bioRxiv Science⌕ Search

Biology subjects

Wegmann, D.

Publications and source records attributed to Wegmann, D..

At least 19 recordsLinked to original sources

Paleogenomic time series reveal past population dynamics and their relevance for the conservation of the Alpine ibex

Endangered species often suffer from genetic threats such as inbreeding depression, the accumulation of mutation load and the general loss of genetic diversity, which may deprive them from future adaptation. Since the genetic make-up of a species is strongly affected by its evolutionary past, however, interspecific comparisons are difficult and absolute levels of genetic diversity may be a relatively poor predictor of extinction risk. To better interpret current levels of genetic diversity of actively managed species, the comparison of present-day genomic data to that of ancient material pre-dating recent anthropogenic population declines has been advocated, but remains rarely done at the evolutionary time scales necessary to understand past population dynamics. Here we demonstrate the usefulness of deep genomic time series through 40 high quality ancient genomes of the Alpine ibex, which faced near-extinction in the 19th century but has since been reintroduced across the Alps and recovered remarkably. The samples, which span the last 14,000 years, evidence strong and stable population structure at the continental scale, paired with frequent extinction and recolonization events at the local scale. These metapopulation dynamics resulted in small and largely isolated populations, with present-day, reintroduced populations only having 20% to 40% less neutral genetic diversity and 3% higher mutation load, likely due to efficient purging in the past. At the species level, however, the loss of genetic diversity was dramatic and reduced to the small fraction that segregated within the only surviving population in the Gran Paradiso area. Our analyses of ancient metapopulation dynamics in Alpine ibex underscore the importance of a healthy genetic pool for successful recolonization and long-term survival, as well as the real possibility of regional extinctions.

evolutionary biology↗

SweepLink: Joint Inference of Demography and Linked~Selection from Time-series Data

Genome-wide time-series data, i.e. allele frequency trajectories tracked across multiple sampling times, are among the richest sources of information for inferring selection. Beyond a beneficial allele's own rise in frequency, such data capture how it drags nearby loci upward via linkage, an effect known as genetic hitch-hiking. Yet most existing tools are single-locus, treating loci independently: they infer site-specific selection coefficients in isolation, then rely on ad hoc window statistics to account for hitch-hiking. Many existing tools further require a predefined population size, or scale poorly when jointly inferring selection and demography, and their power is highly sensitive to a significance threshold. To address these shortcomings, we here present SweepLink, a two-layer Hidden Markov Model that overcomes these limitations by jointly inferring demography and linked selection genome-wide: a spatial layer captures correlations between neighboring selection coefficients, coupled with a temporal Wright-Fisher diffusion layer. As we show with extensive simulations, this setup pushes drift-driven false signals toward neutrality while reinforcing loci that receive support from neighbouring loci, thereby increasing the sensitivity for weak and moderate selection, while matching the power of existing tools to detect strong selection. These simulations further show that SweepLink yields confident posteriors that remain stable at maximal significance, removing the need for arbitrary thresholds. We applied SweepLink to ancient DNA time-series data from the British population, previously analysed with a single-locus tool. SweepLink recovers four of the previously reported signals (LCT, SLC45A2, DHCR7, HERC2), and partially recovers the MHC/HLA signal. It also identifies additional candidate regions, including DPYD, FADS1/2 and OAS1, missed by the prior scan but supported by independent studies.

evolutionary biology↗

Evolutionary history and genomic vulnerability of the extinct giant deer Megaloceros giganteus

