Genetic diversity is a necessary condition for populations to evolve under natural adaptation, artificial selection, or both. However, genetic diversity is often threatened, in particular in domestic animal populations where artificial selection, genetic drift and inbreeding are strong. In this context, cryopreserved genetic resources are a promising option to reintroduce lost variants and to limit inbreeding. However, while the use of ancient genetic resources is more common in plant breeding, it is less documented in animals due to a longer generation interval, making it difficult to fill the gap in performance due to continuous selection. This study investigates one of the only concrete cases available in animals, for which cryopreserved semen from a bull born in 1977 in a lost lineage was introduced into the breeding scheme of a French local dairy cattle breed, the Abondance breed, more than 20 years later.We found that this re-introduced bull was genetically distinct with respect to the current population and thus allowed part of the genetic diversity lost over time to be restored. The expected negative gap in milk production due to continuous selection was absorbed in a few years by preferential mating with elite cows. Moreover, the re-use of this bull more than two decades later did not increase the level of inbreeding, and even tended to reduce it by avoiding mating with relatives. Finally, the reintroduction of a bull from a lost lineage in the breeding scheme allowed for improved performance for reproductive abilities, a trait that was less subject to selection in the past.The use of cryopreserved material is an efficient way to manage the genetic diversity of an animal population, by mitigating the effects of both inbreeding and strong selection. However, attention should be paid to mating of animals to limit the disadvantages associated with incorporating original genetic material, notably a discrepancy in the breeding values for selected traits or an increase in inbreeding. Therefore, careful characterization of the genetic resources available in cryobanks could help to ensure the sustainable management of populations, in particular local or small populations. These results could also be transferred to the conservation of wild threatened populations.The online version contains supplementary material available at 10.1186/s12711-023-00801-6. Genetic variability in populations is necessary to allow for adaptation to changing environments. Indeed, highly diverse populations are more likely to have advantageous or adaptive allelic variations, which lead to a significantly higher evolutionary potential . In addiCryopreservation and the development of gene banks are useful contributions to the conservation of genetic diversity in domestic animals. However, the use of ex situ genetic resources should be combined with in situ breed conservation, as mentioned in the FAO Global Plan of Action . In FranThe long-term cryoconservation of reproductive material  makes it possible to use ancient individuals in order to re-introduce lost genetic diversity in current farm populations . HoweverIn this study, we propose to analyze a concrete case where an ancient cryopreserved bull was used to restore genetic diversity in a selected French local dairy cattle breed, the Abondance breed. We used pedigrees, genotypes and genomic estimated breeding values to determine the impact of the re-use of this bull on the current Abondance breeding scheme with respect to both diversity and performance. The objective was to demonstrate the effectiveness of using ex situ genetic resources to reintroduce genetic diversity and to understand the key parameters that are related to the successful use of cryopreserved collections.The Abondance breed originates from the Chablais region, which is the northernmost part of the French Alps, located between the French shoreline of Lake Geneva and the Valais canton in Switzerland. The geographical isolation of this area has favored the development of an original and hardy cattle population that is adapted to the mountain environment and to farming systems involving transhumance  bulls  were proposed for sale by the end of the 1980s to mate cows on farms. Starting from this time, some original purebred families, such as the Amiens bull lineage, no longer produced approved sires. Finally, this strategy was discontinued at the end of the 1990s and was accompanied by a forced return to a pure breed that may have decreased the genetic variability of the breed due to a bottleneck effect. This trend motivated the breeders’ association to reintroduce genetic diversity by using cryopreserved material in the early 2000s. The Abondance breed is therefore a good case study to investigate the consequences of reintroducing genetic diversity from a former breeding stock.In the current study, we focused on the use of cryopreserved semen from Naif, a bull born in 1977 and a son of Amiens. Its semen was used during two distinct periods, first between 1980 and 1993 and then between 2004 and 2009. We defined two cohorts of contemporary genotyped sires. Cohort 1 corresponds to 62 sires that produced offspring born between 1980 and 1993 along with Naif. Cohort 2 corresponds to 165 sires that produced offspring born between 2004 and 2009, corresponding to the period when Naif was re-used. The Cohort 1 sires were born between 1970 and 1991 and the Cohort 2 sires were born between 1982 and 2007. Then, we defined four female cohorts corresponding to the dams that produced progeny during the same two periods and for which two indices, the dairy merit index and the milk production index, were available. Cohort 1a corresponds to 2443 dams that were mated to sires other than Naif and produced offspring between 1980 and 1993. Cohort 1b consists of the 37 females mated to Naif during the same period. Three females were mated with Naif as well as with another sire for the period 1980–1993 and were thus present in both cohorts. Cohort 2a corresponds to 4092 dams that were mated to sires other than Naif and produced offspring between 2004 and 2009. Cohort 2b corresponds to 25 dams that were mated to Naif during the same period. Fifteen females were present in both cohorts, as they were mated with Naif and other sires during the period 2004–2009. The distribution of the birth years of the four female cohorts are available in Additional file We used a dataset that included all ancestors of the available genotyped individuals in 2017 in the Abondance breeding scheme extracted from the national database. Thus, the pedigree included 25,010 individuals born from 1944 to 2018. The quality of the pedigree was evaluated through the equivalent number of generations with the NGEN module of the PEDIG software . PedigreThe genetic contribution of Naif to the gene pool of a given cohort was defined as the probability that, at any neutral locus, an allele drawn at random in the genotype of a randomly chosen animal in this cohort originates from Naif. This probability was computed from pedigree data using the PEDIG software. The total contributions of Naif were calculated for each year from 1980 to 2017. Then, two types of contributions were defined, an old contribution from the first use of Naif (1980–1993) and a recent contribution from the contemporary use of the cryopreserved semen from Naif (2004–2009). These contributions were calculated using the same method as for the total contributions. The contemporary contribution of Naif was computed by giving it a new identifier when it was used in the second period. Thus, we can compute distinct contributions of Naif when it was used during either the first or the second period since it is considered as two different individuals in the pedigree.We used the genotypes of 6958 individuals obtained with the 50K single nucleotide polymorphism (SNP) chip (Illumina Infinium® BovineSNP50 BeadChip). Quality control was performed by removing SNPs with a call rate lower than 99% and individuals with less than 99% genotyped SNPs. Once this quality control was done, no other SNPs were deleted and 43,801 autosomal SNPs remained in the data sets. However, 26 pairs of SNPs were found to have identical positions in the genome and were removed from the analyses. On average, the marker density was one SNP every 57.2 ± 60.0 kb.The individual observed heterozygosity of Naif, and of sires of the two cohorts, were computed and the mean heterozygosity of Cohorts 1 and 2 was compared using a two-way ANOVA.Inbreeding was assessed from molecular data using runs of homozygosity (ROH). ROH represent long autozygous segments of the genome . Here, a ROH was defined as a homozygous segment of at least 15 SNPs and 1000 kb long, with at least one SNP every 70 kb. Two consecutive SNPs could not be included in the same ROH if they were separated by more than 140 kb. ROH were detected using the “homozyg” PLINK 1.9 function , 23. TheThe 1148 genotyped individuals born in 2017 were grouped according to their relatedness to Naif, forming two groups: one group with a recent link to Naif, which appears to be a father on the pedigree during its second use, and the other group with no link to the recent use of Naif.Principal component analyses (PCA) were conducted with Cohorts 1 and 2 using the ade4 package . Naif waThen, a between-class analysis (BCA) was carried out on the 2017 cohort to specify the family links of the individuals with Naif. The aim of this analysis was to maximize the between-group variance while minimizing the within-group variance. The 1148 individuals were separated into six classes representing the possible combinations of the different uses of Naif: either the absence of any link, or the presence of one or two old or recent links (i.e. due to its first or second use through either a single parent or both of them). Eighty-five individuals had no family link with Naif (0_LWN), 49 individuals had a recent link with Naif through one of their parents (1_LWN_R), 387 