This study addresses the analysis of the morphometric differences based on quantitative traits in the different historical genetic lines of the Spanish Merino sheep breed. The results show the existence of significative differences both in the measurements taken and in the indexes calculated between the different genetic lines. This result could indicate how different genetic lines within the autochthonous Merino breed have developed due to the chosen management models and the adaptation to the specific ecosystems in which the sheep have been bred in pure, closed conditions for centuries, which have been maintained to the present day. Our study complements a previous genomic approach, in which the different historical lines of the breed were characterized, showing the genuine genetic pool of the Spanish Merino, the origin of all the Merino and Merino derived breeds in the world.The Merino breed, which originates from Spain, is the most emblematic livestock breed in the world, since it is the first with a worldwide extension and has had an important impact on the genetic origin of several of the main current sheep populations. For this reason, it is of vital importance to typify the historical genetic lines of the original Spanish Merino breed and thereby ensure the conservation of its variability. In the present study, we used 337 purebred animals  registered in the Genealogical Book of the Native Merino Breed. All the animals were descendants of herds from six ancestral genetic lines . Significant differences were found in all the morphometric traits and indexes between the different genetic lines. Using discriminant analysis, 84% of the animals were classified correctly into their historical genetic lines. Furthermore, the distances between the lines, calculated by a cluster test, showed that Hidalgo, Maesso, and Donoso had the most clearly defined lines, while the Granda, López-Montenegro, and Egea lines were more similar to each other. All this demonstrates the rich genetic variability existing in the genuine gene pool of the Merino sheep breed. The Spanish Merino sheep is considered the most important breed worldwide not only for its history, census, and expansion, but also for its role in the influence of the appearance of new and/or derived breeds . HoweverIn Spain, the economic crisis of the 1960s saw a shift in the selection criterion towards meat production instead of wool production. Some breeders began to use foreign breeds with higher meat aptitude to cross with native Merino mother populations which were not being bred in purity . As a reIn the conservation of genetic resources, the main objective is to preserve variability within populations, following the hypothetical correlation between genetic variation and population viability . MoreoveThe morphological characteristics of the different animals are qualitative, while the structural characteristics are quantitative and, therefore, possible to measure, with morphostructural variables which can be treated statistically . These sFor the study, we used 337 purebred animals  registered in the Genealogical Book of the Native Merino Breed. All the animals were bred in similar conditions on 11 farms that belonged to the historical genetic lines of Donoso (31), Egea (40), Granda (60), Hidalgo (80), Maesso (60), and López-Montenegro (66).A total of 22 measurements were taken for each of the animals in spring, just after the shearing process. The measurements are described in The Body Weight (BW) was measured with a dynamometer, and the animals’ weights in kg were recorded.Measurements were taken on the animals’ left side, with the four feet firmly planted on the floor on a horizontal plane. After that, seven indexes were calculated using some of the measurements to obtain the relationships between 2 lineal dimensions ,19,20:Body Index (BI): (LD/ThP) × 100.Compactness Index (CoI): (BW/WH) × 100.Pelvic Index (PeI): (RW/RL) × 100.Thoracic Index (ThI): (BD/DSD) × 100.Cephalic Index (CeI): (HW/HL) × 100.Proportionality Index (PI): (WH/LD) × 100.Load Cannon Bone Index (LCI): (ACP/BW) × 100.For the statistical analysis, we used the IBM SPSS Statistics 25.0 software for Windows . An analysis of variance (ANOVA) was conducted using the gender and historical genetic lines  as fixed effects. Significant differences between the average values were determined by a post hoc Tukey test. Next, we calculated a principal components analysis (PCA) to define the underlying structure among the variables in the analysis, saving the factorial scores as variables and creating a new variable for each factor in the final solution using the Regression method. After that, a discriminant analysis (DA) was carried out using a stepwise model considering the genetic lines of the animals as a classification factor, introducing in each step the variable that minimizes the global Wilks lambda. The aim of this step was to estimate the proportion of animals that were properly classified into their line, using the discriminant classification method of leave-one-out cross-validation. Finally, we generated a cluster analysis (a simple linkage based on Euclidean distances) and plotted it as a dendrogram.The results of the measurements are shown in Concerning the parameters measured in the head, the lowest values for the HL, HW, WF, and WCh measurements corresponded to the Maesso line and the highest values, except WCh, were shown by the Hidalgo. However, the WF measurement is of particular interest because its point of reference is its distal part, and in the case of the animals of the Hidalgo line, the end of the tuft did not usually end in a straight line but in a kind of peak, in the form of wool-free patches on either side of the head. As regards the other lines, López-Montenegro obtained the second lowest value for HL and WF and the third lowest for HW, while Donoso, Egea, and Granda, with values close to the extremes depending on the measurement, were in the intermediate zone. As for EL, Donoso, followed by Maesso, obtained the lowest values and Hidalgo the highest values, while the values obtained by López-Montenegro, Granda, and Egea were intermediate.As for the trunk and limbs, the lowest values of the measurements SW, NL, WH, BD, HSF, DSD, LD, LBL, RH, RW, RL, BuW, BL, ThP, ACP, PCP, and BW were found in the Maesso line, followed by López-Montenegro, except in the values HL, WH, HSF, and LBL. In contrast, the highest values of SW, NL, WH, BD, HSF, DSD, LD, LBL, HR, RL, BL, and BW were found in the Hidalgo line and the highest values of RW, BuW, ThP, ACP, and PCP were in the Donoso genetic line. Egea and Granda showed intermediate values.The indexes derived from the measurements taken are shown in A discriminant analysis (DA) was carried out, using a stepwise model considering the animals’ historical genetic lines as classification variables. The percentages of correct assignments can be observed in All the measurements and indexes were entered as variables, with 17 variables used in the statistical analysis in the following order: EL (Ear length), BuW (Buttock width), HSF , HW (Head width), CoI (Compact index), WCh (Wool extension on cheeks), ThP (Thoracic Perimeter), BD , NL (Neck length), HL (Head length), BL (Buttock width), BW (Body weight), WH (Withers height), LCI (Load cannon bone index), RH (Rump height), BI (Body index), PI . 84.0% of the animals were classified correctly. All the animals from the Donoso line were correctly classified. The group of Maesso and Hidalgo animals were classified with a high degree of accuracy of 96.7% and 95.0%, respectively. In the case of the López-Montenegro and Egea animals, the values were a little lower  while the animals from Granda line were those with the lowest value of correct classification (61.7%).The dendrogram showed how Egea and Granda were the closest lines according to the morphometric characterization, while Maesso appeared as the family most distant to the other genetic lines as regards corporal conformation . IntermeThe different historical genetic lines of the Spanish Marino have previously been studied using a genomic approach, in which high levels of genetic differences were found between the populations analyzed, as well as high variability within the breed . HoweverTherefore, the data obtained could not be compared with any previously described measurements, except for the WH, 72.5 cm, and the BW, 90 kg, (mean for both sexes) published in the National Breed Information System (ARCA) of the Ministry of Agriculture , or withAs for the ARCA data, the mean of all the animals studied of the six historical lines showed a WH value of 69.92 cm, and a BW of 59 kg, calculated for both sexes. In the case of the WH, the measurement was similar, with a small difference of only 2.58 cm. However, the opposite occurred in the BW, where a significant difference of 31 kg between the data was found. In relation to the results obtained by Diaz Montilla , these dRegarding the differences with the ARCA data, this could be due to the impact made by the Hidalgo lineage, which was commonly used to “re-merinize” crossed Merino flocks. When hybrid Merino flocks are crossed with pure Merino rams, the resulting F1 shows a high manifestation of pure Merino traits . This fact has had a great impact on the current population of the breed in Spain; therefore, the data published in ARCA, although quite high for the breed according to the results obtained in this study, is closer to the average for the Hidalgo BW, 73.35 ± 1.23 kg, than to the average for the six historical lines. In fact, one of the purposes of the present work is to update and complement the ARCA data, in order to produce an integrated, scientifically contrasted zoometric characterization for the breed.Regarding the differences with the measurements obtained by Díaz Montilla , these cThe results of the measurements showed clear differences between the different lines. Here, Maesso was the genetic line with the smallest individuals of all, and the animals were classified as ellipsometric according to the Baronian scale . It was The weight of autochthonous breeds is conditioned by the environment and management in which the animals live, which can give rise to different ecotypes . The morIn this context, the usual practice on Maesso farms has always been to ensure that the replacement animals are small and hardy and can move about efficiently despite the poor resources provided by the southern mountainous areas where their herds live. In particular, it is essential that they are well adapted to the high summer temperatures, following Bergmann’s rule , by whicThe Hidalgo line is made up of animals originally from León (Northern Spain) with a