Primula veris, Dianthus carthusianorum, Medicago falcata, Polygala comosa and Salvia pratensis) occurring in 18 fragments of calcareous grasslands in south eastern Germany. We sampled 1286 individuals in 87 populations and analysed genetic diversity using amplified fragment length polymorphisms. Additionally, we collected data concerning habitat fragmentation  and habitat conditions  of the selected study sites. The whole data set was analysed using Bayesian multiple regressions.Habitat fragmentation is considered to be a main reason for decreasing genetic diversity of plant species. However, the results of many fragmentation studies are inconsistent. This may be due to the influence of habitat conditions, having an indirect effect on genetic variation via reproduction. Consequently we took a comparative approach to analyse the impact of habitat fragmentation and habitat conditions on the genetic diversity of calcareous grassland species in this study. We selected five typical grassland species  contains supplementary material, which is available to authorized users. Calcareous grasslands are important hotspots of plant species diversity in central Europe. They contain many rare and endangered plant species and are of strong conservation interest –3. HowevHowever, fragmentation has not only an impact on biodiversity at the species level, but also on the genetic diversity, due to geographic isolation of smaller populations . In partHowever, the results of many studies dealing with the impact of fragmentation on genetic diversity are inconsistent. Some of these studies support the assumptions derived from the theory of island biogeography , whereasOne of the most important challenges of fragmentation studies is that habitat conditions may differ between remnant grassland patches . Many caHence, genetic diversity may be affected by the interfering effects of both fragmentation and habitat conditions, which might explain the inconsistent results of many fragmentation studies. Moreover, due to different biological traits, patterns of genetic variation differ between plant species  and thesIn this study, we analysed the impact of fragmentation on genetic diversity of several calcareous grassland species in a comparative approach, including both habitat fragmentation and habitat conditions. We used the same analytical approach for all species and applied Bayesian multiple regressions, which enables a detailed interpretation of the data, while being more flexibly adaptable to the data structure than traditional frequentist methods. More specifically we ask the following questions: (i) has fragmentation of calcareous grasslands a significant impact on genetic diversity of plant populations? (ii) is historical landscape configuration more important for genetic diversity than present landscape configuration? (ii) is genetic diversity of grassland plant populations affected by habitat conditions?2013) and perimeter (P) of each grassland fragment as well as the distance (D2013) to the nearest calcareous grassland within the 3 km radius. The shape of the study sites was characterised by the ratio of habitat area to perimeter (HA/P2013), which was small for narrow and elongated grasslands and large for round and compact grasslands.For our study, we randomly selected 18 remnant calcareous grasslands in the valleys of the rivers Naab and Laber on the Franconian Alb in south eastern Germany near Regensburg  and the distance (D1830) to the nearest calcareous grassland within the 3 km radius in 1830. The maps include a detailed legend, which allows the identification of calcareous grasslands. We compared the area covered by calcareous grasslands in 1830 and 2013 and calculated the habitat loss (HL) within the 3 km radius around each of our study sites as a percentage of the grassland area lost since 1830. Additionally, connectivity of each fragment to all other fragments within the 3 km radius in 2013 and 1830 was calculated according to Hanski [Si is the connectivity of the patch i, dij is the distance between patches i and j, and Aj is the area of the patch j [Using historical cadastral maps, which were available from local land surveying offices, we determined the area of the study sites  Arc., Polygala comosa Schkuhr and Salvia pratensis L.), frequently occurring in calcareous grasslands in south eastern Germany. In the field we assessed population size (NI) by counting the number of present individuals (Table P. comosa could not be sampled), leaf material of 15 individuals per population and species was collected  and MseI (40 µM) adaptors (MWG Biotech), following a 5 min heating at 95 °C with a final 10 min step at 25 °C. DNA restriction and adapter ligation were performed in one step by adding a 3.6 μL mixture per reaction containing 2.5 U EcoRI (MBI Fermentas), 2.5 U MseI (MWG Biotech), 0.1 μM EcoRI and 1 μM MseI adapter pair, 0.5 U T4 Ligase with 0.1× of its corresponding buffer (MBI Fermentas), 0.05 M NaCl and 0.5 μg BSA (New England BioLabs) to 6.4 μL of genomic DNA in a concentration of 7.8 ng/μL. Following an incubation at 37 °C for 2 h with a final enzyme denaturation step at 70 °C for 15 min, the restriction-ligation products were diluted tenfold with 1× TE buffer for DNA .DNA adapters were prepared by adding equal volumes of both single strands of EcoRI and MseI primers (MWG Biotech) were added to an AFLP Core Mix  containing 1x Buffer S, 0.4 mM dNTPs and 1.25 U/µL Taq-Polymerase. In a 5 μL reaction volume PCR was performed on at 94 °C for 2 min then 30 cycles of 20 s denaturation at 94 °C, 30 s annealing at 56 °C and 2 min elongation at 72 °C, a final 2 min 72 °C and 30 min 60 °C step for complete extension ending with a final cool down to 4 °C. After PCR, products were diluted 20 fold with 1× TE buffer for DNA.For preselective DNA amplification, 1 μL diluted DNA restriction-ligation product, 0.25 µM preselective EcoRI primers labelled with different fluorescent dyes (Beckman Coulter) were used , 0.25 μM MseI (MWG Biotech) primers and 0.75 μL diluted preselecive amplification product. For detection, EcoRI primers labelled with different fluorescent dyes  were used. PCR parameters used were treated 2 min at 94 °C, 10 cycles 20 s denaturation at 94 °C, annealing 30 s at 66 °C and 2 min elongation at 72 °C, where annealing temperature was reduced every subsequent step by 1 °C, additional 25 cycles of 20 s denaturation at 94 °C, 30 s annealing at 56 °C and 2 min elongation at 72 °C completed by a following 30 min step at 60 °C and a cool down to 4 °C.After an extensive screening of 30 primer combinations, three primer combinations per species were chosen for a subsequent selective PCR reaction. For detection, ed Table . Selecti0.1 buffer for AFLP and pooled. After pooling 5 μL of each selective PCR product of a given sample and adding them to a mixture of 2 μL sodium acetate , 2 μL Na2EDTA  and 1 μL glycogen , DNA was precipitated in a 1.5 mL tube by adding 60 μL of 96% ethanol (−20 °C) and an immediate shaking. DNA was pelleted by 20 min centrifugation at 14,000g at 4 °C, the supernatant was poured off and the pellet was washed once by adding 200 μL 70% ethanol (−20 °C) and centrifugation at the latter conditions and was subsequently vacuum dried in a concentrator [Selective PCR products were diluted with 1× TEentrator , 34.After redissolving the pelleted DNA in a mixture of 24.8 μL Sample Loading Solution  and 0.2 μL CEQ Size Standard 400 (Beckman Coulter), selective PCR products were separated by capillary gel electrophoresis on an automated sequencer . Results were examined using the GeXP software (Beckman Coulter) and analysed using the software Bionumerics 4.6 . From the computed gels, only those fragments that showed intense and articulate bands were taken into account for further analyses. Samples yielding no clear banding pattern or obviously representing PCR artefacts were repeated. Finally, 19 individuals were excluded from the analysis due to the lack of a clear banding pattern. Reproducibility of molecular analyses was investigated with 10% of all analysed samples by means of estimating the genotyping error rate , which wFrom the AFLP bands, a binary (0/1) matrix was created for each species. Based upon this matrix, we calculated the genetic diversity of each population as Nei’s Gene Diversity  using thThe impact of habitat fragmentation and habitat conditions on diversity was analysed using a robust hierarchical Bayesian multiple regression approach with regression parameters for the species level (not shown) and an overarching set of hyperparameters for the species-independent estimation of regression parameters. Predictor variables were grassland fragmentation parameters  and habitat condition parameters . This approach is equivalent, though not identical, to a linear mixed model with random slopes and random intercepts using species as a random effect. The overarching hyperparameters ensure a transfer of information between the species’ parameters that shrinks outliers on species-level towards the main trend. The hyperparameters themselves are therefore suitable to depict the species-independent trends in the data.There was no credible influence of habitat age detectable by the model and, hence, impeded accurate parameter estimation of the remaining parameters. Accordingly, habitat age as a parameter hampered model interpretation, and therefore was excluded from the final analysis. All predictor variables were checked for strong correlations  to check for multicollinearity. Additionally, multicollinearity can be recognised in Bayesian models by extremely broad posterior distributions of correlated parameters. However, this was not the case in the presented analysis. Model stability was verified by re-running the analysis with and without the most strongly correlated predictors. Accordingly, habitat age as a parameter hampered model interpretation and was, therefore, excluded from the final analysis.A Bayesian approach was chosen for being flexibly adjustable to the situation at hand, e.g. it can be easily modified to reduce false positives in parameter estimation or improved to accommodate outliers in the data. In the situation at hand, the applied model could easily be tailored to reflect the hierarchical structure of our data  aiming at a lower limit of 10 k for the relevant parameters. Highest density intervals (HDIs) were computed for the regression coefficients to check if coefficients were credibly non-zero. The predictors’ relative influences were assessed using standardised regression coefficients. A graph was produced by fixing all but the predictors of credible influence to their mean, resulting in a two dimensional scatter plot.2 in 1830–14,881 m2 in 2013 , 97.4% (D. carthusianorum), 97.8% , 98.7% (P. comosa) and 94.3% (S. pratensis). Genetic diversity varied between species and populations. Mean genetic diversity of populations , followed by S. pratensis (0.26), P. comosa (0.25) and D. carthusianorum (0.23). The lowest level of genetic diversity was observed in P. veris (0.18).AFLP analysis resulted in 120 fragments for Considering each species separately and all species together in the hierarchical Bayesian multiple regressions, we observed a credible impact of the distance to the nearest calcareous grassland in 1830 on the genetic variation within populations of the studied grassland species Fig. , 7. The In our study, we observed an impact of the historical landscape configuration on the genetic diversity of the study species, since it depended on the distance to the nearest calcareous grassland in 1830 in the hierarchical Bayesian regressions. During the process of fragmentation the area of the habitat patches usually decreases . In our Aside from decreasing habitat area, isolation of calcareous grasslands is also an important consequence of habitat fragmentation , 8. The Gene flow by pollen is normally restricted to the nearest vicinity of plant populations to distances of less than 1 km , 52. HowSuccisa pratensis depends on the historical landscape structure of the habitat [Dianthus carthusianorum depends on patch connectivity by shepherding [Surprisingly, neither historical nor present habitat connectivity had an impact on genetic diversity in our study. This may be traced back to the fact that the calculated connectivity reflects the spatial structure, but not the real gene flow, which may be strongly affected by the migration of sheep flocks . However habitat , which spherding  and thatpherding , which uBrachypodium pinnatum become increasingly dominant [The results of our analyses indicated no impact of habitat conditions on genetic diversity of the studied calcareous grassland species. It has been demonstrated that, alongside habitat fragmentation, changes in habitat conditions have a strong impact on the species richness and composition of remnant calcareous grasslands fragments , 21. Lacdominant  and littdominant . As a codominant . For theTrifolium montanum is more threatened by the effects of habitat degradation in the short term, than by the effects of fragmentation. Since genetic diversity depends explicitly on the degree of sexual reproduction [Dianthus seguieri, it has recently been shown that increased vegetation height and coverage as well as a high proportion of graminoids due to land use abandonment reduce genetic diversity and seed set [In contrast to the impact of land use on species diversity, the relationship between land use and genetic diversity is much less clear. However, it has already been demonstrated, that seedling recruitment and establishment in grasslands are positively affected by grazing and the removal of litter . Schleunoduction , it appeseed set . As prevseed set  since itHowever, in this study genetic diversity depended neither on vegetation structure, nor on soil nutrient levels. One reason for this observation may lie in the life span of the investigated plant species. It has already been reported that the frequency of plant species in remnant calcareous grasslands depends mainly on their persistence . All speThe results of our study provide evidence that the genetic diversity of calcareous grassland plant species depends on historical landscape configuration, rather than on the present population size or habitat conditions. In practice, efforts to preserve calcareous grasslands mainly concentrate on large fragments exhibiting the typical open-shortgrass habitat conditions and high species diversity. However, a comprehensive conservation approach should also consider the genetic diversity of calcareous grassland plant species. From our results it can be concluded that populations in smaller grassland fragments may, depending on historical landscape configuration, substantially contribute to the genetic variation of the plant species even under conditions of habitat deterioration. Preferably, these populations should therefore be included in strategies to preserve calcareous grasslands as the local biodiversity hotspots they are.