The extinct giant deer (Megaloceros giganteus) was one of the most striking megafaunal species of the Late Quaternary, distinguished by its enormous palmated antlers reaching up to 3.5 m across, the largest known among both living and extinct cervids. Despite its iconic status, little is known about its genomic history prior to extinction [~]8 thousand years ago (kya). We generated the first nuclear palaeogenomes for Megaloceros, represented by nine individuals from Germany ([~]40 kya) and Ireland ([~]11 kya), mapped to a new chromosome-level reference genome of the fallow deer (Dama dama). Phylogenomic analyses placed Megaloceros as sister to Dama (divergence [~]3.5 Ma) and revealed evidence of gene flow with ancestral Cervus lineages. Population analyses identified clear differentiation between German and Irish lineages, with higher genetic diversity in the German individuals. Two genes under strong positive selection, BNIPL and SLC10A7, are associated with apoptosis regulation and skeletal development/bone mineralisation, respectively, and may relate to the species large body and antler size. Demographic reconstructions indicate a long-term decline in effective population size, extremely low heterozygosity, little evidence of extensive runs of homozygosity, and an elevated burden of predicted deleterious alleles. Together, these results suggest that Megaloceros entered the terminal Pleistocene in a genomically fragile state, offering new insight into the biology and evolutionary legacy of one of the largest and most distinctive cervids that ever lived.

evolutionary biology↗

Gene-environment interactions govern early regeneration in fir and beech: evidence from participatory provenance trials across Europe

O_LIForest regeneration is shaped by strong demographic filters during germination and early establishment, yet the relative contributions of provenance and environment remain poorly resolved under natural conditions. C_LIO_LIWe combined climate-chamber trials with a continental-scale, field-based citizen science seed-sowing experiment to quantify germination, early phenological development, and three-year post-germination survival fir (Abies spp.) and beech (Fagus spp.) provenances across Europe. Using mixed-effects models and a state-based Markov framework to reconstruct phenological trajectories, we quantified successive demographic filters from seed to established seedling. C_LIO_LIGermination was structured by genotype-by-environment interactions (G x E) in both genera, but with contrasting expression: fir showed broadly parallel environmental responses among provenances and partial concordance between climate-chamber and field, whereas beech showed strong provenance-by-environment responses and weak predictability from controlled conditions. Seed weight was a key axis in beech (lighter seeds germinated more and developed faster), but confounded with geographic differentiation in fir. C_LIO_LIIn contrast to germination, post-establishment survival was dominated by site-level environmental filtering with limited consistent provenance effects, revealing a life-stage shift from fine-scale G x E during germination and early development to broader environmental control of survival--an important consideration for regeneration--based management and assisted migration. C_LI

evolutionary biology↗

Testing for changes in population trends from low-cost ecological count data

O_LIAccurate and up-to-date knowledge of population trends is essential for effective biodiversity conservation, as is assessing the impact of conservation measures designed to alter these trends. Estimating population trends is challenging, however, either due to altogether insufficient data or due to so-called noisy data that do not readily allow for standard statistical analyses. In addition, many existing methods require monitoring data over long periods of time, which is in contrast to the quick interventions needed by conservation projects, especially when endangered species are involved. C_LIO_LITo address these issues, we here present birp, a novel Bayesian tool that maximizes the power to test for population trends and changes in trends under arbitrary designs, including the canonical before-after (BA), control-intervention (CI) and before-after-control-intervention (BACI) designs often used to assess conservation impact. Our model builds on classic Poisson and negative binomial models for ecological count data and infers changes in population trends jointly from data obtained with multiple survey methods such as track counts, camera trap surveys, or distance sampling, and also from limited and noisy data not necessarily collected in standardized ecological surveys. By focusing on the change itself, our method side-steps common challenges of estimating population trends and does not need to know about absolute population densities or detection probabilities. birp is open-source and available as both a standalone command line tool as well as an R-package for fast and easy use. C_LIO_LIWe illustrate the power of our tool through extensive simulations and show that changes in trends are accurately estimated under various designs, even when data are noisy and sparse, and thereby enables biodiversity research also in regions that are remote and difficult to access. C_LIO_LIUsing birp, we further test for changes in population trends of Tasmanian devils in Australia and of apex predators and their main prey in the Central African Republic. Based on these results we give general guidelines on survey designs that maximize the power to detect trends. C_LI

ecology↗

Local increases in admixture with hunter-gatherers followed the initial expansion of Neolithic farmers across continental Europe

