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Molecular Ecology

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Online ISSN: 1365-294X

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Print ISSN: 0962-1083

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272 reads in the past 30 days

Relationships between richness and biomass for bacteria and fungi as evaluated by regression analyses, considering all soils jointly (a), separately by land use (b), or separately by climate (c). Shaded areas represent 95% confidence intervals for the regression line. Adjusted R² and p‐values (p) are shown for each regression analysis. n.s., not significant.
Box plots comparing richness:biomass ratios for bacteria and fungi among land uses and climates (a) and among land uses under arid, temperate and cold climates (b). In (a), p‐values (p) of two‐way PERMANOVA for the factors land use (LU) and climate (C), and their interaction are shown. Different letters above each box denote significant differences among land uses or climates according to pairwise permutation tests. In (b), different lowercase letters above each box denote significant differences among climates within each land use, and different capital letters denote significant differences among land uses within each climate. The boxes represent the interquartile range (IQR) between the first and third quartiles (25th and 75th percentiles, respectively), and the vertical line inside the box defines the median. Whiskers represent the lowest and highest values within 1.5 times the IQR from the first and third quartiles, respectively. Dots represent outliers.
Random forest mean predictor importance (% increase in MSE (mean square error)) of the studied variables as predictors of the variations in bacterial and fungal richness:biomass ratios across land uses and climates. Significance levels are shown at *p < 0.05 and **p < 0.01. Predictors belonging to the same category were represented with the same colour according to the legend. VE, variance explained; AI, aridity index; BD, bulk density; EC, electrical conductivity; K, extractable potassium; LU, land use; MAP, mean annual precipitation; MAT, mean annual temperature; N, soil total nitrogen; NPP, net primary production; P, available phosphorus; Sand, silt and clay, soil sand, silt and clay contents, respectively; SOC, soil organic carbon.
Relationships between bacterial (a) and fungal (b) richness:biomass ratios and selected environmental variables as evaluated by regression analyses. Shaded areas represent 95% confidence intervals for the regression line. The best model (linear or quadratic) fitting each regression is indicated at the top of each figure in the panel. Adjusted R² and p‐values (p) are shown for each regression analysis. BD, bulk density; MAP, mean annual precipitation; N, soil total nitrogen; Sand, silt and clay, soil sand, silt and clay contents, respectively; SOC, soil organic carbon.
SEM (structural equation modelling) assessing the direct and indirect effects of selected variables on bacterial and fungal richness:biomass ratios. Numbers adjacent to arrows are standardised path coefficients and are indicative of the effect size. Only significant effects (p < 0.05) are indicated, and significance levels are shown at *p < 0.05, **p < 0.01 and ***p < 0.001. Continuous, dashed and dash‐dotted arrows indicate positive, negative and mixed relationships, respectively. R² denotes the proportion of variance explained for every response variable by the model. The model's goodness‐of‐fit was evaluated by the Fisher's C statistic and the AIC value. AI, aridity index; BD, bulk density; K, extractable potassium; N, soil total nitrogen; NPP, net primary production; P, available phosphorus; Sand, soil sand content; SOC, soil organic carbon.
Land Use Interacts With Climate to Influence Microbial Diversity‐To‐Biomass Ratios Across Europe via Soil Organic Carbon and Nitrogen

May 2025

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274 Reads

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219 reads in the past 30 days

Dispersal, Isolation and Local Adaptation Promote Speciation in South American Savannas as Indicated by a Phylogenomic Analysis of a Passerine

June 2025

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226 Reads

Aims and scope


Molecular Ecology is an international journal for research that utilizes molecular genetic techniques to address consequential questions in ecology, evolution, behavior and conservation. Our studies use neutral markers for inference about ecological and evolutionary processes or examine ecologically important genes and their products. We also publish articles on technical methods, computer programs and genomic resource development in our companion journal, Molecular Ecology Resources. We encourage papers that are primarily descriptive or relevant to only one taxon to be submitted to a more specialized journal, such as our sister journal Ecology and Evolution.

Recent articles


Map highlighting the locations (legend) where we derived the 62 root samples as well as (respective smaller symbols in grey) the locations of historic records of the three species (Li and Veresoglou 2023).
Heatmap of the molecular dataset in our study. We highlight in pink boxes the two most abundant taxa (OTU1 and OTU2) and in pink fonds the six taxa that maintained the highest relative abundance (these six taxa cumulatively accounted for over 61% of the reads in our dataset). We note with green stars the two OTUs (Q1 threshold) that were indicators of kernel distributions and in blue the two (Q1 threshold) that were indicators of edge distributions. For more information kindly consult Table 1.
Relationships between (standardised) distance from the kernel (a), organic matter content (b), pH (c) and sand content (d) in the x‐axis and AMF richness (y‐axis). We rarefied the OTU community table to 2500 reads per sample, and the only significant relationship was with distance from the kernel in (a). Both the conditional and marginal R² values for panel (a) were 0.03—that is, the analysis explains a 6% of the variance, split evenly between the effect of distance and the differences in the intercepts across species. Individual relationships between distance and richness, when assessed with non‐parametric Kendall Tau tests, were only significant in the case of Machilus—tau = 0.50, p = 0.0047, but in all cases, there was a trend for richness increases towards the edges of the respective distributions (highlighted with the respective best‐fit lines—for Xanthophyllum there was a positive Pearson correlation of richness with log transformed distance—r = 0.56, p = 0.02). The take home message of the display item is that the distance (even in the crude form with which we were considering it here) from the Kernel is the only effective predictor (other than plant host) in our dataset of AMF richness in the roots of the plants.
(a) ordination graph (RDA) of the two first axes. We used as predictors distance from the Kernel, pH, organic matter and plant species. (b) Partitioning of community variance across three pools: host selectivity, distribution and soil properties. In both the two panels, we Hellinger transformed the community table prior to the analyses. The take‐home message of the display item is that the distribution (i.e., distance from the Kernel of the distribution) explains a small but independent fraction of community variation.
Mycorrhiza at the Cutting Edge: Trees at the Edges of Their Distribution Host Diverse and Distinct AMF Communities
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June 2025

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17 Reads

Abiotic conditions deteriorate predictably at the edges of plant distribution ranges. Any adverse environmental settings could interfere with the functioning of nutritional mutualisms, such as arbuscular mycorrhiza. Documenting how transitioning from the kernel of a plant distribution towards the edges changes how mycorrhiza functions remains nonetheless underexplored. We questioned here whether arbuscular mycorrhizal fungal (AMF) richness and AMF community turnover decline towards the edges of the distribution of three AMF‐associating tree species, Artocarpus hypargyreus, Machilus breviflora and Xanthophyllum hainanense. We assayed roots from 62 tree individuals over a representative fraction of their distribution range and used molecular tools to assay AMF. Contrary to our expectations, we observed increases in AMF richness across marginal populations of those tree species and minimal differences in community turnover. We observed many AMF indicator species of marginal populations of the three hosts, which nonetheless all represented rare AMF taxa. Remarkably, the distance from the kernel of the distribution explained an independent fraction of AMF community variance compared to abiotic parameters and host selectivity. We uncover a novel axis of AMF community variation, the relative distance between the kernel and the edges of the distribution for the plant host. Exploring the significance of this new axis could bring new insights into the functioning of arbuscular mycorrhizal fungi by addressing questions related to how mycorrhizal plants handle stress and what drives host selectivity in the arbuscular mycorrhiza.


Range‐wide population genomic structure of the grey seal inferred by PCA. (A) Map of sampling localities; (B) Range‐wide population structure for PC1‐PC2, suggesting three main genetic clusters; (C) PC1‐PC2 for Northeast Atlantic and Baltic populations only; (D) PC1‐PC2 for Northeast Atlantic populations only. See Figures S7–S10 for additional PC axes. The fraction of total variation in the dataset explained for each PC is provided as a percentage. Localities within a broader region are colour coded with blue from the Northwest Atlantic, orange from the Northeast Atlantic, and red from the Baltic Sea.
Grey seal population genomic structure assessed by admixture analysis in NGSadmix for K2‐6, see Figure S11 for K6‐17. Arrows below K = 6 indicate grey seals that are migrants or admixed individuals between different regional populations.
Comparison of standardised FST values with geographical distances between localities to test for isolation by distance across the grey seal's range (A), as well as within the Northeast Atlantic (B) and Baltic Sea (C).
Connectivity and diversity of grey seals inferred from (A) effective migration parameter (Nm) assessed with divMigrate and (B) genetic diversity estimated as the proportion of heterozygous sites per individual. The legend indicating the sampling locality region is applicable to both subplots. The arrow weight legend for Nm indicates the relative strength of effective migration. Black arrows indicate effective migration between different regions while effective migration within a region is coloured according to the region.
Demographic analyses for the NE Atlantic (orange) and Baltic Sea (red) regions for the past (A) 200 and (B) 20 generations using GONE (Santiago et al. 2020). The NW Atlantic region was excluded from this analysis due to a limited sample size. Forty iterations were run excluding 10% of the population with each iteration to calculate a mean (bolded line) and 95% confidence intervals (shaded ribbons).
Range‐Wide Genomic Analysis Reveals Regional and Meta‐Population Dynamics of Decline and Recovery in the Grey Seal

June 2025

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82 Reads

Wildlife populations globally have experienced widespread historical declines due to anthropogenic and environmental impacts, yet for some species, contemporary management and conservation programmes have enabled recent recovery. The impacts of decline and recovery on genomic diversity and, vice versa, the genetic factors that contribute to conservation success or failure are rich areas for inquiry, with implications for shaping how we manage species into the future. To comprehensively characterise these processes in natural systems requires range‐wide sampling and international collaboration, particularly for species with wide dispersal capabilities, broad geographic distributions, and complex regional metapopulation dynamics. Here, we present the first range‐ and genome‐wide population genomic analysis of grey seals based on 3812 nuclear SNPs genotyped in 188 samples from 17 localities. Our analyses support the existence of three main grey seal populations centred in the NW Atlantic, NE Atlantic and Baltic Sea, and point to the existence of previously unrecognised substructure within the NE Atlantic. We detected remarkably low levels of genetic diversity in the NW Atlantic population, and demographic analyses revealed a turbulent history of NE Atlantic and Baltic Sea grey seals, with bottlenecks in the Middle Ages and the 20th century due to hunting and habitat alterations. We found some localities deviated from isolation by distance patterns, likely reflecting wide‐scale metapopulation dynamics associated with recolonisation and recovery in regions where they were historically extirpated. We identify at least six grey seal genetic populations and reveal marked genetic effects of past declines and recent recovery across the species' range.


