D. angustifolia of 17 populations, which were selected in the tundra zone on the western slope of the Changbai Mountains. Our results showed that the levels of genetic and epigenetic diversity of D. angustifolia were relatively low, and the main variation occurred among different populations (amplified fragment length polymorphism (AFLP): 95%, methylation sensitive amplification polymorphism (MSAP): 87%). In addition, DNA methylation levels varied from 23.36% to 35.70%. Principal component analysis (PCA) results showed that soil properties of different populations were heterogeneous. Correlation analyses showed that soil moisture, pH and total nitrogen were significantly correlated with genetic diversity of D. angustifolia, and soil temperature and pH were closely related to epigenetic diversity. Simple Mantel tests and partial Mantel tests showed that genetic variation significantly correlated with habitat or geographical distance. However, the correlation between epigenetic variation and habitat or geographical distance was not significant. Our results showed that, in the case of low genetic variation and genetic diversity, epigenetic variation and DNA methylation may provide a basis for the adaptation of D. angustifolia.Ecological adaptation plays an important role in the process of plant expansion, and genetics and epigenetics are important in the process of plant adaptation. In this study, genetic and epigenetic analyses and soil properties were performed on  Global warming has many impacts on plants. In particular, a warmer climate may force plant species to migrate from low to high altitudes , which iPhyla canescens showed that the invasive populations in Australia and France had similar or even higher genetic diversity than those in their native areas [Studies shown that some invasive plants have similar or higher genetic diversity in the invaded region than in their original habitat, which may help them adapt to the environment ,17. Studve areas . Generalve areas ,20, therve areas ,21,22. Ave areas . Among ave areas ,25,26. Eve areas ,28,29,30ve areas ,31,32.Deyeuxia angustifolia (Komarov) Y. L. Chang (D. angustifolia), originally distributed on the low altitude birch forest belt of the Changbai Mountains, is one of the dominant herbs under birch forest. However, field observations in the past 30 years have shown that after growth and reproduction, D. angustifolia at lower elevations of the tundra have been connected into patches. The increasing biomass of D. angustifolia has been able to exclude native plants such as Rhododendron chrysanthum Pall. and Vaccinium uliginosum Linn. and became the only dominant species [D. angustifolia was that a large number of forest gaps were formed due to the typhoon that occurred in 1986, which created a natural expansion opportunity for low altitude plants [D. angustifolia plant and the native Rhododendron chrysanthum Pall. plant in the tundra of the Changbai Mountains. The results showed that nitrogen deposition significantly promoted the growth of D. angustifolia. Nutrient disturbance may be the main reason for the expansion of D. angustifolia into tundra ecosystem of the Changbai Mountains [D. angustifolia significantly reduced the species diversity and aboveground biomass of the native plant community, and changed the soil microbial community structure of native plants in the tundra belt [D. angustifolia into the tundra zone and its impact on native plants in the tundra. However, the adaptation mechanism of D. angustifolia invading the tundra is still unclear. In the end, we will gain better knowledge on how D. angustifolia, and probably other invasive species, adapt to stressful environments.The gramineous plant  species ,34. The e plants . In the ountains . The predra belt . ConsequD. angustifolia from different elevations on the western slope of the Changbai Mountains and surveyed the environmental factors of each sampling site in order to detect different environment conditions. Amplified fragment length polymorphism (AFLP) and methylation sensitive amplification polymorphism (MSAP) were used to study the genetic and epigenetic variation of D. angustifolia and to unravel the role of genetic and epigenetic variation in the adaptation of D. angustifolia.In this study, we sampled 17 natural populations of D. angustifolia populations . Pairwise population AMOVA showed similar results  clustering analysis was carried out on 17 populations of D. angustifolia  for each population ranged from 0.015 (P08) to 0.199 (P17), with an average of 0.054; the percentage of polymorphic loci (PPL) ranged from 2.87% (P08) to 37.95% (P17), with an average of 10.59% . Pairwise population AMOVA showed the similar results  was used to show the variation of soil properties in different sampling sites, and PCA explains 75.32% of the variation . PC1  were positively related to the genetic diversity of D. angustifolia; soil C/N, TK and pH  were negatively related to the genetic diversity of D. angustifolia . DNA methylation in plants is usually sensitive to the changes of environmental factors, and the changes of soil temperature had no significant effect on the levels of full methylation and semi-methylation  and environmental factors  were significantly correlated with epigenetic variation (p = 0.033) and geographic distance  were significantly correlated with genetic variation across all sites. When we eliminated the effect of geographic distance or environmental factors, there was no significant correlation between genetic variation and epigenetic variation. In addition, when we eliminated the effect of geographic distance, environmental factors were not significantly correlated with genetic variation and epigenetic variation, but when we eliminated the effect of environmental distance, geographic distance was significantly correlated with genetic variation  and epigenetic variation   .D. angustifolia, a local herbaceous plant in the low altitude area of the Changbai Mountains, has gradually expanded upward to the tundra belt with relatively high altitude in recent years due to natural disasters (typhoons) and air nitrogen deposition. The molecular mechanisms of genetics and epigenetics may provide the basis for the adaptation of D. angustifolia to the new environment. In order to understand how D. angustifolia adapts to the tundra environment through genetics and epigenetics, AFLP and MSAP analyses were carried out on 17 natural populations of D. angustifolia in an elevation gradient on the western slope of the Changbai Mountains, so as to deepen our understanding of the ecological adaptability of D. angustifolia.D. angustifolia was relatively low, and the average values of the Shannon information index (I) and percentage of polymorphic loci (PPL) in 17 populations were 0.035 and 6.79%, respectively  and percentage of polymorphic loci (PPL) were 0.038 and 9.21%, respectively (p < 0.0001). A total of 87% of the epigenetic variation originated among populations, and 13% of the epigenetic variation originated within population  Y. L. Chang (D. angustifolia), a narrow-leaf small reed, is characterized as a hydro-mescophyte perennial herb, with life traits that include fast growth, clonal and sexual reproduction, a short juvenile period, high seed production and anemochory. In July 2017, 17 natural populations of D. angustifolia were selected from the alpine tundra on the western slope of the Changbai Mountains , and all the soil was mixed evenly. About 500 g of soil was obtained at each site. All samples were collected on the same day. After collection, the samples placed in a cooler on ice were brought back to the laboratory for mixing, sifting and root removal. The collected soil samples were used for analysis of physical and chemical properties. Soil temperature was measured in situ using a datalogger .The total DNA of the plant leaves was extracted using a Plant Genomic DNA kit , and the concentration and quality of the DNA were detected using a Nanodrop 2000 spectrophotometer , followed by verification by 1.2% agarose gel electrophoresis. The extracted DNA samples were stored in a refrigerator at −20 °C for later use.TM Thermal Cycler . Based on the number of amplified fragments, 10 DNA samples of each population and 10 selective primer combinations were chosen for the AFLP analyses (The amplified fragment length polymorphism (AFLP) fingerprinting followed the previous protocol of our research team with some modifications . For resanalyses . The fraThe methylation sensitive amplified polymorphism (MSAP) technique, a modification of the amplified fragment length polymorphism (AFLP) markers which replaced the frequent cutter MseI with methylation-sensitive restriction enzymes HpaII (NEB) and MspI (NEB), was used to assess the epigenetic variation within and among populations. The MSAP protocol was performed with some modifications as the previous study described . For AFL4⁺-N (ammonium nitrogen) and NO3−-N (nitrate nitrogen) were extracted with 2 M KCl, NH4⁺-N was determined by the indiophenol blue colorimetric method, while NO3−-N was determined by ultraviolet spectrophotometry. TP  was determined colorimetrically using the molybdate method. AP (available phosphorus) was determined colorimetrically based on the Olsen method. TK  was extracted by incubation with sodium hydroxide, and AK (available potassium) was extracted by incubation with 1.0 mol L−1 ammonium acetate for 0.5 h, followed by filtration. Soil pH was measured on a 1:5 (w/v) ratio in distilled water using a pH meter. Soil moisture was determined after drying at 105 °C for 24 h. The soil C/N ratio was calculated as the TC to TN ratio. Soil temperature of each population was measured in situ using a datalogger.Soil properties were determined within one month after sampling as previously described ,57. In bThe software Quantity One was used to identify and label distinct and independent bands of the electrophoresis. The AFLP and MSAP fragments (bands) were scored as presence (1) or absence (0) of DNA fragments (bands). All fragments from 100 to 600 bp were considered, and a data matrix of the AFLP and MSAP banding patterns of all populations was assembled for further analysis. Since the sensitivity of restriction enzymes HpaII and MspI to the methylation site is different, the HpaII only identifies cytosine lateral hemi-methylation (single chain), while the MspI only recognizes the inner cytosine full-methylation (double chain).The MSAP reactions can be divided into four distinct outcomes: I, fragments present in both profiles (1/1), unmethylated state; II, fragments present only with EcoRI/HpaII (1/0), hemi-methylation of external cytosine; III, fragments present only with EcoRI/MspI (0/1), full-methylation of internal cytosine; IV, fragments absent with both profiles (0/0), uninformative site. The first three results predict different methylation levels, while the last result may have different causes, including methylation mutations or DNA sequence mutations, and it is thus uninformative . For furST values were used to evaluate genetic and epigenetic differentiation among all groups based on GENALEX 6.5. Nei’s genetic distance and Nei’s epigenetic distance matrix were obtained based on GENALEX 6.5. Principal coordinate analysis (PCoA) was used to analyze genetic variation and epigenetic variation between populations. AMOVA and PCoA were also based on GENALEX 6.5 software. The MEGA X [4⁺-N, NO3−-N, AP, pH, moisture and C/N ratio (means ± standard deviation) of each sample was performed with three technical repeats in the laboratory. For TP, TK, AK and AN, a single test of each sample was determined by the Public Technology Service Center of the Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences with a 95% confidence level.According to the binary AFLP and MSAP data matrix, the population genetics software GENALEX 6.5  was usede MEGA X  softwareD. angustifolia in the western slope of the Changbai Mountains was relatively low, which may be due to the short time of expansion into the tundra zone, and the clonal reproduction and genetic drift in the process of expansion. The epigenetic diversity is relatively low, but the level of DNA methylation is relatively high, and the main variations of the genetics and epigenetics of D. angustifolia were from different populations. This may be due to geographical isolation. Sufficient nitrogen, higher soil moisture and lower soil pH may provide a suitable environment for the colonization and expansion of D. angustifolia in tundra. Clonal reproduction and epigenetic regulation, especially DNA methylation, may be involved in the adaptation of D. angustifolia to different environments, which may be a molecular basis for rapid adaptation to tundra environment.Finally, we found that the genetic diversity of


