Populus tremuloides were investigated including 6450 trees across western Canada focusing on adaptation traits and growth. Both genetic parameters and home‐site transplant models were evaluated. We found a genetic trade‐off between growth and early spring leaf flush and late fall senescence. Coefficients of phenotypic variation (CVp) of cell lysis (CL), a measure of freezing injury, shrank from 0.28 to 0.10 during acclimation in the fall, and the CVp slope versus the freezing temperature was significantly different from zero . There was more between‐population genetic variation in fall phenology than in spring leaf phenology. We suggest that P. tremuloides demonstrated a discrepancy between the ecological optimum and the physiological optimum minimum winter temperature. The sub‐optimal growing condition of P. tremuloides is potentially caused by the warmer ecological optimum than the physiological optimum. Assisted migration and breeding of fast growers to reforest cooler plantation sites can improve productivity. Transferring the study populations to less than 4°C of extreme minimum temperature appears safe for reforestation aligning with the historical recolonization direction of the species. This is equivalent to a 5–10° latitudinal northward movement. Fall frost hardiness is an effective criterion for family selection in the range tested in this study.A novel method was tested for improving tree breeding strategies that integrate quantitative and population genetics based on range‐wide reciprocal transplant experiments. Five reciprocal common garden tests of  The sub‐optimality of local Populus tremuloides demonstrated discrepancies between the ecological optimum and physiological optimum and the tree species tended to express a colder ecological optimum than the physiological optimum. Transferring the study populations  appears safe for reforestation, equivalent to 5‐10° latitudinal northward movement. We demonstrate the reasons for adopting assisted migration to counter this maladaptation, especially in reforestation.In this study, we investigated the relationship between local adaptation and genetic parameters of the adaptive traits and growth trait of 22.1The raw growth datasets reanalyzed here are documented in a previous study .2.2Raw bud break scores were obtained from a previous study , and the daily weather dataset covered the 1961–1990 normal period and the actual growing period of trees (1998–2010). The maximum and the minimum temperatures were between ±20°C, with annual precipitation around 474 mm, as a typical continental climate. These weather station data were applied to evaluate the degree days of the spring growing period . Here, the heat‐sum of bud break was calculated with 0°C as the base temperature.To study the correlation between phenology and climatic conditions, Trial #60 was selected in central Alberta, in which individual tree phenology and height were both measured. The daily weather data were retrieved from the 2.4The frost hardiness of northern British Columbia (BC), central Alberta (cAB), and Minnesota (MN) provenance groups were measured at the central Alberta trial (#60). We chose the three populations to represent a northwest to southeast transect of study populations aligning with the species range in order to reveal the full range of the intraspecific genetic variation of the hardiness trait. Lethal damage temperatures of 50% (LT50) and 25% cell lysis (LT25) were quantified and predicted. The experimental design, procedure, raw data, and cell lysis (CL) calculation method are documented by Schreiber, Ding, et al. .REL) was determined by the quotient of the first conductivity measurement after freezing (C1) against the second measurement of the heat‐killed samples (C2)  representing the full continental transect of the population range of the study area. Eight trees were randomly chosen per family and current year twigs per freezing treatment were collected. The collection was repeated three consecutive times with 20–31 days' intervals from 22 August to 10 Oct 2011. All twigs from 48 trees per collection date were cut into 5 cm pieces and placed in 30 ml high‐density polyethylene bottles and were added 5 ml of deionized water before freezing treatments. The freezing treatments during the three sampled times were listed in the Table y Method .The probability of daily freeze–thaw events in winter was calculated from 1960 to 2010 based on the historical daily weather data at the central Alberta site  in Figure 2.5yinkm was the trait observation of the m‐th tree in the k‐th half‐sib family of the i‐th block from the n‐th population; μ was the grand mean; Bi was the fixed effect of i‐th block; Pn was the random effect of n‐th population; and Fnk was the random effect of k‐th half‐sib family within n‐th population; einkm was the random residue of m‐th each tree. The REPEATED statement was used to estimate the within‐plot residual variance and there were ~43 plots nested in each rep. Pn, Fnk, BFink, and einkm were assumed to follow normal independent identical distributions Niid0σpop2, Niid0σf2, Niid0σbf2,Niid0σe2, respectively.Additive genetic and phenotypic variances were calculated with the SAS 9.3 PROC MIXED (SAS Institute). In order to estimate the within‐population variance by each population, we grouped each provenance groups of half‐sib families resulting in the following family effect model:σ^A2) equaled to four times of the half‐sib family variance component (Vf); the phenotypic variance (σ^P2) was (σf2^+σE2^)  was calculated with the following function:Q^st index was calculated asσ^B2 was the between‐population variance component (Vpop); σ^A2 was four times of Vf; Ve was the random residual. Delta method was applied to calculate the standard error of σ^A2, σ^P2, Q^st and h^2. Coefficients of variation (CV) were estimated as.CV of additive genetic variance was (CVA); coefficients of phenotypic variance was (CVP); coefficients of residual was (CVE); m was the trait mean.Additive genetic variance ((t) was the random family effect within trait at~N0,f⊗Vf,where the f was the identity index matrix of half‐sib families and Vf was the half‐sib family variance (1/4σ^Gi); and ewas the residual error, with e~N0Ie⊗VR. The bivariate models were analyzed with the ASREML‐R v3.0 package  was as the followingσ^Gij denotes the estimate of genotypic covariance between traits i and j. σ^Gi was the estimated genotypic variance of trait j, which was the family covariance parameter. To adjust the differences due to different units and scales between growth and adaptive traits, the natural logarithm transformed values of traits were employed.The additive genetic correlation  of the genetic parameters were calculated with the Jack Knife method , when the sample size was not sufficiently large compared to the full field test data (n = 48). To facilitate the convergence of the linear models, a constant of 0.25 was added to each CL observation of the individual tree. We resampled 48 times from the original data with 48 deletions of individuals in total, so there were 48 Jackknife replicates constructed. We estimated five freezing temperatures*three sampling dates*48 Jackknife replications of 48 individuals were used for estimating each genetic parameter per treatment level which reduced standard errors of genetic parameters. The linear regression between genetic parameters and the geographic and climatic variables of provenance groups or DoY were constructed with the lm function in the R program.For the genetic analysis of CL, an individual tree model was used in a reduced function due to the sample size change from ~1290 trees/trial to 48 trees per freezing test temperature, as follows:2.6PDij  is defined as following: at site #70, the average PD of the provenance group BC; at site #10, the average PD of the provenance group AB North; at site #33, the provenance group AB Foothills; at site #60, AB Central provenance groups; and at site #90, SK group. No trial data of MN provenance group are available. Relative PDij was the quotient of PDij and related local productivity . A relative height and relative survival were also fitted as the dependent variables, which were not typical productivity and survival measured directly from the trails but the relative performance contrast against the local seed sources after migration. The distances (dist) of environmental variables  were calculated as the differences between the site condition and the seed origin. Thus, the relationship between the relative productivity and migration distances (dist) was constructed as follows:b0Z and b1Z were the coefficients of the function, and e was the random residual. Here z denoted the transplanting direction. We estimated b0Z and b0Z with two scenarios: upward (dist >0) direction, and downward (dist <0). For example, when the MAT distance was more than zero, the prediction was for an increasing temperature scenario. Furthermore, we set the intercept to zero when dist = 0 and the dependent variable PDijPDlocal=1. To summarize the climatic effects, Principal component analysis (PCA) was used to produce the principal component 1 (PC1) as a combined climatic distance variable with the R function pcomp. PC1 indicated heat and growing degree conditions, which accounted for 57% of the total variance. To evaluate the fitting of models, F‐test of linear regression and adjusted R2 were calculated  that absolute magnitude was more than twice the SE, as well as the height versus growing season length  and bud break was linked to growing season length (rG = −0.7~−0.9). Spring and fall phenology were not correlated, except bud break of score one versus leaf coloration (rG = 0.7).Moderate to high additive genetic correlations were found between height and leaf senescence (rG > 0.6). Leaf phenology duration was negatively related to bud break (score 3) as rG = −0.64 ± 0.06 and positively related to growing season length (rG = 0.47 ± 0.09). Senescence duration was not correlated with leaf out duration but was related to growing season length (rG = 0.79 ± 0.13). The beginning of the phenology stage in each season  was negatively associated with the seasonal duration , but the ends of the phenology stage  were positively related to the duration of the phenology. Avoiding earlier spring frost with a later bud break resulted in a shortened growing season length (rG = −0.9~−0.7); a later senescence led to a longer growing season (rG = 0.3–0.8).Multiple phenology stages were positively correlated with each other within the same season in spring and fall . The VA of leaf senescence ranged from 0 to 5 day2 after Sept. 8th, which followed a quadratic trend over time from phenology phase 1 to 7 within the 18‐day window (R2 = 0.53). The VP increased from 0 to 20 day2 with a quadratic relationship with the average DoY of the phenology (R2 = 0.83). Narrow‐sense heritabilities (h^2) of spring phenology were ~0.9–1, while h^2 of fall phenology was 0–0.39.Among the 43 half‐sib families, for the spring bud break score 1 to 5 analyzed, the CVA and CVP are 0.12~0.15) and after the beginning fall phenology (CVA and CVP~0.01), or the early stage of hardening in September . The CVA adjusted the overestimation of genetic variance partially due to the site environment. The CVA of bud break calculated by heat‐sum was greater (~13%) than the fall leaf senescence (~1%) and bud break (~4%) measured by DoY. The population average of fall phenology correlated with latitude  and MAT of provenance groups .Lager genetic and phenotypic variances occurred at the beginning of spring phenology (h^2 peaked at 0.46 (±0.14) at AB Foothills  and decreased to 0.12 (±0.08) at the BC trial. Genotype by environment effect of height was expected. A positive linear relationship was shown between h^2 and extreme minimum temperature over 30 years (°C) of the trials (EMT) with a Pearson's correlation coefficient as 0.86 (p = .06); the relationship between h^2 and trial latitude (N°) was not significant . CVA of tree height stays around 0.13. CVA and CVP of senescence followed quadratic functions over DoY . The Q^st of spring bud break was moderate to low (~0.2) indicating a low among population variation, while most of the senescence variation was identified among populations  as the northern population shed leaves more than 10 days earlier than the southern (MN) populations.Height 3.3CVA of cell lysis ranged from 0 to 0.10, while CVP ranged from 0.10 to 0.25  under freezing temperatures from 0 to −80°C  over the time series from August 28 to Oct 17 , AB (Middle), MN (South) provenance groups from −20 to −60°C. The 7 Figure . The CVp3.4R2 = 0.52, p < .0001), and to the north with the slope = 0.026 . For the EMT transfer gradients, the correlation was a consistent trend for higher relative height, survival, and productivity when moving to cooler environments. Below the typical altitude of ~1000 m for aspen plantation, assisted migration potentially yielded ~40% more relative productivity compared to the local families when the EMT of sites were 4°C colder, despite the general depression of average growth under cooler climates at sites. No significant local adaptation trend was detected with the multiple environmental distances including the PC multivariate indices. The slopes of each transfer climatic distance ranged from −0.2 to 0.01 when distances were negative, which means a downward transfer, for the temperature‐related distances  the movement was toward a cooler environment, except the altitude and latitude, where negative distances meant downslope or southward transfer  especially during the onset period of hardening . Thus, the ecological optimum tends to be warmer than the physiological optimum.Again, by avoiding the time window of frost and freeze–thaw major events, especially the fall frost Figure , the traR2 = 0.19–0.31***) and latitudinal (R2 = 0.25–0.27***) seed transfer to the cooler and northern sites are secondary options to improve the relative height and productivity. Those aligned with the climatic direction for movement for the EMT. Those factors above are not necessarily determinants of adaptability, though the non‐significant factors could not be excluded from the decision‐making process of planting site selection. We suggest the intraspecific seed transfer direction is promising with the relative productivity increase and successful mitigation stratifies such as assisted migration without maladaptation remain challenging for sustainable forestry and reforestation  and this agrees with the previously reported direction of assisted migration , that is, spring leaf phenology and fall cell lysis. The disparity between ecological and physiological optima can fall around 4°C in terms of EMT, which can be translated to 5~10° northward in the study area , partially due to the coefficients of genetic family models to estimate the additive genetic variance , full‐sib families occurrence in the progeny within the half‐sib families, and limited environment variance to precisely estimate the genetic and phenotypic variances. Howe et al. (VA). The realized growing season was determined by the period from the terminal bud break to leaf senescence, which is a proximation of the growing season length. The earlier stage of spring leaf phenology (in mid‐May) also represented the additive genetic variance level of bud break, compared to the previous arbitrary bud break stage of a single score  will contribute to a potential buffer effect to the foliage loss due to the herbivore during the nutrient‐limited period of stands by mismatching the phenologies between host trees and insects.The leaf flushing date and growing season diverged more among families than between populations, so we suggest a weak frost selection pressure in spring in the study area. The alternative mutualism between herbivore and spring leaf phenology demonstrated earlier and later bud break matched the timing of 4.3P. tremuloides previously. Here, we dissected the extent of intra‐specific selection responses on spring and fall cold adaptation traits and found stronger population differentiation on fall phenology traits than on the spring traits. We demonstrated the species‐range estimation of Q^st for the continuums of spring (0.2–0.3) and fall leaf phenology (0.6–0.9) and identified higher population differentiation of fall phenology than the spring phenology. Therefore, natural selection was prevailing to cause population differentiation of fall phenology rather than the spring phenology  was lower than our results partially due to the limited sampling range of the species  for the total populations including all extreme demes  while spring leaf phenology had less correlation with growth (rG ~ −0.2) based on the species‐range wide samples. We also confirmed that across the species range in western Canada, the increasing growth was associated with late leaf senescence and no survival depression was found when families showed a late onset of leaf senescence; and this trend was previously reported in a within‐population study .Multiple studies in deciduous and conifer species showed the trade‐off between cold adaptation to frost and growth in the boreal species  to track the acclimation weather condition . The genetic parameters of leaf phenology and CL demonstrated different trends as quadratic and linear, respectively, during the process of breaking growth initiation and acclimation. Here, the CL was measured among 48 genotypes from six families compared to over 6000 trees for the height, so CL was not used to infer the genetic correlation with growth.The lethal damage temperatures (LT) linked to the acclimation to winter dormancy Figure  and CVrature R = 0.33 tn Figure . The assrature R = 0.33 tCVP dropped substantially. LT was negatively correlated with the soluble carbon hydrate concentrate in the tissues of temperate trees  was not correlated with the freezing temperature during acclimation that was only expressed as a moderate to a high level at certain stages of the freezing test. A leap value of CVA at −75°C caused the high h^2 and the reasons can lie in the higher standard error of the h^2, sampling error associated with the date or temperature treatment, tissue quality, and experiment conditions. To further confirm −75°C as a freezing temperature for CL trait selection, more repeated tests with extended family sampling are necessary.During the later phase of freezing (<−70°C), the twig could accumulate sugar to raise the tolerance to freezing damage and Previous studies demonstrated the impact of repeated freeze–thaw events in the spring and fall which caused vessel embolism, and therefore, worsened the frost injury and eventually advanced the dieback and mortality rate over winter  ecological optimum than the physiological optimum, which agrees with Rehfeldt et al. . Spring bud break showed a weaker population divergence than fall leaf senescence. The coefficient of additive genetic variance (CVA) in spring phenology and frost hardiness (cell lysis) were about 4‐ and 6‐fold of leaf senescence. The coefficient of phenotypic variation (CVp) for cell lysis ranged from 0.10 to 0.28, and shrank during acclimation. Freezing temperature (°C) was a predictor of the CVP for cell lysis (R2 = 0.33). The VA,CVA, and CVP of the spring leaf phenology demonstrated quadratic trends (R2 = 0.44–0.74) over time, while CVP and CVA for cell lysis followed a linear trend during the acclimation.The correlated response of early bud break and late senescence due to height selection was first confirmed in the western Canadian population collections. Growth variation was positively correlated with lagging fall leaf coloration and abscission ; data curation ; formal analysis ; investigation ; methodology ; software ; supervision ; validation ; visualization ; writing – original draft ; writing – review and editing . Jean S. Brouard: Conceptualization ; data curation ; funding acquisition ; investigation ; resources ; validation ; writing – original draft ; writing – review and editing .The research was supported by the grant from Andreas Hamann's research group and the in‐kind support from Isabella Point Forestry Ltd. and Western Gulf Forest Tree Improvement Program. Funding was provided by an NSERC/Industry Collaborative Development Grant CRDPJ. 349,100‐06 and an NSERC Discovery Grant RGPIN‐330527‐13 through the Government of Canada. We thank Alberta‐Pacific Forest Industries, West Fraser Grande Prairie & High Level (previously Ainsworth Engineered Canada LP), Mercer Peace River Pulp , Western Boreal Aspen Corporation, and Weyerhaeuser Company Ltd. for their financial and in‐kind support.The author declares that they have no conflict of interest.Data S1Click here for additional data file.


