Glycine soja). G. soja, from which domesticated soybeans (Glycine max) were derived, is widely distributed throughout diverse habitats in East Asia . Here, we utilize over 39,000 single nucleotide polymorphisms genotyped in 99 ecotypes of wild soybean sampled across their native geographic range in northeast Asia, to understand population structure and the relative contribution of environment versus geography to population differentiation in this species. A STRUCTURE analysis identified four genetic groups that largely corresponded to the geographic regions of central China, northern China, Korea, and Japan, with high levels of admixture between genetic groups. A canonical correlation and redundancy analysis showed that environmental factors contributed 23.6% to population differentiation, much more than that for geographic factors (6.6%). Precipitation variables largely explained divergence of the groups along longitudinal axes, whereas temperature variables contributed more to latitudinal divergence. This study provides a foundation for further understanding of the genetic basis of climatic adaptation in this ecologically and agriculturally important species.A fundamental goal in evolutionary biology is to understand how various evolutionary factors interact to affect the population structure of diverse species, especially those of ecological and/or agricultural importance such as wild soybean ( Glycine soja  harbors the most diverse of the world's temperate flora   from the USDA soybean germplasm collection. Although the USDA has roughly 1035 wild soybean accessions, these represent only 105 unique locations with respect to longitude (LONG) and latitude (LAT), some of which from southern China were not sampled. We focused on northeast Asia and, in this region, sampled one individual at each geographic location , we used an ad hoc statistic, ΔK, that is based on the rate of change in the log probability of data between successive K values . Using this solution, another longer STRUCTURE run was performed  that produced Bayesian posterior probabilities that determined the assignment of individual ecotypes to each of the groups. For further analyses, we used all ecotypes with Bayesian posterior probabilities ≥0.7, resulting in a sample of 83. We chose this threshold of 0.7 in part because we wanted to include enough accessions to analyze the considerable admixture revealed by the STRUCTURE procedure (see N = 52). All analyses described below therefore were performed on this sample of 83 ecotypes.We found that FST values) between each of the group pairs with the Hierfstat package in R . For each SNP, the three genotypes were coded as 0, 1, and 2, and the genomic distance between each pair of individuals was calculated by averaging the differences across all SNPs with available data for that pair. The genetic distance represented the expected number of different alleles that would be observed in each SNP. To complement the STRUCTURE analysis, we derived a phylogenetic (neighbor‐joining) tree from the pairwise distance matrix using the “nj” function from the package “APE” in R (http://cran.r-project.org/web/packages/ape/index.html).Using the genetic groups identified by the STRUCTURE procedure, we calculated genetic distances (R Goudet . Hierfstockerham . Genetichttp://cran.r-project.org/web/packages/ape/index.html). PCOA calculated in this fashion is equivalent to a principal components analysis of the allelic states within the wild soybean ecotypes. PCOA was useful in producing a series of axes that could be interpreted as separating two or more of the genetic groups. We plotted the PCOA scores for the first three axes to help visualize their effect in differentiating the groups.A principal coordinate analysis (PCOA) of the genetic data was then performed with the pairwise genetic distance matrix, using the PCOA function of the APE package in R (P > 0.05).To assess the potential correlation of environmental and geographic variation with the wild soybean genetic structure, it was necessary to use variables that sufficiently captured the general environmental and geographical differences among the northeast Asian regions sampled. For this purpose, we downloaded 19 environmental variables and four geographic variables available at each ecotype site (resolution 30 arc seconds) through WorldClim  included mean diurnal range (MDR = mean of monthly (maximum temperature – minimum temperature)) and the mean temperature of the wettest quarter (MTW). The precipitation variables (expressed to the nearest mm) included annual precipitation (AP) and precipitation of the wettest month (PWM). These variables tended to show lower correlations with each other and with the geographic variables than other environmental variables. Our intent was to reduce the redundancy inherent among highly associated variables .To sort out the potential effects of natural selection  versus genetic drift  on the genetic variability among the wild soybean accessions, we used Mantel tests of the correlations of genetic, environmental, and geographical distance matrices. We calculated the pairwise genetic distance matrix as described above. With regard to the environmental variables, it was of interest to discover whether nonlinear environmental effects also might impact the genetic structure, as was found by Sork et al. . We therhttp://CRAN.R-project.org/package=vegan). Significance testing of the correlations was performed with 10,000 permutations. We also used partial Mantel tests to discover whether the environmental distances had a significant association with the genetic distances, adjusting for any effects of geographical distances. Significance in this test was interpreted as meaning that genetic variation among the wild soybeans was influenced by environmental selection, whereas a nonsignificant result suggested a role for isolation by distance (genetic drift).We conducted Mantel tests of the correlations of both environmental and geographic distances with genetic distances using the VEGAN package in R  using a full model that included both genetic and environmental/geographic variables.We followed up the CCA with a RDA. RDA is a type of ordination that is the multivariate equivalent of linear regression and can be used to test whether the variation in one set of (independent) variables explains the variation in another set of (dependent) variables. If significance is found in this overall test, RDA can then appropriately be used to estimate the extent of the variation that is explained. We used RDA to test whether the 18 environmental/geographic variables could explain variation in the genetic variables  and to estimate the percentage contribution of this explained variation to the total variation among the genetic groups. We implemented RDA with the VEGAN package in R . This model allowed us to test for environmental effects and their contribution to the explained genetic variation that was independent of spatial (geographic) factors. A second model included the four geographic variables, but adjusted for effects from the 14 environmental variables . This model allowed us to test for geographic effects and their contribution to genetic variation that was independent of the environmental variables. We then summed the estimates of the variation independently explained by environmental and geographic variables and subtracted this sum from the explained variance in the full model to obtain the variance contributed by collinearity of the environment and geography.N = 5); GROUP 2 – northern China (N = 7); GROUP 3 – Korea (N = 45); and GROUP 4 – Japan (N = 26) . These groups primarily describe ecotypes from separate geographical regions: GROUP 1 – central China  and the geographic distances (r = 0.75) were high and statistically significant (P < 0.0001). In the partial Mantel test, the genetic and environmental distances still were significantly correlated  after accounting for geographic distance. This result suggests that, after controlling for isolation by distance alone, environmental selection may well have played a role in shaping genetic variability among the wild soybean accessions. Specific effects of the environmental variables are examined in the CCA results below.In the Mantel tests, correlations of the genetic distances with both the environmental  with the 18 environmental/geographical variables in the wild soybean sample were statistically significant Table . The canCorrelations of the first three pairs of genetic (PCOA) and environmental/geographical variables with their canonical variables are shown in Table 2. There are also moderately high correlations of this canonical variable with mean temperature in the wettest quarter (MTW) and interactions of both temperature variables (MDR and MTW) with each other and with AP. This suggests that besides LAT differences, precipitation , mean temperature of the wettest quarter (MTW), and several temperature‐related interactions are the factors that distinguish wild soybean ecotypes from central China from those in other groups.The second genetic canonical variable loads most highly on PCOA3, the principal component previously seen to separate GROUP 2 from the other groups. The second environmental/geographic variable correlates highly with LAT, PWM, and PWMThe third genetic canonical variable loads most heavily on PCOA2 that previously was seen to separate GROUP 1 ecotypes from the others. Most of the environmental and geographical variables show low correlations with this canonical variable, although there is some association with LAT and ALT as well as with precipitation in the wettest month (PWM) and its square. Thus, there is evidence that beyond LAT and slope differences, precipitation in the wettest month accounts for some of the differentiation of the northern China ecotypes from all others.P < 0.001), with this model explaining 64.0% of the total genetic variation among the wild soybean ecotypes. The partial environment model also showed significance (P < 0.001), suggesting that the environmental variables account for some of the genetic differentiation in the wild soybean ecotypes; that is, populations in the different geographic regions have adapted to different environments. In the partition of the total genetic variation , environmental effects independently contributed nearly 24%, much more than geographic effects (6.6%). Joint (collinear) effects of both of these factors contributed nearly 34%, with