joint species model of frugivory and seed dispersal with bird movement and foraging data from tropical and temperate communities. With this model, we investigate the effect of frugivore loss on seed rain, and compare our predictions to those of standard coextinction models and network metrics. Topological coextinction models underestimated species loss after the removal of highly linked frugivores with unique foraging behaviours. Network metrics informed about changes in seed rain quantity after frugivore loss. However, changes in seed rain composition were only predicted by partner diversity. Nestedness, closeness, and d’ specialisation could not anticipate the effects of rearrangements in plant–frugivore communities following species loss. Accounting for behavioural differences among mutualists is critical to improve predictions from network models.

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Section: Discussionmentioning

confidence: 71%

Section: Introductionmentioning

confidence: 99%

Can network metrics predict vulnerability and species roles in bird‐dispersed plant communities? Not without behaviour

et al. 2019

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“…In the SCMs, we ran 10 3 simulations, and, following (Dalsgaard et al., 2018), we assigned random values to the parameter R of plants and pollinators as we did not have information on the real values of species interaction dependency. In the REW rewiring model, we ran 10 3 simulations, we assigned random probability values of receiving new links to avoid adding additional assumptions and allowed the option of one rewiring attempt with a single partner as in Vizentin‐Bugoni et al (2019).…”

Section: Methodsmentioning

confidence: 99%

An empirical attack tolerance test alters the structure and species richness of plant–pollinator networks

et al. 2020

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Ecological network theory hypothesizes that the structuring of species interactions can convey stability to the system. Investigating how these structures react to species loss is fundamental for understanding network disassembly or their robustness. However, this topic has mainly been studied in‐silico so far. Here, in an experimental manipulation, we sequentially removed four generalist plants from real plant–pollinator networks. We explored the effects on, and drivers of, species and interaction disappearance, network structure and interaction rewiring. First, we compared both the local extinctions of species and interactions and the observed network indices with those expected from three co‐extinction models. Second, we investigated the trends in network indices and rewiring rate after plant removal and the pollinator tendency at establishing novel links in relation to their proportional visitation to the removed plants. Furthermore, we explored the underlying drivers of network assembly with probability matrices based on ecological traits. Our results indicate that the cumulative local extinctions of species and interactions increased faster with generalist plant loss than what was expected by co‐extinction models, which predicted the survival or disappearance of many species incorrectly, and the observed network indices were lowly correlated to those predicted by co‐extinction models. Furthermore, the real networks reacted in complex ways to plant removal. First, network nestedness decreased and modularity increased. Second, although species abundance was a main assembly rule, opportunistic random interactions and structural unpredictability emerged as plants were removed. Both these reactions could indicate network instability and fragility. Other results showed network reorganization, as rewiring rate was high and asymmetries between network levels emerged as plants increased their centrality. Moreover, the generalist pollinators that had frequently visited both the plants targeted of removal and the non‐target plants tended to establish novel links more than who either had only visited the removal plants or avoided to do so. With the experimental manipulation of real networks, our study shows that despite their reorganizational ability, plant–pollinator networks changed towards a more fragile state when generalist plants are lost. A free Plain Language Summary can be found within the Supporting Information of this article.

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“…However, in a plant-pollinator system when highly visited plants were removed, pollination and insect foraging were jeopardized [56]. In addition, recent theoretical development has shown that sometimes extinction rates are not fast enough, so species have time to adapt and generate new links/interactions, and this means that the rules of the assembly of ecological networks include processes that increase the robustness of ecological networks [57]. Therefore, knowing the drivers of the different tolerances of interactive communities to species extinctions could improve our understanding of the dynamics of ecological interactions in a more comprehensive way [54].…”

Section: Measuring Local Diversity Of Interactions (α-Diversity)mentioning

confidence: 99%

“…For example, plant species without a pollen vector may have a chance to reproduce when a new pollinator arrives at its habitat [59]. Even across space, the fact that interactions are filtered and that species do not interact in the same way, may promote ecosystem stability, as if all species where able to interact without interaction filtering then ecological networks would behave as static entities, reducing robustness and ultimately ecosystem functioning [57]. This should be taken into account when explaining the distribution of biodiversity across ecological gradients, as there have been few studies so far that consider the dynamism of species interactions and its contribution to explaining global biodiversity.…”

Section: Measuring the Turnover Of Species Interactions (β-Diversity)mentioning

confidence: 99%

Measuring and Linking the Missing Part of Biodiversity and Ecosystem Function: The Diversity of Biotic Interactions

et al. 2020

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Biotic interactions are part of all ecosystem attributes and play an important role in the structure and stability of biological communities. In this study, we give a brief account of how the threads of biotic interactions are linked and how we can measure such complexity by focusing on mutualistic interactions. We start by explaining that although biotic interactions are fundamental ecological processes, they are also a component of biodiversity with a clear α, β and γ diversity structure which can be measured and used to explain how biotic interactions vary over time and space. Specifically, we explain how to estimate the α-diversity by measuring the properties of species interaction networks. We also untangle the components of the β-diversity and how it can be used to make pairwise comparisons between networks. Moreover, we move forward to explain how local ecological networks are a subset of a regional pool of species and potential interactions, γ-diversity, and how this approach allows assessing the spatial and temporal dynamics of ecological networks. Finally, we propose a new framework for studying interactions and the biodiversity–ecosystem function relationship by identifying the unique and common interactions of local networks over space, time or both together.

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Including rewiring in the estimation of the robustness of mutualistic networks

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 54 publications

References 57 publications

Can network metrics predict vulnerability and species roles in bird‐dispersed plant communities? Not without behaviour

Can network metrics predict vulnerability and species roles in bird‐dispersed plant communities? Not without behaviour

An empirical attack tolerance test alters the structure and species richness of plant–pollinator networks

Measuring and Linking the Missing Part of Biodiversity and Ecosystem Function: The Diversity of Biotic Interactions

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