Sophora alopecuroides (Faboideae) is an endemic species, mainly distributed in northwest China. However, the limited molecular markers range for this species hinders breeding and genetic studies. A total of 20,324 simple sequence repeat (SSR) markers were identified from 118,197 assembled transcripts and 18 highly polymorphic SSR markers were used to explore the genetic diversity and population structure of S. alopecuroides from 23 different geographical populations. A relatively low genetic diversity was found in S. alopecuroides based on mean values of the number of effective alleles (Ne = 1.81), expected heterozygosity (He = 0.39) and observed heterozygosity (Ho = 0.55). The results of AMOVA indicated higher levels of variation within populations than between populations. Bayesian-based cluster analysis, principal coordinates analysis and Neighbor-Joining phylogeny analysis roughly divided all genotypes into four major groups with some admixtures. Meanwhile, geographic barriers would have restricted gene flow between the northern and southern regions (separated by Tianshan Mountains), wherein the two relatively ancestral and independent clusters of S. alopecuroides occur. History trade and migration along the Silk Road would together have promoted the spread of S. alopecuroides from the western to the eastern regions of the northwest plateau in China, resulting in the current genetic diversity and population structure. The transcriptomic SSR markers provide a valuable resource for understanding the genetic diversity and population structure of S. alopecuroides, and will assist effective conservation management. Sophora alopecuroides L. , known as kudouzi in China, is a perennial herb in the genus Sophora (2N = 36). As a wild and moderately xerophytic species, it is mainly distributed in the arid and semi-arid regions of the Asian continent, particularly in the northwest plateau in China [S. alopecuroides confers its excellent drought and alkaline tolerance and anti-sandstorm performance, making it a vital pioneer in environmental protection in northwest China. It is noteworthy for its pharmaceuticals and pesticidal properties, as well as being a valuable source of livestock forage, green manure, windbreaks and nectar [S. alopecuroides is also fine forage for increasing the weight of livestock. According to records in the well-known and comprehensive medical book Compendium of Materia Medica from the Ming dynasty in China, it has been used as traditional Chinese medicine for thousands of years. It possesses high concentrations of secondary metabolites, especially alkaloids, flavonoids and triterpenoids, which exhibit anti-inflammatory, anti-viral and anti-tumor activities [in China . The fuld nectar . Further Sophora N = 36. A Sophora N = 36. AS. alopecuroides focuses mainly on its chemical constituents, pharmacological activity, and endophytic resources [S. alopecuroides to clarify implications for medicine resource exploitation and ecology conservation.Assessment of genetic diversity is a prerequisite for efficiently exploring and utilizing germplasms, and developing different conservation approaches to breeding . Recentlesources –13, whilCicer arietinum) [Psophocarpus tetragonolobus) [Robinia pseudoacacia) [S. alopecuroides. There was a substantial lack of available genomic information, genetic linkage map and polymorphism molecular markers, which had limited genetic researches on S. alopecuroides.SSR markers are regarded as one of the most powerful high-resolution tools for the study of population genetics, phylogenetics, molecular ecology and marker-assisted selection studies in plants for characteristics of codominance, good reproducibility and high polymorphism , 15. Theietinum) , winged nolobus)  and blacoacacia) . HoweverS. alopecuroides. The primary goal of the study was to evaluate the genetic diversity and population structure with a large collection of germplasms based on SSR markers. Specifically, our investigations aimed to: (1) characterize the transcriptome of S. alopecuroides; (2) develop and validate a series of SSR markers to assess the genetic diversity and elucidate genetic relationships and population structure among different populations; and (3) propose effective conservation strategies for the valuable S. alopecuroides.To our knowledge, the present study reports the first transcriptome sequencing of S. alopecuroides were not gathered from any nature reserve or private land. Therefore, collection of the material did not require an additional approval of the ethics committee or other specific permission.Our research did not involve the rare and endangered plants. Samples of S. alopecuroides  according to the manufacturer’s instructions. Pooled samples were prepared by combining equimolar concentration of total RNA for each of three tissues. The integrity of the qualified RNA was analysed through agarose gel electrophoresis and the quality of the total RNA was checked on a NanoDrop 2000 spectrophotometer (Thermo Scientific). The cDNA library for mRNA-seq was prepared with high quality RNA of pooled samples using Illumina Truseq™ RNA sample prep Kit . The cDNA libraries were sequenced using the Illumina HiSeq™ 2500 platform using the paired-end model to obtain read lengths of 150 bp. Sequenced raw data described in this study were uploaded in the Sequence Read Archive (SRA) public database of the NCBI (PRJNA546451).Total RNA was isolated from young fresh leaves, stems and roots of Prior to assembly, the raw paired-end reads were submitted to quality filtering and adaptor trimming using Trimmomatic version 0.36 . Clean r-5) including Pfam protein, Kyoto Encyclopedia of Genes and Genomes (KEGG), Clusters of Orthologous Group (COG), Swiss-Prot protein, the NCBI non-redundant protein (Nr) and non-redundant nucleotide sequences (Nt) to predict possible functional classifications and signaling pathways. Gene ontology (GO) annotation was implemented on Blast2GO [The generated non-redundant unigenes were annotated to public databases using aligned BLASTX  with sevBlast2GO , 26 and Blast2GO .S. alopecuroides were screened for SSRs using the Perl script MISA (https://webblast.ipk-gatersleben.de/misa/). A minimum of ten repeats for mononucleotide SSRs, a minimum of six repeats for dinucleotide SSRs, and a minimum of five repeats for tri-, tetra-, penta- and hexanucleotide were considered as the search criteria for simple sequence repeat in the MISA script. Primer pairs were designed using Primer3 (http://primer3.sourceforge.net/) with the following criteria: annealing temperature of 50–60 °C, optimum primer length of 20–24 bp, a percentage GC in the range of 40%–60%, and predicted product size of 100–400 bp.All unigenes generated by deep transcription sequencing of Na), the number of effective alleles (Ne), number of rare allele (Nr), number of unique allele (Nu), polymorphic information content (PIC), observed heterozygosity (Ho), and expected heterozygosity (He), fixation index (Fis), and information index (I), Hardy-Weinberg equilibrium were estimated using GenAlEx version 6.5 [Each PCR amplification was performed with 1.1 × T3 Super PCR Mix  in a final volume of 20 μl, containing 50 ng DNA, 1 μl each of 10 ng forward and reverse primers. PCR reactions were carried out in the following steps: 5 min pre-denaturation at 94 °C, 35 cycles at 94 °C for 45 s, annealing at an optimal temperature ranging from 55 °C to 60 °C, extension at 72 °C for 45 s, then a final extension at 72 °C for 10 min. Each of the PCR products was confirmed using 6% polyacrylamide gel electrophoresis. We treated these microsatellites as codominant marker when we conducted the following analyses. The genetic diversity indexes including number of alleles  replicates. The most likely values of K were chosen based on ΔK that computed with Structure Harvester [K. The genetic structure plot was drawn by Distruct version 1.1 [Population structure analysis was assessed with STRUCTURE version 2.3  based onarvester . The proarvester  with thesion 1.1 . To revesion 1.1 .F statistics (Fst) among the groups were performed using Arlequin version 3.5 to measure the probable differentiation among different groups.An analysis of molecular variance (AMOVA) and estimate of pairwise Fst / (1 –Fst)) and geographic distance (km) in GenAlEx version 6.5. A heterozygosity excess test at the population level from BOTTLENECK 1.2.02 [Mantel tests were conducted based on genetic distance  consistently located in the southern part of Tianshan Mountains. The number of identified alleles (Na) ranged from 4 to 8, with an average of 5.91. The number of effective alleles (Ne) varied from 2.51 to 2.97, with an average of 2.81. Across the 18 loci, the observed heterozygosity (Ho) varied from 0.38 to 0.65, with an average of 0.55, whereas the expected heterozygosity (He) varied from 0.28 to 0.44, with an average of 0.39. Wright’s fixation index (Fis) varied from—0.65 to—0.23, with an average of—0.38. All statistics of these parameters are summarized in A total of 140 SSR markers were randomly selected and 56 pairs of primers produced good and reproducible banding patterns with expected sizes in the initial screening. Of those successfully amplified primers, only 18 pairs of primers showed higher polymorphism and were used for further genetic diversity and population analysis . Except K for K = 4 (K = 4. The first cluster (Cluster I) contained five populations  located in the southern part of the Tianshan Mountains. The second cluster (Cluster II) grouped together five populations  collected in the northern part of the Tianshan Mountains. The third cluster (Cluster III) contained four populations  and the fourth cluster (Cluster IV) included nine populations . Some populations showed mixed distribution between Cluster III and Cluster IV which were located at the Loess Plateau and Inner Mongolia Plateau in the northwest of China. Interestingly, despite the large distance between Cluster II and Cluster III, there were still some