Pinus radiata D. Don), was able to identify individuals that show high genetic recombination at genomic regions, which potentially include pleiotropic or collocating QTLs responsible for the studied traits, reaching a prediction accuracy of 0.80 in random cross-validation and 0.72 when whole family was removed from the training population and predicted. To identify these highly recombined individuals, a training population was constructed from correlation breakers, created through tandem selection of parents in the previous generation and their consequent mating. Although the correlation breakers showed lower observed heterogeneity possibly due to direct selection in both studied traits, the genomic regions with statistically significant differences in the linkage disequilibrium pattern showed higher level of heretozygosity, which has the effect of decomposing unfavourable genetic correlation. We propose undertaking selection of correlation breakers under current environmental conditions and using genomic predictions to increase the frequency of these ’recombined’ individuals in future plantations, ensuring the resilience of planted forests to changing climates. The increased frequency of such individuals will decrease the strength of the population-level genetic correlations among traits, increasing the opportunity for new trait combinations to be developed in the future.Global climate change introduces new combinations of environmental conditions, which is expected to increase stress on plants. This could affect many traits in multiple ways that are as yet unknown but will likely require the modification of existing genetic relationships among functional traits potentially involved in local adaptation. Theoretical evolutionary studies have determined that it is an advantage to have an excess of recombination events under heterogeneous environmental conditions. Our study, conducted on a population of radiata pine ( Genetic correlations are pairwise trait associations based on pleiotropic mutations2, and linkage disequilibrium (LD) (co-segregation of quantitative trait loci (QTLs))3. Pleiotropic mutations can be present at the allelic level  or at the gene level 2 and appear to be the primary cause of the genetic correlations observed among traits in populations under random mating5. Linkage disequilibrium or a shared environment can also contribute to genetic correlations in a population under non-random mating with the presence of inbreeding6.Global climate change will likely introduce new combinations of environmental conditions into our forest systems, increasing the physiological stress affecting plants and affecting multiple traits in multiple ways. Coping with these ecological changes in the long-term may require the modification of the underlying genetic correlation matrix among functional traits7. Alternatively, high positive genetic correlations between functional traits provide a mean for their co-evolution8. Sgró and Hoffmann9 found changes in environmental conditions to be the cause of changes in the value and direction of genetic correlations in life history traits that were observed in samples from the same population, stressing the need to investigate genetic correlations among different sets of environmental conditions. Previous studies found substantial changes in genetic parameters when estimated in favourable versus stressful environmental conditions11. Although the presence of high recombination through migration might be counteracting local adaptation to the current environmental conditions12, theoretical evolutionary genetics studies have found the evolution of high recombination rates when selection targeted different loci under heterogeneous environmental conditions14. Thus, high recombination rates are crucial in adaptation under rapidly changing environments.Genetic correlations represent evolutionary constraints that have developed over time, and their values and directions can vary according to differences in micro-evolutionary processes along environmental gradients15. In these circumstances, the undesirable genetic patterns between traits can be challenged by specific breeding (complementary mating) and/or selection procedures in order to select ’correlation breakers’  and eliminate the unfavourable trends15. One of the most critical adverse genetic correlation in forest trees is the relationship between tree productivity and wood density. This correlation is often strongly negative e.g. 16. Additionally, both traits seem to be involved in adaptive processes. While productivity in terms of growth speed is considered a means to deal with competition for the sun light from surrounding plants17, wood density is related to resistance to drought stress, through thicker cell walls which prevent cell implosion due to higher negative pressure in the xylem pipelines or embolism19, and to wood stiffness20. Breaking this strongly negative genetic correlation could unlock the genetic potential of trees. Therefore, finding individuals with a much greater recombination rate than normal is strategy to explore the potential of this approach.Adverse genetic correlations constraining genetic improvement progress can similarly be found in breeding populations15. A phenotypic correlation is the product of genetic and environmental correlations and their interplay21. Genetic correlations represent associations among the genetic causes of the traits investigated, which can be affected by many factors such as sampling error, environmental heterogeneity and developmental stage. Genetic correlations can also vary with the expression of different genes in different environments or developmental phases22. While genetic correlations reflect general trends in populations, they can also be modified by the selection of correlation breakers to shift the association between traits in a favourable direction15.Unravelling the mechanisms underlying the observed phenotypic correlations among traits is one of the greatest challenges in quantitative genetics25. Such analyses can be used effectively to select recombinants of known quantitative trait nucleotides (QTNs) to break adverse genetic correlations, especially in cases where genetic correlations are built-up through chromosomal linkages rather than pleiotropic effects26.Current progress in next-generation sequencing has enabled the development of genomic resources for non-model organisms and allowed the closer investigation of correlations among genotypes and phenotypes through association mapping28. However, multi-trait prediction models appear to be less advantageous when traits are uncorrelated, even proving inferior to single-trait models. Additionally, the multi-trait models are not useful for identifying individuals that break unfavourable genetic correlations27.Genetic analyses investigating single-trait versus multi-trait models have found that multi-trait models were superior in the accuracy of estimated genetic parameters such as additive genetic variance, heritability and breeding values, particularly where there are significant genetic correlations between traitsPinus radiata D. Don) population established using tandem selection based on New Zealand’s nation-wide breeding values as a test case15. Using genomic data, we identified individuals that were correlation breakers for growth and wood density traits, and used genomic data to test for high levels of recombination in these individuals. We also investigated how recombination within the population described differs with a another reference population of the same species. With the knowledge that recombination and heterogeneity has been associated with genetic robustness14, we discuss the implications of our results on the potential to select for genotypes that will remain robust under future climate change.Our analysis investigates the genomic features of genetic correlation breakers and the ability to predict these types of individuals using genomics. We propose that genomics will allow us to select individuals with high level of recombination and that these individuals will have favourable combinations of traits that are otherwise negatively correlated at the population level. We used a radiata pine . Selection is usually performed on the basis of a ’growth and form’ (GF) score, which is an artificially created scale from 7 (unimproved) to 30 (highly improved) representing weighted breeding values for growth and form attributes31.The New Zealand (NZ) Radiata Pine Breeding Company’s (RPBC) breeding population includes populations selected for either high wood density or growth and form attributes, using NZ nation-wide breeding values that reflect a common genetic effect across tested environments. The breeding program strategy proposed further developments are outlined in detail in previous studiesThe sample under study was composed of two populations: POP1 was used as the training population, and POP2GF was the population in which to predict individuals with excess recombination using a genomic prediction model. All trials were clonal full-sib families and replicated among sites at Tarawera, Woodhill and Kinleith in the North Island of New Zealand. The POP1 population was established from parents chosen through two selection strategies. One was based on a tandem selection, where 19 parents were first selected for growth and form using GF scores followed by a second round of selection focused on high wood density (HD). This group (POP1HD) formed a population of 160 individuals termed ’correlation breakers’. The second selection strategy in POP1 was based purely on GF scores where 33 parents were selected to create POP1GF, consisting of 304 individuals. POP1GF and POP1HD were established using a single-pair mating design which produced 33 (POP1GF) and 19 (POP1HD) full-sib families. Both populations were planted at Tarawera using a single tree plot, set within replications design with six replications. Each family was represented by 10 genotypes and each genotype was tested in six copies.32.The POP2GF population, consisting of 523 individuals, was also selected based only on GF scores. POP2GF was established using a factorial mating design which produced 42 full-sib families from 24 parents. The POP2GF population was planted across two sites, Woodhill and Kinleith, using an incomplete block design with five replications and nine incomplete blocks (each representing six families) within each replication. Each family included 10 genotypes which were tested in five copies. There were two common parents between POP1GF and POP1HD, 12 common parents between POP1GF and POP2GF and one parent between POP1HD and POP2GF. Full trial details are reported in Li et al.33, straightness (ST9), using a 9 points scale from 1 (crooked) to 9 (very straight)34, diameter at breast height (DBH [cm]), and wood density , using a 9 points scale from 1  to 9 36 as described in Telfer et al.37. In brief, transcriptomic resources, that represented gene expression across broad range of tissues, including compression wood xylem, spring xylem, summer xylem, summer phloem, spring