Pollinators in food webs: Mutualistic interactions increase diversity, stability, and function in multiplex networks

Hale, Kayla R. S.; Valdovinos, Fernanda S.; Martinez, Neo D.

doi:10.1101/791707

Cited by 2 publications

(1 citation statement)

References 83 publications

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“…Classifying these networks would thus require using a specific training dataset composed of microbial networks with known interaction types (as discussed in Supplementary Note S3). Similarly, while our approach is strictly limited to bipartite networks, the increasing availability of multipartite networks, embracing different ecologies (Domínguez‐García & Kéfi, 2024; Pocock et al., 2012), and the recent development of generative models for multipartite networks (Hale et al., 2023) suggest that a classification approach to more complex networks may now be possible. Embracing the challenge of telling apart interaction types in multipartite networks from their structure would also allow to evaluate differences in structure between simulated and empirical multipartite networks.…”

Section: Discussionmentioning

confidence: 99%

Telling mutualistic and antagonistic ecological networks apart by learning their multiscale structure

Pichon,

Le Goff,

Morlon

et al. 2024

Methods Ecol Evol

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Characterizing and understanding the processes that shape the structure of ecological networks, which represent who interacts with whom in a community, has many implications in ecology, evolutionary biology and conservation. A highly debated question is whether and how the structure of a bipartite ecological network differs between antagonistic (e.g. herbivory) and mutualistic (e.g. pollination) interaction types. Here, we tackle this question by using a multiscale characterization of network structure, machine learning tools, and a large database of empirical and simulated bipartite networks. Contrary to previous studies focusing on global structural metrics, such as nestedness and modularity, which concluded that antagonistic and mutualistic networks cannot be told apart from only their structure, we find that they can be told apart by combining a meso‐scale characterization of their structure and supervised machine learning. Motif frequencies appear particularly informative, with an over‐representation of densely connected motifs in antagonistic networks and of motifs with asymmetrical specialization in mutualistic networks. These structural properties can be used to predict the type of interaction with relatively good confidence. Beyond this classical mutualism/antagonism dichotomy, we also find significant structural uniqueness linked to specific ecologies (e.g. pollination, parasitism). Our results clarify structural differences between antagonistic and mutualistic networks and suggest the investigation of the structural uniqueness of specific ecologies as a promising approach for characterizing interactions beyond the coarse antagonistic/mutualistic dichotomy.

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Section: Discussionmentioning

confidence: 99%

Telling mutualistic and antagonistic ecological networks apart by learning their multiscale structure

Pichon,

Le Goff,

Morlon

et al. 2024

Methods Ecol Evol

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Unifying microorganisms and macrograzers in rocky shore ecological networks

Arboleda-Baena,

Pareja,

Poblete

et al. 2023

Preprint

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Over the past decades, our understanding of the vital role microbes play in ecosystem processes has greatly expanded. However, we still have limited knowledge about how microbial communities interact with larger organisms. Many existing representations of microbial interactions are based on co-occurrence patterns, which do not provide clear insights into trophic or non-trophic relationships. In this study, we untangled trophic and non-trophic interactions between macroscopic and microscopic organisms on a marine rocky shore. Five abundant mollusk grazers were selected, and their consumptive (grazing) and non-consumptive (grazer pedal mucus) interactions with bacteria in biofilms were measured using 16S rRNA amplicon sequencing. While no significant effects on a commonly used measure of biofilm grazing (Chlorophyll-a concentration) were observed, detailed image analysis revealed that all grazers had a detrimental impact on biofilm cover. Moreover, different grazers exhibited distinct effects on various bacterial groups. Some groups, such as Rhodobacteraceae, Saprospiraceae, Flavobacteriaceae, and Halieaceae, experienced positive effects from specific grazers, while others, like Rhizobiaceae, Rhodobacteraceae, and Flavobacteriaceae were negatively affected by certain grazers. This study presents the first attempt to construct an interaction network between macroorganisms and bacteria. It demonstrates that the strength of trophic and non-trophic interactions varies significantly depending on the mollusk grazer or bacterial group involved. Notably, certain bacterial groups exhibited a generalized response, while others showed specialized responses to specific macroorganisms in trophic or non-trophic interactions. Overall, this work highlights the potential for integrating microbes into ecological networks, providing valuable insights and methodologies for quantifying interactions across Domains. This research complements the previous ecological network, showing that mollusk grazers interact not only trophically but also non-trophically with epilithic biofilms. It identifies three drivers affecting microbial community assembly, crucial for understanding macro-microorganism dynamics in intertidal systems.

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Pollinators in food webs: Mutualistic interactions increase diversity, stability, and function in multiplex networks

Cited by 2 publications

References 83 publications

Telling mutualistic and antagonistic ecological networks apart by learning their multiscale structure

Telling mutualistic and antagonistic ecological networks apart by learning their multiscale structure

Unifying microorganisms and macrograzers in rocky shore ecological networks

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