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A popular model in continuous phylogeography\u2014with location data provided in the form of latitude and longitude coordinates\u2014describes 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\u2019s spatial distribution. We therefore consider an alternative model, the spatial \u039b-Fleming-Viot process (\u039bFV), which has recently gained popularity in population genetics. Despite the \u039bFV\u2019s robustness to sampling biases, we find that the different assumptions of the \u039bFV and BMP models result in different applicabilities, with the \u039bFV being more appropriate for scenarios of endemic spread, and BMP being more appropriate for recent outbreaks or colonizations.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1008561","type":"journal-article","created":{"date-parts":[[2021,1,6]],"date-time":"2021-01-06T16:42:28Z","timestamp":1609951348000},"page":"e1008561","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":71,"title":["Sampling bias and model choice in continuous phylogeography: Getting lost on a random 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