The Early Peopling of the Philippines based on mtDNA

Miguel Arenas,Héctor Rangel-Villalobos,Catarina Branco,Antonio González-Martín,Elena Campoy,Juan Miguel Baquero,Amaya Gorostiza

doi:10.1038/s41598-020-61793-7

Abstract

Despite the efforts made to reconstruct the history of modern humans, there are still poorly explored regions that are key for understanding the phylogeography of our species. One of them is the Philippines, which is crucial to unravel the colonization of Southeast Asia and Oceania but where little is known about when and how the first humans arrived. In order to shed light into this settlement, we collected samples from 157 individuals of the Philippines with the four grandparents belonging to the same region and mitochondrial variants older than 20,000 years. Next, we analyzed the hypervariable I mtDNA region by approximate Bayesian computation based on extensive spatially explicit computer simulations to select among several migration routes towards the Philippines and to estimate population genetic parameters of this colonization. We found that the colonization of the Philippines occurred more than 60,000 years ago, with long-distance dispersal and from both north and south migration routes. Our results also suggest an environmental scenario especially optimal for humans, with large carrying capacity and population growth, in comparison to other regions of Asia. In all, our study suggests a rapid expansion of modern humans towards the Philippines that could be associated with the establisment of maritime technologies and favorable environmental conditions.

Highlights

Traditionally the goodness of fit analysis is only applied to the preferred scenario, we performed goodness of fit analyses (based on principal component analysis (PCA) and a comparison among the null distributions from the selected SS44) to all the evolutionary scenarios designed in the study
We did not apply partial least square (PLS) components to reduce the number of SS because our selected SS were informative enough and because PLS components can generate SS without biological meaning, being difficult to interpret and leading to biases when comparing components that are calculated separately[42]
If nested scenarios are analyzed together the performance of the method will be poor because nested scenarios under certain evolutionary circunstances can lead to similar genetic signatures (i.e., AllCorr vs AllSouth could be similar in cases where under AllCorr the migration rate from the north is very small by chance sampling from the prior distribution)

Summary

Introduction

Traditionally the goodness of fit analysis is only applied to the preferred scenario, we performed goodness of fit analyses (based on principal component analysis (PCA) and a comparison among the null distributions from the selected SS44) to all the evolutionary scenarios designed in the study. In addtion, for these analyses of goodness of fit we included the position of the real dataset on the simulated parameters landscape for every comparison of scenarios, providing a preliminary view of the fitting of every scenario with the real data. We selected the scenario that better fits with the real data applying the ABC approaches previously described (Pr, Rrej and Rnn)

Methods

Results

Discussion

Conclusion