Albizia saman is a multipurpose tree species of seasonally dry tropical forests (SDTFs) of Mesoamerica and northern South America typically cultivated in silvopastoral and other agroforestry systems around the world, a trend that is bound to increase in light of multimillion hectare commitments for forest and landscape restoration. The effective conservation and sustainable use of A. saman requires detailed knowledge of its genetic diversity across its native distribution range of which surprisingly little is known to date. We assessed the genetic diversity and structure of A.saman across twelve representative locations of SDTF in Colombia, and how they may have been shaped by past climatic changes and human influence. We found four different genetic groups which may be the result of differentiation due to isolation of populations in preglacial times. The current distribution and mixture of genetic groups across STDF fragments we observed might be the result of range expansion of SDTFs during the last glacial period followed by range contraction during the Holocene and human‐influenced movement of germplasm associated with cattle ranching. Despite the fragmented state of the presumed natural A. saman stands we sampled, we did not find any signs of inbreeding, suggesting that gene flow is not jeopardized in humanized landscapes. However, further research is needed to assess potential deleterious effects of fragmentation on progeny. Climate change is not expected to seriously threaten the in situ persistence of A. saman populations and might present opportunities for future range expansion. However, the sourcing of germplasm for tree planting activities needs to be aligned with the genetic affinity of reference populations across the distribution of Colombian SDTFs. We identify priority source populations for in situ conservation based on their high genetic diversity, lack or limited signs of admixture, and/or genetic uniqueness. Through the combination of spatial analysis of genetic characterization data with the outcomes of suitability modeling under past climates, we aim to understand how past climate change and human influences can help explaining contemporary patterns in genetic diversity of Albizia saman, a tree species typically used in agroforestry systems around the world. Our findings suggest that the genetic structure of A. saman populations may partly be explained by the dry forest refugia hypothesis and that  more recent human‐influenced movement of germplasm associated with cattle ranching resulted in profound admixing of most populations. We make a number of recommendations to guide the sourcing of germplasm for future planting endeavors and identify priority areas for in situ conservation of populations. PCR products were run on an ABI PRISM 3730 DNA Analyzer sequencer and sized with GeneScan 500LIZ (Applied Biosystems) standard size. Allele sizes were determined using GeneMapper version 4.0 (Applied Biosystems).Young and healthy leaves of sampled Schuelke . The mas2.3A. saman, following Thomas et al.  around the locations of all the genotyped s et al. . In pracs et al.  and can s et al. .K) tested varied between 1 and 8, using burnin periods of one million steps and 10 million additional replications. For each value of K, we carried out 10 independent repetitions. We used the method of Evanno, Regnaut, and Goudet (K), through calculation of ΔK as implemented in the STRUCTURE HARVESTER software . We only included records located in SDTF as defined by the combination of Etter, McAlpine, and Possingham (2O (PHIHOX), sand % (SNDPPT), silt % (SLTPPT), clay % (CLYPPT), cation exchange capacity (CEC), bulk density (BLD), and coarse fragments > 2 mm (CRFVOL). For the edaphic variables, we calculated a weighted mean across 0–5, 5–15, 15–30, 30–60, and 60–100 cm soil depth values in order to derive a single data value for 0–100 cm. Weights were proportional to the thickness of each soil layer . Collinear explanatory variables were removed based on iterative calculations of variance inflation factors (VIF), retaining only variables with VIFs smaller than 5. VIFs were calculated using self‐assembled R script. The resulting sets of explanatory variables, as well as presence and background points used for model calibrations, are given in Table Presence data collected during our field sampling were complemented with Colombian records extracted from numerous sources , boosted regression trees , random forests (RF), generalized linear models , generalized additive models , multivariate adaptive regression splines (MARS), regression trees (RT), artificial neural networks (ANN), flexible discriminant analysis (FDA), support vector machines (SVM), and the BIOCLIM algorithm. The ensemble modeling was carried out using the BiodiversityR package using default settings. The specifics of how different models are implemented are explained in the package help function and Kindt . As spatTo assess habitat suitability under mid‐Holocene and LGM climate conditions, we carried out projections to two and three climate models, respectively . For characterizing future climate conditions, we used 31 downscaled climate models for the period 2040–2069 based on the representative concentration pathway (RCP) 4.5 scenario of greenhouse gas emissions, prepared for the Fifth Assessment IPCC report (CMIP5)  and Patia  differed significantly from Hardy‐Weinberg expectations  suggest that most of the sampled populations hold comparable levels of diversity  indicated that 10.6% of the total genetic variation resided between populations, compared with 89.4% between individuals within populations. Pairwise FSTRUCTURE showed support for two highly differentiated genetic clusters (K = 2): One grouping trees sampled in the Patía river valley (PAT) and another one grouping all individuals from the rest of the country , and migrated southward and eastward toward the Chicamocha Canyon (CHI) during the period of range expansion of A saman predicted during the LGM. However, our data do not exclude the possibility of an opposite scenario either. Clusters 1 and 3 on the other hand may have formed through prolonged isolation in the middle and upper Cauca river valley around ITU and PAI‐MAT, were favorable climate conditions are likely to have persisted since the LGM or longer. The fact that most populations along the Cauca river valley, as well as those in the Caribbean region contained individuals of mixed origin may–at least partly—be due to human‐influenced movement of planting stock initiated after the European colonization.The four genetic clusters of A. saman is carried out by rodents, tapirs, and peccaries  to cattle would have started much earlier. The largest A. saman trees we measured were found at La Paila (PAI)  Figure . All tre) Figure  were ass) Figure . Thomas ) Figure  similarlCeiba pentandra as we found for A. saman alluding to the possibility of cross‐Andean dispersal, especially because it is less likely that the trees they sampled there established through human intervention. However, as mentioned above, these interpretations have to be treated with caution, and more exhaustive sampling is needed to confirm or refute their validity.Interestingly, the trees we sampled in Tatacoa (TAT) in the upper Magdalena river valley pertained to genetic groups with putative origin in the Cauca river valley and the Caribbean region. This might indicate that these trees were established from germplasm introduced by human activities. However, the possibility that some of the genetic groups arrived there through natural range expansion processes in prehistoric times cannot be ruled out. Our LGM suitability model suggests that the upper Magdalena river valley may have been connected by a continuous suitable area to the Chicamocha canyon and the Caribbean region, facilitating the arrival of cluster 2. On the other hand, a dispersal route across the Andes, connecting the Cauca and Magdalena river valleys might also have existed at one point in prehistory. Cross‐Andean dispersal has been documented previously for SDTF species , which might obscure potential negative effects of more recent fragmentation on progeny. Cascante et al.  investigA. saman in silvopastoral and other agroforestry systems is bound to increase considering the huge pledges made by countries worldwide to restore degraded lands such as Initiative 20 × 20. This initiative aims to initiate the restoration of 20 million hectares of degraded lands in Latin America and the Caribbean by 2020, among others through the use of agroforestry (http://www.wri.org/our-work/project/initiative-20920). Our finding that most of currently suitable areas for A. saman are expected to remain so under climate change, or might even expand, supports its use in future tree planting efforts.Despite the potential negative consequences of fragmentation, Cascante et al.  also fouA. saman tree stand, it will be critical to ensure that planting material has a sufficiently broad genetic basis and is adapted to the current and expected future environmental conditions at each planting site  combined with the Tayrona National Park (ZAT) for cluster 2, la Paila (PAI) for cluster 3, Santa Fé‐Cotove (SFE‐COT) for cluster 2, and the Patia river valley (PAT) for cluster 4. Although populations in the Chicamocha river valley and the Tayrona National Park pertain to the same genetic group, they are geographically isolated and genetically differentiated enough (FST = 0.09; Table A. saman mirror the previously reported genetic uniqueness of C. pentandra trees in the Patia river valley which might point to a broader biogeographical pattern, calling for the urgent establishment of the Patía Fauna and Flora Sanctuary conservation unit proposed by the Colombian national system of national parks (PNNC, Viable populations of ks PNNC, .None declared.ET, LGMH, and CAC designed the study; CAAM, CAC, and LGMH carried out field work; CAAM and JG carried out laboratory work; ET and CAAM performed data curation and statistical analyses and prepared the first draft of the manuscript. All authors contributed to revisions of the manuscript. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Microstructural changes associated with degeneration of dopaminergic neurons of the substantia nigra pars compacta (SNc) in Parkinson's disease (PD) have been studied using Diffusion Tensor Imaging (DTI). However, these studies show inconsistent results, mainly due to methodological variations in delineation of SNc. To mitigate this, our work aims to construct a probabilistic atlas of SNc based on a 3D Neuromelanin Sensitive MRI (NMS‐MRI) sequence and demonstrate its applicability to investigate microstructural changes on a large dataset of PD. Using manual segmentation and deformable registration we created a novel SNc atlas in the MNI space using NMS‐MRI sequences of 27 healthy controls (HC). We first quantitatively evaluated this atlas and then employed it to investigate the micro‐structural abnormalities in SNc using diffusion MRI from 133 patients with PD and 99 HCs. Our results demonstrated significant increase in diffusivity with no changes in anisotropy. In addition, we also observed an asymmetry of the diffusion metrics with a higher diffusivity and lower anisotropy in the left SNc than the right. Finally, a multivariate classifier based on SNc diffusion features could delineate patients with PD with an average accuracy of 71.7%. Overall, from this work we establish a normative baseline for the SNc region of interest using NMS‐MRI while the application on PD data emphasizes on the contribution of diffusivity measures rather than anisotropy of white matter in PD. These dopaminergic neurons contain neuromelanin, loss of which manifests as depigmentation of the SNc. This has been well established as an early histological feature of PD . Typically, these studies initially delineate the SNc and then perform analysis on the computed ROI. Table Taken together, the findings have been widely heterogeneous and perhaps could be implicated to inconsistencies in stages of disease severity as well as to variable sample sizes. More importantly, the techniques employed for delineating the SNc may have a significant bearing on the variability in the reported results. For example, studies have employed T2 weighted, proton density weighted spin echo and inversion recovery based contrasts to precisely localize the SNc ROIs , Bangalore, India. The diagnosis of idiopathic PD was based on the UK Parkinson's Disease Society Brain Bank criteria , and Unified Parkinson's Disease Rating Scale (UPDRS‐III) OFF‐state scores, and levodopa equivalent daily dose (LEDD) were recorded. The OFF state was defined as 12 hr after the last dose of levodopa, and 48 hr after the last dose of a dopamine agonist. Age and gender