transhumant character . HoweverAs regards the López-Montenegro, Granda, Egea, and Donoso lines, although the data show a greater homogeneity, certain factors must be taken into account. The slightly lower values of the López-Montenegro line are largely due to the natural selection system that has historically been applied by breeders to obtain animals which are as well adapted as possible to the environment in which they live. In this way, this line has always avoided making a strong selection for meat traits in order not to lose its genetic heritage. On the other hand, in the Granda, Egea, and Donoso lines, it can be seen from the data that a selection has been made to adapt a rustic breed to market demands in terms of the conformation of animals for meat production, but without losing sight of their historical origin . It is tThe indices were calculated with the purpose of determining somatic states predisposed to certain functionalities. In this way, it was possible to show the relationships between certain elements of height, compactness and length generally used in zootechnics to estimate the animals’ proportions and conformation. The characters obtained from head measurements, such as the cephalic index, are of great ethnological importance, because their variation is not influenced by environmental factors or by the handling of the animals . The aniPrincipal component analysis (PCA) is used as an interdependent technique to identify morphometric parameters that best serve as breed-specific markers . The PCAThe accuracy in the classification using discriminant analysis (DA) was very high. Interestingly, the variable with the highest discrimination impact was Ear length , while In addition, the discriminant assay  classifiThe cluster analysis also establishes quite a few differences between the lines, with Hidalgo being the most different of all. In this case, the morphometric approach used in the results is different from the genomic approach. The genetic differences show the López-Montenegro and Granda lines are the most similar, and that the Donoso is the most differentiated line, rather than the Maesso line . GeneralMorphometry has long been used as a classical approach to characterizing a breed based on its body conformation. However, it is currently in disuse, so there are no studies on the Merino or Merino-derived breeds that allow us to compare the phenotype of the pure Spanish Merino. We consider the results obtained in this work of great importance, since they compare the morphotype of the Spanish Merino with each of its pure genetic lines. Subsequent body measurements could serve as an indicator to ensure that the racial pattern of this ancestral breed, which forms the basis of its rich genetic reservoir, is maintained.This study demonstrates the morphological variability that exists between the different historical genetic lines that make up the Spanish Merino breed. The confirmation of these phenotypic differences, added to the genetic differences previously found in these populations, allows us to define them as strains. It is clear that the high level of variability found in the breed is due to the presence of these historical genetic lines, which have remained pure thanks to the management that has been carried out in the same way for centuries. We can therefore affirm that the variability of the breed is guaranteed so long as the breeding of these historical lines continues, and its rich genetic reservoir is maintained.


The greenhouse effect is a global problem. In view of the intense sunlight radiation in Ningxia , the effect of light-selective sunshade nets of different colors  on the quality and aromatic characteristics of grapes and wine was studied. With the treatments of different nets, the solar radiation intensity was significantly decreased. The sugar contents in both grapes and wines decreased, while the acid contents increased. The contents of total phenols, tannins and flavanols in grapes were increased, while the total flavonoids and anthocyanins were decreased. The contents of most phenolics in wine were increased. The contents of most aromas in grapes and wines under nets were higher than those in the control group. The black group usually possessed the highest variety and content. Red and black nets improved the fruity, floral and sweet aromas of grapes. The white net decreased the green and citrusy aromas. The climatic environment is believed to be perfect for producing high-quality grapes and wine. However, with increasing human activities, greenhouse effect is becoming increasingly serious, leading to rising global temperatures. Simultaneously, the destruction of the ozone layer leads to an increase in solar intensity , which iGrapes is an important fruit distributed worldwide, and wine is receiving increasing attention due to its economic value, elegant taste and health benefits . PhenoliGrapes berries are one of the origins of the phenolic compounds and aromas in wine. The development of grapes is influenced by numerous environmental factors, such as temperature, water and hormones . Thus, mA light-selective sunshade net is one of the cultivation measures that can change the light quality in vineyards. This cultivation measure has been studied by 22.1The Lilan Winery , located in Yong-Ning County, the Ningxia Hui Autonomous Region, was used as the experimental site. Grapevines were planted in 2013 and grown on a north-south line with a spacing of 0.8 m × 3.0 m. The short pruning (double guyot) combined with vertical shoot positioning was used as the trellis system. The trickle irrigation system was adopted in the irrigation regimes. The vineyard soil contained about 80 % sandy soil and 15 % gravel.Ten lines of grapevines were shaded by red (R), white (W) and black (B) light-selective sunshade nets, as shown in 2.2The winemaking process was performed according to the procedure described in our previous study . The har2.3Pure standards were purchased from Sigma-Aldrich Chemical Co. . Solvents of spectrophotometric grade were obtained from J & K Co. Ltd. . A DB-Wax gas chromatography column was purchased from J & W Scientific, Rancho Cordova, CA. All the other chemicals were of analytical grade.2.4The intensity of UV-A and UV-B radiation was detected by a UV detector. The intensity of total radiation was detected by a lux meter. Diameters were detected using a Vernier caliper. The Brix value was measured using a hand refractometer. The total acidity and total sugar and alcohol contents were quantified according to 2.5Phenolic components in grape skins, seeds and wines were detected. The total polyphenol (TP) content was determined using the Folin-Ciocalteu method and expressed as mg gallic acid/g dry weight (DW). The total flavonoid (TFD) content was detected by the Rutin method and expressed as mg (+)-catechin/g DW . The tot2.6Aroma extraction and detection were carried out as previously described . One hunFor solid-phase microextraction (SPME), the extract was heated at 250 °C for 2 h. The aromas were extracted in a 40 °C water bath for 30 min and subsequently desorbed at 230 °C for 3 min into the splitless injection port of a GC–MS instrument fitted with an HP-INNW AX column .Compound profiling was performed using an Agilent 6890 GC–MS system equipped with an Agilent 5975 mass spectrometer and an HP-INNOWAX capillary column (60 mm × 0.25 mm) of 0.25-mm film thickness as described previously . The flo2.7Data are reported as the mean ± standard deviation (SD) values, and the statistical analysis of the data was performed using the IBM SPSS Statistics 21.0 package for Windows . One-way analysis of variance (ANOVA) and Duncan’s multiple range tests were used to determine the significance of the difference among samples, with a significance level of 0.05, by considering the contents of different substances in grapes and wine as dependent variables and the black net, red net, white net and CK treatments as categorical factors.33.1The intensities of UV-A, UV-B and total radiation are shown in 3.2As shown in 3.33.3.1Phenolic compounds in grapes were proven to be influenced by light conditions . As showThe highest content of TFD in grape seeds from 2016 was achieved by the black net-treated group, followed by the red net-treated, CK and white net-treated groups. In 2017, the TFD contents of the three net-treated groups were all significantly higher than the TFD content of the CK group. This finding was different from the trend observed for the skins . Similar3.3.2Phenolic substances contribute greatly to wine quality . In 20163.43.4.1The effect of light-selective sunshade nets on aroma contents has been studied in different species, such as tomatoes  and sweeIn 2016, except for ethyl octanoate, the contents of most esters in grapes grown under different nets were higher than those of the CK group. The contents of total esters in grapes grown under sunshade nets were all higher than those of the CK group. In 2017, the contents of almost all kinds of esters, including the total esters, in grapes grown under the black net were higher than those of the other 3 groups. There were generally more esters in grapes grown under red and black nets than in the other two groups. In 2016, the contents of almost all kinds of acids in grapes grown under different nets were markedly higher than those of the control group . The totNo other volatile substance was detected in the grapes grown under a white net in 2017. In addition, the total contents of the other substances in grapes grown under different sunshade nets were all significantly higher than those of the CK group. In both 2016 and 2017, grapes grown under black sunshade nets had the highest content of other aromas . The totAroma characteristics of grapes. (A), PCA analysis for the effect of vintage on aroma substances. (B), PCA analysis for the effect of treatment on aroma substances. (C) RDA/CCA analysis for the characteristic aroma substances. (D) Random forest analysis for the characteristic aroma substances.In PCA analysis, PC1 and PC2 explained 49.30 % and 22.31 % of the total variance, respectively A and B. In general, the contents of most of the aromas in grapes grown under light-selective sunshade nets were higher than those of the CK group. Grapes grown under nets also have greater varieties of aromas. The black net-treated group usually had the greatest variety and highest content of aromas. This finding is consistent with the studies by 3.4.2Aromas are important to wine quality . Few stuIn addition to ethyl octanoate and 2-methylbutyl acetate, the contents of almost all the esters from CK were lower than those of the sunshade net groups. Except for isoamyl acetate, ethyl hexanoate and 2-methylbutyl acetate in 2017 and ethyl acetate, ethyl butanoate and ethyl decanoate in both 2016 and 2017, wines of the black nets had the highest contents of all the other esters . In 2016In 2016, except for ethyl octanoate, most ester contents in grapes grown under different sunshade nets were higher than those of the CK group. The contents of total esters in grapes grown under sunshade nets were all higher than those of the CK group. In 2017, the contents of almost all kinds of esters, including the total esters, in grapes grown under black nets were higher than those of the other 3 groups. The varieties of esters in grapes grown under red and black sunshade nets were generally greater than those of the other 2 groups. Except for 1-undecanol, isooctanol and isoamyl alcohol, the contents of almost all the other alcohols in wines of grapes grown under sunshade nets were significantly higher than those of the CK group . The conβ-cyclocitral in wines. Furthermore, wines made with grapes grown under red nets in both 2016 and 2017 had the highest content (In addition to the hexanoic acid content in wines in 2016, the contents of almost all the other acids in the wines of grapes grown under different sunshade nets were significantly higher than those of the CK group. Wines of grapes grown under black nets possessed the highest contents for all the other acids in 2016, but similar results were not observed in 2017, except for octanoic acid and acetic acid . This fi content . For the content .