An information-theoretical approach to phylogeography
An information-theoretical approach to phylogeography
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DOI：
10.1111/j.1365-294x.2009.04327.x
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发表时间：
2009-10-01
影响因子：
4.9
通讯作者：
Reid, Noah M.
中科院分区：
文献类型：
--
作者：
Carstens, Bryan C.;Stoute, Holly N.;Reid, Noah M.
地理调查中的数据分析通常以定性的方式进行，或者通过零假设检验进行。前者，即从遗传变异的地理模式中推断种群过程，可能会受到确认偏差的影响，并倾向于过度解释。检验零假设的预测可以说比定性方法更不容易产生偏差，但前提是检验的假设具有生物学意义。由于很难事先知道情况是否如此，因此在地理学研究中普遍需要更多的方法。在这里，我们探讨了一种替代方法，利用信息理论来量化的概率给定的数据多个假设的地理数据分析。我们通过增加IMA中实现的模型选择过程，计算赤池信息准则得分和模型概率来实现这一点。我们生成了一个排名的17个模型，每个模型代表一组历史的进化过程，可能有贡献的进化Plethodon idahoensis，然后量化的相对强度的支持每个假设的数据，使用从信息理论借用的指标。我们的研究结果表明，两个模型有很高的概率给定的数据。这些模型中的每一个都包括人口的分歧和祖先h的估计，这与后代θ的估计不同，推论与该系统中以前的工作一致。然而，模型不一致，其中一个包括迁移作为参数，另一个没有，这表明有两个区域的参数空间，产生模型的可能性是相似的幅度给定我们的数据。模拟研究的结果表明，当数据模拟迁移，大多数的最优模型包括迁移作为一个参数，并进一步说，当所有的共享多态性的结果不完整的血统排序，大多数的最优模型不。结果也可能表明缺乏精确性，这可能是我们收集的数据量的产物。在任何情况下，我们已经应用到我们的数据分析的信息理论的指标是统计上严格的，假设检验方法，但超越了“拒绝/失败拒绝”的二分法的传统假设检验的方式，提供了相当多的灵活性，研究人员。
Data analysis in phylogeographic investigations is typically conducted in either a qualitative manner, or alternatively via the testing of null hypotheses. The former, where inferences about population processes are derived from geographical patterns of genetic variation, may be subject to confirmation bias and prone to overinterpretation. Testing the predictions of null hypotheses is arguably less prone to bias than qualitative approaches, but only if the tested hypotheses are biologically meaningful. As it is difficult to know a priori if this is the case, there is the general need for additional methodological approaches in phylogeographic research. Here, we explore an alternative method for analysing phylogeographic data that utilizes information theory to quantify the probability of multiple hypotheses given the data. We accomplish this by augmenting the model-selection procedure implemented in IMA with calculations of Akaike Information Criterion scores and model probabilities. We generate a ranking of 17 models each representing a set of historical evolutionary processes that may have contributed to the evolution of Plethodon idahoensis, and then quantify the relative strength of support for each hypothesis given the data using metrics borrowed from information theory. Our results suggest that two models have high probability given the data. Each of these models includes population divergence and estimates of ancestral h that differ from estimates of descendent theta, inferences consistent with prior work in this system. However, the models disagree in that one includes migration as a parameter and one does not, suggesting that there are two regions of parameter space that produce model likelihoods that are similar in magnitude given our data. Results of a simulation study suggest that when data are simulated with migration, most of the optimal models include migration as a parameter, and further that when all of the shared polymorphism results from incomplete lineage sorting, most of the optimal models do not. The results could also indicate a lack of precision, which may be a product of the amount of data that we have collected. In any case, the information-theoretic metrics that we have applied to the analysis of our data are statistically rigorous, as are hypothesis-testing approaches, but move beyond the 'reject/fail to reject' dichotomy of conventional hypothesis testing in a manner that provides considerably more flexibility to researchers.