individuals had an old link with Naif through one of their parents (1_LWN_O), 479 individuals were related to the old use of Naif through its both parents (2_LWN_O), one individual was related to the recent use of Naif through its both parents (2_LWN_R), and 147 individuals had one old and one recent link with Naif (2_LWN_OR) , (ii) the dairy merit index  and (iii) the reproduction merit index (REPRO). For the female cohorts, we focused on two milk indices, the milk yield (MILK) and the dairy merit index (INEL). All these values were centered around the 2017 mobile base  and scaled according the 2017 mobile base estimated genetic variances. The mean was set to 100 for ISU and to 0 for REPRO and INEL. For both ISU and INEL, one genetic standard deviation (sd) unit corresponds to 20 points while for REPRO one point corresponds to one genetic sd unit.For the male cohorts, we focused on three multi-trait indices: (i) the total merit index  equivalent number of known generations for the 1980–1993 cohort, to 5.80 (sd = 0.40) for the 2004–2009 cohort. The equivalent number of known generations was 7.57 (sd = 0.08) for the 2017 cohort. The equivalent number of known generations for Cohort 1 (1970–1991) and Cohort 2 bulls (1982–2007) were 2.96 (sd = 0.73) and 4.12 (sd = 0.92), respectively. As expected, the quality of the genealogies increased over time, explaining the different values between the two cohorts  although it no longer produced direct offspring. During its second period of use, from 2004 to 2009, the contribution of Naif increased again because of the increased number of its direct offprings. From 2009 onwards, a slight decline appeared during the following four years, followed by a marked increase from 2014 to 2017 due to the intense use of Naif’s progeny  while that of the 165 sires from Cohort 2 was 31.3% (sd = 1.3). Across the whole genome, the average heterozygosity decreased significantly between the first and the second cohort . Naif had an individual heterozygosity rate of 33.6%, which corresponds to the third quartile of the distribution of Cohort 1, while it was one of the most heterozygous individuals of Cohort 2 , 436 animals had a single family link with Naif through either the maternal or paternal side (1_LWN) and 627 animals had two family links with Naif through both parents (2_LWN), with average inbreeding of 8.67% (sd = 1.81), 8.64% (sd = 1.58) and 8.58% (sd = 1.62), respectively. Inbreeding did not differ significantly according to the number of links with Naif . In addition, 197 individuals had a link to Naif from its second period of use while 951 individuals were not related to the recent use of Naif. Mean inbreeding was 8.74% (sd = 1.62) and 8.01% (sd = 1.51) for unrelated or related individuals, respectively, with the recent use of Naif  appear to be well-separated from the other three groups with no link with Naif (0_LWN) or with links due only to its first use  Fig. .Fig. 6BeThe ISU, INEL and REPRO values of Naif were 70, -24 and 0.8, respectively, those for Cohort 1 were 75.46 (sd = 15.72), − 19.70 (sd = 15.85) and 0.26 (sd = 0.36) and for Cohort 2 were 90.75 (sd = 16.29), − 6.84 (sd = 15.86) and 0.13 (sd = 0.57), respectively  used for AI belonged to a lineage that has since been lost, namely the progeny of a bull called Amiens. Naif was chosen to help “purify” the breed, i.e. to help decrease the proportion of the Red Holstein breed within the Abondance breed. This bull had the advantage of having a large stock of cryopreserved semen available. Thus, it was a good candidate to successfully reintroduce genetic diversity, in particular with a genetic origin from the Amiens lineage. Naif procreated 45 offspring in its first use and, after a 10-year period of inactivity, its semen was used once again to produce 33 offspring in a second use over a shorter time period, indicating a more intensive use of its semen. The years from 2004 to 2007 were marked by an important production of individuals with Naif as a sire, with 30 descendants in those four years alone. These two successive uses led to a large genetic contribution of Naif to the population. Its overall contribution increased in both periods of use and stagnated in between the periods. However, its contribution continued to increase after 2009, even after Naif was no longer used as a sire, through the use of its progeny in the Abondance breeding scheme. Moreover, while the original contribution remained nearly stable from 2004 to 2017, recent contributions increased from 2014 onwards and became almost equivalent to the original contribution. This growing trend shows that the offspring of Naif were not excluded from the Abondance breeding scheme, and in fact appear to have been increasingly used. Although their ISU values were initially lower than the contemporary average, we note that the use of progeny of Naif was primarily motivated by the reintroduction of an ancient lineage in the breed and the improvement of reproductive traits.The analysis of performances revealed that Naif was an average individual during its first use. However, a discrepancy was observed when compared to a more recent cohort, due to the effect of on-going selection for production traits . This high gametic variance, which was associated with a large number of offspring during its reuse, resulted in a rather large panel of diversity among its progeny, which could then be exploited in the breeding scheme. Indeed, the empirical genetic variances for the three indices  of the sons of Naif after its reuse were all larger than the corresponding average within-family genetic variances of other contemporary bulls. Other studies have also revealed that the use of parents that produce more variable gametes may provide a better response to selection by increasing the probability of reproducing high-level genotypes , 34. ThuHowever, in spite of the negative impact on performance traits that were under constant selection, the use of Naif increased the performance of other traits, in particular those that were not or only weakly selected. In our study, the use of Naif contributed performances for reproduction traits, which were not necessarily as much improved as other production traits included in the past breeding goals . Indeed, while Naif was 0.7 genetic sd unit ahead of the other sires used at the time of its second use for REPRO, after two generations, its progeny was still 0.2 genetic sd unit ahead. The gain obtained for a production index such as INEL, but also the progress retained on the REPRO index, allowed the delay due to Naif’s progeny on the ISU total merit index to be absorbed. This underlines the fact that traits such as fertility, calving ease or vitality at birth, which are strongly affected in many dairy breeds by selection that focuses on production traits –37, coulThe study of the 2017 population, nearly two generations after the second use of Naif, highlighted a successful reintroduction of diversity within the breeding scheme. Inbreeding did not increase, and in fact a slight decrease in inbreeding was observed between individuals resulting from recent Naif matings and the rest of the population. This significant difference in inbreeding can be explained by the choice of the females that were mated with Naif. Indeed, bringing back an old bull can favor inbreeding if matings are conducted with related individuals (i.e. its descendants). This can be even stronger if the cryo-preserved individual has been heavily used in the past. Conversely, in the case of Naif we observed a decrease in inbreeding of its offspring resulting from its second use . Finally, our approach could easily be transposed to wild populations for which the reintroduction of genetic diversity from zoos or reserves is a major concern.Additional file 1: Figure S1. Cows from the Abondance breed ruminating on high altitude pastures in the Chablais mountains, © Étienne Verrier (August 2022).Additional file 2: Figure S2. Distribution of birth years for the four female cohorts. The 2443 cows in Cohort 1a are represented in pink, the 4092 cows in Cohort 2a are represented in blue, the 37 cows in Cohort 1b are represented in green and the 25 cows in Cohort 2b are represented in purple.Additional file 3: Figure S3. Classification of different genetical links with the Naif bull for the 2017 cohort. Red: individuals with no genetic family link to Naif (0_LWN); purple: individuals with a recent genetic link with Naif through one of their parents (1_LWN_R); green: individuals with an old link with Naif through one of their parents (1_LWN_O); orange: individuals related to the first use of Naif by both parents (2_LWN_O); magenta: individuals related to the recent use of Naif by both parents (2_LWN_R); blue: individuals with one old and one recent genetic link with Naif (2_LWN_OR).Additional file 4: Figure S4. Annual evolution of the equivalent number of generations from the pedigree data.Additional file 5: Figure S5. Principal component analysis of genotyping data for Cohort 2. Green: individuals with no genetic link to Naif (noLWN); and red: individuals with a genetic link to Naif (LWN). Naif is represented by the blue dot.Additional file 6: Table S1. Average values for the three indices  at two generations after the second use of Naif.Additional file 7: Table S2. Average values for the two indices (INEL and MILK) for female cohorts.Additional file 8: Figure S6. Evolution of GEBV for the three indices  in the Abondance breed. GEBV were assessed in 2017 for all genotyped individuals. Error bars correspond to standard errors.Additional file 9: Figure S7. Annual evolution of average inbreeding (a) and average kinship (b) according to the re-use of Naif frozen semen. Inbreeding and relatedness values were evaluated from pedigree using the MEUW and PAR3 modules, respectively, of the PEDIG software. (a) Individuals resulting from the recent use of Naif are represented in green and individuals not resulting from the recent use of Naif are represented in blue. (b) Kinship within sires is shown in blue, kinship within dams is shown in red, and kinship between sires and dams is in purple. The kinships between Naif and other sires are represented in green. The kinships between Naif and dams are represented in orange. Error bars correspond to standard errors.Additional file 10: Figure S8. Inbreeding depression on reproduction index in 2017 for individuals resulting or not from the reintroduction of the Naif bull. Blue: individuals not resulting from the recent use of Naif—Green: individuals resulting from the recent use of Naif. Regression lines were estimated using an ANCOVA model considering reproduction index (REPRO) as the response variable, and the inbreeding level based on ROH (FROH), the link to the recent use of Naif (NoRECENT_LWN if no link and RECENT_LWN if resulting for reintroduction of Naif) and its interaction as explanatory variables. In blue,