buds, autumn buds, healthy needles, needles infected by Phytophtora pluvialis, seedling phloem and seedling xylem37, were aligned to Pinus taeda reference genome v. 1.01e and used to develop 120 base capture probes. Captured markers were removed if heterozygosity shown in megagametophyte tissues was higher than 5%, average read depth was less than 10, multiple alleles were detected, or only singletons were observed. Additionally, individual datapoints were classified as missing if the ratio between reference and alternative allele was lower than 0.1 and the number of read was less than 1038. The average read depth was 59.2 per marker and 59.04 per individual. Data were further filtered for minor allele frequency (MAF) > 0.01 and missing data were imputed with mean genotype. Total number of markers was 80,159 SNPs after filtering.Genomic data were generated using a previously developed exome capture—genotyping by sequencing platform39. To reduce bias in estimation of LD due to familial structure, we implemented LD corrected for relatedness as proposed by Mangin et al.40 as follows:41. The analysis was performed using ’LDcorSV’ R package42. Additionally, the genomic differences between POP1HD (correlation breakers) and POP1GF populations were investigated through comparison of the mean observed and expected heterozygosity (approximating effective population size) using t test43. The 100 individuals randomly selected from each population were used to estimate the sample mean observed heterozygosity, which was performed 100 times. The trend line of decay in LD was estimated using the Hill and Weir expectation44 as follows: 45. The statistical significance in LD pattern difference between POP1GF and POP1HD in the training population was investigated through a Jennrich test46, testing the null hypothesis that the two correlation matrices are not different from each other. Only scaffolds with at least three overlapping markers between samples were included in this analysis.The analysis of linkage disequilibrium (LD) was based on composite LD correlation ’r’ equivalent to the Pearson’s product-moment correlation coefficient between genotypes of investigated loci47 was used to estimate genetic parameters such as variance components, heritability, and genetic correlations. The multivariate linear mixed model was implemented in the POP1 populations (POP1HD and POP1GF) as follows:48, nth trait, nth trait, and 41:Pinus taeda) reference genome v. 1.01e49) , nth trait, nth trait, nth trait, nth trait, nth trait, nth trait, nth trait, nth trait, and Phenotypic data were standardised for each trait at each site to avoid the problems associated with combining breeding values with different scales into a multi-trait selection index. The ’ASReml-R’ packagenth trait, and nth trait.The multivariate linear mixed model was implemented in the POP2GF population as follows:Trait narrow-sense heritability was estimated as follows:50. The similarity of correlation matrices between samples was compared using the Krzanowski test51 measuring the similarity between first principal components by using ’KrzCor’ function implemented in ’evolQG’ R package52 as follows:ith principal component of the additive genetic variance-covariance matrix of the Ath sample. The phenotypes in the multivariate analysis used to construct additive genetic variance-covariance matrix were standardized in this case.The pair-wise marker-based genetic correlation was estimated in terms of Pearson’s product moment as follows:53 was performed to investigate similarity/dissimilarity between pairs of variance/covariance matrices obtained for each tested population and identify traits which cause differences in these matrices. The method implements the decomposition of evolutionary responses inferred from selection gradient vectors simulated through random skewers54. The similarity in response to selection between two investigated genetic variance/covariance matrices is estimated as the average correlation between vectors of evolutionary responses inferred from the same simulated vectors of selection gradients. The multivariate response to selection is estimated as follows:Selection response decompositionThe correlations of trait-specific vectors are estimated across tested populations for all simulated scenarios, and their average is called the SRD score. If 47 as follows:The prediction of correlation breakers was performed through logistic regression, where the aim was to predict the binary status of the correlation breakers through the genetic markers. The individuals originating from the correlation breakers (POP1HD) population were marked as 1 while individuals from POP1GF population were marked as 0. The POP2GF population was then used as an independent population to identify correlation breakers through the genomic prediction model. The logistic regression was performed in the ‘ASReml-R’ statistical packageThe study complies with Scion internal rules and guidelines for field operations and sampling of genetic material. All permissions required for data collection and sampling of plant tissues for DNA extraction were obtained. There is no permission required for the research on radiata pine in New Zealand.Pinus taeda) reference genome v. 1.01e. We found intensive LD decay in the correlation breaker population (POP1HD) where a composite estimate of linkage disequilibrium 55) was reached at 1 kb. By comparison, this same level of Linkage disequilibrium (LD) decay was investigated within scaffolds which contained at least three Single Nucleotide Polymorphisms (SNPs), and that mapped to the loblolly pine  and reached values from 0.236 to 0.243, the average observed heterozygosity was lower in POP1HD (0.203) compared with POP1GF (0.226). Additionally, we investigated observed heterozygosity in markers from genomic regions with distinct LD pattern between POP1HD and POP1GF (based on results from the Jennrich test) and found higher observed heterozygosity of 0.23 compared to whole-genome observed heterozygosity of 0.203 in POP1HD compared to 0.22 which is similar to the whole-genome observed heterozygosity of 0.226 in POP1GF. Moreover, identity by state (IBS) coefficients were estimated to look at genetic similarity, unbiased by allelic frequencies. We found only subtle differences between the distributions of IBS within each investigated population as well as between them. While the average IBS coefficient within populations ranged from 0.766 (POP2GF) to 0.775 (POP1HD), slightly lower average IBS coefficients were found between populations ranging from 0.764 (POP1HD–POP2GF) to 0.767 (POP1GF–POP1HD and POP1GF–POP2GF) , estimated to be 0.093 (POP2GF in Woodhill) − 0.212 (POP1HD) in the pedigree-based analysis. The highest heritability was observed for wood density (WD), reaching 0.350 (POP2GF in Kinleith) − 0.585 (POP1HD) when estimated from the pedigree across all samples. The marker-based analysis showed a similar pattern with the lowest heritability estimated for ST9, ranging from 0.087 (POP2GF in Kinleith) − 0.225 (POP1GF), and highest heritability estimated for WD, ranging from 0.518 (POP1GF) − 0.608 (POP2GF in Kinleith) Table .Genetic correlations were estimated within each population at each site separately using both pedigree and marker-based analyses. These correlations serve as input parameters to compare differences in the multivariate response to selection between populations and environments. The genetic correlations generally corresponded across the methods. However, the 95% confidence limits were larger in pedigree-based estimates compared to marker-based equivalents, especially in the middle part of the distribution Tables ; Fig. 5.53 based on simulated vectors of selection responses was generally in an agreement between genetic variance-covariance matrices and differences appeared to be caused by environmental rather than genetic heterogeneity. For example, POP1GF and POP2GF, which are both tested in Woodhill and share the highest number of parents, also showed the highest correspondence among the estimates. On the other hand, the lowest correspondence among estimates was obtained between POP2GF tested in two different environments, which indicates the high impact of GxE on the genetic correlation matrix. The lowest correspondence of estimates between the identical genetic material planted at two different environments indicates strong influence or environmental conditions, especially on traits related to growth and stem form. Surprisingly, at high correspondence among estimates was also found between POP1GF and POP1HD, populations with different selection histories but planted in the same environment. A low correspondence was estimated among the POP1HD and POP2GF populations compared with the correspondence among the POP1GF and POP2GF populations, which could be attributable to the difference in selection history  testing prediction accuracy of correlation breaker status within a training population that combined POP1HD and POP1GF, and (2) identifying correlation breakers in POP2GF. The ability to predict the status of correlation breakers in term of prediction accuracy was high and reached 0.80 when leave-one-out scenario was implemented and 0.72 when whole family was removed the from training population and their status predicted. Analysis of the POP2GF population resulted in the identification of 19 individuals with a predicted GEBV higher than 0.75, based on their genomic profile and after back transformation. When these individuals are plotted against their breeding values for DBH and WD, the predicted correlation breakers are in the centre of the distribution, with some cases that are superior for both traits and some that are inferior for both traits . We proposed that the identification of such individuals will lead to improvements in the response to selection in both traits, when traits are adversely correlated, due to decayed genetic correlation. We also postulated that high recombination might also improve population resilience to climate change. Under random mating, the cause of genetic correlations is proposed to be the result of mainly pleiotropic mutations14. Correlation breakers represent individuals that have accumulated an excess of recombination events, and propose their infusion into breeding program/forest plantations to support population resilience under changing environmental conditions. This is especially important in forest trees, as they are long-living organisms facing temporal variability in environmental conditions along their ontogenetical development, exacerbated by climate change. Mathematical modelling found an advantage of recombination at the early stage of adaptation, while selection against recombination was pronounced at later stages to eliminate maladaptive gene flow59. Additionally, the empirical study in lodgepole pine did not find any higher recombination rates between genes associated to different aspects of current climate patterns60. However, if we assume continuously changing environmental conditions with a higher frequency of extreme weather and climate