matched healthy controls with no family history of Parkinsonism or other movement disorders were recruited.Another group of 27 healthy controls  whose NMS‐MRI sequence was acquired as part of a different study  for these subjects were acquired using a single‐shot spin‐echo EPI sequence with repetition time (TR) = 8,583–9,070 ms, echo time (TE) = 60‐62 ms, field of view (FOV) = 128 × 128 × 70m, voxel size = 1.75 × 1.75 × 2mm, slice thickness = 2 mm. Diffusion gradient was applied in 15 directions, with For creating a probabilistic atlas of SNc, a different set of controls were scanned on the same scanner. T1‐weighted images were acquired using the above‐mentioned protocol. Neuromelanin contrast sensitive sequences were acquired using a fast spin echo 3D T1 acquisition with TE: 2.2 ms, TR: 26 ms; flip angle: 20°; reconstructed matrix size: 512 × 512; field of view: 180 × 180 × 50mm; voxel size: 0.9 × 0.9 × 1mm; number of slices: 50; slice thickness: 1 mm; and acquisition time: 4 min 12.9 s. These images covered only the areas between the posterior commissure and inferior region of pons.2.3Probabilistic atlas was built from NMS‐MRI images of 27 subjects as shown in Figure 2.4Two additional sets of snROI markings were generated, one was by manual segmentation (by rater 2 [R2]‐author A.S) and the other was generated by a fully automated deep learning model known as U‐Net  and combined dice scores of atlas with raters R2 and R3 using a Student's 2.5https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FMRIB58_FA) using SyN algorithm and mutual information similarity index in ANTS. The transformation matrix of this registration was used to transform the MD, RD, and AD maps to MNI space. Diffusion measures of bilateral SNc were extracted for all subjects using MNI registered diffusion maps and the atlas described in earlier, which was thresholded at a 0.5 probability.DWI images of patients with PD and healthy controls were manually visualized for quality assessment. All preprocessing steps were done using FSL5.0.9  threshold of 0.05.Statistical analysis on diffusion measures between PD and HC was performed using multivariate analysis of covariance (MANCOVA) model, with FA, MD, RD, AD of left, and right SNc as dependent variables, PD and HC as grouping variables and age and gender as covariates. In addition, an independent To evaluate associations between the microstructural changes to the severity of the disease, the residuals from the diffusion measures after regressing out age and gender were correlated to the UPDRS‐III OFF scores (where available), the age of onset of disease (AoO), duration of illness (DoI) and LEDD of patients with PD.2.7Further, to understand the discriminative power of these diffusion measures, a multivariate random forest (RF) was implemented to delineate PD from the HC. RF is a popular decision tree based machine‐learning algorithm Breiman, . At eachIn this study, RF model was implemented on a total dataset of 232 subjects, out of which 75% was used for training and 25% for testing. A total of eight normalized diffusion measures comprising AD, FA, MD, RD for left and right SNc were used as feature set for the classifier.Number of predictors or features sampled for splitting at each node and the number of trees in the forest were the two primary tuning parameters in the model  was found between overlap of rater's SNc marking and overlap of atlas and rater's markings, thus providing additional support to the reproducibility and validity of the atlas.The probabilistic and thresholded (0.5) atlas of the SNc computed from NMS‐MRI images of 27 controls is shown in Figure 3.3f = 17.28, p = .00004; right snROI: f = 6.470, p = .011), MD , RD  in SNc of PD patients as compared to controls. As shown is Figure f = 0.621, p = .431: right snROI: f = .470, p = .493). The mean and SD of all diffusion measures in HC and PD groups along with their t‐stats and FDR corrected p‐values are shown in Table MANCOVA results showed significantly higher AD , MD , RD  as compared to right SNc. FA of the left SNc was found to be significantly lower as compared to right FA in the PD group  .Asymmetry of all diffusion measures of bilateral SNc was observed for both HC and PD group, with PD group showing a higher significance as shown in Table 3.5L, RDR and RDL were found to be the three topmost ranked features as shown in Table The average classification accuracy, sensitivity and specificity for basic RF model were 73.4%, 0.736 ± 0.01, and 0.731 ± 0.01, respectively, whereas that for RF‐RFE model was 71.7%, 0.736 ± 0.01, and 0.686 ± 0.05, respectively. Average feature ranking was consistent for basic RF and RF‐RFE model wherein MD4We created a probabilistic atlas of the SNc by precisely extracting the SNc ROIs using NM rich MR sequence and employed it to accurately delineate the SNc to create a normative atlas that can be used in future PD studies. We applied this atlas to a large cohort of PD patients to gain understanding of the microstructural abnormalities. Our results not only endorsed earlier findings but also facilitated fresh evidence supporting presence of micro‐structural changes in PD substantia nigra compacta using diffusion MRI analysis. We demonstrated higher diffusivity values in the SNc in PD, with no changes in anisotropy and significant asymmetry of the diffusivity values.The SN is anatomically divided into the SNc and the SN pars reticulata (SNr), where SNc is further subdivided into nigrosomes and the nigral matrix. Nigrosomes are the primary sub regions of the SNc where dopaminergic neurons are degenerated in PD Click here for additional data file.


Vitis vinifera) is one of the most widely cultivated plant species of agricultural interest, and is extensively appreciated for its fruits and the wines made from its fruits. Considering the high socio-economic impact of the wine sector all over the world, in recent years, there has been an increase in work aiming to investigate the biodiversity of grapevine germplasm available for breeding programs. Various studies have shed light on the genetic diversity characterizing the germplasm from the cradle of V. vinifera domestication in Georgia (South Caucasus). Georgian germplasm is placed in a distinct cluster from the European one and possesses a rich diversity for many different traits, including eno-carpological and phenological traits; resistance to pathogens, such as oomycetes and phytoplasmas; resistance to abiotic stresses, such as sunburn. The aim of this review is to assess the potential of Georgian cultivars as a source of useful traits for breeding programs. The unique genetic and phenotypic aspects of Georgian germplasm were unraveled, to better understand the diversity and quality of the genetic resources available to viticulturists, as valuable resources for the coming climate change scenario.Grapevine ( Vitis is present in 10 distribution areas, all in the northern hemisphere: five in North America, where 29 species have been described; four in Asia, with at least 11 species; and only one, Vitis vinifera, in a wide range that includes the Mediterranean, sub-Mediterranean, and Caucasian floristic regions with a spread toward the Pontic, Caspian, and Central Asiatic areas  and a world wine trade worth around EUR 32 billion1. Regions of its cultivation are located roughly between the 35th and 55th northern parallels and between the 25th and 35th southern parallels, in areas with average annual temperatures between 10 and 20°C. These environments are characterized by the alternation of a favorable growing season and an unfavorable cold one. However, the cold winters are not too intense (minimum temperatures range between −10 and 15°C) and the favorable season (average temperature higher than 10°C) is long enough (>200 days) for grapes to ripen  in terms of yields and quality . The curV. vinifera species have been recently found in its domestication cradle, located in Georgia (South Caucasus) (aucasus)   .Georgia counts 48,000 hectares of vineyards and a production of wine and table grapes of 159,000 and 8,000 tons, respectively (see text footnote 1). In 2015, about 100 M liters of wine were produced, 80% of them obtained by white and 20% from red berry grapes. More than 90% of the 2015 production was supported by Kakheti region, producing mainly white and red wines from Rkatsiteli and Saperavi grapes, in the ratio 7:3.The aim of this review is to assess the potential of Georgian cultivars as sources of useful traits for new breeding programs, aiming to face the future challenges that await viticulture worldwide. To do this, we reviewed the particular genetic and phenotypic aspects (such as berry traits and resistance to pathogens) of Georgian germplasm, in the hope of better understanding the diversity and quality of the genetic resources available to viticulturists, coming directly from the origin of domestication.Vitis vinifera is indigenous to Eurasia and it is suggested that the ancestors of the first Vitis genus appeared about 65 million years ago (V. vinifera species includes both cultivated (V. vinifera subsp. sativa) and wild (V. vinifera subsp. silvestris) subspecies, the latter considered the progenitor of subspecies sativa  high population density, with stable settlements and in positions at crossroads of trade flows and cultural trends  are wine grape cultivars, while the others (13) are generally used to produce grapes for fresh consumption. Predominantly, the Georgian vineyards are cultivated with autochthonous varieties: among the recommended wine grapes, 31 are local varieties and seven are international cultivars, while, considering the table grapes, nine of them have a local origin: four are traditional, autochthonous, Georgian cultivars, five are from local breeding outputs, and five are allochthonous varieties . Beside Historical information coupled with archeological and palaeobotanical findings pointed to Georgia as a cradle for grapevine domestication . MoleculAll the outcomes about the genetic characterization of Georgian germplasm reported the uniqueness and originality of this germplasm when compared to the European and Central Asian germplasm . The GeoGenetic variation provides the foundation for any breeding programs, and natural genetic diversity represented historically the major source of variability for crop improvement and adaptation to changing environmental conditions. Given the uniqueness of Georgian germplasm, its strong link with the regions of origin coupled with the evidence of this country being the center of domestication makes this germplasm very attractive for investigation from the perspectives of phenology, grape phenotype and resistance to biotic and abiotic stresses, as sources of new variability for future breeding programs.V. vinifera variability concerning eno-carpological traits . Briefly, the main differences highlighted in V. vinifera species population, higher concentrations in both sugars and acids and thicker skins, ensuring acceptable amounts of phenolics despite the lower accumulation per unit of tissue. Details concerning the results of this comparison are discussed in Sections “Fruit Morphology and Technological Quality of Georgian Cultivars” and “Abiotic Stress Adaptations and Secondary Metabolisms.” It is worth noting that the phenotypic variability reported is due to the genotype, to the environmental growing conditions, and to their interactions. Thus, further studies will be necessary to discriminate these effects, highlighting the role of genotypes.The collaboration among European and Asian researchers makes feasible the comparisons among ampelographic collections using common protocols. Among them, phenotyping was considered in the framework of the COST Action FA1003 , allowinl traits . Table 3Despite the wide variability for berry shapes within the species, Georgian grapes generally have round or slightly elongated small berries  OIV, de, favoredConsidering technological maturity, Georgian records generally show higher concentrations in both sugars and acids than foreign varieties . In the V. vinifera species. This is due to the lighter berries and heavier skins , but the slopecoefficient (60.2) together with the average Browning Intensity Index(27.5) indicated a light symptom appearance in Rkatsiteli grapes. Inphoto-oxidative sunburn, browning symptoms appear due to the reactiveoxygen species (ROS) scavenging activity of phenolics through theiroxidation and consequent polymerization that produce brown pigments could causeproblems in grapes due to chlorophyll overexcitation . Rkatsit pigments. Often,  pigments. Howeveris trait  .The environmental conditions  can affect the pigment accumulation in skins and the modulation of the anthocyanin biosynthetic pathway in berries could be considered as a grapevine