Andaman and Nicobar Islands, a part of South-East Asia, is enriched with the presence of native breeds of livestock  and poultry. There are two native goat breeds, viz., Andaman local goat and Teressa goat in Andaman and Nicobar Islands. However, to date, the origin and genetic makeup of these two breeds have not been detailed. Therefore, the present study describes the genetic makeup of Andaman goats through analysis of mitochondrial D-loop sequence for sequence polymorphism, phylogeographical signaling and population expansion events. The genetic diversity of the Teressa goat was less compared to the Andaman local goat due to its sole presence on Teressa Island. Out of 38 well-defined haplotypes of Andaman goats, the majority of haplotypes belonged to haplogroup A followed by haplogroup B and haplogroup D. The result of mismatch distribution and neutrality tests indicated no population expansion event of haplogroup A and B. Finally, based on poor geographical signaling, we hypothesize that Andaman goats have been imported to these Islands either through multidirectional diffusion or unidirectional diffusion. We justify our hypothesis of multidirectional diffusion on the basis of observation of the haplotype and nucleotide diversity of Andaman goats. Simultaneously, the probability of unidirectional diffusion of goats in these islands from the Indian subcontinent in different spells of domestication events through maritime routes cannot be ignored. Capra hircus), the first domesticated and the most adaptable and widespread species [Capra aegagrus), which is a proven fact based on bone morphological changes with progressive taming [http://dad.fao.org accessed on 15 July 2022). Historically, the domestication process of goats started in the Fertile Crescent of the Near East around 10,000–11,000 years ago [The domestic goat , two distinct well-adapted indigenous goat populations have been demarcated in Andaman and Nicobar Islands. They are the Andaman local goat (ALG) and Teressa goat (TG). No information on the genetic root, diversity and phylogeography of these two well-adapted breeds of goat of this island ecosystem is available.Andaman and Nicobar Islands, a union territory of India, comprises a group of 572 islands. It is situated at the juncture of the Bay of Bengal and the Andaman Sea and is totally separated from the outer world due to its natural geographical barrier. This group of islands had the uniqueness of being a native place of unique breeds of domestic and wild animals. A few examples are Trinket cattle, Nicobari pig, Andaman local pig, Andaman wild boar, Nicobari fowl, Andaman local duck, Andaman local goat and Teressa goat ,13,14,15Conservation of indigenous livestock breeds is important as local breeds are capable of catering for human needs under impending climate change scenarios . MoreoveUsing goat mt. DNA data, a total of six monophyletic highly divergent haplogroups  have been identified . HaplogrThe current study is the first assessment of the genetic diversity and population structure of Andaman goats (ALG and TG). Therefore, this is the first insight into the genetic history of Andaman goats and is a unique comparison with goat sequences reported elsewhere.Sampling was done from the breeding tracts of ALG and TG . ALG sam® basic, Eppendorf, Hamburg, Germany) and isolated DNA samples were stored at −20 °C for further PCR amplification. PCR was carried out to amplify complete mitochondrial D-loop  region using primer sets (Forward: 5′-GCCAATC TCCCTAAGACTCA-3′ and Reverse: 5′-CATCTAGGCATTTTCAGTGC-3′) and PCR conditions as mentioned earlier [https://www.genecodes.com/, accessed on 15 July 2022).Genomic DNA was isolated from goat blood samples with the help of a commercial DNA isolation kit  as per the protocol mentioned by the manufacturer. Quality and concentration of the isolated DNA samples were confirmed by a BioSpectrometer   of goat mitochondrial D-loop.Sequence alignment was done in MEGAX  using Cler 2022) . PhylogeMitochondrial DNA (mt. DNA) D-loop sequence information of ALG  and TG  was submitted to GenBank. To know single nucleotide polymorphism (SNP), the consensus region of D-loop sequence information of 185 goats of ANI was analyzed. Within 137 sequence information of ALG, a total of 89 polymorphic sites and 77 parsimony informative sites were found. Within 48 sequence information of TG, 53 polymorphic sites were detected and all of them were parsimony informative sites. Nucleotide diversity (pi ± SD) values of ALG and TG were 0.01865 ± 0.00093 and 0.01882 ± 0.00104, respectively. The average number nucleotide differences (k) for ALG was 22.608 and for TG, the value was 22.812. Overall transition/transversion bias (R) was 6.078 and 354.733, respectively for ALG and TG.Haplotype details of ALG and TG revealed 38 haplotypes (ANGHT1-ANGHT38) with haplotype diversity (Hd ± SD) of 0.918 ± 0.007. Within the haplotypes, 34 haplotypes were solely represented by ALG, three haplotypes by TG and one haplotype was shared both by ALG and TG. The most frequent haplotype was ANGHT36  followed by ANGHT16 , ANGHT5 , ANGHT6 , ANGHT19 , ANGHT12 and ANGHT37 . This is further to mention that ANGHT1 was represented by six sequences (Frequency = 3.24), two haplotypes (ANGHT2 and ANGHT4) were represented by five sequences each (Frequency = 2.70) and one haplotype (ANGHT38) was represented by four sequences (Frequency = 2.16). The rest of the 27 haplotypes were represented by a single sequence each (Frequency = 0.54). The haplotype frequency of Andaman goats is depicted in Assigned 38 haplotypes of Andaman goats (ALG and TG) were compared with the standard Capra haplogroups . Based on NJ tree , BaysianGenetic affinity of Andaman goats belonging to haplogroup A with goats belonging to different countries representing Eastern Asia (EA), Middle East (ME), Northern Africa (NAF), Northern Europe (NE), Southern Asia (SA), Sub-Saharan Africa (SAF), Southern Europe (SE), South-East Asia (SEA) and Western Asia (WA) was established. All the haplotypes of LAG and TG of haplogroup A clustered with goats of Southern Asia namely India and Pakistan . ANGHT4 To understand the genetic differentiation among A haplogroup goats of Andaman and different regions of the world, pairwise FST distances were calculated. It was found that FST values ranged from 0.01245 to 0.50824 . AndamanFurther, Andaman goats of Haplogroup B were compared with goats belonging to different countries representing Eastern Asia (EA), Middle East (ME), Southern Asia (SA), Sub-Saharan Africa (SAF), Southern Europe (SE), South-East Asia (SEA) and Western Asia (WA). It was found that haplogroup B of Andaman goats clustered with goats of EA (China), SA (India) and SEA  . NetworkPairwise FST distances among B haplogroup sequences of different regions were calculated, and it was found that FST values ranged from −0.22609 to 0.73305 . AndamanIn the present study, one haplotype of Andaman goat  belonged to haplogroup D. The relationship of haplogroup D with goats belonging to different countries representing Central Asia (CA), Eastern Asia (EA), Middle East (ME), Northern Europe (NE), Southern Asia (SA) and Western Asia (WA) was established. Genetic relatedness of Hap D of Andaman goats revealed that Andaman goats belonged to the Southern Asia cluster and were phylogenetically close to the Gohilwadi breed (MH489395) .Pairwise FST distance of the Andaman goat was found lowest with SA (0.89811) . An analMismatch distribution analysis was done to reveal a genetic structure of a population expansion for two goat lineages  . For linThe study of goat genetics is encouraged in the scientific field to understand breeding aspects, the genetic diversity of breeds and the domestication process. Mitochondrial DNA (mt. DNA) is known for its uniqueness of bearing the most informative genetic element as mt. DNA detects introgression from females but not from males . This haStarting with sequence analysis of the D-loop, we have recognized mutation hotspots. It is customary to find mutation hotspots based on nucleotide diversity, sequence variability and parsimony informative sites . Both haOut of six distinct haplogroups of goat, haplogroups A, B and C are said to be of single origin. This hypothesis has been strengthened in the past on the basis of phylogenetic signaling since these three haplogroups are monophyletic . The truTo assess the population expansion events, neutrality tests and mismatch distribution were performed . Here inp ≤ 0.05) except with the goats reared in Eastern Asia (p ≤ 0.05)  . This reThe present study is the first report on the genetic diversity and population structure of Andaman goats  of Andaman and Nicobar Islands, India. It was found that the majority of Andaman goat haplotypes belonged to haplogroup A, followed by haplogroup B and haplogroup D. Phylogenetic analysis of Andaman goats indicated poor geographical signaling; therefore, we hypothesize that Andaman goats have been imported to these Islands either through multidirectional diffusion or unidirectional diffusion.