Alkanes constituted the largest percentage (>40%) in concentrations of the quantified VOCs in NRC, and the exception was Tianjin dominated by aromatics (about 52.34%). Although alkanes were the most abundant VOCs at the cities, the most important VOCs contributing to ozone formation potential (OFP) were alkenes and aromatics. Changchun had the highest OFP (537.3 μg/m3), Tianjin had the lowest OFP (111.7 μg/m3). The main active species contributing to OFP in the nine cities were C2~C6 alkanes, C7~C8 aromatic hydrocarbons, individual cities (Daqing) contained n-hexane, propane and other alkane species. Correlation between individual hydrocarbons, B/T ratio and principal component analysis model (PCA) were deployed to explore the source contributions. The results showed that the source of vehicle exhausts was one of the primary sources of VOCs in all nine cities. Additionally, individual cities, such as Daqing, petrochemical industry was founded to be an important source of VOCs. The results gained from this study provided a large of useful information for better understanding the characteristics and sources of ambient VOCs incities of NRC. The non-carcinogenic risk values of the nine cities were within the safe range recognized by the U.S. Environmental Protection Agency (HQ < 1), and the lifetime carcinogenic risk values of benzene were 3.82 × 10−5~1.28 × 10−4, which were higher than the safety range specified by the US Environmental Protection Agency (R < 1.00 × 10−6). The results of risk values indicated that there was a risk of cancer in these cities.From April 2008 to July 2009, ambient measurements of 58 volatile organic compounds (VOCs), including alkanes, alkenes, and aromatics, were conducted in nine industrial cities  of the Northeast Region, China (NRC). Daqing had the highest concentration of VOCs (519.68 ± 309.88 μg/m O3 is a recognized greenhouse gas [3, which is the root cause of urban photochemical smog [In recent years, with sustained and rapid development of China’s national economy, industrialization and urbanization have been accelerating and the number of vehicles has increased dramatically. Energy consumption has been so significant that air pollution exceeded the environmental carrying capacity, with such pollution in cities and urban agglomerations. This is mainly manifested in the decline of atmospheric visibility and frequent occurrence of heavy pollution episode, such as urban photochemical smog. The importance and role of VOCs on the formation of troposphere ozone  simultaneously in 9 cities of NRC, as shown in A sampling event was conducted in August 2009 in 9 cities. The air quality monitoring campaign represents designed to gain a better understanding of how ground-level ozone was formed and to determine the sources of VOC in the region of northeast China. Another sampling event was conducted in April, July and October 2008, and January 2009 at Shenyang and Tianjin, representing spring, summer, and autumn, winter, respectively. Two of them—Tianjin and Shenyang—were major metropolitan in NRC, at which five daily whole air sample canisters were each collected, five sites at Shenyang and six sites at Tianjin, during April, July and October 2008 and January 2009. The Tianjin and Shenyang were thought to be representative of the major metropolitan emission sites, respectively. The sampling dates were selected based on a forecast of meteorological conditions, each event lasted eight to ten consecutive days, and the weather during the sampling days was generally fine and the wind speeds calm as predicted by National Weather Service, favoring pollutant accumulation and interaction. Periods with such weather conditions were deliberately sought to maximize VOC signals and increase the likelihood of successful identification of sources using PCA. To sub-atmospheric sampling was used, the evacuated canister (b20 motor) was used, which was placed on the “roof platform” for 1 h sampling. No sources  immediately influenced the sampling. The flow rate is about 0.9 mL/s. A pressure gauge was used to check the completion of each sample collection (atmospheric pressure). 2O behind in the first trap. The second cryotrap, which contains Tenax, was cooled to −30 degrees Celsius, which allows the trapping of VOCs while letting CO2 pass through. From Module 2, VOCs were back-flushed at 190 degrees Celsius and then focused again at −160 degrees Celsius in the Module 3 trap. The Module 3 trap then was rapidly heated to 60 degrees Celsius for 30 s. Helium was used as the purge gas for the cryogenic pre-concentrator and the carrier gas for the GC. Column DP-624 was initially held at 40 degrees Celsius for 3 min, then was raised to 90 degrees Celsius at a rate of 8 degrees Celsius each minute; then to 200 degrees Celsius at a rate of 6 degrees Celsius each minute, and finally was held for 9 min. Standard gas mixtures were VOC calibrating gas (1.0 ppm (parts per million), including 64 compounds, Spectra Gases To-15 ), PAM calibrating gas  and 4-bromofluorobenzene .VOC analysis procedures followed the US Environmental Protection Agency (US EPA) methods TO-15. An 800-mL aliquot of air sample from each canister was concentrated using a three-stage cryofocusing pre-concentration system . The VOCs were separated on a capillary column  and quantified with a positive ionization detector. First, ambient air samples were pumped into the pre-concentrator, which has 3-stage cryotraps (Module 1 to Module 3). VOC compounds were initially trapped cryogenic on glass beads of Module 1 at −150 degrees Celsius by liquid nitrogen, then they were recovered by desorbing at 10 degrees Celsius to leave most of the liquid HFor the quality assurance and quality control, a laboratory blank sample was processed for ten samples with the same procedure as the real samples and no detectable VOCs were present. Field blanks were treated and no significant contamination was found. The total recovery efficiencies of VOCs ranged from 70% to 130% and the variance coefficient was less than 20%. Relative correlation for standard cure (standard gas including three concentration grads: 1 ppb (parts per billion), 2.5 ppb and 5 ppb) was in the range of 0.998–1.000 and the relative standard deviations of the response factor ranged from 2% to 25%.The analyzed 58 VOC species from sampling could be categorized into 3 groups, including alkanes, alkenes (included acetylene) and aromatics, as shown in jiC is the mass contribution of species j from source i (μg/m3) and jMIR is the MIR value of species j, as proposed by Carter [The maximum incremental reactivity (MIR) method can be used to identify species of highly reactive VOCs, and it was widely used in scientific studies . To effey Carter .PCA can provide a more transparent view of the accumulated information. Factor analysis attempts to identify underlying variables, or factors, that explain the pattern of correlations within a set of observed variables. PCA is often used in data reduction to identify a small number of factors that explain most of the variance observed in a much larger number of manifest variables. In this study, PCA was carried out on selected 58 VOCs. The next step was to rotate the initial factor matrix. The rotation phase of factor analysis attempt to transform the initial matrix into one that was easier to interpret . The varIn this study, health risk assessment method was based on new methods of health evaluation inhaled route for a particular pollutant place in 2009 proposed by the US EPA (EPA-540-R-070-002), calculated as follows: iEC is the exposure concentration of VOCsi, the unit is μg/m3; ρ(iCA) is the environmental concentration of VOCsi, the unit is μg/m3; ET is the exposure time, the value is 24, the unit is h/d; EF is Exposure frequency, the value is 365, the unit is d/a; ED is the exposure time, the value is 70, the unit is a; AT is the average time, the unit is h, the value is 70 × 365 × 24; HQ is the non-carcinogenic risk Hazard quotient, ρ(iRfC) is the reference concentration of VOCsi, the unit is μg/m3; R is the lifetime carcinogenic risk value, ρ(iIUR) is the unit inhalation risk of VOCsi [3)−1; HI is the hazard index.In the formula, of VOCsi , the uni3) was significantly higher than that in the other urban areas, 5 times higher than Shenyang (75.20 ± 40.09 μg/m3). Large fractions of aromatic compounds, especially toluene, were observed in nine cities. There had similar levels of light alkanes, as well as rich industries, which might explain the similarities. They had a high concentration of propane, likely due to the widespread domestic and vehicular use of liquefied petroleum gas. High levels of acetylene, toluene, ethylene, and ethane at this site probably originated from several anthropocentric sources such as vehicle exhaust, petrochemical industries, and industrial uses of solvents. Vehicular emissions were clearly identifiable from the significant levels of isobutane, isopentane, and benzene. It can be seen from 3, 109.68 ± 23.27 μg/m3 and 104.31 ± 46.04 μg/m3) was similar to the corresponding concentrations in Guangzhou (176.12 μg/m3), Xinken (132.20 μg/m3) [3), these cities had major manufacture including electronics, communication, paper, garments and textiles, food, shoes, and plastic. Tianjin’s transport industry was significantly developed, named the cargo distribution center of China. Additionally, Anshan was the iron and steel product center of China, with the production transported all over the world. Just as Shanghai was the center of the Yangtze River Delta region of China. Daqing and Taiwan had the same concentrations , Taiwan09 μg/m3 , because in 2019 , the con in 2019 .In this study, the atmospheric VOCs of Shenyang and Tianjin were mainly observed. Samples of daily variation of VOCs were collected in April, July, October 2008 and January, July 2009 and in urban areas, respectively, which were taken as representative samples of winter, spring, summer and autumn.3), and Tianjin was higher in winter (171.65 ± 95.96 μg/m3). The VOCs concentrations during the autumn and winter can be attributed to: (a) increased emissions from heating sources; (b) reduced photochemical degradation of some VOCs by solar radiation in winter. In contrast, the lowest concentrations in summer of two cities were attributable to the absence of seasonal sources , higher percentage in vapor phase, wash out effect, and photochemical degradation.The low level of VOCs in spring and summer can likely be attributed to the climate characteristics of Shenyang and Tianjin were windy in spring and rainy in summer. Compared with spring and summer, the atmospheric stability and convection were weaker in autumn, due to the vertical mixing and diffusion of pollutants were reduced. In addition, the sunshine duration was significantly shortened in autumn, and the intensity of solar radiation and photochemical reaction decreased, leading to the degradation effect being weakened. so that the concentrations of VOCs reached their higher values in autumn. In winter, although January was the heating season, the fixed combustion emissions increased, which brought severe VOCs contamination.Similar features were also founded for the average contributions of species groups across the four seasons for two cities . Aromati3) than in 2009 (104.31 ± 46.04 μg/m3), it was 2 times. Three species groups also were significantly lower, and the proportion of the three VOCs groups