Ancient genomes reveal over two thousand years of dingo population structure.

Dingoes are culturally and ecologically important free-living canids whose ancestors arrived in Australia over 3,000 B.P., likely transported by seafaring people. However, the early history of dingoes in Australia-including the number of founding populations and their routes of introduction-remains uncertain. This uncertainty arises partly from the complex and poorly understood relationship between modern dingoes and New Guinea singing dogs, and suspicions that post-Colonial hybridization has introduced recent domestic dog ancestry into the genomes of many wild dingo populations. In this study, we analyzed genome-wide data from nine ancient dingo specimens ranging in age from 400 to 2,746 y old, predating the introduction of domestic dogs to Australia by European colonists. We uncovered evidence that the continent-wide population structure observed in modern dingo populations had already emerged several thousand years ago. We also detected excess allele sharing between New Guinea singing dogs and ancient dingoes from coastal New South Wales (NSW) compared to ancient dingoes from southern Australia, irrespective of any post-Colonial hybrid ancestry in the genomes of modern individuals. Our results are consistent with several demographic scenarios, including a scenario where the ancestry of dingoes from the east coast of Australia results from at least two waves of migration from source populations with varying affinities to New Guinea singing dogs. We also contribute to the growing body of evidence that modern dingoes derive little genomic ancestry from post-Colonial hybridization with other domestic dog lineages, instead descending primarily from ancient canids introduced to Sahul thousands of years ago.

Population genetic simulation: Benchmarking frameworks for non-standard models of natural selection.

Population genetic simulation has emerged as a common tool for investigating increasingly complex evolutionary and demographic models. Software capable of handling high-level model complexity has recently been developed, and the advancement of tree sequence recording now allows simulations to merge the efficiency and genealogical insight of coalescent simulations with the flexibility of forward simulations. However, frameworks utilizing these features have not yet been compared and benchmarked. Here, we evaluate various simulation workflows using the coalescent simulator msprime and the forward simulator SLiM, to assess resource efficiency and determine an optimal simulation framework. Three aspects were evaluated: (1) the burn-in, to establish an equilibrium level of neutral diversity in the population; (2) the forward simulation, in which temporally fluctuating selection is acting; and (3) the final computation of summary statistics. We provide typical memory and computation time requirements for each step. We find that the fastest framework, a combination of coalescent and forward simulation with tree sequence recording, increases simulation speed by over twenty times compared to classical forward simulations without tree sequence recording, although it does require six times more memory. Overall, using efficient simulation workflows can lead to a substantial improvement when modelling complex evolutionary scenarios-although the optimal framework ultimately depends on the available computational resources.