the remaining variance (36%) being unexplained.The RDA showed that the full model using all 18 environmental and geographic variables was highly significant , and southern regions. Our study focused on northeast Asia  and all other groups, especially GROUP 1  we found was between GROUP 3 and 4, with ecotypes in these groups found in Korea and Japan.The FST value between GROUP 3 and 4 of 0.1 corresponds to a number of migrants (Nm) equal to 9  ecotypes may have resulted from a unidirectional movement (Korea to northern China) in this species due to climate changes. It also is likely that the admixture in both Chinese groups can be explained by their small sample sizes.Incomplete lineage sorting previously has been suggested as a possible mechanism that explains why wild soybeans from southern China are more related to those from northern rather than central China  than in LAT , so it was not surprising to see that the first environmental/geographical canonical variable was highly correlated with LONG. In our sample, LONG reflects proximity to the ocean, a factor that especially influences precipitation in this region. Thus, mean values for AP for ecotypes in GROUP 3 and 4 that are closer to the ocean are higher  than those for GROUP 1 and 2 (157 and 172). As a consequence, AP was the most important contributor to the first environmental/geographical canonical variable that was responsible for the separation of these two groups from the other two continental (China) groups.The CCA demonstrated a strong association of the environmental/geographical and genetic variables across the range of wild soybeans we sampled. The first genetic canonical variable was highly associated with PCOA1 that separated wild soybean ecotypes from the continent, the Korean peninsula, and the Japanese islands see Fig. . The fouThe interaction of the two precipitation variables, AP and precipitation in the wettest month (PWM), also contributed heavily to the first environmental/geographic canonical variable Table . This sur = −0.64, P < 0.0003), although not with mean diurnal temperature range, MDR . MTW was most highly associated with the second environmental/geographical canonical variable  from the other groups  contributed much more than geographic effects (6.6%) to the differentiation of the wild soybean genetic groups. There was considerable collinearity (33.8%) between both of these factors, probably because of the wide geographic sampling and some moderate correlations of the precipitation and temperature variables with LONG and LAT. As far is known, our study is the first to partition the genetic variance among wild soybeans into environmental and geographic components, and therefore no comparable studies are available for comparison. Environmental and geographic factors have been assessed in other species and, as expected, their contributions vary. For example, Sork et al.  found thArabidopsis  by downloading the specific accessions we used.The data can be accessed in the Soybase database  geographic and environmental variables for each of the four genetic groups.Figure S1. Statistics from the STRUCTURE analysis  is 4.Figure S2. Neighbor‐joining tree generated from the 99 wild soybean ecotypes.Figure S3. A venn diagram showing the proportion of genomic variance explained by environmental variables (light blue), geography (light green), and the joint effects of environment and geography (blue‐green).Figure S4. Linear regression plots illustrating the joint effects of PWM on PCOA1 (A) and MDR on PCOA1 (B) at the mean of AP and at 2 standard deviations above and below the AP mean.Click here for additional data file.


Anopheles funestus s.s. is a primary vector of malaria in sub-Saharan Africa. Despite its important role in human Plasmodium transmission, evolutionary history, genetic diversity, and population structure of An. funestus in southern and central Africa remains understudied. We deep sequenced, assembled, and annotated the complete mitochondrial genome of An. funestus s.s. for the first time, providing a foundation for further genetic research of this important malaria vector species. We further analyzed the complete mitochondrial genomes of 43 An. funestus s.s. from three sites in Zambia, Democratic Republic of the Congo, and Tanzania. From these 43 mitogenomes we identified 41 unique haplotypes that comprised 567 polymorphic sites. Bayesian phylogenetic reconstruction confirmed the co-existence of two highly divergent An. funestus maternal lineages, herein defined as lineages I and II, in Zambia and Tanzania. The estimated coalescence time of these two mitochondrial lineages is ~500,000 years ago  with subsequent independent diversification. Haplotype network and phylogenetic analysis revealed two major clusters within lineage I, and genetic relatedness of samples with deep branching in lineage II. At this time, data suggest that the lineages are partially sympatric. This study illustrates that accurate retrieval of full mitogenomes of Anopheles vectors enables fine-resolution studies of intraspecies genetic relationships, population differentiation, and demographic history. Further investigations on whether An. funestus mitochondrial lineages represent biologically meaningful populations and their potential implications for malaria vector control are warranted. These data represent a drastically reduced incidence of malaria since 20002. However, progress has plateaued in recent years and incidence and mortality have remained essentially the same in 2015 and 20161. The decline of malaria can be attributed to several causes, including a rise in coverage of vector control2. However, phenomena such as changing vector foraging and resting behaviors4, and the development of insecticide resistance6, have led to heterogeneity and stagnation in the success of malaria control worldwide. Population genetic and genomic methods, as a result of advances in sequencing strategies, are becoming useful tools for understanding and monitoring vector population diversity9, dispersal10 and dynamics with the ultimate goal of informing malaria control12.In 2016, there were approximately 216 million cases of malaria and approximately half a million deaths, most of which occurred in sub-Saharan AfricaAnopheles funestus sensu stricto (hereafter “An. funestus”) is a major malaria vector throughout sub-Saharan Africa and poses a significant threat to malaria control and elimination due to its high vectorial capacity, expansive distribution, and high rates of insecticide resistance13. While studies of the other major regional malaria vectors in the An. gambiae sensu lato species complex have been frequent over the past four decades, research on An. funestus has remained at a trickle, with very few investigations during recent decades. Notably, this dearth is largely due to the relative difficulty of rearing An. funestus in laboratories. With the advent of cytogenetic studies in the 1980’s, as well as modern and more sophisticated molecular techniques, population studies of field-caught mosquitoes have become more common13. Additionally, the establishment of laboratory colonies of An. funestus within the last decade14 has finally allowed for more complex genetic and genomic studies of this species15.An. funestus across its entire geographic range in sub-Saharan Africa25. These broad-scale studies largely agree that An. funestus populations can be split into major western and eastern groups17. Interestingly, there is compelling evidence for the hypothesis that the Great Rift Valley serves as an important barrier to gene flow between these populations20, which has similarly been documented for An. gambiae26. Additionally, Michel et al.17 reported two mitochondrial lineages (I and II) of An. funestus based on partial mitochondrial gene sequences (COI and ND5), which are not reflected in parallel nuclear microsatellite analyses. While mitochondrial lineage I makes up the majority of samples found in countries throughout sub-Saharan Africa, individuals belonging to lineage II have only been reported in the southeastern range of this species in Mozambique, Madagascar, northeastern Tanzania, and northern Zambia27.However, there are still only a limited number of genetic studies  of An. funestus have focused on only limited regions in western and eastern Africa, while An. funestus populations in central and southern Africa, such as Zambia and the Democratic Republic of the Congo, remain greatly understudied. More specifically, investigations of An. funestus maternal lineages have also been limited within southern Africa, and have solely relied on partial mitochondrial gene sequences (COI and ND5)25. A key challenge to such studies is the unfinished nature of the published mitochondrial reference genome (GenBank: DQ146364.1), which is incomplete and lacking ~27.5% of the genome sequence, mainly in coding regions28. This incomplete reference represents a hurdle to future research in the field, as mitochondrial genomes (hereafter “mitogenome”) can serve as an important source of markers for population genetic studies, and also provide insight into evolutionary relationships within the An. funestus species complex. Further, the absence of large-scale mitogenome and nuclear genome data of wild-caught An. funestus makes it difficult to catalogue genetic variation in natural populations and determine population structure and dispersal rates.To date, fine-scale population genetic studies of 30, the rare occurrence of recombination31, and a higher mutation rate than the nuclear genome32. Mitochondrial sequence polymorphisms may be particularly useful to study sex-biased dispersal known to occur in some anopheline mosquitoes (including An. funestus)34. Mitogenomes have smaller effective population sizes than autosomal DNA, enabling better discrimination between populations due to the rapid effects of genetic drift. Additionally, mitochondria contain multiple genomic copies making mitogenomes amenable targets for sequencing at high coverage. Understanding historical gene flow and genetic structure via analysis of mitogenomes is a step toward revealing contemporary vector population dynamics and accurate discrimination between lineages and sub-populations. In turn, this information will contribute to an enhanced appreciation of malaria transmission dynamics, especially if vector genetic diversity reflects differences in biology, behavior, permissiveness to Plasmodium parasites35, or