Leaves of herbarium specimens illustrating the variation in leaf shape found in Pyrus of southwestern Kyrgyzstan.
Results of analyses of genetic structure of Pyrus in Kyrgyzstan. (A) Map of sampling locations, with the genetic composition of each population as identified by STRUCTURE admixture proportions (from 2B) indicated by the pie charts. Inset shows the study region in Central Asia. (B) Results of STRUCTURE analysis at K = 3, with each bar representing an individual, and the colours representing the genetic groups to which individuals are assigned. Populations of origin are indicated with black bars below the individuals. Population names with asterisks indicate the presence of clonal individuals as indicated by identity analysis. Putative hybrids detected by STRUCTURE are denoted by dots. (C) Results of Principal Components Analysis (PCA) of genetic variation in the full data set, showing PCs 1 and 2, with individuals coloured by STRUCTURE admixture proportions.
Hybrid index assessment of putative hybrids identified by PCA and STRUCTURE with error bars representing 95% credible intervals for: (A) P. korshinskyi x P. communis and (B) P. korshinskyi × P regelii.
Results of analyses of genetic structure within Pyrus korshinskyi in Kyrgyzstan (unique P. korshinskyi dataset). (A) Results of STRUCTURE analysis at K = 2, with each bar representing an individual, and the colours representing the genetic groups to which individuals are assigned. Populations of origin are indicated with black bars below the individuals, with grey bars indicating population type (i.e., wild, propagated, or ex situ). (B) Results of Principal Components Analysis (PCA) of genetic variation in the dataset containing only P. korshinskyi, showing PCs 1 and 2, coloured by population type.
Preserving Wild Pears: Using Genomic Data to Assess Species Boundaries, Interspecific Hybridization, and Genetic Diversity to Inform Conservation

Wild congeners of cultivated fruit trees are vital sources of genetic diversity for crop improvement and key targets for conservation. However, cultivating crops within the range of rare wild congeners increases the risk of interspecific hybridization, threatening the genomic integrity of wild species. This is a concern for Pyrus in Central Asia, where a critically endangered wild species, P. korshinskyi, coexists with cultivated Pyrus communis and another widespread species, P. regelii, forming a species complex in which species boundaries are unclear, complicating conservation efforts. Here, we sought to assess: (1) the distinctiveness of species, (2) the extent to which interspecific hybridization and introgression may reduce the genetic integrity of P. korshinskyi, and (3) genetic diversity and structure within P. korshinskyi. Using RAD sequencing to genotype 185 individuals from 13 presumed wild and ex situ populations in Kyrgyzstan, we found that P. korshinskyi is genetically distinct, highly morphologically variable, but occasionally hybridises with both P. regelii and P. communis. Morphometric analyses indicate that the parental species and hybrids can be differentiated based on leaf characters. Unexpectedly, several reportedly wild populations of P. korshinskyi were found to be clonally propagated; unfortunately, because Pyrus exhibits gametophytic self‐incompatibility and all of the trees are the same genotype, these propagated populations are mate limited, limiting their conservation value. While P. korshinskyi populations are genetically diverse, further efforts are needed to preserve wild genetic diversity ex situ. These findings guide conservation strategies to maintain genetic integrity and diversity of P. korshinskyi both in situ and ex situ, underscoring the importance of genetic analyses for conserving crop wild relatives, especially in complex cultivated‐wild mosaics.


Behavioural measurements for alarm cue‐ and control cue‐exposed guppies. (A) Distance travelled (cm) (proxy for activity), (B) time spent in shelter and in outer edge of tank (s) (proxy for boldness) and (C) number of squares explored (proxy for exploration) were measured in a modified open field test. (D) Preference for a container containing a female shoal over an empty container (proxy for shoaling) was measured in a shoaling test. A positive number indicates a preference for the container containing a shoal. Linear mixed models were run with each behavioural measurement as the outcome and cue type as a predictor. Tank was included as a random effect in all models. Sex was included as a fixed effect in all models, and body mass was included as a fixed effect in all models except for the shoaling one. Significance of cue in the linear mixed models is shown on each plot.
Differential methylation analysis results. (A and B) Heatmap of differentially methylated regions (DMR) with hierarchical clustering of samples for (A) females and (B) males. Each row is a DMR and each column is an individual. Scaled percent methylation for each DMR is displayed in the heatmap. (C and D) Proportion of DMSs and differentially methylated regions (DMRs) that are hypo‐ or hyper‐methylated for (C) females and (D) males. Direction of methylation is determined by comparing alarm cue fish to control fish such that hypermethylation means there is more methylation in the alarm cue fish. Asterisk indicates a significant difference between the proportion of hyper‐ and hypo‐methylation found using a Chi‐Square goodness‐of‐fit test (p < 0.0001). (E and F) Proportion of CpGs and DMSs, located in exons, intergenic regions, introns or promoters for (E) females and (F) males. Asterisks indicate significant differences from the null distribution (constructed from the distribution of all CpGs) found using a G‐test (p < 0.05 for all).
Developmental Behavioural Plasticity and DNA Methylation Patterns in Response to Predation Stress in Trinidadian Guppies

June 2025

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14 Reads

Early‐life experiences can predict the environments experienced later in life, giving individuals an opportunity to develop adaptive behaviour appropriate to a likely future environment. Epigenetic mechanisms such as DNA methylation (DNAm) have been implicated in developmental behavioural plasticity; however, studies investigating this possibility are limited in taxonomic breadth and ecological relevance. We investigated the impact of early‐life exposure to predation stress on behaviour and DNAm in the brains of Trinidadian guppies (Poecilia reticulata). We exposed guppies throughout development to either an alarm cue (conspecific skin extract), inducing predation stress, or a control cue (water) for 8 weeks and then raised them to adulthood under identical conditions. Then, we conducted two behavioural assays, an open‐field and a grouping test, before performing whole‐genome bisulfite sequencing on whole brains. Guppies exposed to the alarm cue during development exhibited increased grouping (shoaling) in adulthood compared to those exposed to the control treatment, but there were no detectable impacts on activity, boldness, or exploratory behaviour. We also identified stable shifts in brain DNAm in response to developmental alarm cue exposure in genes involved in behavioural regulation. Some differentially methylated sites were significantly associated with shoaling propensity in both males and females. Additionally, males and females differed in the magnitude of DNAm responses and the genes impacted, suggesting distinct roles for DNAm between the sexes. This study shows how early‐life predation stress can induce behavioural changes in adulthood and that shifts in neural DNAm could be an underlying mechanism responsible for these changes.


Assigning Phenologically Asynchronous Moths to Source Populations Using Individual Genotypes

June 2025

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33 Reads

The spruce budworm (Choristoneura fumiferana; SBW) is a periodically outbreaking forest insect pest that affects the boreal forests of North America through extensive defoliation and tree mortality. Causes of widespread spatial synchrony of SBW outbreaks remain a key question in the ecology and management of this species. While the Moran effect (correlated favourable environmental conditions) and density‐dependent dispersal (from epicentres of demographic explosions) have been proposed and supported as drivers of synchronised outbreaks, the relative contribution of long‐distance dispersal is still poorly understood. In this study, we use a novel approach to distinguish resident from migrant moths and to assign migrants to likely source clusters with the goal of better characterising regional dispersal. First, we characterise the genetic diversity and structure of resident SBW larvae and three phenologically separated groups of moths over one flight season using Genotyping‐by‐Sequencing. Then, using a novel machine learning approach, we assign putative migrants to their likely source populations. We hypothesised that migrant moths and resident larvae would be genetically distinct and could be assigned to source populations. Our findings revealed complex patterns of moth dispersal and population differentiation within a single season, including two spatially overlapping genetic clusters. We observed subtle but significant genetic differences between resident larvae and migrant moths, supporting the hypothesis that long‐distance dispersal contributes to outbreak dynamics and synchrony. These insights enhance our understanding of SBW population dynamics and suggest that effective management strategies, such as the Early Intervention Strategy (EIS), must account for the role of dispersal in mitigating the detrimental effects of major outbreaks.


Nest architecture of the stingless bee Tetragonula carbonaria (left) and its cryptic congener T. hockingsi (right).
Population structure of Tetragonula carbonaria and T. hockingsi on Australia's east coast (a) Map of sample locations used for population genomic analyses. Inset: map of Australia showing sampling region in red. (b) Principal component analysis (PCA) of 0.35M linkage (LD)‐filtered SNPs for all samples. (c) PCA of 0.25M linkage‐filtered SNPs for all T. carbonaria samples. (d) Admixture analysis using 0.25M T. carbonaria linkage‐filtered SNPs. Optimal K value bordered in red. (e) PCA of 0.17M linkage‐filtered SNPs for all T. hockingsi samples. (f) Admixture analysis using 0.17M T. hockingsi linkage‐filtered SNPs. Optimal K value bordered in red.
Genetic differentiation between Tetragonula carbonaria and T. hockingsi, and between T. carbonaria's three discrete populations (North, Central and South) (a) Number of SNPs falling into different Fst intervals in each between‐population or between species comparison. (b) Proportion of SNPs falling into coding regions (CDS; light blue), untranslated (UTR)/intronic (green) and intragenic regions (blue) for each range of Fst values for each comparison.
Patterns of caste‐biased gene expression in Tetragonula carbonaria (a) Principal component analysis of log‐transformed gene expression for 14,553 genes among queens, males, foragers and within‐nest workers (n = 6 for each group). (b) upSet plot showing numbers of differentially expressed genes shared between different pairs of castes within abdominal tissue. (c) upSet plot showing numbers of differentially expressed genes shared between different pairs of castes within head tissue. (d) GO terms enriched (unadjusted p < 0.01) among differentially expressed genes between workers, queens and males. Only the top 15 terms per comparison are shown. (e) Log(2) fold change in expression between queens and workers in T. carbonaria (x‐axis) and Apis mellifera (y‐axis; values obtained from Warner et al. 2019) for 8982 orthologous genes. Line of best fit generated in R via lm(); statistics via Pearson correlation.
Genomic parameters of Tetragonula carbonaria genes grouped by queen‐worker caste expression bias. (a) Log(10) nucleotide diversity; (b) population sequence divergence (dxy) between T. carbonaria and T. hockingsi; (c) −log2 neutrality index (NI); and (d) dN/dS ratio. Significance values indicated by four asterisks (p < 0.001) or ns (non‐significant) are the result of two‐sided Wilcoxon tests following correction for multiple comparisons, and dashed lines represent median values of the three sets of genes.
Multi‐Omic Analysis Reveals Population Differentiation and Signatures of Social Evolution in Tetragonula Stingless Bees

June 2025

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94 Reads

Stingless bees in the genus Tetragonula are social insects with a fully sterile worker caste, and are therefore well‐placed to provide insights into the genomic changes associated with ‘superorganismal’ life histories. Here we assemble the genome of Tetragonula carbonaria and characterise the population structure and divergence of both T. carbonaria and its cryptic congener T. hockingsi in eastern Australia, revealing three distinct populations for T. carbonaria and two partially differentiated subpopulations for T. hockingsi. We then combine our genomic results with RNA‐seq data from different T. carbonaria castes (queens, males, workers) to test two hypotheses about genomic adaptations in social insects: the ‘Relaxed Constraint’ hypothesis, which predicts indirect, and therefore relaxed, selection on worker‐biased genes; and the ‘Adapted Worker’ hypothesis, which predicts intensified positive selection on worker genes due to their evolutionarily novel functions. Although we do not find a direct signal of either weaker purifying selection or elevated positive selection in worker‐biased genes based on deviations from neutral expectations of nucleotide change between the two species, other evidence does support a model of relaxed selection on worker‐biased genes: such genes show higher nucleotide diversity and greater interspecies divergence than queen‐biased genes. We also find that differentially caste‐biased genes exhibit distinct patterns of length, GC content and evolutionary origin. These findings, which converge with patterns found in other social insects, support the hypothesis that social evolution produces distinct signatures in the genome. Overall, Tetragonula bees emerge as a valuable model for studying the genomic basis of social complexity in insects.