Melampsora larici‐populina) expands clonally and linearly along the Durance River, in the Alps. The colonization dynamics observed in 2004 showed two phases with different genetic outcomes. Upstream, fast colonization maintained high genetic diversity. Downstream, the colonization wave progressively faltered, diversity eroded, and differentiation increased, as expected under recurrent founder events. In line with the high dispersal abilities of rust pathogens, we provide evidence for leapfrog dispersal of clones. Our results thus emphasize the importance of colonization dynamics in shaping spatial genetic structure in the face of high gene flow.The genetic consequences of range expansions have generally been investigated at wide geographical and temporal scales, long after the colonization event. A unique ecological system enabled us to both monitor the colonization dynamics and decipher the genetic footprints of expansion over a very short time period. Each year an epidemic of the poplar rust ( In population genetics, dispersal has long been viewed as a force that homogenizes allelic frequencies across populations and balances the effect of drift in equilibrium models Slatkin . NeverthThe hallmark of range expansion under restricted gene flow is an erosion of genetic diversity together with an increase in genetic differentiation along axes of expansion. A particular genetic pattern during expansion is the increase in frequency of a rare variant at the front of a colonization wave, the so‐called surfing phenomenon  subjected to a linear and continuous range expansion, surveyed in real time. We take advantage of a particular ecological corridor, the Durance River (southern France). This simple ecological system provides the opportunity to analyze in parallel colonization dynamics and the resulting population structure. Deeply embanked in the French Alps, the Durance River valley (ca. 145 km long and a few km wide) channels the dispersal of M. larici‐populina in a downstream direction , which is restricted to the uppermost part of the valley. Then, the fungus switches to asexual reproduction on poplars and performs several rounds of clonal multiplication until leaf‐fall. The early stages of the epidemic thus occur each year in the uppermost part of the valley where larches and poplars coexist, and then, the disease spreads southward, downstream in the valley. Dispersal occurs along a 1D and continuous domain, as wild poplar stands are distributed all along the riversides.Here, we aim to document how genetic differentiation arises in a natural population of the poplar rust fungus . Disease incidence was evaluated as the number of infected leaves found over the search time elapsed during each survey.To represent disease progression in continuous time and space, data interpolation was performed using the mba.surf function in the R‐package MBA (R Development Core Team 10.5061/dryad.249kj74s). We focus on the 252 indivi‐duals assigned as wild isolates in Xhaard et al.  is not sufficient to discriminate clones, due to the very likely occurrence of scoring errors. This leads to the occurrence of distinct MLGs differing at few alleles. We thus used an approach recommended by Arnaud‐Haond et al.  based ongsim software   were very small (<10−15). Based on these results, we can confidently assess that all the MLGs we identified can indeed be considered as true clonal lineages.We further used MLR = (G − 1)/(N − 1), G being the number of clonal lineages, and N the number of sampled individuals; (2) the slope β of the Pareto distribution, summarizing the distribution of clonal membership within a population. β is an index of clonal evenness, describing the relative abundance among clonal lineages  The clonal richness was estimated by Â), standardized to the smallest sample size , and unbiased gene diversity (HE)  including clonal replicates, to detect the genetic consequences of clonality; and (2) retaining a single copy of each clonal lineage. For each set, gene diversity within locations was estimated through the mean number of alleles per locus (HE) Nei . GeneticFST‐based Reynolds distance  using the networks  down to Monêtier (≈90 km from the source) until early September Fig. B. Then, FST downstream the valley, using Prelles as a reference  downstream from Monêtier  suggesting that our assessment of clonal distribution was not biased by differences among sites in sampling effort. The several occurrences of MLL1 enabled to test for an increase in frequency along the invaded domain, as expected under the surfing hypothesis. However, the correlation between the frequency of MLL1 and geographical distances was very weak , indicating that this clonal lineage did not surf on the Durance River. Alternatively, we observed an erratic distribution of MLL1 along the invaded domain, which may result from LDD events or from the limited sample size. We therefore tested whether the lack of detection of MLL1 between Saint‐Clément and Monêtier could be due by chance. Assuming an even distribution of this lineage along the invaded domain, the probability to detect it given the sample size follows a Binomial law . The global frequency of MLL1 was set at 0.04 (10 occurrences over 252 samples), which amounts to the most conservative estimates. The probability that we observed by chance no individual belonging to MLL1 between Chateauroux and Rochebrune  was very low (PX=0)B( = 0.03). The gap in MLL1 detection between Saint‐Clément and Monêtier would not result from a limited sample size. The resampling of MLL1 at very distant sites would thus provide evidence for a leapfrog dispersal event.One clonal lineage (noted MLL1) was found at several instances and at distant locations  (Klopfstein Ne) .Theoretically, the distribution of individual invasive success is linked to effective population size (Narrow corridors, such as the Durance Valley, are expected to raise the odds of surfing events (Fayard et al. The poplar rust colonization of the Durance River valley is a neat ecological system to decipher the population genetic outcome of range expansion. In this study, we showed how the colonization dynamics paralleled the variation in population genetic indices. Depending on the demographic scenario of colonization, some sites were at genetic equilibrium, other displayed strong hallmarks of founder event even if they were colonized early. Our main result was that the increase in genetic differentiation went hand in hand with an erosion of genotypic diversity along the invaded domain. The next step will consist in examining several instances of colonization to gain more insights on the interplay between colonization dynamics, dispersal regime, and genetic consequences of the Durance invasion. It is much likely that the strength of the epidemics and the colonization dynamics would differ among years. It would thus be very interesting to compare the resulting population genetic structure to identify the common features and differences across years. Do we always observe an increase in genetic differentiation? Is the slope of increase inversely proportional to the speed of colonization? Is the patchy distribution of clonal lineage a general rule? Is it possible that some genotype surf the valley after all?None declared.Figure S1. Frequency distribution of the number of different alleles between pairs of MLGs of poplar rust.Click here for additional data file.