insecticide susceptibility36, all of which have consequences for malarial disease management, surveillance, and control measures.Complete mitogenomes are particularly useful for reconstructing phylogenies and inferring population history due to haploid maternal inheritanceAn. funestus across a critically understudied geographic region, and to further examine the evolutionary history and distribution of mitochondrial lineages, we performed shotgun mitogenome sequencing of An. funestus samples from northern Zambia, southeastern Democratic Republic of the Congo (hereafter “DRC”), and southern Tanzania. We first generated a new An. funestus mitochondrial reference genome that filled gaps in the existing incomplete reference (GenBank: DQ146364.1)28 and then assembled and annotated 43 An. funestus mitogenome sequences from these regional collections. Bayesian phylogenetic and classical population genetic analyses were performed to characterize An. funestus mitochondrial lineages, document their distribution in southern and central Africa, and assess their demographic history. Notably, the data generated in this study are part of an initial collection effort to build a digital repository of genomic data from field-caught An. funestus across southern and central Africa.To investigate the degree of genetic diversity in An. funestus samples were chosen to geographically represent this species in southern and central Africa where we are actively conducting research on malaria transmission. In total, 43 An. funestus samples were selected for further sequencing  achieved49.Lupiro is located within Kilombero Valley in southern Tanzania, a zone of intense perennial malaria transmission50. Each identified mosquito was placed individually into a labelled 0.6 mL microcentrifuge tube containing silica gel desiccant and cotton wool and stored either at room temperature or frozen at −20 °C until laboratory processing. Genomic DNA extractions were performed on the head and thorax for each individual mosquito as previously described51. Quantitation of the genomic DNA was performed using a Qubit 2.0 Fluorometer  and genomic libraries were prepared as described, using an input of 10 ng of genomic DNA52. Indexed libraries were pooled and sequenced in a single lane on an Illumina HiSeq4000 to generate 150 bp paired-end reads. Sequencing was performed at the University of California-Davis DNA Technologies Core. Demultiplexed Illumina raw reads obtained from DNA Technologies Core were trimmed using Trimmomatic version 0.3653. We used the typical trimming parameters “ILLUMINACLIP:’{input.adapters_file}’:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36”, which removes adapters, trims low quality or N bases below quality 3, scans the read with a 4-base sliding window cutting when the average quality per base drops below 15, and dropped reads below 36 bp long.Field-caught mosquitoes were morphologically identified to species using standard keys at the time of collectionAn. funestus mitochondrial reference (GenBank: DQ146364.1)28 was used as a ‘seed’ sequence to generate a new and complete mitogenome reference with MITObim v1.8 with 10 iterations, default parameters, and trimmed Illumina reads from sample AF13ICNC14-10654. Subsequently, raw Illumina sequence reads for each sample were aligned to the newly generated An. funestus mitogenome reference AF13ICNC14-106, using BWA alignment tool v0.7.7 56. Duplicate sequences were identified and excluded from downstream analysis using Picard Suite v1.117 MarkDuplicates57. Aligned reads were realigned around indels (insertions and deletions) using GATK v3.7 RealignerTargetCreator and IndelRealigner. Variants with respect to AF13ICNC14-106 were identified with GATK HaplotypeCaller (ploidy set to 1)58. Indels and single nucleotide polymorphisms (SNPs) with signals of low mapping or genotyping quality were excluded with GATK VariantFiltration, using the following filters recommended by GATK: quality by depth (QD < 2.0), Fisher strand bias (FS > 200.0), mapping quality (MQ < 40.0), the Mann-Whitney rank sum test (ReadPosRankSum < − 20.0)59. To create the consensus mitogenome sequence for each sample from the variant files, the GATK tool FastaAlternateReferenceMaker was used. The mitogenome coverage for each sample was calculated using the software GATK v3.7 (DepthOfCoverage with parameters mmq > 20 and mbq > 20)58.The incomplete An. funestus mitogenomes were aligned using MUSCLE with and without full mitogenomes from An. gambiae (GenBank: L20934.1) and An. minimus (GenBank: KT895423.1) as outgroups62. It is important to account for recombination when reconstructing evolutionary histories because homologous recombination has a profound impact on evolutionary trajectories and therefore the interpretation of inferred phylogenies. We used the 3SEQ software which implements a fast non-parametric recombination detection algorithm to infer recombinant tracts along the mitogenomes to rule out the possibility of recombination in our N = 43 An. funestus mitogenome alignment63. Maximum likelihood trees of the 43 samples were generated in SeaView v4, using PhyML and GTR substitution model, and default parameters with 1000 bootstrap replicates64. BEAST2 v2.4.5 was used to conduct phylogenetic analyses as well as generate estimates of divergence times and population size, and determine demographic history of southern and central African An. funestus65. Analyses were performed using a substitution rate of 1.2 × 10−8 mutations per site per year, following estimates from Brower66. Markov chains were run for 100 million generations or until convergence, with 10 million generations of each run discarded as burn in, and chains sampled every 10000 generations. Both HKY and GTR substitution models were used in combination with gamma site-specific rate variation (G) and a proportion of invariant sites (I) parameters, strict and relaxed log normal molecular clocks, as well as constant and Bayesian skyline population models. To compare models, the Path Sampler application from BEAST2 v2.4.5 was used to generate marginal likelihood estimates and the model with the highest estimate was used for demographic and population history inference65. Tracer v1.667 was used to inspect convergence and confirm effective sample sizes were greater than 200 for parameters of interest. Tracer v1.6 was also used to generate Bayesian skyline plots. Because our evolutionary rate was in years, effective population size was confounded with generation time; we used a generation time of 3/5268 to convert estimates of population diversity to Ne in our coalescent analyses. LogCombiner was used to resample 10000 trees from BEAST2 analysis and then TreeAnnotator was used to generate Maximum Clade Credibility (MCC) trees65. The multiple alignment of the 43 An. funestus mitogenomes was further analyzed using TCS statistical method as implemented in PopArt v1.7 to produce a mitochondrial haplotype network70.The 43 newly generated An. funestus samples sequenced in this study compared to previously known An. funestus diversity we extracted the NADH dehydrogenase subunit 5 (ND5) sequences from our 43 mitogenome sequences and aligned them using MUSCLE to 400 published partial ND5 sequences (834 bp)17. To further explore the diversity of our samples in the context of this large pan-African dataset, we used PhyML to generate a maximum likelihood tree of the ND5 alignment using the GTR nucleotide substitution model and 1000 bootstrap replicates71. tcsBU was used to visualize the TCS haplotype network generated by TCS v1.21 for partial ND5 sequences72.To investigate how the genetic diversity of An. funestus populations, we estimated the strength of association between phylogenetic relationships and sampling locations for the complete An. funestus mitogenome sequences using the software package BaTS73. BaTS generates a parsimony score (PS)74 and association index (AI)75 to assess the extent of geographical association with phylogenetic structure across the entire tree, as well as maximum monophyletic clade size statistics (MC)73 to determine the association for particular sampling locations.To determine the extent of geographic structure in our 76. These statistics test the null hypothesis that populations are: neutral, of constant size, are in panmixia, and have no recombination. Arlequin v3.5 was used to calculate the mismatch distributions to test signal for population spatial expansion77. The raggedness index and SSD were used to evaluate how well the sample conforms to the null model of either demographic or spatial expansion. Mantel tests were used to evaluate for correlation between genetic distance and physical distance using the APE package in R v3.3.079.DnaSP v5 was used to generate general diversity statistics, conduct neutrality tests, and examine demography62. Transfer RNA (tRNAs) were identified by their putative secondary structures using tRNAscan-SE80. The ribosomal RNA genes (rrnL and rrnS) were identified by sequence similarity to the available homologous sequences using MITOS81.Protein coding genes were identified and annotated manually by sequence similarity to the previous reference genome (GenBank: DQ146364.1) as well as the orthologous sequences of other anopheline speciesAn. funestus mitogenome sequences are available in the GenBank Database under the following accession numbers: MG742157-MG742199.The 43 newly generated An. funestus from three regions across southern and central Africa  were subjected to whole genome shotgun sequencing  was generated and the remaining 42 mitogenomes were assembled. On average, mitogenome coverage was 350×, ranging from 32× to 477× across the 43 samples , as is typical for the mitogenomes of many arthropod and anopheline taxa82. The mean nucleotide diversity (π) in the 43 An. funestus mitogenomes was 0.00625  and An. arabiensis (π = 0.0046)84. The multiple alignment of the 43 mitogenomes revealed a total of 567 polymorphic sites. These variable sites defined a total of 41 mitogenome haplotypes from the 43 sampled individuals , while An. minimus appears to have split from An. funestus approximately 5.36 million years ago (95% HPD: 4.06–6.68 Mya).We found 160 nucleotide differences between haplotypes in lineage I and II on average, with 47 fixed differences between the two mitochondrial lineages. Lineage II contained longer branch-lengths between samples than samples within lineage I, an observation which is also reflected