eco-physiological adaptation mechanism . ConsideV. vinifera, and are susceptible, at different levels, to several pathogens , while non-vinifera species, from North American and Asian, are resistant to fungi and tolerant to viruses and some bacteria (Erysiphe necator) and downy mildew (caused by the oomycete Plasmopara viticola) are two of the most important  and V. vinifera, were undertaken at the beginning of the XX century . Furthermore, studies on the effects of CO2 and temperature on downy and powdery mildews showed that the disease incidence of downy mildew increases with rises in gas and temperature, while an increase in CO2 did not influence powdery mildew incidence , is an actively triggered procedure initiated by fungal elicitors or other elicitors  and resistant  cultivars  protein  analysis on a range of North American and Asian Vitis species has led to the identification of 28 resistance (R) loci (Resistance to P. viticola) confer different degrees of resistance to disease, ranging from partial to total resistance   loci . These Rsistance . The maj(MrRPV1) ; (ii) RpRR genes ; (iii) Ry mildew ; (iv) Rpproteins ; (v) Rpvnfection . The othV. vinifera germplasm  have been identified, through a GWAS (Genome Wide Association Study), in the genetic background of the Georgian ermplasm   , Convolvulus arvensis (bindweed), and Vitex agnus-castus (chaste tree) (Vitis species and V. vinifera varieties studied have been found to be resistant or tolerant to the GY phytoplasmas (Flavescence dorée (FD) and Bois noir (BN) are the more important diseases of the grapevine yellows (GY) complex, responsible for severe yield losses in vineyards worldwide . FD and tis spp. . Consequtis spp. . On the solani’)  is occaste tree) . The epite tree) . Moreovete tree) . Unfortuoplasmas .Surveys conducted in vineyards of Khaketi and Shida Kartli regions in eastern Georgia highlighted a wide diffusion of BN, while FD was not reported . MoreoveV. vinifera subsp. silvestris is considered the progenitor of cultivated species. In the last two decades, an increase interest in preserving wild genetic resources has led to surveys on Georgian land aimed to localize and gather wild grapevine material. The plant material collected in these surveys is summarized in the P. viticola infection  and biotic stresses, as well as the whole genome analysis of the most performing Georgian cultivars, in order to identify the genetic regions related to such valuable traits. A step toward this direction has been performed by GDL and SLT conceived the work. MS, VR, and GDLwrote the introduction and genetic variability section. GC wrote theclimate section. LR wrote the section on phenotype. SLT wrote the section on resistance to fungal pathogens. FQ wrote the section on susceptibility to phytoplasma diseases. DM, OF, and PAB critically revised the manuscript. All the authors read and approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Xylocopa virginica, across 14 sites spanning more than 450 km, including dense urban areas and intensive agricultural habitat. X. virginica is a large bee which constructs nests in natural and human‐associated wooden substrates, and is hypothesized to disperse broadly across urbanizing areas. Using 10 microsatellite loci, we detected significant genetic isolation by geographic distance and significant isolation by land use, where urban and cultivated landscapes were most conducive to gene flow. This is one of the first population genetic analyses to provide evidence of enhanced insect dispersal in human‐altered areas as compared to semi‐natural landscapes. We found moderate levels of regional‐scale population structure across the study system (GʹST = 0.146) and substantial co‐ancestry between the sampling regions, where co‐ancestry patterns align with major human transportation corridors, suggesting that human‐mediated movement may be influencing regional dispersal processes. Additionally, we found a signature of strong site‐level philopatry where our analyses revealed significant levels of high genetic relatedness at very fine scales (<1 km), surprising given X. virginica's large body size. These results provide unique genetic evidence that insects can simultaneously exhibit substantial regional dispersal as well as high local nesting fidelity in landscapes dominated by human activity.Human activity continues to impact global ecosystems, often by altering the habitat suitability, persistence, and movement of native species. It is thus critical to examine the population genetic structure of key ecosystemservice providers across human‐altered landscapes to provide insight into the forces that limit wildlife persistence and movement across multiple spatial scales. While some studies have documented declines of bee pollinators as a result of human‐mediated habitat alteration, others suggest that some bee species may benefit from altered land use due to increased food or nesting resource availability; however, detailed population and dispersal studies have been lacking. We investigated the population genetic structure of the Eastern carpenter bee,  One common result of intensive human land‐use practices, such as urbanization and cultivation, is the creation of homogenized altered habitats that may functionally isolate remnant habitat patches  levels of regional‐scale population structure, (b) signatures of regional genetic isolation by geographic distance (IBD) and isolation by resistance (IBR) across contemporary land use, and (c) levels of fine‐scale genetic structure. Specifically, we hypothesized that (a) X. virginica exhibits low levels of regional‐scale population genetic structure because of the species’ large body size and thus potential for long‐distance dispersal, (b) X. virginica populations experience greater genetic differentiation with increasing geographic distance but lower genetic differentiation across more urbanized and cultivated habitats, due to higher nesting availability in human‐altered landscapes, and (c) at local scales, X. virginica exhibits fine‐scale genetic structure indicative of high levels of nest‐site fidelity and philopatry.In this study, we utilized highly polymorphic microsatellite loci in 22.1Xylocopa virginica (N = 598) were collected from 33 sites across 14 regions that spanned a ~ 450 km corridor in central northern Texas between May and July in 2013 (Table SD = 145.25 km). Bees were collected within a 50 x 50 m plot established in an area with abundant floral resources . Markers were grouped into two multiplexes with each primer at 2 µM per mix, and forward primers contained a fluorescent tag  to detect individual markers during electrophoresis. Each multiplex was amplified in a 15 uL PCR using 7.5 µl of Qiagen 2x Multiplex PCR Master Mix, 1.5 µl primer mix, approximately 1–2 ng genomic DNA, and 3 µl of RNase‐free water. PCR conditions were the same for each multiplex: initial heat activation at 95°C for 15 min, then 30 cycles of 94°C for 30 s, 60.7°C for 90 s, and 72°C for 60 s, and a final extension step of 60°C for 30 min. Labeled PCR products were run on a 3,730 Sequencer (Applied Biosystems) at the Center for Biomedical Research Support DNA Sequencing Facility at the University of Texas at Austin. Alleles were called using GENEMARKER version 2.4.0 (SoftGenetics) and checked by eye. Thirty randomly chosen individuals (5%) were re‐genotyped to confirm accuracy of allele calls and to detect any genotype shifts or errors. Genotypes at all loci were found to be identical between runs.Genomic DNA was amplified at 10 polymorphic microsatellite loci Table  using th2.3X. virginica is not known in Texas, we tested for the presence of full siblings in our dataset using COLONY v. 2.0.6.4  within each population and locus. Effective population size (eN) of each site was estimated using COLONY 2.0.3.4  in the sampled populations, we used STRUCTURE v 2.3  the hypothesis that gene flow of X. virginica would be similarly limited by the same land uses that have been previously documented to limit gene flow in a number of bee species, specifically open water, impervious surfaces, and cropland  that urban, farmed, and forested habitats would have lower resistance to gene flow than open grassland habitats which contain little woody substrate. In this case, we assigned developed land, cultivated land, and forest habitats a resistance value of 0.1 and all other land cover types within the study area a value of 0.9. Finally, we tested a set of hypotheses (Set C) that single land uses are independently driving gene flow, with each of the land‐use types in Hypothesis B as single terms, with each assigned a resistance value of 0.1, and all other land‐use types assigned a resistance value of 0.9  were included in this analysis. Pairwise geographic distances between individuals were calculated using Loiselle's Fij , but similar to FIS levels observed in another recent study of X. virginica  were also low, and ranged from 41 at site RRL and 144 at both HRM and RCR, although some sites had an estimated Ne of infinity, likely due to low sample sizes at those sites . Examination of pairwise FST and GʹST across sites indicated significant differentiation at all GʹST comparisons . When only 19 sites were included in the IBD analysis, we also found significant IBD, but the F and p values were much lower than for the model with all sites included . Models including forest cover at resistance value = 0.3 (but not 0.1) were also significant , the 0‐ to 0.25‐km scale , and the 0.25‐ to 0.5‐km scale , where individuals sampled within these distances were significantly more related to each other than expected at random. Average genetic relatedness declined as distance increased , and even stronger support for significant genetic isolation by land‐use resistance (IBR), where our results indicated that human‐altered habitats are likely the most conducive habitats for 4.1X. virginica, either through human‐facilitated or natural dispersal. These results contrast previous pollinator studies investigating landscape drivers of genetic structure which have suggested that urban and cropland use can be highly restrictive to gene flow  suggested that open agricultural landscapes enhanced gene flow more than presumed preferred stream habitats, where natural forested habitats acted as barriers  across the entire study area. Geographic IBD results from genetic drift differentiating allele frequencies at a faster rate than dispersal and gene flow can normalize them Rousset, ; IBD has4.2ijF ≅ 0.03–0.06) and at spatial scales as small as 0.25 km was astonishingly high compared with similar studies in bees. For example, Lopez‐Uribe et al. , but the genetic relatedness for C. inaequalis was an order of magnitude lower than found within our study . Studies of other bee species have found similar relatedness levels to our results (Fij = 0.05–0.06), but at much larger spatial scales  and northeastern edge (sites MA and SM) of the study region in very rural areas. The remaining sites are more closely clustered around the urban areas of Austin and Dallas, and had higher levels of admixture, indicating that there is more genetic mixing between these sites despite being located further apart. This might be explained partially by the fact that most of the sites in the study are located within 70 km or less to the same major highway, the Interstate 35 corridor, one of the largest freight flows in the United States (U.S. Department of Transportation, In contrast, MA and SM are more closely located to the less‐traveled Interstate 30 (see Figure 4.4Xylocopa virginica displays both regional and localized levels of population genetic structure related to their nesting resource specialization and exceptionally high levels of natal site fidelity. Although this fine‐scale genetic relatedness and significant IBD indicate somewhat limited dispersal in this species, isolation‐by‐land‐use analyses reveal that urban, agricultural, and woody landscapes may facilitate X. virginica gene flow across the study region. Overall, these results suggest that there may be positive implications for the conservation of pollination services both locally and globally, as the nearly 400 Xylocopa species are important pollinators for both crops and native plants throughout the world (Chaves‐Alves et al., X. virginica and other wood‐nesting pollinators will likely benefit, reinforcing wild pollination services in both urban and agricultural areas.There have been many recent reports of wild bee declines across the globe, most often linked to human‐induced habitat change (Cameron et al., 5https://doi.org/10.7291/D1Z39RIndividual microsatellite genotypes, individual sample IDs, and DNA extraction plate information are deposited as a.csv file on DRYAD, Supplementary MaterialClick here for additional data file.