Synoeca cyanea and the lowland Synoeca aff. septentrionalis. We analyzed several sources of data including multilocus DNA sequence, climatic niche models and chromosomal features. We find support for idiosyncratic phylogeographic patterns between these wasps, involving different levels of population structure and genetic diversity, contrary suitable climatic conditions during the last glaciation, and contrasting historical movements along the AF. Our data indicate that neotectonics and refugia played distinct roles in shaping the genetic structure of these wasps. However, we argue that eustatic changes influenced the demographic expansion but not population structure in AF biota. Notably, these wasps exhibited chromosomal clines, involving chromosome number and decreasing of GC content, latitudinally oriented along the AF. Together, these results reinforce the need to consider individual organismal histories and indicate that barriers and refugia are significant factors in understanding AF evolution.Phylogeographic studies have sought to explain the genetic imprints of historical climatic changes and geographic barriers within the Brazilian Atlantic Forest (AF) biota, and consequently two processes of diversification (refugia and barriers) have been proposed. Additionally, there is evidence that eustatic changes influenced the biogeographic history of the AF. Here we evaluate these contrasting diversification processes using two AF social wasp species – the mid-montane  Nevertheless, the magnitude of these biological effects during glaciations is generally dependent on the species’ environmental requirements, vagility3, latitude and topography4. Multiple investigations have shown the intrinsic role of glacial-interglacial cycles for northern hemisphere taxa6, revealing a process of range contraction during glaciation7. Also, studies on tropical organisms such as Australian and African forest-dwelling species have revealed range contraction during the Last Glacial Maximum (LGM), around 21 thousand years ago (21 kya)11. However, range and population expansions even during the LGM have been proposed for some cold/arid-tolerant biota13.Intense climatic swings during the late Quaternary had notable effects on the population dynamics, range and genetic diversity of many organisms14 and Carnaval et al.15 (hereafter CM model), suggests that climatically stable areas , even during the LGM, were restricted to its northern areas  and central areas , whereas the southern portion of the AF would have been climatically unstable. Moreover, the north and south AF represent two distinct climatic regions16, reinforcing the idea that these two geographic areas provided different paths to the diversification history of AF species. Notably, several phylogeographic studies are consistent with the CM model in showing higher genetic diversity, strong population structure and an absence of demographic expansion signals within populations in the north and central portion of the AF, whereas populations in the southern portion show lower genetic diversity, signals of demographic expansion (due to recent re-colonization events from north populations) and weaker population structuring21.The high biotic diversity within Neotropical forests presents a conundrum that has challenged evolutionary and biogeographic biologists to explain the cause of these diversity patterns in such environments. Hence, particular models considering historical mechanisms capable of promoting diversification have been invoked to explain the diversity pattern in the eastern South America, particularly the Brazilian Atlantic Forest (hereafter AF). The model of climatically stable habitat through time in the AF, proposed by Carnaval & Moritz25, including the newly proposed influence of sea-level changes on the biogeographic history of the AF26. In contrast to refugia scenarios, genetic predictions regarding barrier hypotheses include older divergence times (due to vicariant events occurring deeper in the past), interruptions of gene flow that coincide with geographic barriers, and populations showing smooth demographic oscillations27. Another alternative hypothesis to the phylogeographic patterns observed in AF species, termed the “Atlantis Forest hypothesis”, suggests that populations actually expanded, even during the LGM, in response to the expansion of the AF onto the Brazilian emerged continental shelf26.However, other studied species reveal genetic responses contrary to that expected by the CM model, suggesting instead a meaningful role for geographic barriersSynoeca . These are aggressive, medium-sized social wasps with black or metallic colours that build nests directly attached to tree branches28. In Brazil, these wasps are popularly known as “marimbondo (or caba) tatu”, meaning “armadillo” wasp, because the envelope of their nests resembles the back of an armadillo (Dasypodidae). The mid-montane Synoeca cyanea  inhabits the southern and central portions of AF; it is found regularly in semi-deciduous and gallery forests, showing a strong trend for mid-elevation habitats in lower latitudes, reaching up to 1100 meters above sea level (a.s.l.)29  is restricted to the AF coastline, occupying dense ombrophilous forest and occasionally restinga habitat   proposed for AF biota.We evaluated these models using two species of Neotropical swarm-founding wasps within the genus 32. On the other hand, only a few AF phylogeographic studies have explored chromosome data, despite karyotypic differences having the potential to act as a genetic barrier34, and consequently might reveal strong evidence of population structuring . Here, we used independent data including multilocus DNA sequence, climatic niche models, and chromosomal features to investigate and compare the phylogeographic patterns of these two parapatric social wasps. Additionally, we explored whether these armadillo wasps experienced the scenarios proposed by the contrasting diversification models suggested for AF biota, and also evaluated the potential role of the Brazilian emerged continental shelf during the LGM on their demographic histories. Specifically, if multiple processes  drove the diversification and historical demography in AF biota, we expect that mid-montane (S. cyanea) and lowland  species would have experienced different patterns of historical population dynamics, which would argue against a single dominant explanation for diversification within this region. Thus, we first compared levels of population structure, historical demographic change, and suitable climatic conditions during the LGM to assess the roles of barriers and refugia in shaping the genetic structure of AF social wasps. Second, assuming that the north and south AF could act as distinct climatic regions, we inferred the putative geographic origin and historical movements along the AF for these species. Finally, we collected chromosomal data in search for additional evidence of phylogeographic structure and the directionality of their population dispersal.The combination of phylogeographic analysis and ecological niche modeling (ENM) has proven to be a powerful approach providing remarkable insights regarding microevolutionary processesS. cyanea, n = 37 from 25 localities; S. aff. septentrionalis, n = 24 from 17 localities) with the aim of covering their respective ranges . Each bidirectional sequence was assembled into contigs and edited using Sequencher v.5.2.4 . Multiple sequence alignments were generated using Muscle 3.739 implemented in MEGA version 6.0640. We checked for recombination in each protein-coding gene using the pairwise homoplasy index (PHI) test implemented in SplitsTree v4.14.241.We removed thorax or hind leg tissues from each specimen sampled  and extracted total genomic DNA using the DNeasy Blood & Tissue Kit . We amplified multiple genetic loci as follows: mitochondrial (mtDNA) fragments of 16 S ribosomal DNA (16 S), 12 S ribosomal DNA (12 S), cytochrome oxidase subunit I (COI), cytochrome oxidase subunit II (COII), cytochrome b (CytB), and nuclear (nDNA) fragments of carbamoylphosphate synthetase (CAD) and elongation factor 1-alpha F2 copy (EF1α) (Table 42 implemented in the program Network 4.613 (www.fluxus-engineering.com). We defined haplogroups within the context of putative geographic barriers proposed for the AF within the range of both armadillo wasp species (see results section). Measures of genetic differentiation  among haplogroups were estimated for each locus using DnaSP v5.10.143. We also investigated the degree of genetic structure among haplogroups, among localities within haplogroups, and within localities using analysis of molecular variance  in Arlequin 3.5.1.245 with 10,000 iterations.Relationships among haplotypes and their spatial distribution were assessed using median-joining networks46. The most appropriate partition scheme and the best fitting model for each partition were selected using PartitionFinder v1.1.147 under the Bayesian information criterion (BIC). Bayesian Inference (BI) was conducted using MrBayes v3.2.348 with two independent runs of 50 million generations each with four chains (temp = 0.1) and sampled every 10,000 generations. Tracer v1.649 was used to assess convergence in the Markov chains by considering the effective sample size (ESS > 200). We removed as burn-in the first 20% of sampled generations and produced a maximum credibility tree. Trees were visualized and edited using FigTree v1.4.2.Prior to phylogenetic inference, alignments for each locus were concatenated and codon positions delimitated using Mesquite v3.02S) for each locus using DNAsp v5.10.143.To compare the amount and patterns of genetic diversity between the two armadillo wasp species, we calculated the nucleotide diversity (π), number of haplotypes (H), haplotype diversity (Hd) and number of polymorphic/segregating sites  implemented in BEAST v1.8.254. This coalescent method increases the precision to infer demographic dynamics, even with a small number of individuals, by incorporating multiple unlinked loci rather than the use of a single locus55. We selected a strict clock for each EBSP run based on haplogroup-level structure within each species. We also specified a substitution rate for mtDNA sequences at 1.2–1.5% per million years calibrated to other insects56, and estimated substitution rates for the nDNA data set. Analyses were run with a random tree and linear model. We performed two replicate runs with 100 million of generations, with trees sampled every 10,000 generations. Tracer v1.649 was used to verify convergence through ESS values (>200). Replicate runs were combined after a burn-in of 10% using LogCombiner v1.8.2.Additionally, we reconstructed the historical demography over time for each haplogroup using the Extended Bayesian Skyline Plot method  using the maximum entropy algorithm in MAXENT v3.3.3k59. Bioclimatic variable layers were limited to eastern South America. Occurrence points were gathered through specific literature, surveying, specimens donated, loaned and/or examined from entomological collections from museums or universities listed in the Acknowledgements section  and we checked auto-correlated variables by PCA. Simultaneously, we verified the percentage contributed to each bioclimatic layer and we excluded the variables with non-contributions. The models were run with the following parameters: quadratic, product, threshold and hinge, 500 iterations, regularization multiplier equal 1, thirty percent of random test and 10 replicates subsampled. Finally, present-day ENMs were projected into bioclimatic variables predicted for two different past scenarios, Last Interglacial  and LGM (21 kya) from MIROC (Model of Interdisciplinary Research on Climate) and CCSM3 (Community Climate System Model) models.Initially, we verified and excluded correlated bioclimatic layers based on Principal Component Analysis (PCA) using PAST v3.0254 using the mtDNA-only data set. This model considers heterogeneous rates in a diffusion process across the branches of the phylogeny62 and allows for probabilistic inference of historical movements. We used the Jitter option (with 0.5) to add random noise to samples with similar coordinates. We applied a GMRF skyride model as demographic prior for RRW analyses