β-cyclocitral, Furfural, Ethyl acetate, Octanoic acid, Styrene, 2-Methylbutyl acetate, Geranyl acetate, Isooctanol, Ethyl heptanoate, Undecylenic acid, Phenylethyl alcohol and Nonanal were influenced more by the vintage, while the other substances were more affected by the treatment methods. The contents of Ethyl octanoate, Isoamyl acetate, Phenylethyl alcohol, Ethyl decanoate, Succinic acid, Ethyl acetate and β-cyclocitral were apparently higher than that of the other substances. Ethyl acetate, Isoamyl acetate, Ethyl octanoate and Ethyl decanoate contributed most in ester aroma components. Octanoic acid, Succinic acid, Acetic acid and Hexanoic acid contributed most in acid aroma components. Isobutyl alcohol, 1-Hexanol, 2,3-Butanediol and Phenylethyl alcohol contributed most in alcohol aroma components (In PCA analysis, PC1 and PC2 explained 35.81 % and 23.60 % of the total variance, respectively A and B. mponents C. The components D.Fig. 3AIn general, the contents of most of the aromatic substances in wines made of grapes grown under light-selective sunshade nets were higher than those of the CK group. Wines made from the net treatment group also have more varieties of aromas. The black net-treated group usually had the greatest variety and highest content of aromatic substances, indicating that the light-selective sunshade net is a potential ideal cultivation measure in vineyards to improve the varieties and contents of aromatic substances in wines.3.53.5.1According to the odor properties of different aromatic substances detected in grapes, we summarized the aromatic contents of different series in grapes under different treatment conditions . The bla3.5.2β-cyclocitral and nonanal were higher than 20, indicating that these compounds may greatly influence wine aromas.The odor profile is a good tool to evaluate the taste and quality of wine . Althougβ-cyclocitral. The floral odor is mainly due to damascenone and β-cyclocitral. The intensities of green and citrusy aromas were low, chiefly from damascenone and nonanal, with low OTs, respectively. The intensity of fatty acids was extremely low and mainly from ethyl decanoate. From The descriptors of a component might not be unique. One compound could be described by several different descriptors . Several4Light-selective sunshade nets were proven to significantly decrease the radiation intensity. The treatments decreased the sugar contents in both grapes and wines, meanwhile, increased the acid contents. This is beneficial for improving wine grape quality in northwest China regions such as Ningxia. The contents of TP, TT and TFA in grapes were increased, while the TFD and TA contents were decreased. The contents of the majority of phenolic substances in wine were increased. Most of the aromatic substance contents in grapes and wines receiving light-selective sunshade net treatment were higher than those of the control group. Red and black nets could improve pleasant aromas, such as fruity, floral and sweet aromas, while white nets could decrease pleasant flavors. Overall, light-selective sunshade nets, especially the black net, were shown to be an ideal material for solving the problem of relatively high sugar and low acid contents in Northwest China. Simultaneously, light-selective sunshade nets were proven to be beneficial to the development of most phenolic substances and aromas. However, each kind of net has its own advantages and disadvantages. Which kind of net should we use? Should we combine several nets with different colors? The situation will depend on the specific circumstances in different regions. In addition, light-selective sunshade nets can not only decrease the solar radiation intensity but also influence the temperature and humidity. Thus, the relative changes may be the result of multifactor interactions. Which factor plays the main role? What should we do in production practice? Further efforts are needed to interpret the influencing mechanism in more detail.Yanlun Ju: Resources. Uwamahoro Francoise: Conceptualization, Methodology, Software, Investigation, Writing – original draft. Demei Li: . Yang Zhang: Conceptualization, Methodology, Software, Investigation, Writing – original draft. Bochen Liu: Conceptualization, Methodology, Software, Investigation, Writing – original draft. Miao Sun: . Yulin Fang: Resources. Xiaofeng Wei: Conceptualization, Methodology, Software, Investigation, Writing – original draft.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Regionally coordinated green development has been widely documented in China. However, most previous studies have investigated it from the perspective of linearity, while the spatial correlation of green development is nonlinear. Based on 48 cities in Bohai Rim, this study used a social network analysis to measure the spatial network, with an emphasis on the internal structure of regional green development, and analyzed the driving factors of regionally coordinated green development from the perspective of nonlinearity. We found that large cities have formed a “siphon effect” and that the polarization of eco-efficiency has become increasingly serious. There are limited connections, some of which are redundant, in the spatial network of green development, while the stability of the network is strong. Additionally, reducing the differences in environmental regulation approaches among cities can have a positive impact on the spatial correlation and spillover effect of green development, thereby promoting regionally coordinated green development among cities in the Bohai Rim. Since the 18th National Congress of the Communist Party of China, the characterization of the development of the Chinese economy has changed from rough development to sustainable development. However, increasing environmental pollution and extensive resource utilization have severely restricted the overall modernization process. The 5th Plenary Session of 19th Central Committee of the Communist Party of China further emphasized that China must adhere to green development goals and create a harmonious, coexisting relationship between humans and environment. The report of the 19th National Congress of the Communist Party of China proposed the regionally coordinated development strategy. Therefore, the rational layout of green industries, the coordination of the green development of various regions, and the improvement in the efficiency of resource utilization have great significance in the high-quality development of a green economy.Due to differences in resource endowments, geographical locations, economic foundations, and development strategies and guidelines, Chinese green development processes show large regional differences and serious polarization. Guided by the goal of ecological civilization construction, all regions are continuing to explore new green development paths. Although regional green development with a focus on urban agglomerations has achieved certain results, the problems posed by regional imbalances in green development remain prominent, and the situation is troubling. For this reason, the “Overall Plan for Ecological Civilization System Reform” issued by Chinese government proposes an enhancement of the integrity and coordination of the reform of the ecological civilization system. Regionally coordinated green development advocates for industrial-level green development, with endowments to various regions, and promotes a new, integrated green development pattern; this builds an efficient, comprehensive, open, and shared green development system by integrating the resources of various cities in the region. It is conducive to the overall improvement of green development.The results of the current research, which surround the theoretical connotations, measurement methods, and implementation paths for green development, are very fruitful; however, room for improvement remains in regionally coordinated green development. Based on the inherent requirements of China’s real-world development, this research focuses on regionally coordinated green development. This analysis is achieved through empirical measurements of China’s urban-level, coordinated green development capabilities and systematic analyses of the internal evolution mechanism and external driving mechanism of coordinated green development. This study provides a useful, decision-making reference point for the realization of the long-term goal of a “Beautiful China”.Eco-efficiency measurement methods assess productivity under environmental restraints and have been widely used to evaluate the performance of green development approaches. Eco-efficiency measurement methods were first introduced by Schaltegger and Sturm  and haveSpatial correlation, in relation to eco-efficiency, has also been widely investigated by scholars recently. Using Markov chain, Pan et al.  studied Some scholars investigated the integrated green development in China and most of them supported the idea that integrated development would enhance green productivity efficiency. Yang et al.  suggesteIn the literature, most studies have approached the spatial correlation of green development in a linear way. However, considering that connections