Data from: Varyingly hungry caterpillars: predictive models and foliar chemistry suggest how to eat a rainforest

A long-term goal in evolutionary ecology is to explain the incredible diversity of insect herbivores and patterns of plant host use in speciose groups like tropical Lepidoptera. Here we used standardised food-web data, multigene phylogenies of both trophic levels and plant chemistry data to model interactions between Lepidoptera larvae (caterpillars) from two lineages (Geometridae and Pyraloidea) and plants in species-rich lowland rainforest in New Guinea. Model parameters were used to make and test blind predictions for two hectares of exhaustively sampled forest. For pyraloids we relied on phylogeny alone and predicted 54% of species level interactions, translating to 79% of all trophic links for individual insects, by sampling insects from only 15% of local woody plant diversity. The phylogenetic distribution of host plant associations in polyphagous geometrids was less conserved, reducing accuracy. In a truly quantitative food-web only 40% of pair-wise interactions were described correctly in geometrids. Polyphenol oxidative activity (but not protein precipitation capacity), was important for understanding the occurrence of geometrids (but not pyraloids) across their hosts. When both foliar chemistry and plant phylogeny were included, we predicted geometrid-plant occurrence with 89% concordance. Such models help to test macroevolutionary hypotheses at the community level.

进化生态学的长期目标之一，是阐释昆虫植食者的惊人多样性，以及热带鳞翅目（Lepidoptera）这类物种丰富类群所呈现的宿主植物利用模式。本研究依托标准化食物网数据、两个营养级的多基因系统发育树以及植物化学数据，对新几内亚物种丰富的低地雨林中，两类支系（尺蛾科Geometridae与螟蛾总科Pyraloidea）的鳞翅目幼虫（毛虫）与植物间的互作关系开展建模。随后利用模型参数，对两公顷完成全面采样的森林开展盲测预测与验证。针对螟蛾总科类群，本研究仅借助系统发育信息即可完成预测：仅通过采样当地15%的木本植物多样性对应的昆虫样本，即可预测54%的物种水平互作，对应个体昆虫间79%的营养联系。多食性尺蛾类的宿主植物关联的系统发育分布保守性较弱，导致预测精度降低。在完整的定量食物网中，尺蛾类的两两物种互作仅能被正确描述40%。多酚氧化活性（而非蛋白沉淀能力），是阐释尺蛾类（而非螟蛾总科类群）在宿主植物上的分布的关键影响因素。当同时纳入叶片化学特征与植物系统发育信息时，我们对尺蛾-植物分布的预测一致性可达89%。此类模型有助于在群落水平检验宏进化假说。