events62, evolutionary processes might change direction which would shift back to the early stage of adaptation, and an excess of recombination would be advantageous. The decomposition of contemporary genetic correlations will not only release new genetic variation but also increase progress in evolutionary responses to new environmental conditions. Nevertheless, such a putative positive effect is restricted only to the genetic correlations caused by LD, while those caused by pleiotropic mutations should remain unaffected by recombination rates. However, pleiotropy-related genetic correlations could still be influenced by new environmental conditions through other mechanisms9.Additionally, an excess of recombination events has been shown to be favourable under heterogeneous environmental conditions63. Therefore, the establishment of a specific population containing correlation breakers is required. Such populations can be created through tandem selection as implemented in this study, or by complementary mating where the best performing individuals for each unfavourably correlated trait are mated15. Therefore, being a correlation breaker is more a relative concept than heritable feature. However, our analysis of observed heterogeneity discovered that, while the population of correlation breakers showed lower levels of heterozygosity across the whole genome, possibly due to directional selection for both traits (DBH and WD), they also showed increased level of observed heterozygosity in regions with statistically different patterns in LD. These regions might contain groups of pleiotropic or collocating QTLs for selected traits. Our multivariate analysis discovered that the tandem selection strategy disrupted most of the commonly expected correlations in the population apart from the correlation among BR9 and ST9  as having with no relatedness and inbreeding, the markers can trace both the relatedness created recently within breeding program and also relatedness and population structure that existed prior to the formation of the base population65. Additionally, genomic markers enable tracing of the Mendelian sampling term and linkage disequilibrium between QTLs and markers as oppose to the expected identity by descent derived from pedigree information66. On the other hand, such a clear pattern was not observed in the selection response decomposition, where the differences between correlation patterns appeared to be caused by environmental rather than genetic heterogeneity . These individuals are centred in the middle of the distribution with regard to breeding values for DBH and WD, with some having breeding values above average in both traits to some having breeding values below average in both traits . Our training population was established from sets of progenies derived from parents selected with a tandem selection strategy, and progeny of parents from a directional selection strategy. The ability to distinguish LD decay pattern developed from exome capture markers Fig.  was crucits Fig. . However68. However, there are concerns that such natural mechanisms are not fast enough to cope with the current speed of climate change without human intervention, and a more active approach needs to be applied. An assisted gene flow approach69 was proposed as a mechanism to shift current populations towards their future optimal conditions as predicted by climate models73. The current development of genomic resources in forest trees enables a deeper insight into adaptation processes through the detection of selection signals at the genome level75. However, adaptive traits are rather complex, and capturing selection signals through association mapping can be challenging76. Therefore, genomic selection77 or genome-wide selection scans79 based on the deployment of genetic markers to predict phenotype through multivariate regression models seems to be a more feasible solution to predict adaptive traits. Arenas et al.80 proposed genomic prediction of putative adaptive traits in small natural population of relict species to optimize conservation management.Forest trees are mostly widespread species occupying geographically large and environmentally highly heterogeneous areas which, accompanied with intensive long-distance gene flow, should support the rapid adaptation of populations to new environmental conditions82, agriculture crops84 and forest trees91. Compared to animals and plants, however, forest trees have high genetic diversity, extreme genome lengths  showed much more rapid LD decay compared with the other samples . Chao et al.63 found differences in the long-range level of LD between different wheat populations and attributed it to differences in breeding and selection history. However, they did not find any differences in LD decay, and each population captured a comparable amount of recombinant events.The radiata pine genomic resources generated within our project were assembled against the loblolly pine draft reference genomeakers POPHD showedakers POPHD showedakers POPHD showed82.In contrast, our samples do show a difference in LD decay, with the fastest decay apparent in the correlation breakers population, which provides evidence that a higher amount of recombination events is being captured in this sample compared with others. This pattern is expected, as the correlation breakers population was selected to break the adverse pattern in genetic correlations between two highly complex traits, which would require a higher level of recombination between a large number of QTLs associated with the different traits. Additionally, the increased recombination rate in this population could stimulate an increased response to selection and a decreased loss of additive genetic variance over time97. The reduced level of heterozygosity can be connected to accumulation of favourable alleles for QTLs involved in genetic architecture of both traits under selection (DBH and WD). Additionally, despite the relatively high conservedness of exome regions, as evidenced by similar IBS within and between populations—Fig. Since the expectation for entclass1pt{minimaentclass1pt{minima65 through the construction of a marker-based relationship matrix98. The implementation of this type of relationship matrix in genetic evaluations involves the simple substitution of the pedigree-based alternative and does not require any additional data treatment. The spectral decomposition of the marker-based relationship matrix found a strong difference in population structure between the correlation breakers that were selected for HD and other samples selected for GF. While samples selected for GF show high dispersion across the investigated space, the correlation breakers population was concentrated in the middle  selection be undertaken using genomic predictions by increasing the frequency of ’recombined’ individuals. We propose that high levels of recombination will also confer long-term resilience in planted forests, vital to the success of these populations under a changing climate. The increased frequency of such individuals may decrease the strength of the population-level genetic correlations among traits, which will, in turn, increase the opportunity for new trait combinations to be developed in the future.Supplementary Information.


In the context of rapid urbanization and limited land amount, it is essential to scientifically evaluate the urban land green use efficiency (ULGUE) to promote regional sustainable development. Current studies are of great value for enriching the theoretical system and application research of ULGUE. Still, most of them only consider industrial pollution but ignore carbon emission as an essential environmental influencing indicator. This paper introduced carbon emissions into the input-output indicator system, measured ULGUE of 57 cities in the Yellow River Basin (YRB) over the 2004–2017 periods using the super-efficiency slacked-based measure (Super-SBM) model, analyzed its spatio-temporal patterns with the kernel density estimation (KDE) model and spatial autocorrelation model, and then identified the influencing factors with the Spatial Durbin model (SDM). As shown by the results, firstly, the ULGUE in the YRB over the 2004–2017 periods showed a trend of first decreasing and then increasing. Secondly, the ULGUE exhibited spatio-temporal imbalance characteristics across the YRB. Thirdly, ULGUE was the interaction of multiple indicators, and its influencing factors had spatial spillover effects. All in all, this paper is fundamental to the high-quality development of cities in the background of the Chinese policy of “carbon peak, carbon neutralization”. Under the dual constraints of China’s protection policy of farmland and ecological land, the urban construction land stock is limited, and the outward expansion of urban space is somehow hindered . Fr. Fr21]. The cities in the upstream region mainly displayed the characteristics of the spaced distribution of high-efficiency cities with low environmental pollution and low development degree, while the capital cities had lower efficiencies. Among the midstream cities, there were fewer high-efficiency cities in 2004 and nine super-efficient cities in 2017, including Yulin, Zhengzhou, Lvliang, Yangquan, Yan’an, Changzhi, Linfen, Xinzhou, and Datong. In addition, the general land use in Shaanxi Province showed inefficient characteristics. Although Xi’an had reached the efficiency value of 1 during the 2006–2009 periods, with the biggest built-up area, the most extensive stock of fixed assets, and the largest number of employee inputs, it was difficult to achieve higher ULGUE at a lower carbon emission level with carbon emission constraints. In the downstream area, Zibo, Jinan, Dongying, Heze, and Binzhou cities achieved the DEA efficiency of ULGUE in 2017. The middle reaches of the Yellow River basin had the largest number of cities, and their efficiency values directly affected the level of urban land green use in the entire basin.In this paper, the KDE model was used to measure the dynamic characteristics of their distribution for the whole, upstream, midstream, and downstream of the YRB in 2004, 2008, 2012, and 2017, respectively . Beyond From a basin-wide perspective, the position of the main peak showed a trend of first leftward and then substantial rightward shift from 2004 to 2017, indicating that the ULGUE in the YRB experienced a trend from gradually decreasing to rapidly increasing during the study period. Among them, the main peak shifted slightly to the right from 2004 to 2008, then shifted significantly to the left from 2008 to 2012, and shifted even more significantly to the right from 2012 to 2017, presenting large volatility. The slight increase in the height of the main peak indicates that the difference between ULGUE of cities gradually decreases. Moreover, the simultaneous existence of the main and secondary peaks reveals that the bi-polar pattern of ULGUE has been present during the study period. The gradual steepening of the shape of the main wave peak indicates that the proportion of low-value areas of ULGUE