were similar between 2008 and 2009. With the successful convening of the Olympic Games, Tianjin’s air quality reached international standards during the Olympic Games, indicated that the effective implementation of the Olympic security program played a key role. The concentration of Shenyang was similar between 2008 and 2009, indicated that the impact of implementation of the Olympic security program was small., because Shenyang was not the venue for the Olympic Games and far from Beijing, it had less impact on Beijing.The observations for Tianjin were displayed during July 2008 and 2009 in 2, NO2, CO, O3 and VOCs observed at Shenyang. The observed VOCs enhancements  were associated with highly elevated SO2, NO2 and CO, with ozone concentrations showed an opposite trend to other substances. For Fushun, the SO2, NO2, CO, O3 and VOCs were associated same as Shenyang on 15 and 17 July, however the occurrence of high values on other dates was inconsistent, which may be related to the influence of adverse meteorological conditions. Overall, this suggested that the observed high levels of VOCs may be attributed to same sources or processes. In most cases, a clear relationship between these high ozone level and either VOCs levels or NO2 and CO levels was observed.3 and secondary organic aerosols. The concentration and chemical reactivity of VOCs in the atmosphere of different cities were different. In order to effectively control the VOCs in the atmospheric environment, the MIR method was used to determine the active components and key species in VOCs.VOCs as a primary pollutant in the atmosphere, greatly impact the formation of O3, 426.90 μg/m3, 262.60 μg/m3, 537.30 μg/m3, 121.60 μg/m3, 346.20 μg/m3, 175.70 μg/m3, 222.10 μg/m3 and 111.70 μg/m3. The highest OFP was 537.30 μg/m3 in Changchun, and the lowest is 111.70 μg/m3 in Tianjin.The OFP of the top 10 species of VOCs in nine cities were dominated by alkenes and aromatics, and the top 10 species accounted for 60.1~76.5% of the OFP of TVOCs. The species of the highest OFP level in VOCs of nine cities as follows: toluene of Harbin, toluene of Daqing, propylene of Huludao, toluene of Changchun, 1,3-butadiene of Shenyang, toluene of Jilin, toluene of Anshan, ethylene of Fushun, toluene of Tianjin. Toluene and ethylene were the mainly active component of each city in the top 10 species of OFP. There were five kinds of alkanes in the top 10 species of OFP in VOCs in Daqing. The top 10 species of OFP at Tianjin and Anshan were mainly aromatic hydrocarbons. Therefore, the main active species in the NRC were C2~C6 alkanes, C7~C8 aromatic hydrocarbons, individual cities (Daqing) included n-hexane and propane and other alkane species.3 at Shenyang and 213.03 μg/m3 at Tianjin), which was in line with the highest concentration of VOCs in autumn at the two cities. It showed that changes in the concentrations of VOCs had a certain impact on OFP. At Shenyang, the OFP of other seasons had not change much (112.24~131.35 μg/m3). At Tianjin, the followed by January 2009 (206.20 μg/m3), April 2008 (136.00 μg/m3), July 2008 (111.70 μg/m3), July 2008 (109.06 μg/m3). In January of Tianjin, the concentration of TVOCs was the lowest in four seasons, but the OFP was higher. The concentration of alkenes in January (19.69 μg/m3) was higher than other seasons (4.34~10.19 μg/m3), and the MIR of alkenes was higher than alkanes and aromatics. The MIR method was used to evaluate the contributions of VOC groups to ozone formation, indicated the amount of O3 increased for every 1 g added of hydrocarbons.3 and 111.7 μg/m3), the OFP of aromatics accounted for the main part in 2008 (47.96 μg/m3), and alkanes dominated in 2009 (46.24 μg/m3).Additionally, 2) of the different hydrocarbons with ethyne and i-pentane were calculated for the nine cities  suggested that their primary source was combustion, except Huludao. The majority of aromatic compounds also correlated well with i-pentane indicated that gasoline evaporation was likely an additional source (Shenyang being an exception with a correlation coefficient of below 0.5). Finally, the good correlation of long-chain alkanes (C6~C10) with i-pentane indicated that solvent evaporation was more likely the main source for those gases.In NRC, Short-chain alkenes  and aromatic compounds showed a good correlation with ethyne  was considered. Correlation and ratios between the BTEX . The B/T ratio in nine cities of NRC in this study ranged from 0.2 to 1.39 . To idenBefore applying PCA to the VOC data, the individual sample was inspected and those with unusually high or low concentrations were identified as possible outliers, the sensitivity analysis was conducted. The suspected outliers were removed from the dataset one at a time until a stable PCA result was achieved and further exclusion of samples in a random fashion had very little effect on the PCA outputs.In total, 37 species at Shenyang and 44 species at Tianjin were chosen for PCA analysis, these were the most abundant compounds (accounting for 72.42% at Shenyang and 87.23% at Tianjin of the TVOCs) and efficient tracers for major VOCs sources. Five and six components were extracted from the application of PCA to the data of VOCs at Shenyang and Tianjin, respectively . Here, KThe components in Factor 1: High factor loading were found for ethyne, alkenes, alkanes and aromatics, species were so complex. Ethyne were associated with combustion, and the aromatics and alkanes were from industry combustion. The possible sources included industry combustion and vehicle exhausts.Factor 2: 1,3-butadiene in the atmosphere was the characteristic product of internal combustion engines . and proFactor 3: I-pentane was a marker for gasoline evaporation. Additionally, ethylbenzene, phenylethylene and p,m,o-xylene had high factor loading in this factor. It was known that these species had the largest portion in solvents , which wFactor 4: Isoprene was one of the most reactive hydrocarbon species and was used as a tracer for biogenic emissions, the possible sources was biogenic emissions.Factor 5: high factor loading was found for cyclohexane. Cyclohexane was often used as a solvent for rubber, paints, varnishes. Additionally, cyclohexane also was adhesive thinners, and oil extractants. The high factor loading was observed suggesting sources of solvent dominated the compound.From the PCA-resolved result of VOCs at Shenyang a, the soThe components in Factor 1: The high-carbon chain n-alkanes had high loadings, such as heptane, n-octane and n-pentane, and the proportion of n-alkanes of high-carbon chain in coal-burning gas was large. Although i-pentane was a marker for gasoline evaporation, but emitted during combustion processes. Therefore, the factor was label as “industrial fuel combustion”, respectively.Factor 2 and Factor 6: In Factor 2, the vehicular internal engine combustion and the unburned vehicle emissions can be identified by high factor loading on propene, 1,3-butadiene and alkanes. In Factor 6, 1-butylene were relatively simple, mainly from traffic emissions. So species in Factor 2 and Factor 6 could result from vehicle emissions.Factor 3 and Factor 5: In the two Factors, high factor loading was found for ethylbenzene, xylene isomers, phenylethylene and propylbenzene isomers. which indicated species could result from the evaporation of solvents, such as paints.Factor 4: Isoprene had high loading and was used as a tracer for biogenic emissions. Thus, it was label as “biogenic emissions”.From the PCA-resolved result of VOCs at Tianjin b, the so−3~5.46 × 10−1, within the safety range recognized by the US Environmental Protection Agency (HQ < 1). The lifetime carcinogenic risk value of benzene was between 3.82 × 10−5~1.28 × 10−4, which was higher than the safety range stipulated by the US Environmental Protection Agency (R < 1.00 × 10−6), indicated that there was a risk of carcinogenesis, and the human body was exposed to the environment for a long time. It may induce immune diseases of the lymphatic system and leukemia, which should be paid more attention.Benzene, toluene, ethylbenzene, p,m-xylene, and o-xylene are important VOCs species in the atmosphere. −2) was higher than that in eight other cities in NRC. The HQ values of p,m-xylene at Harbin and Tianjin were higher than at Guangzhou, and lower in other cities. However, all the HQ values in nine cities were higher than at Beijing, indicated that other cities had a higher non-carcinogenic risk of p,m-xylene than Beijing. In general, the nine cities of NRC had higher health risks  than other cities. In particular, it should be noted that the carcinogenic risk of benzene in the region had exceeded the safety threshold. Therefore, it is necessary to decrease the emission of benzene, control and reduce their carcinogenic risk.In this study, the lifetime carcinogenic risk quotient of benzene at Fushun, Jilin and Harbin was higher than Guangzhou and Beijing, whereas the lifetime carcinogenic risk quotient of benzene at Shenyang and Changchun was lower than that of Guangzhou and Beijing. Benzene had the highest non-carcinogenic risk quotient in the nine cities of NRC, whereas Guangzhou and Beijing had the highest non-carcinogenic risk quotient of toluene, and the HQ value toluene of Harbin  were alkenes and aromatics. For OFP of seasonal characteristics at Shenyang and Tianjin. This result suggested that even though the measured concentrations of alkanes were the highest, their contributions to OFP were less than alkenes and aromatics, due to the lower hydroxyl radical reactivity. Using B/T ratio of 0.6 ± 0.2, three groups of cities were identified: three cities  with the ratio in the range of 0.4–0.8, Huludao with a ratio between 0.6–1 (0.98), four cities  with a ratio lower than 0.4. The B/T ratio of about 0.6 and the good correlation with i-pentane, suggested vehicular emissions (vehicular combustion and gasoline evaporation) were likely the main sources of the identified hydrocarbons for those cities. Combustion and vehicular emissions were still the primary source of non-methane hydrocarbons for the second group of cities. Lower ratio of the third group suggesting these cities affected multiple pollution sources. Four sources were identified for VOCs by the PCA model at Shenyang: (1) combustion (included industry combustion and vehicle exhausts); (2) sources of liquefied petroleum gas/natural gas or solvent; (3) vehicle exhausts; (4) biogenic emissions. And four sources were identified at Tianjin: (1) Industrial fuel combustion; (2) vehicle emissions; (3) use of solvent; (4) biogenic emissions.−5~1.28 × 10−4, higher than the safety range specified by the US Environmental Protection Agency (R < 1.00 × 10−6), indicate that there is a risk of cancer. The HI values in nine cities of NRC were higher than other cities, and the health risk level was higher than that of other cities. The non-carcinogenic risk values of Shenyang, Fushun, Changchun, Jilin, Harbin, Daqing, Huludao, Anshan and Tianjin were within the safe range recognized by the U.S. Environmental Protection Agency (HQ < 1), and the lifetime carcinogenic risk value of benzene was 3.82 × 10This study provides a data base for the health risk assessment and ambient air quality in nine cities in NRC, and accumulates relevant basic data to support the research about the changes of VOCs in China in recent years. This information is crucial in pollution control and risk management.