in the diversity statistics Table . The mos85. The single-SNP-based TaqMan assay targets a SNP at position 405 in the sequence of COI, where two states are considered: a T or a C85. This definition of lineages misidentifies ~30% of our lineage II samples as lineage I.Within the two main lineages, we found several well-supported clades Figure . In lineAn. funestus  and lineage II (p = 0.001**) had significantly related pairwise nucleotide identity and geographic distance matrices.Plots of geographic distance relative to nucleotide identity are shown in Figure An. funestus diversity, we constructed a maximum likelihood tree and haplotype network from a large data set including published partial mitochondrial ND5 gene sequences available from GenBank17 and the derived corresponding partial gene sequences from our 43 mitogenomes. The topology of the ML tree . Our divergence estimates fell on the low end of estimates from previous studies91, and specifically, our estimate of divergence between the two lineages is younger than that reported by Michel and colleagues, who used the same mutation rate (1.1–1.2% per million years) to generate an estimate of ∼850,000 years17.The Bayesian coalescent analyses of the complete An. funestus samples harbored a genomic signature of historic population expansion for the total population as well as for lineage I, though not for lineage II  began approximately 80,000 years ago  share a nucleotide polymorphism (a ‘T’) with lineage I instead of the diagnostic SNP used in the TaqMan assay to define lineage II (a ‘C’). This finding reinforced the importance of complete mitochondrial sequences for accurate characterization of An. funestus diversity and/or revision of the assay to accurately reflect the new mitogenome data and diversity within An. funestus. We found 47 mitogenome-wide fixed SNP differences between the two lineages that may more accurately discriminate between lineages and would benefit future studies that aim to describe An. funestus lineage composition, distribution and biology in sub-Saharan Africa. A maximum likelihood tree using the partial ND5 gene of our samples along with those from Michel et al.17  experienced a demographic expansion. At this time, data suggest that the lineages are at least partially sympatric. Preliminary analyses have indicated that lineages I and II may differ in habitat and/or foraging preferences (unpublished observations); ongoing work is exploring these trends. Such a difference in behavior may have important implications for vector control. Though it is tempting to theorize that lineage I and II may represent reproductively isolated populations because of the strong separation of clades in mitogenome comparisons, these results will have to be interpreted within the context of future nuclear genomics and hybridization experiments between the two lineages. Given our findings, further investigations on whether the An. funestus mitochondrial lineages represent biologically meaningful populations are warranted.We have illustrated that Supplementary Information


Avoidance of mating between related individuals is usually considered adaptive because it decreases the probability of inbreeding depression in offspring. However, mating between related partners can be adaptive if outbreeding depression is stronger than inbreeding depression or if females gain inclusive fitness benefits by mating with close kin. In the present study, we used microsatellite data to infer the parentage of juveniles born in a French colony of feral pigeons, which allowed us to deduce parent pairs. Despite detectable inbreeding depression, we found that pairwise relatedness between mates was significantly higher than between nonmates, with a mean coefficient of relatedness between mates of 0.065, approximately half the theoretical value for first cousins. This higher relatedness between mates cannot be explained by spatial genetic structure in this colonial bird; it therefore probably results from an active choice. As inbreeding but not outbreeding depression is observed in the study population, this finding accords with the idea that mating with genetically similar mates can confer a benefit in terms of inclusive fitness. Our results and published evidence suggest that preference for related individuals as mates might be relatively frequent in birds. The pattern of occurrence and the consequences of inbreeding are of major interest in evolutionary and behavioural ecology, because inbreeding depression is considered to play a central role in the evolution of mating systems and dispersal –3. InbreHowever, even in species in which inbreeding depression occurs it does not follow that mating among relatives should always be disfavoured. Firstly, when unrelated mating partners are scarce relative to related partners, the cost of inbreeding depression may be lower than that of forfeiting mating . SecondlOnly a handful of empirical studies have reported mating preferences for related partners in animals (but see for example –19), wheColumba livia, is well suited to perform such comparison. Firstly, this is a socially monogamous species [The rock pigeon,  species ; secondl species ,24. ThusIn the present study, we used microsatellite data to infer parentage of offspring produced in a dovecote located near Paris, France, allowing us to infer breeding pairs. We first tested the impact of within-pair relatedness on the number of offspring produced, to evaluate the fitness consequences of inbreeding in this population. We then compare the genetic relatedness of the inferred breeding pairs to the genetic relatedness expected under random mating, to determine whether relatedness influences mate choice in rock pigeon.Columba livia is considered a domestic species in France, ethical approval was not required by the French equivalent of the Institutional Animal Care and Use Committee . The study was performed in only one location: a city owned dovecote. The study was authorized by the local authorities of the city (French "mairie" of Fontenay-sous-bois). The approval from the local authorities in Fontenay-sous-bois was obtained prior to sample collection.The study was carried out following the recommendations of the European Convention for the Protection of Vertebrate Animals used for Experimental and Other Scientific Purposes. Because the procedures used in the study (fitting with a colored ring and 1–3 μl blood draw) were minimally painful and because the pigeon Columba livia) living in an urban dovecote in Fontenay-sous-Bois, France . The dovecote is a small building (about 2.5 x 2.5 x 2.5 m) located in a park, where 90 nesting sites are available. Fifty kilograms of seeds are provided each week with grit and water. The pigeons have free access to the dovecote. Adults were captured either before sunrise, to catch the individuals that had spent the night in the dovecote, or in the daytime, to catch those using the dovecote mainly for feeding. A total of nine captures was performed over a 20-month period. Adults were forced out of the dovecote through a tissue tunnel and into cages placed on the floor. Juveniles were regularly captured in their nest, shortly before fledging, during the weekly maintenance of the dovecote. A total of 372 individuals  was captured. During each capture birds caught for the first time were fitted with a coloured ring. Our recapture data showed that the proportion of marked animals varied between 90 and 94% of the total number of pigeons. To perform parentage analyses (see below), we therefore conservatively assumed that 90% of parents were sampled.We studied a population of free-living pigeons (genescan 3.1 and genotyper 2.5 (Applied Biosystems). Negative controls were used to check for cross-contamination between tubes during DNA extraction and amplification.From each individual, we sampled 1–3 μl of blood from the tarsal vein using a fine needle. Blood was stored in buffer PBS + EDTA (3% v/v) and kept frozen at -20°C until DNA extraction. We extracted DNA using the Qiagen (Hilden Germany) Tissue Kit and following the manufacturer’s protocol for DNA extraction from blood samples. DNA was eluted in 2x 100 μl AE buffer (Qiagen) and stored at -20°C. We amplified seven microsatellite loci developed for domestic breeds of the rock pigeon . As feracervus 3.0 [One locus, CliμD19, showed a very high proportion of non-amplifying alleles (0.60) and was thus excluded from further analyses. The six other loci showed no evidence of heterozygote deficit, suggesting that null alleles, if present, were at low frequency . Among trvus 3.0 ,28 was hrvus 3.0 . The gencervus 3.0 [Parentage analyses were conducted following the likelihood-based approach implemented in the program rvus 3.0 ,28. For Ten juveniles, four candidate mothers, and four candidate fathers were excluded from the analyses, because they did not amplify at one or more loci. Overall, 120 (88%) juveniles were assigned to a parent-offspring trio . These assignments allowed us to infer 70 different parent pairs . Some males and females contributed to more than one of the 70 different mother-father pairs inferred. In total, 51 (44%) of the 116 candidate males and 49 (48%) of the 102 candidate females reproduced, i.e., they were assigned as parent to at least one juvenile at 95 or 80% confidence levels. Very similar results were obtained when the proportion of sampled parents and the error rates were set to alternate values (not shown).Parentage was inferred for all offspring produced in the dovecote during the 20 months of the study.  