Data from: Phylogeographic model selection leads to insight into the evolutionary history of four-eyed frogs

Phylogeographic research investigates biodiversity at the interface between populations and species, in a temporal and geographic context. Phylogeography has benefited from analytical approaches that allow empiricists to estimate parameters of interest from the genetic data (e.g., θ = 4Neμ, population divergence, gene flow), and the widespread availability of genomic data allow such parameters to be estimated with greater precision. However, the actual inferences made by phylogeographers remain dependent on qualitative interpretations derived from these parameters’ values and as such may be subject to overinterpretation and confirmation bias. Here we argue in favor of using an objective approach to phylogeographic inference that proceeds by calculating the probability of multiple demographic models given the data and the subsequent ranking of these models using information theory. We illustrate this approach by investigating the diversification of two sister species of four-eyed frogs of northeastern Brazil using single nucleotide polymorphisms obtained via restriction-associated digest sequencing. We estimate the composite likelihood of the observed data given nine demographic models and then rank these models using Akaike information criterion. We demonstrate that estimating parameters under a model that is a poor fit to the data is likely to produce values that lead to spurious phylogeographic inferences. Our results strongly imply that identifying which parameters to estimate from a given system is a key step in the process of phylogeographic inference and is at least as important as being able to generate precise estimates of these parameters. They also illustrate that the incorporation of model uncertainty should be a component of phylogeographic hypothesis tests.

系统地理学（Phylogeography）研究在时间与地理框架下，种群与物种交界区域的生物多样性。系统地理学得益于诸多分析方法，这些方法可帮助实证研究者从遗传数据中估算目标参数（例如θ=4Neμ、种群分化、基因流）；而基因组数据的广泛普及，让这类参数的估算精度得到显著提升。然而，系统地理学家所做出的实际推论，依然依赖于从这些参数数值中推导得出的定性解读，因此可能陷入过度解读与确认偏误的误区。为此，本文主张采用一种客观的系统地理学推断方法：先基于观测数据计算多种种群历史模型的概率，再借助信息论对这些模型进行排序。我们以巴西东北部两种四眼蛙姐妹物种的分化过程为研究对象，使用通过限制性酶切位点关联测序（restriction-associated digest sequencing）获得的单核苷酸多态性（Single Nucleotide Polymorphisms）数据，展示了该方法的应用流程。我们针对九种种群历史模型，估算了观测数据的复合似然度，随后借助赤池信息准则（Akaike Information Criterion）对这些模型进行排序。研究表明，在与数据拟合度不佳的模型下估算参数，很可能得到会引发虚假系统地理学推论的参数结果。我们的研究结果明确证实：针对特定研究系统确定需估算的参数，是系统地理学推断流程中的关键步骤，其重要性至少不亚于精准估算这些参数本身。此外，本研究还表明，模型不确定性的纳入应当成为系统地理学假说检验的必要组成部分。