Mechanical therapies, such as distraction osteogenesis, are widely used in dental clinics. During this process, the mechanisms by which tensile force triggers bone formation remain of interest. Herein, we investigated the influence of cyclic tensile stress on osteoblasts and identified the involvement of ERK1/2 and STAT3.Rat clavarial osteoblasts were subjected to tensile loading  for different time periods. RNA and protein levels of osteogenic markers were determined using qPCR and western blot after inhibition of ERK1/2 and STAT3. ALP activity and ARS staining revealed osteoblast mineralization capacity. The interaction between ERK1/2 and STAT3 was investigated by immunofluorescence, western blot, and Co-IP.The results showed that tensile loading significantly promoted osteogenesis-related genes, proteins and mineralized nodules. In loading-induced osteoblasts, inhibition of ERK1/2 or STAT3 decreased osteogenesis-related biomarkers significantly. Moreover, ERK1/2 inhibition suppressed STAT3 phosphorylation, and STAT3 inhibition disrupted the nuclear translocation of pERK1/2 induced by tensile loading. In the non-loading environment, inhibition of ERK1/2 hindered osteoblast differentiation and mineralization, while STAT3 phosphorylation was elevated after ERK1/2 inhibition. STAT3 inhibition also increased ERK1/2 phosphorylation, but did not significantly affect osteogenesis-related factors.Taken together, these data suggested that ERK1/2 and STAT3 interacted in osteoblasts. ERK1/2-STAT3 were sequentially activated by tensile force loading, and both affected osteogenesis during the process.The online version contains supplementary material available at 10.1186/s12860-023-00471-8. Mechanical therapies have emerged as common choices in dental clinics, such as distraction osteogenesis (DO), orthodontic tooth movement, and maxillary advancement/expansion. It is well known that tensile force stimulates osteogenic activity, thus driving bone formation. In recent years, researchers have been paying increasing attention to mechanotransduction, by which mechanical force is translated into biochemical signals and thereby initiates osteogenesis.Osteoblasts, which are derived from mesenchymal stem cells, are the main bone-forming cells in response to mechanical loading . It has Extracellular signal-regulated kinase 1/2 (ERK1/2) and signal transducer and activator of transcription 3 (STAT3) are two key mechanosensory molecules. ERK1/2 reportedly mediates the conversion of external mechanical stimulation to intracellular biochemical signals , 7 and tA number of biological processes have been proposed to involve crosstalk between signaling pathways. ERK1/2 and STAT3 may coordinate in various ways to modulate physiological processes. In mechanical loading-responsive myocardial cells, ERK1/2 and STAT3 were the downstream effectors of mechanical signal transmission . CrosstaIn this study, we first examined the effects of cyclic tensile stress on osteogenesis-related factors in rat primary osteoblasts. Next, inhibition of ERK1/2 or STAT3 was carried out on osteoblasts to determine their roles and potential interdependences.Cyclic tensile loading  was imposed on osteoblasts using the Flexcell tension system for 6, 12, 24, and 48 h. Osteoblasts not subjected to load served as the control group. To investigate the effects of tensile loading on osteogenesis in osteoblasts, the expression of osteogenesis-related genes was examined by qPCR and western blots. Compared with the control group, mRNA levels of Osterix, COL-I, OCN, Runx2, and ALP demonstrated varying degrees of increase after strain load. Osterix mRNA level was significantly upregulated as early as 6 h of strain loading. Runx2 and ALP mRNA expression peaked at 24 h and then decreased at 48 h. COL-I and OCN mRNA expression increased over time, peaking at 48 h  for 12 h, followed by 24 h of mechanical loading. The mRNA levels of osteogenesis-related factors were measured using qPCR. There was a significant increase in COL-I, ALP, and Runx2 mRNA expression after tensile stress loading compared to the control group. It was observed that inhibition of ERK1/2 greatly reduced the expressions of COL-I, ALP, and Runx2 regardless of loading presence Fig. A. ConsisThe mineralization capacity was further examined by ALP and ARS staining. As illustrated in Fig. To assess STAT3 involvement in osteogenesis, we treated the cells with STAT3 inhibitor Stattic (5µM) for 6 h, followed by 24 h of loading. There was no significant difference between the control group and the control + Stattic group in terms of the mRNA levels of COL-I, ALP, and Runx2. In response to mechanical loading, mRNA expression of COL-I, ALP, and Runx2 increased, but STAT3 inhibition dramatically reduced it . In the control group, both ERK1/2 and STAT3 were located in the nucleus and cytoplasm. STAT3 was mostly found in the nucleus in the control + U0126 group, suggesting ERK1/2 inhibition impacted STAT3 localization. However, STAT3 inhibition did not significantly alter ERK1/2 localization in the control + Stattic group. As a result of tensile loading, STAT3 and ERK1/2 were found to accumulate near nuclear regions. In contrast, in the tensile stress + U0126 group, ERK1/2 and STAT3 were more localized in the cytoplasm. Furthermore, STAT3 inhibition decreased the loading-induced nuclear translocation of ERK1/2, as demonstrated in the in the tensile stress + Stattic group Fig. B.Western blot analysis further clarified the mutual relationship between STAT3 and ERK1/2 in a more quantitative sense. First, we detected the levels of STAT3 and pSTAT3 in whole cell lysates, finding that total STAT3 protein expression was not significantly different across groups. The control + U0126 group displayed a significantly higher level of pSTAT3 protein than the control group. However, loading-induced pSTAT3 upregulation was reduced after ERK1/2 inhibition Fig. C. Our neWe then extracted nuclear proteins and used western blots to measure nuclear pERK1/2 and pSTAT3 expression. As shown in Fig. A variety of mechanical therapies have been developed to correct craniofacial skeletal or/and dental problems, including distraction osteogenesis and orthodontic tooth movement. Molecular mechanisms by which external mechanical stimulation is translated into cells and then affects cell activities are always of interest. Currently, the purpose of our study was to examine the effect of tensile loading on calvarial osteoblasts, as well as the involvement of ERK1/2 and STAT3.The Flexcell tension system is widely used to apply stretch forces to cell populations. The strain regimes vary depending on the type of cell population studied, and the responses induced by stretch forces vary among different types of cells. In osteoblastic cells, elongation from 0.25%  to 24%  significThe upregulation patterns of osteogenic markers in our study were correlated with osteoblast differentiation profiling to some extent. Runx2 and Osterix are two master transcription factors that are involved at the early stage of osteoblast differentiation . These tIt has been reported that ERK1/2 activation directly promotes the expression of Runx2 and OCN . As mentIncreasing evidence suggests that STAT3 plays a role in osteogenesis depending upon cell lineages, maturation status, and external stimuli . AccordiAdvances in decoding the crosstalk between ERK1/2 and STAT3 in specific settings may provide novel insights into the mechanism behind cellular events. The inhibition of ERK1/2 in esophageal cancer activated STAT3 and the activation of STAT3 reversed the therapeutic effect of ERK1/2, resulting in drug resistance . In our However, the study had certain limitations. First, late activation of STAT3 at 24 h of tensile loading may involve the synthesis of intermediate factors. Therefore, methods such as cycloheximide chase analysis could be used to further explore potential protein synthesis or degradation. Second, a more thorough elucidation of their roles in osteogenesis could be achieved by using silencing RNA to directly target ERK1/2 and STAT3 gene expression.2.Murine primary osteoblasts were obtained as described in previous research , 25. Bri5 cells/ml. When the cells reached 80% confluence, cells were subjected to cyclic tensile stress at 10% magnitude and 0.5 Hz frequency for various durations . Cells not exposed to tensile stress served as the control group. After mechanical loading, cells were harvested and tested for changes in the expression of various genes and proteins [Cells were seeded onto collagen I-coated six-well BioFlex® plates  at a density of 4 × 10proteins .ERK1/2 inhibitor (U0126 solution): U0126 powder was dissolved in dimethyl sulfoxide (DMSO) to 50µM concentration as the storage solution.STAT3 inhibitor (Stattic solution): Stattic powder was dissolved in DMSO to 20µM concentration as the storage solution.Total RNA was extracted by Trizol reagent  according to instructions. Then cDNA synthesis and subsequent amplification were performed in compliance with the manufacturer’s protocols . Gene-specific primers Table  were desTotal cellular protein  and nuclear protein  were extracted using the corresponding kit. The sample concentration was determined by a BCA protein assay kit . After being mixed with loading buffer, the protein sample was boiled at 95 °C for 5 min. The protein sample was separated by SDS polyacrylamide gel electrophoresis and transferred to PVDF membrane. Later, the blots were blocked in 5% fat-free skimmed milk for one hour at room temperature, followed by incubation with primary antibodies  against Osterix , COL-I , OCN , Runx2 , ALP , ERK1/2, pERK1/2 , STAT3, pSTAT3, and GAPDH  overnight at 4 °C. After a three-time wash in TBST solution, membranes were probed with goat anti-rabbit IgG conjugated secondary antibody  for one hour at room temperature. The blots were cut prior to hybridization with antibodies during blotting. The membranes were processed with enhanced chemiluminescence detection reagents  and visualized.After treatment, the cells were washed with PBS, fixed with 4% paraformaldehyde at room temperature for 30 min, penetrated with 0.5% Triton X-100 for 20 min, blocked with 0.5% BSA for one hour, and incubated with primary antibodies  at 4 °C overnight. After washing thrice, cells were processed with rhodamine-labeled anti-rabbit secondary antibodies and fluorescein isothiocyanate-labeled anti-mouse secondary antibodies for one hour. Cell nuclei were stained with DAPI and images were observed with the fluorescence microscope .The total protein was extracted as mentioned before. Co-IP was conducted as described previously . 10% of The mineralization solution was prepared as previously described  . The quantitative analysis of Alizarin red staining images was performed by Image J software according to the standard protocols as described in our previous research .P < 0.05 was considered to be statistically significant.All quantitative data from three experiments were expressed as mean ± standard deviation and analyzed by GraphPad Prism . Tests for normality and homoscedasticity were performed in GraphPad Prism according to the instruction guide. Data were assessed through two way-ANOVA  or one-way ANOVA (for three or more groups) followed by post-hoc Tukey test. Below is the link to the electronic supplementary material.Supplementary Material 1


Antilocapra americana peninsularis) is an endangered pronghorn subspecies that is almost entirely held in captivity. Captive breeding has increased the number of pronghorns from 25 founders in 1997 to around 700 individuals today, but it is unclear how the genetic diversity of the captive herd may have changed over time. We therefore generated and analysed data for 16 microsatellites spanning 2009–2021. We detected a decline in heterozygosity and an increase in the proportion of inbred individuals over time. However, these trends appear to have been partially mitigated by a genetically informed breeding management attempt that was implemented in 2018. We also reconstructed the recent demographic history of the Peninsular pronghorn, revealing two sequential population declines putatively linked to the desertification of the Baja California peninsula around 6000 years ago, and hunting and habitat loss around 500 years ago, respectively. Our results provide insights into the genetic diversity of an endangered antelope and indicate the potential for genetically informed management to have positive conservation outcomes.Endangered species with small population sizes are susceptible to genetic erosion, which can be detrimental to long-term persistence. Consequently, monitoring and mitigating the loss of genetic diversity are essential for conservation. The Peninsular pronghorn ( Some authors have even argued that Earth’s biodiversity is entering a sixth mass extinction event, characterised by the unprecedented loss of diversity at all levels6. Consequently, nowadays the persistence of many species is critically dependent on intensive management actions such as captive breeding, habitat restoration and reintroduction programs.Many species have experienced severe declines over the past two centuries as a result of growing anthropogenic pressures including direct exploitation, habitat destruction and climate change7. For example, species like the Kakapo (Strigops habroptilus), Przewalski’s horse  and giant Galapagos tortoise (Chelonoidis niger), among many others, would have gone extinct without human intervention and ex situ management10. Captive breeding is frequently used for the preservation of threatened species and, in some cases, for the rehabilitation of declining populations14. However, it can sometimes inadvertently lead to genetic or behavioural changes that are not always beneficial15. For example, when selective pressures in captivity differ to those that are usually encountered by a species in the wild, maladaptive alleles or behaviours can rise to high frequency in captive populations, which can compromise the survival of individuals after they are reintroduced into the wild17. Furthermore, in small captive populations, strong