Study species and sampling sites across Denmark. We sampled Entomobrya nicoleti (a) across 49 non‐acidic grasslands, that is, grasslands on more or less calcareous soils (Staalsen 2024), in Denmark (b) with the aim of obtaining 50 specimens from each site. The purple dots on the map (b) represent sites where this goal was reached, and the specimens sequenced and analysed (N=25$$ N=25 $$), while orange dots represent sites where less than 50 individuals were found (N=21$$ N=21 $$). Yellow dots/triangles represent grasslands where no specimens were found (N=3$$ N=3 $$). We furthermore sampled 13 conventional agricultural fields (triangles) to evaluate if they represent barriers to E. nicoleti, and did not find any specimens in any of those fields (Table S1).
Illustration of demographic scenario. Figure illustrating the demographic scenario and parameters estimated by fastsimcoal2. N1 and N2 are current effective population sizes of two populations after a split. NAncestral is the ancestral effective population size prior to the split. Split time indicates the time before present when the two current population merged into one ancestral population. Gene flow, as indicated by the purple double‐sided arrows, shows potential gene flow between the populations at intervals between present and when the split occurred.
Changes in gene flow, cropland cover, and population demography over the last 4000 years. The figure shows trajectories of (1) effective population size for all populations represented as a mean of z‐transformed trajectories including 95% CI (purple), (2) agricultural history, presented as percentage of cropland cover (orange), (3) predicted gene flow rate including 95% CI (red), based on estimates from the fastsimcoal2 analysis described in the methods 2.3.7.
Population demography. Stairway plots (a) showing estimated effective population size (Ne) for each population from present and 50,000 years back in time. Most populations have experienced drastic decreases in Ne in recent times. Note that the y‐axes are scaled with 105$$ {10}^5 $$. Estimates of mean genetic diversity (b), represented as π$$ \pi $$, are of relatively even magnitude across populations. π$$ \pi $$ was estimated on neutral, mono‐ and bi‐allelic sites.
Population structure. Pie charts visualising a conStruct analysis showing clear structure that results from isolation‐by‐distance (a) and no structure when removing the effect of isolation‐by‐ distance (b). The four colours in the pie charts represent each of the four spatial layers (K=4$$ K=4 $$) (c) and deep branches on a UPGMA dendrogram (d) with a red * indicating bootstrap confidence above 70% (1000 replicates). For actual percentages, see Figure S5. A principal component analysis (e) on neutral bi‐allelic variants shows population clustering resembling isolation‐by‐distance.
Gene Flow Disruption and Population Declines in a Soil Arthropod in Fragmented Habitats

June 2025

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47 Reads

The intensification of land use over past millennia has accelerated habitat loss and fragmentation. This is hypothesized to lead to reductions in population sizes and restrictions in gene flow, processes that amplify genetic drift with profound negative impacts on species and populations. However, empirical data on the population genetic impacts of habitat fragmentation remain limited, particularly for presumed abundant species such as insects. Reports of dramatic insect and arthropod declines are increasing, and their short generation times and limited dispersal capacities make them especially vulnerable to habitat fragmentation. To substantiate the hypothesis that habitat fragmentation negatively impacts genetic composition and demography, we combined historical agricultural land use data from Denmark with whole‐genome resequencing of 25 populations of the collembolan Entomobrya nicoleti from natural grasslands. Abundance data indicate that agricultural expansion reduces habitat suitability and fragments populations. Demographic modelling shows that intensification of agricultural land use coincides with severe declines in effective population sizes. It is likely that these declines have yet to reach their full effect on current levels of genetic diversity because of the ‘drift debt,’ where the genetic diversity of recently declined populations will erode over future generations. Gene flow estimates revealed sharp recent declines that coincide with agricultural intensification. Our results underscore that even seemingly abundant species in fragmented landscapes can experience severe reductions in effective population size and gene flow. These demographic shifts predict future genetic erosion, highlighting the delayed yet inevitable consequences of habitat fragmentation for population persistence.


(A) Geographic distribution of sampled individuals sequenced in this study, with locations marked and coloured by species. Each point represents a sampling site, with approximately four individuals per location, though exact numbers vary (see Table S1 for details). The inset highlights sampled locations in French Polynesia. White asterisks represent the recently colonised populations. (B) Phylogenetic relationships across South Pacific white‐eyes, including a dense sampling of silvereye subspecies using a coalescent‐based approach with SVDquartets. (C) A DensiTree representing the variability in the inferred phylogenetic relationships inferred with SNAPPER, highlighting areas where the tree space is more densely populated. The root canal, or the phylogeny with the highest clade support, is highlighted by the darkest lines.
(A) Genomic PCA plots representing the two axes that explain most variation (PC1 on the x‐axis and PC2 on the y‐axis) for the three datasets all samples; southern Melanesian populations only; and a group consisting of Australian, New Zealand and outlying islands (ANZO). (B) NGSadmix bar plots for all samples (K = 3), Southern Melanesia (K = 4) and ANZO (K = 7). Each K is the most likely number of genetic groupings detected for each dataset. Within each population, each bar is the estimate of the individual's ancestry proportion from each of the assumed ancestral populations.
Phylogenetic network representing the evolutionary relationships and reticulate evolution across (A) silvereyes and other South Pacific Zosterops species, (B) Southern Melanesian silvereyes and (C) silvereyes from the ANZO cluster, which include continental species that show little clustering partly due to high levels of gene flow as shown in the TreeMix maximum‐likelihood tree on the right. The main directions of gene flow are shown on the map of Australia. Colours indicate different populations in panels A–C, while in the map (C), they represent migration weights.
(A) Adjusted predictions estimated with brms of FST on islands and the continent. (B) Adjusted predictions of the effect of the distance on FST levels among continental populations (yellow) and islands (blue). The uncertainty bands are darker for lower values and lighter for higher values, representing 50%, 80% and 95% credibility intervals.
Islands Promote Diversification of the Silvereye Species Complex: A Phylogenomic Analysis of a Great Speciator

June 2025

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58 Reads

Geographic isolation plays a pivotal role in speciation by restricting gene flow between populations through distance or physical barriers. However, the speciation process is complex, influenced by the interplay between dispersal ability and geographic isolation, as seen in “great speciators” – bird species that simultaneously have broad island distributions but high levels of subspecific diversity. Comparing genomic population differentiation in species that occupy both continental and island settings can reveal the effects of different forms of geographic isolation and validate if the primary mechanism proposed to catalyse a great speciator pattern, that is, dispersal reduction following island colonisation, has occurred. The highly diverse white‐eye family Zosteropidae includes several great speciators, including the silvereye (Zosterops lateralis), with 16 subspecies (11 occurring on islands), distributed on the Australian continent and numerous southwest Pacific islands. We compared continental and island patterns of divergence using whole genome and morphological data. Australian mainland populations showed a low genetic population structure, lack of isolation by distance patterns and low morphological diagnosability, suggesting that the species' dispersal propensity in a continental setting is sufficient to overcome multiple forms of geographic barriers and large geographic distances. In contrast, except for island populations less than 200 years old, most island populations were highly genomically structured with clearer morphological diagnosability even if separated by relatively short geographic distances. The inferred reduction of dispersal propensity in island situations is consistent with the proposed model of great speciator formation on islands. Our phylogenomic analyses also allowed resolution of the silvereyes' evolutionary position, showing their relatively early emergence (~1.5 Mya) within the rapidly radiating Zosteropidae, while population‐level analyses demonstrated where morphological subspecies and genomic data align and disagree. However, the silvereye example also shows how uncertainties about relationships remain when reconstructing evolutionary history in rapidly radiating groups, even when whole genome data is available. Altogether, our results show how within‐species genomic and morphological patterns measured over broad spatial scales and with varying geographic contexts can help reveal when particular stages of speciation such as great speciators are likely to emerge.


DDM1 Controls Heritable Natural Epigenetic Variation in Arabidopsis

As a part of epigenetic modifications, DNA methylation involves the addition of a methyl group to cytosine. In plants, this process occurs in three sequence contexts (CG, CHG and CHH) through two pathways: de novo DNA methylation and DNA methylation maintenance. DNA methylation is highly conserved between ecotypes due to its heritability and role in genomic stability. However, numerous naturally occurring differentially methylated regions (NMRs) exist between ecotypes, which are also heritable and contribute to differential gene expression and phenotypic variation. Decreased DNA methylation observed in mutants of RdDM or DNA methylation maintenance pathways highlights the potential roles of these pathways in NMR formation and inheritance. Nevertheless, the complexity of plant DNA methylation across different contexts and pathways has made the contributions of these two pathways in NMR formation and inheritance remain unclear. To address this, we chose two Arabidopsis thaliana ecotypes, Col‐0 (Col) and C24, and utilised mutants of pol4/5 and ddm1 in both ecotypes. By examining the changes in NMRs within these mutants, we aimed to elucidate the roles of DNA methylation pathways in NMR formation and inheritance. Our results revealed (1) NMRs can be categorised into three types, but classification is not solely based on methylation contexts; (2) compared to RdDM, the DNA methylation maintenance pathway plays a more dominant role in NMR formation and is less influenced by SNPs; (3) DNA methylation maintenance, rather than RdDM, controls NMR inheritance. Our findings highlight the role of the DNA methylation maintenance pathway in NMR formation and inheritance.