Corynosoma  parasites in northern European marine, brackish, and freshwater seal populations. We collected Corynosoma population samples from Arctic, Baltic, Ladoga, and Saimaa ringed seal subspecies and Baltic gray seals, and then applied COI barcoding and triple‐enzyme restriction‐site associated DNA (3RAD) sequencing to delimit species, clarify their distributions and community structures, and elucidate patterns of intraspecific gene flow and genetic diversity. Our results showed that Corynosoma species diversity reflected host colonization histories and population sizes, with four species being present in the Arctic, three in the Baltic Sea, two in Lake Ladoga, and only one in Lake Saimaa. We found statistically significant population‐genetic differentiation within all three Corynosoma species that occur in more than one seal (sub)species. Genetic diversity tended to be high in Corynosoma populations originating from Arctic ringed seals and low in the landlocked populations. Our results indicate that acanthocephalan communities in landlocked seal populations are impoverished with respect to both species and intraspecific genetic diversity. Interestingly, the loss of genetic diversity within Corynosoma species seems to have been less drastic than in their seal hosts, possibly due to their large local effective population sizes resulting from high infection intensities and effective intra‐host population mixing. Our study highlights the utility of genomic methods in investigations of community composition and genetic diversity of understudied parasites.Studies on host–parasite systems that have experienced distributional shifts, range fragmentation, and population declines in the past can provide information regarding how parasite community richness and genetic diversity will change as a result of anthropogenic environmental changes in the future. Here, we studied how sequential postglacial colonization, shifts in habitat, and reduced host population sizes have influenced species richness and genetic diversity of  Corynosoma species seems to have been less drastic than in their seal hosts, possibly due to their large local effective population sizes resulting from high infection intensities and effective intra‐host population mixing. Our study highlights the utility of the 3RAD method in investigations of community composition and genetic diversity of understudied parasites.Acanthocephalan parasite communities in northern European landlocked seal populations are impoverished with respect to both species and intraspecific genetic diversity. Interestingly, the loss of genetic diversity within  Instead, the molecular markers indicated that 32 individuals should be assigned to C. magdaleni, while the remaining three specimens represented a possibly undescribed cryptic species, “Candidatus Corynosoma nortmeri.” Genetic studies from other parts of the world have likewise found evidence of both over‐  examine whether widely distributed Corynosoma species exhibit intraspecific genetic structuring with respect to seal host (sub)species or geographic areas, and (3) infer whether intraspecific genetic diversity in the parasites reflects the widely differing levels of genetic variability found in their seal hosts. To these ends, we first sequenced the standard “DNA barcode” section of the mitochondrial COI gene from 578 Corynosoma individuals collected from Arctic, Baltic, Ladoga, and Saimaa subspecies of ringed seal and Baltic gray seals, and then confirmed our barcode‐based species delimitations by performing restriction‐site associated DNA sequencing (RADseq) on a subset of individuals representing different COI clades. After species delimitation, we inferred Corynosoma community composition in the focal seal (sub)species and estimated the levels of population‐genetic differentiation among, and genetic diversity within, the different host populations and geographic areas. Based on the sequence of postglacial colonization and current population sizes of the focal seal (sub)species, we hypothesized that: (i) Corynosoma community richness should decrease from the Arctic toward the Baltic Sea and the two small and isolated landlocked populations, (ii) widespread Corynosoma species should exhibit population‐genetic differentiation across geographic areas and host (sub)species, and (iii) intraspecific genetic diversity should decrease along with Corynosoma community richness, and should reflect the levels found in the seal hosts.The main aims of our study were to: (1) infer whether community composition and species diversity of 22.1The 578 adult acanthocephalan worms studied herein were collected during necropsies of the digestive tracts of 18 Arctic ringed seals, 12 Baltic ringed seals, 25 Saimaa ringed seals, 4 Ladoga ringed seals, and 18 Baltic gray seals  from Svalbard to serve as an outgroup in phylogenetic analyses based on RADseq data  in accordance with the manufacturer's instructions. Specimens from bearded seals were extracted using the bead‐based BOMB DNA extraction protocol species from those extracts having a concentration above 1 ng/μl. In addition to this random sampling, we included 3 additional individuals based on their potentially interesting positions on the COI barcode NJ tree , we selected a subset of the barcoded individuals for RAD sequencing as follows: from 10 to 14 individuals of Appendix .Corynosoma worms, we used the novel 3RAD approach of Bayona‐Vásquez et al.  based on the K2P model. We then queried the barcodes against previously published Corynosoma sequences on GenBank using the BLAST search tool and assigned our COI barcode sequences to species based on their top match statistic , position on the NJ tree, and ABGD results.A neighbor‐joining (NJ) tree for the 578 COI sequences was constructed in MEGA‐X based on Kimura's  two‐parafinerad_input.py written by E. M. Ortiz and available via https://github.com/edgardomortiz/fineRADstructure‐tools. In the fineRADstructure clustering algorithm, 100,000 Markov chain iterations were used, with a burn‐in of 100,000 iterations and with sampling occurring every 1000 iterations. Next, the SNP data (.vcf file format from ipyrad) were imported into R v. 4.0.2  and retained the number of PCs with the lowest mean squared error. Finally, we constructed a maximum‐likelihood (ML) phylogeny based on the concatenated RAD loci in IQ‐TREE v. 2.2.0 species and geographic areas, we constructed a TCS haplotype network species and geographical areas by estimating overall and between‐population ΦST values in Arlequin v. 3.5.2.2 species  ranging from 1 to 10 and chose the value of K for which the cross‐entropy criterion was the lowest.Finally, we performed a more detailed analysis of intraspecific genetic structuring within 2.6Corynosoma species collected from each seal (sub)species. Haplotypes were inferred from distance matrices, nucleotide diversity was estimated based on K2P distances among haplotypes, and the site‐specific maximum proportion of missing data was set to 0.05.To estimate population‐specific diversity in the full mitochondrial COI barcode dataset, we used Arlequin to estimate standard diversity indices  for the population samples of each To obtain corresponding estimates for the RADseq data, we used the four species‐level datasets Appendix  to estim33.1Corynosoma in the full COI barcode dataset  with a published sequence of C. magdaleni (GenBank acc. no. EF467872) could be distinguished on the NJ tree species . Pairwise estimates of differentiation were highest between the two landlocked populations and their marine relatives, while the populations from lakes Saimaa and Ladoga were less differentiated from each other . However, this result mainly reflects the differentiation of C. semerme samples originating from Arctic ringed seals from the populations in Baltic ringed and gray seals and Ladoga ringed seals, while ΦST values estimated across the latter three populations were not statistically significantly different from zero species . When considering pairwise FST values across host (sub)species, differentiation between C. strumosum collected from Baltic ringed and gray seals was low and did not differ from zero statistically (FST = 0.003 [−0.001–0.007]), but all other values were statistically different from zero . The SNP‐based Admixture analysis indicated three ancestral populations within C. strumosum: from the Arctic, Baltic Sea, and Lake Saimaa . For this species, the lowest pairwise value occurred between the samples from Baltic and Ladoga ringed seals, but the 95% confidence interval of the estimate overlapped with that of the differentiation across Baltic ringed and Baltic gray seals . For Corynosoma sp. 2, a meaningful test of differentiation could be done only between the population samples from Baltic ringed and Baltic gray seals; differentiation was statistically significant in this case species, estimates of within‐population gene and nucleotide diversity were in general similar when considering the relatively wide standard deviations of the values . Within C. semerme, expected heterozygosity was lowest in the Ladoga population and highest in the Arctic, and the estimates of all populations were statistically significantly different from each other . Expected heterozygosity was likewise statistically significantly different in the populations of Corynosoma sp. 2 originating from Baltic ringed and Baltic gray seals, although the absolute values of the estimates were relatively similar (Table p < .0001).In line with the mitochondrial results, the fineRADstructure analysis estimated lower co‐ancestry (and hence higher genotypic variation) for s Figure . Based ols Table . However4Corynosoma species exist in the study system and within each seal population. According to our Hypothesis (i), we expected to find a gradient of decreasing species diversity from the abundant Arctic ringed seal to the small and isolated seal populations of lakes Saimaa and Ladoga. Next, we took a closer look at geographical and host‐associated differentiation as well as population‐level genetic diversity within those Corynosoma species that were found in multiple seal (sub)species. In this case, we expected to find geographical and/or host‐associated population‐genetic structuring within widespread Corynosoma species (Hypothesis (ii)), as well as a gradient of genetic diversity from the Arctic toward the small landlocked populations (Hypothesis (iii)). Below, we relate our findings to results from prior genetic studies of northern European ringed seals as well as to diversity patterns observed in other differentially isolated host–parasite systems.Parasite species richness as well as genetic diversity within parasite species are expected to be influenced by the phylogeographical histories and population sizes of their hosts , which was based on the colonization sequence and the current population sizes of the focal seal (sub)species  and clupeids (Clupeidae) , life‐cycle disruption, or host population size will require molecular screening of Corynosoma communities in intermediate and paratenic hosts in the Arctic and in the Baltic Sea.By contrast, the 4.3Corynosoma species that were found in more than one seal (sub)species. Reflecting geographical isolation, the Arctic population was divergent from the Baltic Sea and freshwater populations of both C. strumosum and C. semerme species are genetically differentiated. While population differentiation shows broad concordance with spatial patterns found in the seal hosts, some discordances are also evident. In particular, the loss of genetic diversity in landlocked Corynosoma populations appears to have been less drastic than in seals. Retention of genetic variation in Corynosoma may have been facilitated by high within‐host population sizes and efficient outbreeding arising from continuous mixing of parasite infrapopulations through intermediate hosts. On long‐time scales, such differential erosion of genetic variation in fragmented host–parasite systems could fundamentally alter adaptive potential in coevolving hosts and parasites ; data curation (lead); formal analysis (lead); funding acquisition ; investigation ; methodology ; project administration (supporting); resources (supporting); visualization (lead); writing – original draft (lead); writing – review and editing (supporting). Eeva Ylinen: Data curation (supporting); funding acquisition ; investigation (supporting); methodology (supporting); resources (lead); writing – review and editing (supporting). Mervi Kunnasranta: Conceptualization (supporting); funding acquisition (lead); investigation (supporting); project administration ; resources ; supervision ; writing – review and editing (supporting). Simo N. Maduna: Formal analysis (supporting); methodology (supporting); resources (supporting); writing – review and editing (supporting). Tuula Sinisalo: Investigation (supporting); resources (supporting); validation (supporting). Craig T. Michell: Investigation (supporting); methodology (supporting); resources (supporting). Kit M. Kovacs: Investigation (supporting); resources (supporting); writing – review and editing (supporting). Christian Lydersen: Investigation (supporting); resources (supporting). Evgeny Ieshko: Investigation (supporting); resources (supporting). Elena Andrievskaya: Investigation (supporting); resources (supporting). Vyacheslav Alexeev: Investigation (supporting); resources (supporting). Sonja Leidenberger: Investigation (supporting); methodology (supporting); writing – review and editing (supporting). Snorre B. Hagen: Resources (supporting); writing – review and editing (supporting). Tommi Nyman: Conceptualization (lead); data curation (supporting); formal analysis (supporting); funding acquisition ; investigation ; methodology ; project administration (lead); resources ; supervision (lead); visualization (supporting); writing – original draft (supporting); writing – review and editing (lead).The authors declare no conflicts of interest.https://doi.org/10.5281/zenodo.7965559, GenBank accession numbers: OQ471326–OQ471903, NCBI Sequence Read Archive: BioProject PRJNA937090.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 at Appendix S1Click here for additional data file.Appendix S2Click here for additional data file.Appendix S3Click here for additional data file.Appendix S4Click here for additional data file.