Because not all adults were present in the dovecote during the 20 months and because we could not precisely evaluate the length of their stay in the dovecote, we measured the reproductive success of a parent pair as the number of juveniles that they produced by clutch. Feral pigeons lay one to two eggs per clutch. The reproductive success of the observed parent pairs was thus between one and two.We used a generalized linear model with Binomial response to assess whether the probability of a pair having two offspring per clutch (given that the pair reproduced) was influenced by the relatedness between parents and by average monthly temperature and total precipitations during the time of incubation and the first weeks of growth of the chicks. All calculations were performed with the R statistical program using the “glm” function from the lme4 package .r unbiased estimator of genetic relatedness [Finally, we examined whether the breeding pairs observed in our study were on average more related than an equal number of pairs selected at random i) among all males and females present in the dovecote, and ii) among all males and females identified as breeders. We used the software program GenAlEx 6  to calcuatedness . This inatedness . Negativatedness , i.e. thWe studied whether the relatedness between mates had an influence on their reproductive success, measured as the number of juveniles per clutch . Mean temperature and total precipitations at the time of breeding were not retained during model selection based on AIC criteria, suggesting that breeding success is not affected by climatic conditions in our study population. We found that the probability of a pair having two offspring per clutch was negatively impacted by the level of relatedness within pairs . The average relatedness value was very close for couples inferred with a 95% (mean r = 0.063) or 80% (mean r = 0.061) confidence level.p-value = 0.0005).We found that the 70 breeding pairs were on average more related that an equal number of pairs taken at random among all males and females present in the dovecote. The difference in level of relatedness between breeding pairs and random pairs was estimated at 0.07 and was highly significant  and between an equal number of pairs selected at random among all males and females identified as breeders. We found that these random couples had a mean relatedness of -0.014±0.21. The difference in level of relatedness between observed pairs and random pairs was estimated at 0.10 and was highly significant . The abovementioned hypothesis can therefore be rejected.IS = 0.009), which confirms that no genetic differentiation is present within the population. Finally, to our knowledge, genetic structure has never been detected within a colony in any species of colonial bird. We therefore think that the presence of two genetically differentiated groups of pigeons within the dovecote is unlikely.Please note that we only tested the possibility that adults reproducing in the dovecote and those using the dovecote only for feeding were genetically differentiated. It remains theoretically possible that two genetically differentiated groups of pigeons reproduce in the dovecote but we think that it is unlikely. Feral pigeons have high dispersal abilities and at the spatial level, genetic structuration is detectable in urban pigeons only when populations are separated by more than 20 km . Within IS) values [A higher relatedness between mates might also have been observed in our study, because couples were inferred from their offspring: if mate choice is not dependent on relatedness but rather if related couples have a better reproductive output on average, then this could result in the observed pattern. However, our data clearly show that the opposite is true: related couples tend to have lower reproductive success. Yet as we did not consider pairs that failed to reproduce, we probably underestimated the average relatedness between mates. We therefore conclude that feral pigeons probably choose genetically similar individuals as mates, thus increasing inbreeding levels in the population. This is in agreement with the findings of a study on the spatial genetic structure of feral pigeon populations in France: the large majority of the studied populations showed positive inbreeding coefficient (F) values , suggestThe first question that deserves discussion is the mechanism that leads to the observed level of inbreeding. Genetic similarity between mates can be observed even if mate choice is random with respect to kinship in the presence of a small-scale genetic population structure. In such a case, encountered potential mates are more genetically similar than random mates from the population . HoweveThe second question concerns the fitness consequences of the level of inbreeding that we observed in the population (0.062), i.e., what might be the selective pressure responsible for the evolution of a kin preference? Given that the widespread low level of inbreeding observed in this population seems to be the result of an active choice, it seems likely that this level of inbreeding would be beneficial. A first possibility would be that the preference for mates of intermediate genetic similarity observed in our study population leads to optimal outbreeding . HoweverA second hypothesis would be that mating with a related partner can be advantageous, not because it maximises the offspring’s fitness, but because it increases the parent’s inclusive fitness . For thiExperimental choice studies in a variety of species have demonstrated that close relatives are usually avoided as mating partners . Yet inbS1 Table(XLSX)Click here for additional data file.


We live in a world where digital trails of different forms of human activities compose big urban data, allowing us to detect many aspects of how people experience the city in which they live or come to visit. In this study we propose to enhance urban planning by taking into a consideration individual preferences using information from an unconventional big data source: dataset of geo-tagged photographs that people take in cities which we then use as a measure of urban attractiveness. We discover and compare a temporal behavior of residents and visitors in ten most photographed cities in the world. Looking at the periodicity in urban attractiveness, the results show that the strongest periodic patterns for visitors are usually weekly or monthly. Moreover, by dividing cities into two groups based on which continent they belong to , it can be concluded that unlike European cities, behavior of visitors in the US cities in general is similar to the behavior of their residents. Finally, we apply two indices, called “dilatation attractiveness index” and “dilatation index”, to our dataset which tell us the spatial and temporal attractiveness pulsations in the city. The proposed methodology is not only important for urban planning, but also does support various business and public stakeholder decision processes, concentrated for example around the question how to attract more visitors to the city or estimate the impact of special events organized there. Outputs of analyses on digital footprints can provide novel insights into how people live and experience the city, revealing important aspects of human mobility including tourism. The most widespread way of extracting information from digital traces is to use mobile phone records , 2 whichThe focus of this paper is on geo-tagged photographs shared by users through online social media platforms , as publicly shared photographs present a feedback on urban design and planning in a qualitative way and almost in a real time which can be a significant addition to more traditional methods, e.g., surveys . Namely,In our study we use publicly available data from the website sfgeo.org, which collects records about photographs shared on the most popular photograph sharing websites  . From thR2 ≅ 0.85) [We found that among ten most photographed places in the world there are five US and five European cities. Since the raw dataset does not contain information about user home locations, which is important to distinguish between a resident and a tourist, we assigned home locations to users following the procedure described in our previous paper : residen ≅ 0.85) , the num ≅ 0.85) .Rather than focusing only on aggregated urban attractiveness as done in , in thisperiodogram plots the power versus frequency, so the seasonal patterns show up as large spikes located at their frequencies [A quantitative way to detect periodicity in a regular series of data is to inspect its power spectrum. The power spectrum is the discrete Fourier transform of the auto covariance function of the data series. The quencies . In our As expected, the most important cycles are weekly and yearly with the former being the most important for all cases except for tourists in Berlin, augmented with harmonics forming the shape of the weekly and yearly patterns. Also for most cities it is apparent that tourists have a much stronger yearly component than residents corresponding to the more seasonal nature of tourism; more generally, we see that for residents the weekly period and periods shorter than a week are relatively more important, while for visitors, longer periods are more pronounced. This is in accordance with the observations made previously on the aggregated time series and the general assumption that for residents, the weekly cycle plays a more prominent role where several harmonics with periods shorter than a week contribute, while the number of visitors is expected to have an important yearly pattern with more variation, the shape of which can be made up of the significant harmonics with periods larger than a week.Apart from looking at sources of periodicity  in the time series, we look at the variation of photograph numbers in general and also after decomposing the time series into deterministic and random components. Looking at the distribution of raw daily photograph numbers at first, we get that the distributions can be well approximated by a log-normal distribution . An example for New York City is shown in At denotes temporal attractiveness, f(t) is a deterministic sequence and ut is the noise component, with the two options being models with additive and multiplicative noise, respectively. Further, the deterministic sequence consists of two parts: trend (dt) and seasonality (st): f(t) = st × dt, while the stochastic sequence is representative of the random variations. To carry out the decomposition, we need to specify the periodicity in the seasonal component; based on the power spectrum, we chose this to be one week. Illustration of this procedure for New York City is shown in Figs Next, we look at how time series of daily photograph numbers can be decomposed into the combination of deterministic and stochastic components and estimate the relative importance and again approximate the distribution of these. We use the following two options to decompose the attractiveness time series:ε > = < |ut − 1|> for multiplicative noise, giving the relative importance or weight of noise when compared to the real activity and the deterministic component. Looking at values in We consider the noise as the most interesting component because it may represent the special events in a city as deviations from the regular