Colour morphs of rock‐dwelling ‘mbuna’ cichlids from Lake Malawi. (a) Metriaclima zebra BB male, (b) M. zebra BB female, (c) M. callainos blue male, (d) M. zebra OB female, (e) M. zebra O female, (f) M. callainos white female. Blue males and blue females are nearly indistinguishable, as are white males and white females. Four examples of the OB male phenotype are presented in Figure 3. Photos by Ad Konings.
Comparison of the normal and inverted regions on LG5. (a) Plot of the relative gene locations between the M. zebra UMD2a reference and the inverted haplotype of L. trewavasae and M. zebra. Regions of poor alignment of UMD2a against L. trewavasae are indicated by red bars. (b) Detailed coordinates in the UMD2a reference and the inverted haplotypes from the LatrZW and MezeOBm assemblies. Inverted triangles denote regions of high transposable element density in the inverted sequences. A gap of unknown size in LatrZW indicates where two contigs were joined based on the reference.
An assessment of the frequency of OB males in populations around Lake Malawi. High frequencies are defined as populations with greater than 1% OB males, while low frequencies are defined as populations with less than 1% OB males. These frequencies were estimated through informal surveys by Ad Konings. Not indicated are many other populations segregating OB that do not have notable frequencies of OB males. Species/populations with OB poolseq data are indicated in bold.
Plots of ZW sex‐specific SNP density across the inverted region for comparisons of males and females analysed by pool‐seq. Comparisons of BB males to various OB females plotted against (a) L. trewavasae (LatrZW) and (b) M. zebra (MezeOBm). Comparisons of BB males to BB females (i.e., not including an inversion difference) plotted against (c) L. trewavasae (LatrZW) and (d) M. zebra (MezeOBm). Dashed lines indicate approximate inversion boundaries.
Decreases in ZW SNP density within the inversion correspond to regions of high transposable element density. The fraction of bases annotated as transposable elements is plotted along with the ZW sex‐specific density in 200 kb windows across the inverted region of LG5 in the LatrZW assembly.
A Chromosome Inversion Creates a Supergene for Sex and Colour in Lake Malawi Cichlids

June 2025

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68 Reads

Cichlid fishes have the highest rates of evolutionary turnover of sex chromosomes among vertebrates. Many large structural polymorphisms in the radiation of cichlids in Lake Malawi are associated with sex chromosomes and may also carry adaptive variation. Here, we investigate the structure and evolutionary history of an inversion polymorphism that includes both a ZW sex locus and an orange‐blotch colour polymorphism in the rock‐dwelling cichlid fishes of Lake Malawi. We use long‐read sequencing to characterise the sequence and breakpoints of the inversion. We quantify allele frequency differences across the inversion in population samples of the genera Metriaclima and Labeotropheus. We also examine expression differences of genes in the inversion. The simple inversion spans 7 Mb and is flanked by CACTA transposons that may have catalysed the rearrangement. The region includes ~600 genes, several of which show large differences in expression. Some of these genes are candidates for the sex and colour phenotypes. This inversion is an accessible model system for studying the role of structural polymorphisms and sex chromosome turnover in the adaptive radiation of cichlids in the lakes of East Africa.


Sampling locations and study system. (A) The North Sea–Baltic Sea salinity gradient, with sampling locations for S. marinoi. Salinity measurements for the period 2010–2018 used for the interpolation were downloaded from ICES (ICES Ocean Hydrography, 2020. ICES. Copenhagen) and Sharkweb (https://sharkweb.smhi.se/hamta‐data/). The inset map in the top‐left corner shows the broader geographic region. Our Zenodo repository contains replicates of the Baltic Sea map for several other environmental gradients, including temperature, pH and nutrient concentrations. (B) Light micrograph of a S. marinoi culture (scale bar = 10 μm). (C) Scanning electron micrograph of S. marinoi strain RO5AC (scale bar = 5 μm). The micrograph shows the linking spines that connect individual cells, resulting in chain formation. The SEM image was obtained by the Centre for Cellular Imaging at the University of Gothenburg and the National Microscopy Infrastructure, NMI (Sweden, VR‐RFI 2016‐00968). (D) Heatmaps showing multigenerational stepping‐stone connectivity (16 and 64 generations) between the sampling locations for a particle size of S. marinoi, calculated from the seascape connectivity model. Data were averaged across all months, drift depths and drift durations. Multigenerational connectivity represents the probability to go from locality X to Y using stepping‐stone dispersal over n generations. Dark blue values represent lowest connectivity, whereas dark red values represent highest connectivity. Dispersal from locality X to Y is different than going from Y to X, because of possible asymmetric water transport. Connectivity values for the 64‐generation heatmap are generally lower than those for the 16‐generation heatmap, despite allowing for more generations: In consecutive iterations of the model, particles are lost when dispersing out of the domain (and no new particles are generated), and therefore, multigenerational connectivity needs to be interpreted in a relative sense. This is also true within localities (e.g., from locality X to locality X), as the probability of local retention or self‐recruitment differs among sites in the seascape due to spatial differences in oceanographic circulation patterns.
Population structure of S. marinoi. (A) PCA of the allele frequencies, showing clear distinction between samples from the North Sea and the Baltic Sea. (B) Isolation‐by‐distance plot. Distance is measured as the multigenerational stepping‐stone connectivity, across 64 generations (see Figure 1D). Each pair of localities is plotted twice due to asymmetric water transport between localities. (C) Pairwise genome‐wide FST between all localities, showing the lowest levels of population differentiation between localities from the Baltic Sea. (D) Measures of genetic variation: Nucleotide diversity π, population mutation rate θW (Watterson's theta) and Tajima's D. Values were averaged across each contig for each locality, and visualised as boxplots. Outlier SNPs were removed prior to creating the plots in panels (A–C). These plots show results from SNPs filtered at a minimum coverage of 40×. The colours from the legend in the bottom‐left corner refer to corresponding colours in panels (A–D). The green colour (‘North Sea vs. Baltic Sea’) compares the genetic distance between sites in the North Sea and the Baltic Sea.
Outlier SNPs and genes associated with the Baltic Sea environmental gradients. (A) Table showing the number (#) of outlier SNPs and genes in each tested category for the 40× minimum coverage dataset (20× minimum coverage between brackets). For the GEA analyses, union refers to the full set of SNPs or genes found by one or both approaches (i.e., LFMM/BayPass), and intersection refers to outlier SNPs and genes that were part of the overlap of both approaches. The bottom‐two rows refer to outlier SNPs or genes that overlapped between the FST outliers and the union lists of the GEA outliers. (B) Types of outlier SNPs for the categories in (A). The labels of the vertical axis correspond with the labels in (A). (C) Types of outlier SNPs for the categories in (A), only showing SNPs located in exons. The labels of the vertical axis correspond with the labels in (A). (D) Manhattan plots showing outlier SNPs associated with the environment of the Baltic Sea as estimated by LFMM, shown separately for both PC axes. The dotted line represents the 1% FDR significance threshold. Different contigs are indicated with alternating shades of grey. The coloured dots represent outlier SNPs that overlap with FST outliers (dark blue), BayPass' GEA test (yellow) or both (green). Plots (B–D) show results obtained at a minimum coverage of 40×.
(A) GO enrichment results (biological process) for the outlier genes. The GO enrichment results shown in this figure are based on the outliers detected using a minimum coverage of 40× and only include outlier genes (i.e., genes with outlier SNPs) that had at least one outlier missense SNP. Three categories are shown: (i) FST outliers (outliers between the North Sea and the Baltic Sea), (ii) GEA outliers for PC1 (north–south gradient in the Baltic Sea) and (iii) GEA outliers for PC2 (east–west gradient in the Baltic Sea). For the GEA outliers, GO enrichment is based on the union of both GEA approaches (LFMM/BayPass) For the GEA PC2 set, GO terms were summarised with REVIGO (Supek et al. 2011). Bars are coloured by topGO's p‐value. The height of the bars indicates the proportion of genes with a given GO term that are enriched relative to the total number of genes with this GO term in the genome of S. marinoi. (B and C) Barplots showing the overlap between the outlier genes and differentially expressed genes in S. marinoi in response to low salinity. Outlier genes are subdivided into three categories: FST outliers, GEA outliers for PC1 and GEA outliers for PC2. Panel (B) indicates the FST outliers and the full set of GEA outliers detected by the union of both approaches (LFFM/BayPass). Panel (C) indicates the GEA outliers detected by both LFMM and BayPass. The dark shades indicate results from outliers detected at a minimum coverage of 40×, whereas the light shades correspond with outliers detected at a minimum coverage of 20×. Gene expression data were obtained from eight strains, originating from localities A, B, D, F, I, J, K and P, which were exposed to salinities mimicking the Baltic Sea salinity cline (24, 16 and 8). We tested for differentially expressed (DE) genes for each salinity contrast (8–16, 16–24 and 8–24) within each strain and by combining data from all strains, resulting in a total of 7676 differentially expressed genes (= average & strain‐specific response in the plot). For all combinations of strains, we also tested for interaction effects for each salinity contrast, thus testing for significant strain‐specific responses: This resulted in 3958 differentially expressed genes (= interaction effects in the plot). A significant overlap between the outlier and differentially expressed genes is indicated with asterisks.
Genome‐Wide Adaptation to a Complex Environmental Gradient in a Keystone Phytoplankton Species

June 2025

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121 Reads

Marine phytoplankton play essential roles in global primary production and biogeochemical cycles. Yet, the evolutionary genetic underpinnings of phytoplankton adaptation to complex marine and coastal environments, where many environmental variables fluctuate and interact, remain unclear. We combined population genomics with experimental transcriptomics to investigate the genomic basis underlying a natural evolutionary experiment that has played out over the past 8000 years in one of the world's largest brackish water bodies: the colonisation of the Baltic Sea by the ancestrally marine diatom Skeletonema marinoi. To this end, we combined target capture of the entire nuclear genome with pooled shotgun sequencing, and showed that the method performs well on both cultures and single cells. Genotype–environment association analyses identified > 1000 genes with signals of selection in response to major environmental gradients in the Baltic Sea, which apart from salinity, include marked differences in temperature and nutrient supply. Locally adapted genes were related to diverse metabolic processes, including signal transduction, cell cycle, DNA methylation and maintenance of homeostasis. The locally adapted genes showed significant overlap with salinity‐responsive genes identified in a laboratory common garden experiment, suggesting the Baltic salinity gradient contributes to local adaptation of S. marinoi. Taken together, our data show that local adaptation of phytoplankton to complex coastal environments, which are characterised by a multitude of environmental gradients, is driven by widespread changes in diverse metabolic pathways and functions.