Kryptolebias marmoratus), hereafter “rivulus,” is an intertidal fish species restricted to the highly fragmented New World mangrove forests of Central America, the Caribbean, the Bahamas, and Florida. Mangrove patches are biological islands with dramatic differences in both abiotic and biotic conditions compared to adjacent habitat. Over 1,000 individual rivulus across 17 populations throughout its range were genotyped at 32 highly polymorphic microsatellites. Range-wide population genetic structure was evaluated with five complementary approaches that found eight distinct population clusters. However, an analysis of molecular variance indicated significant population genetic structure among regions, populations within regions, sampling locations within populations, and individuals within sampling locations, indicating that rivulus has both broad- and fine-scale genetic differentiation. Integrating range-wide genetic data with biophysical modeling based on 10 years of ocean current data showed that ocean currents and the distance between populations over water drive gene flow patterns on broad scales. Directional migration estimates suggested some significant asymmetries in gene flow that also were mediated by ocean currents and distance. Specifically, populations in the center of the range (Florida Keys) were identified as sinks that received migrants  from other populations but failed to export individuals. These populations thus harbor genetic variation, perhaps even from extirpated populations across the range, but ocean currents and complex arrangements of landmasses might prevent the distribution of that genetic variation elsewhere. Hence, the inherent asymmetry of ocean currents shown to impact both genetic differentiation and directional migration rates may be responsible for the complex distribution of genetic variation across the range and observed patterns of metapopulation structure.Passive dispersal via wind or ocean currents can drive asymmetric gene flow, which influences patterns of genetic variation and the capacity of populations to evolve in response to environmental change. The mangrove rivulus fish ( Gene flow, the exchange of genetic material between populations, can drive spatial patterns of genetic variation , which iPatterns of gene flow in species that actively disperse are often symmetrical . SpeciesKryptolebias marmoratus  and genetic differentiation was nonsignificant  as horizontal diffusivity coefficients within the turbulent diffusion model of CMS  at random times with the location of each particle retained every half hour. Particles were tracked for 63 days, which is the average time rivulus spend in the egg before successfully hatching (33 days) plus the twice the standard deviation . A 63-day tracking period was chosen so that the simulations include 97.5% of the observed variation in successful hatching time. Because eggs or adults in flotsam that wash ashore can be exported again with ebbing tides, particles could not become stranded when encountering land masses.To estimate oceanic connectivity, we used Connectivity Modeling System CMS;  to trackPop1→ Pop2≠ Pop2→ Pop1), estimates were retained as asymmetric values for directional migration analyses and made symmetric for analyses with genetic differentiation estimates. For the symmetric dataset, TSIB was summed per population pair regardless of direction (TSIBPop1 → Pop2+ TSIBPop2 →Pop1). While the summed TSIB is referred to as symmetric, the values include natural occurring asymmetries in ocean currents because the values are summed by population pair not averaged. Hence, the symmetric dataset is mathematical symmetric but still includes asymmetric processes that maybe evolutionarily relevant. However, direct connectivity via ocean currents did not exist for every population pair or in both directions; therefore, TSIB for each population pair was divided by the total TSIB across all population pairs and used as the relative probability of successful dispersal. The relative probability of successful dispersal was used to construct an undirected and directed graph  in the package igraph  were natural log transformed and multiplied by −1 before using igraph to find the shortest path. The transformation produces positive edge values with high relative dispersal success having smaller numbers . The exponential of the shortest path multiplied by −1 was divided by the number of nodes  to get the per simulation relative probability of dispersal based on time. Summing transformed relative probabilities and taking the exponential of the sum multiplied by −1 is equivalent to multiplying two relative probabilities. Hence, resulting values indicate the relative probability of the most direct path scaled by the number of intermediate nodes.From the resulting particle trajectory files, the amount of time any particle spent within the receiving population’s 10 km buffer  was calculated and summed for each source—sink combination. Some particles were only inside the population buffer at one time point, potentially due to the time interval of sampling across the simulation or ocean currents pushed the particle rapidly through the buffer. In either case, not incorporating those particles would misrepresent connectivity; therefore, any particle with only one time point was assigned a half hour of residence which was the time interval of sampling. Because oceanic connectivity is inherently asymmetric  sNMFs3r  STRUs3r  s3r , migration rates (ΔM), and inbreeding coefficients (ΔF) respectively. For all Bayesian analyses , convergence was assessed with Tracer v1.7.1 ) and water distance log10-transformed before downstream analyses , and their two-way interaction as predictor variables  against each linearized genetic differentiation metric  and the fourth root of the migration rate, transformed for normality. Models then were compared using AICc following To evaluate isolation-by-distance and isolation-by-oceanography patterns, all genetic differentiation estimates were linearized  with >10% of the variance attributed to each level  and Northern Caye (NC) were most genetically dissimilar  and Indian River (IR)  and New Smyrna (NS) were the most genetically distinct , nonsignificant (Jost’s D) with 0.08 < p < 0.15, or significant  was best with an AICcw > 0.99 and separated from the next best model by >10 AICc units  were significant (p < 0.03); both OCS and the interaction between OCS and water distance were significantly negatively associated with genetic distance, while water distance was significantly positively associated with genetic distance , we evaluated the function on symmetric data and compared the results with ecodist. Our function and ecodist produced the same correlation values but the custom function identified OCS as significant in the partial Mantel tests with GST (p = 0.016), G′ST (p = 0.025) and RST (p = 0.005) but marginally non-significant with Jost’s D , not significant (D: p = 0.146) or significant (RST: p = 0.042). While our custom function and ecodist calculate significance similarly, our function first converts the matrix into a vector and permutes the vector rather than matrix thereby changing, compared to existing software, the permutation procedure on which the null distribution is generated. Our custom function identified a significant negative Mantel’s r  between water distance and migration rate and a nonsignificant positive association between migration rates and OCA after controlling for water distance (p = 0.146). The full MLPE model (Migration Rate ∼ OCA + Distance + OCAx Distance) was the most likely model and ∼4.6 AICc units lower than the second-best model (Migration Rate = OCA + Distance) and ∼5.4 AICc lower than the model with water distance alone with an AICcw > 0.86 (p = 0.005) and the interaction between water distance and OCA (p = 0.009) were significantly negatively associated with migration rate, while OCA alone was nonsignificant  with 1,120 fin clips genotyped at 32 highly polymorphic microsatellite markers collected from across rivulus’ range. With coarse resolution (K = 8), methods were partially congruent, showing overlapping clusters and assignments ; howeverssimilar . NeitherK. marmoratus species complex that includes K. hermaphroditus, simultaneously identified large blocks of shared ancestry, novel patterns of admixture , and distinct population clusters , an AMOVA suggests significant population structure between regions , populations within regions, sampling locations within populations, individuals within sampling locations, and within individuals . Strong S) and its interaction with water distance as important predictors of genetic differentiation  were positively associated with migration rates when the distance between populations over water was low to moderate  with large confidence intervals . Ocean sWe found low rates of directional migration overall, but high migration rates (>0.15) were detected from Everglades (EG) and Fort Lauderdale (FL) to Florida Keys (UK and LK). Importantly, such migration was not reciprocated; that is, EG and FL did not receive many migrants from Florida Keys . Indeed,Using 10 years of biophysical simulations, we found that ocean currents are important predictors of both genetic differentiation and asymmetric gene flow, shedding light on potential drivers of rivulus’ relatively chaotic distribution of genetic variation and demonstrating that ocean currents can modulate gene flow in an intertidal species with internal fertilization and no pelagic larvae. However, the coarse resolution of current OGCM used within our biophysical models likely missed interactions between surface currents, topography, and landmass distributions that might further influence retention and export of migrants and, as a result, metapopulation structure and the spread of genetic variation across the landscape. We suggest that the Florida Keys, although central to the range, act as a genetic sink that retains migrants but does not export individuals to other populations. This could potentially be due to fine scale oceanographic processes such as eddies or tides that are currently difficult to incorporate in biophysical models. Coincidentally, new ocean modeling approaches utilizing unstructured meshes are being developed that may offer greater resolution into hydrological dynamics in coastal and intertidal systems . Increas