gradually declines.From a sub-regional perspective, the temporal evolutionary changes of ULGUE in the YRB, on the other hand, varied widely. The small difference in the height of the main peak and the secondary peak in the upstream area from 2004 to 2012 indicates that the changes in ULGUE are small. However, from 2012 to 2017, the main peak in the upstream was significantly elevated and shifted from a broad peak to a sharp peak, indicating that the difference of ULGUE among the upstream cities is gradually expanding and the proportion of the high-value area is gradually increasing. The main peak in the midstream region shifted slowly to the right, suggesting that the ULGUE displays a slowly increasing trend. Nevertheless, the height of the main peak decreased year by year, and there was a tendency for the sharp peak to turn into a broad peak, indicating that the difference between cities increased and the proportion of high-value areas declined. The main peak of the downstream region showed a “jump” change, shifting sharply right from 2004 to 2008, but shifting sharply left from 2008 to 2012, and shifting slightly right from 2012 to 2017. That is to say, the efficiency values have gone through a “rising-declining-rising” process. The width of the main peak of the KDE curves remained relatively stable, indicating that the variation of ULGUE among the downstream cities does not change to a great tent. The primary and secondary peaks of the KDE curves in the upstream, midstream, and downstream regions were all present, revealing that the differences in ULGUE are always present.From 2004 to 2017, the results of Moran’s index of ULGUE in the YRB were negative for 4 years and positive for 11 years . In addiFurther, this paper reported the local Moran’s index, namely the “LISA” aggregation index of ULGUE . From 20The independent variables were logarithmized to enhance the smoothness of the data, reduce covariance, and mitigate heteroskedasticity. Prior to conducting the regression analysis, a test for multicollinearity was first performed, and the mean VIF was 3.06, much less than 10, indicating that there is no multicollinearity among the selected variables. Furthermore, unit root tests and cointegration tests were carried out. There was a long-term equilibrium relationship between ULGUE and the selected variables, which can be subjected to panel regression analysis.p-value was less than 0.1. Therefore, the SDM model should choose the fixed effects for the regression. The LR test and Wald test both passed the significance test at the 1% level, implying that the SDM model will not degenerate into the SAR model and SEM model in the regression analysis. According to the results of LR tests for time-fixed, spatial-fixed, or time-spatial-fixed effects, the time-spatial-fixed effect would be suitable for the SDM model  was also significant at the 1% to 10% level. Under the premise that both models were applicable, the SDM model that combined both could be chosen for empirical analysis. Then, the Hausman test was conducted for the choice of random or fixed effects and the DM model .The regression results of the SDM model are shown in The regression coefficient of employment structure was −0.071, which negatively affected ULGUE at a 5% significant level, which is consistent with the findings of Ji et al. . AdditioThe coefficient of environmental regulation was −0.105 and was significant at a 5% statistical level, indicating that the level of environmental regulation harms ULGUE, and there is no “Porter’s hypothesis” for ULGUE in the YRB, which is consistent with the findings of Hao et al., Wang et al., and Shuai et al. ,100,101.The coefficient of real estate development intensity was −0.028, which was significant at the 10% statistical level, indicating that high-intensity real estate development will suppress the regional ULGUE. In the “land economy” of China, government departments have promoted economic growth to a certain extent by granting land and bringing in real estate developers. Still, the use of land resources has shifted to commercial and residential land. What’s more, the restricted land use pattern is not conducive to developing urban ecological functions.The spatial spillover effects of the SDM model were measured, namely direct effect, indirect effect, and total effect decomposition . Except The indirect effect of governmental expenditure intensity was significantly positive at the 1% level, indicating that the increased fiscal expenditure of local government positively affects the ULGUE in neighboring cities. Local governments have multiple game relationships in the face of environmental pollution and environmental management problems in socio-economic development. Under the pressure of economic growth and political promotion, local governments’ interventions in socio-economic development are spatially competitive. Apart from that, local city governments’ policy measures and strategic behaviors in land development and utilization, investment in urban infrastructure, and industrial layout will also have positive spatial transmission to the neighboring cities through demonstration, imitation, learning, and competitive effects.The indirect effect coefficient of the industrial structure was −0.738, which passed the significance test at the 10% level, indicating that the degree of second industry proportion of a city has a negative spillover effect on the ULGUE of its neighboring cities. In comparison to the tertiary sector, a higher share of the secondary industry will take up more land, consume more energy, and cause more industrial pollution and carbon emissions . The spaThe indirect effect coefficient of environmental regulation was 0.868, which passed the significance test at the 5% level. The increase in environmental regulation intensity in local cities is conducive to promoting the improvement of ULGUE in neighboring cities. Beyond that, industries of high energy consumption, high pollution, and high emission industries are forced to improve pollution emission treatment technology in the production process and raise awareness of green production. The cities have similar industrial structure characteristics to the YRB, which is densely populated with resource-based cities. In the context of China’s policy of intensive energy use, industrial pollution remediation, and haze remediation, there is competition for political achievements in each city. Moreover, the improvement of environmental regulations in one city will trigger the surrounding cities to follow suit, which in turn propels the improvement of ULGUE.In this study, robustness tests were performed by replacing the independent variables. Specially, we use the share of the tertiary sector in GDP to represent the industrial structure and the centralized sewage treatment rate to represent environmental regulation and again run the regression referring to Xue et al.’s study . The resTo test the robustness of the findings, this paper used the inverse distance matrix to replace the geographic distance matrix for the regression analysis of the SDM model. As shown in The YRB spans eight provinces, and the 57 sample cities selected in this paper have variability in geographic location, economic development level, resource endowment, and government management characteristics . In thisRegarding the indirect effects, spatial spillover effects were mainly manifested in midstream and downstream cities. To be specific, the economic development levels of midstream and downstream cities significantly negatively affected the surrounding cities’ ULGUE. Unlike the sparse distribution of upstream cities, the midstream and downstream cities are more closely connected due to geographical and economic factors. When the scale of a city far exceeds that of its neighboring cities, it will form a siphon effect, attracting a large amount of labor, capital, and new industries and forming an unbalanced development structure . FurtherThe YRB is the main supply base of energy resources in China and an ecological corridor connecting the Qinghai-Tibet Plateau, Loess Plateau, and North China Plain. Since the ecological protection and high-quality development of the YRB have been promoted as a national strategy, a lot of scholars have paid extensive attention to the sustainable development of this region ,108,109.With economic globalization, substantial carbon emissions and industrial pollution have created an enormous obstacle to sustainable development . At the From the perspective of natural factors, improper soil management can cause a reduction in the carbon sink capacity of the land . The gloThe construction of a low-carbon city is a multidisciplinary and jointly guided vision of the future city, whose definition and criteria are dynamic. Moreover, low-carbon development requires bottom-up low-carbon behaviors, up-bottom policy guidance, and technical support that constantly running through it. At present, cities around the world are exploring new models of low-carbon development . In orde(1)Controlling the land supply amount, adhering to the urban development boundary, and improving ULGUE within the city’s built-up areas. The government should consider factors such as the carrying capacity of resources and the environment and the suitability of land space development. In addition, the economic development patterns should be based on local conditions. Meanwhile, urban development mode driven by the expansion of construction land together with the “pie” type of urban development should be strictly forbidden. It is also essential to promote the structural reform on the supply side of urban land and change from the development mode of traditional urban space expansion land to the optimization mode of urban land space layout. Moreover, upstream and midstream cities need to enhance the construction of green space facilities to increase the expected output of land. Besides, the downstream cities should promote the development of the real economy and reduce the real estate economic bubble with consideration of the economic development level.(2)Accelerating the green manufacturing industry, with the green sustainable development concept as the guidance. Industrial structure optimization and moderate intensification are encouraged to provide the ecological foundation for the green sustainable and healthy development of cities and livable cities. The long-standing development mode in the YRB has resulted in a large proportion of traditional manufacturing and resource-based industries in the industrial structure. At the same time, there is a low proportion of high-tech, advanced manufacturing, and modern service industries as well as a severe phenomenon of industrial homogeneity. It is necessary to rely on industrial transfer and industrial structure upgrading to promote the advanced manufacturing industry. With energy conservation and emission reduction as the policy guidance, the government should not only accelerate the implementation of peak planning and action plans for major cities and energy carbon-emitting industries, but also incorporate the implementation of carbon-emission control and air pollutant emission reduction into the central environmental protection inspectors, local party, and government ecological and environmental leadership of the responsibility audit system.