and mtDNA substitution rate estimated as applied in EBSP analyses (see above).To infer the historical movements of both species, we applied a Bayesian phylogeographic approach under a relaxed random walk (RRW model) diffusion model across continuous space in BEAST v1.8.249 was used to verify convergence through ESS values (>200). Replicate runs were combined after a burn-in of 10% using LogCombiner v1.8.2, and the maximum clade credibility tree was accessed using TreeAnnotator v1.8.2. Posteriorly, we generated a visual representation of the historical movements experienced by these armadillo wasps in Google Earth v7.1.5 (Google Inc.) using the Continuous Tree module in SPREAD v1.0.663.We performed four replicate runs of 200 million generations each and sampled at every 10,000 steps. Tracer v1.6et al.64. Only sampling events that preserved live larvae were available for cytogenetic analysis  following Schweizer65 with modifications proposed by Guerra & Souza66. Due to few slides available for some populations, some slides were first stained with fluorochrome and subsequently stained with Giemsa®.Mitotic metaphases were obtained from neural ganglia cells during the third instar of larvae according to Imai et al.67.Chromosomal observations and image capture were conducted using both epifluorescence photomicroscope Olympus BX51 equipped with a DP-72 digital camera and DMRA2 Leica . We observed a minimum of five metaphases per slide to assure unambiguous chromosome number counts. The best quality metaphase plate was used for karyogram preparation using Adobe Photoshop CS6 v13.0. Finally, the chromosomes were arranged in decreasing order of size and classified according to Levan S. aff. septentrionalis sequences had a single base pair indel approximately in the middle of the 16 S sequences. The PHI test yielded no statistically significant evidence for recombination in each protein-coding gene. All sequences are deposited in GenBank and their respective accession numbers are presented in Supplementary Table Our sequence data set contained a total of 3621–3622 base pairs (bp) composed of mtDNA protein-coding COI (472 bp − 13.04% of data set), tRNA-Leu + COII (47 + 582 bp − 17.37%), CytB (433 bp − 11.96%), mtDNA non-protein-coding 16 S (508–509 bp − 14.03%), 12 S (354 bp − 9.77%), and nDNA introns from CAD (361 bp − 9.97%) and EF1α (873 bp − 24.11%). No premature stop codons were found within protein-coding sequences. Half of the S. cyanea and S. aff. septentrionalis   in the mtDNA-only data set (12 S and EF1α were invariable)  Figs . This resee Fig.  and S5 asee Fig. . FurtherS. aff. septentrionalis, two haplogroups based on both mtDNA and nDNA data sets were identified as follows: (1) the northern AF (NAF) haplogroup composed of individuals from Alagoas, Sergipe and Pernambuco states; and (2) the central and southern AF (CSAF) haplogroup composed of individuals from Bahia, Espírito Santo and São Paulo states. These haplogroups exhibited high degrees of differentiation in mtDNA but moderate differentiation in nDNA as reveled by Dxy and AMOVA (Tables Dxy) between S. aff. septentrionalis haplogroups are higher when compared with S. cyanea haplogroups; the only exception was that COI exhibited higher genetic differentiation in S. cyanea haplogroups  has lower genetic diversity than S. aff. septentrionalis  was largely supported for all loci (approximately reduced by 3-fold in S. cyanea) , CytB (Tajima’s D) and COI (Fu’s Fs); S. cyanea haplogroup SGL – CytB ; and S. aff. septentrionalis haplogroup CSAF – CytB , COI , COII (Fu’s Fs), and CAD  (see Table S. cyanea population size in the NGL (~2-fold increase), SGL (~1.5-fold increase) and S. aff. septentrionalis CSAF (~2-fold increase) from 100 kya to present   climate, but slight range contractions under LIG (warm-wet) and the present climate for both species. Results obtained under the MIROC model exhibited some differences when compared to the CCSM3 model, an expected result due to the different conditions simulated from these models (CCSM3 is derived from colder climatic conditions when compared to MIROC)69. ENMs for S. cyanea under LIG and current climatic conditions yielded similar reconstructions to each other and predicted highest climatic suitability compatible with the current distribution of this wasp. However, the LGM projection predicted that populations shift to lower elevations in two large areas, one in São Paulo, Mato Grosso, Mato Grosso do Sul states and Paraguay, and the other in the Espírito Santo, Rio de Janeiro and Bahia states, as well as into the exposed continental shelf. While the ENMs for S. aff. septentrionalis predicted higher climatic suitability in the Brazilian coastal lowlands under LIG (with slight range retraction) and the present climate, they also indicated range expansion into the continent and into the emerged continental shelf during LGM  values (86) Fig. . Interessee Fig. . Both MIsee Fig. .Figure 4S. cyanea and S. aff. septentrionalis  this wasp began to spread to the north ~2.60 million years ago (mya), reaching São Paulo state; (2) around ~1.50 mya it reached Minas Gerais state; (3) around ~960 kya lineages from Rio Grande do Sul dispersed toward Misiones, Argentina; and (4) around ~770 kya lineages dispersed into Espírito Santo, Bahia and eastern Paraguay. For S. aff. septentrionalis the RRW model indicates the northern AF  as the putative ancestral location and infers two main colonization events: (1) an initial southern dispersal ~70 kya, reaching Bahia state, and (2) lineages reaching Espírito Santo and São Paulo states ~9 kya.Our Bayesian reconstruction of spatial-temporal movements (RRW model) suggested putative ancestral locations and colonization routes for lis Fig. . The RRWSynoeca cyanea showed asymmetric karyotypes with large variation in chromosome size, representing three distinct karyotypes with the following chromosome numbers: 2n = 38 , 2n = 40  and 2n = 42   Fig. . AdditioS. aff. septentrionalis, we found symmetric karyotypes with a slight decreasing of chromosome size, representing three distinct karyotypes with the following chromosome numbers: 2n = 38 , 2n = 40  and 2n = 41   Fig. . The kar3/DAPI fluorochrome staining revealed distinct patterns regarding GC-rich sites (CMA3 positive) between the target species as well as among karyotypes. Depending on the chromosome, these sites were distributed in both chromosomal arms, in the terminal position, near the centromere, or throughout the entire chromosome  neotectonic and refugia played different roles for each species in shaping their respective genetic structures; (2) both species underwent demographic expansions even during the last glaciation; (3) they displayed contrasting patterns of spatial-temporal dispersal, and (4) they experienced substantial chromosomal changes associated with a latitudinal cline along the AF.The AF biome is characterized by high biological diversityS. cyanea and S. aff. septentrionalis revealed two major haplogroups in each species that are each distributed along a latitudinal gradient. For S. cyanea the main phylogeographic break (~2.60 mya) was coincident with the Guapiara lineament located in the southern São Paulo state  the population genetic structure of the bee species Melipona quadrifasciata (0.23–0.84 mya)71, the snake species Bothrops jararaca (3.87 mya)72 and the toad species Rhinella crucifer (0.6 mya)24. A plausible explanation for why the genetic structure of S. cyanea has been influenced by the Guapiara lineament may be the strong association of this species with montane regions, especially in São Paulo and Paraná states , as also observed for some other AF organisms73. Notably, this armadillo wasp species exhibited long-term persistence in areas of predicted refuges   that falls into the Pleistocene, a period marked by intense climatic swings75. Together, this evidence suggests that the genetic structure of S. aff. septentrionalis populations was deeply influenced by climatic changes and consequently both Pernambuco and Bahia refuges could have served as a buffer to maintain its higher level of genetic diversity compared to S. cyanea.For the lowland see Fig. . SynoecaS. cyanea  and specimens with 2n = 41 chromosomes .Although we found strong evidence for a different primary role driving the genetic diversity of each AF species (a neotectonic barrier versus climate change), we cannot discard the possibility of a potential secondary role of these factors in shaping the genetic structure of AF biota. For example, we found unsuitable habitat during cooler periods (LGM) in the southern portion of the current distribution of on Table ; Fig. 3.S. aff. septentrionalis is stronger than in S. cyanea, as indicated by comparison of Dxy and AMOVA values  , reflecting the intrinsic low mutation rate, large effective population size and differential retention of ancestral polymorphisms in nDNA data80.Phylogeographic structure in ues Fig.  and S5, see Fig.  and S5. see Fig. , accompaSynoeca species in order to evaluate the potential role of the Brazilian emerged continental shelf during the LGM as proposed by the Atlantis hypothesis. Based on our tests of demographic change  (~130–185 kya)  during the chromosomal evolutionary history of Synoeca. Additionally, the intrapopulational chromosome heteromorphism witnessed in these insects is additional evidence of considerable amount of chromosomal changes in their karyotypes. Considering our phylogeographic analyses for these species , these insects showed remarkable intraspecific variation involving chromosome number and GC/AT-rich segments along their geographic range. While ies Fig. , as discies Fig.  and S2, 3+ sites) within these Synoeca species: (1) GC content decreased during their chromosomal microevolutionary history; and (2) change in the chromosomal distribution of GC content seems to be more dynamic in S. cyanea, especially between pericentromeric regions (Fig. 91. Although we did not perform cytogenetic tests to detect heterochromatic regions and/or specific families of repetitive DNA, it is possible that GC-rich sites in Synoeca represent these types of genomic regions, as verified in other insects including beetles92 and hymenopterans including solitary93 and social91 wasps.Integrating cytogenetic and phylogeographic analyses also revealed two intriguing features about GC content (here as CMAons Fig. . It is nSynoeca karyotypes. Notably, these rearrangements are common evolutionary changes that have been reported for multiples species94, and seem to be frequent during the chromosomal evolutionary history of swarm-founding social wasps95. These chromosomal rearrangements can produce karyotype asymmetry, chromosome number change and loss of chromosomal segments, as well as producing occasionally post-zygotic reproductive barriers, limiting gene flow89. Assuming that chromosomal rearrangements are frequent in social wasps and that chromosomal changes can accumulate within isolated populations, karyotypic differences, barriers and refugia could together reduce gene flow over time among Synoeca populations, triggering the phylogeographic patterns here described.Chromosomal Robertsonian fusions and fissions seem to have played a significant role in the intense reshuffling of Supplemental materials