genetic drift and the increased probability of mating between close relatives can decrease genome-wide heterozygosity and lead to inbreeding depression22. The fitness costs associated with inbreeding have been documented across taxonomic groups and include negative effects on litter size, longevity, female reproduction, male fertility and weight, in addition to hereditary defects26, all of which can have a strong impact on population viability.For many species, captive management has been the only option for persistence28 and reduced genetic diversity has been associated with increased extinction risk and reduced adaptive potential31, knowledge of the effects of captive breeding on genetic diversity is crucial. In this regard, time-series genetic data from captive populations can be particularly useful35, as they can shed light on changes in key genetic characteristics of a population such as allelic richness, heterozygosity and the effective population size (Ne); measures that reflect a combination of the speed of allele frequency change through genetic drift, the efficacy of selection and expected genetic diversity levels for selectively neutral loci37.Given that conserving genetic diversity and minimising inbreeding are important goals of most if not all captive breeding programmesAntilocapra americana) is the only extant species of the North American family Antilocapridae39. Pronghorns are thought to have been historically abundant, with documents from the 1800s suggesting that roughly 30–40 million individuals inhabited North America prior to the westward settlement of humans on the continent41. Nevertheless, current pronghorn numbers have been severely affected by habitat fragmentation and overhunting, with many populations having declined or disappeared entirely44. Nowadays, four pronghorn subspecies are recognized: the American pronghorn (A. a. americana), the Sonoran pronghorn (A. a. sonoriensis), the Peninsular pronghorn (A. a. peninsularis) and the Mexican pronghorn (A. a. mexicana)45. The American pronghorn is the most widespread subspecies, with the Sonoran, Peninsular and Mexican subspecies occupying more peripheral southern areas45. Of these subspecies, the Peninsular and Sonoran are currently under national and international protection48. Overall, the pronghorn is one of the many species currently undergoing captive breeding and translocation, with independent breeding programs active in the USA and Mexico51.The pronghorn  and a breeding plan was developed that focused on minimizing the relatedness of potential partners. Group-based management was implemented at one of the pens. At this pen, which consisted of only breeding females, two sexually mature males, selected on the basis of molecular estimates of kinship , were introduced and kept there until the following year57, while genetic diversity appears to be somewhat lower for the Sonoran58 and Peninsular pronghorn subspecies59. Moreover, the American subspecies shows little evidence of population genetic structure57 while population genetic differentiation at the subspecies level is more pronounced, revealing clear genetic discontinuities between geographically isolated populations59.Previous population genetic studies of pronghorns reported moderate to high levels of genetic diversity in the American subspecies51, which may have been further exacerbated by small population sizes and inbreeding over the past few decades of captivity. Alternatively, or additionally, dramatic ecological changes during the last glacial maximum  resulted in the desertification of most of the Baja California peninsula, reducing water availability62 and likely contributing to a gradual reduction in pronghorn numbers over thousands of years.The reasons for the relatively low genetic diversity of the Peninsular pronghorn subspecies are unknown, with two  explanations being possible. The first of these is that human induced habitat loss, competition with domestic animals and uncontrolled hunting may have caused the Peninsular pronghorn to decline over the past three centuries63 to evaluate support for alternative demographic scenarios that could explain the low genetic diversity of the Peninsular pronghorn, and to estimate relevant parameters such as the current Ne and the strength and timing of historical declines. We hypothesised that the collapse of the Peninsular pronghorn may have been driven by a combination of historical ecological changes and more recent anthropogenic pressures. We furthermore hypothesised that, although the captive breeding programme has been successful in increasing the number of individuals, there may have been some unavoidable loss of genetic diversity and an increase in inbreeding over time, although we expected that some of these changes might have been mitigated by the breeding management attempt.Here, we generated a time series dataset of multilocus microsatellite data for the captive Peninsular pronghorn spanning the period 2009–2021 inclusive. We first evaluated changes in genetic diversity, heterozygosity and inbreeding over the past 13 years. We then used approximate Bayesian computationp = 0.02) but when the cohorts were analysed separately this was only present in 2009 . Analysing the full dataset of 124 individuals  was slightly but not significantly  lower than expected heterozygosity  and ranging from zero to 0.53  from 16.6% in 2009 to 55.5% in 2021 , internal relatedness (IR) and homozygosity weighted by locus (HL)—showed consistent trends of declining heterozygosity over time . Nevertheless, for the marker-based estimates  only Ho was significant and only for the period 2009–2018. None of the estimates were significant for the period 2018–2021 (Table f > 0.25) declined from 26.3% in 2018 to 11.1% in 2021  years ago, whereas second decline appears to date back to around 554 (95% CI 7–2420) years ago  to evaluate four alternative historical demographic scenarios , a tendency for reduction was observed for He  and Ho . Furthermore, all three frequency-based estimates of individual heterozygosity  revealed significant decreases in heterozygosity during the course of the study with, for example, sMLH falling by around 27% over the past 13 years. This pattern was mirrored by the TrioML based inbreeding coefficient, which showed a gradual reduction in the number of non-inbred and weakly inbred individuals and a concurrent increase in the number of moderately to highly inbred individuals over time. These findings are consistent with the theoretical expectation of zygosity being a function of the breeding system65. Thus, inbreeding directly reduces heterozygosity by increasing the proportion of homozygotes relative to random expectations, but only indirectly affects allelic richness. By contrast, genetic drift directly affects allelic diversity but only indirectly impacts heterozygosity67. Therefore, our results suggest that inbreeding is currently the predominant force shaping the genetic diversity of the Peninsular pronghorn.Although we did not find any statistically significant temporal changes in several marker-based estimates of genetic diversity  in comparison to the total size of the captive herd . Incomplete sampling may be particularly important regarding the outcomes of management actions, as the effect of breeding recommendations will not be fully realised if animals with unknown relationships are allowed to mate as well as animals of known kinship. Consequently, the reduction in the loss of diversity that we observed after the implementation of the genetically informed breeding attempt may be conservative in the sense that more thorough sampling might have produced an even better outcome. Regardless, given that it is not possible to provide breeding recommendations for unsampled individuals, a strong case can be made for increasing sampling rates into the future. This would help to further optimise partner selection and thereby ensure the best possible retention of genetic diversity and reduction of inbreeding. Larger sample sizes of genotyped individuals would also be beneficial for the ongoing monitoring of genetic changes within the captive population.A second important caveat is that our sample size of individuals was modest . This work did not require any approval from the ethical committee since no experiments on live animals were performed, aside from the routine tagging that was performed by trained personnel and according to the conservation programme internal schedule. All procedures were in compliance with the ARRIVE guidelines for how to report animal experiments91.All samples were collected by the management team of the Peninsular Pronghorn Conservation Programme under the registration key DGVS‐UMA‐VL‐3755‐BC given to the management unit by the Mexican Secretariat of Environment and Natural Resources. All procedures were approved by the authorized personal of the Valle de los Cirios Flora and Fauna Protection Area and followed the guidelines of the American Society of Mammalogists , 1 mM MgCl2, 0.2 mM deoxynucleoside triphosphates, 0.05% bovine serum albumin, 0.5 U of Taq DNA Polymerase (Invitrogen), and 0.5 µM of each primer. The polymerase chain reaction profile consisted of an initial denaturalization step at 95 °C for 5 min, followed by 35 cycles of 60 s each at 95 °C, annealing by ramping from 55 to 60 °C, followed by 60 s extension at 72 °C. Cycles were terminated with a final extension stage of 10 min at 72 °C. PCR products were resolved on an Applied Biosystems 3730XL capillary sequencer at the University of Arizona Gene Core Facility and alleles were scored using PeakScanner v1.0 (Applied Biosystems).DNA extractions were performed using DNeasy Blood and Tissue Kit  following the manufacturer’s protocol. DNA concentration was determined using a NanoDrop2000 (Thermo Scientific™) and each extract was adjusted to a concentration of approximately 100 ng/µl. We amplified 16 microsatellite loci previously described for pronghorn96. After that, we imported the binned microsatellite data into the R environment97 (R version 4.1.2) and converted it into a GENIND object using adegenet98. We quantified the amount of missing data per locus and per individual using the R package poppr99 and removed all individuals that failed to genotype at four or more loci. We also used package PopGenReport100 to estimate null allele frequencies. To test for linkage disequilibrium (LD) between pairs of loci, we used poppr. For this analysis, we used the standardized index of association (rbarD)101 and the number of permutations was specified using the ‘sample = 999’ argument. We further tested for Hardy–Weinberg equilibrium using the pegas package102. Finally, in order to determine the uniqueness of the genotypes we used “mlg” function as implemented in poppr. All of the above analyses were performed on the complete dataset and separately for each year. Whenever multiple tests we used, the resulting p-values were adjusted for the false discovery rate (FDR) using the R package stats97.Genotypes were binned to size classes using FlexiBinA), allelic richness (Ar), expected heterozygosity (He) and observed heterozygosity (Ho) were calculated for the full dataset and separately for each year using the R packages adegenet98 and hierfstat103. Multilocus heterozygosity was quantified as standardized multilocus heterozygosity (sMLH), internal relatedness (IR) and homozygosity weighted by locus (HL)104 using R packages Rhh105 and inbreedR106. We used COANCESTRY v. 1.0107 to calculate individual inbreeding coefficient using the TrioML method108, using 10,000 reference individuals and bootstrapping on 10,000 samples. Following Marshall et al.109, we designated inbreeding coefficients (f) of zero as ‘none’, below 0.125 as ‘low’, 0.125 ≥ f < 0.25 as ‘moderate’, and f ≥ 0.25 as ‘high’. Wilcox tests were then used to test for significant differences in the diversity indices using the R package stats. We also used generalized linear models (GLMs) to quantify the strength of diversity decline over the years  using the R package lme4110. Finally, using the “predict” function in the R package stats and the slope of the GLM spanning 2009–2018, we determined the modeled value for each diversity estimate assuming that no genetically based breeding management attempt had been implemented.The number of alleles  from 2016, which was the year represented by the largest number of individuals.To investigate the demographic history of the Peninsular pronghorn, we used approximate Bayesian computation (ABC) as implemented in DIYABC v. 2.0Ne) change over time. The mutation rate was set to range between 1e−2 and 1e−5. Priors for the timing of events and the magnitude of changes of Ne  constant Ne over time; the alternative scenarios invoked: (b) a recent reduction caused by overexploitation and habitat loss, (c) a historical reduction caused by the desertification of the Baja California peninsula, and (d) a combination of recent and historical reductions, expressed as a two-step model (Supplementary Fig. 113 to estimate the posterior probability of each scenario based on the 1% of simulated data sets for each model that produced summary statistics closest to the observed values. The error rate was estimated using prior data space and the posterior distributions. The posterior error rate represents the proportion of wrongly identified scenarios over the 1000 test datasets63. Based on the best supported scenario, local linear regression was used to estimate the posterior distributions of the parameters. Specifically, a logit transformation of parameter values was performed and the 1% closest simulated datasets to the observed were used for regression and posterior parameter estimation113.ABC allows the evaluation of alternative demographic scenarios, expressed as a stepwise series of population size changes, and then uses summary statistics from the observed and simulated datasets to estimate parameter values and to assess the relative support for each scenario. We first developed four alternative demographic models intended to describe plausible patterns of effective population size (Supplementary Information.