between regions are complicated, the spatial connections of green development approaches should be approached in a nonlinear way. Moreover, in the past, the central government of China has assigned great importance to the integrated development of the Bohai Rim area and believe it will reshape the industry distribution and alleviate the environmental problem in the Bohai Rim area. However, there has been little research about whether this is applicable. In addition, as the government is increasingly interested in regionally coordinated green development, there is some evidence that green development remains uneven across China’s regions; however, little is known about the “how, when and why” of the occurrence of such progress. To fill the above research gaps, this paper makes the following contributions.Firstly, this paper uses a window–super-slack-based measure to calculate eco-efficiency, indicating the level of green development in each city in the Bohai Rim area. Additionally, we measured the degree of regionally coordinated green development using a gravity model. The strength of the spatial correlations can quantify the spatial correlation of green development among cities in the Bohai Rim area, which can reflect the ability of cities to have and receive influence surrounding green development from other cities. Secondly, this paper focuses on a spatial network analysis of green development, which shows the internal characteristics and mechanisms of the green-development-related spatial correlation and spillover effect. Thirdly, this paper provides support for the regionally coordinated green development of the Bohai Rim area and discusses the applicability of it through a QAP, which was used to explore the driving factors of regionally coordinated green development, providing empirical support for the integrated development policy. This study has practical significance for policymakers in formulating policies scientifically and can improve the effectiveness of policy implementation in regionally coordinated green development.This paper is organized as follows: A DEA posits no requirement for the shape of the production function for inputs and outputs and is able to evaluate complex production relations. If there are multiple decision-making units (DMUs) in the frontier, then the traditional slack-based measure (SBM) fails to determine the differences among the DMUs. Aiming to solve this problem, this paper used an adjusted SBM  and brouk, with the possible production set of {, x ≥ ∑j=1,j≠k) is the measured super efficiency and can be larger than 1.Which is subject to:m,nR represents the spatial correlation between city m and city n; mP and nP are the populations of city m and city n, respectively; mG and nG are the GDPs for city m and city n, respectively; mEE and nEE are the eco-efficiencies of city m and city n, respectively; m,nK shows the ratio of eco-efficiency of city m to the sum of eco-efficiency of city m and city n; m,nD is the economic distance between city m and city n; m,nd is the geographic distance between city m and city n. mg and ng are the GDP per capita for city m and city n, respectively. By the calculation, the correlation between the eco-efficiencies of cities in the Bohai Rim area form the following matrix: 48×48.After the measurement of eco-efficiency, we constructed the green development network for the Bohai Rim area. Each city in the Bohai Rim area was regarded as a “point” in the network, and the spatial connections of eco-efficiencies formed the “line” between each point. In the literature, the correlation for the spatial perspective of eco-efficiencies between two regions has been measured by the VAR model. However, the VAR model fails to reflect the dynamic change in the network and is very sensitive to the lag effect of the variables. Therefore, this study used a modified gravity model, as follows:To measure the inside structure of the network in our study, we used a social network analysis. The social network analysis consisted of two major parts: a centrality analysis and a block model analysis. For the centrality analysis, we used a degree centrality measurement, a closeness centrality measurement, and a betweenness centrality measurement to measure the roles and effects of the various regions in the eco-efficiency network. The degree centrality measurement was used to represent the beneficial between-region correlations inside the eco-efficiency network. The degree centrality measurement was divided into an in-degree centrality measurement and an out-degree centrality measurement. In terms of the in-degree centrality measurement, a larger number indicates that the region in question has a greater correlation with other regions in the eco-efficiency network. As for the out-degree centrality measurement, a larger number indicates that the region in question poses a greater spillover effect on other regions in the eco-efficiency network. The closeness centrality measurement determines how simple it would be for a region to develop an internal connection with other regions in the eco-efficiency network, with each region playing a central actor role. The betweenness centrality measurement is used to determine which region will play the intermediary role (or the “Bridge” role) in the eco-efficiency network.The block model analysis involves conducting a spatial cluster analysis for the whole network; this analyzes the connections between each group and provides more intuitive insight into how each region is connected in the whole network. This method first divides regions into different plates and then studies the spillover effect between each plate. Using block model analysis, the internal structural status of the network can be revealed. This analysis is especially useful when there are potential clusters in the network.Due to contradictory assumptions surrounding independent variables, traditional studies using econometric methods to analyze the spatial spillover effects of eco-efficiency often show errors, such as biased estimation results; meanwhile, the QAP method can overcome these potential biases. Since the QAP method is based on the permutation of the matrix, the correlation coefficient of the two matrices is given by comparing the similarity in the lattice values of the two square matrices, and the nonparametric test is carried out using the given coefficients. The QAP method does not require an assumption that the independent variables are opposed to each other, and the test results are more robust. Therefore, this study adopts the QAP method to analyze the spillover effects and potential spatial drivers of eco-efficiency. The specific model is as follows:i indicates the depreciation rate, and i and t indicate the cities and years, respectively. As in [Considering the accessibility and availability of data, this study exploited the annual data of 48 cities in Bohai Rim from 2005 to 2017. Most data came from the China Urban Statistical Yearbook, China Environmental Statistical Yearbook. y. As in , 2005 waThe eco-efficiencies of 48 cities were calculated using the adjusted SBM model. We found that eco-efficiency increased over time, and it coincided with the raising environmental consciousness in China. Additionally, Beijing, Cangzhou, Ordos, Qingdao, and Yantai all showed high eco-efficiency from 2005 to 2017; meanwhile, Rizhao, Zaozhuang, Anyang, Puyang, Huludao, Chaoyang, and Fuxin showed consistently low eco-efficiency. Additionally, Weihai, Yangquan, Handan, Xinzhou, and Tangshan showed fluctuating eco-efficiency performance. In addition, the eco-efficiency in the Bohai Rim area was found to be unstable over time. Among the 48 cities in the Bohai Rim area, there we found that less than one quarter of cities had good eco-efficiency performance. The difference in eco-efficiency levels among cities was found to be significant. We found that cities showing high eco-efficiency scores usually had good transport systems and strong economic development, such as provincial capitals or coastal cities. Additionally, the cities that had low eco-efficiency scores were usually the cities with exhausted nature resources. We found a cluster phenomenon among the geographical locations; this adjusted with the passage of time, which is consistent with previous papers ,26,27. FThrough a social network analysis, we found that the actual eco-efficiency correlation among cities in the Bohai Rim area was 527 in 2017, while the maximum correlation among all 48 cities in the Bohai Rim area was 2256. The network density was 0.234, meaning that only 23.36% of eco-efficiency associations can be observed. This indicates that the eco-efficiency associations among cities in the Bohai Rim region are low; thus, the capacity for regionally coordinated eco-efficiency development still has potential for improvement. From The mean of the closeness centrality measure was 80.5, with a maximum of 106 and a minimum of 54, indicating an overall balance. Additionally, we found that the closeness centrality measure was higher than 70 for the majority of cities, except for Dongying, Ordos, Qingdao, Yantai, Weihai, Jinan, and Zibo. Considering that the closeness centrality measure represents the ease of transport to other cities in the network, the whole eco-efficiency network is centered in those cities. Roughly speaking, due to great economic development, widely connected transportation, and advanced technology, the eastern coastal cities showed a strong connection with other cities in the eco-efficiency network. However, inland cities were found to have weaker and fewer connections with other cities because of the relatively minimal economic development and inconvenient transportation connections.The mean of the betweenness centrality measure was found to be 16.75, with a maximum value of 146.17 and a minimum value of 0. From This may be because Bayannur City’s own eco-efficiency is low, its economic level is underdeveloped, and it is unable to form a correlation with other cities in terms of green technology development and environmental regulation, etc.; additionally, its ability to influence and be influenced by other cities is poor. Overall, it can be seen that, within the Bohai Rim region, Dongying and Ordos are the center points of eco-efficiency development; additionally, the spatial linkage and spillover effects of eco-efficiency growth have been formed especially in inland areas and eastern coastal areas.In the previous section, we demonstrated that different cities have different performances in the centrality analysis. Specifically, we found that eastern coastal cities and cities with high GDP play an important role and may work as the center or “bridge” in the network structure of eco-efficiency. To further analyze the internal characteristics of the spatial network of eco-efficiency across cities, the present section adopts a block model analysis. According to the literature, where the maximum segmentation depth is limited to 2 and the maximum concentration is limited to 0.2, the 48 cities in the Bohai Rim area were separated into 4 blocks, as shown in The eco-efficiency correlations among the plates in the Bohai Rim area are displayed in For the first plate, there are 35 internal relationships, 123 overflow relationships that receive influence from other plates, and 182 overflow relationships that influence other plates. There are more overflow relationships which influence other plates than those that receive influence. Additionally, the expected internal relationship ratio (42.55%) is higher than the actual internal relationship ratio (16.13%). Therefore, the first plate is regarded as the “Net overflow plate”, which includes 21 cities, such as Anyang, Bayannaoer, and Baoding. The second plate is the “Agent plate”, including 13 cities, such as Chaoyang, Chifeng, and Dandong. In this plate, the total number of internal relationships is 30, and the overflow relationships (33) of influence received from other plates are fewer than the total spillover relationships influencing other plates (97). Additionally, the actual internal relationship ratio (23.62%) is close to the expected internal relationship ratio (25.53%). The third plate is in the “Two-way overflow plate” class, and includes Tianjin, Taiyuan, and Hohhot, etc., and this plate receives influence from other plates and poses influence on other plates in equal measure. Most cities in this plate are provincial capital cities and normally have high economic development, advanced green technology, and convenient traffic connections; therefore, these cities are more likely to have a significant impact on the cities of other plates. For the fourth plate, there are more overflow relationships of received influence (199) than there are spillover relationships of influence on other plates (93). The actual internal relationship ratio is 16.96%, which is higher than the expected internal relationship ratio (14.90%); therefore, the fourth plate is considered to be the “Main benefit plate”.In order to further analyze the connections inside the eco-efficiency network, we used Ucinet to generate density matrices between each plate. The density of the whole eco-efficiency network was found to be 0.23. Therefore, when the value of a given plate’s density is bigger than 0.23, the value should be 1 or 0. Using this method, we were able to obtain an image matrix for every plate. From To sum up, the green production factors in the Bohai Rim area appear to gravitate to the eastern coastal areas, forming a siphon effect in their relationships with large cities. The polarization of eco-efficiency is becoming increasingly serious; that is, the spatial distribution pattern of eco-efficiency in the Bohai Rim area is “stronger in the east” and “weaker in the west”.p ≥ 0 indicates a probability that the regression coefficient is no less than the observed regression coefficient after the random permutation. Additionally, p ≤ 0 indicates the probability that the regression coefficient is no more than the observed regression coefficient after the random permutation.We found that the cities in the Bohai Rim area form a network in terms of eco-efficiency and different cities play different roles in the eco-efficiency network. However, it is still unclear how this eco-efficiency network will change over time and what factors will influence this change. In order to obtain a better understanding, we conducted a quadratic assignment procedure for analysis. From We found that the correlation coefficient for geographic distance was −0.279. Additionally, its standardized regression coefficient was −0.285, which is significant at 1%. This means that geographic distance reduces the spatial correlation and spillover effect of eco-efficiency among cities in the Bohai Rim area. Existing studies  have conWe found that the correlation coefficient of economic difference was 0.464. Additionally, its standardized regression coefficient was 0.447, which is significant at 1%. This means that those cities which have larger economic difference are more likely to form a spatial correlation and spillover effect with eco-efficiency. In previous studies, economic development has been found to have a great influence on regional eco-efficiency . HoweverWe found that the correlation coefficient of urbanization difference was 0.254. Additionally, its standardized regression coefficient was 0.061, which is significant at 5%. This means that larger urbanization difference promotes spatial correlation and spillover effects among cities, which can also be explained by the theory international movement factors. Urbanization means that more people are flocking from rural areas to urban areas, and the increase in urban populations brings about the agglomeration of industries. Therefore, differences in urbanization between cities indicate differences in production factors, such as capital, labor, and technology, making it more likely that advanced green energy technologies will be transferred between regions. This flow of green energy technologies can have a positive impact on the spatial network of eco-efficiency in the Bohai Rim area.We found that the correlation coefficient of environmental policy difference was −0.116. Additionally, its standardized regression coefficient was −0.052, which is significant at 1%. This means that smaller environmental policy difference among cities leads to more connections with other cities. In the literature, environmental policy has been found to play an important role in the monitoring of the environmental performance of polluters [The regression coefficients of technology difference and industrial structure difference are not statistically significant. However, Tang et al.  found thIn summary, differences in economic development and urbanization can strengthen connections in an eco-efficiency network, while differences in geo-distance and environmental policies can weaken the connections in an eco-efficiency network. From the perspective of integrated development of eco-efficiency in the Bohai Rim area, integrated development does not mean that all cities should have similar GDP growth or urbanization progress. However, it is important to set up a role model for other cities to follow. Those cities could be provincial capitals, which comprise the “bridge” in the eco-efficiency network. As for environmental policies and transportation, the integrated development of the Bohai Rim area may require fully accessible environmental policy implementation and the development of convenient transportation across the region.Through measuring eco-efficiency, we explored the network of eco-efficiency among cities in the Bohai Rim area. This paper finds that, while eco-efficiency in the Bohai Rim area has increased over time, coinciding with increased environmental consciousness in China, the eco-efficiency of cities in the region shows a “strong east and weak west” spatial distribution pattern, with significant spatial differences. Additionally, we found that there is a “club convergence” phenomenon in terms of geographic eco-efficiency, this appears to adjust over time. In terms of the eco-efficiency network, there are no isolated cities in the network, but different cities play different roles. Through conducting a social network analysis and a block model analysis, we found that a siphon effect of “eastern coastal areas”—in terms of the green production factors in the Bohai Rim area—further aggravated the polarization of eco-efficiency. Finally, through a quadratic assignment procedure analysis, we found that the homogenization of environmental policies could further strengthen the spatial correlation and spillover effects of eco-efficiency in the Bohai Rim area. Additionally, difference in economic development and urbanization can strengthen the eco-efficiency connections, while the difference in geo-distance can weaken the eco-efficiency connections in the eco-efficiency network.Based on the above analysis and research findings, the insights of this research are as follows: (1) It remains important to strengthen the integrated development of the Bohai Rim region. Although we have found spatial correlations in this study, many cities still have low eco-efficiency, and the network structure of eco-efficiency in the Bohai Rim area still has room for improvement. (2) We advise that the Chinese central government pays more attention to the eco-efficiency of coastal cities and provincial capitals. In the eco-efficiency network, coastal cities and provincial capitals play important roles and work as “bridges”, which means that their eco-efficiency level can significantly influence the eco-efficiencies of other cities. (3) In order to promote the eco-efficiency of the Bohai Rim area, it is essential to develop basic transportation and focus on coordinating environmental policies. In addition, central government should actively encourage communication and cooperation between cities and motivate cities with low eco-efficiency to learn from cities with high eco-efficiency. (4) The aggregation of relevant resources and elements, such as policies, funds, and technology, should be promoted, and the effect of work synergy should be amplified. (5) Green finances should be vigorously developed, and effective approaches to the development of green finances should be actively explored through innovations in financial organizations, financing models, service methods, and management systems.This study used a social network approach and the QAP model to analyze city-level regional green development, degree of synergy, and influencing factors; our findings enrich the research on green development and spatial synergy to a certain extent, but deficiencies and imperfections remain. First of all, this paper only analyzes the spatial correlation of the green development in the urban agglomeration around the Bohai Sea, and the selected samples are limited. In the future, it is possible to compare the green development level, spatial correlation, and synergy degree of different urban agglomerations. Secondly, due to limited data, this study selected the pollutants emitted by industry. Additional contaminants should be added to the analyses in the future. Finally, the research object of this paper was cities; if the research focus was extended to a county level, then a more objective conclusion might be obtained.


