A necessary component of understanding vector‐borne disease risk is accurate characterization of the distributions of their vectors. Species distribution models have been successfully applied to data‐rich species but may produce inaccurate results for sparsely documented vectors. In light of global change, vectors that are currently not well‐documented could become increasingly important, requiring tools to predict their distributions. One way to achieve this could be to leverage data on related species to inform the distribution of a sparsely documented vector based on the assumption that the environmental niches of related species are not independent. Relatedly, there is a natural dependence of the spatial distribution of a disease on the spatial dependence of its vector. Here, we propose to exploit these correlations by fitting a hierarchical model jointly to data on multiple vector species and their associated human diseases to improve distribution models of sparsely documented species. To demonstrate this approach, we evaluated the ability of twelve models – which differed in their pooling of data from multiple vector species and inclusion of disease data – to improve distribution estimates of sparsely documented vectors. We assessed our models on two simulated datasets, which allowed us to generalize our results and examine their mechanisms. We found that when the focal species is sparsely documented, incorporating data on related vector species reduces uncertainty and improves accuracy by reducing overfitting. When data on vector species are already incorporated, disease data only marginally improve model performance. However, when data on other vectors are not available, disease data can improve model accuracy and reduce overfitting and uncertainty. We then assessed the approach on empirical data on ticks and tick‐borne diseases in Florida and found that incorporating data on other vector species improved model performance. This study illustrates the value in exploiting correlated data via joint modeling to improve distribution models of data‐limited species.

中文翻译：

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通过联合建模整合相关物种的信息，改进记录稀疏的疾病媒介的分布模型

了解媒介传播疾病风险的一个必要组成部分是准确表征媒介的分布。物种分布模型已成功应用于数据丰富的物种，但对于记录稀疏的向量可能会产生不准确的结果。鉴于全球变化，目前尚未得到充分记录的向量可能变得越来越重要，需要工具来预测其分布。实现这一目标的一种方法可能是基于相关物种的环境生态位不是独立的假设，利用相关物种的数据来了解记录稀疏的载体的分布。相关地，疾病的空间分布对其向量的空间依赖性存在天然依赖性。在这里，我们建议通过将层次模型联合拟合多个媒介物种及其相关人类疾病的数据来利用这些相关性，以改进记录稀疏的物种的分布模型。为了证明这种方法，我们评估了 12 个模型（这些模型的不同之处在于它们汇集了来自多个媒介物种的数据和包含疾病数据）改善稀疏记录媒介的分布估计的能力。我们在两个模拟数据集上评估了我们的模型，这使我们能够概括我们的结果并检查其机制。我们发现，当焦点物种记录稀疏时，合并相关向量物种的数据可以减少不确定性，并通过减少过度拟合来提高准确性。当媒介物种的数据已经纳入时，疾病数据只能略微提高模型性能。然而，当其他向量的数据不可用时，疾病数据可以提高模型的准确性并减少过度拟合和不确定性。然后，我们评估了佛罗里达州蜱和蜱传疾病的经验数据的方法，发现结合其他媒介物种的数据可以提高模型性能。这项研究说明了通过联合建模利用相关数据来改进数据有限物种的分布模型的价值。