Integrating urban and rural areas is the only way to achieve sustainable regional development. To comprehensively build an evaluation index system for new urbanization and rural revitalization (NU-RR), taking the Yellow River Basin (YRB) as research object, and the coupled coordination degree (CCD) model, relative development degree (RDD) model and gravity model are used to quantitatively measure the spatial and temporal patterns, synchronous development status and spatial linkages of the coupled coordination of NU-RR from 2005 to 2020. The factors influencing the CCD are identified with the help of the geographic detector model. The findings indicate that: (1) From 2005 to 2020, the combined indexes of NU-RR in the YRB show an increasing trend, while rural revitalization is growing slower than new urbanization. (2) The CCD of NU-RR in the YRB shows spatial structure characterized by “high in the east and low in the west” and undergoes an evolutionary process of “low coupling-medium coupling-high coupling”. (3) The spatial disparities in the state of coupled and coordinated development of different cities are significant, mainly showing the spatial distribution characteristics dominated by the lagging new urbanization. (4) The spatial connection of CCD is networked and polarized, and the interprovincial barrier effect is weakened. (5) Total retail sales of consumer goods per capita and local general public budget expenditure as a share of GDP are the primary influencing elements affecting the CCD of NU-RR in the YRB. The interaction is manifested as bivariate enhance and nonlinear enhancement. The study’s findings can guide decisions to promote high-quality urban-rural integration development in the YRB. Urbanization is a vital force in the modernization of countries and regions. According to the UN Sustainable Development Agenda 2030, more than 50% of the global population resides in cities, and the world urbanization rate is expected to approach 60% by around 2030 . Rapid uNU-RR are a symbiotic and co-prosperous community of destiny, and the deep integration of the two is the essential focal point for building a new type of industrial-agricultural relationship. New urbanization is the power source of rural revitalization, while rural revitalization is the endogenous power of new urbanization. New urbanization guides the transfer of urban talent, capital, technology and other factors to rural areas, stimulates rural economic potential, encourages the modernization and restructuring of rural enterprises, and realizes the optimal allocation of rural resource factors, etc. Rural revitalization provides a spatial carrier for the construction of new urbanization, expands the scale of towns, and provides cities with agricultural products, labor, raw materials and other elements. The development of cities will inevitably have a radiating effect on the countryside, and the development of the countryside can also force the cities to accelerate their development. Thus, studying the relationship between NU-RR is academically significant and practically significant.Multiple aspects of the relationship between NU-RR, including one-way influence and mutual connection, have been explored in previous studies. Regarding one-way influence, both domestic and international researchers have focused on the issue of urbanization on rural development, mainly focusing on the impact effects of urbanization on rural revitalization , includiIn addition to coupling coordination evaluation of the two major systems, scholars have analyzed the primary influencing factors of CCD using various methods such as decoupling and traditional econometric regression models. Decoupling models provide the possibility to investigate the role of subsystems in coupling coordination or barrier factors from an internal perspective , mainly As an important strategic region of the country, the YRB has an important position in the national economic development pattern. Existing studies mainly focus on the coupling relationship between new urbanization and the ecological environment , green dOverall, the research on the relationship between NU-RR has yielded rich results. However, there is still a need for study in terms of research breadth, scope, and methodology from the coupling perspective. First, regarding the breadth of the research, the current studies only pay attention to the static CCD of a region, ignoring the crucial aspect of the spatial correlation of CCD between urban and rural areas in various regions. Secondly, in terms of research scale, most studies study the coupling relationship between NU-RR based on the national or provincial scale. Still, little attention has been paid to the watershed scale, especially the YRB, an important national strategic area. Thirdly, in terms of research methods, existing studies focus on a single influencing factor for the coupling and coordination of NU-RR, and lack research on the interaction of factors leading to spatial differentiation. Therefore, this study evaluates the coupling and coordination of NU-RR based on the data of 99 prefecture-level cities in the YRB from 2005 to 2020 using the CCD model, RDD model and gravity model. On this basis, the influence factors of the CCD are analyzed using the geographic detector model.This article is structured as follows. Section 2 describes the study design and study area. Research methods in Section 3. Results analysis for Section 4. Section 5 analysis of influencing factors. Section 6 provides a discussion of the article and draws the main conclusions.This paper’s study design and the application approach are depicted in YRB refers to the entire Yellow River system, from its source to the ocean. This river has an impact on the area’s geography and ecology since it crosses the three significant geographic gradients of east, central, and west while flowing through the provinces of Qinghai, Gansu, Ningxia, Inner Mongolia, Shaanxi, Shanxi, Henan, Shandong, and Sichuan (regions) . At the http://www.ngcc.cn/ngcc/).The information required for this study’s calculations was acquired from the 2006–2021 China Urban Statistical Yearbook, the China Urban and Rural Construction Statistical Yearbook, and the yearbooks for nine provinces (cities and autonomous regions) in the YRB. The PM2.5 pollution data is derived from the global PM2.5 satellite raster data published by Columbia University’s Center for Socioeconomic Data and Applications. Leagues, autonomous areas, and cities with more missing data were ultimately excluded, leaving 99 prefecture-level cities in the YRB to be included in the study, and some cities with missing data were filled in using interpolation of adjacent years. Base map from National Center for Basic Geographic Information  and focus on fundamental needs in urban-rural areas, including government support and human resources . In geneEnvironmental conditions mostly pertain to the outside environment, including society, the market, and the level of information. The social environment refers to the fixed asset investment condition. Fixed asset investments are essential to socialized reproduction, strengthening the economy and bridging the earnings gap between urban-rural dwellers. The vigorous and sustainable development of the market can promote the rational allocation of urban-rural resources and bring new impetus to urban-rural development. The development of urban and rural areas together is given new life by the degree of information technology. It aids in removing the information “barrier” that separates urban and rural communities.Xij, yij are the standardized data, denoting the standardized values of the ith sample and the jth index , max xij and min xij are highest and lowest values of the corresponding indicator data. Wj denotes the weights of each variable; Xi represents the composite index of new urbanization; Yi denotes the composite index of rural revitalization.Each index is normalized using the maximum-minimum standardization method. The weights used to generate the comprehensive indices of NU-RR are then determined using the entropy weight method. The following are the formulas:U1 is the overall index of new urbanization, U2 is the overall index of rural revitalization; C represents the coupling degree; D is the coordination degree; T is the comprehensive coordination index between systems; The values of a and b are set to equal, both being 0.5, by the recommendation made in the report of the 19th Party Congress to integrate urban and rural development. D has a value between 0 and 1. The greater the value of D, the better the coupling coordination between NU-RR in the YRB. Referring to relevant research findings ; h = 1, …L, is the stratification of the variable X; N and Nh are the number of samples within the research area and the detection area; σ2 and A statistical method called the geographical detector can reflect the spatial disparity of geographical elements and identify the underlying reasons for those features . This stThe amount of economic, social, spatial, and green urbanization in the YRB exhibits a rising trend year by year from 2005 to 2020, as shown in As shown in NU, RR, T, C, and D in the YRB from 2005–2020 were calculated using the above methods. As shown in (1) The composite index of NU-RR displays an uptrend. The growth trajectory is always between the development level of NU-RR, showing that the influence of NU-RR on the T value of the YRB is the same. (2) From the coupling degree, the development trends of NU-RR in the YRB are primarily stable and highly coupled during the study period, indicating that cities and villages have strong interaction. (3) From the perspective of CCD, the CCD of NU-RR in the YRB has been improving consistently, and the advancement is positive overall. From 2005 to 2020, the CCD has risen from 0.37 to 0.57, experiencing a development process from “low degree coordination” and “moderate coordination” to “highly coordination”. From 2005 to 2007, they were in a state of poor coordination, which may be related to the fact that the YRB, a crucial region for reducing poverty in China, has yet to properly address poverty issues such as the large number of rural poor and the high proportion of people living in deep poverty, resulting in a lack of effective interaction mechanisms between urban and rural areas. From 2008 to 2013, they were in an intermediate coordination state. From 2014 to 2020, they were highly coordinated, mainly thanks to the implementation of the National New Urbanization Plan (2014–2020), which has boosted urban and rural development.To investigate the spatial distribution features of the CCD of NU-RR in the YRB. 2005, 2010, 2015, and 2020 are chosen as the years for analysis. Arc GIS 10.8 software is used to