rhythm of activities. To quantify the importance of the noise, we report average normalized residuals in Looking at the distribution of individual components, we get that in the case of a multiplicative noise, the random component can also be described with a log-normal distribution for residents and gives a good approximation in the case of tourists for most cities. The trend distribution can also be approximated by a log-normal distribution; here, it gives a good fit in the case of tourists in London, Barcelona, Chicago and Los Angeles and residents in New York, while for the other cases the null hypothesis can be formally rejected, but the distribution is still rather close to a log-normal see . To quanth July. This seems to be the most photographed event for the American people. Furthermore, in New York City, we found outliers related to the Thanksgiving Day for each year. For example, we found an interesting outlier for residents in Barcelona on 27 May 2009, when the soccer team won the Champions League.We also extracted a list of what we called “outliers”, for each city. Particularly interesting in this sense are the outliers relative to the random variations, because it is supposed to be able to retrace special events. It is generally more interesting in this case detecting the activity of residents. Regarding the American cities, we noted that, in fact, from the outliers of the residents it is possible to retrace the main important events for the population. In particular, in each city (except Los Angeles) there is a trace of the 4si(t) denotes the number of shared photographs in cell i at time t and di,j is the distance between cells i and j. When all the activities are concentrated only around one single point, the value of V is equal to its minimum, zero. By normalizing V with the densities of activity in each cell, we obtain the weighted average distance Dv (the “Venables distance”):Having examined temporal patterns in the data, we now proceed with examining if there is an interrelation between spatial and temporal patterns of photographic activity. Similarly to , we use Dv during the day as people converge around these during the day. For more polycentric cities, where places are spatially less separated, we expect a smaller variation of Dv than the one observed for monocentric cities as places are more “mixed”, i.e., people and activity need not concentrate in just one area or few areas during the day. In order to make sure that values of Dv are comparable among different cities, they should be normalized by the city dimension, dilatation attractiveness:A denotes city official area. We compute Dil for typical weekdays and weekend days in each city, as shown in Dil is lower that means the attractiveness of the city is not equally distributed in all city areas, but it “collapses” in few places that are close together.In a monocentric city, i.e., where attractive places are spatially “segregated”, we expect a large variation of the average distance nsion, A , giving Dv during the day that gives a measure of the maximum spatial spread of high density locations [We can conclude that generally European cities are characterized by lower values of dilatation , with the exception of Barcelona and Paris, which, unlike more “dilated” US cities also show several peaks on weekends. The US cities are in general characterized by higher values of dilatation , but their curves are flatter than the European ones with San Francisco being the most “dilated” US city. The most interesting difference between a weekday and a weekend pattern can be observed in a case of Barcelona in the early morning. There are several possible explanations for that—one of them being people who go out late at night are distributed more evenly in the city and not only in the center. Moreover, one can also notice an interesting invert peak of dilatation in Washington DC around 7 AM when on a weekday everyone is getting up in the morning, while on weekends everyone is still sleeping. From these curves, we can extract another feature: the dilatation index, as the ratio between the maximum and the minimum ocations :μ=maxt(μ) of the cities. When comparing values of dilatation indices for the US and European cities, we can notice a tendency for former ones to mostly have a smaller dilatation index that the latter ones. Lower values of parameter μ denote that the average distance Dv stays approximately the same throughout the day, meaning that no matter which hour of the day it is, the spatial spread of the high density locations does not change significantly. When places of activity are more entangled, then we talk about more “mixed” cities. In the opposite case of large values of μ, the spatial organization of the different high-density locations changes significantly along the day.In The common reason why people travel and visit new cities, as either domestic or foreign visitors, is to experience new places they feel attracted to. A very direct measure of this attractiveness is the number of publicly shared photographs that people take in cities all around the world. However, a measure of urban attractiveness is not static as it evolves over time, sometimes resulting in considerable variations over the course of a year or even over a single week or a day. In this study we thus conducted an analysis of these temporal variations, providing and comparing longitudinal characteristic for urban attractiveness patterns. Moreover, spatial variations of attractiveness cannot be separated from temporal variations. The movement itself usually is not the goal of an individual, but an intermediate step that is accomplished in a given period of TIME to reach SPACE where the individual can take advantages of interest (attractiveness). Following this logic, in our study we have analyzed not only the daily temporal dynamics (necessary for moving between two places in the territory), but also the periodicity in weeks and months. Extrapolating this information can be very useful for studying attractiveness in general, but especially for the estimation of large flows of users and for preparing to manage them, for example during special events or particular situations.The novelty of our study is in the kind of data considered: geo-tagged photographs shared using the most popular photograph sharing platforms . Different sources of big data have been used in planning science for a long time now , but photographs can give us something more useful for urbanism in general—information about what people consider important and attractive, for any reasons, in the city. For top ten most photographed cities in the world , we showed the general temporal distribution comprehending the daily, weekly and month-by-month variation to detect behavior of residents, domestic and foreign visitors. Results of our analysis on temporal distribution showed that for US cities visitors’ behavior in general is more similar to domestic visitors’ behavior, while for EU cities, visitors’ general behavior is more similar to the foreign ones. We also looked at the Fourier spectrum to identify the most important periodicity of attractiveness. Periodicity is what happens to the flow within any given period or season, and is related to shifts in arrivals according to the day of the week and/or the week of the month. The causes of periodicity are many and varied. We did not analyze the causes, but we checked how and when periodicity is distributed in the city over the time. This is quite significant in urban science. When seasonal flow demand decreases, in fact, this has both direct and indirect effects upon city economy and wellness. What a city should do is to provide price incentives to encourage the off season traveler with discounts and other associated benefits. Knowing the periodicity of big flows could concretely help administrations, operators and users to manage transportation, organize events and choose the period of vacation.Our results suggest that for residents, the weekly pattern is most pronounced, while for visitors the yearly pattern is more important. We considered the time series activities for residents and visitors, extracting trend, seasonality and random events. We found that on average, the random component constitutes about 30%-60% of activity ; this also means that in the case of a special event, it can be much higher. This method could be very interesting in particular for small cities, where special events are not so frequent as in metropolitan areas and thus it is possible to really take in account the impacts of them compared to the normal trend. Although in all cases we showed results only for our ten cities, the same methodology can be also applied to other cities as well.Finally, we applied two spatio-temporal measures, as space and time cannot be considered just separated contributors: the dilatation attractiveness and the dilatation index, which give us measures of how the attractiveness is distributed across the city area, by combining spatial and temporal distribution. These measures revealed even more of differences between European and US cities. Namely, by looking at the “dilatation attractiveness” index, we found that the US cities in general have higher values of dilatation attractiveness and less variations compared to European cities. This means that the US cities are more polycentric and “mixed” cities, perhaps because of the fact that on average they are bigger than European cities. In fact, for a higher value of “dilatation attractiveness” city is more “dilated”, more active in all their space. This is also true for the second index, which is the ratio between the maximum and minimum value of “dilatation attractiveness” during the day. In conclusion, we measured and analyzed spatio-temporal configuration of attractiveness in different cities and we found important similarities and symmetries among them. This confirms previous research on urban human behavior displaying high predictability in general , 31.S1 FigAverage daily and weekly time series and monthly activity for each of the cities included in our analysis.(PDF)Click here for additional data file.S2 FigPower spectrum of activity time series for each of the cities separately for residents (left column) and visitors (right column).(PDF)Click here for additional data file.S1 TableList of most significant periods identified in the power spectra for each city.(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.