We simulated long-term (>20 yrs.) connectivity patterns driven by remotely-sensed sea surface height and evaluated results against estimates of genetic distance among populations of anemonefish, Amphiprion bicinctus, along the eastern Red Sea coastline. Predicted connectivity was remarkably consistent with genetic population data, demonstrating that circulation features  formulate physical pathways for gene flow. The southern basin has lower physical connectivity than elsewhere, agreeing with known genetic structure of coral reef organisms. The central Red Sea provides key source regions, meriting conservation priority. Our analysis demonstrates a cost-effective tool to estimate biophysical connectivity remotely, supporting coastal management in data-limited regions.Coral reefs rely on inter-habitat connectivity to maintain gene flow, biodiversity and ecosystem resilience. Coral reef communities of the Red Sea exhibit remarkable genetic homogeneity across most of the Arabian Peninsula coastline, with a genetic break towards the southern part of the basin. While previous studies have attributed these patterns to environmental heterogeneity, we hypothesize that they may also emerge as a result of dynamic circulation flow; yet, such linkages remain undemonstrated. Here, we integrate satellite-derived biophysical observations, particle dispersion model simulations, genetic population data and ship-borne  These fragile ecosystems face serious threats from climate change, ocean acidification, destructive and unsustainable fishing practices, and water-polluting land-use activities3. Metapopulation connectivity, the exchange of individuals and genes among geographically separated sub-populations, is a key process for maintaining biodiversity and resilience in coral reef ecosystems4. Effective descriptions of connectivity pathways hence remain a major goal in marine ecology and conservation6.Coral reefs occupy less than 0.2% of the world’s oceans, yet they host 35% of all known marine species7. Connectivity pathways in marine systems are hence inherently driven by an interplay of physical factors  and larval traits 9. Ocean circulation may thus act as a vector or inhibitor of gene flow across various spatial scales11. The integration of oceanographic models with empirical genetic data has revealed the power of physical pathways to predict spatial population structuring for a range of species13. Tracing large-scale physical connectivity pathways would thus bring insights into population dynamics and spatio-temporal patterns of gene flow16, ultimately facilitating the effective design of marine protected area networks17. Circulation models of sufficient resolution and quality, however, are often lacking in remote areas of conservation concern, primarily due to the high costs of in situ data collection18 and logistical issues in politically diverse regions, e.g. the Arabian Seas16 and the Caribbean11.The life-history of most marine species is characterized by a dispersive larval phase that connects relatively sedentary adult populations20. Remote sensing may thus provide a cost-effective approach to estimate physical pathways of larval connectivity in regions of limited data availability.Satellite-derived observations can effectively provide global views of natural processes, including surface water movement at synoptic scales not feasible through conventional field measurementsin situ profiles, particle dispersion simulations and genetic population data to visualize meso/large scale physical pathways and to investigate their role in driving gene flow among coral reef systems in the Red Sea. Reef communities (including benthic invertebrates and fishes) have been shown to exhibit remarkable genetic homogeneity across most of the Arabian Peninsula coastline, except at the southern part of the basin24. Here, we hypothesize that dynamic circulation flow could reflect strong basin-wide physical connectivity; yet, such linkages remain undemonstrated. We computed long-term (>20 yrs.) connectivity estimates of simulated particles driven by satellite observations of sea level elevation to create a measure of pairwise oceanographic distance among coastal populations. The outputs were then linked with an existing dataset of genetic distance among populations of anemonefish, Amphiprion bicinctus, along the eastern Red Sea coastline15. Our approach presents a blueprint for the utilization of satellite-derived sea surface observations to predict avenues of connectivity in marine metapopulations.Here, we implement a multidisciplinary approach, integrating satellite-derived observations, ship-borne vice-versa. Occasionally, these circulation pathways were interrupted or redirected locally by smaller scale eddies and filaments  remotely from space, we detected signs of water mass advection and meso-scale eddy formations along the entire Red Sea basin Fig. . Water mnts Fig. . Circulants Fig. . Altimet −1 Fig. . Simultain situ observations, suggesting it adequately captures surface current and circulation patterns in the Red Sea ast Fig. . Particlast Fig. . To assetes Fig. . Again, A. bicinctus along the east coast of the Red Sea15. Based on these particle tracking simulations, we calculated a distance matrix of Mean Connection Time (MCT in days) between release locations27. Briefly, MCT is defined as the average time for a passive particle released at site (i) to reach site (j), with low MCT values indicating high connectivity, and vice-versa (see methods). Results showed that mean patterns of genetic distance (pairwise FST) among sampling sites paralleled the overall MCTs among release sites, indicating that genetic differentiation between populations are linked to physical oceanographic pathways  and IBD model   Fig. . A combi01) Fig. . The choIt has to be acknowledged that the estimated network metrics (MCT and source/destination strength) were based on the 19 genetic-sampling regions only. Regardless of how well the pre-selected sites spread along the east side, they do not necessarily represent basin-scale patterns of coral reef networks. To address this issue, as well as to gain further confidence in the aforementioned outcomes, we repeated the experiment by releasing particles at 60 sites , covering the majority of the Red Sea coral reef complexes Fig. . Overall29. Biophysical dispersal models can simulate patterns of connectivity in coral reef ecosystems; yet, in many tropical regions uncertainties arise due to inadequate oceanographic knowledge and data. Our analysis highlights the successful use of satellite-derived surface currents in dispersal models. The geostrophic currents effectively predicted patterns of spatial genetic variation in a species with pelagic larval dispersal. Our results reveal that the surface water circulation of the Red Sea is a key factor in regulating patterns of gene flow among distant coral reef communities.Unequivocally, a deeper understanding of the key ecological processes of marine larval dispersal and recruitment requires a better comprehension of the physical pathways of connectivity24 as well as marked genetic homogeneity across a range of coral reef taxa  along most of the east Red Sea coast (>1500 km), excluding the southern end. Overall, our analysis showed that the dynamic surface circulation may play a key role in these genetic population patterns. Geographic distance was also a significant explanatory variable of genetic structure. We acknowledge that the genetic data used here were irregularly distributed along the Arabian coastline , hence physical proximity may have increased significance of the IBD relationship  of the Red Sea. Previous studies have demonstrated a surprisingly homogeneous community structurehip Fig. . This aghip Fig. . To acco000 km of26. In contrast, the southern end of the Red Sea (e.g. Farasan Islands) exhibited the lowest source/sink strength, indicating poor connectivity with distant reefs in the Red Sea. The described patterns concur markedly with recent genetic research in the region, demonstrating homogeneity in population structure along the east Red Sea coast that is interrupted by a sudden genetic break at around ~19°N in two different taxa30. The reduced circulation activity26, and the low source/sink connectivity dynamics in the southern region, may not be the only reasons for the genetic heterogeneity between the southern end and the rest of the basin. The regional circulation dynamics of the southern Red Sea (water influx from the Indian Ocean) could also partly explain the heterogeneity in genetic population  in comparison to the rest of the basin36, facilitating water mass exchange among coral reefs by mesoscale features. We show water mass movement from coastal coral reefs to offshore waters and towards distant coastal regions. Besides mere transport, these productive water masses may be important for larval survival. Increased productivity and encounters with mesoscale eddies have been shown to enhance survival of pelagic larvae38. These survival advantages may carry over beyond transitions into oligotrophic waters39 and potentially beyond settlement38  in less than two weeks, theoretically facilitating successful dispersal for the majority of coral reef species11. The circulation speed reported here is largely coherent with previous studies26, but even faster coastal currents (30 cm s−1) have been observed in the Red Sea40. Maximum reported eddy velocities have been shown to reach up to 100 cm s−1 35.The speed of water mass transport is another important factor, as larvae have to reach their destination within a species-specific window of time Coral reefs are among the most biologically diverse ecosystems on Earth. This biodiversity translates directly into food security and societal support through fisheries, recreation, and tourism. Tracing connectivity patterns could improve the design of marine reserve networks in vulnerable biodiversity hot-spots. Sensors onboard satellite platforms continuously sample the Earth at synoptic scales, providing long-term global observations of the oceanic surface, thereby providing a cost-effective tool to detect and model coral reef connectivity. By sensing major avenues of connectivity from space, we provide a baseline towards effective management of a fragile ecosystem, the Red Sea. Our approach could be applied to any region where shortages of detailed oceanographic information are hampering coastal management. Present day precautionary conservation measures and management practices may have a profound influence on the future functioning of coral reef ecosystems, and consequently for society.et al.26.Remotely-sensed Geostrophic currents: Geostrophic velocities were used in the numerical simulation of particle dispersion (see below) to calculate connectivity between the coral reefs. To compute the geostrophic velocities, the Absolute Dynamic Topography (ADT) data, the latest version of the SSALTO/DUACS products distributed by AVISO based on multi-mission satellite altimeter observations, were acquired from ftp://ftp.aviso.oceanobs.com/global/dt/upd/msla/merged/. Daily data were mapped with a Cartesian 1/4 degree grid and geostrophic velocities were computed from ADT. A detailed methodological approach on satellite-derived geostrophic currents and its application in the Red Sea can be found in Zhan http://www.ngdc.noaa.gov/mgg/global/ etopo5.HTML.To plot the bathymetry in the Red Sea Fig. , we obta41.Coral Reef positions: The Global Distribution of Coral Reefs dataset (version 2010) compiled by the UNEP-WCMC was used to locate the position of coral reefs in the Red Sea  measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua) and the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) produced by the NASA Ocean Biology Processing Group, and available from the NASA website (http://oceancolor.gsfc.nasa.gov/). All figures were produced using regionally-tuned chlorophyll algorithms, designed and validated for the Red Sea43. The aim of this study was to use ocean colour signals as a tracer of spatial variations, rather than an estimate of absolute chlorophyll values . The example images were selected based on several criteria, such as cloud-free composites, connecting disparate coral reefs across the two continents, depicting coastal currents, cyclonic and anti-cyclonic features, and focusing on the oligotrophic spring and summer months . The ocean colour images were derived from MODIS  calculating the euphotic depth from the surface chlorophyll concentration of each profile 45; and (ii) dividing the euphotic depth by 4.6 to approximate the 1st optical depth, which was assumed to be the average penetration depth of the satellite signal over the spectral range. For further details see Brewin et al.42.Chlorophyll in-water measurements: We used these datasets to validate the satellite-derived chlorophyll over coral reefs, and show that surface currents transfer productive water masses between distant coral reefs across and along the Red Sea basin. The chlorophyll data were collected during a research cruise as part of the Research Cruises Expedition Programme of the Red Sea Research Center at KAUST. In March 2010, the R/V “Aegaeo” sampled a substantial part of the Red Sea (from 17°N to 28°N), sampling