populations  from the Cluster II mixed with different populations in Cluster III.Population structure estimation for the whole panel of accessions was inferred using the Bayesian clustering approach implemented in STRUCTURE version 2.3.4. The most optimal number of clusters was identified using the model value Δor K = 4 , suggestor K = 4 . The resS. alopecuroides, but communicating genetic information also occurred between different clusters. Finally, we found four distinguishable clusters identified according to the unrooted phylogenetic tree constructed using the MEGA programme, consistent with the above-mentioned model-based population structure and PCoA analysis  among the 23 populations ranged from 0.01 to 0.16 based on the SSR data  and other regions. Beyond that, the AMOVA at the population levels based on sampling locations showed a peculiar pattern. According to the structure analysis, four clusters were considered for AMOVA analysis. It was observed that the genetic diversity due to within-population variation was about 91.98% and the genetic variation among populations accounted for only 8.02% , indicating that there was no pattern of isolation by distance among the different populations  values, indicating a slight excess of heterozygosity in S. alopecuroides. Excess heterozygosity may be ascribed to a genetic bottleneck. According to the Wilcoxon signed-rank test, all clusters had a significant excess of heterozygosity under the IAM and TPM (P < 0.01), whereas a significant bottleneck was only detected in Cluster III and Cluster IV (P < 0.01). In addition, mode-shift tests showed that all clusters displayed shifted modes, indicating that S. alopecuroides experienced a recent bottleneck , together accounting for 95.77% of all the SSRs identified.Polymorphic microsatellite mining and the genetic variability of SSR markers are now often extensively exploited in non-model plants using a deep transcriptome sequencing approach . HoweverS. alopecuroides were similar to those reported in S. moorcroftiana [Sophora. Moreover, the repeat numbers in the SSR motifs were generally skewed towards fewer repeats with an increase in length of the repeat motif, suggesting that S. alopecuroides might have undergone a long evolutionary history; Species with long evolutionary histories tend to have an abundance of short motifs [The characteristics of the SSR markers in roftiana , anothert motifs .S. alopecuroides has been valued since the earliest times as ecological and economic herbaceous plants, comprehensively utilized for its important role in soil and water conservation across its natural distribution, and unique medicinal properties. Unfortunately, after the field investigation, we found that the limited distribution regions together with the deteriorating ecological environment and human disturbance in the northwest plateau in China, had caused the size of populations and the level of genetic diversity of this species to shrink, which could be further confirmed by the molecular diversity analysis based on the SSR markers  in this study. A total of 18 polymorphic SSR markers were tested and generated alleles for S. alopecuroides, which provided a relative full assessment of the genetic diversity and population structure of this species for the first time at population level. Compared with other species in the genus Sophora [S. fulvida , S. prostrata , S. molloyi , S. tetraptera , S. microphylla , S. chathamica , S. godleyi , S. longicarinata , S. japonica , it is clear that S. alopecuroides exhibits relatively low genetic diversity. Besides, most species in Sophora are monoecious, insect-pollinated and self-compatible [S. alopecuroides is no exception, and these factors would lead to its relatively low level of genetic diversity.Genetic diversity in a natural population results from the interaction of drift, migration and selection, all of which are necessary for population persistence, adaptation and evolution . It is k Sophora , 43, suc2 = 0.2371 (P = 0.495), indicating that geographic distance had very little effect on genetic variance. Although the Mantel test results indicated that the genetic clustering was only very tenuously related to geographical origin of the populations, and only a moderately positive IBD was detected among all S. alopecuroides samples. Geographic isolation and various climate variation would be major factors that have shaped the current distribution patterns of this species; Many mountains and plateaus resulted in scattered distribution in habitats, which would limit pollen and seed dispersal among populations. Geographic isolation is also expected to have significant effects on the genetic structure of populations, which is a major precursor of allopatric speciation [S. alopecuroides in the Cluster I was significant differentiated from Cluster II. Numerous mountains and valleys, especially the Tianshan Mountains, constitute geographic barriers between these two groups, thereby leading to the observed high genetic differentiation between them. Coincidentally, an increase number of researches had demonstrated similar phenomenons that many species’ distributions and genetic diversity were deeply influenced by geographical isolation in Northwest China, such as Gymnocarpos przewalskii [Hexinia polydichotoma [Stuckenia filiformis [Combined with the analyses on population structure, PCoA and NJ analyses based on SSR markers, the 23 populations were divided into four distinct genetic clusters significantly related to their geographical origins, further confirmed by the results of hierarchical AMOVA (P < 0.01). From the Mantel test results, we found a low correlation coefficient, reciation , 46. Acceciation , it is eewalskii , Hexinia71 P = 0.5, indicaliformis .Malus sieversii), common walnut (Juglan regia), peach (Prunus persica) and others. In some way, the Silk Road acted as a corridor for species gene flow across topographically diverse landscape and over large distances. Coincidentally, according to our filed investigation, the current distribution areas of S. alopecuroides are along the famous Silk Road which originated in the historical capital of Shanxi Province (Chang’an), went through Gansu Province via Lanzhou city and Dunhuang city along the Hexi Corridor, and continued westward along the northern foot of the Eastern Tianshan Mountains. Thus, the current spatial of S. alopecuroides is more likely to be explained by the fact that it was carried as medicine by ancient traders and herders for its anti-inflammatory and analgesic pharmacological effects when they were on a long trip along the Silk Road, and this transport facilitated human-mediated gene flow among autochthonous S. alopecuroides populations.Archaeological evidence and historical records document extensive spread and migration of many plants along the Silk Roads , such asS. alopecuroides. Artificial cultivation will be an effective approach to securing supplies for medicinal purposes, generating not only beneficial ecological effects but also considerable economic benefit. Establishing a protection area within the core distribution area where the excavation of S. alopecuroides was strictly forbidden, which must be implemented immediately for in situ conservation. Several populations from the northern and southern parts of Tianshan Mountains had the higher genetic diversity, which should be prioritized for in situ conservation. A significant number of rare alleles detected in the southern parts of Tianshan Mountains would contribute to S. alopecuroides breeding, since these rare alleles would be diagnostic for particular genotypes or even have an influence on phenotypic richness. Meanwhile, a germplasm resource nursery or bank through the collection of seeds from different geographic locations should be established urgently. Furthermore, improving the genetic diversity within populations requires the monitoring of crossing among populations. When ex situ conservation is implemented, sample collection should cover as many populations as possible from the entire natural range to capture the full extent of genetic variability. In conclusion, combining in situ and ex situ conservation approaches would be the best strategy to preserve the valuable genetic resources of S. alopecuroides.Genetic variation plays a crucial role in maintaining a species’ evolutionary potential to cope with continually changing environment. Therefore, maintaining species diversity is one of the main objectives of current species conservation , 53. TheS. alopecuroides with 118,197 assembled transcripts of mean length 735 bp was generated and a total of 18 markers were validated in 260 accessions from 23 populations. As a result, genetic diversity was discovered to be relatively low, mainly due to the deteriorating ecological environment and declining population numbers of S. alopecuroides. The complex geographic distribution divided S. alopecuroides into four distinctive genetic clusters. Geographical isolation together with human migration and trade along the Silk Road would influence the distribution of S. alopecuroides and promote the spread of S. alopecuroides from the western to eastern part of the northwest plateau in China. To the best of our knowledge, this is the first attempt to exploit a transcriptome database and develop a set of SSR markers in S. alopecuroides, a resource that will facilitate in-depth understanding of genetic diversity and population genetic structure and also promote researches on evolution ecology, conservation genetics and genetic breeding of S. alopecuroides in the future.A large-scale transcriptome dataset from S1 FigTotal of 10,680 unigenes were assigned into 31 functional groups of five cluster.(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(TIFF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S5 FigGenetic and geographical distances showed low correlation.(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(XLS)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.


