Although long‐standing theory suggests that biotic variables are only relevant at local scales for explaining the patterns of species' distributions, recent studies have demonstrated improvements to species distribution models (SDMs) by incorporating predictor variables informed by biotic interactions. However, some key methodological questions remain, such as which kinds of interactions are permitted to include in these models, how to incorporate the effects of multiple interacting species, and how to account for interactions that may have a temporal dependence. We addressed these questions in an effort to model the distribution of the monarch butterfly Danaus plexippus during its fall migration (September–November) through Mexico, a region with new monitoring data and uncertain range limits even for this well‐studied insect. We estimated species richness of selected nectar plants (Asclepias spp.) and roosting trees (various highland species) for use as biotic variables in our models. To account for flowering phenology, we additionally estimated nectar plant richness of flowering species per month. We evaluated three types of models: climatic variables only (abiotic), plant richness estimates only (biotic) and combined (abiotic and biotic). We selected models with AICc and additionally determined if they performed better than random on spatially withheld data. We found that the combined models accounting for phenology performed best for all three months, and better than random for discriminatory ability but not omission rate. These combined models also produced the most ecologically realistic spatial patterns, but the modeled response for nectar plant richness matched ecological predictions for November only. These results represent the first model‐based monarch distributional estimates for the Mexican migration route and should provide foundations for future conservation work. More generally, the study demonstrates the potential benefits of using SDM‐derived richness estimates and phenological information for biotic factors affecting species distributions.

中文翻译：

---

具有物候信息的生物预测因子改善了墨西哥帝王蝶迁徙的范围估计

尽管长期存在的理论表明，生物变量仅在局部尺度上与解释物种分布模式相关，但最近的研究表明，通过结合生物相互作用所告知的预测变量，对物种分布模型 (SDM) 的改进。然而，一些关键的方法论问题仍然存在，例如允许在这些模型中包含哪些类型的相互作用，如何结合多个相互作用物种的影响，以及如何解释可能具有时间依赖性的相互作用。我们解决了这些问题，以模拟帝王蝶 Danaus plexippus 在秋季迁徙（9 月至 11 月）通过墨西哥期间的分布，该地区即使对这种经过充分研究的昆虫也有新的监测数据和不确定的范围限制。我们估计了选定的花蜜植物（马利筋属）和栖息树木（各种高地物种）的物种丰富度，用作我们模型中的生物变量。为了解释开花物候，我们还估计了每月开花物种的花蜜植物丰富度。我们评估了三种类型的模型：仅气候变量（非生物）、仅植物丰富度估计（生物）和组合（非生物和生物）。我们选择了带有 AICc 的模型，并另外确定了它们在空间保留数据上的表现是否优于随机模型。我们发现，考虑物候的组合模型在所有三个月中表现最佳，在辨别能力方面优于随机模型，但在遗漏率方面则优于随机模型。这些组合模型还产生了最生态现实的空间模式，但对花蜜植物丰富度的模拟响应仅与 11 月的生态预测相符。这些结果代表了墨西哥迁徙路线的第一个基于模型的君主分布估计，应该为未来的保护工作提供基础。更一般地说，该研究证明了将 SDM 衍生的丰富度估计和物候信息用于影响物种分布的生物因素的潜在好处。