Emerging diseases of wildlife origin are increasingly spilling over into humans and domestic animals. Surveillance and risk assessments for transmission between these populations are informed by a mechanistic understanding of the pathogens in wildlife reservoirs. For avian influenza viruses (AIV), much observational and experimental work in wildlife has been conducted at local scales, yet fully understanding their spread and distribution requires assessing the mechanisms acting at both local, (e.g., intrinsic epidemic dynamics), and continental scales, (e.g., long‐distance migration). Here, we combined a large, continental‐scale data set on low pathogenic, Type A AIV in the United States with a novel network‐based application of bird banding/recovery data to investigate the migration‐based drivers of AIV and their relative importance compared to well‐characterized local drivers (e.g., demography, environmental persistence). We compared among regression models reflecting hypothesized ecological processes and evaluated their ability to predict AIV in space and time using within and out‐of‐sample validation. We found that predictors of AIV were associated with multiple mechanisms at local and continental scales. Hypotheses characterizing local epidemic dynamics were strongly supported, with age, the age‐specific aggregation of migratory birds in an area and temperature being the best predictors of infection. Hypotheses defining larger, network‐based features of the migration processes, such as clustering or between‐cluster mixing explained less variation but were also supported. Therefore, our results support a role for local processes in driving the continental distribution of AIV.

中文翻译：

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人口，迁徙和温度是美国水禽在大陆范围内禽流感的动力。

野生生物起源的新兴疾病正越来越多地扩散到人类和家畜中。对野生动植物水库中病原体的机械理解有助于对这些种群之间的传播进行监测和风险评估。对于禽流感病毒（AIV），已经在地方范围内进行了许多野生动植物的观察和实验工作，但要充分了解其传播和分布，需要评估在地方范围（例如，内在的流行动态）和大陆范围内起作用的机制， （例如，长距离迁移）。在这里，我们结合了有关低致病性，美国的AIV型AIV具有新颖的基于网络的鸟类带/恢复数据应用程序，用于调查基于迁徙的AIV驱动程序及其与特征明确的本地驱动程序（例如人口统计学，环境持久性）相比的相对重要性。我们在反映假设的生态过程的回归模型之间进行了比较，并使用样本内和样本外验证评估了其预测时空AIV的能力。我们发现，AIV的预测因子与当地和大陆尺度上的多种机制相关。随着年龄的增长，强烈支持以当地流行病动态为特征的假说，某个地区和温度的候鸟按年龄的特定聚集是感染的最佳预测因子。假设定义了迁移过程中基于网络的较大功能，例如集群或集群间混合说明了较少的差异，但也得到了支持。因此，我们的研究结果支持了局部过程在推动AIV大陆分布方面的作用。