Melipona subnitida, a tropical pollinator of key biological and economic importance inhabiting one of the driest and hottest regions of South America. Our results reveal four genetic clusters across the species’ full distribution range. All populations were found to be under a mutation–drift equilibrium, and genetic diversity was not influenced by the amount of reminiscent natural habitats. However, genetic relatedness was spatially autocorrelated and isolation by landscape resistance explained range‐wide relatedness patterns better than isolation by geographic distance, contradicting earlier findings for stingless bees. Specifically, gene flow was enhanced by increased thermal stability, higher forest cover, lower elevations, and less corrugated terrains. Finally, we detected genomic signatures of adaptation to temperature, precipitation, and forest cover, spatially distributed in latitudinal and altitudinal patterns. Taken together, our findings shed important light on the life history of M. subnitida and highlight the role of regions with large thermal fluctuations, deforested areas, and mountain ranges as dispersal barriers. Conservation actions such as restricting long‐distance colony transportation, preserving local adaptations, and improving the connectivity between highlands and lowlands are likely to assure future pollination services.Habitat degradation and climate change are currently threatening wild pollinators, compromising their ability to provide pollination services to wild and cultivated plants. Landscape genomics offers powerful tools to assess the influence of landscape modifications on genetic diversity and functional connectivity, and to identify adaptations to local environmental conditions that could facilitate future bee survival. Here, we assessed range‐wide patterns of genetic structure, genetic diversity, gene flow, and local adaptation in the stingless bee  As restMelipona subnitida Ducke, 1911 (known as the Jandaíra bee) is a key pollinator of native plants and local crops and one of the most widely used stingless bee species for honey production  Since natural habitats harbor floral resources and nesting sites  using Qiagen's DNeasy Blood and Tissue Kit, according to the manufacturer's protocols. DNA integrity was then verified on 1% agarose gels, and its concentration was quantified with a Qubit 2.0 Fluorometer (Invitrogen). Only samples containing nondegraded DNA, concentrations of at least 20 ng/μl, and a total DNA amount greater than 2 μg were used for subsequent RAD sequencing.2.2SgrAI restriction enzyme. The resulting fragments were tagged with individual barcodes, which were then multiplexed and sequenced using 100‐bp single‐end methodology on the HiSeq 2000 platform (Illumina). Libraries were created at the same time and ran on the same sequencing run on two different lanes. Total number of generated reads per individual ranged from ~550,000 to 2.3 million. Samples were then demultiplexed and barcode sequences trimmed to result in final fragment lengths of 92 bp. Quality filtering was done in SAMtools during the genotyping stages, and coverage was limited to loci having less than 500× to limit the incidence of possible contaminants. Stacks was then used to cluster loci from a single individual and generate a RAD reference, allowing two haplotypes from each locus. The de novo clustering of sequences into RAD tags was performed using VELVET (version 1.2.10), considering a minimum cluster depth of 5 and maximum of 1,500, a maximum number of two haplotypes per cluster, and a maximum of three variants per cluster. SNP calling was performed using SAMtools (version 0.1.16), and loci harboring SNPs were included in the final genotype (VCF) table if they had at least 6× individual sequence coverage over at least 75% of the population, individual per locus genotype quality scores of at least 10, a minimum FASTQ quality score of 20, and a minimum distance to other SNPs of 50 bp . A variant was cataloged when it was present in a single sample .DNA samples were then shipped to Floragenex, Inc.  for RAD library preparation, Illumina sequencing, and bioinformatic processing. Briefly, libraries were prepared based on the genome size of this species , a wrapper for VCFtools , read depth (20–50), linkage disequilibrium , and strong deviations from the Hardy–Weinberg equilibrium . Additionally, we removed any potential loci under selection detected through genome scans. FST outlier tests were applied after adjusting p‐values using the genomic inflation factor (λ), and setting false discovery rates to q = 0.05, using the Benjamini–Hochberg algorithm  package  was allowed to vary between 1 and 10, with 10 replicate runs for each k‐value, and the best k was chosen based on cross‐entropy and cross‐validation errors , expected heterozygosity (HE), nucleotide diversity (π), and inbreeding coefficient . Effective population size (Ne) was also estimated employing the linkage disequilibrium method implemented in NeEstimator 2.0.1  into habitat  and nonhabitat . The percentage of habitat cover in 2‐km‐radius buffers surrounding our sampling locations was then calculated, as such radius comprises the estimated foraging distance for this species  for each location (N = 56 locations). We then used the nlme package . All models were validated by plotting residual versus fitted values and by checking for residual autocorrelation.Aiming to assess the influence of habitat amount on genetic diversity, we reclassified a high‐resolution land cover/land use map for 2013 , to test for higher gene flow across forested areas. To test for a reduced gene flow across highlands and corrugated terrains, we created elevation and terrain roughness surfaces, using raw elevation and terrain roughness maps. While elevation was retrieved from WorldClim (http://www.worldclim.org/), terrain roughness was created from this elevation layer using the Terrain Analysis plug‐in in QGIS V2.14. To evaluate resistance due to environmental conditions, we created three additional resistance surfaces containing the raw values from those WorldClim bioclimatic variables explaining most variation across our study region . We thereby tested for reduced gene flow across areas with higher temperatures, higher temperature range, and higher precipitation. To assess isolation by geographic distance (IBD), we created a last resistance surface replacing all pixel values in our elevation map with 0.5. Using the program Circuitscape V4.0 , we evaluated fine‐scale spatial genetic structure by quantifying spatial autocorrelation in genetic relatedness. To do so, we used local polynomial fitting (LOESS) of pairwise relatedness to pairwise geographic distance , created with the dredge function from the MuMIn (v1.4) package  as predictors in our MLPE models. We used the Akaike information criterion (AIC) to compare models containing all possible combinations of noncollinear predictors , and false discovery rates were set using the Benjamini–Hochberg algorithm at a rate of q = 0.05  and only considered as candidate loci those shared between all runs for each environmental variable. Full R scripts of LFMM can be found in the LEA website (http://membres-timc.imag.fr/Olivier.Francois/LEA/index.htm). In order to map adaptive genetic variability, we used the adegenet package , but not for LD or HWE. We first ran a principal component analysis using all 19 WorldClim bioclimatic variables plus altitude and forest cover , to select a set of orthogonal variables explaining most environmental variation across our study area. Since the first four principal components accounted for 92.08% of total variance, we selected the four variables that were most strongly correlated with these axes. The selected variables  were then used to run latent factor mixed models (LFMMs), aiming to identify possible associations between SNPs and environmental variables, while accounting for the underlying population structure  using two different clustering approaches  but similar heterozygosity and inbreeding as the genetic clusters located at the distribution's core (Pop 2 and Pop 3). This finding suggests that M. subnitida colonized these peripheral regions more recently, whereas clusters Pop 2 and Pop 3 had more time to accumulate a higher genetic variability, as stated by the central–peripheral hypothesis , out of 18 analyzed species to date  are key mediators of genetic connectivity in this stingless bee species, and support earlier findings stressing out the role of elevation as a bee dispersal barrier (Jackson et al., M. subnitida, temperature annual range and forest cover were highly correlated, so we could not disentangle the relative contribution of each. Our work is nevertheless the first to reveal a significant effect of forest cover on stingless bee gene flow, a long‐standing expectation for this group of bees with restricted dispersal (Jaffé, Pope, et al., Eciton burchelli Westwood, 1842 (Pérez‐Espona, McLeod, & Franks, Even though thermal stability was the main factor explaining gene flow patterns in k‐values. Additionally, LFMM were calibrated based on the distribution of adjusted p‐values, so the incidence of false discovery rates was low (François et al., A. mellifera (Chávez‐Galarza et al., M. subnitida are expected to shift to higher elevations by 2050 (Supporting Information Figure Our environmental association tests can be considered robust to deviations from the underlying demographic structure, as candidate loci were intersected across different M. subnitida and highlight the role of regions with large thermal fluctuations, deforested areas, and mountain ranges as dispersal barriers. Moreover, our work unravels previously unknown patterns of local adaptation in these bees. This knowledge could help guide future conservation actions such as avoiding the transportation of colonies beyond 300 km, preserving highland and lowland populations separately, and conserving or restoring foothill and mountain forests. Considering the high biological and economic importance of this native pollinator, such conservation efforts will be easily offset if honey production and pollination services are maintained in the future.Taken together, our findings shed important light on the life history of https://doi.org/10.6084/m9.figshare.7863416.v1Genotype and sequence data have been deposited in FigShare: None declared. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Infertility is considered a major public health issue, and approximately 1 out of 6 people worldwide suffer from infertility during their reproductive lifespans. Thanks to technological advances, genetic tests are becoming increasingly relevant in reproductive medicine. More genetic tests are required to identify the cause of male and/or female infertility, identify carriers of inherited diseases and plan antenatal testing. Furthermore, genetic tests provide direction toward the most appropriate assisted reproductive techniques. Nevertheless, the use of molecular analysis in this field is still fragmented and cumbersome. The aim of this review is to highlight the conditions in which a genetic evaluation (counselling and testing) plays a role in improving the reproductive outcomes of infertile couples. We conducted a review of the literature, and starting from the observation of specific signs and symptoms, we describe the available molecular tests. To conceive a child, both partners' reproductive systems need to function in a precisely choreographed manner. Hence to treat infertility, it is key to assess both partners. Our results highlight the increasing importance of molecular testing in reproductive medicine. During the last few decades, there have been a series of striking advancements in reproductive and laboratory medicine that have essentially caused these two fields to become inextricably connected. Laboratory medicine now plays a critical role in all stages of the reproductive process, from diagnostic approaches to the choice of the most complex therapy.In particular, genetic tests are carried out for three main purposes in reproductive medicine: the identification of the infertility causes, identification of genetic diseases transmissible to offspring, and optimization of the assisted reproductive technology (ART) Fig. .Fig. 1ThThe overall fertility rate is decreasing; for example, in the US, 12% of women receive fertility treatment over the course of their lifetimes, so it is important to emphasize the fertility journey of couples [A medical evaluation is indicated when the couple fails to achieve pregnancy after 12 months of regular, unprotected sexual intercourse . ConfirmIn addition, genetic tests are also indicated for the identification of genetic diseases that are transmissible to the offspring: preconception screening allows couples who are