{"title":"Impacts of emergent structural topologies on the stability of an adaptive plant-animal network","authors":"Min Su, Zhongyi Wang, Qi Ma","doi":"10.1016/j.biosystems.2025.105522","DOIUrl":null,"url":null,"abstract":"<div><div>Plant species, interacting with both pollinators and herbivores, form a 3-guild ecological network that encompasses mutualistic and antagonistic subnetworks. These plant-animal interactions typically evolve through adaptive interaction switching, which tends to promote species abundance. In this study, we constructed an adaptive plant-animal network and explored how interaction rewiring among species from different guilds influences the structure and stability of this 3-guild network. Our findings reveal that interaction rewiring dynamically reshapes network architecture, with plant degree centrality correlations between mutualistic and antagonistic subnetworks emerging as a critical determinant of community stability. Notably, extreme centrality correlations, driven by interaction switching, can undermine network stability. In contrast, moderate correlations enhance resilience, rendering optimized networks more robust than their randomly assigned counterparts. These results underscore the importance of interaction switching in shaping the structure and stability of 3-guild ecological networks. Moreover, these findings provide mechanistic evidence that plant generalism fundamentally influences resilience in optimized networks, mediated through the evolutionary trade-off between herbivore defense and pollinator attraction.</div></div>","PeriodicalId":50730,"journal":{"name":"Biosystems","volume":"254 ","pages":"Article 105522"},"PeriodicalIF":1.9000,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biosystems","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0303264725001327","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Plant species, interacting with both pollinators and herbivores, form a 3-guild ecological network that encompasses mutualistic and antagonistic subnetworks. These plant-animal interactions typically evolve through adaptive interaction switching, which tends to promote species abundance. In this study, we constructed an adaptive plant-animal network and explored how interaction rewiring among species from different guilds influences the structure and stability of this 3-guild network. Our findings reveal that interaction rewiring dynamically reshapes network architecture, with plant degree centrality correlations between mutualistic and antagonistic subnetworks emerging as a critical determinant of community stability. Notably, extreme centrality correlations, driven by interaction switching, can undermine network stability. In contrast, moderate correlations enhance resilience, rendering optimized networks more robust than their randomly assigned counterparts. These results underscore the importance of interaction switching in shaping the structure and stability of 3-guild ecological networks. Moreover, these findings provide mechanistic evidence that plant generalism fundamentally influences resilience in optimized networks, mediated through the evolutionary trade-off between herbivore defense and pollinator attraction.
期刊介绍:
BioSystems encourages experimental, computational, and theoretical articles that link biology, evolutionary thinking, and the information processing sciences. The link areas form a circle that encompasses the fundamental nature of biological information processing, computational modeling of complex biological systems, evolutionary models of computation, the application of biological principles to the design of novel computing systems, and the use of biomolecular materials to synthesize artificial systems that capture essential principles of natural biological information processing.