Opportunistic attachment assembles plant–pollinator networks

Ponisio, Lauren C.; Gaiarsa, Marília Palumbo; Kremen, Claire

doi:10.1111/ele.12821

Cited by 84 publications

(100 citation statements)

References 68 publications

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“…The finding also highlights the role of common and abundant plants as reservoirs of symbiotic fungal diversity and thus contributors towards maintenance of ecosystem functions. Reliance of the symbiotic network on the most common hosts has earlier been demonstrated for aboveground mutualistic systems, such as plant–pollinator networks (Ponisio, Gaiarsa, & Kremen, ). Furthermore, Winfree, Williams, Dushoff, and Kremen () showed that network robustness in plant–pollinator systems is largely reliant on the most common plant species.…”

Section: Discussionmentioning

confidence: 88%

Non‐random association patterns in a plant–mycorrhizal fungal network reveal host–symbiont specificity

et al. 2018

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Arbuscular mycorrhizal (AM) fungi are obligate plant symbionts that have important functions in most terrestrial ecosystems, but there remains an incomplete understanding of host–fungus specificity and the relationships between species and functional groups of plants and AM fungi. Here, we aimed to provide a comprehensive description of plant–AM fungal interactions in a biodiverse semi‐natural grassland. We sampled all plant species in a 1,000‐m2 homogeneous plot of dry calcareous grassland in two seasons (summer and autumn) and identified root‐colonizing AM fungi by SSU rDNA sequencing. In the network of 33 plant and 100 AM fungal species, we found a significant effect of both host plant species and host plant functional group on AM fungal richness and community composition. Comparison with network null models revealed a larger‐than‐random degree of partner selectivity among plants. Grasses harboured a larger number of AM fungal partners and were more generalist in partner selection, compared with forbs. More generalist partner association and lower specialization were apparent among obligately, compared with facultatively, mycorrhizal plant species and among locally more abundant plant species. This study provides the most complete data set of co‐occurring plant and AM fungal taxa to date, showing that at this particular site, the interaction network is assembled non‐randomly, with moderate selectivity in associations between plant species and functional groups and their fungal symbionts.

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Section: Discussionmentioning

confidence: 88%

Non‐random association patterns in a plant–mycorrhizal fungal network reveal host–symbiont specificity

et al. 2018

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“…having a specific degree distribution; Caldarelli, ), but deviate from these rules in small yet informative ways (specifically, about prey selection or predator avoidance). Opportunistic attachment and topological plasticity have been suggested as mechanisms that can move the system away from predictions based on power laws (Ramos‐Jiliberto et al, ; Ponisio, Gaiarsa & Kremen, ). We suggest that deviations from the power law be analysed as having intrinsic ecological meaning: why there are more, or fewer, species with a given frequency of interactions may reveal reasons for and/or constraints on particular species interactions.…”

Section: What Can We Learn With Ecological Network?mentioning

confidence: 99%

Analysing ecological networks of species interactions

et al. 2018

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Network approaches to ecological questions have been increasingly used, particularly in recent decades. The abstraction of ecological systems - such as communities - through networks of interactions between their components indeed provides a way to summarize this information with single objects. The methodological framework derived from graph theory also provides numerous approaches and measures to analyze these objects and can offer new perspectives on established ecological theories as well as tools to address new challenges. However, prior to using these methods to test ecological hypotheses, it is necessary that we understand, adapt, and use them in ways that both allow us to deliver their full potential and account for their limitations. Here, we attempt to increase the accessibility of network approaches by providing a review of the tools that have been developed so far, with - what we believe to be - their appropriate uses and potential limitations. This is not an exhaustive review of all methods and metrics, but rather, an overview of tools that are robust, informative, and ecologically sound. After providing a brief presentation of species interaction networks and how to build them in order to summarize ecological information of different types, we then classify methods and metrics by the types of ecological questions that they can be used to answer from global to local scales, including methods for hypothesis testing and future perspectives. Specifically, we show how the organization of species interactions in a community yields different network structures (e.g., more or less dense, modular or nested), how different measures can be used to describe and quantify these emerging structures, and how to compare communities based on these differences in structures. Within networks, we illustrate metrics that can be used to describe and compare the functional and dynamic roles of species based on their position in the network and the organization of their interactions as well as associated new methods to test the significance of these results. Lastly, we describe potential fruitful avenues for new methodological developments to address novel ecological questions.

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“…The previous subsection synthesised two frameworks to model population dynamics, one assuming static interactions (i.e., Lotka–Volterra type models) while the other allowing plastic interactions (i.e., consumer–resource model by Valdovinos et al .). This subsection synthesises studies that provide further understanding of the highly plastic nature of mutualistic interactions (CaraDonna et al ; Ponisio et al ).…”

Section: Towards Predicting the Dynamics Of Mutualistic Networkmentioning

confidence: 99%

Mutualistic networks: moving closer to a predictive theory

2019

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Plant–animal mutualistic networks sustain terrestrial biodiversity and human food security. Global environmental changes threaten these networks, underscoring the urgency for developing a predictive theory on how networks respond to perturbations. Here, I synthesise theoretical advances towards predicting network structure, dynamics, interaction strengths and responses to perturbations. I find that mathematical models incorporating biological mechanisms of mutualistic interactions provide better predictions of network dynamics. Those mechanisms include trait matching, adaptive foraging, and the dynamic consumption and production of both resources and services provided by mutualisms. Models incorporating species traits better predict the potential structure of networks (fundamental niche), while theory based on the dynamics of species abundances, rewards, foraging preferences and reproductive services can predict the extremely dynamic realised structures of networks, and may successfully predict network responses to perturbations. From a theoretician's standpoint, model development must more realistically represent empirical data on interaction strengths, population dynamics and how these vary with perturbations from global change. From an empiricist's standpoint, theory needs to make specific predictions that can be tested by observation or experiments. Developing models using short‐term empirical data allows models to make longer term predictions of community dynamics. As more longer term data become available, rigorous tests of model predictions will improve.

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Opportunistic attachment assembles plant–pollinator networks

Cited by 84 publications

References 68 publications

Non‐random association patterns in a plant–mycorrhizal fungal network reveal host–symbiont specificity

Non‐random association patterns in a plant–mycorrhizal fungal network reveal host–symbiont specificity

Analysing ecological networks of species interactions

Mutualistic networks: moving closer to a predictive theory

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