planning to become pregnant to know their reproductive risk a priori. Normally, gametes with genetic or chromosomal alterations have reduced reproductive potential. Thanks to ART, many of these difficulties can be overcome, and therefore, genetic tests  have the crucial impact of monitoring the possible transmission of these genetic alterations to the offspring , 5.Another application field of molecular diagnostics is related to the antenatal diagnosis. To date, the diagnostic options for couples at risk of transmitting a specific inherited disorder to their offspring are preimplantation genetic testing (PGT) and prenatal diagnosis (PND). These two diagnostic procedures share the same purpose but differ in diagnostic time, type of sampling, and laboratory procedures. In addition to the more traditional laboratory investigations, it is now undisputed that molecular biology methods for PGT support the efficacy of ART techniques, contributing significantly to their success  , 6.To optimize the application of genetic tests in clinical practice, in this review, we discuss (1) the genetic conditions related to infertility, including the common and rare ones that are case appropriate; (2) the diagnostic strategies in families at risk of known monogenic disease transmission; and (3) the impact of PGT in the optimization of ART techniques.https://www.nih.gov), OMIM (https://www.omim.org) and OrphaNet (https://www.orpha.net/consor/cgi-bin/index.php).The literature review was carried out according to PRISMA guidelines. No temporal restrictions were applied. The research was performed using the following keywords: genetic cause of infertility, genetic cause of male infertility, genetic cause of female infertility, mutations and infertility, molecular diagnostics in infertile couples, molecular diagnostics and reproductive medicine, PGT techniques, and PGT and ART. All the papers found were carefully read and evaluated before their inclusion. No unpublished studies were taken into consideration. In addition, the following databases were also consulted to verify gene/phenotype associations: NIH : OMIM (Online Mendelian Inheritance in Man) reports more than 200 genetic conditions related to male infertility, ranging from the most common clinical presentations of infertility to the rarest complex syndromes in which signs and symptoms are beyond the reproductive problems . In mostToday, the presence of alterations in the spermiogram is the first indication for genetic tests, particularly in cases of severe oligospermia (< 5 million/ml)  . InteresGenetic disorders related to male infertility include whole chromosomal aberrations , partial chromosomal aberrations   but is 0.84% in newborns [newborns . Structunewborns , 12; thenewborns , 110. InPartial chromosomal aberrations Microdeletions in the long arm of the Y chromosome (Yq), named the AZF (Azoospermia Factor) region, have been found in 8–12% of azoospermic men and 3–7% of oligozoospermic men [6 spermatozoa/ml) associated with primary testiculopathy, and ICSI is required to overcome infertility [rmic men , resultirmic men . The AZFertility .Male offspring will carry the same father’s Yq microdeletions or even a worse one; therefore, genetic counselling is mandatory . ParentsThe rearrangement of the AZFc zone is responsible for 60% of all Yq microdeletions . The AZFSingle gene mutations This section will focus on the noteworthy single gene disorders that have clinical relevance for male infertility  gene or by a deletion affecting the FMR2  gene. Carriers of the female FMR1 premutation  or FMR2 microdeletion show menstrual dysfunction, diminished ovarian reserve, and premature ovarian failure [ failure , 126.In addition to the family history, in the case of women with these clinical manifestations, the possibility of a molecular test should be considered. The most common genetic contributors to POI are X-chromosome-linked defects. In rare cases, the cause is an alteration in an autosomal chromosome . IdentifIt is well known that in 20–25% of cases, perinatal mortality is caused by inherited chromosomal or genetic alterations . Thanks The American College of Obstetricians and Gynecologists has issued standard recommendations for ethnic and general population genetic screening in couples based on reproductive age . TestingIn this context, genetic counselling is crucial for recognizing the genetic risk, referring patients appropriately and informing patients about genetic issues that are relevant to decision-making . In factCurrently, during the antenatal period, a variety of techniques are available to identify a transmissible disorder to the offspring in the presence of carrier or affected couples. Each of these techniques can be applied only during a specific time period of pregnancy or at different embryo stages in the IVF protocol.Invasive PND is usually performed on DNA extracted from fetal cells obtained by chorionic villus sampling (CVS) (between the 11th and 13th weeks of gestation) or from amniocytes (from the 15th to the 20th week), and the result is obtained in 7 or 15 days, respectively . The molAn increasing amount of interest has been shown regarding the noninvasive prenatal diagnosis (NIPD) of monogenic disease that is able to detect fetal genetic alterations in maternal blood at an early gestational age (approximately 10 weeks). However, although noninvasive prenatal testing (NIPT) of cell-free fetal DNA (cffDNA) for the screening of chromosomes 21, 18, 13, X and Y has been clinically adopted, NIPD remains a challenge . Very rePGT has the same diagnostic motivation as the traditional PND, with the advantage of advancing the timing of diagnosis at the embryo stage. Only disease-free embryos are transferred to the mother, avoiding recourse to therapeutic abortion. Even for couples who are able to conceive naturally, PGT requires the application of IVF techniques, including (a) the collection of gametes from both partners; (b) the fertilization of the oocyte by intracytoplasmic sperm injection (ICSI); (c) the embryo biopsy, which allows one or more cells from the blastomere or trophectoderm to be taken 3 or 5 days, respectively, postfertilization; (d) molecular analysis and (e) the embryo transfer.PGT protocols are set to start from a small amount of biological sample, ranging from 1 to 10 cells from the embryo at the cleavage stage or blastocyst stage. Conflicting opinions are reported on the detrimental effects of embryo biopsy and mosaicism events between cleavage-stage and blastocyst embryos. Linan et al. demonstrated that the concordance of diagnosis in embryos that were double biopsied on D3 and D5 is 67.8% lower than previously reported, supporting the use of blastocyst biopsies instead of cleavage-stage embryo biopsies . RecentlPGT includes whole genome amplification (WGA) to obtain a sufficient quantity of genomic DNA for one or more molecular investigations , 142. SeHuman embryos that are developed in vitro show a great deal of acquired chromosomal abnormalities; for this reason, PGT for aneuploidy (PGT-A) has been developed to select euploid embryos that are suitable for transfer .PGT-A is primarily indicated for couples with advanced maternal age, recurrent implantation failure, recurrent abortions, or severe male infertility. Meiotic errors are one of the main causes of the low success rate (~ 30%) of in vitro fertilization techniques. Randomized studies and meta-analyses have shown that the PGT-A technique does not increase the live birth rate but decreases the miscarriage rate and increases the efficiency of IVF techniques , 151.The evolution of PGT-A techniques started with a limited number of chromosomes analyzed by fluorescence in situ hybridization (FISH) in 1995 . It was Currently, the most commonly used technique is NGS. This method involves the amplification of the genome from a single cell by WGA, the preparation of a DNA library, starting directly from the amplified DNA, and the subsequent sequencing of a pool of libraries in parallel, each identified by a specific “barcode” sequence. Finally, an analysis software that compares the sequences obtained in each sample with respect to the “human hap-map reference genome” allows the identification of the possible presence of chromosome aneuploidies . LiteratHowever, it was soon clear that the gold standard was to develop a method for the analysis of both monogenic diseases and PGT-A at the same time. Indeed, as previously discussed, the very recent innovation for this purpose is the use of NGS to analyze single gene mutations and chromosomal copy number variations to select euploid disease-free embryos . CurrentHealthy reproduction can be affected by unhealthy environmental and lifestyle factors, increasing paternal and/or maternal age, anatomical or genetic anomalies, systemic or neurological diseases, infections, trauma, and antibody development , 159. AsThe first stage in assessing a couple with potential fertility problems involves the substantial synergy between the patient’s medical history, physical examination, instrumentation analysis (pelvic ultrasound in women) and laboratory tests  to identify any underlying pathology and possible risk factors. Although this evaluation should be performed simultaneously for both partners, the male partner analysis is required only in 18% of cases . Figure Many couples learn that they are at high risk of having a child that is affected by a genetic disease only when the woman is already pregnant or after giving birth. Thus, a detailed family history for a couple planning to undergo ART is a useful tool for identifying genetically at-risk couples and can improve the medical care of these patients. A number of important factors must be considered when collecting a detailed family history in the context of family planning . These iBRCA1 and BRCA2 are the most common genes related to hereditary breast and ovarian cancers. Molecular screening for these genes has greatly increased in recent years due to technological simplification and the availability of specific drugs that are suitable for patients with mutations in these genes. As a consequence, several affected and nonaffected young females have been found to be carriers of a BRCA mutation. This information should be taken into account when reproductive choices are planned, considering the potential risk of treating these patients with hormonal therapy.NGS-based strategies offer the opportunity not only to optimize genetic testing in reproductive medicine  but also to tailor the therapeutic decision on the basis of the specific genomic features of the patients. Common observation reveals that patients with the same diagnosis may respond differently to therapies, and it is becoming increasingly evident that this differential responsiveness may be due to specific DNA variations at the genomic level. The challenge for future medicine is to move from a population-based view to an individually based one. Novel technologies are driving this revolution. In the context of reproductive medicine, for example, it has been established that specific single nucleotide variants in the genes encoding for hormonal receptors are able to influence the efficacy of hormonal therapies in inducing ovulation . In addiCurrently, both the entire diagnostic pathway and the effectiveness of genetic analysis for infertility suffer from an approach that is ineffective: only a few genetic variables are studied, each through a specific molecular diagnostic procedure. This makes the process of genetic investigation fragmented and cumbersome, with a negative impact on the couple, in addition to the psychological distress caused by infertility. Therefore, despite the large potential of genetic tests to provide real insights into infertility causes, this crucial tool is still used without a method tailored to diagnostic needs regarding infertility. However, recent developments in new sequencing technologies have made it possible to compact one or more tests into a single NGS-based analysis, thus reducing diagnostic costs and time. The European Society of Human Genetics (ESHG) and the European Society of Human Reproduction and Embryology (ESHRE) have recently issued a recommendation for the development and introduction of extended carrier screening . AlthougThe general state of health in the reproductive environment is gaining increasing attention and clinical relevance. Therefore, reproductive specialists have the task of evaluating infertile couples by considering both their general and reproductive health, since the relative conditions of comorbidity can influence their reproduction. Medicine is undergoing an important transformation from a reactive to a preventive approach: the future will focus on the integrated diagnosis, treatment and prevention of diseases in individual patients.