The replacement of hunter-gatherer lifestyles by agriculture represents a pivotal change in human history. The initial stage of this Neolithic transition in Europe was instigated by the migration of farmers from Anatolia and the Aegean basin. In this study, we modeled the expansion of Neolithic farmers into Central Europe from Anatolia, along the Continental route of dispersal. We employed spatially explicit simulations of palaeogenomic diversity and high-quality palaeogenomic data from 67 prehistoric individuals to assess how population dynamics between indigenous European hunter-gatherers and incoming farmers varied across space and time. Our results demonstrate that admixture between the two groups increased locally over time at each stage of the Neolithic expansion along the Continental route. We estimate that the effective population size of farmers was about five times that of the hunter-gatherers. Additionally, we infer that sporadic long distance migrations of early farmers contributed to their rapid dispersal, while competitive interactions with hunter-gatherers were limited. TeaserThe first farmers of continental Europe increasingly admixed over time with indigenous hunter-gatherers.

genomics↗

Inference of Locus-Specific Population Mixtures From Linked Genome-Wide Allele Frequencies

1Admixture between populations and species is common in nature. Since the influx of new genetic material might be either facilitated or hindered by selection, variation in mixture proportions along the genome is expected in organisms undergoing recombination. Various graph-based models have been developed to better understand these evolutionary dynamics of population splits and mixtures. However, current models assume a single mixture rates for the entire genome and do not explicitly account for linkage. Here, we introduce TreeSwirl, a novel method for inferring branch lengths and locus-specific mixture proportions by using genome-wide allele frequency data, assuming that the admixture graph is known or has been inferred. TreeSwirl builds upon TreeMix that uses Gaussian processes to estimate the presence of gene flow between diverged populations. However, in contrast to TreeMix, our model infers locus-specific mixture proportions employing a Hidden Markov Model that accounts for linkage. Through simulated data, we demonstrate that TreeSwirl can accurately estimate locus-specific mixture proportions and handle complex demographic scenarios. It also outperforms related D- and f-statistics in terms of accuracy and sensitivity to detect introgressed loci.

genomics↗

Accurate Bayesian inference of sex chromosome karyotypes and sex-linked scaffolds from low-depth sequencing data

1The identification of sex-linked scaffolds and the genetic sex of individuals, i.e. their sex karyotype, is a fundamental step in population genomic studies. If sex-linked scaffolds are known, single individuals may be sexed based on read counts of next-generation sequencing data. If both sex-linked scaffolds as well as sex karyotypes are unknown, as is often the case for non-model organisms, they have to be jointly inferred. For both cases, current methods rely on arbitrary thresholds, which limits their power for low-depth data. In addition, most current methods are limited to euploid sex karyotypes (XX and XY). Here we develop BeXY, a fully Bayesian method to jointly infer the posterior probabilities for each scaffold to be autosomal, X-or Y-linked and for each individual to be any of the sex karyotypes XX, XY, X0, XXX, XXY, XYY and XXYY. If the sex-linked scaffolds are known, it also identifies autosomal trisomies and estimates the sex karyotype posterior probabilities for single individuals. As we show with downsampling experiments, BeXY has higher power than all existing methods. It accurately infers the sex karyotype of ancient human samples with as few as 20,000 reads and accurately infers sex-linked scaffolds from data sets of just a handful of samples or with highly imbalanced sex ratios, also in the case of low-quality reference assemblies. We illustrate the power of BeXY by applying it to both whole-genome shotgun and target enrichment sequencing data of ancient and modern humans, as well as several non-model organisms.

genomics↗

The spatial distribution and temporal trends of livestock damages caused by wolves in Europe

