Phylogeographic inference allows reconstruction of past geographical spread of pathogens or living organisms by integrating genetic and geographic data. A popular model in continuous phylogeography—with location data provided in the form of latitude and longitude coordinates—describes spread as a Brownian motion (Brownian Motion Phylogeography, BMP) in continuous space and time, akin to similar models of continuous trait evolution. Here, we show that reconstructions using this model can be strongly affected by sampling biases, such as the lack of sampling from certain areas. As an attempt to reduce the effects of sampling bias on BMP, we consider the addition of sequence-free samples from under-sampled areas. While this approach alleviates the effects of sampling bias, in most scenarios this will not be a viable option due to the need for prior knowledge of an outbreak’s spatial distribution. We therefore consider an alternative model, the spatial Λ-Fleming-Viot process (ΛFV), which has recently gained popularity in population genetics. Despite the ΛFV’s robustness to sampling biases, we find that the different assumptions of the ΛFV and BMP models result in different applicabilities, with the ΛFV being more appropriate for scenarios of endemic spread, and BMP being more appropriate for recent outbreaks or colonizations.

文字记录推断可以通过整合遗传和地理数据来重建病原体或活生物体过去的地理分布。一种流行的连续植物志模型，以纬度和经度坐标的形式提供位置数据，描述了在连续的空间和时间中以布朗运动（布朗运动系谱，BMP）传播，类似于连续性状进化的类似模型。在这里，我们表明，使用此模型的重建可能会受到抽样偏差的强烈影响，例如缺少某些地区的抽样。为了减少采样偏差对BMP的影响，我们考虑从欠采样区域添加无序列样本。尽管这种方法减轻了采样偏差的影响，在大多数情况下，由于需要事先了解暴发的空间分布，因此这不是可行的选择。因此，我们考虑了另一种模型，即空间Λ-弗莱明-维特过程（ΛFV），该模型最近在群体遗传学中得到普及。尽管ΛFV对采样偏差具有鲁棒性，但我们发现ΛFV和BMP模型的不同假设导致不同的适用性，其中ΛFV更适合于地方性传播的情况，而BMP更适合于近期的暴发或殖民化。