During the last glacial–interglacial cycle, Arctic biotas experienced substantial climatic changes, yet the nature, extent and rate of their responses are not fully understood1–8. Here we report a large-scale environmental DNA metagenomic study of ancient plant and mammal communities, analysing 535 permafrost and lake sediment samples from across the Arctic spanning the past 50,000 years. Furthermore, we present 1,541 contemporary plant genome assemblies that were generated as reference sequences. Our study provides several insights into the long-term dynamics of the Arctic biota at the circumpolar and regional scales. Our key findings include: (1) a relatively homogeneous steppe–tundra flora dominated the Arctic during the Last Glacial Maximum, followed by regional divergence of vegetation during the Holocene epoch; (2) certain grazing animals consistently co-occurred in space and time; (3) humans appear to have been a minor factor in driving animal distributions; (4) higher effective precipitation, as well as an increase in the proportion of wetland plants, show negative effects on animal diversity; (5) the persistence of the steppe–tundra vegetation in northern Siberia enabled the late survival of several now-extinct megafauna species, including the woolly mammoth until 3.9 ± 0.2 thousand years ago (ka) and the woolly rhinoceros until 9.8 ± 0.2 ka; and (6) phylogenetic analysis of mammoth environmental DNA reveals a previously unsampled mitochondrial lineage. Our findings highlight the power of ancient environmental metagenomics analyses to advance understanding of population histories and long-term ecological dynamics.

Late Pliocene and Early Pleistocene epochs 3.6 to 0.8 million years ago1 had climates resembling those forecasted under future warming2. Palaeoclimatic records show strong polar amplification with mean annual temperatures of 11–19 °C above contemporary values3,4. The biological communities inhabiting the Arctic during this time remain poorly known because fossils are rare5. Here we report an ancient environmental DNA6 (eDNA) record describing the rich plant and animal assemblages of the Kap København Formation in North Greenland, dated to around two million years ago. The record shows an open boreal forest ecosystem with mixed vegetation of poplar, birch and thuja trees, as well as a variety of Arctic and boreal shrubs and herbs, many of which had not previously been detected at the site from macrofossil and pollen records. The DNA record confirms the presence of hare and mitochondrial DNA from animals including mastodons, reindeer, rodents and geese, all ancestral to their present-day and late Pleistocene relatives. The presence of marine species including horseshoe crab and green algae support a warmer climate than today. The reconstructed ecosystem has no modern analogue. The survival of such ancient eDNA probably relates to its binding to mineral surfaces. Our findings open new areas of genetic research, demonstrating that it is possible to track the ecology and evolution of biological communities from two million years ago using ancient eDNA.

We develop an analytical framework for predicting the fitness of hybrid genotypes, based on Fisher’s geometric model. We first show that all of the model parameters have a simple geometrical and biological interpretation. Hybrid fitness decomposes into intrinsic effects of hybridity and heterozygosity, and extrinsic measures of the (local) adaptedness of the parental lines; and all of these correspond to distances in a phenotypic space. We also show how these quantities change over the course of divergence, with convergence to a characteristic pattern of intrinsic isolation. Using individual-based simulations, we then show that the predictions apply to a wide range of population genetic regimes, and divergence conditions, including allopatry and parapatry, local adaptation and drift. We next connect our results to the quantitative genetics of line crosses in variable or patchy environments. This relates the geometrical distances to quantities that can be estimated from cross data, and provides a simple interpretation of the “composite effects” in the quantitative genetics partition. Finally, we develop extensions to the model, involving selectively-induced disequilibria, and variable phenotypic dominance. The geometry of fitness landscapes provides a unifying framework for understanding speciation, and wider patterns of hybrid fitness.

Highlights d We recovered genomic data from ancient bears directly from cave sediments d Ancient Mexican black bears are ancestrally related to Eastern American black bears d The Mexican and Yukon populations of extinct giant shortfaced bears were distinct d Our results bring ancient eDNA into the era of population genomics

When divergent populations form hybrids, hybrid fitness can vary with genome composition, current environmental conditions, and the divergence history of the populations. We develop analytical predictions for hybrid fitness, which incorporate all three factors. The predictions are based on Fisher's geometric model, and apply to a wide range of population genetic parameter regimes and divergence conditions, including allopatry and parapatry, local adaptation, and drift. Results show that hybrid fitness can be decomposed into intrinsic effects of admixture and heterozygosity, and extrinsic effects of the (local) adaptedness of the parental lines. Effect sizes are determined by a handful of geometric distances, which have a simple biological interpretation. These distances also reflect the mode and amount of divergence, such that there is convergence toward a characteristic pattern of intrinsic isolation. We next connect our results to the quantitative genetics of line crosses in variable or patchy environments. This means that the geometrical distances can be estimated from cross data, and provides a simple interpretation of the "composite effects." Finally, we develop extensions to the model, involving selectively induced disequilibria, and variable phenotypic dominance. The geometry of fitness landscapes provides a unifying framework for understanding speciation, and wider patterns of hybrid fitness.

MotivationThe simplifying assumptions that are used widely in theoretical population genetics may not always be appropriate for empirical population genetics. General computational approaches that do not require the assumptions of classical theory are therefore quite desirable. One such general approach is provided by the theory of absorbing Markov chains, which can be used to obtain exact results by directly analyzing population genetic Markov models, such as the classic bi-allelic Wright–Fisher model. Although these approaches are sometimes used, they are usually forgone in favor of simulation methods, due to the perception that they are too computationally burdensome. Here we show that, surprisingly, direct analysis of virtually any Markov chain model in population genetics can be made quite efficient by exploiting transition matrix sparsity and by solving restricted systems of linear equations, allowing a wide variety of exact calculations (within machine precision) to be easily and rapidly made on modern workstation computers.ResultsWe introduce Wright–Fisher Exact Solver (WFES), a fast and scalable method for direct analysis of Markov chain models in population genetics. WFES can rapidly solve for both long-term and transient behaviours including fixation and extinction probabilities, expected times to fixation or extinction, sojourn times, expected allele age and variance, and others. Our implementation requires only seconds to minutes of runtime on modern workstations and scales to biological population sizes ranging from humans to model organisms.Availability and ImplementationThe code is available at https://github.com/dekoning-lab/wfesSupplementary information Supplementary data are available at Bioinformatics online.

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Joint phylogenetic analysis of ancient DNA (aDNA) with modern phylogenies is hampered by low sequence coverage and post-mortem deamination, often resulting in over-conservative or incorrect assignment. We provide a new efficient likelihood-based workflow, pathPhynder, that takes advantage of all the polymorphic sites in the target sequence. This effectively evaluates the number of ancestral and derived alleles present on each branch and reports the most likely placement of an ancient sample in the phylogeny and a haplogroup assignment, together with alternatives and supporting evidence. To illustrate the application of pathPhynder, we show improved Y chromosome assignments for published ancient DNA sequences, using a newly compiled Y variation dataset (120,908 markers from 2,014 samples) that significantly enhances Y haplogroup assignment for low coverage samples. We apply the method to all published male ancient DNA samples from Africa, giving new insights into ancient migrations and the relationships between ancient and modern populations. The same software can be used to place samples with large amounts of missing data into other large non-recombining phylogenies such as the mitochondrial tree.