A key component of microbiome research is understanding the role of host genetic influence on gut microbial composition. However, it can be difficult to link host genetics with gut microbial composition because host genetic similarity and environmental similarity are often correlated. Longitudinal microbiome data can supplement our understanding of the relative role of genetic processes in the microbiome. These data can reveal environmentally contingent host genetic effects, both in terms of controlling for environmental differences and in comparing how genetic effects differ by environment. Here, we explore four research areas where longitudinal data could lend new insights into host genetic effects on the microbiome: microbial heritability, microbial plasticity, microbial stability, and host and microbiome population genetics. We conclude with a discussion of methodological considerations for future studies. For humans and animals, host genes play a role in shaping the gut microbiome. However, measuring these effects is difficult because host genetic and environmental similarities are often correlated. For instance, relatives often live together and share similar diets and lifestyles—forces that can also change gut microbial communities. Watching the microbiome over time, through longitudinal sampling, can help solve this problem by breaking gene-environment correlations. Here, we review this idea and several other research areas where longitudinal data will be helpful for understanding host genetic effects on the microbiome. We believe this approach will shed new light on the evolution of host–microbe relationships and can inform new microbiome therapies. For instance, few genetic variants in hosts are consistently identified as having gut microbial correlations across population-specific studies (reviewed in 3). Further, in humans, very few microbial taxa are heritable , and those that are most frequently heritable have widely varying heritability estimates across studies .Gut microbial taxa – including those under host genetic influence – have widespread effects on host health and physical functioning, as reviewed in recent studies.22 and genetic similarity between hosts is often confounded by shared environments . As such, even studies with large sample sizes struggle to detect host genetic effects in the face of environmental correlations.3 For example, Gacesa et al. 24 found that across 8,208 individuals from 2,756 family units, only 6.6% of microbes were heritable. In contrast, 48.6% of microbial abundances were affected by cohabitation, but because relatives often cohabitate, the correlation between shared genes and shared housing may have obscured the researchers’ ability to detect heritable microbes.A major challenge with estimating microbiome heritability is the difficulty in isolating host genetic effects from other drivers of microbiome variation–especially variation in the environment. Host environments play a strong role in shaping gut microbiome composition,25 An additional complication is that genetic effects on dynamic host traits can be themselves dynamic. For example, the heritability of dynamic host traits, such as body mass index (BMI), changes over an individual’s lifetime as the relative importance of environmental and behavioral factors increase compared to genetic effects.27 Similar age-related changes in heritability have also been found in the gut microbiome.21This complication is exacerbated by the dynamic nature of microbiome community composition within a host over time. For example, a study in which 20 individual hosts were sampled daily for 6 weeks found that variations in the abundances of most microbes were greater within individual hosts than between hosts.20 note that in a study of age and microbiome composition, the association of Christensenellaceae abundance with age is based on one data point per host, such that if individuals in a certain age class have a similar diet or share other life history traits, the Christensenellaceae result may reflect between-cohort differences rather than a true age-related increase in Christensenellaceae abundance. Sampling the same host multiple times can reveal age-related changes in microbial abundances within individual hosts. Repeated sampling may also help to ameliorate the sample size and power limitations discussed in the past work3.To date, most research on host genetic effects on the gut microbiome relies on cross-sectional data, but time-series data can supplement our understanding of the relative role of genetic processes in the microbiome in ways that cross-sectional data cannot. Broadly, sampling the same individual over time can help to reveal environmentally contingent host genetic effects, both in terms of controlling for environmental differences and in comparing how genetic effects differ by environment. Longitudinal sampling can also minimize cohort effects. For example, In this review, we highlight four areas where time-series data are particularly valuable for understanding host genetic effects on the microbiome and the implications of these patterns for evolution and host health. These four areas are (1) heritability of microbial phenotypes, (2) local adaptation and phenotypic plasticity of the microbiome, (3) lifetime stability and dynamism of microbial communities, and (4) microbiome population genetics. Additionally, we provide a roadmap for other researchers by summarizing methods for visualizing and modeling longitudinal microbiome data with associated host genetic information and proposing future directions for the field.24 Larger sample sizes, which are often advocated for in studies of host genetic effects on microbiome composition,24 cannot necessarily overcome the limitation that host genetic similarity is often correlated with environmental similarity.Quantifying the heritability of complex, dynamic phenotypes requires parsing environmental effects from genetic effects. In the case of the gut microbiome, it is common to define phenotype heritability as the proportion of variation in either microbiome community composition or the abundances of individual microbial taxa that can be explained by host genetic variance.21 Likewise, including multiple samples per subject over extended time scales can also help break the genetic relatedness/environmental similarity correlation that can affect heritability estimates in cross-sectional designs, as relatives may share environments at a given time point, but undergo individualized changes in their physical and social environments over time. Further, repeated sampling from the same individual removes cohort effects when testing how environments or host traits affect heritability estimates. For example, the relationship between host age and microbiome heritability can be better teased out when, for instance, all four-year-old hosts in the data set were not born in the same calendar year, and thus have dissimilar early life environmental effects. Comparing heritability estimates across environments is particularly important because heritability estimates of individual microbes are environmentally dependent.1 This effect has been demonstrated experimentally in a study of the corn rhizosphere across field sites 28 and in the observational study of baboon gut microbes described above.21 By repeated sampling of individual hosts across changing environments, it is possible to paint a broader picture of the context of the changing relative contributions of additive genetic versus environmental effects on microbial composition (as shown in 29 (reviewed in 30).One approach that can help disentangle gene/environment correlations is to use study designs that leverage longitudinal microbiome samples from many individual hosts. For instance, a study using 16,234 samples from 585 wild baboons found that using multiple samples per host led to a striking increase in the number of heritable microbial taxa detected versus using one sample per host.31 In agricultural and livestock research, understanding the relationship between host genotype, microbes, and phenotype allows for targeted breeding of desired traits,1 such that parsing host gene-by-environment interactions on host phenotypes, as well as gene-environment-microbiome interactions, is a high priority.32 One such example is a longitudinal study of piglets that determined heritable microbes are associated with growth rates.33 Agricultural and livestock systems also provide great experimental setups for quantifying microbial heritability in changing environmental contexts. For example, planting inbred maize lines in different fields over 2 years revealed host genotype effects on microbiome rhizosphere colonization throughout differing environments.34Longitudinal microbiome data are already being used to understand heritability in some health and agricultural applications. For example, questions about the heritability of early life gut colonization and community assembly in humans have been addressed by repeated sampling of multiple sets of triplet infants.36 Longitudinal data can reveal if gut microbial dynamics are individualized. Combining this information with data on host genetics could further reveal if host genotype predicts plasticity in the microbiome phenotype. For example, past studies have shown that when hosts undergo a change in their environment by moving to a new area, their microbiomes likewise change as they pick up local microbes.37–39 Further, the rate of local microbial acquisition varies between hosts. Six humans sampled pre-immigration and monthly for up to 9 months post-immigration to the US varied in the length of time; it took them to acquire local microbes, but by 9 months their guts had the same dominant taxa as long-term US residents.37 Similarly, a study of dispersing baboons found that males who had lived in a social group for less than a year varied considerably in how similar their gut microbiota were to other group members, but this variation decreased over time.39 Time-series samples from dispersing primates and other animals that periodically change environments, such as migratory birds, can provide natural experiments to test which environmental characteristics predict if hosts acquire local microbes, the length of time over which such shifts happen, and if the rate of acquisition has a genetic component. Quantifying the rate of change also provides insight into the mechanisms by which hosts acquire local microbes. An immediate change could suggest diet is the primary driver, whereas gradual acquisition of local microbes implicates horizontal transmission from conspecifics or other drivers.Gut microbiome community composition is a plastic phenotype, but the dynamics of this plasticity are not well understood.40 and woodrats acquire microbes that allow them to degrade dietary plant toxins.41 Longitudinal studies on these and other systems could further test local adaptation by experimentally swapping hosts between different environments and testing if