Recently, there has been an increasing interest in conservation of native genetic resources of chicken on a worldwide basis. Most of the native chicken breeds are threatened by extinction or crossing with ecotypes.Six Saudi native chicken breeds including black naked neck, brown frizzled, black, black barred, brown and gray were used in the current study. The aim of the current study was to evaluate genetic diversity, relationship and population structure of Saudi native chicken breeds based on 20 microsatellite markers.IS]). The phylogenetic relationships of chickens were examined using neighbor-joining trees constructed at the level of breeds and individual samples. The neighbor-joining tree constructed at breed level revealed three main clusters, with naked neck and gray breeds in one cluster, and brown and frizzled in the second cluster leaving black barred in a separate one.A total of 172 alleles were detected in Saudi native chicken breeds across all 20 microsatellite loci. The mean number of alleles per breed ranged from 4.35 in gray breed to 5.45 in normally feathered black with an average of 8.6 alleles. All breeds were characterized by a high degree of genetic diversity, with the lowest heterozygosity found in the brown breed (72%) and the greatest in the frizzled and black barred populations (78%). Higher estimate of expected heterozygosity (0.68) was found in both black breeds  compared to the other chicken populations. All studied breeds showed no inbreeding within breed (negative inbreeding coefficient [FIt could be concluded that the genetic information derived from the current study can be used as a guide for genetic improvement and conservation in further breeding programs. Our findings indicate that the Saudi native chicken populations have a rich genetic diversity and show a high polymorphism. The importance of keeping genetic diversity in animals and poultry is supported worldwide by the Food and Agriculture Organization . Most ofIt can be assumed that indigenous breeds contain genes and alleles relevant to their adaptation to particular environmental circumstances and specific breeding strategies ,6. UsingTo our knowledge, Saudi native breeds segregating for major genes such as naked neck and frizzle genes were not included in any study. Therefore, the current study aimed at investigating the genetic diversity, relationship and population structure of Saudi native chicken carrying naked neck and frizzle genes based on 20 microsatellite markers.A total of 2,700 one-day old Saudi native chicks, representing six breeds  (450 each) were kept in the poultry research station, Qassim University. The present breeds were collected and propagated under an extensive breeding program to purify and conserve native genetic recourses. All breeds were kept under similar management and environmental conditions. The care and handling of chickens were in accordance with regulations of animal care and ethics committee of Qassim University. The name, abbreviation, morphological appearance and photo for each population are summarized in A total of 144 blood samples were collected from the different chicken populations (24 each). Approximately one mL blood/bird from a wing vein was collected in ethylenediamine tetraacetic acid (EDTA) tubes and stored at −20°C. DNA was extracted from 0.5 mL of whole EDTA blood using ILLUSTRA blood mini spin kit . Then, DNA samples were taken to determine the quantity and quality of DNA using Thermo Scientific Nano Drop 8000 UV-Vis Spectrophotometers. The selected microsatellites were amplified using ProFlex PCR System Applied Biosystems . The polymerase chain reaction (PCR) was performed for each locus in 10 μL reactions consisted of 2 μL of Genomic DNA (20 ng), 5 μL 2×PCR AmpliTag gold PCR Master mix , 0.4 μL primer mix (50 pmoles) and 2.6 μL DNase free water. The PCR program was carried out at 95°C for 5 min, followed by 30 cycles of 95°C for 30 s. Annealing temperature (ranged from 58°C up to 64°C depending on primers sequence) was determined for each primer for 30 s. and 72°C for 30 s, and final extension at 72°C for 10 min. Following the completion of the PCR cycles, 3 μLof the reaction products was mixed with 1 μL 6×gel loading dye and then loaded into each well of vertical 8% polyacrylamide gel made with 1×tris base, boric acid and EDTA buffer at 100 V for 60 to 90 min and stained with Ethidium bromide (1%). A 50 bp DNA ladder was used to estimate allele sizes in base pairs (bp). A reference bird was used to compare and correct allelic size in each gel. Twenty microsatellite markers distributed on 14 autosomal chromosomes and previously used by Fathi et al [T]), expected heterozygosity estimated within a population (HS) and Hardy Weinberg equilibrium (HWE) using GENETIX program [Genetic variability was estimated per locus and across all loci for each population by allelic frequencies, observed heterozygosity, expected heterozygosity in all populations , FST , and FIS  and values for each breed were calculated using the FSTAT 2.9.3 [The FAT 2.9.3  and GENEAT 2.9.3 . The folWhere, HT, total gene diversity or expected heterozygosity in the total population as estimated from the pooled allele frequencies; HI, intra-population gene diversity or average observed heterozygosity in a group of populations; HS, average expected heterozygosity estimated from each subpopulation.Genetic distance was computed between populations without a bias correction . These gAcross the 20 microsatellite markers studied, a total of 172 alleles were identified in all native chicken breeds . All stuIT, FST, and FIS) estimated according to Weir and Cockerham [O) and expected heterozygosity (HE) were recorded based on allele frequency data for each locus per breed (T) across all native chicken breeds. However, the mean of HE recorded in the current study was lower than that reported by Zhang et al [E was higher than that of Hillel et al [T) for all studied breeds was 0.85 (MCW016), which meant that this locus was a highly informative locus among all loci. On the other hand, the value of HT for MCW012 was 0.48, indicating a slightly informative locus.Fixation indices . A positive number for FIS means deviation from HWE. Only MCW0069, MCW0165, MCW0103, MCW0123, and MCW0295 showed a positive number. Out of all used markers, 75% showed negative figures. Relatively, low average FIS (−0.08) across all loci in the resent study was recorded, indicating non-random mating in the Saudi native chicken breeds. However, FIS is used to obtain a deeper insight in appraising the degree of in-breeding and endangerment potentiality and is considered as an important tool to judge the conservation priority [IS is less than 0.05, the breeds are not in danger. On average, the genetic differentiation index, FST, among breeds was 0.02 (ST (0.357) owing to line differences was recorded in pure-bred commercial chicken [The Fpriority . Accordiwas 0.02 . About 202 . The mean FIS for Saudi native chicken breeds was lower than zero (−0.07). These values were similar to that reported by Tadano et al [IS, full- or half-sib mating should be avoided to prevent inbreeding depression. Among breeds, the mean FIS values varied from −0.01 for naked neck black breed to −0.10 in both normally feathered brown and black barred. The mean FST value of 0.02 indicates that approximately 2% of the total genetic variation is caused by breed differences, whereas the remaining amount (98%)was due to differences among individuals within breeds. With respect to HWE, black chicken breeds  and normal brown breed exhibited a deviation from Hardy–Weinberg equilibrium (dHWE). Out of all 120 HWE tests in 6 breeds tested, dHWE at the 5% level recorded 12.9%. Contrary with our results, Ding et al [All studied breeds showed no inbreeding within breed (negative inbreeding coefficient [Fno et al  for 12 cno et al  and Dingno et al  for Turkng et al  reportedMolecular information of genetic diversity is playing an important role in conservation of chicken resources. Genetic distance based on Nei’s unbiased values is presented in The unrooted NJ tree derived from the Nei’s standard genetic distance of six Saudi native chicken breeds is given in In conclusion, determining genetic biodiversity between chicken breeds is considered as an initial step of strategic plan for genetic characterization and conservation of Saudi native chickens. Evaluation of genetic diversity among Saudi native chicken breeds based on 20 microsatellite markers studied in the current study was efficient and gained reliable results.


Understanding the interaction between life history, demography and population genetics in threatened species is critical for the conservations of viable populations. In the context of habitat loss and fragmentation, identifying the factors that underpin the structuring of genetic variation within populations can allow conservationists to evaluate habitat quality and connectivity and help to design dispersal corridors effectively. In this study, we carried out a detailed, fine‐scale landscape genetic investigation of a giant panda population from the Qinling Mountains for the first time. With a large microsatellite data set and complementary analysis methods, we examined the role of isolation‐by‐barriers (IBB), isolation‐by‐distance (IBD) and isolation‐by‐resistance (IBR) in shaping the pattern of genetic variation in this giant panda population. We found that the Qinling population comprises one continuous genetic cluster, and among the landscape hypotheses tested, gene flow was found to be correlated with resistance gradients for two topographic factors, slope aspect and topographic complexity, rather than geographical distance or barriers. Gene flow was inferred to be facilitated by easterly slope aspect and to be constrained by topographically complex landscapes. These factors are related to benign microclimatic conditions for both the pandas and the food resources they rely on and more accessible topographic conditions for movement, respectively. We identified optimal corridors based on these results, aiming to promote gene flow between human‐induced habitat fragments. These findings provide insight into the permeability and affinities of giant panda habitats and offer important reference for the conservation of the giant panda and its habitat. Gene flow is one mechanism that can maintain population genetic diversity and increase the likelihood of evolutionary adaptation, while demographic isolation will induce population genetic structuring and even lead to reproductive isolation and eventually speciation Slatkin, . SeveralFor species with moderate dispersal, an isolation‐by‐distance (IBD) model has been classically invoked to describe the relationship between geographic and genetic variation. It predicts lower genetic similarity between individuals with increasing geographical distance, as the homogenizing effects of gene flow diminish across space  is endemic to China and restricted to the mountains on the eastern edge of the Qinghai–Tibetan Plateau and to the north of the Sichuan basin. The giant panda's cryptic behaviour has made it difficult to directly observe important life history processes, including dispersal behaviour, rendering approaches such as landscape genetics a promising solution to shed light on this process using an indirect method. To date, however, the sparse sampling that has been possible within and among the isolated regional populations of this species have resulted in only a few landscape genetic studies, including Zhan et al.  due to long isolation and substantial habitat fragmentation, does the Qinling population feature low genetic diversity compared to other giant panda populations; (b) as the Qinling population is subjected to considerable human activity, IBB, IBD, IBR or a combination of these models which play the main force in shaping the genetic differentiation pattern of this giant panda population. In answering these questions, conservationists can propose more reasonable and effective strategies in conserving the species and its habitats.22.1The Qinling Mountains, which are located in the south of Shaanxi province in central China, have been recognized as a natural boundary between north and south China, and it is also currently the northernmost distributional border of the giant panda. Different from the semi‐arid northern slopes of the area, the southern slopes feature a mild and moist habitat, providing giant pandas with a natural refugium  following the manufacturer's instructions. Twelve microsatellite loci, Ame‐μ10, μ11, μ13, μ15, μ22, μ24, μ26, μ27, AY79, AY95, AY217, and AY161213  and a final step of 60°C for 30 min. Ta was decreased by 2°C every second cycle from 60°C to a touchdown temperature (48–50°C), which was used for an additional 25 cycles .We extracted total DNA from faecal samples using the Qiagen DNeasy Stool kit (QIAGEN2.3A), the expected heterozygosities (HE) and the observed heterozygosities (HO) were computed using FASTAT 2.9.3.2 ) was calculated using GIMLET V1.3.1 Valière,  to confi