Recent population declines to the high elevation western North America foundation species whitebark pine, have been driven by the synergistic effects of the invasive blister rust pathogen, mountain pine beetle (MPB), fire exclusion, and climate change. This has led to consideration for listing whitebark pine (WBP) as a threatened or endangered species under the Endangered Species Act, which has intensified interest in developing management strategies for maintaining and restoring the species. An important, but poorly studied, aspect of WBP restoration is the spatial variation in adaptive genetic variation and the potential of blister rust resistant strains to maintain viable populations in the future. Here, we present a simulation modeling framework to improve understanding of the long-term genetic consequences of the blister rust pathogen, the evolution of rust resistance, and scenarios of planting rust resistant genotypes of whitebark pine. We combine climate niche modeling and eco-evolutionary landscape genetics modeling to evaluate the effects of different scenarios of planting rust-resistant genotypes and impacts of wind field direction on patterns of gene flow. Planting scenarios showed different levels for local extirpation of WBP and increased population-wide blister rust resistance, suggesting that the spatial arrangement and choice of planting locations can greatly affect survival rates of whitebark pine. This study presents a preliminary, but potentially important, framework for facilitating the conservation of whitebark pine. Pinus albicaulis) is one of the most intensively studied North American conifers, in part due to its unique relationship with the grizzly bear (Ursus arctos horribilis), Clark's nutcracker (Nucifraga columbiana), and over 20 other wildlife species , and the introduced invasive fungal pathogen white pine blister rust  have led to consideration for listing the species as threatened under the Endagered Species Act in 2010 , intensifying interest in developing strategies for its conservation and management . In the US Northern Rockies, offspring of over 1300 phenotypic selections are under evaluation in support of active restoration by planting proven, rust-resistant seedlings which have a combination of no-spot, needle-shed, bark reaction and shoot resistance mechanisms .We used CDMetaPOP  blister rust screening program . This allowed us to model evolution of WPBR resistance based on a single locus under selection with a single genotype being selected for.CDMetaPOP implements natural selection analogously to the adaptive-, or fitness-landscape of allele frequencies as originally envisioned by Wright . This fuhttp://www.fs.fed.us/database/feis/plants/tree/pinalb/all.html accessed September, 2015). We implemented a size-based fecundity model to determine the number of seeds produced at a given basal area per stand following the individual tree DBH conversion to basal area: Basal Area = 0.00007854 * DBH2. To obtain a size-based seed production per individual tree, we used the value of 500 cones per 1 basal area  following an isolation-by-distance movement pattern similar to pollen dispersal: probability of cone dispersal to a new stand location was a function of the inverse-square Euclidean distance with a 30 km maximum distance threshold , as well as a broader distribution of planting across the entire extent outside of wilderness areas Figures . Each blGST) across all loci using the method of Nei  for each block of scenarios are shown in Figure Block 2 scenarios are shown in Figure Overall population mean allelic diversity is shown in Figure Genetic differentiation for each zone is shown in Figure When we included the effects of directional wind resistance on pollen dispersal we see an overall increase in genetic differentiation across all scenarios Figure  with theThe goal of this paper is to provide an example of integrating species distribution modeling with landscape genetic simulation of neutral gene flow and adaptive evolution. Our specific focus was on exploring the effects of different levels of pathogen lethality and gene flow on the evolution of blister rust resistance in WBP and the effectiveness of several scenarios of planting rust resistant genotypes of WBP in different spatial configurations. This is the first simulation experiment to examine local and regional demogenetic patterns to the placement of resistant individuals, and the first to quantify differences in adaptive evolutionary processes as a function of directional and isotropic resistance to dispersal.We first presented climate niche models for WBP and WPBR distributions. We used the climate niche models with a new eco-evolutionary landscape genetics model to simulate demogenetic responses with and without the presence of the disease agent, white pine blister rust. These models allowed us to produce baseline null models of a ”healthy” disease-free system . This allowed us to quantify how much the introduction of disease resistant genotypes might mitigate the effects of WPBR and to evaluate this model systems sensitivity to the extent and pattern of introduction of disease resistant genotypes. Our results demonstrate that different patterns of planting resistant genotypes can influence genetic outcomes, and that genetic diversity and differentiation are more sensitive than population dynamics Figure . FurtherOur results also show that large differences in predicted genetic differentiation are produced when models use simple isolation-by-distance assumptions as compared to when they implement more realistic spatial processes, such as isolation by resistance. Specifically, scenarios that incorporated the influence of wind resistance on the ability of pollen to disperse resistant genes through the landscape produced much higher rates of local extirpation along with higher genetic differentiation Figure . These sTo produce reliable inferences about implications of adaptive variation, researchers must unambiguously determine whether markers for key adaptive traits, such as blister rust resistance, are under selection and identify the factors in the environment that drive that selection  and assess sensitivity and uncertainty in these systems. For example, simulations could explore the effects of habitat quality and density-dependent processes (Pfluger and Balkenhol, EL, ZH, MM, and SC designed research. EL ran simulations. EL and ZH performed the analyses. EL, ZH, MM, and SC interpreted the data and wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Pisum sativum, L.) is a major pulse crop used both for animal and human alimentation. Owing to its association with nitrogen-fixing bacteria, it is also a valuable component for low-input cropping systems. To evaluate the genetic diversity and the scale of linkage disequilibrium (LD) decay in pea, we genotyped a collection of 917 accessions, gathering elite cultivars, landraces, and wild relatives using an array of ∼13,000 single nucleotide polymorphisms (SNP). Genetic diversity is broadly distributed across three groups corresponding to wild/landraces peas, winter types, and spring types. At a finer subdivision level, genetic groups relate to local breeding programs and type usage. LD decreases steeply as genetic distance increases. When considering subsets of the data, LD values can be higher, even if the steep decay remains. We looked for genomic regions exhibiting high level of differentiation between wild/landraces, winter, and spring pea, respectively. Two regions on linkage groups 5 and 6 containing 33 SNPs exhibit stronger differentiation between winter and spring peas than would be expected under neutrality. Interestingly, QTL for resistance to cold acclimation and frost resistance have been identified previously in the same regions.Pea ( Pisum sativum, L.), although as ancient as 10,000 yr  often result from a complex evolutionary history, including domestication bottlenecks, selection of favorable alleles, secondary admixture, or introgression of genetic material from wild relatives into cultivars. Studying these processes in crop species has proved of tremendous interest to evolutionary geneticists and breeders alike . The genomic regions underlying many of these key adaptations are of particular interest, yet only a few have been roughly identified, mainly using QTL mapping approaches. The advent of high-throughput genotyping technologies in pea enables one to look for the footprints of selection using a different approach, scanning the genome to identify unusual regions in terms of diversity, LD, allelic differentiation and other genomic characteristics. These so-called genome scans have been applied in other crops, and represent an interesting complement to more targeted QTL, or association mapping, approaches . While genetic diversity and genetic structure have been investigated on various collections in pea, examination of LD patterns have been conspicuously scarce to date (The present study aimed to (i) describe the diversity patterns in a collection of germplasm representing elite cultivars, landraces, and wild peas; (ii) assess LD decay in different panels, and try to correlate it with the genetic characteristics of the panels; and (iii) identify genomic regions that might have been under selection either during domestication or postdomestication.Pisum accessions was selected from various collections. Overall, the goal was to represent the full spectrum of genetic variability from wild peas (Pisum fulvum and P. sativum elatius) and old landraces to modern elite cultivars, and also spanning the usage spectrum . Other genetic stocks and reference lines were included, in particular