(3)Actively promoting the synergistic development among cities and city agglomerations in the YRB in the context of regional integrated development. Departments should take the core cities and metropolitan areas as the entry point, pull the surrounding cities with their comprehensive advantages, further link the surrounding node cities to form a radiation circle, and gradually drive the development of the small and medium-sized cities. Since there is an increasing spatial spillover effect of ULGUE, the governmental departments should actively review the spatial interaction between local cities and neighboring cities and strengthen the role of linkage control between cities in land urbanization. Apart from that, the ULGUEs of the Lanzhou-Xining Urban Agglomeration and Hohhot-Baotou-Ordos-Yulin Urban Agglomeration is relatively high, while the Guanzhong Plain Urban Agglomeration is at a lower efficiency level. It is of great significance to use the positive spillover effect of government financial support, industrial restructuring, and infrastructure construction to promote the positive transmission of ULGUE in cities.This paper may have the following research shortcomings. First of all, due to the lack of accurate and complete energy consumption data for prefecture-level cities, this paper used the carbon emission dataset based on the nighttime light inversion provided by Chen et al. with a time cut-off of 2017 . SecondlThe increasing urbanization rates have made cities the regional centers of economy, population, and resources. For cities worldwide, it is a common problem to balance economic, social, and ecological benefits and achieve high efficiency with the currently limited amount of land is a common problem. In addition, the resident’s livelihood and the production of industry all produce large amounts of greenhouse gas emissions and environmental pollution, which will affect the sustainable development of the YRB. In this paper, an improved input-output indicator system was developed to measure regional ULGUE, which can also be used in other regional and national studies. From the perspective of urban carbon emissions as undesirable outputs, this paper measured the ULGUE of 57 prefecture-level cities in the YRB over the 2004–2017 periods using the Super-SBM model, analyzed their spatio-temporal divergence characteristics with the KDE model and spatial autocorrelation, and explored the influencing factors and spatial spillover effects. The main conclusions are as follows.(1)The temporal change patterns of ULGUE in the Yellow River Basin exhibited a “U”-shaped feature. Namely, it declined first and then increased with the development of the cities. The ULGUE in the YRB decreased from 2004 to 2010, while rising to a higher level from 2010 to 2017, which indicates the possible existence of an environmental Kuznets curve in the development of the urban economy. The average efficiency in the upstream area was the highest, followed by the downstream area and the midstream area (lowest). Although most upstream cities have a pattern of “low development level and low emission” pattern, the downstream cities have an agglomeration characteristic of “high development level and high emission”. There are many resource-based cities in the midstream, with significant differences among cities. This provides empirical support for the recognition of ULGUE in multiple types of cities.(2)ULGUE in the upstream, midstream, and downstream areas presented distinctive spatial non-equilibrium characteristics. Especially, the bi-polar pattern of ULGUE had been present during the study period. The spatial autocorrelation of ULGUE during 2004–2013 was insignificant, but a significant positive spatial correlation increased yearly after 2013, indicating that the positive inter-regional synergy feature is becoming more decisive annually. Meanwhile, the natural resource endowments can also influence the spatial distribution of ULGUE. What’s more, the Local Moran’s index displayed a “north high and south low” distribution. The Inner Mongolia Plateau and Loess Plateau regions were the high-value areas for ULGUE, while the Guanzhong Plain was a low-value aggregation area. That is to say, areas with low topographic relief and low elevation are easy for urban construction, but not easy for intensive use of land resources. In this regard, more scientific urban planning and policy support are needed.(3)Under the spatial spillover effect, the influencing factors of ULGUE showed a complex mechanism. From the perspective of a single city, the regional economic development significantly improved the ULGUE, while the fiscal expenditure intensity, employment structure, environmental regulation, and development intensity significantly negatively affected the ULGUE. Considering the decomposition of direct effects, indirect effects, and total effects, the fiscal expenditure intensity, environmental regulation, and industrial structure (high percentage of tertiary industry) had a significant positive spatial spillover effect. Apart from that, the total effects of fiscal expenditure intensity, environmental regulation, and industrial structure were all positively significant. Besides, the midstream and downstream were more prone to economic siphoning impacts, resulting in inefficient ULGUE of the neighboring cities. In short, reasonable policy regulations are needed to adjust the ULGUE of a region.


Monitoring the genetic diversity of small populations is important with respect to conserving rare and valuable chicken breeds, as well as discovery and innovation in germplasm research and application. Restriction fragment length polymorphisms (RFLPs), the molecular markers that underlie multilocus DNA fingerprinting (MLDF), have historically been employed for this purpose, but over the past two decades, there has been an irreversible shift toward high-throughput single-nucleotide polymorphisms (SNPs). In this study, we conducted a comparative analysis of archived MLDF results and new data from whole-genome SNP genotyping (SNPg) among 18 divergently selected breeds representing a large sample of the world gene pool. As a result, we obtained data that fit the general concept of the phylogenetic distribution of the studied breeds and compared them with RFLP and SNP markers. RFLPs were found to be useful markers for retrospective assessment of changes in the genetic architecture and variability underlying the phenotypic variation in chicken populations, especially when samples from previous generations used for MLDF are unavailable for SNPg. These results can facilitate further research necessary to assess the possibility of extrapolating previous MLDF results to study the long-term dynamics of genetic diversity in various small chicken germplasm populations over time. In general, the whole-genome characterization of populations and breeds by multiple SNP loci will further form the basis for the development and implementation of genomic selection with the aim of effective use of the genetic potential of the domestic gene pool in the poultry industry. Gallus gallus) is the most common type of poultry )each gel . The sizere used , allowinThe number and distribution of bands (DNA fragments) on the filter were specific to each individual. Pairwise comparison of all profiles for two different breeds was carried out, and the data were entered into a table for use in calculations. Information on individual filters was combined for the subsequent compilation of a matrix of interbreed distances and the construction of a phylogenetic tree for 18 breeds.FST values  and thestimated .The phylogenetic tree was built using the T-REX web server  using th® DNA chips produced by Illumina for the GWMAS Consortium [The genomic DNA samples were genotyped using chicken 60K SNP BeadChipnsortium . All obtnsortium . GenotypFST genetic distances were calculated using the EIGENSOFT program [® chips . Observed (OH) and expected heterozygosity (EH), unbiased expected heterozygosity (UEH), allelic diversity (RA), and coefficient of inbreeding (FIS) were calculated in the diveRsity R package [Pairwise  program  based on package . Multidi package .A phylogenetic tree for 18 breeds was built using the Neighbor-Net algorithm implemented in SplitsTree v. 4.14.6 . This trp = 3.20 × 10−8) and ZS  and the highest in CBl (p = 2.34 × 10−17), MG (p = 1.30 × 10−17), and YC (p = 1.80 × 10−18).As shown by the results of the genetic diversity study using MLDF, more than 100 DNA fragments per comparison (gel) were detected in the patterns of hybridization of the oligonucleotide probe with chicken genomic DNA . The disAs shown in The use of multilocus analysis technology made it possible not only to identify intrapopulation differences but also to calculate genetic distances to build a phylogenetic tree that displays the origin and degree of relationship in 18 divergently selected breeds. The resulting tree  had fourIn addition, as a result of a complementary analysis using MLDF , the genOH value was found in the BL population (0.273 ± 0.001) and the highest in the UG population (0.373 ± 0.002). The EH and UEH values were also the lowest in BL , whereas the highest values were in MG . The lowest RA value was detected in BL (1.716 ± 0.001), confirming the lower OH and EH values in this breed, and the highest RA value was determined in MG (1.944 ± 0.001). According to the FIS coefficient, almost none of the populations manifested inbreeding.The results of the assessment of genetic diversity using DNA chips are shown in FST genetic distances  in the Admixture cluster analysis showed that the most likely number of clusters  in our breed sample was 16 , which iHinfI restricted chicken DNA revealed a highly polymorphic pattern of DNA fingerprinting. In 20 unrelated broilers, an average of 27.8 bands per individual was detected in the range of 3 to 23 kb. Additionally, according to Epplen et al. [In this work, using the multilocus probe (GTG)5, we performed the fingerprint typing of 18 chicken breeds kept in small, closed gene pool flocks. The use of oligonucleotide (microsatellite) probes, including the (GTG)5 probe employed in this study and others, has been widely established in MLDF investigations. For example, Haberfeld et al.  exploredn et al. , the besn et al. ,63,64, wn et al. ) enabledA comparative evaluation of  authors ,66.As for the MLDF procedure itself, it is well established that it is subject limitations and drawbacks. If it were a universal, comprehensive, highly reliable, straightforward, and highly effective method, it would still be widely used today. However, it has been gradually discontinued since the early 2000s and is rarely revisited. This technique was largely abandoned because it used anonymous loci and was difficult to perform , and only few samples could be compared at the same time (as it is