Goudet, , which w Goudet, , respect2.4K = 1–10 were tested using an admixture model with correlated allele frequencies. For each simulation, 106 Markov Chain Monte Carlo (MCMC) iterations were used after 105 burn‐in simulations. Each value of K was repeated using 20 independent runs. The log likelihood of the posterior probability (LnPr(X|K)) and its rate of change (ΔK) were both evaluated to infer population clustering.A Bayesian clustering approach, implemented with STRUCTURE v2.3.4 , rivers, vegetation, roads and human disturbances, with genetic differentiation patterns based on an IBR model McRae, .First, a simple resistance surface representing the distance effect alone (referred to as the model IBD in the following text) was constructed by setting a value of “1” to all the raster cells using CIRCUITSCAPE V3.5  when the following criteria were met: (a) the partial Mantel test between genetic distance and landscape resistance model must be significant when the effect of IBD was excluded (GD~LR|IBD); (b) the partial Mantel test between genetic distance and the model IBD was not significant when the landscape resistance effect was excluded (GD~IBD|LR). A model could be supported independently of other candidate models if (c) the partial Mantel test between genetic distance and the true landscape model was significant when the effect of the reduced model was excluded (GD~LM|RM) and (d) the partial Mantel test between genetic distance and the reduced model was not significant when the effect of the landscape model was excluded (GD~RM|LM).1|2 = GD~LR1|LR2 – GD~LR2|LR1, where the LR1 represented the resistance matrix of landscape variable 1 and LR2 represents the resistance matrix of landscape variable 2. The GD~LR1|LR2 is the partial Mantel correlation between genetic distance and landscape variable 1 when partialling out the effect of landscape variable 2. The best candidate model should therefore possess positive RS value for all comparisons and has to pass the causal modelling criteria against the model IBD.Rather than simply competing the alternative landscape models against model IBD, a set of candidate models with similar parameters as the top model were assessed against each other by estimating their relative support (RS). Relative support can be quantified as: RSr value when partialling out the effect of the model IBD. We kept the parameter for the first variable stable while altering the parameter values for the second variable to create a series of bivariate models. The best parameter for the second landscape variable was identified by comparing the partial Mantel r value removing the effect of the model IBD of bivariate models with each other. Then, we kept the parameter values for the second landscape variable constant but altered the parameter values for the first landscape variable to obtain the best parameter for the first landscape variable in a similar way. We re‐optimized the remaining variables by adding them one at a time to the initial bivariate model until the parameter set for the best‐supported model did not change. Multivariate models were also evaluated using their RS values, by altering the model parameters for one variable while holding the others constant until variable parameters did not change. In order to be accepted, the multivariate model had to pass the two‐step criteria of the causal modelling to show it was better than model IBD. In addition, a landscape variable was excluded from the best‐supported multivariate model, if it did not pass the causal modelling criteria with a reduced model, if the GD~(A+B+C)|(A+B) model was significant, and the GD~(A+B)|(A+B+C) was not significant.Considering the complex landscape giant pandas live in, a multivariate approach was also used to explain gene flow within this population. The multivariate resistance surface matrix was equal to the sum of univariate resistance matrices of each landscape variable. We started with a simple model with only two landscape variables which had the highest partial Mantel 2.7wi) among models averaged from the global model to identify the best model. Rβ2, which compares a model with fixed effects to a null model (we used the model IBD as our null model) with only random effect and an intercept, was also calculated, using Kenward‐Roger F and degrees of freedom, with the “KRmodcomp” function from the R package “pbkrtest “. To correct for the dependency among data points, a maximum‐likelihood population‐effect (MLPE) method was followed  Degree of overlap with the area featuring the lowest resistance and highest current density. Overlapping with low resistance area, corridors could provide giant pandas with even and continuous pathways facilitating their dispersal and movement. (b) Reconnecting populations divided by major roads. There are several major roads running through the Qinling Mountains, which markedly divide and isolate habitat patches and local populations. To counter this barrier effect and promote gene flow, corridors should cross the major roads to connect neighbouring habitat patches. (c) Avoid farmlands by more than 1 km and residences by 2 km. Human activities impede utilization of corridors. According to a GIS study on giant pandas at different spatial scales, anthropogenic activities still disrupt the animals’ movements at a relative large range. In particular, farmland impedes giant pandas’ movements at a 1 km range, while the residences have larger disturbance range at 2 km  and expected heterozygosity (HE) were 0.488 and 0.447, respectively, similar to previous studies  was K = 1. When K > 1, the proportion of the individual assigned to each cluster was approximately equal, indicating there was no genetic structure in Qinling giant panda population  between pairwise genetic distance and Euclidean distance in our sample set, indicating that IBD did not play a key role in the genetic differentiation pattern of the Qinling giant panda population. Compared to the females showing significant correlations in the first two distance classes, significant positive correlations in males were still found until 9.5 km , although the bootstrap error bars showed the r value for males was not significantly greater than that for females.The spatial autocorrelation analysis including all individuals suggested local genetic structure within the study area. Based on the Mantel correlogram, a significant positive correlation between the pairwise genetic distance and Euclidean distance occurred in the first two distance classes . By ranking the partial Mantel r values of different models, slope aspect was found to be most correlated to genetic distance after partialling out the effect of the model IBD ; namely, more variance in pairwise genetic distance could be explained by including aspect and TC at the same time. Based on the above results, we suggested that aspect and TC more predominantly influenced gene flow within the Qinling giant panda population than other six factors.By changing the parameter values of either factor and keeping that of the other one constant, we finally found that the best‐supported multivariate model included aspect and TC, removing the effect of the model IBD Table . The par3.4Rβ2 < 0.01%), suggesting that the geographic distance is not the main force driving gene flow in the Qinling giant panda population. Two variables, the model IBD and slope, were excluded from the full model due to collinearity. The best model included two variables, aspect and topographic complexity , Niuweihe (NWH), Xinglongling–Taibaishan (XT), Tianhuashan–Jinjiliang (TJ) and Pingheliang (PHL) from west to east. These habitat components are separated by major roads and human activities. Solely based on the current density map, we could identify multiple narrow high current density pathways as candidates for potential corridors to connect habitat components. However, frequent anthropogenic disturbance could hamper corridor utilization by animals and diminish its efficiency in promoting gene flow. Thus, according to three quantitative criteria established in this study, we finally identified an optimal corridor for each pair of adjacent habitat components Figure b, which 44.1Here, we report the first comprehensive study to explore the population and landscape genetic structure of the Qinling giant pandas, with a large noninvasive genetic sample set. We found the lowest genetic diversity in the Qinling population among all panda populations, consistent with our hypotheses and the conclusions drawn by previous studies using similar molecular marker . One of the primary reasons for this observation might be natal dispersal pattern in giant pandas. In our analysis, male giant pandas represented closer genetic relationships than females in proximal distance. This finding is in accordance with a recent study in giant pandas (Hu et al., We only detected significant genetic correlations between individuals in close distance classes, indicating that IBD does not explain spatial genetic structure of giant pandas in Qinling Mountain, a result confirmed by the Mantel test result (Wild giant panda habitat includes complex environment components. Landscape factors including extreme topographic and vegetation transitions are considered to affect giant panda movements and habitat selection (Hu, Schaller, Pan, & Zhu, We investigated eight key landscape features using a relatively large genetic data set, and ultimately, slope aspect proved to be strongly associated with genetic variation in the Qinling population, followed by topographic complexity. Qinling giant pandas appear to have a preference for the east‐facing slopes. This preference has also been found in brown bears from the Carpathians Mountains, considered to be related to food availability in the breeding season (Cotovelea, The south side of the Qinling Mountains provides suitable habitats to sustain a high‐density giant panda population. This habitat is located in central China and is in the East Asian Monsoon Region. The continuous ridge of the Qinling Mountains obstructs cold air from the north and traps warm and wet monsoon air from the Pacific Ocean in summer, thus providing suitable climatic conditions for giant pandas. At a finer scale, east‐facing slopes not only have sufficient solar radiation in the morning, but also avoid overtranspiration in the afternoon, which helps to keep a suitable microclimate for the development of bamboo. Previous field surveys have also shown that bamboo forest on east/south‐facing slopes grow faster and accumulate biomass more quickly (Pan et al., Topographic complexity is related to a series of habitat conditions with variable solar radiation and soil moisture, possibly influencing plant growth and hindering animal movements, but has seldom been considered in previous ecological research on giant pandas. Here, we could infer that giant pandas use less complex land surfaces, different from conclusions for some other bear species which have been suggested to prefer areas with complex topographies (Apps, McLellan, Woods, & Proctor, Concluding the results of IBB, IBD and IBR tests, gene flow was significantly influenced by only two landscape factors over the geographic scale defined in this study. By evaluating different landscape genetic hypotheses, a relatively comprehensive understanding of the key factors shaping genetic structure and gene flow can be achieved. Multihypothesis approaches enable researchers to interpret the relationship between spatial heterogeneity and population genetic variation at more precise spatial scales. This is crucial for the conservation of endangered animal populations, considering that strategies based on incorrect inferences could result in the waste of limited conservation resources and, most importantly, may miss the opportunity to retrieve critically endangered populations. In consequence, more and more landscape genetic studies employ this approach. However, the number of empirical studies that have fully adopted it remains few (but see Cushman & Landguth, 4.3We did not detect any genetic substructure in the Qinling population so far, which is important given that this giant panda population already has relatively low genetic diversity due to its past demographic history. However, roads and logging continue to fragment the Qinling Mountains, and are ultimately likely to impact on gene flow to some extent. If no action is taken, the legacy of previous actions and future infrastructure development could result in the demographic isolation among local patches, and impact on the long‐term survival of this unique giant panda population.In this context, we identified several corridors to connect the key habitat components in order to promote gene flow within this unique giant panda population. Among five habitat components, XT sustains the largest local population, playing the role of demographic “source” in the region (Pan et al., None declared.https://doi.org/10.5061/dryad.5sh56g0The microsatellite data available from the Dryad Digital Repository:  Click here for additional data file.