continuous fluorescence vertical profiles at several stations using a WET Labs ECO-FLNTUs (FLNTURTD-964) fluorometer attached to a CTD. The fluorometer was laboratory calibrated prior to each cruise. For each : We used an existing genetic population dataset of the anemonefish Amphiprion bicinctus (Rüppel 1830), comprising a total of 991 samples collected between October 2009 and August 2011 from 19 locations along 1500 km of the Saudi Arabian coastline of the Red Sea. We used these dataset to link the satellite-derived patterns of surface circulation with patterns of spatial genetic variation in a species with pelagic larval dispersal. All samples were genotyped at 38 microsatellite loci and data were analysed according to Nanninga et al.15. We used a matrix of pairwise genetic distance (linearized FST) between sampling locations.Genetic datasetet al.)15. We released a particle every 5 days during the whole period of available geostrophic currents (1992–2012), and monitored distribution patterns of each individual particle for a period of 2 years. Each simulation was integrated for 2 years based on daily velocity fields. The choice of the 2-year timeframe was a compromise between investigating a manageable number of particles and obtaining a meaningful number of connections between distant sites, offering a sensible representation of the average surface circulation in the Red Sea. Moreover, by releasing particles every 5 days during this 20 year period, our analysis adequately captured variability within the circulation regime. In total, ~650,000 particles were released from each site, totaling ~12 million particles. The simulation was conducted using the Connectivity Modeling System (CMS)46, which is a multi-scale probabilistic model of particle dispersal based on a stochastic Lagrangian framework. A land mask boundary condition was used to account for the convoluted coastline and less accurate remotely-sensed data in the offshore area. Further details can be found in Paris et al.46.To investigate particle patterns in the Red Sea Fig. , the parWe repeated the same experiment as above with an expanded set of release sites in order to cover the full potential network in the Red Sea. In total, we released particles at 60 different sites along the west and east coast Fig. . To reduin situ chlorophyll data, we provided evidence that geostrophic currents carry productive water masses between distant coral reefs, which may be important for larvae survival25 , we recorded the time of the first connection taken to reach site (j). A connection was counted once a particle reached the radius (<0.5°) within the domain of the site (j). The MCT between two sites, (i) and (j), was calculated by averaging the time of first connections within a period of 2 years. As a result, the MCT represents the overall connectivity patterns during the particle release period (1992–2012); it is independent of distance (km), integration time, and the number and intensity of circulation features. For instance, a passive particle might need 230 days to travel from site PR  to site CAN , whereas it may take 368 days to travel the opposite way  computed with a linear regression between distance matrices with the coefficient computed with Generalised Additive Model (GAM)28 regression, to model the non-linearity between circulation distances and genetics , we followed the approach of Wanget al.27, we calculated the source and destination strength for each of the 19 sites. The source strength represents the capacity of a site to transport particles to other sites. The source strength of a site (i) is defined as the average number of connections of site (i) with other sites. The higher the number, the higher the likelihood of connections of a specific site with others . In turn, the destination strength of site (i) is defined as the fraction of particles released at other sites that reach site (i). This analysis allows for the description of the main source-sink dynamics between the simulated populations.Based on Mitarai A hierarchical clustering method was used to identify physically interconnected sub-regions in the Red Sea Fig. , based o49. Regardless, Fig. 26.There are limitations in altimeter-derived geostrophic currents in near-shore coastal regions  and most spatially extensive dataset available. MCT was derived from this 20-year climatological mean and every particle was tracked for 2 years after the release from each site, offering an adequate representation of the average surface circulation in the Red Sea. We are hence confident that the physical pathways used in this study constitute a suitable representation of surface flow (see Supplementary Text –2012 and50. Dynamic circulation and regional winds could trigger sediment resuspension in Red Sea coral reef complexes16, potentially affecting algorithm performance. On the other hand, such processes could serve as an important source of nutrients to adjacent waters51. In shallow regions, the chlorophyll signal may also be obscured by the effect of seafloor reflectance. A combination of these factors can result in an overestimation of chlorophyll31. Validation studies, however, have shown generally good agreement between satellite-derived and in situ chlorophyll measures in offshore and shallow reef-bound coastal waters44 (see also Fig. in situ chlorophyll measurements, suggesting the dispersal of productive water masses.Finally, there are limitations in remotely-sensed ocean-colour observations in shallow waters. For instance, in optically-complex waters such as coral reef regions, yellow substances, particulate matter and coloured dissolved organic matter do not covary in a predictable manner with chlorophyll valuesSupplementary Information


Natrix natrix helvetica and the eastern subspecies N. n. natrix, whereas in the other, more easterly, contact zone two lineages meet that are currently identified with N. n. natrix and N. n. persa. This second contact zone runs across Central Europe to the southern Balkans. Our analyses reveal that the western contact zone is narrow, with parapatrically distributed mitochondrial lineages and limited, largely unidirectional nuclear gene flow. In contrast, the eastern contact zone is very wide, with massive nuclear admixture and broadly overlapping mitochondrial lineages. In combination with additional lines of evidence , we conclude that these differences reflect different stages in the speciation process and that Natrix helvetica should be regarded as a distinct species. We suggest a nomenclatural framework for presently recognized grass snake taxa and highlight the need for reconciling the conflicts between genetics and taxonomy.Recent studies found major conflicts between traditional taxonomy and genetic differentiation of grass snakes and identified previously unknown secondary contact zones. Until now, little is known about gene flow across these contact zones. Using two mitochondrial markers and 13 microsatellite loci, we examined two contact zones. One, largely corresponding to the Rhine region, involves the western subspecies  Currently, there are more than 30 species concepts distinguished, with an ever increasing number6. While the application of different concepts lead to a taxonomic inflation for some regions and some groups, the number of Central European vertebrate species remained generally stable, suggesting their diversity is well understood. An exception to that rule might be bats8. With respect to Central European snakes, the number of recognized species did not change for more than a century15, even though some southern European taxa, like Elaphe sauromates16, Macroprotodon brevis and M. mauritanicus18, Malpolon insignitus19, Natrix astreptophora20, Vipera graeca21, and Zamenis lineatus22, have been elevated to species status within the last two decades and a new species of viper  has been recently discovered in the Alps23.Even though species delimitation became a Renaissance issue in zoology, with new approaches being developed for assessing species boundariesNatrix natrix sensu lato) are the most abundant and one of the most widely distributed snake species of the Palaearctic region25. For a long time, little was known about their genetic and phylogeographic structuring. Based on external morphology, many subspecies have traditionally been recognized26, suggestive of pronounced phylogeographic structuring. Indeed, in a pioneering nearly range-wide study using mitochondrial DNA (mtDNA), not less than 16 distinct genetic lineages were identified26. However, the majority of these lineages conflicts with previously recognized subspecies and only two lineages match with morphologically defined taxa. In phylogenetic analyses, these lineages correspond to 16 terminal clades that cluster in three major clades  and concordant morphological and genetic evidence, Ibero-Maghrebian grass snakes have recently been split off as the distinct species N. astreptophora20.Grass snakes  were combined with evidence from 13 highly polymorphic nuclear microsatellite loci.The present study aims at examining and comparing differentiation and gene flow across the two contact zones of genetic lineages of different age and phylogenetic hierarchy. For doing so, the previous samplingNatrix natrix helvetica (‘blue lineage’) and the nominotypical subspecies (‘yellow lineage’) and another contact zone further in the east, which was identified for the first time in a previous study26. This second contact zone runs across Central Europe to the southern Balkans and concerns two genetic lineages (‘red’ and ‘yellow lineages’), which occur within the distribution ranges of N. n. natrix and N. n. persa. However, neither of these subspecies is congruent with the two genetic lineages, and both lineages occur within the range of either taxon and beyond, suggesting that a taxonomic revision is required26. Only one of these lineages, the ‘yellow lineage’, occurs naturally in the Rhine region, i.e. in the contact zone with N. n. helvetica.The focus of the present study lies on two contact zones of distinct genetic lineages of grass snakes Fig. , one in b gene = cyt b) were sequenced . Mitochondrial sequences of 391 specimens were available from previous studies29 and merged with new data for 1,197 grass snakes. For 15 samples, mtDNA could not be sequenced. In addition, samples were genotyped at 13 polymorphic nuclear microsatellite loci. Laboratory procedures for mtDNA and microsatellites followed previous studies26. The microsatellite data of 1,484 samples included those for 31 grass snakes from a previous study20. For 119 samples, for which mtDNA sequences were available, no microsatellite data could be generated. Approximately 200 samples from Switzerland were studied in the laboratory of the University of Basel, whereas the majority of samples was processed in the laboratory of Senckenberg Dresden. Fragment lengths of the two data sets were calibrated using 31 samples processed in both laboratories. For detailed sample information, see Supplementary Table In total, 1,603 samples  were used in the present study. No snakes were sacrificed for the present study. All sampling and methods were carried out in accordance with relevant guidelines, regulations and best ethical and experimental practice of the Senckenberg Nature Research Society. Two mitochondrial DNA fragments  analyses using raxml 7.2.831 including previously published data29, the GTR + G model and a fast ML search with 100 bootstrap values. Then, for the lineages involved in the studied contact zones, alignments of each mtDNA block were examined using popart (http://popart.otago.ac.nz) and the implemented parsimony network algorithm of tcs32. Based on haplotypes, uncorrected p distances (means) were calculated using MEGA 7.0.2133 and the pairwise deletion option.Mitochondrial sequences were aligned using arlequin 3.5.1.234. The presence of null alleles was examined using micro-checker 2.2.335. There was no evidence for linkage disequilibrium, null alleles or a deviation from HWE. Microsatellite data were then analyzed with the unsupervised Bayesian clustering approach of structure 2.3.437 using the admixture model and correlated allele frequencies. structure searches in the data set for partitions that are, as far as