From the eighth century onward, the Indian Ocean was the scene of extensive trade of sub-Saharan African slaves via sea routes controlled by Muslim Arab and Swahili traders. Several populations in present-day Pakistan and India are thought to be the descendants of such slaves, yet their history of admixture and natural selection remains largely undefined. Here, we studied the genome-wide diversity of the African-descent Makranis, who reside on the Arabian Sea coast of Pakistan, as well that of four neighboring Pakistani populations, to investigate the genetic legacy, population dynamics, and tempo of the Indian Ocean slave trade. We show that the Makranis are the result of an admixture event between local Baluch tribes and Bantu-speaking populations from eastern or southeastern Africa; we dated this event to ∼300 years ago during the Omani Empire domination. Levels of parental relatedness, measured through runs of homozygosity, were found to be similar across Pakistani populations, suggesting that the Makranis rapidly adopted the traditional practice of endogamous marriages. Finally, we searched for signatures of post-admixture selection at traits evolving under positive selection, including skin color, lactase persistence, and resistance to malaria. We demonstrate that the African-specific Duffy-null blood group-believed to confer resistance against Plasmodium vivax infection-was recently introduced to Pakistan through the slave trade and evolved adaptively in this P. vivax malaria-endemic region. Our study reconstructs the genetic and adaptive history of a neglected episode of the African Diaspora and illustrates the impact of recent admixture on the diffusion of adaptive traits across human populations.

Plankton seascape genomics show different trends from large-scale weak differentiation to micro-scale structures. Prior studies underlined the influence of environment and seascape on a few single species differentiation and adaptation. However, these works generally focused on few single species, sparse molecular markers, or local scales. Here, we investigate the genomic differentiation of plankton at macro-scale in a holistic approach using Tara Oceans metagenomic data together with a reference-free computational method to reconstruct the FST-based genomic differentiation of 113 marine planktonic species using metavariant species (MVS). These MVSs, modelling the species only by their polymorphism, include a wide range of taxonomic groups comprising notably 46 Maxillopoda/Copepoda, 24 Bacteria, 5 Dinoflagellates, 4 Haptophytes, 3 Cnidarians, 3 Mamiellales, 2 Ciliates, 1 Collodaria, 1 Echinoidea, 1 Pelagomonadaceae, 1 Cryptophyta and 1 Virus. The analyses showed that differentiation between populations was significantly lower within basins and higher in bacteria and unicellular eukaryotes compared to zooplantkon. By partitioning the variance of pairwise-FSTmatrices, we found that the main drivers of genomic differentiation were Lagrangian travel time, salinity and temperature. Furthermore, we classified MVSs into parameter-driven groups and showed that taxonomy poorly determines which environmental factor drives genomic differentiation. This holistic approach of plankton genomic differentiation for large geographic scales, a wide range of taxa and different oceanic basins, offers a systematic framework to analyse population genomics of non-model and undocumented marine organisms.

We provide a novel methodology for computing the most likely path taken by drifters between arbitrary fixed locations in the ocean. We also provide an estimate of the travel time associated with this path. Lagrangian pathways and travel times are of practical value not just in understanding surface velocities, but also in modelling the transport of ocean-borne species such as planktonic organisms, and oating debris such as plastics. In particular, the estimated travel time can be used to compute an estimated Lagrangian distance, which is often more informative than Euclidean distance in understanding connectivity between locations. Our methodology is purely data-driven, and requires no simulations of drifter trajectories, in contrast to existing approaches. Our method scales globally and can simultaneously handle multiple locations in the ocean. Furthermore, we provide estimates of the error and uncertainty associated with both the most likely path and the associated travel time.