spatially display the data from the four years in the YRB . The folThe CCD of NU-RR demonstrates a good development trend, mainly manifested by the increasing number of highly coordinated cities, whose proportion rose from 2.02% in 2005 to 81.8% in 2020, with an increased rate of 79.78%. The number of low and moderately coordinated cities shows a decreasing trend, and the proportion of low-coordinated cities decreases the most, reaching 100%. However, few cities have attained extreme coordination, and there is still space to enhance the YRB’s growth of urban-rural integration.Regional differences in the CCD of NU-RR are weakened, manifesting as medium and highly coordinated urban expansion and low coordinated urban contraction, showing a relatively balanced development trend. From 2005 to 2020, moderate and highly coordinated cities continued to emerge. After Chengdu and Qingdao, highly coordinated cities expanded from downstream cities such as Yantai, Weihai, Jinan, and Zhengzhou to middle and upstream cities, now covering almost the entire YRB. In contrast, the scope of low-coordinated cities has gradually shrunk from the middle and upper reaches to the downstream regions. Currently, there is no low-degree coordinated city, and the whole basin presents a moderate and highly coordinated development trend.By basin, the downstream cities change less, forming a structural feature with Qingdao as the centre and gradually radiating outward. By 2020, all downstream cities will have achieved excellent coordination. The coupling coordination in the midstream cities has changed, evolving from low degree coordination and moderate coordination to high coordination, with only the northern part of Shanxi and part of Gansu in medium coordination in 2020, while the rest of the cities have achieved high coordination. The changing trend of upstream areas is also significant, among which Chengdu has the most developed economy and reached a high level of coordination in 2005, while Longnan city lags and is still at a low level of coordination in 2015, mainly due to its closed geographical environment, natural resource conditions, location, transportation, and economic development level lagging, which seriously hinder the development of urban-rural integration.According to the spatial distribution of the relative development types of NU-RR in the YRB , the spaThe RDD of NU-RR is generally in a benign development stage. The primary manifestation is that the number of cities with NU-RR simultaneously developing increases, its proportion grew from 9.09% in 2005 to 21.21% in 2020, and the decrease percentage reached 17.17%. The cities with lagging rural revitalization have no prominent characteristics in terms of quantity.Cities lagging in rural revitalization show a certain degree of regional lock. From 2005 to 2020, the cities lagging in rural revitalization are permanently distributed in Lanzhou, Baotou, Taiyuan, and Chengdu. The rural construction of these cities always needs to catch up to the urban development, indicating that these cities have earlier developed economic conditions and more muscular economic strength. Urbanization will inevitably have a siphoning effect on resources, talents and other factors in the countryside, thus inhibiting the ability of rural areas to develop themselves.The spatial differences in different time dimensions are significant. In 2005, the new urbanization lagging cities occupied almost the whole YRB. Since China’s urbanization development was still in its early stages, urban construction was relatively slow. The abundance of natural resources in rural areas was a growth catalyst for rural development. The spatial characteristics of 2010 and 2015 showed no significant change. Shuozhou, Zhengzhou, and Xi’an have changed from synchronous development to lagging new urbanization, while Xinzhou and Jincheng have changed from lagged rural revitalization to balanced development of both. Most synchronous growth cities in 2020 were in the YRB’s middle reaches. Rural revitalization lagging cities were scattered in various provinces, and the number of new urbanization lagging cities was reduced. However, they still occupy most of the YRB.This study calculates the coupling and coordination link strengths of 99 prefecture-level cities in the YRB for 2005, 2010, 2015, and 2020 using the gravity model to understand better the spatial connectivity network features of the CCD of NU-RR. To better visualize the presentation, the values of gravitational force higher than one are screened to analyze the network connection ability of CCD among cities. From Evolution from point-axis to group-type spatial structure. The intensity of coupled and coordinated spatial linkages among cities in the YRB gradually increased from 2005 to 2020, and the connection network showed complex structural characteristics. In 2005, 27 groups of cities, such as Yantai-Qingdao, Binzhou-Dongying, and Dezhou-Jinan, were closely connected, while the rest were not. In 2020, the coupling coordination and connection strength among cities will have significantly increased, with more than 100 groups of highly connected cities and a stable triangular structure such as Taiyuan-Yangquan-Jinzhong and a radial structure with developed cities such as Jinan, Zhengzhou, and Chengdu as the core nodes have been formed.The provincial barrier effect gradually weakened. The interaction between cities is steadily enhanced, showing the characteristics of inter-provincial spatial structure. In 2005, the spatial scope of the coupled and coordinated spatial connection between cities radiated the features of adjacent hinterland structures; the spatial tightness effect is significant, but the cross-provincial link is weak. As time evolves, the regional differentiation pattern is easing. By 2020, the number of cities with inter-provincial connections will gradually increase. However, connecting cities in nearby provinces is only possible because of geographical limitations. Cities upstream and downstream of the YRB are not yet sufficiently connected.The spatial polarization effect is significant. From 2005 to 2020, due to the disparity in the economic growth levels of cities in the upper, middle, and lower reaches, the lower reaches’ agglomeration degree of spatial connectivity enhanced, and the spatial agglomeration effect was significant, followed by cities in the middle reaches. In contrast, the downstream towns, except for Sichuan Province, have gradually strengthened their linkages, while cities in the rest of the provinces are relatively weak. The trickle-down effect of the downstream cities should be fully exploited in the future, establish a “one-to-one” city support mechanism and cross-regional cooperation mechanism, and promote the spatial linkage and synergistic development in the east, middle, and west of the YRB.The CCD is chosen as the dependent variable, and 11 independent variables are chosen that may influence the CCD of NU-RR. Article selection 2005, 2010, 2015, and 2020 as time points, Jenks’ natural best breakpoint grading method stratifies the selected independent variables. After converting it from a numerical to a typological quantity, the main factors affecting the CCD of the two in different years and their temporal variation characteristics are explored with the help of a geographical detector.X10), q(X5), q(X7), q(X9), and q(X11). Among them, total retail sales of consumer goods per capita (q>0.4) is the primary influencing factor. The proportion of local general public budget expenditure to GDP, population density, and fixed asset investment per capita (q>0.3) are significant influencing factors. The number of international Internet users has the weakest explanatory power and is an essential influencing factor. After 10 years of development, the changes in 2015 are q(X10), q(X5), q(X7), and q(X2). Among them, in addition to total retail sales of consumer goods per capita, the share of local general public budget expenditure in GDP also rises as a major influence factor, population density remains an important factor, and the urban-rural income ratio becomes an essential influence factor. q(X10), q(X5), q(X7) in 2020. Compared with 2015, the core and significant impact factors are consistent, and the impact of the urban-rural income ratio is insignificant. Overall, the dominant factors affecting the CCD of NU-RR in the YRB are X10 and X5, the important influencing factors are X7 and X9, and the basic influencing factors are X2 and X11, as shown in The results of factor detection are shown in X10) has the most significant impact on the CCD of the two, with q-values reaching above 0.4 in 2005, 2010, 2015, and 2020, with an increasing explanatory role trend. In recent years, the living standard of urban and rural residents in the YRB has improved significantly, and the consumption capacity has increased significantly, accelerating the circulation of resources, capital and other factors between urban and rural areas and strengthening the development potential of the market environment. (2) Local general public budget expenditure as a share of GDP (X5) has a significant driving effect on both, with the coefficient of action showing steady growth from 2005 to 2020 and only showing a slight fluctuation in 2015, probably caught by the fragile nature of the YRB, the lagging economic development and the extensive area of the poor region, the government increases investment in urban and rural infrastructure, thus alleviating the predicament of rural backwardness. (3) Population density (X7) significantly affects urban-rural coupling coordination, with the explanatory effect first weakening and then increasing from 2005 to 2020. As the phenomenon of “hollowing out” of rural areas intensifies, attracting young and strong laborers to return is essential. The YRB should strengthen policy guidance and financial support to encourage elite talents to return to their hometowns to start businesses and promote rural prosperity. (4) The per capita fixed asset investment (X9), the core growth pole of economic growth, reflects the state’s investment in urban and rural areas to improve infrastructure and optimise livelihood projects, etc. In 2005, the economic development of the areas in the YRB was in its initial stage, so it was necessary to increase the investment in new infrastructure through social fixed asset investment to tap the growth potential and promote regional integrated development.Specifically, (1) Total retail sales of consumer products per capita Click here for additional data file.