Leptobrachium boringii, a typical “ring” element characterized by disjunct ranges in the mountains, by integrating time-calibrated gene tree, genetic variability, individual-level clustering, inference of population splitting and mixing from allele frequencies, and paleoclimatic suitability modeling.The distribution of genetic diversity and the underlying processes are important for conservation planning but are unknown for most species and have not been well studied in many regions. In East Asia, the Sichuan Basin and surrounding mountains constitute an understudied region that exhibits a “ring” of high species richness overlapping the eastern edge of the global biodiversity hotspot Mountains of Southwest China. We examine the distributional history and genetic diversification of the Emei mustache toad The results reveal extensive range dynamics, including secondary contact after long-term isolation via westward dispersal accompanied by variability loss. They allow the proposal of a model that combines recurrent contractions caused by Quaternary climatic changes and some failed expansions under suitable conditions for explaining the shared disjunct distribution pattern. Providing exceptional low-elevation habitats in the hotspot area, the eastern edge harbors both long-term refugial and young immigrant populations. This finding and a synthesis of evidence from other taxa demonstrate that a certain contributor to biodiversity, one that preserves and receives low-elevation elements of the east in this case, can be significant for only a particular part of a hotspot. By clarifying the low variability of these refugial populations, we show that discordant mitochondrial estimates of diversity can be obtained for populations that experienced admixture, which would have unlikely left proportional immigrant alleles for each locus.Dispersal after long-term isolation can explain much of the spatial distribution of genetic diversity in this species, while secondary contact and long-term persistence do not guarantee a large variation. The model for the formation of disjunct ranges may apply to many other taxa isolated in the mountains surrounding the Sichuan Basin. Furthermore, this study provides insights into the heterogeneous nature of hotspots and discordant variability obtained from genome-wide and mitochondrial data. Understanding the spatial distribution of genetic diversity within species and the underlying processes is important for conservation –3. QuateRegional biogeographic studies have often identified different major processes that shape the patterns of intraspecific genetic variability, for example, evolutionary melting pots , 10, hisThe Sichuan Basin and surrounding mountains Fig.  in southLeptobrachium boringii , has a typical disjunct distribution in the mountains at the edges of the basin and hotspot .In this study, we examine the distributional history and genetic diversity of the Emei mustache toad, focusing on two questions: (1) is the distribution highly dynamic over time, and what are the effects of range dynamics, and (2) does the eastern edge of the Mountains of Southwest China hotspot provide a long-term refugium harboring high genetic variation? We obtained sequence data for two mitochondrial loci and genome-wide single nucleotide polymorphism (SNP) data for samples from all of the corresponding known ranges. By combining time-calibrated gene tree, genetic diversity, genetic clustering, population splitting and mixing inference, and paleoclimatic suitability modeling, we inferred considerable range dynamics underlying the genetic and distribution patterns and a long-term refugium in the hotspot edge area, with the lowest genetic variability despite immigration. In addition, we demonstrated significant discordance between variability estimates from genome-wide SNPs and mitochondrial DNA sequences.The mitochondrial DNA dataset contained 1831 sites and 83 haplotypes after removing alignment gaps in the control region. Among the ingroup members, 282 sites were variable, and 227 sites were parsimony informative. Haplotype designation and GenBank accession numbers are given in Table S1. The genome-wide SNP main dataset contained 10,869 SNPs, from 7045 to 10,423 with a mean of 9305 SNPs (85.6%) for the ingroup and 6986 and 5680 SNPs for the two outgroup individuals. The 4-individual dataset with no missing loci included 16,888 SNPs. The Sequence Read Archive (SRA) accession numbers under BioProject PRJNA551927 are presented in Table S1. These three datasets are available on Dryad (10.5061/dryad.t4b8gtj0c).For the BEAST analysis, a three-partition scheme was selected for the mitochondrial dataset: the protein-coding genes with the TrN + G model, tRNA genes with the K80 + I model, and control region with an HKY + I + G model. An adequate effective sample size of greater than 700 was achieved for each parameter , and theThe first two principal coordinates obtained by PCoA jointly accounted for 71.9% of the observed genome-wide SNP variation among individuals Fig. . Three dP < 0.05) involving some Middle, East, and South populations were added, with a resulting topology highly consistent with that of the neighbor-joining tree , with the highest values for some South and East populations and lowest ones across the West region. Average identity-by-state distances of 0.032, 0.076, 0.075, and 0.058 were obtained for the West, Middle, East, and South regions, respectively. When compared to the outgroup species, again, much lower variation was estimated for the West representative. From the 4-individual SNP dataset without missing loci, heterozygous loci proportions of 0.0616, 0.1462, 0.1614, and 0.1152 were obtained for West representative bf71, East representative fjnB02, and the two outgroups ms07 and yb01, respectively.Various levels of regional genetic variation and a decline in variability along the path of sequential westward divergence were inferred from the main SNP dataset Fig. , Table 1P > 0.05), and only a weak correlation was detected between the heterozygosity and haplotype number . For example, for the five single-haplotype sampling sites, expected heterozygosities of 0.0300–0.0565 were obtained, covering a 56.1% range of the estimates. In 24 sampling sites, both mitochondrial and SNP data were obtained for at least two individuals. Similarly, when only these 74 individuals were considered, all the three mitochondrial diversity measures (results not shown) were not correlated with the SNP expected heterozygosity (P > 0.05).A similar variability pattern was not obviously represented in the mitochondrial diversity measures Table . The nucL. boringii in different periods was not equally stable  populations in the eastern edge of the Mountains of Southwest China hotspot. The molecular results suggest a Late Pliocene to early Pleistocene origin for the West clade endemic to the hotspot edge, in which the area around sites 21–24 and 26 has consistently been modeled to be suitable for mphibian , 79, mammphibian , insect mphibian , and flomphibian , 80–82. mphibian , 48, 83.In this hotspot, except for the southwestern finger, which overlaps the Nujiang Valley, the eastern edge is the only area that provides low-elevation habitat, say, below 1000 m. Most areas of the hotspot exceed 3000 m due to the rapid uplift of the Hengduan Mountain region mainly between the late Miocene and Late Pliocene , 84–87, L. boringii.In addition to implying that long-term occupation does not necessarily correlate with large genetic variation at the western edge of the Sichuan Basin Figs. , 2 and 5Different RADseq and mitochondrial variations associated with admixture have been reported in other taxa –99. The A scenario of dispersal after long-term isolation can explain much of the spatial distribution of genetic variation in the Emei mustache toad, yet long-term persistence and secondary contact do not guarantee high variability. For many other taxa isolated in the mountains surrounding the Sichuan Basin, a model that combines recurrent contractions caused by Quaternary climatic changes and some failed expansions under suitable conditions for explaining scattered ranges can be considered when examining their evolutionary histories. More generally, this study provides insights into the heterogeneous nature of biodiversity hotspots and the discordant variability estimates using genome-wide and mitochondrial data.Leptobrachium boringii were used as the ingroup. The congeners L. liui and L. leishanense, with two tadpole samples each, were used as outgroups according to the current phylogenetic hypotheses [A total of 301 individuals, including 289 tadpoles, from 28 sampling sites representing all the known ranges of potheses , 76–78. nad1 fragment consisted of partial tRNA-Leu (16 bp) and NADH dehydrogenase subunit 1 genes; the D-loop fragment of ~ 1080–1350 bp in length contained regions of the cytochrome b gene (78 bp) and a control region with the tRNA-Thr, tRNA-Pro, and tRNA-Trp genes between them. For the nad1 fragment, the primers Leu-1 L and Leu-5 L from Zheng et al. [D-loop fragment were newly designed in this study: Vcb-1 L (5′-ATCggCggAC AACCTgTCgA AgACC-3′), Vcb-3 L (5′-CAACCTgTCg AAgACCCCTA CgT-3′), Vcr-2H (5′-gTggCTgATC CACCggAAgg TAAg-3′), and Vcr-4H (5′-gCTgATCCAC CggAAggTAA gATC-3′). Sequences of the light-strand encoded tRNA-Pro gene were converted into complementary strand sequences. Alignment was conducted with ClustalX version 2.1 [The 745-bp g et al.  and ND1-g et al.  were usesion 2.1  and visunad1 and D-loop fragments were concatenated for analysis. The nucleotide diversity and haplotype diversity were estimated for each local population using DnaSP version 6.10.01 [nad1 gene sequence. Because suitable fossil calibrations were not available, an evolutionary rate of 0.69 ± 0.3% divergence per million years per lineage commonly used for the anuran nad1 gene was used to calibrate the tree [The  6.10.01 . To exam 6.10.01 . The bes 6.10.01  using ththe tree , 104–106the tree  to ensurMseI restriction enzyme, T4 DNA ligase, ATP, and MseI adaptor containing barcode information. The restriction ligation was heat inactivated at 65 °C. The product was digested with the restriction enzyme HaeII at 37 °C and subsequently purified using Agencourt AMPure XP beads (Beckman Coulter). PCR was performed using the purified product and Phusion Master Mix (New England Biolabs) with universal and index primers. The PCR products were purified and pooled and then electrophoresed on 2% agarose gel. Fragments of 425–450 bp in size (with indexes and adaptors) were isolated, purified and diluted for sequencing using the PE150 Illumina Hiseq 2000 platform.Genome-wide SNP data from RADseq loci were obtained for 75 ingroup representatives from 25 sampling sites  and two outgroup individuals using the SLAF-seq  approachRaw reads were checked for quality using FastQC version 0.11.7 . Data clThe expected heterozygosity for each sampling site was estimated using Arlequin version 3.5.2.2 . In addiAn identity-by-state distance matrix between individuals was constructed in PLINK version 1.9  and usedL. boringii.As an approach to assess range stability, the last interglacial (LIG), last glacial maximum (LGM), and mid-Holocene (MH) potential distributions were modeled for comparison. Climate reconstructions for these periods are frequently used examples of Quaternary climate fluctuations. The analyses were conducted at a spatial extent of 21.5–34°N and 100–114°E, extending not substantially from the Sichuan Basin and surrounding mountains and the range of r| > 0.8), the variables with smaller contributions according to jackknife analysis of variable importance to model development were excluded [Ecological niche modeling was conducted using Maxent version 3.4.1 , 119. Niexcluded , 122. Seexcluded , and theexcluded . Model pThe niche model was projected onto paleoclimate reconstructions in WorldClim with the same resolution of 2.5 arc-minutes. The resolution of the LIG  layers was changed from 30 arc-seconds to 2.5 arc-minutes through averaging . For LGMAdditional file 1: Table S1. Sample information and haplotype designation. (XLS 87 kb)