possible, in linkage equilibrium and HWE. The Monte Carlo Markov chains ran for 1 million generations, including a burn-in of 250,000 generations. Calculations were repeated ten times for Ks ranging from 1 to 10. The optimal number of clusters was determined using the ΔK method38 with the software structure harvester39. structure results were visualized using distruct 1.140.All 13 microsatellite loci were tested for Hardy-Weinberg equilibrium (HWE) and linkage equilibrium using 29. These lineages are expected to contribute to nuclear genomic admixture, which is why a stepwise approach was applied to assess their genetic impact and to single out the extent of admixture between the red and yellow lineages. This approach takes into account that structure is known to identify only the uppermost hierarchical level of genetic partitioning38, i.e. the clusters reflect the most differentiated genetic units. Accordingly, a first calculation comprising all available samples resulted in two clusters, one corresponding to helvetica (blue lineage) and the other to all other lineages.In the southern Balkans and the Baltic Sea region, additional genetic lineages occurhelvetica hybrids, hybrid genotypes were modelled using hybridlab 1.041. For doing so, 20 non-admixed representatives of each lineage occurring in the Rhine contact zone were selected (blue and yellow lineages) as pure parental genotypes. Using these data, 20 genotypes of each hybrid class  were inferred and the simulated hybrid data were then subjected to structure analyses, together with the data of the 20 pure grass snakes of each cluster, to obtain a threshold for Q values for distinguishing pure animals, hybrids and backcrosses. Based on this threshold, all genotypes with genetic impact of helvetica were removed and a second structure run included then only the remaining samples. With this run, admixture of the red and yellow lineages with the other geographically neighbouring lineages .To explore which individuals represent structure clusters, population genetic diversity values, pairwise FST values and Analyses of Molecular Variance (AMOVAs) were calculated using convert 1.3142, arlequin34 and fstat 2.9.3.243. Admixed individuals were excluded.In addition, for the microsatellite data of the adegenet44 to assess the distinctiveness of the genetic lineages without underlying population genetic presumptions. Two different PCAs were run, one including genotypes of all samples of the blue, red and yellow lineages and their hybrids and another one with samples of the yellow and red lineage without influence of helvetica and the adjacent eastern lineages. Obviously introduced individuals were excluded.Microsatellite data were also examined using Principal Component Analyses (PCA) as implemented in the R package hzar45. This software fits molecular genetic data to classical equilibrium cline models using the Metropolis-Hastings Markov chain Monte Carlo algorithm. Cline fitting was performed by adding geographical information of sampling sites to genetic information. To acknowledge for mountain ranges, two transects were selected. The transect for the contact zone in the Rhine region ran from southern France to northeastern Germany  and the transect for the eastern contact zone, from northern Germany to northern Hungary . The eastern transect was not extended to the southern Balkans because the grass snakes are there genetically impacted by other genetic lineages. Using arcgis 10.2 (http://www.esri.com/arcgis), samples were arbitrarily pooled by dividing each transect into segments of 10 km length. Samples within 50 km left and right of each segment were assigned to one sampling unit.To examine gene flow across contact zones, cline analyses were calculated for microsatellite and mtDNA data using the R package Q of cluster membership (as inferred by structure) for each pooled collection site was then calculated. For mitochondrial data, the frequency of haplotypes of the blue lineage (western contact zone) or the yellow lineage (eastern contact zone) was used. These four data sets (mtDNA and microsatellites for each contact zone) were processed independently. Using a burn-in of 10,000 iterations, followed by additional 90,000 iterations, fifteen implemented models were fitted to the mean proportions of cluster membership or haplotype frequencies. The best cline model was then selected based on the lowest AIC score  meets with the yellow lineage (currently identified with N. n. natrix) in a secondary contact zone in the Rhine region, and that another, more easterly and much wider, contact zone of the yellow and red lineages runs across Central Europe to the Balkans. The latter two lineages are distributed within the ranges of what is currently identified with the grass snake subspecies N. n. natrix and N. n. persa, but these lineages conflict with morphology-based taxonomy so that neither the distribution ranges of the lineages and subspecies nor morphology and genetics are congruent26. Rather, each subspecies corresponds to several distinct mitochondrial lineages and some of these lineages are shared between the two subspecies. Thus, there is obviously a pronounced conflict between morphological variation and genetics. Whereas the geographical distribution of haplotypes of the blue and yellow lineages abuts, with virtually no sympatric occurrences . One population in the Neander valley close to Düsseldorf, Germany, seems to consist exclusively of such allochthonous grass snakes and has been suspected to be introduced for a long time47. In addition to these non-native grass snakes, eight individuals of other mitochondrial lineages from the Mediterranean  were found in southern Great Britain and Hesse, Germany  were separated from the yellow haplotype cluster by at least 40 mutational steps and from the red cluster by a minimum of 54 steps. With only 12 haplotypes differing by maximally two mutation steps, there was distinctly less variation in the blue lineage compared to the two eastern lineages. In the yellow lineage, 43 haplotypes with maximally 15 mutations occurred; in the red lineage, 33 haplotypes differing by maximally 10 mutations.In parsimony network analyses, each lineage corresponded to a highly distinct haplotype cluster. For ND4 Fig. , top, thb  was highly distinct from the yellow and red lineages, with some mismatches reflecting mitochondrial introgression mainly from helvetica into the eastern group. In contrast, the eastern lineages showed weak differentiation and massive overlap. The PCAs corroborate furthermore that our definition of admixed individuals is appropriate because hybrids were intermediate also in the PCA, independently from population affiliation, HWE or linkage equilibrium.The PCAs using microsatellite data Fig.  are in lThe cline analyses revealed completely different patterns in the two contact zones Fig. . For the48. They can be interpreted as ‘windows on the evolutionary process’49 and ‘natural laboratories for evolutionary studies’50. Hybridization can provide important insights into divergence and speciation processes56 and, thus, contributes to a better understanding of evolution. Based on extensive sampling of approximately 1,600 grass snakes and using a powerful data set of 13 microsatellite loci and two mitochondrial markers, this study presents a fine-scale analysis of gene flow across two secondary contact zones of grass snake lineages.Hybrid zones are regions in which genetically distinct populations meet and produce hybrid offspringN. n. helvetica and the ‘yellow lineage’ is limited and largely unidirectional from helvetica into the ‘yellow lineage’, with a cline width of less than 50 km in the contact zone, gene flow in the contact zone of the ‘yellow’ and ‘red lineages’ is extensive and in both directions. This contact zone is wide (cline widths for microsatellites and mtDNA approx. 680 km and 360 km), and the ‘yellow’ and ‘red lineages’ seem to be panmictic there , crested and marbled newts , pond turtles , and wall lizards , taxa which are all regarded as distinct species. Generally, a steep cline across a narrow hybrid zone suggests lower hybrid fitness and selection against hybrids67, corresponding to intrinsic isolating mechanisms, which may also include assortative mating.Our data provide evidence for hybridization in the contact zone of ems Fig. , left. IFST value of 0.11 (microsatellites) of the involved lineages is low. Similar, also geographically wide-ranging, admixture among distinct taxa is observed for instance in European pond turtles  and rabbits . Enigmatic is the more or less exclusive presence of mitochondrial haplotypes of the ‘red lineage’ in the central part of the contact zone and the more or less exclusive presence of ‘yellow haplotypes’ in the southernmost part , this together with the limited gene flow in the narrow contact zone raises the question whether N. n. helvetica represents a distinct species and not only a subspecies.In summary, we found good agreement between the studied mitochondrial lineages and nuclear genotypes of grass snakes. Using 13 highly polymorphic microsatellite loci, distinct clusters were revealed that correspond to previously identified mitochondrial lineages70. Moreover, different species concepts can lead to different conclusions about species status, and there are currently more than 30 species concepts used6. However, to bypass conflicts between different species concepts, it has been suggested to unite their common elements and to characterize species primarily as independent evolutionary lineages72. Morphology, reproductive isolation, ecological niches or reciprocal monophyly are understood as different lines of evidence for species status71.Species conceptualization and delimitation is a complicated issue. In particular, it is difficult to distinguish whether observed differences are on the species or population level71. Regarding reproductive isolation, species boundaries are known to be ‘semipermeable’74 and approximately 10% of animal species are known to hybridize75. Speciation may occur despite continuous gene flow81 and high abundances of hybrids within a hybrid zone are not uncommon48. Obviously, reproductive isolation in nature is a matter of degree82, with the complete lack of interbreeding and hybridization representing only the most extreme condition6. Hybridization between sister species in narrow contact zones is also known from other European snake species, like Vipera aspis and V. latastei in Spain83. Therefore, the limited gene flow between N. n. helvetica and the ‘yellow lineage’ fits in that pattern and is not contradicting species status.It is important that these lines of evidence emerge at different times in the speciation processN. n. helvetica into the ‘yellow lineage’ in a narrow contact zone, the morphological distinctness of N. n. helvetica25, its placement in another deeply divergent clade than eastern grass snake lineages26, and the considerable age of its mitochondrial lineage28  as a distinct species.Considering the largely unidirectional gene flow from 28 (Supplementary Fig. FST value compared to the differentiation of N. helvetica. Our structure analyses (Fig. 26, supporting their conspecificity. These lineages are currently identified in the Balkans with N. n. persa, and some of them with N. n. natrix in more northerly regions26.In contrast, we regard the ‘yellow’ and ‘red lineages’ as conspecific, representing a less advanced stage in the speciation process. This assessment is supported by their lacking morphological differentiation, their wide hybrid zone with panmictic large-scale gene flow, the placement of their younger mitochondrial lineages in the same more inclusive cladeses Fig.  also proN. helvetica as a full species necessitates that all nominal subspecies assigned to the same major clade in phylogenetic analyses26 have to be transferred to N. helvetica, resulting in a revised taxonomy (Table 26 refer only to taxa within, but not across, the newly delimited species, so that the suggested taxonomy does not contribute to further nomenclatural confusion, but reflects deep genetic divergences and discontinuities much better than before. However, we wish to underline that further research is needed for reconciling the conflicts between genetics and morphology of the individual subspecies within each of the three grass snake species.Nomenclaturally, the recognition of my Table . FortunaSupplementary Information