fitness improves over time as they acquire the microbes of their new environment. More broadly, longitudinal data could reveal if host-associated microbes shift the mean or variance of a host phenotype. A shift in the mean could lead to a host phenotype better adapted to the current environment, but with less flexibility to adapt to a changing future environment. In contrast, a microbiome that increased variance in a phenotype could lead to increased capacity to adapt to environmental change.42Longitudinal studies could also further our understanding of when a plastic microbiome is adaptive and to what degree host genetics affects microbial plasticity. The ability to acquire the local microbiome may provide key fitness advantages to the host; indeed, studies have shown that bean bugs acquire bacteria from the soil that makes them resistant to pesticides,Salmonella typhi alters virulence factor expression in response to temperature 43), and temperature-dependent and other environmentally dependent changes in gut microbial gene expression could be a key component of temporal variation in host–microbiome interactions.44An additional aspect of microbial plasticity that could be considered in longitudinal designs is the plasticity of individual microbial taxa. Changes in microbial gene expression under different environmental conditions have been well studied in pathogenic bacteria  with resistance to medical interventions.49 Longitudinal data sets could be used to test if taxa that demonstrate stability over a host’s lifetime are those with abundances that are already linked to host genetics, i.e., heritable. In support of this, microbiome taxa that have abundances that are highly personalized to individual hosts tend to be more heritable than taxa that exhibit similar abundance patterns across hosts.51 Individual taxon stability could also be measured as genetic stability; 46 studied SNP (single nucleotide polymorphism) haplotypes of microbes and SVs  and found smaller differences within than between hosts and that the degree of microbial genetic stability differed between microbes. A recent study3 states that at least one group of bacterial species that are genetically stable over time, Bifidobacterium, also has high heritability. Additionally, there may be selection at the level of gene functions or pathways, rather than at the level of individual microbial taxa, and selection could also occur on pathways or functional roles filled by multiple taxa.52 In support of the idea that hosts and their microbiomes experience selection for some functions to be stable over time, there is a phylogenetic signature for heritability such that taxa that are more closely related–and therefore may have similar functions and occupy similar niches – may exhibit similar host genetic effects.1 Future studies could further explore the link between host genetics and functional stability.It is possible that the stability of some microbiome phenotypes has a host genetic component; i.e., stability could be heritable. In other words, host genetics could control the day-to-day change in the microbiome. Although, to the best of our knowledge, this hypothesis has not been tested with real-world data, there are several reasons why maintaining a more stable microbiome can be beneficial for the host. For example, temporal stability of ecosystem functions makes ecosystem services likewise more stable, and therefore reliable;21 and studies have found that the stability of microbiome community composition changes with age, sickness, and other traits.53 How individual hosts vary in their degree of microbial stability, which microbes are most affected, and quantifying the degree of environmental versus genetic drivers of this variation at different temporal time scales are key questions for understanding host–microbiome interactions.54 A likely mechanism for host genetic control of microbial stability is changes in host gene expression, as host gene expression differences have been linked to host genetic control of the microbiome.55 Future work could explore if the same changes in gene expression that control microbial taxa abundances at individual time points also regulate the stability of taxa or overall community composition.Combining host genetic information with microbiome time-series data and associated environmental metadata can reveal under what circumstances microbes are stable and provide insight into stability mechanisms. Even highly heritable microbial taxa demonstrate considerable variation in abundance over a host’s lifetime,57 will be particularly valuable for parsing out a second potential mechanism of lifetime microbial stability. Glucocorticoids have a pervasive genetic component,58–60 and a study on primate microbial endocrinology61 proposes that host–microbiome signaling via the endocrine system may promote microbiome stability. This suggests that one potential mechanism for the host genetic influence on microbial stability is via glucocorticoid levels.Additionally, microbiome time-series data sets that include hormone information, such as those that measure metabolites of steroid hormones in fecal samples,62 Further, due to rapid generation times in bacteria, ecological and evolutionary processes operate at a similar temporal scale and may interact to shape microbial populations.63 Longitudinal data may be useful in teasing apart host population-level drivers of microbial differences by breaking these correlations and to address several unique questions: Is it possible to detect evidence of phylosymbiosis  or patterns of co-diversification  after controlling for ecological differences? Do host populations demonstrate microbial change at different rates, and how is this rate affected by local landscape characteristics? Similarly, does a population always look like itself, or do environmental shifts cause host-associated microbial communities from temporally and geographically separated host populations to resemble each other?Genetic differences between host populations are also often correlated with environmental, social, and spatial characteristics, making it difficult to determine the relative contributions of these factors in shaping local microbiomes.67 but in humans, few microbes show biogeographical patterns that match host biogeography (reviewed in 63). Time-series data are useful because even among microbes whose phylogenies parallel host genetic relationships, environmental traits may modify these signatures. For example, microbiota from oysters in environmentally disturbed sites no longer exhibit genetic relationships that parallel their hosts’ population genetic structure.68 This phenomenon has also been shown experimentally; the microbiotas of woodrats brought into captivity and placed on uniform diets showed stronger host phylogenetic structuring than when they were in the wild, suggesting that signatures of phylosymbiosis on microbial composition may be obscured by strong environmental effects in wild populations.69 Time-series data can also reveal if landscape modification is reflected in microbiome changes. A recent study by Couch et al.62 advocates incorporating spatial elements to model how the landscape interacts with host ecology to influence population-level microbial variation. Past work has shown that geographic and environmental traits structure microbiomes between host populations of mice, tortoises, and baboons.70–72 By sampling host populations over time, as they shift their landscape use, it will be possible to test if changes in home range use parallel changes in microbial communities. Observational studies of long-term wild study systems will be of particular importance in answering these questions, as microbial patterns observed in controlled lab systems  do not necessarily transfer to what is observed in their wild counterparts.73 These questions are also timely, as climate change causes shifts in local environments and in animal ranges.Time-series data can help place evidence of phylosymbiosis or co-diversification in an ecological context. Past work has shown that the genetic structure of hosts may parallel (or not) the genetic structure of members of their microbial communities. For example, genetically similar stickleback fish populations have more similar microbes,74 A second complication is that some studies are constrained to irregular sampling intervals, yielding datasets with uneven time between samples. Further, the unit of analysis also varies across studies; in this review, we have focused on repeated sampling of the same individual host, but some longitudinal studies may use cage, inbred line, social group, or population as their level of analysis.Incorporating host genetic information into longitudinal microbiome models presents a unique statistical challenge, as the wide range of longitudinal study designs that can test different aspects of host genetic effects  have a similarly wide range of statistical approaches. Even within the microbiome-diet subfield, there are no standardized best practices for longitudinal study designs.75–77 Some common methods of sample preservation do not yield consistent microbiome profiles when subjected to freeze-thaw cycles and other stresses common to fieldwork,76 although this can somewhat be ameliorated by treating data as compositional.78 Batch effects must also be controlled for, as samples collected over extended time periods are often not processed at the same time.79–81 Approaches to account for batch effects include running technical replicates across multiple plates and including sequencing plates in statistical models,21 and using decontam 82 or other software to identify and remove contaminants between batches.81An additional set of complications that especially affect longitudinal microbiome studies are those associated with data collection and processing, which broadly fall under the issue of reproducibility in microbiome studies.83–86 As an alternative, horizon plots can show the temporal dynamics of more taxa in a more condensed visual space. Several R packages provide horizon plot functionality, including CNEr and TSFEL.88 BiomeHorizon is the first R package designed to apply horizon plots to microbiome data.47Once sequence data are processed, the first step in any longitudinal analysis, whether it includes host genetic information or not, is visualizing which taxa are changing over time. Stream or line graphs are a common approach for visualizing temporal microbial changes for a handful of microbes in a limited number of hosts.86. Ridenhour et al. propose an autoregressive integrated moving average (ARIMA) time-series model modified to handle microbiome dynamics89. This model uses untransformed count data as the input, and incorporates environmental differences. A study by Wanger et al.90 focus on time-varying analyses of overall changes in microbial diversity. Chen and Li91ʹproposed ZIBR (two-part zero-inflated beta regression model with random effects) model accounts for the high number of zeros