Phylogenetic reconstruction of African pseudocrenilabrine cichlids. (A) Sampling map of East African lakes and their drainage basins. Basins are outlined in white with a solid line and sub‐basins are outlined in white with a dotted line. For samples collected from rivers or small water bodies, sampling points are shown and numbered within each sub‐basin (e.g., Rovuma 1 is the first location within the Rovuma sub‐basin). The central Jordan river system, the sampling location of Astatotilapia flaviijosephi, is not shown. Photos by Luka Moritz Blumer (Astatotilapia calliptera), Alex Hooft van Huysduynen/Hannes Svardal/Ilia Artiushin/Valentina Burskaia (all other Malawi species), George Turner (Astatotilapia sp. ‘Ruaha blue’), Robert Taylor (Pseudocrenilabrus philander, available at http://specify‐attachments‐saiab.saiab.ac.za/originals/sp6‐3284146409238926730.att.JPG, licensed under CC by 4.0), and the South African Institute for Aquatic Biodiversity (Serranochromis angusticeps, available at https://www.inaturalist.org/photos/97168187, licensed under CC by 4.0). (B) A maximum likelihood phylogenomic tree for Malawi and non‐Malawi African pseudocrenilabrine cichlids (n = 612), rooted to a reconstructed ancestral state sample (see methods). The scale bar gives genomic distance in coalescent units and is based on the amount of discordance in gene trees. The Malawi radiation (highlighted in grey), containing 239 species in our callset, has been visually collapsed into five major groups, each comprising of the ecomorphological groups described by Malinsky et al. (2018). The ecomorphological groups have been approximately sized according to the number of species in each group. Non‐Malawi species were assigned to 10 phylogenetic groups and members are coloured according to their group membership. The Lake Victoria Region Superflock (LVRS) sub‐group of the Victoria group is highlighted in orange. Sampling locations for each sample are given after a dash. Samples which were collected from major lakes (or affluents of major lakes) are labelled with the lake name. All other samples are labelled with the sub‐basin name and location number which corresponds to the map in a. Nodes A‐E mark split times for the Malawi radiation (estimated 1.17–1.19 Mya), the Malawi/Victoria clade (4.56–4.65 Mya), the CSA group (5.12–5.18 Mya), the Pseudocrenilabrus group (6.99–7.06 Mya), and the Orthochromis group (1.62–1.66 Mya) respectively.
Potential genetic contributions of non‐Malawi African pseudocrenilabrine cichlids into the Malawi radiation. (A) ABBA‐BABA statistics (D‐statistic and f4‐ratio) for tests where species P1 = Victoria (VIC) and P2 = Malawi (MAL, positive values) or P1 = Malawi and P2 = Victoria (negative values), and P3 = non‐Malawi (AFR). Non‐significant test trios are marked in grey and significant trios are coloured according to the non‐Malawi group the P3 species is from. Significant positive tests indicate significant excess allele sharing between Malawi species and the P3 non‐Malawi species indicated, whereas significant negative tests indicate significant excess allele sharing between Victoria group species and the P3 species. On the right‐hand side is the percentage of trios that are significant for each P3 species. (B) f4‐ratios of significant trios where species P1 = Astatotilapia flaviijosephi (Victoria group), P2 = Malawi, and P3 = non‐Malawi species A. sp. ‘Ruaha blue’, A. gigliolii, I. loocki, O. sp. ‘Red cheek’ or O. malagaraziensis. The six chosen P3 non‐Malawi species were selected as representatives for their respective groups. Trios are separated into one of the seven Malawi ecomorphological groups (Diplotaxodon, Rhamphochromis, A. calliptera, ‘mbuna’, shallow benthic, deep benthic, and ‘utaka’). Boxplots are coloured according to the P3 non‐Malawi group. Pairwise significant differences in the mean f4‐ratio between groups (Tukey's HSD test, p < 0.05) are indicated using letters to the side of each box; groups with different letters have significantly different means, whilst groups that share a letter do not.
Recent gene flow between Rovuma Astatotilapia calliptera populations and Astatotilapia gigliolii. f4‐ratio of significant tests (100% of all tests) where P1 = Astatotilapia flaviijosephi (Victoria group), P2 = Malawi (L. fuelleborni, C. chrysonotus, D. limnothrissa, M. subocularis, A. peterdaviesi and R. woodi), Astatotilapia calliptera populations from the Rovuma catchment or Astatotilapia calliptera populations not from the Rovuma, and P3 = A. gigliolii. Sampling locations of A. calliptera and A. gigliolii samples are shown below.
Signatures of gene flow in windows along the genome. (A) Dinvestigate fdM windows of 50 snps for trios where species P1 = Victoria group, P2 = Malawi and P3 = Astatotilapia gigliolii group, Orthochromis group, Astatotilapia. sp. ‘Ruaha blue’, Pseudocrenilabrus group or CSA group. Windows with excess allele sharing between Malawi and a non‐Malawi group have a positive fdM value, whereas windows with excess allele sharing between Victoria and a non‐Malawi group have a negative fdM value. The blue dotted line marks the mean fdM value across all windows for each P3 group and the red dotted line marks the threshold of significance (top and bottom 0.05%), used to select candidate windows for gene flow. (B) Distribution of candidate fdM 50snp windows across the genome. For visualisation purposes, windows are artificially enlarged by a 10 kb buffer (adding 10 kb before the start and after the end of each window). Windows are coloured according to which P3 non‐Malawi group shows significant excess allele sharing with Malawi; CSA = purple, A. sp. ‘Ruaha blue’ = blue, Pseudocrenilabrus = pink, Orthochromis = green and A. gigliolii = red.
Genome Analyses Reveal Diverse Riverine Genetic Contributions to the Lake Malawi Cichlid Radiation

June 2025

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80 Reads

Comparative studies of whole genomes have increasingly shown that genetic introgression between closely related species is surprisingly common across the tree of life, making the description of biodiversity and understanding the process of speciation complex and challenging. The adaptive radiation of cichlid fishes in Lake Malawi, that is characterised by hybrid origins and cases of recent introgression, provides a valuable model system to study the evolutionary implications of introgression. However, many potential sources of introgression into the radiation have not yet been investigated. Here we use whole genome data from 239 species from Lake Malawi and 76 species from surrounding African river and lake systems to identify previously unknown introgression events involving the Malawi radiation. Computing genome‐wide excess allele sharing (ABBA‐BABA statistics) and window‐based statistics, we find that three independent riverine cichlid lineages show significantly higher allele sharing with the Malawi radiation than expected, suggesting historical genetic exchange. Introgressed haplotypes are distributed relatively uniformly across the Malawi radiation, indicating that most hybrid‐derived polymorphism was acquired and sorted before the formation of the contemporary Malawi radiation. Our results point towards several previously unknown contributors to the Malawi cichlid hybrid swarm and show that the history of one of the largest vertebrate radiations is more complex than previously thought.


Dispersal, Isolation and Local Adaptation Promote Speciation in South American Savannas as Indicated by a Phylogenomic Analysis of a Passerine

South American savannas are a disjunct biome with an unclear evolutionary history. We tested hypotheses about their Quaternary history and the evolution of savanna cores through fragmentation or dispersal from the Cerrado. We used genomic data (genotyping‐by‐sequencing) and ecological niche models of the Burnished‐buff Tanager (Stilpnia cayana Linnaeus 1766) to evaluate intraspecific differentiation, gene flow, past range shifts and landscape genomics association. We found clear genomic differences between populations on each side of the Amazon basin and high admixture in Marajó Island and Bolivia. Landscape genomics analysis indicated that the Amazon River, isolation by distance and temperature predict genomic differentiation in this bird. Taken together, the results suggest that a combination of dispersal from the Cerrado, isolation due to geographic distance, and the Amazon River basin, and local adaptation shaped species diversification.


Introduction to the sex determination system of citrus mealybugs and the conditions it creates for sex‐biased genes. (a) Overview of paternal genome elimination (PGE). Males and females have extremely different morphologies despite a lack of sex chromosomes. Throughout their lives, males only express one copy of their genome, their maternal haplotype. At the time of reproduction, only maternal alleles are passed to the next generation by males. Females express and pass on both maternal and paternal alleles. (b) Considerations for selection on sex‐biased genes under PGE. Alleles are exposed to selection (i.e., expressed, + signs) under very different conditions depending on the sex of expression. Although only sex‐limited genes should be exposed strictly to one selective regimen, sex‐biased genes should face either male‐ or female‐selective conditions more often than the reserve. Specifically, male‐biased genes are expressed in roughly half the population under an equal sex ratio, are haploid in expression most of the time, and must pass from father to daughter to be expressed in a male again in the following generation. Unbiased genes are expressed in the full population, but in a haploid or diploid state depending on whether they are in males or females, respectively. Finally, female‐biased genes are also expressed in half the population and predominantly in a diploid state. These conditions should have differing impacts on the strength of selection. Predictions (stronger/weaker) are shown relative to a diploid unbiased allele baseline. Representations of mealybugs were created with biorender.com.
Polymorphism and sex‐biased gene expression. Colors correspond to the bias classes defined in Table 1 and letters denote significant differences such that classes with different letters are significantly different and a > b > c. Semi‐transparent points show individual gene values. Top left: Nonsynonymous variants per nonsynonymous site (pN). Female‐biased genes hold the most variation, followed by male‐biased genes, and then unbiased genes hold the least. Top right: Synonymous variants per synonymous site (pS). Synonymous variation follows the same pattern as nonsynonymous variation: Female‐biased genes hold the most, followed by male‐biased and unbiased genes. Bottom: Scaled polymorphism rate (pN/pS). Considering both classes together, female‐biased genes hold more scaled variation than either unbiased or male‐biased genes, which do not differ from each other.
Divergence rates across sex‐bias classes. Top left: Nonsynonymous substitutions per nonsynonymous site. Female‐biased genes show the most nonsynonymous change, followed by unbiased genes, with male‐biased genes showing the least nonsynonymous change. Top right: Synonymous substitutions per synonymous site. Female‐biased and male‐biased genes show less scaled synonymous divergence than unbiased genes. Bottom: Scaled divergence (dN/dS). Overall female‐biased genes evolve the fastest between species, followed by male‐biased, and finally unbiased genes. Groups with different letters are significantly different from each other, with values a > b > c, and colors follow the categorization from the methods.
Adaptive evolution of sex‐biased genes under PGE. The proportion of substitutions driven by positive selection (α) when considering all polymorphisms (left) or excluding nonsynonymous polymorphisms with frequency < 0.2 (right). The latter should upwardly bias alpha if weakly deleterious variants segregate below this frequency. In both cases, female‐biased genes show less adaptation than unbiased genes, although male‐biased genes cannot be differentiated from either class when not filtering polymorphisms. After this filter, however, both male‐biased and unbiased genes evolve significantly more adaptively than female‐biased genes. We checked the robustness of our findings by further increasing the stringency to exclude nonsynonymous polymorphisms with frequency < 0.4 but found the same pattern of less adaptation in female‐ than unbiased or male‐biased genes (Figure S2).
Contrasting Evolutionary Trajectories Under Paternal Genome Elimination in Male and Female Citrus Mealybugs