Do scaling relations always provide the means to anticipate the relationships between the size of cities, costs of maintenance, and the socio-economic benefits resulting from their growth? Scaling laws are considered a universal principle that describes the development of complex systems such as cities. It seems that regardless of their location or history, the growth of cities is associated with the super-linear or sublinear scaling of features such as the amount of space required, infrastructure, or human activities. However, the results of our research, based on grouping by Self-Organizing Maps, reveal some limitations in the application of scaling laws: the trends of urban growth behave in a different manner when we consider both a large and diverse collection of cities and a subset of cities alike. This finding complements the existing body of knowledge on the growth of cities and allows for a more accurate prediction of their future. Scaling laws have been present in the debate on urban development and in the accompanying research since around 2006–2007 ,2. TheseA subsequent analysis of the statistical data supports the hypothesis that the number of important characteristics that describe a city along with its growth (number of inhabitants) varies exponentially; with an exponent greater than 1 in the case of, for instance, earnings and the number of innovations, and less than 1 when describing the supporting infrastructure ,2.Since then, many studies have focused on similar dependencies: network connectivity , urban sHowever, studying a large and diverse group of cities may lead to the omission of the specific causes of globally-applicable rules .Accordingly, the following is proposed: if scaling laws are valid for entire urban systems, they should also be considered important locally—not just in the consideration of cities from more than one country or region , but within the boundaries of one country looking at groups of cities demarcated by their similarities.Cities in different geographic areas at different times  obtain dOur research shows that they are not, and this may serve as the basis for a broader discussion on the causes of the scaling laws lying within the boundaries of cities.The study involved 881 of the current (2016) 915 Polish cities , regardless of their size, within the administrative borders adopted by the Central Statistical Office.The study excluded only those cities that have received municipal rights during the audited period or at a later date, or those without consistent statistical data.Due to administrative changes, the studies covered the cities during the years 2002–2012. For Poland, this period was marked by a period of rapid development, which likely has an impact on the results of the studies.The method of determining the groups from a larger set of cities had to be objective and based on many data points, but with no assumptions made on the nature of the subsets  and correlations between the data.Although some other techniques of examining multidimensional data exist (such as deep learning theory ), it wasIt was decided that the indicators related to the metabolism of the city will consist of features describing the city itself, and that they will serve (on the whole) as the basis for their subsequent grouping.Increasing population, area, diversity, and multiplicity of connections allow cities to increase the speed and efficiency of the acquisition, processing, and—ultimately—dissipation of energy, which comprise the basis of the city metabolism as researched for many years . Therefo10 indicators (features of cities) were constructed. They were used to create a database describing city metabolism across 3 main sectors of the city: natural, economic, and housing :Feature 1 (F1): [factorized area of forests / city area]Feature 2 (F2): [total usable floor space of  dwellings / city area]Feature 3 (F3): [total income of city residents (in terms of energy intensity) / city area]Feature 4 (F4): [budget expenditures of the local government / total income of city residents]Feature 5 (F5): [budget expenditures per capita]Feature 6 (F6): [number of enterprises / city area]Feature 7 (F7): [number of enterprises per capita]Feature 8 (F8): [Enterprise Diversity Index (EDI)]Feature 9 (F9): [gas consumption in households / total usable floor space of dwellings]Feature 10 (F10): [electricity consumption in households / total usable floor space of dwellings]The database transferred for further analysis was kept anonymous, as the grouping was carried out solely on the basis of a set of characteristics. City names and their locations were encoded.The sources of data are provided in the Appendix in mi, i = 1,…,881, be a town described by a real vector 1×110xi = , where 1×11zi,k contains the variable k = 1, …, 10 measured in each of the 11 years (2002–2012). The set of all 881 towns forms a matrix 881×110A = [881×11Z1 … 881×11Zk = 10]. The aim is to group the towns, according to their similarities, on an array of nodes (neurons). This process, called mapping, was described by Kohonen .(DOCX)Click here for additional data file.


Oncorhynchus mykiss) to Chile in South America, initially through stocking and later through aquaculture escapes, provide a unique setting to contrast these two pathways. Using a panel of single nucleotide polymorphisms, we found contrasting genetic metrics and patterns among naturalized trout in Lake Llanquihue, Chile's largest producer of salmonid smolts for nearly 50 years, and Lake Todos Los Santos (TLS), a reference lake where aquaculture has been prohibited by law. Trout from Lake Llanquihue showed higher genetic diversity, weaker genetic structure, and larger estimates for the effective number of breeders (Nb) than trout from Lake TLS. Trout from Lake TLS were divergent from Lake Llanquihue and showed marked genetic structure and a significant isolation‐by‐distance pattern consistent with secondary contact between documented and undocumented stocking events in opposite shores of the lake. Multiple factors, including differences in propagule pressure, origin of donor populations, lake geomorphology, habitat quality or quantity, and life history, may help explain contrasting genetic metrics and patterns for trout between lakes. We contend that high propagule pressure from aquaculture may not only increase genetic diversity and Nb via demographic effects and admixture, but also may impact the evolution of genetic structure and increase gene flow, consistent with findings from artificially propagated salmonid populations in their native and naturalized ranges.Different pathways of propagation and dispersal of non‐native species into new environments may have contrasting demographic and genetic impacts on established populations. Repeated introductions of rainbow trout ( We amplified 96 SNPs . SNPs with the lowest information content within each group of loci in LD were also excluded. Information content was gauged by Shannon–Weaver index calculated in GENALEX 6.5 software  and between streams  following Benavente et al.  and expected heterozygosities (HE) and allelic richness (AR). Calculation of HO and HE was conducted in GENALEX; AR was estimated in HierFstat v0.04‐10 package  to evaluate deficit or excess of heterozygotes on deviations from HWE proportions were calculated in GENALEX. We built boxplots for each genetic diversity index and inlet stream within lakes and tested for statistical differences (p < .05) between lakes using the nonparametric Mann–Whitney U test in R.We tested for significant differences between lakes on observed  in GENEPOP. Using STRUCTURE, we tested K values ranging from one to five, with 50,000 iterations of burn‐in and a run length MCMC of 250,000; all these runs were replicated ten times. These runs were conducted using an ancestry mixture model and locality information priors to improve the detection of structure when genetic structure is weak  theta (θST/[1 − θST]) and geographic distances to test for isolation by distance between inlet streams within each lake. The Mantel test was conducted in ade4 software package based on 100,000 permutations , six loci that were found in LD with more informative markers , and three monomorphic markers . This filtering procedure yielded a set of 86 reliable SNPs for subsequent analyses  varied little across collections (10–15%). We thus opted for retaining related individuals as the incidence of family structure was low  and Lake TLS . Spatial (interpopulation) variance was greater for rainbow trout from Lake TLS  than from Lake Llanquihue . Based on these results, we followed Benavente et al. . Inbreeding coefficients f showed no evidence for heterozygote deficit or excess and were consistent with fit to HWE proportions within collections.Range of values for all three parameters—S Figure . HO ran3.4θST ranged between −0.022 and 0.156; the strongest divergence was found among rainbow trout from inlets located in different lakes .Patterns of isolation by distance differed significantly between lakes. Rainbow trout from Lake Llanquihue showed no significant relationship between genetic and geographic distances, whereas rainbow trout from Lake TLS exhibited a significant relationship . All point estimates were finite and bound by finite 95% CIs. Values for LD Nb ranged between 86 and 223 individuals  for Lake Llanquihue and between 13 and 164 individuals  for Lake TLS.Estimates of LD 4Nb). Using a suite of 86 SNPs, we found higher genetic diversity and larger estimates of number of breeders in Lake Llanquihue, a lake heavily influenced by aquaculture escapes and subject therefore to higher propagule pressure than Lake TLS. Genetic structure and a significant isolation‐by‐distance pattern were evident in Lake TLS, but not in Lake Llanquihue, in line with secondary contact between two introductions in opposite shores of the lake. Divergence between lakes was also large, suggesting trout inhabiting these two lakes originated from different donor populations.We evaluated how different pathways of rainbow trout invasion, exemplified by two Patagonian lakes with and without the influence of trout aquaculture, have resulted in differences in population genetics metrics and patterns such as diversity, structure, divergence, and effective number of breeders  were higher for trout from Lake Llanquihue than from Lake TLS. We hypothesize that high propagule pressure from late aquaculture escapes, which followed an early and brief stocking phase, explain these differences. From 1910 to 1916, between 50,000 and 100,000 rainbow trout eggs were imported from Germany and maintained in a temporary hatchery located at the outlet of Lake Llanquihue, Maullin River, for stocking purposes  were larger for rainbow trout from Lake Llanquihue than from Lake TLS. Estimates of LD Nb from this study were consistent with Benavente et al. , suggesting estimates of LD Nb are potentially converging toward the effective number of breeders in the entire metapopulation, meta‐Nb. Yet, differences in meta‐Nb for rainbow trout between lakes could persist if meta‐Nb is much smaller than the sum of local LD Nb, which may occur under gene flow asymmetry between populations  among native populations has been related to a high risk of extinction from genetic stochasticity (Palstra & Ruzzante, Salvelinus namaycush with a founding size of only two individuals that established a self‐sustaining population (Kalinowski, Muhlfeld, Guy, & Cox, Nb is small.Small effective population size (and None declared.https://doi.org/10.5061/dryad.gd844.Data available from the Dryad Digital Repository at Conception or design of the work: DG‐U IA LWS JES GG. Acquisition, analysis, or interpretation of data for the work: CBC‐A MIA SSM RG DG‐U. Drafting the work or revising it critically for important intellectual content: CBC‐A LWS JES IA GG SSM DG‐U. Final approval of the version to be published: all authors. Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved: all authors. Click here for additional data file. Click here for additional data file. Click here for additional data file.