desirable to conduct comparisons on the same filter). However, based on our many years of experience  and thiNevertheless, in this work, we found a suitable way to analyze previous fingerprints and build the corresponding phylogenetic tree using the tree inference from incomplete matrices for 18 divergently selected breeds surveyed in the 2000s. The resulting tree topology  was largThe two phylogenetic trees generated with RFLP markers  and SNPsThe presented analysis of two approaches to assess genetic diversity provides a deeper understanding of the features of the genomic architecture of chicken breeds and populations. In addition, we compared the degree of variability within populations using two methods and correlated the results with historical breed data. The two approaches used in our study to assess genetic relatedness between chicken populations revealed differences between Asiatic breeds  and genetically rooted light-layer breeds, such as RW and Pu. Similar results were reported in a study by Malomane et al. .The optimal probable number of admixture-inferred clusters (i.e., 16 ancestral populations; Additionally, some individuals of the UG and YC breeds had genetic components of different origins, indicating the need for further study of these breeds. The first of these is a game breed, and the second has game chicken roots in its origin. The game type also stands out as an independent evolutionary branch of domestic chicken breeding according to the concept proposed by Moiseyeva et al. ; therefoCollectively, when comparing phylogenies from MLDF and genome-wide SNPg, a clear concept of the distribution within this large global sample of chicken breeds can be tracked, which conforms to known models of their evolutionary subdivision ,71. On tIn the present study, we attempted to assess the biodiversity of a large number of chicken breeds from the RRIFAGB bioresource collection. In genetic and phylogenetic studies of 18 divergently selected breeds, we not only assessed the general phylogeny of breeds but also considered how two types of markers were able to reflect these phylogenetic relationships. On the basis of MLDF data, we obtained a matrix of interbreed distances, calculated population genetic indicators characterizing the genetic variability of breeds, and built phylogenetic trees and cluster models. Our analysis results show that the MLDF method remains a fairly accurate method for retrospective analysis of changes in the genetic divergence of populations over time, although the method itself is already very outdated. Although there are examples of comparisons of multilocus DNA fingerprints and microsatellites to determine genetic distances in chicken , such a


Amphibolurus muricatus). We leveraged a large captive experiment that manipulated parental and offspring thermal environment to simultaneously estimate the genetic and non‐genetic drivers of variation in colouration. We found that the overall brightness, the elongation of the longitudinal stripes on the dorsum and the contrast between light and dark patches of the pattern were all heritable. Colouration varied according to the age of the hatchling; however, the thermal environment of neither the parents nor offspring contributed significantly to colouration. It appears that developmental plasticity and maternal effects associated with temperature are not important drivers of variation in our measures of colouration.The underlying drivers of variation in the colouration (colour and pattern) of animals can be genetic, non‐genetic, or more likely, a combination of both. Understanding the role of heritable genetic elements, as well as non‐genetic factors such as age, habitat or temperature, in shaping colouration can provide insight into the evolution and function of these traits, as well as the speed of response to changing environments. This project examined the genetic and non‐genetic drivers of continuous variation in colouration in a lizard, the jacky dragon ( Jacky dragons exhibit high variation in dorsal colour and pattern. We investigated the role of heritable genetic and non‐genetic drivers of this variation in colouration. We found only genetic sources contributed to variation in colouration. Females were monitored for gravidity, and most deposited their eggs in nests within their cage. Jacky dragon eggs were collected after deposition and then incubated at a constant temperature of 28°C, which typically produces a 50/50 sex ratio  in opaque plastic enclosures (500 mm × 300 mm × 300 mm) with sand substrate. Each breeding cage was assigned to a basking treatment at the beginning of the breeding season, which could be different for an individual across the 2 years of measurement. The basking lamps created a temperature gradient within the cage (~24–57°C) that allowed individuals to thermoregulate to their preferred temperature while the lamp was on  and 2016/2017 breeding season (n = 104). A total of 259 photographs were taken (one of each jacky dragon). Photographs were discarded if they were not in focus enough to analyse (n = 80). We aimed to take photographs of jacky dragons at 30 days old . Immediately following photography, individual body surface temperatures were recorded using a non‐contact infrared thermometer.We quantified three aspects of jacky dragon colouration using digital photography following methods by Troscianko and Stevens  and EndlWe photographed jacky dragons in a large cardboard box to prevent escape. The box was lined with a sheet of white PTFE (polytetrafluoroethylene) to standardize the background of each photograph. A Spectralon 99% white reflectance standard  was included in each image to standardize the photograph for analysis. An Iwasaki eyeColour MT70D E27 6500K arc lamp, a broad‐spectrum light source (300–700 nm) with the UV coating removed (sanded lightly with wet/dry sandpaper), was used as the light source for photography  and visually inspected for correct exposure using the histograms associated with the images and focus using the photo screening function in the program micaToolbox   Table . Second,t Tables . Both els Tables .lm’ function from the ‘stats’ R package , but contrast  and brightness  were not. However, body temperature also differed between study years . To investigate the effect of body temperature, we ran simple linear regressions using the ‘2.4MCMCglmm’ R package  separately, using models that contained identical fixed and random effects. All models included the fixed effects of: sex , parental treatment (long‐bask and short‐bask), offspring treatment (long‐bask and short‐bask), lizard age when photographed (days) and sampling year (2015 and 2016). A previous study with a subset of these lizards found evidence for a three‐way interaction effect between sex, offspring and parental basking treatment on offspring behaviour , models also included random effects. Each model included random intercepts for maternal identity , parental cage  and offspring cage (to account for early development conditions not associated with basking treatment). Models also included a random intercept for lizard identity, which linked an individual's data to the pedigree.In our pedigree, there was a small amount of uncertainty of paternity. A small percentage of hatchlings in our sample (6%) were from females that were housed with different males between seasons, and sperm storage is possible in this species  and variance (σ2) within the models. Parameter estimates were considered significant when 95% credible intervals did not include 0, and the ‘pMCMC’ values calculated by ‘MCMCglmm’ were <0.05  estimates from each animal model were used to calculate heritability (h2) of their respective colouration metric . The narrow‐sense heritability (h2) is defined as the proportion of phenotypic variance (σp2) explained by additive genetic variance (σA2), which can be estimated using the following formula:Variance (σp2) is the sum of all variance components, including the residual variance (σR2), σA2, variance (σ2) due to mother identity and variance (σ2) from offspring and mother housing enclosures . Thus, the formula that was used to estimate heritability in this study was as follows:Phenotypic variance σp is the s3We recorded substantial variation across our three colouration metrics, ‘brightness’, ‘elongation’ and ‘contrast’ Endler, . ‘Brighth2) of brightness was 0.1619 , elongation was 0.1810 , and contrast was 0.2304 . There were also non‐genetic factors that explained significant variation in jacky dragon colouration metrics. Lizard age affected all colouration metrics, as lizards increased in age (days) dorsal colouration became brighter, more elongated and higher in contrast. Sampling year affected two colouration metrics (brightness and contrast); animals in the 2016 sampling year were brighter with higher contrast  and degree of difference between light and dark patches of the pattern (contrast). The thermal environment of the parents and offspring that we predicted may have functional importance for these lizards did not significantly influence their colouration. However, more immediate elements of the environment and the individual, as captured in variation across years and animal age, had significant influences on colouration, suggesting that colouration in this lizard is responsive to short‐term conditions.h2 = 0.16–0.23) suggest that colouration could exhibit a robust evolutionary response to selection. Heritability of colouration is often high across a wide array of animal taxa (Table h2 = 0.16–0.23). This finding could be attributed to low additive genetic variance or high non‐genetic drivers of phenotypic variance that we could not account for in this study. For example, unmeasured components of individual quality (e.g. resource provisioning and immunocompetence) could be important non‐genetic drivers of variation in jacky lizard colouration, as occurs for many elements of colouration across animals  in order to examine other non‐genetic drivers of variation, our significant effects of lizard age and sampling year are likely attributed to uncontrolled ‘immediate’ factors. The effect of lizard age is most likely attributed to shedding cycles differing over the age range at which the photographs were taken (range: 6–62 days). During this time, agamid species can shed their skin 1–2 times depending on individual growth rate Doneley, . LeadingIn this study, we examined genetic and non‐genetic drivers of variation in a type of animal colouration that is often neglected in this field—continuous variation in colouration and colour pattern. Moreover, we applied methods for quantifying colour patterns Endler,  to one oLS and KU conceived the ideas and designed the methodology; RR collected data in 2016/17; RR, JR, KU and LS performed the statistical analysis; RR, LS, KU and JR all contributed to manuscript preparation and approved the submitted version.The authors declare that they have no conflict of interest.https://publons.com/publon/10.1111/jeb.14066.The peer review history for this article is available at Appendix S1Click here for additional data file.