June 2025

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48 Reads

Most studies of sex‐biased genes explore their evolution in familiar chromosomal sex determination systems, leaving the evolution of sex differences under alternative reproductive systems unknown. Here we explore the system of paternal genome elimination employed by mealybugs (Hemiptera: Pseudococcidae) which have no sex chromosomes. Instead, all chromosomes are autosomal and inherited in two copies, but sex is determined by the ploidy of expression. Females express both parental alleles, but males reliably silence their paternally inherited chromosomes, creating genome‐wide haploid expression in males and diploid expression in females. Additionally, sons do not express alleles directly inherited from their fathers, potentially disrupting the evolution of male‐benefiting traits. To understand how these dynamics impact molecular evolution, we generated sex‐specific RNAseq, a new gene annotation, and whole‐genome population sequencing of the citrus mealybug, Planococcus citri. We found that genes expressed primarily in females hold more variation and evolve more quickly than those expressed in males or both sexes. Conversely, we found more apparent adaptation in genes expressed mainly in males than in those expressed in females. Put together, in this paternal genome elimination system there is slower change on the male side but, by increasing selective scrutiny, an increase in the degree of adaptation in these genes. These results expand our understanding of evolution in a non‐Mendelian genetic system and the data we generated should prove useful for future research on this pest insect.


Introgression, Phylogeography, and Genomic Species Cohesion in the Eastern North American White Oak Syngameon

Hybridization and interspecific gene flow play a substantial role in the evolution of plant taxa. The eastern North American white oak syngameon, a group of approximately 15 ecologically, morphologically and genomically distinguishable species, has long been recognised as a model system for studying introgressive hybridization in temperate trees. However, the prevalence, genomic context and environmental correlates of introgression in this system remain largely unknown. To assess introgression in the eastern North American white oak syngameon and population structure within the widespread Quercus macrocarpa, we conducted a rangewide survey of Q. macrocarpa and four sympatric eastern North American white oak species. Using a Hyb‐Seq approach, we assembled a dataset of 3412 thinned single‐nucleotide polymorphisms (SNPs) in 445 enriched target loci including 62 genes putatively associated with various ecological functions, as well as associated intronic regions and some off‐target intergenic regions (not associated with the exons). Admixture analysis and hybrid class inference demonstrated species coherence despite hybridization and introgressive gene flow (due to backcrossing of F1s to one or both parents). Additionally, we recovered a genetic structure within Q. macrocarpa associated with latitude. Generalised linear mixed models (GLMMs) indicate that proximity to range edge predicts interspecific admixture, but rates of genetic differentiation do not appear to vary between putative functional gene classes. Our study suggests that gene flow between eastern North American white oak species may not be as rampant as previously assumed and that hybridization is most strongly predicted by proximity to a species' range margin.


Experimental evolution treatment descriptions. Brassica rapa plants evolved in response to eight treatments. Plants evolved in limestone or tuff soil (coloured brown or grey respectively), with or without aphid herbivores, and hand‐ or bee pollination. Eight generations of selection were imposed, and all plants were grown for two additional generations without insects to reduce maternal effects, leading to a total of 10 generations. Treatment names are as follows: LHB, limestone soil, herbivory, bee pollination; LHH, limestone soil, herbivory, hand pollination; LNHB, limestone soil, no‐herbivory, bee pollination; LNHH, limestone soil, no‐herbivory, hand pollination; THB, tuff soil, herbivory, bee pollination; THH, tuff soil, herbivory, hand pollination; TNHB, tuff soil, no‐herbivory, bee pollination; TNHH, tuff soil, no‐herbivory, hand pollination.
Genomic divergence (Fst) associated with ecological factors. (A) Multidimensional scaling of genetic distance (FST) between evolved (G10) and generation 1 (G1) plants. Smaller distances between shapes indicate less genomic divergence. Plants from G1 are plotted as a black square, plants that evolved in tuff soil are coloured in green, and plants that evolved in limestone soil are coloured in blue. Plants that evolved with herbivory are plotted as triangles, and plants that evolved without herbivory are plotted as circles. Plants that evolved with hand pollination are plotted as filled shapes, and plants that evolved with bee pollination are plotted as shapes with stripes. The plotted distance matrix is an average between the two replicates. (B) Genomic divergence is explained by soil, herbivory, pollination, and their interactions. A linear model with residual randomization permutation procedure (RRPP) was run 1000 times without replacement, and F values from the model are shown on the x‐axis. Larger F values indicate more genomic differentiation associated with that factor.
Adaptive genomic evolution differs between tuff and limestone soil. (A, B) The mean proportion (± 95% confidence intervals) of the same SNPs identified in both genome‐wide Cochran–Mantel–Haenszel (CMH) tests and the CLEAR method are plotted separately for tuff (A) and limestone (B) soils. (A) Plants that evolved in tuff soil are coloured in green (n of SNP observations for tuff soil model = 856,932) and (B) Plants that evolved in limestone soil are coloured in blue (n of SNP observations for limestone soil model = 811,795). Differences in lowercase and uppercase letters indicate significant pairwise differences between treatments of tuff soil and limestone soil, respectively. Plants that evolved with herbivory are plotted as triangles, and plants that evolved without herbivory are plotted as circles. The pollination regime is shown on the x‐axis. (C–J) Genome‐wide scans for signatures of selection are represented using Manhattan plots. The y‐axis shows the −log10 p‐value corrected by FDR. The significantly evolved SNPs using both CMH tests and the CLEAR approaches are coloured black. The x‐axis shows the genomic regions of B. rapa with its 10 chromosomes. Manhattan plots are shown for each treatment for plants that evolved (C) in tuff soil with herbivory and bee pollination, (D) in limestone soil with herbivory and bee pollination, (E) in tuff soil without herbivory and with bee pollination, (F) in limestone soil without herbivory and with bee pollination, (G) in tuff soil with herbivory and hand pollination, (H) in limestone soil with herbivory and hand pollination, (I) in tuff soil without herbivory and with hand pollination, and (J) in limestone soil without herbivory and with hand pollination.
Bee pollination enhances adaptive genomic differentiation. SNPs showing patterns consistent with selection were identified in both Cochran–Mantel–Haenszel (CMH) tests and the CLEAR method. A genomic PCA was performed with the 2424 candidate SNPs presumed to be under selection. The two replicates are plotted for each treatment, and each treatment replicate mean is plotted as a larger, filled centroid. Plants from generation 1 are plotted as black squares, plants that evolved in tuff soil are coloured in green, and plants that evolved in limestone soil are coloured in blue. Plants that evolved with herbivory are plotted as triangles, and plants that evolved without herbivory are plotted as circles. Plants that evolved with bee pollination are plotted as shapes with stripes, and plants that evolved with hand pollination are plotted as filled shapes. Note that individuals are plotted as open shapes, thus, their pollination regime is not displayed.
Bumblebee Pollination and Herbivory Alter Genomic Adaptation of Plants to Soil

Adaptation to environmental heterogeneity is a key driver of between‐population genomic differentiation, yet we know very little about how genomic divergence is affected by adaptation to multiple ecological factors. Using an experimental evolution approach, we whole‐genome re‐sequenced ‘fast‐cycling’ Brassica rapa plants which had evolved during eight generations of selection from different combinations of soil type, aphid herbivore presence or absence, and either bee‐ or hand pollination. Our results show that bumblebee pollination was the strongest driver of genomic divergence and that the degree of genomic divergence was strongly associated with the number of SNP markers identified in genomic selection scans. Furthermore, we found that the number of SNPs under selection was affected by herbivory in a soil‐dependent way. More specifically, aphid herbivory was associated with an increased number of selected SNPs for bee‐pollinated plants that evolved in tuff soil but was associated with a decreased number of selected SNPs for bee‐pollinated plants that evolved in the more resource‐limiting limestone soil. We also found that a higher number of selected SNPs was associated with higher rates of phenotypic evolution for 27 phenotypic traits including morphology and scent. Finally, we found that variation in pleiotropy between treatments was related to both the degree of genomic divergence and the number of SNPs under selection. Our results demonstrate that different soil types promote unique adaptive genomic architectures in response to biotic interactions, thus increasing genomic divergence between plant populations.


statistics for 45 nine‐spined stickleback populations. (a) Nucleotide diversity (π) across the autosomal chromosomes. (b) LD calculated as the harmonic mean of r² for SNPs located 100–200 kb apart within LG4. (c) Inbreeding coefficients (FIS) with standard deviations. (d) Relatedness (rxy) for pairs of individuals within populations with colours representing the proportion of pairwise comparisons within a population falling in a specific relatedness class. For the LD decay curve for each population, see Figure S2.
Variations in recent historical Ne estimates obtained with GONE. Range of Ne estimates for each population over 1–50 generations before present, calculated using two maximum recombination fractions (hc = 0.01 in light grey, and hc = 0.05 in dark grey). Median values are marked by matching triangles. Populations are ordered as in Figure 1, and the colour bar on top represents the ecotypes. For the trajectories of each population, see Figure S4. Estimates at one generation before present (NeGONEC$$ {N}_{e\ GONE}^C $$) were used in analyses.
Comparison of estimated contemporary effective population size (NeC$$ {N}_e^C $$) using GONE and CurrentNe2 for 45 nine‐spined stickleback populations. (a) and (b) show NeC$$ {N}_e^C $$ estimates obtained from GONE with hc = 0.01 and hc = 0.05, respectively. The black dashed line represents the 1:1 slope, and the grey shaded area indicates populations for which NeC$$ {N}_e^C $$ could not be estimated using CurrentNe2. The shapes denote whether the estimates were affected by migration, while the colours represent the ecotypes.
Temporal reconstruction of effective population size (Ne) for 45 nine‐spined stickleback populations over the past 30,000 years. (a) The colours represent Ne estimates at key time points with the scale given at the top. The estimates for the last 200 years were derived with GONE (hc = 0.01; results with hc = 0.05 are shown in Figure S8), while those for 4000 to 30,000 years ago were obtained with MSMC2. Asterisks indicate estimates influenced by population structure as inferred by manual inspection following the patterns in fig. 2F of Santiago et al. (2020). Long‐term Ne values were calculated from genetic diversity (π). Key time points were chosen to reflect significant geographic events: 4000 years ago marks the onset of the current Baltic Sea stage, 12,600 years ago the start of the Baltic Ice Lake stage, and 21,000–27,000 years ago corresponds to the Last Glacial Maximum. The colour next to population labels represents the ecotypes. (b) The demographic history of a representative population, FIN‐PYO, over the past 80,000 years. The dashed lines connect the time points in panels (a) and (b).
Estimating Recent and Historical Effective Population Size of Marine and Freshwater Sticklebacks