in longitudinal data. SynTracker is designed for the level of microbial strains93 and provides a user-friendly walkthrough of microbiome time-series analyses, as well as R and matlab tutorials. For a more detailed discussion of statistical challenges and approaches in analyzing longitudinal microbiome data, see the thorough review on the topic by Kodikara et al.94There are several other software packages in R and other statistical environments that are specifically designed to analyze longitudinal microbiome data, and future studies could explore integrating host genetic information as well. TIME, a web-based interface, guides users through multiple workflows to visualize microbial abundances and co-occurrences over time, and to predict causality 95 multispecies time-series data using first-order multivariate autoregressive (MAR(1)) models 96), generalized additive models (GAMs),51 and the stochastic logistic model with environmental noise (SLM).97Host relatedness data may also be integrated into other common models for measuring temporal variation in ecological systems, including generalized Lotka–Volterra equations,99 The ACE model, which is often used in twin studies, can be modified for repeated measures.100 Quantitative genetics models can also be extended to include genetic effects on the host and genetics effects on the microbiome.42 Bayesian models could also be used, including R packages for calculating heritability estimates from generalized linear mixed-effects models using a Markov chain Monte Carlo approach . To address the issue of compositionality that is inherent to microbiome studies and has the potential to affect heritability estimates, we recommend permuting host identity in any heritability models .To incorporate longitudinal data into heritability models, the Animal Model includes options for multiple samples per individual.103–105 Further, technical advances will improve our ability to characterize temporal changes in host gene–microbiome interactions. For example, database improvements with more GWAS studies will allow for more functional gene annotations.63 Although this review focused on the gut microbiome, incorporation of host genetic effects into longitudinal studies of other bodysite microbiomes, such as the oral microbiome,107 skin,109 and vagina,110 can provide similarly exciting insights into links between microbial dynamics and host health.The incorporation of host genetic data into longitudinal microbiome studies opens up many exciting directions in the microbiome field. The future work will leverage a combination of existing collections, natural history studies of human and wild animal populations, and controlled experimental approaches.111–113 We point out that many of these areas also have host genetic components. These include quantifying disease risk;113 understanding intra-individual variation;112 linking early life effects with lifetime microbiome consequences;111 matching microbial and host aging, modeling social dynamics and transmission, uncovering which microbial taxa are heritable, and linking microbial dynamics to host fitness;104 and modeling gene recombination rates and characteristics of microbes under selection.63In addition to the four topic areas we focused on in this review, several recent articles highlight crucial areas where longitudinal microbiome data can contribute.114 and personalized therapeutics.103 Understanding temporal dynamics and host genotype effects could help contribute to our understanding of the stable colonization of therapeutic microbes.63 Further, they could contribute to the expanding field of microbiome breeding, defined as conducting artificial selection on microbiomes that “seeks to change the genetic composition of microbiomes in order to benefit plant or animal hosts”115.The results from such studies have broad real-world applications to human health. Detailing the relationship between the gut microbiome and host genetics could be important for translational medicine applications


Hyperoodon ampullatus, using whole‐genome resequencing data (n = 37) from five regions across the North Atlantic Ocean. We found a range‐wide pattern of isolation‐by‐distance with a genetic subdivision distinguishing three subgroups: the Scotian Shelf, western North Atlantic, and Jan Mayen regions. Signals of elevated levels of inbreeding in the Endangered Scotian Shelf population indicate this population may be more vulnerable than the other two subgroups. In addition to signatures of inbreeding, evidence of local adaptation in the Scotian Shelf was detected across the genome. We found a long‐term decline in effective population size for the species, which poses risks to their genetic diversity and may be exacerbated by the isolating effects of population subdivision. Protecting important habitat and migratory corridors should be prioritized to rebuild population sizes that were diminished by commercial whaling, strengthen gene flow, and ensure animals can move across regions in response to environmental changes.Information on wildlife population structure, demographic history, and adaptations are fundamental to understanding species evolution and informing conservation strategies. To study this ecological context for a cetacean of conservation concern, we conducted the first genomic assessment of the northern bottlenose whale,  Declining genetic diversity in a population is one factor that can lead to detrimental outcomes, including inbreeding depression and loss of adaptive evolutionary potential. Understanding the processes that underlie contemporary distributions of genetic variation can guide conservation strategies to preserve and recover at‐risk populations. Genomic approaches are becoming increasingly important for delineating population subdivisions and investigating genetic variation to inform regional management targets for conservation priority  during these periods, as observed in the widespread population bottleneck in killer whales  of the North Atlantic Ocean, primarily found along the edge of the continental slope , and Jan Mayen (Iceland). Our objective was to use high‐resolution genomic data to examine northern bottlenose whale population structure, demographic history, changes in genetic diversity, and for the first time, to consider indices of inbreeding and evidence for selection across geographic regions in the North Atlantic.22.1Balaenoptera musculus) reference genome obtained from NCBI database (accession no. GCA_009873245.3) with SatsumaSynteny version 2 , and cow (Bos taurus).The northern bottlenose whale reference genome was derived from an individual in the Scotian Shelf. The tissue sample was collected by dart biopsy from a live animal in 2016 as described in Feyrer et al. . DNA was2.2Forty‐nine tissue samples for resequencing data were collected from live wild animals 2003–2019) in the North Atlantic Ocean, specifically in the Scotian Shelf, Newfoundland, Labrador, Canadian Arctic, and Jan Mayen regions  with VCFtools version 0.1.17  , and pruning for linkage‐disequilibrium , and the Scotian Shelf.We estimated changes in 2.5To evaluate levels of inbreeding, we calculated runs of homozygosity (ROH). In a population with higher levels of inbreeding, a greater number and lengths of ROHs are expected , dependent on levels of diversity , representing a 0.05 p‐value cutoff, and a window size of 100 kb overlapping by 10 kb with a minimum of two significant markers. Genes located within 20 kb of the significant SNPs were pulled from the reference genome annotation using MAGMA  is accompanied by a reduction in haplotype diversity due to a “hitch‐hiking” effect, allowing genomic regions under selection to be located from haplotype structure Stephan, . Extende33.1The northern bottlenose whale reference genome assembly was 2.3 Gb in length, consisting of 67,191 scaffolds. BUSCO analyses detected 76% complete mammalian orthologues Table . Chromos3.2The genetic variant data set for all 49 individuals  resulted in 7,636,698 variants, which were filtered down to 3,891,367 SNPs before applying further analysis‐specific filters such as minor allele frequency. Sample coverages are shown in Table 3.3K = 2 genetic clusters supported the Scotian Shelf as a separate ancestral genetic source, and K = 3 additionally supported Jan Mayen as a genetic source , over K = 1 (CE = 0.51), based on their correspondence with PCA results, sampling distribution, and known geographic concentrations of northern bottlenose whales. Expected heterozygosity ranged from 0.170–0.177 and observed heterozygosity ranged from 0.172–0.184 (Table FST) ranged from 0.002–0.020 (Table R2 of 0.86 (p = .032)   Figure . Correla) Figure . Given t) Figure , we comp3.4Ne) increased during the last glacial period (between 11.7–115 kya), including the last glacial maximum (between 19–26.5 kya), followed by a steady decline before commercial whaling began in the North Atlantic Ocean . Total ROH length was shorter in Jan Mayen  compared to western North Atlantic  and the Scotian Shelf   Table , however3.6PMM1 and PSMD12) that appeared in a high number of candidate regions that were over‐represented in the reference annotation, which may have resulted from annotating the genome without mRNA data, affecting the low confidence in locations of these two genes. GO analyses revealed weak enrichment in genes across the GO categories of biological processes, molecular function, and cellular components  and limited by small sample sizes from Labrador (n = 3) and Newfoundland (n = 6), the subtle variation found in these areas may be important given the species' low overall genetic diversity.Genomic analyses supported that the most prominent genetic subdivision is between the Scotian Shelf and northern regions, and revealed new evidence of genetic subdivision between Jan Mayen and western North Atlantic regions. This supports the identification of at least three northern bottlenose whale genetic subpopulations: the Scotian Shelf, western North Atlantic, and Jan Mayen, and corroborates previous population structure assessments using more limited microsatellite analyses , combined with a possible greater demographic decline, indicate a higher risk of negative genetic effects, such as inbreeding depression. In Jan Mayen, lower mean values of ROH, could reflect a larger population size relative to other parts of the species' range. ROH values indicate the highest degree of inbreeding comes from individuals in the Scotian Shelf, although the range of values overlaps those found across the western North Atlantic. While an assembly with higher continuity could improve ROH estimates, these results are consistent with predictions in smaller populations. For example, killer whale populations that have gone through population bottlenecks exhibit higher ROH estimates  and in BioProject PRJNA809035.This article has earned an Open Data badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The data is available on Zenodo (Appendix S1Click here for additional data file.