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作 者:Amanda Caroline DUDCZAK Gabriel Massaccesi DE LA TORRE Lorena EUCLYDES Karla Magalhães CAMPIÃO
机构地区:[1]Department of Zoology,Federal University of Paraná,Curitiba,Brazil [2]Postgraduate Program in Zoology,Federal University of Paraná,Curitiba,Brazil [3]Postgraduate Program in Ecology and Conservation,Federal University of Paraná,Curitiba,Brazil
出 处:《Integrative Zoology》2022年第4期530-542,共13页整合动物学(英文版)
摘 要:Interactions among living beings are the structuring basis of ecosystems,and studies of networks allow us to identify the patterns and consistency of such interactions.Antagonistic networks reflect the energy flow of communities,and identifying network structure and the biological aspects that influence its stability is crucial to understanding ecosystem functioning.We used antagonistic anuran interactions-predator-prey and host-parasite-to assess structural patterns and to identify the key anuran species structuring these networks.We tested whether anuran body-size and life-habit are related to their roles in these networks.We collected individuals of 9 species of anurans from an area of the Atlantic Forest in Brazil and identified their prey and helminth parasites.We used network(modularity,specialization,and nestedness)and centrality metrics(degree,closeness,and betweenness)to identify the role of anuran species in both networks.We then evaluated whether anuran body-size or life-habit were related to anuran centrality using generalized linear mixed models.The networks formed specialized interactions in compartments composed by key species from different habits.In our networks,anurans with rheophilic and cryptozoic habit are central in predator-prey networks,and those with larger body size and arboreal and cryptozoic habit in the host-parasite network.This study represents a step towards a better understanding of the influential factors that affect the structure of anuran antagonist networks,as well as to recognize the functioning roles of anuran species.
关 键 词:DIET disease food web helminth infection network centrality
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