June 2025

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15 Reads

Effective population size (Ne) is a quantity of central importance in evolutionary biology and population genetics, but often notoriously challenging to estimate. Analyses of Ne are further complicated by the many interpretations of the concept and the alternative approaches to quantify Ne utilising different properties of the data. Each method is also informative over different time scales, suggesting that a combination of approaches should allow piecing together the entire continuum of Ne, spanning from the recent to more distant past. To test this in practice, we inferred the Ne continuum for 45 populations of nine‐spined sticklebacks (Pungitius pungitius) using whole‐genome data with both LD‐ and coalescent‐based methods. Our results show that marine populations exhibit the highest Ne values in contemporary, recent, and historical times, followed by coastal and freshwater populations. The results also demonstrate the impact of both recent and historical gene flow on Ne estimates and show that simple summary statistics are informative in comprehending the events in the very recent past and aid in more accurate estimation of NeCNeC {N}_e^C , the contemporary Ne, as well as in reconstruction and interpretation of recent demographic histories. Although our sample size for each large population is limited, we found that GONE can provide reasonable Ne estimates. However, due to challenges in detecting subtle genetic drift in large populations, these estimates may represent the lower bound of Ne. Finally, we show that combining GONE and CurrentNe2, both sensitive to population structure, with MSMC2 provides a meaningful interpretation of Ne dynamics over time.


Actual change in the number of occupied 10 km squares (1971–2007) by nightjar across the entirety of Britain and Northern Ireland. Data derived from Sharrock (1976) Gribble (1983), Morris et al. (1994), Conway et al. (2007), Balmer et al. (2013).
Modern breeding range map (A) and sampling locations of (B); historic and modern (C; n = 13 population centroids) nightjar samples. (A) Eurasian range map from (IUCN 2023), dark orange = breeding and light orange = found on passage migration only. (B, C) colours reflect assigned regions to each sample. Region classifications for each sample can be found in Table S1.
PCA biplots of genetic similarity. In all biplots, individuals with > 50% missingness (n = 15) have been removed from analysis. Plot (A) All (modern & historic samples), (B) Historic samples only and (C) Modern samples only. In the case of plots (A, B), the main plots are cropped subplots of the embedded plots (top right), which show all samples. The dashed boxes in the embedded plot show the cropped area presented in the main plot. The plot has been cropped to remove the effect of strongly differentiated individuals on interpreting the genetic structure. Where appropriate, regional groupings (coloured circles and triangles) are presented as 95% confidence ellipses.
Weighted regression of global heterozygosity over time. The black line represents the weighted regression line, adjusted for sampling intensity across years, with 95% confidence intervals shown in grey. The observed slope and associated p‐value from the randomisation test are presented on the plot. Inset barplot shows differences in average global heterozygosity between the modern and historic samples, with error bars reflecting standard deviation. Throughout figure, orange = historic and blue = modern samples.
Average FROH within 100 kb windows across modern samples within each regional category. Boxes represent median (midline) first and third quartiles, and whiskers reflect value ranges.
The Genomic Signature of Demographic Decline in a Long‐Distance Migrant in a Range‐Extreme Population

June 2025

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48 Reads

Migratory birds are inherently vagile, a strategy that may reduce the impacts of habitat loss and fragmentation on genetic diversity. However, specialist resource requirements and range‐edge distribution can counteract these benefits. The European nightjar (Caprimulgus europaeus) is a long‐distance migratory bird and resource specialist. Like other long‐distance migrants, nightjar populations have declined across the British Isles and Northwestern Europe over the past century. With this decline well documented in the British Isles, there is a need to quantify its genetic impacts. We applied full genome resequencing to 60 historic (1841–1980) and 36 contemporary British nightjars. Nightjars exhibited a statistically significant 34.8% loss in heterozygosity and an increase in inbreeding over the last ~180 years, showing a departure from panmixia towards weak spatial structure in the modern population. Such fine‐scale structuring in migratory birds is rare. Our results provide a case study of fragmentation's impact on a species with specialist resource requirements at its range limit. Similar demographic declines in nightjars and other long‐distance migrants across Northern and Western Europe suggest that genetic patterns seen in the British population may reflect those in other nightjar populations and European avifauna. Whilst our results indicate no immediate conservation concern, they depict a trajectory of declining genetic diversity, increasing inbreeding and genetic structure, potentially shared with other migratory species. Our study highlights the value of applying spatiotemporal population genetics analysis to migratory birds, despite their inherent vagility.


(a) Sampling locations of European anchovy including distribution of Northern (dark blue), Southern (light blue) and Cadis (red) clusters. For sampling details see Table S1. (b) Visualisation of population structure using a Principal Component Analysus (PCA) of all 40 individuals clustering in three separate clusters: Northern (dark blue), Southern (light blue) and Cadis (red). (c) ADMIXTURE analysis with most likely scenario of three clusters (K = 3). Individual admixture proportions are shown for each individual. Locations: IR_S, Ireland; KAT, Kattegat; NS, North Sea; POR_S, Portugal‐South; POR_W, Portugal‐West.
Manhattan plot based on sliding‐window genetic differentiation FST comparing the Northern and Southern clusters. Inner panel shows detailed plot for chromosome 4. Regions above the 98% percentile of the empirical distribution are marked with discontinuous blue line. The average FST is represented by a discontinuous red line.
Visualisation of the ontology terms detected during Gene Ontology and KEGG analysis when comparing the Northern and Southern European anchovy clusters, including biological processes, cellular components and metabolic and signalling pathways. (a) Lollipop chart with number of genes involved in enriched GO terms on the x‐axis and (b) ClueGO network for a better biological interpretation of functionally grouped genes. Analyses were conducted using the zebrafish Ensembl Gene IDs, only considering GO terms with corrected p values < 0.05 and selecting the major significant GO term as the representation of the group.
Demographic history reconstruction of European anchovy populations using the PSMC (Pairwise Sequentially Markovian Coalescent) method. A universal mutation rate of 1 × 10⁻⁸ and generation time of 1 year were used. All individuals were included in the analysis and each PSMC curve corresponds to a single individual. The three clusters are colour‐coded: Northern (dark Blue), Southern (light blue) and Cadis (red). The shaded area indicates the Last Glacial Period (LGP).
Resolving the Population Structure and Demographic History of the European Anchovy in the Northeast Atlantic: Tracking Historical and Contemporary Environmental Changes

June 2025

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97 Reads

The spatial distribution of the European anchovy has expanded in the northern part of its range in the Northeast Atlantic in recent decades. However, whether this results from a northward range shift of southern conspecifics or the expansion of a local northern population is unknown. Using for the first time whole‐genome sequencing, we explore current patterns of genetic diversity and population sub‐structuring of European anchovy in the Northeast Atlantic, with special focus on recently expanded North Sea areas. Genomic data suggested three distinct groups: Northern (North Sea and Kattegat), Southern (Ireland and Central Portugal) and Cadis (South Portugal). Despite most of the genome being homogenised by high levels of gene flow characteristic of small pelagic fish, several large regions of high genetic differentiation were observed. This suggests that genomic population boundaries might be maintained by local adaptation within chromosome structural variants (inversions). Admixture analysis indicates that the ongoing northern range shift involves both migrants of southern origin and expansion of the local North Sea population. Historical demographic inference suggests that anchovies survived the last glacial period with small population sizes, followed by a split into the current Northern and Southern groups at the end of the last glacial maximum. The Southern group then expanded into the North Sea as the ice sheets retreated, in an expansion involving a large number of individuals, which is consistent with the retention of most of the genetic diversity. In comparison with other small pelagic fish, the genetic patterns found in anchovies (deeply divergent groups, no loss of genetic diversity during expansion, mixing between groups) align well with those found in European sprat, while sardines fit the pattern of expansion of a leading‐edge population, with reduced genetic diversity and much shallower divergence between populations. This study contributes to a better understanding of population structure, range shifts and local adaptation in small pelagic fish under climate change, informing conservation and management efforts.


Admixture Affects the Rate and Repeatability of Experimental Adaptation to a Stressful Environment in Callosobruchus maculatus

Hybridisation and admixture are common in nature and can serve as important sources of adaptive potential by generating novel genotype combinations and phenotypes. However, hybrid incompatibilities can also reduce hybrid fitness. Given the pervasiveness of admixture and its potential role in facilitating adaptation, understanding how admixture influences the rate and repeatability of evolution is critical for advancing our understanding of evolutionary dynamics. Yet, few studies have examined how patterns of evolutionary repeatability in admixed lineages are shaped by strong ecological pressures. In this experiment, we evaluated patterns of evolution and repeatability in admixed and non‐admixed cowpea seed beetles (Callosobruchus maculatus) adapting to a novel, stressful host: lentil. Specifically, we asked (1) whether admixture facilitates adaptation to lentil, (2) whether repeatability is greater in admixed or non‐admixed lineages, and (3) to what extent repeatability in admixed lineages is driven by selection on globally adaptive alleles versus epistatic effects and hybrid incompatibilities. We found that admixture facilitated adaptation to lentil, and evolutionary rescue–defined as adaptation that prevents population extinction–occurred in all lineages. Evolutionary repeatability was highest in two admixed lineages, though evident across all lineages. Adaptation to lentil appeared largely driven by selection on globally adaptive alleles. Nevertheless, even under conditions of evolutionary rescue in a marginal environment, the purging of hybrid incompatibilities contributed substantially to repeated evolution in admixed lineages.