Camellia hainanica, which is common in China’s Hainan Province, is an important woody olive tree species. Due to many years of geographic isolation, C. hainanica has not received the attention it deserves, which limits the exploitation of germplasm resources. Therefore, it is necessary to study population genetic characteristics for further utilization and conservation of C. hainanica. In this study, 96 individuals in six wild Camellia hainanica populations were used for ploidy analysis of the chromosome number, and the genetic diversity and population structure were investigated using 12 pairs of SSR primers. The results show complex ploidy differentiation in C. hainanica species. The ploidy of wild C. hainanica includes tetraploid, pentaploid, hexaploid, heptaploid, octoploid and decaploid species. Genetic analysis shows that genetic diversity and genetic differentiation among populations are low. Populations can be divided into two clusters based on their genetic structure, which matches their geographic location. Finally, to further maintain the genetic diversity of C. hainanica, ex-situ cultivation and in-situ management measures should be considered to protect it in the future. C. oleifera is the most important woody oil crop in China, and has excellent potential for development. This tree species plays an essential role in ensuring national grain and oil safety, socialist economic construction and ecological civilization construction . We used ArcGIS (Esri) programs to draw a population distribution map based on population distribution coordinates. Plant individuals are randomly selected from each population, and the number of samples chosen depended on the size of the population. Ninety-six plant individuals from six wild C. hainanica populations were assigned. We collect fresh leaf material from each individual and stored it at −80 °C for DNA extraction.Wild C. hainanica .The DNeasy Plant Mini Kit  was used to extract the DNA of the leaf sample, and the operation of this application was strictly conducted according to the manufacturer’s instructions. Likewise, a NanoDrop 2000 spectrophotometer  and agarose gel assay were used to measure the quality of the DNA samples. Twelve highly polymorphic SSR markers were used to genotype 96 samples of ainanica . The tarainanica . The uniainanica . PCR ampC. hainanica were collected and placed in an ice box, and celllysates  and DAPI staining solution  were placed in an ice box to precool. Approximately 0.5 g of fresh leaf tissue was chopped using a sharp razor blade in a 55 mm plastic Petri dish containing 300 of µL celllysate and 10 µL of PVP aqueous solution . The separation buffer containing the nuclei was filtered into a 1.5 mL centrifuge tube using a 40 µm nylon filter, and 200 µL of DAPI staining solution was immediately added. After incubation for 30 s in an ice bath, the fluorescence intensity of DAPI-stained nuclei was determined using a Partec CyFlow® Space flow cytometer . Each DAPI-stained nuclei solution was tested for three replicates, and each repetition comprising 5000–10000 counts was measured. Camellia fluviatilis (2n =2x =30), a diploid relative species, was used as the control (The young leaves of  control . Somatic control .The nonamplified (null) alleles at each microsatellite locus were detected using Micro-Checker v2.2.3 software . The allhttps://web.stanford.edu/group/pritchardlab/structure_software/release_versions/v2.3.3/html/structure.html) is more reliable than other clustering methods for cluster analysis of mixed ploidy populations , the relative DNA content of each plant was determined. The results indicated that the relative DNA content in hexaploid plants was 1.5-fold higher than that of in tetraploid plants , 1C. Thevia the ploidy test of every individual. We found that the numbers of tetraploid, pentaploid, hexaploid, heptaploid, octoploid and decaploid plants were 1, 2, 9, 13, 29, and 42, respectively, in six wild C. hainanica populations, which showed that the higher the ploidy was, the greater the number of plants. In addition, octaploids and decaploids accounted for approximately 74% of all samples, and the number of tetraploid, pentaploid, hexaploid and heptaploid plants was lower than that of octaploids and decaploids in each population . The genetic variation within a population is much higher than the genetic variation among populations.Population genetic differentiation analysis showed that the degree of genetic differentiation among populations was low (Fst<0.100) . The coeC. hainanica populations come from different provenances  increased gradually with increasing K value , although the expected heterozygosity is higher than the results of previous research for other Camellia species (o = 0.451) estimates were also lower than the expected heterozygosity (He = 0.706), indicating significant heterozygosity deficits in all populations. Meanwhile, we found that the average inbreeding coefficient was positive (Gis = 0.333) for all C. hainanica populations, and the level of inbreeding was higher, which may have resulted from geographical isolation between islands and continents.The genetic diversity of a species reflects its evolutionary and adaptive potential. The more genetic variation a species has, the more adaptive it is  species , and the species . SimilarFst underestimated genetic differentiation between provenances, while lower genetic differentiation occurred among the six provenances tested. Meanwhile, the analysis of molecular variance (AMOVA) revealed that the genetic variation within the population accounted for 85% of the total genetic variation, which is consistent with the low FST values (Fst<0.100) among provenances. The results are similar to those of previous studies of the genus Camellia, and island isolation may play a key role in this genetic differentiation  complex ploidy differentiation in C. hainanica; (2) low genetic diversity and genetic differentiation among populations are low; and (3) the analysis of genetic structures in different populations, showing that C. hainanica can be divided into two clusters. Finally, to maintain the genetic diversity of C. hainanica and improve its potential application value, we must implement a protection strategy and then protect populations with low genetic diversity from evolutionary threats.The study of the genetic diversity, genetic differentiation and genetic structure of 10.7717/peerj.14756/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.14756/supp-2Supplemental Information 2Red circles represent samples from Tunchang, Wuzhishan, Qiongzhong and green circles represent samples from Qionghai, Chengmai, Dingan.Click here for additional data file.


Populus genetic diversity and allow enabling molecular breeding to support accurate early selection, increasing the efficiency, and reducing the time and costs of poplar breeding, that, in turn, will improve our capacity to face or prevent the emergence of new diseases or pests.Poplar is one of the most important forest trees because of its high economic value. Thanks to the fast-growing rate, easy vegetative propagation and transformation, and availability of genomic resources, poplar has been considered the model species for forest genetics, genomics, and breeding. Being a field-growing tree, poplar is exposed to environmental threats, including biotic stresses that are becoming more intense and diffused because of global warming. Current poplar farming is mainly based on monocultures of a few elite clones and the expensive and long-term conventional breeding programmes of perennial tree species cannot face current climate-change challenges. Consequently, new tools and methods are necessary to reduce the limits of traditional breeding related to the long generation time and to discover new sources of resistance. Recent advances in genomics, marker-assisted selection, genomic prediction, and genome editing offer powerful tools to efficiently exploit the  Populus, which are traditionally clustered into six sections known as Populus, Tacamahaca, Leucoides, Turanga, Abaso, and Aigeiros . I. Icis- oo et al.  that revIn recent years, genetic studies have allowed to dissect the genetic bases of biotic resistances in poplar and this information has the potential to be exploited for tree breeding, particularly for implementing MAS and, consequently, marker-assisted breeding (MAB). Under certain scenarios, it was shown that MAS can be used to replace or improve truncation selection based on phenotypic values  and has The knowledge acquired in QTL mapping and GWAS shows that, in several cases, a few QTLs or QTNs with large effects underlie the genetic bases of biotic stress resistance . MoreoveThe application of GP, which aims to estimate the breeding value of individuals using only genotypic information , for theR genes (CRY and genes encoding for proteinase inhibitors) [Alternative ways to accelerate the genetic gain in poplar breeding are based on genetic modification. Recently, genetic-engineering applications in poplar have been reviewed and include studies related to the generation of transgenic poplars resistant to diseases by expressing antibacterial and antifungal genes  or insect ibitors) . Even thibitors) . Moreoveibitors) .The development of the new GE technologies based on CRISPR /Cas is revolutionizing the concept of genome modification because allows the introduction of insertions, deletions, or the substitutions of single base or short sequences at specific sites in a target genome in highly efficient, simple, and versatile way ,121,122 WRKY18 and WRKY35, contributed to unravelling their involvement in P. tomentosa resistance to Melampsora [P. tomentosa by activating the biosynthesis of proanthocyanidins [Even if GE in poplar is mainly focused for the improvement of growth in plantations and the use in the pulp and bio-refinery industries ,124, applampsora ; throughyanidins . These eyanidins .Biotic stresses represent major threats in both natural populations and plantations of poplar and forest trees in general. The extent of biotic stresses is intensifying as global warming is rapidly enlarging the distribution and incidence of pests and pathogens towards higher latitudes and their occurrence in new territories ,29. For One of the main issues of poplar breeding is tied to the biology of poplar that, as for other perennials, displays late expression of relevant traits and late development of reproductive structures, characteristics that prolong selection cycles and, consequently, the duration of breeding programmes. In addition, long breeding cycles should be supported by long-term financial investments. Nonetheless, market needs can change over time and the new clones developed by traditional breeding might not guarantee the expected return investments. The usual intensive cultivation of monoclonal plantations of a small number of elite cultivars, strongly adapted to the pedo-climatic conditions of specific territories, is often criticized by environmental associations, more favourable to greater biodiversity as in natural forests, and restrictions have been posed also on the cultivation of hybrid clones in protected areas and bio-conservation sites along rivers. In this condition, selected clones of native species, alone or in mixed clones, could represent a useful alternative to interspecific hybrids in more sustainable poplar plantations. For this reason, it is necessary targeting poplar genetics to guarantee the sustainability of wood productions in the areas where the spread and emergence of pests and pathogens are expected, also because of the higher frequency and incidence of abiotic stress conditions. It is also important to improve the annual rate of genetic gain in poplar breeding programmes using fast and precise applications and exploiting untapped sources of genetic variability, taking advantage of the large poplar genetic variation worldwide and creating large genetic panels that should be shared between research groups from different countries and tested in multiple environments. The molecular characterization of these collections, in combination to common garden phenotypic data, can contribute to the unlocking of this genetic diversity available to breeders and allows the acquisition of new knowledge  to be integrated into breeding and deployment strategies. The development and use of more diverse genotypes and the maintenance of large well-adapted populations will result in a significant increase in frequency of potentially beneficial allelic variants.The annotated poplar genomes and the new genotyping platforms have largely contributed to the mining of the interaction between poplar and pests or pathogens, but the current knowledge is still insufficient to face climate-change challenges. For example, because of the rapid LD decay measured for poplar, many molecular markers are needed to capture the effects of all QTLs. Moreover, strong LD between markers and causative variants that control the traits of interest is desirable to achieve high values of predictive ability in GP. The available SNP arrays, developed using candidate-genomic-region approaches ,69, reprThe understanding of the relationship between changing environment and the spread of poplar diseases, using accurate prediction models of the effects of climate change on poplar plantations, is pivotal to define new breeding objectives based on the enlargement of the genetic backgrounds in parentals for the generation and selection of new clones with large biodiversity and adaptability to changing environments. Finally, the activation of new cooperation projects, involving geneticists, eco-physiologists, wood technologists, and stakeholders, as poplar growers and wood users, would lead to the definition of short- and medium-term objectives for the development of innovative genetic materials with wide variability to cope with future highly uncertain environments and for the definition of cost- and time-effective strategies that meet rapid changes even considering the ecological and economic contexts.


