Out-of-sample predictions from plant–insect food webs: robustness to missing and erroneous trophic interaction records

With increasing biotic introductions, there is a great need for predictive tools to anticipate which new trophic interactions will develop and which will not. Phylogenetic constraint of interactions in both native and novel food webs can make some novel interactions predictable. However, many food webs are sparsely sampled, or may include inaccurate interactions. In such cases, it is unclear whether modeling methods are still useful to anticipate novel interactions. We ran bootstrap simulations of host-use models on a Lepidoptera–plant data set to remove native trophic records or add erroneous records in order to observe the effect of missing or erroneous data on the prediction of interactions with novel plants. We found that the model was robust to a large amount of missing interaction records, but lost predictive power with the addition of relatively few erroneous interaction records. The loss of predictive power with missing records was due to inaccuracy in estimating phylogenetic distance between native and novel hosts. Removal of interaction records proportionally to their encounter frequency in the field had little effect on the loss of predictive power. Host-use models may have immediate value for predicting novel interactions from large, but sparsely sampled databases of trophic interactions.

随着生物引种事件日益增多，学界亟需能够预判哪些新型营养级互作（trophic interactions）能够建立、哪些无法建立的预测工具。本土与新兴食物网中互作的系统发育约束（phylogenetic constraint），可使部分新型互作具备可预测性。然而，多数食物网存在采样不足的问题，或包含不准确的互作记录。在此类场景下，建模方法是否仍可有效用于预判新型互作，尚不明确。我们在鳞翅目-植物数据集（Lepidoptera–plant data set）上开展寄主利用模型（host-use models）的自助法模拟（bootstrap simulations）：通过移除本土营养级互作记录，或添加错误互作记录，以观测缺失或错误数据对新型植物互作预测的影响。研究结果显示，模型对大量缺失的互作记录具备鲁棒性，但仅添加少量错误互作记录便会丧失预测能力（predictive power）。缺失记录导致的预测能力下降，源于本土与新型寄主间系统发育距离（phylogenetic distance）估算的偏差。按照野外遭遇频次按比例移除互作记录，对预测能力下降的影响微乎其微。寄主利用模型可从规模庞大但采样不足的营养级互作数据库中开展新型互作预测，具备即时应用价值。