Wolf populations are recovering and expanding across Europe, causing conflicts with livestock owners. To mitigate these conflicts and reduce livestock damages, authorities spend considerable resources to compensate damages, support damage prevention measures, and manage wolf populations. However, the effectiveness of these measures remains largely unknown, especially at larger geographic scales. Here we compiled incident-based livestock damage data across 21 countries for the years 2018, 2019 and 2020, during which 39,262 wolf-caused incidents were reported from 470 administrative regions. We found substantial regional variation in all aspects of the data, including the primary target species, the density of damages, their seasonal distribution, and their temporal trend. More than half of the variation in damage densities across regions is explained by the area of extensively cultivated habitats occupied by wolves and namely natural grasslands and broad-leaved forests. Regional variation in husbandry practices and damage prevention, while difficult to quantify at a continental scale, appear important factors to further modulate these incidents. As illustrated with detailed data from Germany, for instance, the relationship between the number of wolf units and damages is diminishing over time, suggesting some adaptation of livestock owners and local authorities to their presence, for example by increasing prevention efforts. As we argue, temporal trends of damage incidents, which are robust to variation in data collection across regions, are thus informative about the local intensity of the wolf-human conflict. We estimated increasing trends for the majority of regions, reflecting the current expansion of wolves across the continent. Nonetheless, many of these increases were moderate and for more than one third of all regions, trends were negative despite growing wolf populations, thus indicating that wolf-livestock conflicts can be successfully mitigated with proper management.

ecology↗

Between fishing and farming: palaeogenomic analyses reveal cross-cultural interactions triggered by the arrival of the Neolithic in the Danube Gorges

While early Neolithic populations in Europe were largely descended from early Aegean farmers, there is also evidence of episodic gene flow from local Mesolithic hunter-gatherers into early Neolithic communities. Exactly how and where this occurred is still unknown. Here we report direct evidence for admixture between the two groups at the Danube Gorges in Serbia. Analysis of palaeogenomes recovered from skeletons revealed that second-generation mixed individuals were buried amidst individuals whose ancestry was either exclusively Aegean Neolithic or exclusively local Mesolithic. The mixed ancestry is also reflected in a corresponding mosaic of grave goods. With its deep sequence of occupation and its unique dwellings that suggest at least semi-sedentary occupation since the late Mesolithic, the area of the Danube Gorges has been at the center of the debate about the contribution of Mesolithic societies to the Neolithisation of Europe. As suggested by our data, which were processed exclusively with uncertainty-aware bioinformatic tools, it may have been precisely in such contexts that close interactions between these societies were established, and Mesolithic ancestry and cultural elements were assimilated.

genomics↗

Climate-induced range shifts drive adaptive response via spatio-temporal sorting of alleles

Quaternary climate fluctuations drove many species to shift their geographic ranges, in turn shaping their genetic structures. Recently, it has been argued that adaptation may have accompanied species range shifts via the "sieving" of genotypes during colonisation and establishment. However, this has not been directly demonstrated, and knowledge remains limited on how different evolutionary forces, that are typically investigated separately, interacted to jointly mediate species responses to past climatic change. Here, through whole-genome resequencing of over 1200 individuals of the carnation Dianthus sylvestris coupled with integrated population genomic and gene-environment models, we reconstruct the past neutral and adaptive landscape of this species as it was shaped by the Quaternary glacial cycles. We show that adaptive responses emerged concomitantly with the post-glacial range shifts and expansions of this species in the last 20 thousand years. This was due to the spatial sorting of adaptive alleles through time, as populations expanded out of restrictive glacial refugia into the broader and more heterogeneous range of habitats available in the present-day inter-glacial. Our results reveal a tightly-linked interplay of migration and adaptation under past climate-induced range shifts that has acted to shape the spatial patterns of adaptive genetic variation we see in the species today.

evolutionary biology↗

The mixed genetic origin of the first farmers of Europe

The precise genetic origins of the first Neolithic farming populations, as well as the processes and the timing of their differentiation, remain largely unknown. Based on demogenomic modeling of high-quality ancient genomes, we show that the early farmers of Anatolia and Europe emerged from a multiphase mixing of a Near Eastern population with a strongly bottlenecked Western hunter-gatherer population after the Last Glacial Maximum. Moreover, the population branch leading to the first farmers of Europe and Anatolia is characterized by a 2,500-year period of extreme genetic drift during its westward range expansion. Based on these findings, we derive a spatially explicit model of the population history of Southwest Asia and Europe during the late Pleistocene and early Holocene. One-Sentence SummaryEarly European farmers emerged from multiple post LGM mixtures and experienced extreme drift during their westward expansion.