Neotoma magister) are found in metapopulations distributed throughout the Interior Highlands and Appalachia. Historically these metapopulations persisted as relatively fluid networks, enabling gene flow between subpopulations and recolonization of formerly extirpated regions. However, over the past 45 years, the abundance of Allegheny woodrats has declined throughout the species’ range due to a combination of habitat destruction, declining hard mast availability, and roundworm parasitism. In an effort to initiate genetic rescue of a small, genetically depauperate subpopulation in New Jersey, woodrats were translocated from a genetically robust population in Pennsylvania (PA) in 2015, 2016 and 2017. Herein, we assess the efficacy of these translocations to restore genetic diversity within the recipient population.Allegheny woodrats (We designed a novel 134 single nucleotide polymorphism panel, which was used to genotype the six woodrats translocated from PA and 82 individuals from the NJ population captured before and after the translocation events. These data indicated that a minimum of two translocated individuals successfully produced at least 13 offspring, who reproduced as well. Further, population-wide observed heterozygosity rose substantially following the first set of translocations, reached levels comparable to that of populations in Indiana and Ohio, and remained elevated over the subsequent years. Abundance also increased during the monitoring period, suggesting Pennsylvania translocations initiated genetic rescue of the New Jersey population.Our results indicate, encouragingly, that very small numbers of translocated individuals can successfully restore the genetic diversity of a threatened population. Our work also highlights the challenges of managing very small populations, such as when translocated individuals have greater reproductive success relative to residents. Finally, we note that ongoing work with Allegheny woodrats may broadly shape our understanding of genetic rescue within metapopulations and across heterogeneous landscapes.The online version contains supplementary material available at 10.1186/s12862-022-02083-w. Neotoma magister) require rock habitats , located primarily in high elevation areas throughout the Appalachian Mountains and Interior Highlands [Landscape-scale anthropogenic disturbance can cause habitat loss and fragmentation, thereby spatially isolating local wildlife populations and impeding functional connectivity –4. Speciighlands , 11. Theighlands , 13. If ighlands –16.New Jersey (NJ) is home to a single remnant population of Allegheny woodrats, located ~ 240 km from the nearest extant population in Pennsylvania. Individuals sampled in 2009, 2011, and 2012 and genotyped at 11 microsatellite loci had relatively low genetic variability, as indicated by allelic diversity and observed heterozygosity   can yield a low probability of identity , thus anotypes; –26, evennotypes; . Even renotypes; , 27–30. N. magister, including 119.8 Gb from the paired-end (PE) library and 17.8 Gb from the mate-paired (MP) library  of raw sequence data from We initially identified 627,421 high-quality SNPs. Of these, we selected 192 SNPs to include in a Fluidigm SNPtype assay. We subsequently excluded 58 loci for reasons outlined in the methods . Of the remaining 134 loci, at least 128 loci amplified for each of the 318 woodrats genotyped  of 9.8 × 10–3. By contrast, 134 SNP loci generate values of 5.0 × 10–27 (PID) and 3.1 × 10–14 (PIDsib). If the more conservative data set of 70 loci is used, the PID is 1.9 × 10–13 and the PIDsib is 3.1 × 10–7 across all 50 individuals. Furthermore, our results indicate that a much smaller panel of SNPs might be utilized in subsequent studies to achieve a PID < 0.0001  at 11 microsatellite markers resulted in a PParentage analysis revealed that the six woodrats translocated from Pennsylvania to New Jersey produced a minimum of thirteen offspring Table . The femOf the 82 tissue samples collected from the Palisades population, five were collected in 2009, thirteen in 2011, nine in 2015, eighteen in 2016, sixteen in 2017, eight in 2018 and thirteen in 2019. Once a Bonferroni correction was applied, a single locus was found to be out of Hardy–Weinberg equilibrium, and only in 2019 (exhibiting evidence of heterozygote excess). STRUCTURE analysis of resident individuals and those translocated to NJ provided evidence of two genetically distinct clusters when the most conservative data set (70 loci) was utilized. The population-wide genetic composition changed following translocation events in 2015, as illustrated by a shift from the blue cluster associated with the resident population prior to human-mediated gene flow, to an increase in the orange cluster associated with the genetic profiles of the PA individuals  and expected heterozygosity (HE) were substantially lower in New Jersey than at sites in Indiana (IN) and Ohio  New Jersey  fell between 0.25 and 0.37. Median HO and HE were 0.32 and 0.31, respectively.We identified 20 publications for which a Fluidigm SNPtype assay was used to genotype individuals at relatively few loci (38–192 SNPs) and Hed Table . SpeciesNeotoma has undergone whole-genome sequencing [Neotoma magister are particularly limited [ID estimates associated with this assay, we anticipate a similar approach could be used to non-invasively monitor Allegheny woodrat populations from hair or fecal samples.The data described herein represent only the second time a member of the genus quencing . Genetic limited , yet eve limited  and iden limited –44. Geno limited –33. Give limited , wolves, limited , 33. GivO and HE were apparent as soon as 2016, making observed levels comparable to those among woodrat populations in IN and OH, and persisted through the end of the monitoring period in 2019. We also compared genetic variability in the NJ population to that of other species and determined that observed heterozygosity of New Jersey’s woodrats caught before 2015 was notably lower than any species listed as “Least concern” by the IUCN. Following translocations, HO and HE for the NJ population fall within the range of estimates generated across species. Increased abundance since 2015 provides additional evidence of potential genetic rescue. As such, this study joins relatively few in providing evidence of an increase in population size or growth rate following assisted gene flow  and clean-up  kits in accord with the manufacturers’ instructions. We conducted three lanes of paired-end and one lane of mate-paired sequencing using an Illumina HiSeq2500. We used Trimmomatic [We extracted deoxyribonucleic acid (DNA) from a tail clip of a single mmomatic  to removmmomatic . We usedmmomatic  to condummomatic , implememmomatic , was useMus musculus protein sequences from the UniProtKB database (www.uniprot.org) and used the protein2genome setting in MAKER to generate gene annotations. These annotations were subsequently used to train SNAP [Mus musculus expressed sequence tag (EST) sequences to the genome using BLAST and used InterProScan to identify putative protein domains. Finally, all ab initio gene predictions supported by protein, EST or InterProScan evidence were promoted to gene annotations.We used the MAKER 2.28 pipeline  to annotain SNAP  and genehttp://broadinstitute.github.io/picard) to sort and identify duplicate reads. We used the GATK 3.6 pipeline [We identified SNPs as in Doyle et al. . Brieflypipeline , 73 to rpipeline , ultimatpipeline  to find pipeline  was usedWe trapped and subsequently genotyped 82 woodrats sampled from the Palisades, NJ between 2009 and 2019 Tables , includi2 > 0.2 and removed one marker in each case, creating a reduced dataset of 70 loci in which all SNPs are in linkage equilibrium.SNP calls were edited using the Fluidigm® Genotyping Analysis Software. We excluded markers from downstream analysis when data did not cluster into distinct homozygous and heterozygous states and if minor allele frequencies were less than 0.025. We used chi-squared tests implemented by GenAlEx , 26 to tN. magister utilized relatively small panels of 11–22 microsatellite markers , using each marker panel. We additionally calculated PIDsib, which represents a conservative upper bound for the likelihood that two individuals sampled from a population will have the same genotype by chance [Previous studies of s e.g., , 84, 85), 85N. may chance , 33. Thiy chance , 33).We used Cervus 3.0.7  to assigSTRUCTURE 2.3.4 , 83, STRO and HE were not provided, we averaged across per-locus values or population-specific values when able. When interpreting these results, an important caveat is that we do not have a robust understanding of how SNP HE, HO and allelic diversity vary with categorizations such as body size, conservation status, habitat, migratory behavior, taxonomic group and trophic class. In contrast, these relationships have been extensively studied utilizing estimates of genetic variation generated with microsatellite loci [We used GenAlEx  to calcuite loci –93.Additional file 1: Microsatellite genotypes of woodrats sampled in 2009, 2011 and 2012 in the Palisades, NJ. The spreadsheets labeled “Raw Data” and “data GenAlEx” include the genotypes for twenty-eight individuals genotyped at 11 microsatellite loci. These individuals had relatively low genetic variability, as indicated by number of alleles (spreadsheets AFP and AGL), observed heterozygosity and expected heterozygosity (spreadsheet HFP).Additional file 2: Summary statistics associated with nuclear genome sequencing, assembly and annotation.Additional file 3: SNP genotypes of woodrats sampled in Indiana, New Jersey, Ohio and Pennsylvania. The spreadsheet labeled “Raw Data” includes the genotypes for 318 individuals genotyped at 134 SNP loci.Additional file 4: Relationship between probability of identity (PID), probability of identity between siblings (PIDsib) and the number of genotyped SNP or microsatellite loci.Additional file 5: Comparison of microsatellite- and SNP-based genetic variation.