Vitis vinifera L. subsp. sylvestris (Gmel.) Hegi and cultivated (V. vinifera L. subsp. vinifera) grapevine in the western Balkan region and Central Europe have not been studied together previously, although this area has a rich viticultural past. Here, we studied wild grapevine populations sampled from their natural habitats in several countries of the western Balkan region and Central Europe. Their genetic diversity and structure were compared to cultivars that are traditionally in use in this region. A sample set of 243 accessions was genotyped at 20 nuclear microsatellite loci, including 167 sylvestris and 76 diverse vinifera cultivars. The genetic diversity of the wild grapevines was lower than that of cultivars by all genetic parameters. Both hierarchical and nonhierarchical clustering methods differentiated two main groups, indicating clear separation between wild and cultivated vines but also revealed clear gene flow between the cultivated and wild gene pools through overlaps and admixed ancestry values in the graphs. There was greater affinity to the wild grapes in Central European cultivars than in Balkan cultivars. Fine arrangement of the structure among cultivated grapevines showed differentiation among Central European and Balkan cultivars. These results confirm the divergence of wild grapes from vinifera and highlight the “crossroad” role of the western Balkan peninsula in the broader context of European viticulture. The genetic diversity and relationship between wild ( V. v. subsp. sylvestris) is a close relative of the cultivated grapevine (V. v. subsp. vinifera) and the only endemic taxon within the Vitaceae family in Europe [V. sylvestris are endangered and fragmented, in some cases down to <10 individuals [V. sylvestris is an officially protected species , while in Croatia, it has “least critical (LC)” status according to the Flora Croatica Database [Wild grape , Bosnia and Hercegovina (P03), Slovenia (P05), Hungary (P07), and Germany (P08)  .V. v. subsp. vinifera varieties from the corresponding countries were included. According to historic records, summarized in the Vitis International Variety Catalogue database (www.vivc.de) [n = 27), Germany (n = 16), Croatia (n = 17), France (n = 4), Bosnia and Hercegovina (n = 2), Italy (n = 1), Switzerland (n = 1), Austria (n = 1), Montenegro (n = 1), Slovenia (n = 1), Balkan (n = 1), and unknown (n = 4).For analysis of genetic structure and differentiation between wild and cultivated grapevines in these regions, seventy-six vivc.de) , wide geDNA was extracted from young leaves. Total genomic DNA was extracted using the NucleoSpin Plant II kit . The extracted DNA was quantified and used at a working DNA concentration of 1 ng/μL. Twenty microsatellite loci were analyzed to study the genetic diversity of the samples: VMC1B11 ; VMC4F3.The KAPA Fast Multiplex PCR Kit (2x)  was used to set up reaction mixtures containing master mix, 100 pmol of each primer, and ~ 1 ng template DNA. Amplification was performed in ABI 9700 thermal cyclers  using the following program: three min initial denaturation at 95 °C, followed by 30 cycles of denaturation at 95 °C (15 s), annealing at 60 °C (30 s), and extension at 72 °C (30 s). A final extension was performed at 72 °C for seven min. DNA of two certified cultivars from Julius Kühn-Institut laboratory, “Muscat á petits grains” and “Cabernet franc,” were amplified and used as references to standardize the allele calls.Amplified products were resolved using capillary electrophoresis on an ABI 3130xl Genetic Analyzer  using GeneScan-LIZ 500 as an internal standard. Peaks were identified by size and height with GeneMapper 5.0 software .Different measures of genetic variability among 243 unique genotypes at 20 SSR loci were calculated. The number of alleles per locus (Na), number of effective alleles (Ne), private alleles, observed (Ho) and expected (He) heterozygosity, and Fixation index (F) were calculated for each locus over both wild populations and cultivated grapevines using GenAlEx 6.5 .Genetic relationships among accessions were assessed by distance-based cluster analysis using the neighbor-joining method (NJ) as implemented in the MEGA 6.0 software  using thST and pairwise Nei’s genetic distance for each pairwise combination of populations in GenAlEx software [The genetic differentiation between populations was carried using pairwise Fsoftware .Bayesian model-based cluster analysis executed in STRUCTURE  was applV. sylvestris [The genetic indices of the 167 wild and 76 cultivated accessions at 20 nuclear SSR loci were calculated  after thlvestris . The expvinifera (0.759) were greater than that of any wild population, which ranged from 0.437 in P08 to 0.593 in P06. The He values were greater for vinifera grapevines than for sylvestris. Fixation index values were positive for all analysed groups except for cultivated varieties, indicating heterozygosity deficiency or greater inbreeding within wild populations. The group of cultivars had a negative F, indicating more heterozygosity. The F values within sylvestris were least for P07 (0.013) and greatest for P05 (0.146)Genetic diversity indices at the population level show that the number of alleles per locus of cultivated grapevines was greater (8.600) than in wild grapevines, where Na values ranged from 3.100 (Hungarian wild set = P07) to 5.300 (Bosnian wild set = P03) . The ovesylvestris populations and the group of cultivated varieties was estimated using pairwise Fst values and Nei’s genetic distance.  to 0.625 (cultivated group–Germany sylvestris P08). The FST values ranged from 0.067 for the Croatian sylvestris (P04) and Slovenian sylvestris (P05) up to 0.163 for the sylvestris from Croatia (P01 and P02) but was greatest for sylvestris P01-cultivated samples (0.187).Genetic distance among the eight istance. . The Neisylvestris, PA were observed at 15 out of 20 SSR. The number of PA was lower in sylvestris (22) than in the cultivated vinifera (35), but not drastically so. In cultivated grapevines, PA were found at 14 out of 20 SSR loci. In this study, 22 sylvestris PA were observed in seven out of eight populations. The greatest number of private alleles was identified in P06 (6) followed by P05 (5), P03 (4), and P08 (3), all populations from different countries.Fifty-seven private alleles (PA) were found at 19 out of 20 SSR markers in the studied set . In sylvvinifera and sylvestris subspecies, which formed two main clusters, one of which contained cultivated grapes (C1), and other (C2), the wild grapes from Germany, Hungary, Slovenia, Croatia, and Bosnia and Herzegovina  that are closely allied with the cultivars. Wild accessions tend to cluster together with individuals from the same population due to high within-population genetic similary. The geographic clustering can be seen in the vinifera set originating from at least 10 different countries tending to differentiate between Western and Eeastern European cultivars.The neighbor-joining (NJ) cluster analysis showed clear differentiation between the zegovina . One setsylvestris with sylvestris-C2). Despite the strict pre-analysis selection of presumed pure sylvestris, PCooA revealed slight overlap between C1 (vinifera) and C2 (sylvestris), indicating gene flow from vinifera to sylvestris and vice versa, even though the vinifera set was half the size of the wild set.The structure and the correlations among individuals were analysed using PCooA based on the genetic distance matrix of 20 SSRs. PCooA projections were plotted in a two-dimensional scatter plot . The PCoV. sylvestris and 76 V. vinifera) into two clusters: sylvestris and vinifera  and P02 (Imotski). Populations P07 (Hungary) and P08 (Germany) had only one such accession. The greatest percentage of admixed genomes among cultivated V. vinifera was observed in Western wine cultivars: “Pinot meunier,” “Burgunder gross,” “Moehrchen,” “Suessschwarz,” “Adelfraenkisch,” “Gewuerztraminer,” “Riesling blau,” “Chatus,” and “Ondenc.” Other cultivars were all of V. vinifera ancestry with Q > 75  and the lowest for P08 (Germany), indicating heterozygosity decreased from east to west. The east-west pattern of decreasing diversity was documented in other studies [sylvestris populations in the Rhine floodplains followed a south-north migration route after glaciation retreated, being thus more related to the Southwestern European gene pool. Decreasing heterozygosity was confirmed by the fixation index, in general highlighting inbreeding in all sylvestris populations, while the cultivated set had a negative fixation index, showing greater genetic diversity than sylvestris. However, this situation is not expected in sylvestris, as the presumed ancestor of cultivated grapes. The FST statistics showed low differentiation between vinifera and sylvestris, in agreement with previous results [sylvestris–sylvestris (Croatia–Slovenia), but even greater between sylvestris  and cultivated varieties. Genetic diversity by all measures was reduced in sylvestris populations, indicating their isolation, where sibmating leads to inbreeding and loss of alleles and heterozygosity. Private alleles can be useful indicators of gene flow [sylvestris from vinifera [sylvestris than in the cultivated set, there were many private alleles in the sylvestris populations from all countries, highlighting the local richness of the wild populations. There was continuous reduction in genetic variability of Vitis sylvestris populations, leading to loss of alleles and heterozygosity. In general, perennial fruit crops such as grape are the descendants of spontaneous or controlled crosses between geographically and genetically distant individuals, resulting in new variants carring novel combinations of alleles not find in the wild [Genetic diversity in the studied set of individuals, expressed through the expected and observed heterozygosities of SSR markers, was greater in  studies ,16,18,20 studies ,24. It i results ,49. It wene flow  or used vinifera . In thisthe wild . Therefothe wild .vinifera –sylvestris set, the NJ, PCooA, and STRUCTURE analyses confirmed clear separation of the two groups. Both groups showed high assignment