evolutionary biology↗

Selection on ancestral genetic variation fuels parallel ecotype formation in bottlenose dolphins

What are the mechanisms that allow species to extend their ranges and adapt to the novel environmental conditions they find in the newly available habitat? The study of parallel adaptation of pairs of populations to similar environments can provide great insights into this question. Here, we test for parallel evolution driven by niche specialization in a highly social marine mammal, the common bottlenose dolphin, Tursiops truncatus, and investigate the origins of the genetic variation driving local adaptation. Coastal ecotypes of common bottlenose dolphins have recurrently emerged in multiple regions of the world from pelagic ecotype populations, when novel habitat became available. Analyzing the whole genomes of 57 individuals using comparative population genomics approaches, we found that coastal ecotype evolution was relatively independent between the Atlantic and Pacific, but related between different regions within the Atlantic. We show that parallel adaptation to coastal habitat was facilitated by repeated selection on ancient alleles present as standing genetic variation in the pelagic populations. Genes under parallel adaptation to coastal habitats have roles in cognitive abilities and feeding. Therefore, parallel adaptation in long-lived social species may be driven by a combination of ecological opportunities, selection acting on ancient variants, and stable behavioural transmission of ecological specialisations. Tried and tested genetic variation that has been subject to repeated bouts of selection, may promote linked adaptive variants with minimal pleiotropic effects, thereby facilitating their persistence at low frequency in source populations and enabling parallel evolution.

evolutionary biology↗

Identifying loci under selection via explicit demographic models

Adaptive genetic variation is a function of both selective and neutral forces. To accurately identify adaptive loci, it is thus critical to account for demographic history. Theory suggests that signatures of selection can be inferred using the coalescent, following the premise that genealogies of selected loci deviate from neutral expectations. Here, we build on this theory to develop an analytical framework to identify Loci under Selection via explicit Demographic models (LSD). Under this framework, signatures of selection are inferred through deviations in demographic parameters, rather than through summary statistics directly, and demographic history is accounted for explicitly. Leveraging on the property of demographic models to incorporate directionality, we show that LSD can provide information on the environment in which selection acts on a population. This can prove useful in elucidating the selective processes underlying local adaptation, by characterising genetic trade-offs and extending the concepts of antagonistic pleiotropy and conditional neutrality from ecological theory to practical application in genomic data. We implement LSD via Approximate Bayesian Computation and demonstrate, via simulations, that LSD has i) high power to identify selected loci across a large range of demographic-selection regimes, ii) outperforms commonly applied genome-scan methods under complex demographies, and iii) accurately infers the directionality of selection for identified candidates. Using the same simulations, we further characterise the behaviour of isolation-with-migration models conducive to the study of local adaptation under regimes of selection. Finally, we demonstrate an application of LSD by detecting loci and characterising genetic trade-offs underlying flower colour in Antirrhinum majus.

evolutionary biology↗

Kinship, acquired and inherited status, and population structure at the Early Bronze Age Mokrin necropolis in northern Serbia.