A molecular analysis using informative SNP markers in 1570 clones of cassava from Vietnam reveals varietal composition from farmers’ field and genebank collectionsHo = 0.43) was slightly higher than expected (He = 0.40) across SNP markers, suggesting an excess of heterozygotes plants. Diversity indexes indicated that cassava populations from North West and Eastern Vietnam are genetically diverse (mean He = 0.40). Genetic parentage tests identified 85 unique genetic groups within the varieties KM94, KM419, BRA1305, KM101, KM140, PER262, KM60, KM57 and two unidentified varieties, which accounted for 82% of the frequency distribution. KM94 is the most dominant variety in Vietnamese farms surveyed (38%), reflecting its superior quality and productivity. Discriminant analysis of principal components (DAPC) revealed four main subgroups, which were partially corroborated by neighbor joining (NJ) analyses. After removing duplicates, 31 unique genotypes were distributed across five of the agro-ecological zones. These were well distributed in the subgroups revealed via DAPC and NJ analyses. The genetic groups identified herein could be used to select unique accessions that should ideally conform with ex situ germplasm collections and identify areas where on-farm conservation programs should be targeted. Newly identified genotypes may also contribute as genetic breeding resources that could be used to adapt cassava to future changes and farmers’ needs.Cassava is the most important smallholder cash crops in Southeast Asia and is especially used in industrial products. Yet, systematic genetic studies on molecular markers from Vietnamese germplasm have not been considered for breeding and conservation programs. We conducted a molecular analysis of 1570 clones of cassava germplasm from farms across six agro-ecological zones using informative SNP markers. We unraveled the genetic diversity and population structure and provided insights into the value of breeding and conservation programs. Duplicated genotypes comprised 98% of the total sample of the Central Highlands region. Ninety-six SNPs were amplified Central Highlands and South East provinces had the highest allelic richness, covering up to 83% of alleles. The average observed heterozygosity (The online version containssupplementary material available at 10.1007/s11103-021-01124-0. In a second step, we randomly selected 12 households in each selected village. Villages located in areas with higher cassava production were more likely to be selected for the study. For selecting households for the study, we assigned equal probability of being selected to the list of households made available for each selected village. The final study consisted in 949 households in 79 villages across the country.Each selected household responded a survey question about the cassava production and commercialization. Additionally, each household were asked to provide a cassava sample for each of the cassava varieties reported by the household. The data and sample collection was conducted between October and December 2015. Each collection site was geo referenced using a GPS device and the local variety names were recorded from farmers. A total of 1570 cassava samples were collected in major cassava planting areas across Vietnam  in Hanoi, essentially using the CTAB-based DNA extraction protocol described by Doyle and Doyle . GenotypSamples were compared with the single nucleotide variation library at the CIAT. This library is based on 2000 diverse genotypes, and comprises materials from ex situ collections that were assembled in national programs in Latin America  and Asia . A comparative analysis was also performed using a genetic duplicate test based on the homozygous/heterozygous allele-call correspondence differences (< 3%) implemented in the next-generation sequencing experience platform , gene diversity , and polymorphism information contents (PIC) were determined using the diversity tool of the NGSEP platform  platform.To determine genotype by genotype associations, discriminant analysis of principal components (DAPC) was performed using the Adegenet library that was developed for R. We used Bayesian information criteria to define numbers of subgroups for this dataset. We generated a genetic distance matrix using Power Marker V. 3.25 and used the “Shared allele” method to estimate genetic distances. Clustering was performed using the neighbor joining (NJ) method with MEGA v. 6 software, and was visualized and edited using the interactive tree of life .Based on the genotype data generated using the 1570 samples collected across Vietnam, genotype frequencies were analyzed using geo referenced information. The genotype frequencies data matrix was plotted using the Geographic Information System ArcGIS Desktop Help 10.2 Geostatistical Analyst  was the most commonly recognized cultivar group , bamboo leaf (9.3%), KM94 (7.8%), green (4.3%), white (4.1%), and hybrid (4.0%) were the best known, and %, bambooKM94).Geographic patterns of variety naming revealed that upland areas below 250 m above sea level (m a.s.l) had the highest diversity of names (82%). The remainder (12%) were found in mountain areas between 250 and 1000 m a.s.l. The South Eastern region had the highest diversity of nomenclature, with 35 different names, followed by the Central Highlands and North Central Coast regions, with 32 and 31 names, respectively. In the provinces Phú Yên, Bình Phước, Bình Thuận, and Tây Ninh, farmers used more than 12 variety names. But in the Tuyên Quang province, only one variety name was used by farmers (Ho = 0.43) was higher than expected (He = 0.40) across all 96 SNP markers, suggesting an excess of heterozygotes plants among the 1570 individuals evaluated, and among genotypes from the same agro-ecological zones  ranged from 0.43 to 0.46, and the North Central Coast zone had the highest level at 0.46  suggest that cassava samples from the North West and East zones have the highest genetic diversity (mean He = 0.40).Ninety-eight percent of genotypes had at least one duplicate among all samples, and 85 different cassava genetic groups were finally identified . The highest percentage of duplicated genotypes was detected in the Central Highlands (24.5%), whereas duplication in North West and South Central Coast zones did not exceed 12%  with a genotype reference set of 2000 unique improved landrace varieties from the Germplasm Banks of Latin American  and Asia .Among varieties from Vietnamese farmer’s fields, KM94, KM419, BRA1305, KM101, KM140, PER262, KM60, KM57, and two unidentified varieties (G7 and G44) represented 82% of the frequency distribution  representing the total genetic variability in Vietnam. DAPC analyses identify clear groups and subdivisions. The scatterplots in Fig. The average total genetic distance between genotypes of different agro-ecological zones was 0.025, with values ranging from 0.0050 to 0.046 Table . The smaGenetic relationships between the present samples were identified in cluster analyses using the NJ method. The dendrogram in Fig. SNP data from 1570 samples were analyzed in genetic duplication tests using NGSEP. These computations allowed identification of a set of 31 unique genotypes that were not duplicated with the other 1540 samples collected in Vietnam or with the 2000 genotypes of the CIAT cassava reference library  was higher than expected (He = 0.40) in whole cassava samples and within each agro-ecological zone across all loci, suggesting high heterozygosity among the genotypes cultivated in Vietnam. Outcrossing  and inbreeding-sensitive crops such as cassava are expected to have higher numbers of DNA polymorphisms  for a wide geographic variety of germplasms. Similarly, Peña-Venegas et al. . Despite the introduction and release of new varieties in Vietnam, proportions of LAC landraces were low in Vietnamese farmers’ fields, suggesting that these varieties contribute to the observed heterozygosity among cassava germplasm cultivated in Vietnam  from studies using SSR markers, and these observations are concordant with our results. The high heterozygosity observed here and in previous studies was not corroborated by studies in Africa Fregene et al. Ninety-six percent of the present SNP markers were effective for detecting genetic variability in the whole sample set Table . The avei et al. , despites et al.  reporteds et al. , who useDo Ha Tay or Bamboo Leaf. This variety was exclusively found in the North of Vietnam below 750 m ASL. In addition, the variety KM101 (CMR29-56-101) was distributed across 6.3% of the country and was first used in 1989 as a high yielding variety in Vietnam. KM101 was officially released in 2015. This variety has been conserved at the CIAT genebank under the names TAI121 and TAI14, and according to pedigree and parental knowledge, was identified as CMR 29-56-101 (CMR 23-149-128 × Rayong1). KM140 showed a frequency distribution of only 6% and has been identified as a hybrid of KM98-1 and KM 36 by HLARC. This variety was released in 2017 to replace the main variety KM94 because its starch contents exceeded 26.2%. We also identified an important variety (G44) in Vietnamese farmers’ fields that was not found in any of the genebanks consulted. This unknown variety is closely related to KU50 and has a frequency distribution of 4% in the northern provinces of Lai Châu and Lào Cai. A second Latin American landrace from Peruvian origin, coded as PER262 at the CIAT genebank, had a 3.4% frequency distribution across North, Central and South of Vietnam. PER262 was identified as a duplicate of the strains CR63 and TAI9 (CIAT genebank accessions): this accession is showing high levels of disease resistance to cassava mosaic disease cause by the Sri Lanka Mosaic Virus . Similarly, the last three most frequently found strains had first-degree relationships with a Latin American landrace that is present in the CIAT genebank. KM57 is known as VNM8 in the CIAT genebank and Xanh Vinh Phú in the HLARC genebank, and is related in first degree to PER262. This variety probably was grown in Vietnam prior to the breeding efforts by the CIAT and partners in Thailand, suggesting that it was grown at that time as a “farmer selected” material from PER262. The second unidentified variety among the top most frequently observed plants is related to the Peruvian landrace PER308 (CIAT genebank). Accordingly, KM60  was closely related to the Colombian landrace COL1684, and was used in the first breeding efforts by the CIAT and partners in Thailand  and a recently released variety from the national breeding program (KM419) Thus, cassava breeding in Vietnam has made remarkable progress since 1988, with prevalent adoption of locally selected genotypes since Vietnam began its cooperation with CIAT and the Asian Cassava Research Network Fig. . For morThe KM419 cassava production area was more than 50,000 ha in 2013, and its advantages over KM94 include 28% greater root yields (34.9–54.9 t/ha), starch contents (27.8%–30.7%), root dry matter contents (15.6–21.6 t/ha for 7–10 months after planting). This variety also has good root shape with white flesh, and is highly adaptable to various production conditions  and North Central Coast zones (Nghệ An and Quảng Bình), which are priority areas for on-farm conservation programs. Similarly, it is necessary to include other areas that were identified in the spatial distributions, such as those with high allelic richness in the provinces of Gia Lai and Đăk Lăk  and Bình Phước and Đồng Nai  in the cultivated cassava materials that are currently present in Vietnam, and these were structured into four clusters using DAPC and NJ analyses  is distributed in all agro-ecological zones of Vietnam and was the dominating variety cultivated by farmers in this study (38%). We conclude that the genetic diversity in Vietnamese cassava farmers’ fields is only partially covered by CIAT accessions, suggesting that local varieties should be incorporated into germplasm banks of future collections.Electronic supplementary material 1 (PDF 513 kb)Electronic supplementary material 2 (PDF 171 kb)Below is the link to the electronic supplementary material.