We estimated excess mortality by comparing deaths from January 2020 up to June 2021 vs 2016–2019, using a generalized additive model. We estimated a total of 21,699  excess deaths across the 21 cities. Overall relative excess mortality was highest in the Metropolitan (Santiago) and the North regions , followed by the South and Center regions (17.6% and 14.1%). At the city-level, the highest relative excess mortality was found in the Northern cities of Calama and Iquique (around 40%). Cities with higher residential overcrowding had higher excess mortality. In Santiago, capital of Chile, municipalities with higher educational attainment had lower relative excess mortality. These results provide insight into the heterogeneous impact of COVID-19 in Chile, which has served as a magnifier of preexisting urban health inequalities, exhibiting different impacts between and within cities. Delving into these findings could help prioritize strategies addressed to prevent deaths in more vulnerable communities.We estimated excess mortality in Chilean cities during the COVID-19 pandemic and its association with city-level factors. We used mortality, and social and built environment data from the SALURBAL study for 21 Chilean cities, composed of 81 municipalities or “The online version contains supplementary material available at 10.1007/s11524-022-00658-y. The COVID pandemic had caused more than 5 million confirmed deaths worldwide as of October 2021 . These lDuring the pandemic, various countries have reported heterogeneous impacts of COVID-19 across their territory . PotentiHere, we aim to describe the EM during the COVID-19 pandemic in 2020 and the first half of 2021 in 21 Chilean cities and their corresponding 81 municipalities, and to explore associations with city and municipality-level social and built environment factors. While most descriptions of EM have focused on countries in the Global North –26, we fThis analysis was conducted as part of the SALURBAL study, which collects data on health and the environment in cities of 11 Latin American countries , 32, defst 2021, covering the period of deaths occurring from January 1st 2016 to July 3rd, 2021 .We obtained mortality data from January 2016 to June 2021 by age, sex and municipality from the vital registration system of Chile  .We obtaiTo characterize the social environment of each city and municipality we used four indicators: two for educational attainment , poverty (% of the population that cannot fulfill their basic needs), and overcrowding (% of households with 2.5 or more people per bedroom). Educational attainment and poverty are used as indicators for area-level socioeconomic status, which has been found to be predictive of COVID-19 transmission and mortality in several settings , 35, 36.2, 2020) and air pollution  [To characterize the built and physical environment of each city and municipality, we used data on population density  , 38, con5, 2018) . We also5, 2018) , 40.The main objectives were to estimate EM from January 2020 to June 2021 in 21 Chilean cities and their respective 81 municipalities, and to explore associations between city- and municipality-level social and built environment indicators and EM. We conducted this analysis in three steps. First, we described average monthly deaths by period  overall, for SALURBAL Chilean cities , and by sex and age groups .Second, we estimated weekly and total (sum for the whole period) EM ) in cities pooled by four macroregion, each city, and each municipality. We calculated EM by computing a smoothed estimate of expected death counts for each week of 2020 and up to June 2021 based on data from 2016 to 2019, using a negative binomial generalized additive model (GAM), as detailed in Basellini et al , with a Absolute EM was defined as the difference between observed deaths and expected deaths (obtained from the GAM model) divided over the population in 2020 and 2021, and relative EM was defined as EM over the number of expected deaths. We show these results in three ways: overall, summing over all 81 municipalities of Chile belonging to SALURBAL; by city and municipality; by the 4 macroregions.Third, we studied the associations between social and built environment factors and EM at the ecological level at two different levels. We ran a linear model at the city-level to estimate the strength of associations between these factors and relative and absolute EM in January 2020-June 2021, adjusting for age (% of the population aged 65 or above). We repeated the same process for the municipalities belonging to three large metropolitan areas in Chile: the conurbation of Santiago in the Metropolitan Region, Valparaíso in the Valparaíso Region and Concepción in the Biobío Region. We selected these areas as they are the only ones with five or more sub-units, allowing for an exploration of within-city differences. In a secondary analysis, and to acknowledge uncertainty around the estimation of EM, we used the same procedure as Basellini et al  , respectively. Increases were especially salient in men and older adults, with similar patterns in the whole country compared to SALURBAL cities. Figure Table Figure Figure Figures We found a different pattern in the three metropolitan areas. First, municipalities within Santiago with higher educational attainment had lower EM , while municipalities with higher overcrowding and poverty had higher EM . These associations also existed with absolute EM. We found an association between population density and relative and absolute EM in Concepción. Last, we found an association between education and relative EM, and between overcrowding and absolute EM in Valparaíso, but these associations were inconsistent in the absolute and relative scales.rd), as compared to the 2016–2019 period, resulting in around 22,000 total excess deaths from January 1st, 2020 to July 3rd, 2021. This increase was more marked among men and among the 20–39 years age group and older adults (60 to 74 and older than 75 years old). We found a heterogeneous impact between and within cities, and over time, with the Santiago Metropolitan region being the most affected region in 2020 , and other regions in the following months (December 2020 to June 2021). Last, we found that cities with higher levels of overcrowding had higher EM, while areas of Santiago with higher overcrowding and lower educational attainment had higher EM, as did areas of Concepción with higher population density.In this study, we found that all-cause mortality in Chile was 13% and 11% higher during 2020 and 2021 . Second, educational attainment  are measures of socioeconomic status [We found different associations in the metropolitan areas of Concepción and Valparaíso. First, in Valparaíso we found inconsistent associations between educational attainment and overcrowding with relative and excess EM, while we found no associations for the same indicators in Concepción. These differences with Santiago could be due to three potential reasons. First, the low sample sizes  and EM. We did not find any association, except for population density in Concepción. Population density have been reported previously as potential factor driving EM  at the cst 2021, to cover the period of deaths in study. Due to the dynamics of the pandemic in Chile, we examined EM up to June 2021 to account variability in the different regions of Chile, but it implies that we include the possible effect on mortality of the vaccines, since the vaccination campaign started on February 2021, reaching coverages over 70% at the beginning of July. If the distribution of these vaccines occurred unevenly, especially by the factors we examined in this study, some of our results for the first half of 2021 may be related to these patterns in vaccine rollout. There are risk factors and indicators of health access that could have affected EM but were outside the scope of this study. Future work would consider the role these play as mediators in the pathway between socioeconomic status  and mortality. Relatedly, and given the limited number of municipalities within each city, we had a limited capacity to examine within-city inequalities outside of Santiago. Despite that, our results highlight the situation of other cities than the capital, less explored in our country by now. Last, our PM2.5 data relies on satellite imagery and corresponds to 2018, and may represent and incomplete picture of air quality conditions in these cities.We acknowledge some limitations. There may be delays in registration, although in Chile, deaths should be registered up to three days according to the law. Regardless, we allowed for up to 2 months in delays by using data on deaths registered up to September 1We found a heterogeneous increase of mortality in urban areas of Chile during the COVID-19 pandemic, with the capital and Northern cities being most impacted with relative EM of upwards of 20% from January 2020 to June 2021, and peaks of up to 150% in the beginning of winter 2020 in Santiago. We showed that EM has been higher during 2021 than 2020 outside the Metropolitan region, suggesting the importance to continue analyzing the geographic heterogeneity in EM during the pandemic. EM was higher in cities with higher levels of housing overcrowding and, within Santiago, in municipalities with higher overcrowding and lower educational attainment, indicating the impact of social inequalities on health outcomes in Chile. Continuing the monitoring of EM is crucial to evaluate the effects of the pandemic in the following years. Furthermore, a geographically and socially disaggregated reporting of EM is useful to understand the entire impact of the pandemic, allowing for the prioritization of specific population groups and territories in interventions such as vaccination campaigns.Supplementary file1 (DOCX 1.26 MB)Below is the link to the electronic supplementary material.