to their own cluster, confirming the representativeness of the studied set. The accessions falling in the transition zone in NJ, the overlapping zone in PCooA or the admixed part in Structure suggest a common gene pool for the two groups. Despite the strict pre-selection of true-to-type sylvestris individuals and a cultivated set originating from at least 10 different countries that was only half as large, classification  and ordination (PCooA) methods both showed bidirectional gene flow between the vinifera and sylvestris gene pools.By using the combined sylvestris shown here was reported previously by various authors [sylvestris cluster. Most of these wine grapes, except “Riesling Weiss,” “Pinot,” and “Gewürztraminer,” are minor varieties or were rediscovered in recent times. The subsequent clusters encompass mainly Hungarian varieties, followed by Croatian and Bosnian with some intermixed Hungarian varieties. Clustering far from sylvestris and high vinifera ancestry values point to a different geographic origin, most likely the Near East [vinifera cluster, while cultivars from Germany were grouped close to the sylvestris cluster. “Riesling blau,” the cultivar in the transitional zone in NJ, shared more alleles with sylvestris than with the vinifera group in this set of samples . The same pattern of allele sharing from sylvestris was seen in some other Western European cultivars in other genetic comparison studies [sylvestris in a Structure plot [vinifera. One should take into consideration the limited power of SSR markers to resolve subtle phylogenetical relations among closely related individauls [The NJ cluster analysis was carried out without considering the geographic origin of the samples. However, the cultivated set displays a clear structured arrangement of accessions by ancestry, diversity and putative geographic origin. “Elbling Blau” and eight wine grapes in the first three independent small subclusters are first-degree related to ‘Savagnin’  and ‘Pinot’. The genetic proximity of “Savagnin”/“Traminer” and “Pinot” to  authors ,14,49. A authors  in the sear East . In the  studies . When anure plot , but SNPividauls ,53.Because of its reduced number of dimensions, PCooA provides less-detailed insight into the genetic structure of the studied set and does not allow conclusions as to what extent some population/individual influenced the other. However, this analysis facilitates detection of an intermediate position between the groups (overlaps). Clustering methods give more detailed information on the genomic nature of the studied set. Both groups showed very high average probabilities of assignment to their own cluster, consistent with being genetically different.Vitis sylvestris populations represent small, isolated populations surviving in mountain or floodplain forests in close proximity to rivers. The remaing populations of Vitis sylvestris are considered an “endangered species” [Vitis taxa that escaped from vineyards [Outlier groupings or diversity was not observed, giving insight into the similarity of the studied wild populations from this area. Greater diversity was observed in the eastern than in the western wild populations, and southern populations were more diverse than the northern ones, so the decrease in diversity can be traced from east to west, and from south to north. Ketch island in Germany was the northernmost population, while Dalmatian populations were most southern. In the Balkans, one study assumed there was a past metapopulation covering the broader area of Danube river . Severalspecies” . Severalineyards . GizdavaV. v. subsp. sylvestris resources.This study demonstrated that populations of wild grapes are highly fragmented into small, isolated populations that represent valuable genetic resources. Additional conservation efforts are needed to protect the remaining genetic variation of V. vinifera and V. sylvestris populations showed clear distinction between cultivated and wild grapevines. Despite strict selection of pure sylvestris and a considerably smaller set of vinifera cultivars included in analysis, all statistical and phylogenetic methods showed high assignment of vinifera to vinifera and sylvestris to sylvestris clusters, but always with visible overlaps between the groups. Through hierarchical phylogenetical clustering, vinifera cultivars were shown to be the source of genetic variation in the studied set. Allelic richness of the wild gene pool from various countries indicated local adaptation of sylvestris populations and assignment to its population of origin, confirming a valuable source of genetic variation in the studied populations. Close affinity was seen between the studied sylvestris populations and Western European cultivars, suggesting a common genetic origin. In contrast, cultivated grapes from the Balkan area were distant from the sylvestris populations in this sample set, suggesting a different geographic and genetic pool of domestication. However, the general questions relating the time and direction of introgression between these two subspecies are still open and could be resolved with a broader sample set and whole-genome data.Analysis of Western Balkan and Central European


Mitochondrial DNA (mtDNA) analysis is a critical tool in assessing the maternal origin, phylogeny, and population structure of domestic animals. Guangxi cattle are located in southernmost China, where the pure Chinese indicine resources might be preserved. In this study, we sequenced the complete mtDNA of three cattle breeds in Guangxi Province for phylogenetic analysis. The aim of this study was to determine the maternal origin and phylogenetic status of Guangxi cattle.n = 21), Nandan (n = 18), and Weizhou cattle (n = 17) using mitogenome sequencing. Our results show that Bos indicus sub-haplogroup I1a predominates in Guangxi cattle breeds. Population structure by multidimensional-scaling analysis significantly differentiates Weizhou from the other two breeds (Longlin and Nandan). Moreover, the mtDNA haplotype composition and FST values indicate that the formation of Longlin and Nandan breeds may have been affected by Indian indicine, whereas, Weizhou island might have preserved pure Chinese indicine cattle due to its geographical isolation. We speculate that following the initial entry of zebu into southern China, the subsequent introgression of Indian indicine may have influenced the matrilineal origin of local breeds in southwestern China.Guangxi Province is located in the southernmost region of China, adjacent to the hotspot that is considered to be the putative migratory corridor or domestication area for Chinese indicine cattle. Here, we investigated the evolutionary status of Guangxi native breeds, Longlin ( Bos taurus (taurine) and Bos indicus (indicine) [Bos taurus originated in Near East, and then spread into East Asia and thought to have reached China at least ~3900 years before present (YBP) [Bos indicus originated from Indus Valley about 80000 YBP, and spread eastwards to Southeast Asia and southern China <4000 YBP [The formation of modern Chinese cattle breeds is the result of multiple and successive invasions throughout history. Based on the morphological and genetic evidence, Chinese cattle have been mainly classified into two types: ndicine) ,2,3. Bosnt (YBP) . Previou4000 YBP ,6. A rec4000 YBP . GuangxiBos indicus lineage, a novel sub-haplogroup, named I1a, has been described by Chen et al. [Maternally inherited mitochondrial DNA (mtDNA) has been extensively used to determine the genetic variation and phylogenetic relationships of domestic cattle, but related studies have been mostly restricted to the short hypervariable region, which makes it impossible to clearly distinguish between some important ancient branches within the tree ,5,10. Prn et al.  on the bn et al. .Bos indicus and Bos taurus mtDNA haplotypes while Weizhou only harbor Bos indicus haplotypes [Bos indicus Y3 lineage [Several previous studies have examined the genetic diversity and origin of Guangxi cattle breeds using both mitochondrial and Y-chromosome data. Early studies using mtDNA control region have suggested that both Nandan and Longlin have plotypes ,14,15. A lineage . Here, wA total of 56 ear tissues were collected from three cattle breeds, including 18 Nandan (ND), 21 Longlin (LL), and 17 Weizhou (WZ) from Guangxi Province . To miniGenomic DNA was extracted by the standard phenol-chloroform method . Paired-The complete mitochondrial genome sequence was obtained for each of the three Guangxi cattle breeds including Nandan, Longlin, and Weizhou. All the sequences were deposited in GenBank under the accession numbers MN714163–MN714218.FST values using the SPSS version 18.0 software package . A neighbor-joining tree was constructed in MEGA 5.0 [Measures of mitogenome sequence variation, including numbers of haplotypes and variable sites, haplotype diversity (Hd), nucleotide diversity (Pi), and the average number of nucleotide differences (k), were calculated using the program DnaSP v 5.10 . The anaMEGA 5.0 , and theMEGA 5.0 . The BayMEGA 5.0  under thMEGA 5.0 . ConvergAnalysis of mitogenome sequences  revealed 308 variable sites among the 56 samples, defining a total of 34 haplotypes a. The mtBos grunniens mtDNA sequence (accession no. AY684273) as an outgroup , combinoutgroup b. The BaWithin I1 lineage, Indian cattle clustered at the root segment, which represents the ancestral indicine mtDNA haplotypes b. The map < 0.05). Geographic distribution of breeds (among groups) represented no significant effect and accounted for only 3.64% of the total variability. Estimate of pairwise-population FST values indicated a high genetic differentiation between Indian and Weizhou cattle . Among the Guangxi breeds, Weizhou was the most differentiated from the other two breeds, with FST values ranging from 0.221  to 0.081 . In contrast, there was limited genetic differentiation between Nandan and Longlin . In other words, Nandan and Longlin breeds shared a similar genetic background. MDS analysis was also performed to better visualize the breed genetic relationships using European taurine cattle (AY676855–AY676873) as an outgroup  in Guangxi native cattle. I1 was the predominant mtDNA haplogroup, whereas the haplogroup I2 was rare and T3 and T4 only detected in limited Longlin and Nandan cattle. The composition of the mtDNA haplogroups in Guangxi breeds were congruent with those previously presented ,15. MoreThe analyses of the genetic divergence reveal that Nandan cattle are much closer to Indian cattle 0.14143) than to Weizhou cattle 0.22131). In addition, three Longlin  and one Nandan cattle  carried mtDNA haplotypes closely related to Indian indicine b. This f2131. In 143 than All Weizhou cattle belonged to the I1a sub-haplogroup. Weizhou is an island where cattle were imported from Leizhou Peninsula 100 years ago . The higIn conclusion, our findings revealed the large genetic divergence between Weizhou and the other two Guangxi Breeds (Longlin and Nandan). Sub-haplogroup I1a was the major domestication event in southern China. Weizhou Island might preserve the precious genetic resource of Chinese indicine.


