In the twenty-first century, ticks and tick-borne diseases have expanded their ranges and impact across the US. With this spread, it has become vital to monitor vector and disease distributions, as these shifts have public health implications. Typically, tick-borne disease surveillance (e.g., Lyme disease) is passive and relies on case reports, while disease risk is calculated using active surveillance, where researchers collect ticks from the environment. Case reports provide the basis for estimating the number of cases; however, they provide minimal information on vector population or pathogen dynamics. Active surveillance monitors ticks and sylvatic pathogens at local scales, but it is resource-intensive. As a result, data are often sparse and aggregated across time and space to increase statistical power to model or identify range changes. Engaging public participation in surveillance efforts allows spatially and temporally diverse samples to be collected with minimal effort. These citizen-driven tick collections have the potential to provide a powerful tool for tracking vector and pathogen changes. We used MaxEnt species distribution models to predict the current and future distribution of Ixodes pacificus across the Western US through the use of a nationwide citizen science tick collection program. Here, we present niche models produced through citizen science tick collections over two years. Despite obvious limitations with citizen science collections, the models are consistent with previously-predicted species ranges in California that utilized more than thirty years of traditional surveillance data. Additionally, citizen science allows for an expanded understanding of I. pacificus distribution in Oregon and Washington. With the potential for rapid environmental changes instigated by a burgeoning human population and rapid climate change, the development of tools, concepts, and methodologies that provide rapid, current, and accurate assessment of important ecological qualities will be invaluable for monitoring and predicting disease across time and space.

在二十一世纪，壁虱和壁虱传播的疾病在美国范围内不断扩大，影响范围日益扩大。随着这种传播，监测这些媒介和疾病的分布已变得至关重要，因为这些变化对公共卫生产生了影响。通常，tick传播疾病的监测（例如莱姆病）是被动的，并依赖于病例报告，而疾病风险则通过主动监测来计算，研究人员从环境中收集s。病例报告为估计病例数提供了依据；但是，它们提供的关于媒介种群或病原体动态的信息很少。主动监视在本地范围内监视壁虱和虫病原体，但这是资源密集型的。结果，数据通常会在时间和空间上稀疏汇总，以增强统计能力来建模或识别范围变化。通过让公众参与监视工作，可以以最小的努力收集时空上不同的样本。这些公民驱动的tick收集有潜力提供强大的工具来追踪病媒和病原体的变化。我们使用MaxEnt物种分布模型来预测当前和将来的物种分布通过使用全国性的公民科学tick收集计划，在美国西部的太平洋硬x。在这里，我们介绍了两年来通过公民科学壁虱收集产生的利基模型。尽管公民科学馆藏有明显的局限性，但这些模型与加利福尼亚利用30多年传统监视数据的先前预测的物种范围保持一致。此外，公民科学还可以扩展我的理解。太平洋分布在俄勒冈州和华盛顿。随着人口的迅速增长和气候变化的迅速发展，潜在的环境迅速变化，提供对重要生态质量进行快速，最新和准确评估的工具，概念和方法的开发对于跨时期的监测和预测疾病将具有不可估量的价值。和空间。