Bos taurus) breeds including the threatened Danish Jutland cattle. We examined population structure and genetic diversity within breeds and lineages genotyped across 770K single nucleotide polymorphism loci to determine (a) the amount and distribution of genetic diversity in native breeds, and (b) the role of genetic drift versus selection. We further investigated the presence of outlier loci to detect (c) signatures of environmental selection in native versus commercial breeds, and (d) native breed adaptation to various landscapes. Moreover, we included older cryopreserved samples to determine (e) whether cryopreservation allows (re)introduction of original genetic diversity. We investigated a final set of 195 individuals and 677K autosomal loci for genetic diversity within and among breeds, examined population structure with principal component analyses and a maximum‐likelihood approach and searched for outlier loci suggesting artificial or natural selection. Our findings demonstrate the potential of genomics for identifying the uniqueness of native domestic breeds, and for maintaining their genetic diversity and long‐term evolutionary potential through conservation plans balancing inbreeding with carefully designed outcrossing. One promising opportunity is the use of cryopreserved samples, which can provide important genetic diversity for populations with few individuals, while helping to preserve their traditional genetic characteristics. Outlier tests for native versus commercial breeds identified genes associated with climate adaptation, immunity and metabolism, and native breeds may carry genetic variation important for animal health and robustness in a changing climate.Native domestic breeds represent important cultural heritage and genetic diversity relevant for production traits, environmental adaptation and food security. However, risks associated with low effective population size, such as inbreeding and genetic drift, have elevated concerns over whether unique within‐breed lineages should be kept separate or managed as one population. As a conservation genomic case study of the genetic diversity represented by native breeds, we examined native and commercial cattle ( These are the Westergaard‐, Vesterbølle‐, Oregaard‐ and Kortegaard‐lineages , suggesting strong drift and high rates of inbreeding. The low NE for the Jutland cattle breed was supported by Pertoldi et al. . In Denmark, cryopreservation is now used for native livestock breeds including the Jutland cattle, and these collections can provide genetic material for ongoing conservation efforts. Specifically, Hertz et al.  by supplying genetic material from less related earlier generations of animals. This strategy can be applied to native breeds of cattle and other species by cryopreservation of spermatozoa and oocytes in a gene bank Curry, . An incrz et al.  simulatez et al. .Emerging genomic methods provide exciting prospects for identification of genetic variation and selection in native breeds, yet additional efforts are needed for native breed conservation management to benefit fully from these new approaches  with the help of cattle owners and managers, and obtained semen samples from Viking Genetics (http://www.vikinggenetics.com/). Samples were genotyped with the Illumina BovineHD array with 776,665 SNPs at Genoskan A/S  following the manufacturer's protocol and based on the UMD 3.1 bovine genome assembly. Duplicate samples of two individuals, one old bull post‐1980 and one from the Faroe Islands, were included and allowed comparison of genotyping consistency. The data were filtered in PLINK 1.9 . We then examined genetic structure in the data with PCA in the adegenet package  is determined with a cross‐validation procedure, where the optimal K‐value has the lowest cross‐validation error relative to alternate K‐values   to build a maximum‐likelihood tree and examine signs of possible admixture among populations. We analysed 195 individuals and 595,025 SNPs pruned for MAF and genotyping success in PLINK, as outlined above, and plotted a maximum‐likelihood tree with the R script plotting_funcs.R provided in the source code for TreeMix (https://bitbucket.org/nygcresearch/treemix/wiki/Home). To measure the amount of genetic diversity within breeds, and within Jutland cattle lineages, we calculated polymorphism (P%), observed (HO) and expected heterozygosity (HE) with standard error, and pairwise identity by descent (IBD) between individuals within breeds/lineages in PLINK. Furthermore, we calculated NE with 95% confidence intervals in NeEstimator v2.1  per population in R with a script (https://johnbhorne.wordpress.com/2017/07/12/identifying-private-snps-in-r/) for the HierfStat package , and because the Jutland cattle lines have small populations and low genetic diversity where LD is expected to be high, we filtered the data to remove highly linked SNPs. For filtering, we used the PLINK formula (–indep 50 5 2), where 50 is the size of the sliding window , 5 is the number of SNPs shifted in each step and 2 is the variance inflation factor . We used equalized population sizes and an alpha‐level  of 0.01 for detecting loci that deviated significantly from the neutral distribution, whereby loci with q‐values <0.01 were considered outliers. We performed seven separate tests for outlier loci . We followed the approach in Porto‐Neto et al. (https://www.ncbi.nlm.nih.gov/gene/) for information on gene function in cattle and other species (primarily records from Homo sapiens and Mus musculus). We centred on genes relevant to selection in cattle and our investigation of native breeds . We followed the approach of Caniglia et al.  in PLINK for representative groups including two native breeds from different environments and one commercial breed. These were the native Jutland cattle (old bulls post‐1980), the native Western Norwegian Red‐polled cattle and the commercial Holstein breed. ROH represent long homozygous segments in individuals where both parents have transmitted identical haplotypes, and can indicate various processes that include selection and recent inbreeding  and modern SDM‐1965  found in the Westergaard‐lineage and the highest value (84.1%) observed in the Western Norwegian Fjord cattle  and Oregaard the highest (n = 32), with intermediate values for Vesterbølle (n = 5) and Westergaard (n = 9).Results for ed Table . Genetict value 8.1% obsert value 8.1% obserle Table . The rest value 8.1% obsert value 8.1% obser3.2n = 229 loci, T2: n = 62, T3: n = 336, T4: n = 199, T5: n = 235, T6: n = 425 and T7: n = 766 outliers. After examination of all outliers , milk production (n = 11), reproduction (n = 35), physical appearance (n = 2), climate adaptation (n = 6), behaviour and cognition (n = 10), hormones (n = 2), infection and immunity (n = 11), metabolism (n = 15), and sensory including olfaction (n = 3), vision (n = 3) and hearing  linked to production traits, milk and reproduction to T7 where we detected numerous genes associated with production traits (n = 27), reproduction (n = 11) and other traits (Table n = 4). In contrast, genes associated with physical appearance were found only in comparisons involving the Western Norwegian Red‐polled cattle, and all results were related to the polled phenotype exhibited by this breed. Enrichment analyses identified GO categories (n = 19) for production traits that included cellular response to growth factors, muscle development and bone formation (Supporting Information Table n = 6) primarily linked to transmembrane transport. For behaviour and cognition, we identified GO categories (n = 11) including locomotory behaviour and synaptic signalling (Supporting Information Table n = 3) for sensory perception of sound and mechanical stimulus, including equilibrioception . Genes linked to other types of traits produced no significant GO results. Examination of ROH found that none were shared among all individuals within breeds. We plotted ROH shared among at least six individuals per breed, and focal genes found within outlier flanking regions, across all autosomal chromosomes  and reproduction. The distribution of results was uneven, ranging from T2 where we identified one flanking gene  than in the 20 modern samples of SDM‐1965 . Mean IBD was also higher (0.216 vs. 0.160) and P% very similar (81.3 vs. 81.2). The post‐1980 samples of Jutland cattle did not exhibit PA, but the 14 sampled bulls showed higher HE and lower IBD than all contemporary Jutland lineages, and higher HO than all but one of them  was the only group that did not exhibit any PA. This is likely explained by its variants being shared among contemporary lineages, which seems consistent with the TreeMix results of admixture involving this group. In contrast, the old bulls pre‐1980 (SDM‐1965) exhibited seven PA, which seems consistent with the group's high levels of heterozygosity. The two Norwegian breeds also showed a high number of PA, despite modest values for NE. The small remaining populations of Vesterbølle and Westergaard, which represent the upper and lower NE estimates for Jutland lineages  with four and 21 individuals, respectively, illustrate the difficulties inherent in managing such lineages as separate units. As suggested for isolated populations of wild species with small NE  and potentially also for other areas within biology and evolution . The overall distribution of results among outlier tests appears to support our expectations of broad genome‐wide differences among the investigated populations and the evolutionary uniqueness of native breeds. Danish native cattle exhibited differentiation from commercial breeds, which has also been reported from other regions . Furthermore, the implementation of an appropriate breeding plan will help increase the NE, which will in turn reduce the fluctuations in allelic frequencies across generations. At the same time, assortative mating strategies may be chosen for individuals that carry genes of interest, to augment the relevant allelic frequencies and reduce the risk of their loss from the population.Our analyses demonstrate the potential use of genomics for investigating the genetic structure and unique variation of native domestic breeds, and how these may differ from commercial breeds in traits relevant for production and environmental tolerance. Our results, using northern European extensive native cattle as a case study, also underline how these differences may have implications for human‐induced artificial and natural selection of both native and commercial breeds. We show the potential for addressing basic evolutionary inquiries and applied conservation questions that can help managers create conservation plans to preserve genetic uniqueness while maintaining in situ selection in native domestic breeds, so these can continue to be shaped by their local environmental conditions even if the None declared.https://doi.org/10.5061/dryad.hr5d4g8. Data include the PLINK files  for 195 individual cattle and 677,311 autosomal single nucleotide polymorphism loci based on the UMD 3.1 bovine genome assembly ((https://www.ncbi.nlm.nih.gov/genome?term=bos%20taurus)) and filtered in PLINK for genotyping success of minimum 98%. Additionally, we include an Excel file with information on each of the 195 individuals, including PLINK family ID (FID) and individual ID (IID), breed/lineage and, if relevant, identity codes such as Danish studbook number.Data for this study are available at  Click here for additional data file. Click here for additional data file. Click here for additional data file.