If parasites transmit more readily between closely related hosts, then parasite burdens should decrease with increased genetic diversity of host populations. This important hypothesis is often accepted at face value—notorious epidemics of crop monocultures testify to the vulnerability of host populations that have been purged of diversity. Yet the relationship between genetic diversity and parasitism likely varies across contexts, differing between crop and noncrop hosts and between experimental and natural host populations. Here, we used a meta‐analytic approach to ask if host diversity confers protection against parasites over the range of contexts in which it has been tested. We synthesized the results of 102 studies, comprising 2004 effect sizes representing a diversity of approaches and host‐parasite systems. Our results validate a protective effect of genetic diversity, while revealing significant variation in its strength across biological and empirical contexts. In experimental host populations, genetic diversity reduces parasitism by ∼20% for noncrop hosts and by ∼50% for crop hosts. In contrast, observational studies of natural host populations show no consistent relationship between genetic diversity and parasitism, with both strong negative and positive correlations reported. This result supports the idea that, if parasites preferentially attack close relatives, the correlation of genetic diversity with parasitism could be positive or negative depending upon the potential for host populations to evolve in response to parasite selection. Taken together, these results reinforce genetic diversity as a priority for both conservation and agriculture and emphasize the challenges inherent to drawing comparisons between controlled experimental populations and dynamic natural populations. We conducted a large meta‐analysis to test the long‐standing hypothesis that genetic diversity limits parasitism in host populations. We find support for this hypothesis as well as clear evidence that the protective effect of genetic diversity varies across contexts. In experimental host populations, we find that genetic diversity leads to reductions in parasitism that are moderate for noncrop hosts and very strong for crop hosts. These results support a key assumption of major evolutionary hypotheses that argue for parasites as the selective force maintaining sexual reproduction and multiple mating. They also emphasize genetic diversity as a sustainable solution for suppressing disease in crop fields. In contrast, our study did not find a consistent negative correlation between genetic diversity and parasitism across observational studies of natural host populations. This analysis did, however, suggest a negative relationship between genetic diversity and parasitism in the subset of observational studies that surveyed populations of threatened host species. As a whole, our study underscores the potential for genetic diversity to strongly limit parasitism while highlighting biological and empirical factors that may influence where and when we see a relationship between genetic diversity and parasitism. These findings further emphasize that conserving genetic diversity is critical for defending crops and threatened populations against outbreaks.Why do some host populations suffer from large parasite burdens, while others do not? This is a central question in the study of host‐parasite interactions, and the answer determines best practices for food security and conservation. A prominent hypothesis argues that genetically diverse host populations experience less parasitism than genetically depauperate ones Haldane . This idMost tests of the monoculture effect compare parasitism in crop mixtures and monocultures  AND (parasit* OR infect* OR pathogen OR disease OR susceptib* OR epidemic OR resist*)We received weekly Web of Science alerts of new search results through December 2019. We also searched Agricola  and ProQuest (abstracts with genetic diversity AND [disease resistance OR pathogen OR parasite]). Finally, we searched reference lists of reviews , a subset of host genotypes, or transformed values. Genetic diversity of hosts had to be intraspecific and explicitly controlled or measured.We use the term “parasite” to refer to parasites and pathogens . Nonetheless, we excluded a few studies from which we could not calculate an appropriate effect size—one noncrop experimental study that did not report standard deviations and four observational studies with fewer than four sampled populations.One hundred and two studies met our inclusion criteria Fig. . We attrBoth authors extracted and checked all data. We collected data from tables, figures  and ΔAICc, the difference in AICc values of the focal model and the model with the lowest AICc . ΔAICc less than two generally indicates that a model has substantial support. We also calculated the Akaike weight (w), which is interpreted as the probability that a model is the best model among the candidates. If a clear set of best models  was identified, we further evaluated the factors in those models as potential contextual variables mediating the strength of the monoculture effect. Alternately, a large set of best models with low weights (w near 0) suggested that no single factor or subset of factors played a unique role in mediating the strength of the monoculture effect . For three effect sizes in two studies , so we treated all effect sizes sharing controls as part of the same experiment. We examined four contextual variables: parasite life cycle, parasitism metric, mixture evenness, and inoculation mode (Table S1). In the next section, we describe an alternate approach to incorporate the 47 crop studies that did not report standard deviations.Eight crop studies fit our inclusion criteria and reported standard deviations, as required for calculating standard effect sizes for experimental studies. For these eight studies, we calculated Hedges’ z, the recommended statistic for correlational studies , and after analysis we transformed z back to r for interpretation . In the model, we treated all effect sizes sharing low‐diversity controls as part of the same experiment. We included study type as a contextual factor. Experimental crop studies lacking standard deviations were excluded in this analysis because we were unable to convert to a common effect size. This exclusion did not undermine our goal for this analysis, which was to directly test for differences between study types. We did not aim to estimate the mean effect of genetic diversity on parasitism across all studies, because variation between study types suggested that this estimate was not meaningful.To further test observed differences between study types, we combined experimental and observational data by converting correlation coefficients to Hedges’ g, the log response ratio (lnRR) can be calculated without standard deviations and weighted using sample sizes instead of sampling variances  for interpretation. We excluded 90 effect sizes because means of zero prevented calculation of lnRR.Forty‐seven experimental crop studies did not report standard deviations. These represented the majority of the crop literature and spanned a wider time period (1977‐2017) than the eight studies with standard deviations (2000‐2019). To incorporate these studies, we applied an alternate effect size and weighting approach to all 55 studies. In contrast to Hedges’ Triticum. These taxonomic factors were not included in the smaller crop analysis due to insufficient sample sizes.To account for the hierarchical structure of the data, we calculated the weighted mean effect size for each experiment within a study and for each study. We then calculated the weighted means across experiments and studies. We calculated bootstrap confidence limits by sampling 10,000 times with replacement  between parasitism in high‐ and low‐diversity host populations   Fig. . This esc < 2 and low weights (0.05 ≤ w ≤ 0.12). The best model contained no contextual factors (Table S3A). We estimated the mean effect of each contextual factor by averaging its regression coefficient across the 95% confidence set of models. No contextual factor had a substantial and consistent effect on the relationship between genetic diversity and parasitism (Table S3B). Additionally, in models fit with factors individually, the mean effect of genetic diversity did not differ significantly between levels of any factor . These results indicate a limited role for the tested contextual factors in explaining variation in the effect of genetic diversity.We tested if 11 contextual factors explained this variation. Model selection analysis identified nine models with ΔAICg = −1.945   Fig. .I2 of 91.5% indicated substantial variation in the effect of genetic diversity (Table c < 2 and moderately low weights (0.10 ≤ w ≤ 0.23). Each of these models included mixture evenness as a factor (Table S4A). Averaging values of regression coefficients across models, we found that genetic diversity reduced parasitism more when cultivars were mixed unevenly  (Table S4B). A model with only evenness showed that diversity strongly reduced disease in even mixtures  but even more so in uneven mixtures  . Diversity also reduced parasitism more when plants were directly inoculated with parasites . A model with only inoculation mode showed that diversity reduced parasitism with direct inoculation  but only marginally with passive parasite exposure in field settings  .The The alternate analysis of all 55 crop studies further supported a major reduction in parasitism with genetic diversity. In this analysis, a negative effect of genetic diversity on parasitism would manifest as a value below one for the response ratio . The weighted mean response ratio across the 235 experiments was 0.493 , indicating a reduction in parasitism of over 50% in mixtures relative to monocultures. The weighted mean response ratio across the 55 studies was consistent with this result    Table . This fiI2 of 53.2% indicated moderate variation in the effect of genetic diversity (Table c < 2 and low weights (0.03 ≤ w ≤ 0.07). All top models included conservation status and parasite life cycle (Table S6A). Averaging values of the regression coefficients across models, we found that the correlation between diversity and parasitism varied substantially with conservation status , parasite life cycle , and island status  (Table S6B). Fitting models with factors individually, we found a negative correlation of genetic diversity with parasitism for threatened taxa  but not unthreatened taxa . This distinction weakened when we relaxed our criterion for classification as threatened (Results in the Supporting Information). We also found a negative correlation across island  but not mainland populations . Finally, a model with only parasite life cycle found a negative correlation for simple life cycle parasites  and a positive one for complex life cycle parasites , but these did not differ significantly from zero .The g = −0.570 , P < 0.001) with an I2 of 84.5% (Table P = 0.002) , while observational studies did not  (Table S7), although the mean estimates for these two groups overlapped .Finally, we directly tested whether study type contributed to variation among effect sizes in a combined dataset. Across experimental and observational data, genetic diversity had a negative relationship with parasitism  similar to that of Ekroth et al. (g = −0.353)  Fig. . Reversa53) Fig.  allows d53) Fig.  that did53) Fig.  includeda priori hypotheses. Modeling this approach, Ganz and Ebert  and our study found substantial variation in the effect of genetic diversity. Ekroth et al. (2019) linked this variation to study setting and parasite functional group: genetic diversity reduced parasitism in field settings but not lab settings and for microparasites but not macroparasites. In contrast, we did not identify any contextual factors that explained variation in effect sizes . This may reflect differences in our datasets. Notably, Ekroth et al. , inclusion evaluations (S2), extracted data, calculations of effect sizes (S3), and analysis scripts (S4‐S8) are available as Supporting Information for review on the GitHub Repository genetic‐diversity‐and‐disease. Data files and analysis scripts are also deposited in the Dryad Digital Repository Associate Editor: R. SnookSupplementary MaterialClick here for additional data file.Table S1. Biological and methodological variables included in tests for context dependence.Table S2. Formulas used in effect size calculationsTable S3. Analysis of context dependence in experimental studies of noncrop systems.Table S4. Analysis of context dependence in the subset of crop studies reporting standard deviations.Table S5. Analysis of context dependence in the full collection of crop studies.Table S6. Analysis of context dependence in observational studies of noncrop systems.Table S7. Combined analysis of experimental and observational data.Figure S1. Testing for context dependence in experimental studies of noncrop systems.Figure S2. Testing for context dependence in experimental studies of crop systems.Figure S3. Testing for context dependence in observational studies of noncrop systems.Click here for additional data file.Supplementary MaterialClick here for additional data file.Supplementary MaterialClick here for additional data file.