Multispecies coalescent models used to estimate population demographic parameters in the hybrid zone. (a) The introgression model (MSC‐I) includes nine parameters: six population sizes (θN, θS, θhybrid, θN.anc, θS.anc, θroot), one divergence time (τroot), one hybridization time (τhybridization), and one introgression probability (φ) defined as the proportion of the hybrid population genome originating from one of the parental populations (the proportion of the genome originating from the other population is 1−φ). Parameters for the MSC‐I were estimated separately for each hybrid sample site. (b) The migration model (MSC‐M) includes six parameters: three population sizes (θN, θS, θroot), one divergence time (τroot), and two migration rates (MN→S, MS→N). Migration rate is the expected number of migrants from the donor population to the recipient population per generation.
Phylogeny and geographic distributions of Sceloporus tristichus and S. cowlesi in the Southwestern United States based on mitochondrial DNA (mtDNA). (a) Phylogenetic relationships based on the ND1 gene estimated using maximum likelihood (rooted with S. occidentalis). The four mtDNA clades that are found in the hybrid zone are colour‐coded. (b) Geographic distributions of S. tristichus (circles) and S. cowlesi (triangles), the distributions of the clades detected in the hybrid zone, and the mtDNA species boundary (dashed line). (c) Pie‐charts showing mtDNA haplotype frequencies in the hybrid zone. Sample sizes are shown in parentheses. The shaded areas show habitat distributions of Petran Montane Conifer Forest (dark grey), Great Basin Conifer Woodland (light grey), and Great Basin Grassland (unshaded/white).
Phylogeography of Sceloporus tristichus and S. cowlesi in the Southwestern United States based on nuclear DNA. (a) Phylogenetic relationships estimated with the concatenated ddRADseq loci using maximum likelihood. mtDNA species assignments are shown for each sample. Phylogenetic tips are labelled by their geographic origin (State), using abbreviations shown on the maps in (b–d). Based on the K = 3 analysis in (c), S. cowlesi nuclear DNA (brown) does not approach the hybrid zone, which is made up of the nuclear DNA of southern (green) and northern (orange) populations of S. tristichus. The barplots show SNP‐based admixture results for models assuming K = 2, K = 3, and K = 4 populations. The geographic distributions of these populations are in (b) K = 2, (c) K = 3 (best‐fit model), and (d) K = 4. Pie‐charts on the maps show individual admixture proportions.
Patterns of hybridization and introgression for the Sceloporus tristichus hybrid zone. (a) Hybrid index plot shows decreasing northern ancestry along the hybrid zone. The posterior mode (dot) and 95% credible interval (error bars) is shown for each sample. The points are ordered by population mean and then individual estimates. (b) Histogram of hybrid index values across all eight populations shows a high frequency of late‐stage backcrosses. (c) Nuclear and mtDNA clines (with 95% confidence intervals).
Hybrid Zone Analysis Using Coalescent‐Based Estimates of Introgression and Migration in Plateau Fence Lizards (Sceloporus tristichus)

Coalescent modelling of hybrid zones can provide novel insights into the historical demography of populations, including divergence times, population sizes, introgression proportions, migration rates and the timing of hybrid zone formation. We used coalescent analysis to determine whether the hybrid zone between phylogeographic lineages of the Plateau Fence Lizard (Sceloporus tristichus) in Arizona formed recently due to human‐induced landscape changes, or if it originated during Pleistocene climatic shifts. Given the presence of mitochondrial DNA from another species in the hybrid zone (Southwestern Fence Lizard, S. cowlesi), we tested for the presence of S. cowlesi nuclear DNA in the hybrid zone as well as reassessed the species boundary between S. tristichus and S. cowlesi. No evidence of S. cowlesi nuclear DNA is found in the hybrid zone, and the paraphyly of both species raises concerns about their taxonomic validity. Introgression analysis placed the divergence time between the parental hybrid zone populations at approximately 140 kya and their secondary contact and hybridization at approximately 11 kya at the end of the Pleistocene. Introgression proportions estimated for hybrid populations are correlated with their geographic distance from parental populations. The multispecies coalescent with migration provided significant support for unidirectional migration moving from south to north, which is consistent with spatial cline analyses that suggest a slow but steady northward shift of the centre of the hybrid zone over the last two decades. When analysing hybrid populations sampled along a linear transect, coalescent methods can provide novel insights into hybrid zone dynamics.


Landscape Genomics Provides Insights Into Climate Change‐Driven Vulnerability in Torrent Frogs (Ranidae: Amolops)

Anthropogenic climate change has caused widespread loss of species biodiversity and ecosystem productivity worldwide, with amphibians being particularly affected. Predicting the future of amphibians, a critical group for maintaining biodiversity and for balancing ecosystem structure and function, is essential for effective conservation planning in the Anthropocene. In this study, we used Amolops species as a model to assess their vulnerabilities under future climate change. Through genotype–environment association (GEA) analyses, we identified climate‐associated SNPs, revealing that temperature and precipitation were key drivers for local adaptation in these species. Genetic offset analysis showed that the marginal and high‐latitude populations of the Amolops mantzorum and Amolops monticola groups were at greater risk of local extinction as a result of a mismatch of genetic‐environmental associations under future climate conditions. Ecological niche models predicted that, from 2011 to 2100, approximately 67% of Amolops species would experience significant habitat loss. We introduced the life strategy index (LSI) to assess species vulnerability, considering the interplays of evolution, ecology, and colonisation. Our LSI analysis showed that Amolops deng and Amolops tuberodepressus face a high extinction risk, in contrast with A. mantzorum, features strong adaptability and a low extinction risk. The LSI framework not only enables the systematic assessment of species vulnerability but also identifies key contributing factors through comprehensive evaluation across ecological, evolutionary, and colonisation dimensions, thereby facilitating the development of targeted conservation strategies.


Genetic dissimilarity between 16S/COI‐tagged GenBank accession records from identical voucher specimens across Amphibians, Birds, Mammals and Reptiles. Only pairs of sequences that exhibit divergence (i.e., non‐identical sequences) are included in the plot. Orange lines represent average dissimilarity values.
In Vouchers We (Hope to) Trust: Unveiling Hidden Errors in GenBank's Tetrapod Taxonomic Foundations

Genetic repositories are invaluable resources foundational to various biological disciplines. While their data and metadata reliability are essential for robust research outcomes, numerous studies have highlighted data quality and consistency issues. Here, we detect and quantify errors at the most fundamental level by analysing the congruence of sequences derived from the same genetic marker and specimen voucher across tetrapods. Our analysis reveals that 32% of re‐sequenced vouchers (with identical field or museum numbers) yield unequal sequences, ranging from a few mutations to significant divergences (0.06%–33.95%). These divergences may result from sample misidentification, labelling errors, fidelity disparities between sequencing methods, or contamination at various stages of the research process. Our findings demonstrate errors within GenBank at its most basal level and suggest that, although undetectable, a similar error rate likely exists in non‐re‐sequenced data. These previously overlooked errors are concerning because they arise from replicated experiments, which are uncommon, and raise serious questions about the reliability of non‐re‐sequenced specimens. Such errors can compromise the accuracy of biodiversity assessments (e.g., taxonomic assessment, eDNA and barcoding), phylogenetic analyses and conservation planning by artificially inflating the intraspecific divergence or misidentifying (to‐be‐described) species. Additionally, the accuracy of large‐scale biological studies that rely on such data can be compromised. Our concerning results call for protocols ensuring sample traceability to the specimens or tissues during the whole process of data generation, analysis and deposition in a database. We propose a third‐party annotation system for individual GenBank records that would allow flagging common errors and alert both the original submitter and all users to potential problems without modifying the original records.


Convergent Evolutionary Dead‐End and Breakdown of Hard Chorion in Parental‐Egg‐Care Fish Reproductive Strategies

June 2025

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65 Reads

Fish exhibit a diverse array of reproductive strategies adapted to various ecological niches. Parental egg‐care, including live‐bearing, mouth‐brooding, and male egg protection by brood pouches, represents an effective strategy for ensuring larval survival and has emerged independently in multiple lineages. Despite the recognised evolutionary bias that favours a strategy transition from non‐carer to egg‐carer, the genetic mechanisms underlying this bias and the commonalities among parental egg‐care species remain elusive. This study explores the relationship between egg‐care and the chorion hardening system crucial for protecting eggs in non‐care species. By analysing whole genome sequences of 240 species of Acanthopterygii across 25 orders, we discovered that multiple genes associated with chorion hardening have become pseudogenes in various egg‐care species, indicating a collapse of the chorion hardening system in these fish. These findings suggest that the evolutionary bias in fish reproductive strategies not only aims to enhance survival efficiency but also imposes a constraint on egg‐care species, preventing them from reverting to a reproductive strategy relying on a hardened chorion. In particular, alveolin, previously characterised as a single mutant resulting in significantly fragile chorion in medaka, suggests a strong correlation between egg‐care strategy and gene loss. Our results suggest an evolutionary dead‐end because gene loss may impose an evolutionary constraint at the behavioural level. The observed association between gene loss and reproductive strategies provides insights into suitable reproductive environments for each species and may facilitate non‐invasive estimation of reproductive strategies in species with unknown breeding strategies.


Genomic Analysis of Hair Sheep From West/Central Africa Reveals Unique Genetic Diversity and Ancestral Links to Breed Formation in the Caribbean

June 2025

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98 Reads

Cameroon Blackbelly sheep are a domestic breed of hair sheep from West/Central Africa. They are popular with small‐holder farmers in Cameroon as they are highly resilient to local environmental challenges and are prolific a‐seasonal breeders. The aim of this study was to characterise the genetics of Cameroon Blackbelly sheep in relation to global sheep populations and to investigate their relationship to Caribbean hair sheep. We first examined the genetic diversity of the Cameroon Blackbelly breed relative to global sheep populations using 50K SNP data. We also used whole genome sequence data to further investigate relationships between Cameroon Blackbelly and breeds from Africa and Europe, as well as the Barbados Blackbelly breed from the Caribbean, which is phenotypically similar to Cameroon Blackbelly. ADMIXTURE results based on 50K and WGS data demonstrated both West/Central African and European ancestries for the Barbados Blackbelly sheep. Results from f4‐statistics‐based qpAdm analyses supported these findings. Local ancestry inference identified several genomic regions in Barbados Blackbelly with high proportions of West/Central African ancestry. One of these, on OAR3, includes various keratin genes, suggesting that these genes may play a role in the shared coat phenotypes of the Barbados Blackbelly and Cameroon Blackbelly. This result is consistent with previous reports of adaptive introgression of coat characteristics in both wild and domesticated species. The findings of our study support the view that sheep were transported from West/Central Africa to the Caribbean as part of the transatlantic slave trade and European colonisation, similar to introductions proposed for cattle and goats.


Journal metrics


4.5 (2023)

Journal Impact Factor™


27%

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8.4 (2023)

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32 days

Submission to first decision


1.244 (2023)

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