Leptinotarsa decemlineata Say) in potato (Solanum tuberosum L.) fields each year, whether crop rotation and intervening edge habitat also affect genetic variation among populations is unknown. We investigated the role of landscape configuration and composition in shaping patterns of genetic variation in CPB populations in the Columbia Basin of Oregon and Washington, and the Central Sands of Wisconsin, USA. We compared landscape structure and its potential suitability for dispersal, tested for effects of specific land cover types on genetic differentiation among CPB populations, and examined the relationship between crop rotation distances and genetic diversity. We found higher genetic differentiation between populations separated by low potato land cover, and lower genetic diversity in populations occupying areas with greater crop rotation distances. Importantly, these relationships were only observed in the Columbia Basin, and no other land cover types influenced CPB genetic variation. The lack of signal in Wisconsin may arise as a consequence of greater effective population size and less pronounced genetic drift. Our results suggest that the degree to which host plant land cover connectivity affects CPB genetic variation depends on population size and that power to detect landscape effects on genetic differentiation might be reduced in agricultural insect pest systems.Landscape structure, which can be manipulated in agricultural landscapes through crop rotation and modification of field edge habitats, can have important effects on connectivity among local populations of insects. Though crop rotation is known to influence the abundance of Colorado potato beetle (CPB;  CPB originally colonized the Central Sands during the 1860s Riley, , but did22.12 area in the Columbia Basin, and an 8,736 km2 area in the Central Sands, collecting CPB from commercial agricultural fields in eight locations in the Columbia Basin of Oregon and Washington and nine locations in the Central Sands of Wisconsin between 2014 and 2016 . The final SNP dataset consisted of 7,408 SNP loci for 93 beetles in the Columbia Basin and 106 beetles in the Central Sands and was used to detect associations between genetic differentiation and landscape resistance. In addition, we sequenced a 544 base‐pair fragment of the mitochondrial genome (COI‐COII), using the method described in Crossley et al. (MK605454‐MK605457). The mitochondrial data provide an independent measure of population structure, and due to a smaller effective population size, can reveal strong patterns of genetic differentiation or changes in population size .We obtained land cover rasters from the Cropland Data Layer (USDA‐NASS) for the years 2007 to 2015 and quantified the proportion of the landscape within our study extents that was occupied by major land cover types (which occupied at least 2% of the landscape in either study extent) each year using FRAGSTATS v4  and axis loadings from principal components analysis (done with prcomp in R) on distances with all variables. This resulted in the removal of fallow cropland and wetlands. This analysis also revealed that “shrubland” and “grassland/pasture” classifications were frequently interchanged (possibly erroneously) among years; we therefore combined these land cover classifications into “grassland/shrubland” for landscape genetics analysis. We averaged resistance distances for each land cover type across years and standardized resistance and geographic distances by dividing by the standard deviation, as recommended for downstream landscape genetics analysis . We then calculated the potato land cover interspersion and juxtaposition index, which describes the degree of intermixing (as a percent) of potato with other land cover types; values of this index can range from 0 to 100, with high values indicating that potato land cover is adjacent to many other land cover types. Values of all metrics were represented as averages (± standard error) across sites and years (2007–2015).To assess the suitability of landscapes in the Columbia Basin and Central Sands for CPB overwintering, we quantified the percent sand in the soil, and the amount and configuration of potato field and forest edges. We calculated the percent sand in the soil from the Gridded Soil Survey Geographic Database  and average nucleotide diversity (π) in sampled CPB populations using the populations module of Stacks  using lm in R. We also calculated average pairwise FST between CPB populations using the method of Weir and Cockerham  would exhibit lower genetic diversity, due to the negative effect of crop rotation on CPB dispersal and abundance in potato fields  than in the Central Sands (0.005 ± 0.000) and was highest between Columbia Basin and Central Sands populations (0.041 ± 0.000).Colorado potato beetle populations in the Columbia Basin exhibited lower genetic diversity and higher genetic differentiation than populations in the Central Sands. Nucleotide diversity  ranged from 0.0056 to 0.0063 in the Columbia Basin and 0.0073 and 0.0080 in the Central Sands. Observed heterozygosity was 0.51% ± 0.01% in the Columbia Basin and 0.67% ± 0.02% in the Central Sands. Considering only sites that were polymorphic among populations, observed heterozygosity was 19.4% ± 0.4% in the Columbia Basin and 21.6% ± 0.8% in the Central Sands. Pairwise Mitochondrial DNA sequencing identified only two haplotypes in the Columbia Basin, one of which constituted 99% (182/183) of the Columbia Basin beetle samples, while the other was a singleton Figure . Wiscons3.22) than in the Central Sands (16 km2), and the amount of potato land cover was more variable across years in the Central Sands (coefficient of variation: 19.3% vs. 5.8% in the Columbia Basin).Land cover composition differed greatly between the Columbia Basin (Oregon and Washington) and the Central Sands (Wisconsin) study extents, with shrubland and grains (mainly wheat) dominating in the Columbia Basin, and forest, corn, and beans prevailing in the Central Sands between 2007 and 2015  for potato land cover were moderately correlated with estimates of landscape resistance for other land cover types in both regions. In the Columbia Basin, resistance distances for grain (abundance in landscape: 59%), forest (58%), and bean (57%) land cover exhibited the highest correlation with that of potato, whereas in the Central Sands, grassland/shrubland (abundance in landscape: 77%), corn (74%), water (69%), and sand (65%) exhibited the highest correlation.Estimates of landscape resistance to gene flow  in the Central Sands .BEDASSLE models exhibited good convergence among chains after four million steps. Upper 95% confidence limits of scale reduction factors estimated with Gelman–Rubin tests were close to one  Table . Effect αE/αD = 3,329), followed by corn (87), water (67), and grassland/shrubland (50), but only the effect of potato in the Columbia Basin was significantly different from other land cover effects (p < .001). In the Central Sands, land cover types with relatively higher average effect sizes comprised corn (αE/αD = 40), forest (31), and water (29). When all land cover types were included in the same model, effect sizes became more even across land cover types   than in the Columbia Basin . Nucleotide diversity (π), but not observed heterozygosity, decreased with increasing crop rotation distance in the Columbia Basin, though this relationship was only marginally significant  Figure .4We used contemporary samples of land cover and SNP genotype data to detect associations between landscape resistance and genetic differentiation among CPB populations in the Columbia Basin (Oregon and Washington) and Central Sands (Wisconsin). We hypothesized that CPB genetic differentiation would decrease with increasing potato, shorter rotational distances (in space), and greater abundance of forest land cover and sandy soil between sites. Conversely, we expected genetic differentiation would increase with increasing grassland/shrubland, grain, water cover and with larger rotational distances. Models that independently considered land cover effects on genetic differentiation identified a strong effect of potato land cover in the Columbia Basin but no landscape effects in the Central Sands Figure . CompariImportantly, we also observed a correlation between potato field rotation distances and nucleotide diversity, suggesting that crop rotation, in addition to reducing the timing and abundance of CPB infestations in potato fields, also acts to reduce the genetic diversity of local CPB populations.4.1In the Columbia Basin, sites connected by a high amount of potato land cover exhibited slightly lower genetic differentiation, consistent with the hypothesis that having an abundance of host plants facilitates dispersal and gene flow over the landscape. A similar relationship was found between genetic differentiation and grain (predominantly wheat) land cover. Wheat typically follows potato in crop rotation schemes in the Columbia Basin, but has not been a prevalent crop in the Central Sands since the early 1900s. Wheat is known to be a barrier to CPB dispersal by walking  in potato‐producing regions in the Midwest , whereas alternative host plants in the Columbia Basin tend to occur in isolated riparian habitats  land cover. One such environmental factor could be climate: There is much lower moisture availability in the Columbia Basin than in the Central Sands, which could reduce the amount of time and distance over which CPB can disperse before succumbing to desiccation and starvation.4.3Crop rotation is a powerful management practice, leveraging one of the most malleable features of the agricultural landscape: the spatiotemporal connectivity of crop land cover. The geographic distance between rotated potato fields and composition of the intervening landscape affects CPB dispersal and abundance (Huseth et al., The authors declare no competing interests.MSC conceived of the study, gathered and analyzed data, and wrote the manuscript. SIR gathered data, provided funding, and wrote the manuscript. SDS gathered data, provided funding, and wrote the manuscript. Click here for additional data file.