accessions exhibiting some resistance to Aphanomyces euteiches (A. euteiches (Aphanomyces breeding) provided by M.L. Pilet-Nayel (UMR IGEPP Rennes); (iv) a set  of 97 elite cultivars from Europe, Canada. and the US; and (v) a set of 57 genotypes representing mostly wild material and old landraces from the Middle-East  provided by P. Smýkal . Genetic structure and MAF distribution).A total of 917 P. sativum recombinant inbred line populations (http://support.illumina.com/array/array_software/genomestudio.ilmn). When necessary, GenoPlots were edited manually so that three genotype clusters AA, AB, and BB could be obtained. Filters were applied to discard SNPs with missing rate ≥0.2, and/or heterozygosity ≥0.05, in further analyses.The total sample was genotyped using the newly developed custom Infinium BeadChip  Genopea 13.2K SNP Array as described in For genetic structure analyses, SNPs with minor allele frequencies <0.02 were discarded, while, for LD analyses, the minor allele frequency (MAF) threshold was 0.05. Only the very best markers were kept , essentially those with a high-quality GenoPlots score and a high mapping quality on individual and consensus maps of biparental populations as described in To investigate the genetic structure of our material, we used two methods: the first, Discriminant Analysis on Principal components (DAPC), implemented in the Adegenet R package , and the number of discriminant axes was set to six for a proportion of explained variance of 35.7%.K) ranging from 1 to 20 with five replicates for each value of K and using the “simple prior” option . To evaluate the repeatability across runs, and rule out for true multimodality (as opposed to cluster labels switching), we ran the program CLUMPP v.1.1.2 using the Greedy algorithm (FastStructure was run for a number of clusters (lgorithm . The putR. The tree was edited and displayed using the Interactive Tree of Life website (http://itol.embl.de/). Finally, we also examined the MAF distribution overall, and in different sets identified through the structure analysis above , as well as the joint distribution of allele frequencies between pairs of such ensembles.Using the same genotypic data, we also calculated a simple distance matrix between individuals by counting the number of different alleles over the total number of alleles, accounting for missing data. This distance matrix was used to create a hierarchical tree with the Ward clustering algorithm with the hclust function in r2 values were calculated between SNPs located on the same linkage groups and plotted against pairwise genetic distance using custom python scripts and functions from an expanded version of the EggLib package  as described in To evaluate the magnitude of the effect of sample structure on the value of LD values, we computed the ST and Jost’s D genome-wide on an SNP basis, and plotted the values obtained ordering them using the genetic map. FST measures the proportion of the variance in allele frequencies attributable to variation between populations . The method use a Bayesian framework to estimate the posterior probability of a locus being under selection by contrasting two alternative model of divergence from a ancestral population, one with selection and one without  is 0.298.Overall, the proportion of admixed individual, and the level of admixture, is lower when using the DAPC method than with fastStructure, as seen in other studies . The Aphanomyces breeding subset is strongly imbalanced, with most of its accessions found in either group 2, 11, or 16 (140 genotypes out of 176). As stressed before, groups 2 and 11 contain almost exclusively related genotypes from this subset. Accessions from the Winter pea breeding subset are scattered across different groups , but with a majority assigned to winter groups . Accessions from the Elite cultivar panel are predominantly dry seeds peas (group 8 and 14). Finally, and as expected, the accessions from the Wild/landrace panel are found almost exclusively in the two groups gathering the wild peas and old landraces of Middle-Eastern origin (groups 6 and 15). Overall, these groups are also found on the distance tree, although a few get mixed .i.e., alleles that are, say, at a low frequency in wild peas are less likely to be also at low frequency in cultivated peas. The distribution observed in wild peas is more akin to what is observed under a classic mutation/drift equilibrium model. There is probably a strong effect of the ascertainment scheme.Materials and Methods), wild peas appeared less polymorphic than cultivated peas with this set of markers. For example, the 17 accessions of P. fulvum are only polymorphic for 1764 SNPs . While many more markers were polymorphic in the accessions indicated as P. s. elatius (9172 markers), their relative allele frequencies differed drastically from those of cultivated pea.In our data, probably due to the ascertainment bias following the SNP selection for the Genopea 13.2K array . Using the same accessions, but calculating IBS using only SNPs polymorphic in P. fulvulm, we observe that P. fulvum accessions are more spread out, while the group gathering Asian sativum is essentially unchanged.r2 estimator as a function of the genetic distance. Considering the whole dataset, there is a steep decay in the LD values as the genetic distance increase; for example, the median value does not exceed 0.05 at a distance of 5 cM. r2 values, and there is almost no LD in wild peas. This dependence upon the sample can also be observed by contrasting the panels available; for example, in the Elite cultivar subset, comprising mainly dry seeds pea from North America and Europe, the level of LD is substantially higher than in other subsets, although still decreasing rapidly (data not shown). This could be due to a history of higher levels of genetic drift, since the genetic basis in this sample is narrower.We investigated the patterns of LD decay in the genome using the classic r2V estimator —the FST being high across the whole genome. For the spring/winter comparison, a clear peak of differentiation could be observed in the middle of linkage group 6, while most of linkage group 5 showed high FST values. A more rigorous approach to detect significant outliers was undertaken, using the Bayesian framework described in q-value set at 0.05 , 33 SNPs were detected as exhibiting significantly higher levels of differentiation than expected under neutral divergence from an ancestral population. These SNPs . A first simple way to visualize such regions is to plot the SNP-wise Fese SNPs  were locHe, Nei’s heterozygosity) could be noted along the genome in either winter peas or spring peas. Such dips have frequently been observed in domesticated species, and can be the result of selective sweeps around loci that have been particularly instrumental in the domestication process . In order to make sure that this difference was not a byproduct of the difference in sample sizes, we generated 50 datasets for both the spring and winter peas containing the same number of individuals than the wild peas, and calculated the mean He over these 50 datasets. The strong difference remained, showing it was not due to differences in sample size, but more likely to the SNP selection process .Finally, no obvious “dip” in genetic diversity , end-use  has already been described in P. sativum mapping population has introduced a bias in the survey of the diversity of wild accessions where the number of polymorphic markers was lower and the level of resolution achieved poorer. A resampling procedure ensured that this effect was not a side-effect of a lower sample size. Yet, the IBS networks of individuals belonging to the wild/landrace group showed interesting patterns of groupings among this group. Using SNPs that are polymorphic among P. fulvum accessions allowed better visualization of this group’s diversity. Regardless of the true level of genetic diversity in the wild material, the high genetic diversity in the cultivated material could indicate a scenario involving a weak bottleneck at domestication, possibly complemented by the diversity of uses, and the contrasting environmental conditions experienced in pea-producing areas worldwide.In the present study, the panel used for SNP discovery was of modest size, comprising 16 accessions chosen to span the widest spectrum possible, so as to minimize ascertainment bias. However, the SNP selection criterion of being polymorphic in at least one ST peak nicely coincides with the position of this QTL. In particular, one of the markers detected under selection is in common between our studies [NT6083 in Medicago truncatula and M. falcata. As in Arabidopsis thaliana, the CBF genes have been shown to produce key transcriptional activators for cold acclimation  was observed to drop below what they called a basal level (0.2) at a distance of ca. 10–15 cM , P. fulvum, and P. abyssinicum, which might have been domesticated independently  in pea are not well known, and the taxonomy within the elations . Currentendently .Pisum genus, its domestication, and subsequent breeding should probably use whole-genome resequencing data to alleviate the ascertainment bias issue. A more balanced sample with a larger diversity of wild and old landraces would also be useful. Once a workable domestication model identified, the systematic search for the footprints of the domestication at the molecular level should prove more fruitful.A more in-depth investigation of the evolutionary history of the www.g3journal.org/lookup/suppl/doi:10.1534/g3.117.043471/-/DC1.Supplemental material is available online at Click here for additional data file.