Reji Chacko Merin, Albouy Camille, Altermatt Florian, Casanelles-Abella Joan, Brändle Martin, Boussange Victor, Campell Fadri, Ellis Willem N, Fopp Fabian, Gossner Martin M, Ho Hsi-Cheng, Joss Alain, Kipf Pascal, Neff Felix, Petrović Andjeljko, Prié Vincent, Tomanović Željko, Zimmerli Nik, Pellissier Loïc
Land Change Science, Swiss Federal Research Institute WSL, Birmensdorf, Switzerland.
Institute of Terrestrial Ecosystems, Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland.
Sci Data. 2025 Jul 9;12(1):1164. doi: 10.1038/s41597-025-05487-7.
Understanding how species interact within ecological networks is essential for predicting the consequences of environmental change, from trophic cascades to broader changes in species distributions and ecosystem functioning across large spatial scales. To facilitate such explorations, we constructed trophiCH: a country-level trophic meta-food web (henceforth "metaweb") that includes vertebrates, invertebrates, and vascular plants within Switzerland, based on literature published between 1862 and 2023. Our comprehensive dataset catalogues 1,112,073 trophic interactions involving 23,151 species and 125 feeding guilds (e.g., fungivores). Thirty percent of species-level interactions were empirically documented. Additional species-level interactions were inferred by resolving coarser taxonomic records (e.g., inferring links from "species A feeds on genus B") based on habitat co-occurrences. While explorations of large-scale food webs have often relied on modelling approaches due to data gaps, this empirically based metaweb paves the way for data-driven studies of real-world food webs across space and time. By integrating the metaweb with local species assemblages knowledge, future studies can gain insights into broad patterns of food web structure across spatial scales.
了解物种在生态网络中的相互作用对于预测环境变化的后果至关重要,这些后果涵盖了从营养级联到物种分布和生态系统功能在大空间尺度上的更广泛变化。为了便于进行此类探索,我们构建了trophiCH:一个国家级的营养元食物网(以下简称“元网络”),它基于1862年至2023年期间发表的文献,纳入了瑞士境内的脊椎动物、无脊椎动物和维管植物。我们全面的数据集记录了1112073种营养相互作用,涉及23151个物种和125个取食类群(例如食真菌动物)。30%的物种层面相互作用有实证记录。其他物种层面的相互作用是通过基于栖息地共现情况解析更粗略的分类记录(例如从“物种A以属B为食”推断联系)来推断的。虽然由于数据缺口,对大规模食物网的探索通常依赖于建模方法,但这个基于实证的元网络为跨时空对现实世界食物网进行数据驱动的研究铺平了道路。通过将元网络与当地物种组合知识相结合,未来的研究可以深入了解跨空间尺度的食物网结构的广泛模式。