Do bipartite binary antagonistic and mutualistic networks have different responses to the taxonomic resolution of nodes

ECOLOGICAL ENTOMOLOGY(2020)

引用 2|浏览8
暂无评分
摘要
1. Bipartite network analyses are increasingly being used to better understand mutualistic and antagonistic plant-insect interactions at the community level. As a result of taxonomic limitations, it is usually very difficult to identify all nodes of a network down to the species level and many studies leave some specimens identified as lower resolution taxa. Accordingly, we do not know how much a lower resolution taxonomic representation changes the network structure compared with a representation with all nodes at species level. 2. The present study aimed to test whether insect-plant networks built using different combinations of taxonomic levels can still preserve the same basic structure of networks built only with species. 3. In total, 73 bipartite published interaction networks (mutualistic and antagonistic) were selected, which were turned into binary networks and reconstructed using the nodes classified as species, genus, family or order (representing different levels of classification difficulty). The network structures were compared using their binary representations mainly using connectance, NODF (Nestedness metric based on Overlap and Decreasing Fill) and modularity. 4. The mutualistic network structure was strongly linearly related to the original network structures if all nodes were grouped up to genus level. In antagonistic networks, the structure was related to the original network only if nodes were only grouped at the species level. 5. The findings of the present study are especially helpful for comparative network studies, such as those assessing the effects of environmental gradients. For mutualistic networks, Citizen Science programmes can provide useful ecological indicators, even with its taxonomic limitations.
更多
查看译文
关键词
Bipartite networks,connectance,insect-plant networks,Node identification,NODF,modularity
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要