Twenty-four ancient genomes with an average sequencing coverage of 0.85{+/-}0.25 X were produced from the Mokrin necropolis, an Early Bronze Age (2,100-1,800 BC) Maros culture site in Serbia, to provide unambiguous identification of biological sex, population structure, and genetic kinship between individuals. Of the 24 investigated individuals, 15 were involved in kinship relationships of varying degrees, including 3 parent-offspring relationships. All observed parent-offspring pairs were mother and son. In addition to the absence of biological daughters, we observed a number of young women and girls with no biological relatives in our sample. These observations, together with the high mitochondrial diversity in our sample, are consistent with the practice of female exogamy in the population served by Mokrin. However, moderate-to-high Y-chromosomal diversity suggests a degree of male mobility greater than that expected under strict patrilocality. Individual status differences at Mokrin, as indicated by grave goods, support the inference that females could inherit status, but could not transmit status to all their sons. The case of a son whose grave good richness outstrips that of his biological mother suggests that sons had the possibility to acquire status during their lifetimes. The Mokrin sample resembles a genetically unstructured population, suggesting that the communitys social hierarchies were not accompanied by strict marriage barriers.

genomics↗

A sparse occupancy model to quantify species interactions in time and space

Camera traps and acoustic recording devices are essential tools to quantify the distribution, abundance and behavior of mobile species. Varying detection probabilities among device locations must be accounted for when analyzing such data, which is generally done using occupancy models. We introduce a Bayesian Time-dependent Observation Model for Camera Trap data (Tomcat), suited to estimate relative event densities in space and time. Tomcat allows to learn about the environmental requirements and daily activity patterns of species while accounting for imperfect detection. It further implements a sparse model that deals well will a large number of potentially highly correlated environmental variables. By integrating both spatial and temporal information, we extend the notation of overlap coefficient between species to time and space to study niche partitioning. We illustrate the power of Tomcat through an application to camera trap data of eight sympatrically occurring duiker Cephalophinae species in the savanna - rainforest ecotone in the Central African Republic and show that most species pairs show little overlap. Exceptions are those for which one species is very rare, likely as a result of direct competition.

ecology↗

Detecting selection from linked sites using an F-model

Allele frequencies vary across populations and loci, even in the presence of migration. While most differences may be due to genetic drift, divergent selection will further increase differentiation at some loci. Identifying those is key in studying local adaptation, but remains statistically challenging. A particularly elegant way to describe allele frequency differences among populations connected by migration is the F-model, which measures differences in allele frequencies by population specific FST coefficients. This model readily accounts for multiple evolutionary forces by partitioning FST coefficients into locus and population specific components reflecting selection and drift, respectively. Here we present an extension of this model to linked loci by means of a hidden Markov model (HMM) that characterizes the effect of selection on linked markers through correlations in the locus specific component along the genome. Using extensive simulations we show that our method has up to two-fold the statistical power of previous implementations that assume sites to be independent. We finally evidence selection in the human genome by applying our method to data from the Human Genome Diversity Project (HGDP).

genomics↗

Estimating and accounting for genotyping errors in RAD-seq experiments

In non-model organisms, evolutionary questions are frequently addressed using reduced representation sequencing techniques due to their low cost, ease of use, and because they do not require genomic resources such as a reference genome. However, evidence is accumulating that such techniques may be affected by specific biases, questioning the accuracy of obtained genotypes, and as a consequence, their usefulness in evolutionary studies. Here we introduce three strategies to estimate genotyping error rates from such data: through the comparison to high quality genotypes obtained with a different technique, from individual replicates, or from a population sample when assuming Hardy-Weinberg equilibrium. Applying these strategies to data obtained with Restriction site Associated DNA sequencing (RAD-seq), arguably the most popular reduced representation sequencing technique, revealed per-allele genotyping error rates that were much higher than sequencing error rates, particularly at heterozygous sites that were wrongly inferred as homozygous. As we exemplify through the inference of genome-wide and local ancestry of well characterized hybrids of two Eurasian poplar (Populus) species, such high error rates may lead to wrong biological conclusions. By properly accounting for these error rates in downstream analyses, either by incorporating genotyping errors directly or by recalibrating genotype likelihoods, we were nevertheless able to use the RAD-seq data to support biologically meaningful and robust inferences of ancestry among Populus hybrids. Based on these findings, we strongly recommend carefully assessing genotyping error rates in reduced representation sequencing experiments, and to properly account for these in downstream analyses, for instance using the tools presented here.

evolutionary biology↗