High-performance micro-electro-mechanical system (MEMS) gyrocompasses for north-finding systems have been very popular for decades. In this paper, a MEMS north-finding system (NFS) based on virtual maytagging (VM) is presented for the first time. In stark contrast to previous schemes of MEMS-based NFSs  and the abandoning rate table, we developed a honeycomb disk resonator gyroscope (HDRG) and two commercial accelerometers for azimuth detection. Instead of the physical rotation of the integrated turntable in traditional NFSs, the vibratory working modes of the HDRG are rotated periodically with electronic control to reduce the uncertainty in the azimuth. After systematically analyzing the principle of NFSs with VM, we designed tests to verify the practicability at the sensor level. A bias instability of 0.0078°/h can be obtained during one day with VM in an HDRG. We also implemented comparative north-finding experiments to further check our strategy at the system level. The accuracy in the azimuth can reach 0.204° for 5 min at 28.2° latitude with VM and 0.172° with maytagging. The results show that without any mechanical turning parts, VM technology makes it possible to develop high-precision handheld MEMS NFSs. Therefore, the development of north-finding strategies has not been suspended in recent years. Digital magnetic compasses (DMCs) are widely used because of the advantages of low cost and integration. However, the working principle of DMCs  indicates that the accuracy can be easily degraded in magnetically compromised environments3. GPS-based NFSs are another common orientation technology. With separated GPS antennas, the azimuth information of body can be easily obtained, but they fail in GPS-denied environments  because the satellite signals cannot be received5. The traditional method of using reference celestial bodies  for orientation continues to this day. Although the error in measurement is controllable with excellent sensors and does not accumulate with time, it is seriously restricted by climatic conditions6.The orientation is indispensable for navigation and guidance, while north-finding with an accuracy of several milliradians (mrad) is now an urgent need for practical applications 7. However, the Earth’s rotation rate is only approximately 15°/h, thus challenging the performance of gyroscopes8. Traditional devices such as dynamically tuned gyroscopes, ring laser gyroscopes and fiber optic gyroscopes have been generally used for accurate orientation, but they are bulky and expensive13. In recent years, great progress has been made in Coriolis vibratory gyroscopes, in which the long-term bias drift and environmental adaptability can be improved effectively in hemispherical resonator gyroscopes. However, the cost is still high for north-finding gyroscopes with an accuracy of several mrad15.Inertial NFSs, which are commonly called gyrocompasses, integrate inertial sensors  to find north. The orientation information can be obtained by measuring the Earth’s rotation and gravity vector. Without any external auxiliary information, the gyrocompass is not susceptible to interference and, therefore, provides an effective scheme for orientation under the complex and harsh environmental conditions mentioned above16. Additionally, the continuous improvement in the performance makes it possible to achieve accuracies of several mrad in the azimuth with MEMS gyroscopes17. However, due to the limitations of the manufacturing process and control scheme, the long-term bias drift of MEMS gyroscopes still causes issues19. Making use of a continuous rotation to create a modulation of the Earth’s rotation rate, carouseling can be performed to identify the azimuth free from the influence of bias and scale-factor errors of the gyroscope20. Another technique that can be conducted to achieve bias rejection is maytagging, which is implemented by turning the gyroscope ±180°. Taking advantage of the gyroscope output in two positions, the bias can be observed and eliminated from the measured values of the azimuth21. However, both methods require a rate table, which restricts further integration and makes the system complicated.In contrast, with reduced size, weight, and power, MEMS gyroscopes have inherent advantages and can thus be implemented in the design of miniaturized and handheld NFSs23.In this paper, we established a MEMS gyrocompass with the VM technique. Without any physical rotation parts, we implemented an HDRG and two commercial accelerometers in a north-finding system. Instead of rotating the gyroscope with a rate table, the vibratory working modes of the HDRG are rotated periodically with electronic control to reduce the uncertainty of the azimuth. After systematically analyzing the principle of NFSs with VM, we designed tests to verify our scheme. The results show that the accuracy of the azimuth can reach 0.22° for a 5-min averaging time at 28.2° latitude with VM. Furthermore, the approach in this paper can also provide new ideas for other similar sensing and measurement systemsn > frame), is a local tangent plane reference system of the Earth’s surface and is used to calculate the orientation information of the body. Ωie (15.041067°/h) is the rotation rate of the Earth, g is the acceleration of gravity, and λ and L  are the longitude and latitude, respectively. The x-axis, y-axis, and z-axis of the < n > frame  point North, East, and Down. As shown in Fig. b > frame) is rigidly attached to the body with a gyrocompass. The x-axis, y-axis, and z-axis of the < b > frame  point Front, Right, and Below. An HDRG (ωbx) and two commercial accelerometers , the direction cosine matrix (DCM) from < n > frame to < b > frame  can be expressed as:Rz(ϕ) represents the rotation around the z-axis at ϕ degrees, Ry(θ) represents the rotation around the y-axis at θ degrees, and Rx(γ) represents rotation around the x-axis at γ degrees. As shown in Fig. n > frame :As shown in Fig. b > frame, the following can be obtained:If we transform Eq.  to the <Moreover, the ideal value of the sensors can be obtained by expanding Eq.  as folloϕ can be calculated according to Eq. (θ and γ) from the second and third expressions, while the HDRG  of the HDRG. To increase the signal-to-noise ratio of the displacement detection, improve the voltage utilization efficiency, and complete the electrical adjustment of the resonator, electrodes are designed inside and outside the honeycomb structure24. Two silicon-on-insulator (SOI) wafers are used to fabricate the substrate and structure layer. First, the wire and anchor on the substrate are formed by deep reactive ion etching (DRIE). After generating the thermal oxide layer for protection, the substrate is bonded with a structure layer. Second, the aluminum pads are patterned for signal extraction after moving the handle wafer by chemical and mechanical polishing. Finally, the resonator structure and electrodes are formed by DRIE25.As shown in Fig. n = 2 wine-glass modes, which are composed of 0° and 45° modes with the same mode shape . The working frequency is 4.4 kHz, and the quality factor (Q) is 580k. The initial frequency split of the working modes is 0.2 Hz. Considering the two-axis lumped mass vibration model, the actual value of the HDRG that is applied in the force-to-rebalance (FTR) mode can be expressed as26:b(α) is the bias error of the HDRG and is the periodic function of the driving angle α. As shown in Fig. a, the quadrature motion q and the driving angle α are the orbital elliptic parameters that are used to describe the trajectory of the gyroscope. The angle that the ellipse makes with the x-axis is represented by α. a is the trajectory of vibration for a perfect gyroscope, while q is nonzero in the presence of imperfections . However, after quadrature tuning and frequency tuning in the HDRG, the trajectory is almost a straight line, that is, a>>q27. Δ(1/τ) represents the damping asymmetry of the resonant structure and can be calculated as Δ(1/τ) = 1/τ1 − 1/τ2. τ1 and τ2 are the attenuation coefficients of the damping principle-axis. θτ is the azimuth of damping. Actually, b(α) will drift with environmental factors , therefore bringing uncertainty to the azimuth.As shown in Fig. b(α) has an opposite sign when α = 0° and α = 90°. At the same time, the ideal value of the HDRG remains unchanged, which provides the possibility for estimating and eliminating the bias error of the HDRG. Therefore, by substituting Eq.  mechanically, virtual maytagging can compensate for the bias by electrical modulation without any physical turning parts. As shown in Fig. T1 and T2 are the times of α = 0° and α = 90°, respectively, which are equal in length. There should be a transition time Tt between the two states for the rotation of α27. In this paper, the timing configuration is set as T1 = T2 = 120 s and Tt = 30 s for completing the north-finding process in 5 min. The ordinates of Fig. T1 (VM: α = 0°), it will become negative during T2 (VM: α = 90°). It should be noted that the unstable working state of the HDRG causes the unavailability of bias during Tt. Therefore, the bias of two adjacent work states can be added to estimate and eliminate the bias of the HDRG , which is also the reason for the lack of data during the first period of T1.By summing the two equations in Eq. , the tema. First, the displacement signal of the driving mode passes through the C/V converter and becomes the voltage signal. Second, the amplitude and phase information of the vibration is obtained through the demodulation module. Finally, the PID controller and phase-locked loop are used to control the amplitude and phase, respectively. In both the FTRL and QNL, the vibration information of the sensing mode is used to control the HDRG. Regarding the FTRL, the in-phase signal (I) is used to suppress the displacement of the sensing mode, and the output of the HDRG (angular velocity) can be obtained by solving the rebalanced force. In the QNL, the quadrature-phase signal (Q) is utilized to eliminate the quadrature error of the HDRG, and the output of the PID controller in the QNL is the quadrature tuning voltage. In fact, to realize mode matching in an HDRG, a frequency tuning voltage is implemented. Approximately 5 mHz of circumferential frequency fluctuation can be observed30, which is within the mechanical bandwidth of the working mode (7.6 mHz).As shown in Fig. α = 0°. When Option 2 of the VS is selected, the driving mode and sensing mode are exchanged in the measurement and control loops. In this situation, the HDRG works in the state of α = 90°. Therefore, α can be controlled conveniently by setting the option of the VS. Since the VS and sequence controller can both be programmed in the FPGA, there is no need to add other auxiliary equipment to realize VM of the HDRG.The virtual switch (VS) is placed in the measurement and control loops of the HDRG. Two options (Option 1 and Option 2) are placed in the VS, which works according to the instructions of the sequence controller. Specifically, when Option 1 of the VS is selected, the driving mode is connected to the AGCL, and the sensing mode is connected to the FTRL. At this time, the HDRG works in the state of K11, K12, K21, K22) is an indispensable part of the measurement and control loops of the HDRG. In contrast to the traditional working mode of the HDRG, under the working mode of VM, the measurement and control loops of the HDRG require periodic switching by the VS. As shown in Fig. K11 ≠ K21 or K12 ≠ K22, the loop gains of the driving and sensing modes are not equal when the VS is set to Option 1 (α = 0°) and Option 2 (α = 90°). In these circumstances, the circuit noise, bandwidth, and range of the two working states are not the same, which affects the normal use of the HDRG under VM. Therefore, to match the above performance of the HDRG in two working states, the gain coefficient should meet the following relations:The adjustable gain module  can be calculated through the measurement value of two accelerometers  of north-finding is tested for further verification. The azimuth angle of the turntable is rotated to seven positions , and the averaging time is set to 5 min. The results of three cycles are shown in Fig. The performance of the reported MEMS gyrocompass is listed in Table In this paper, a MEMS NFS based on VM technology is presented for the first time. In contrast to the method of mechanical modulation with an integrated turntable, the electronic modulation of the HDRG is applied to the MEMS gyrocompass, which aims to suppress the long-term bias drift of the HDRG through the periodic rotation of vibration modes. Experiments are also carried out to verify the effectiveness of VM. The bias repeatability of the HDRG is improved from 0.128°/h to 0.012°/h, and the bias instability reaches 0.0078°/h at integration times of 7000 s. Furthermore, the SD of north-finding at a fixed position is 0.061° (≈1 mrad), and the ICA of seven positions can reach 0.204° at an averaging time of 5 min with VM, which is similar to that of mechanical modulation . Therefore, high-precision MEMS NFSs can potentially be developed without any mechanical turning parts.


NLPs inle genes , leadingle genes . Nitratele genes . In new le genes  further le genes .NLP2 displayed rapid nuclear retention in response to nitrate, a typical post-translational regulation process known for NLP7 . Using cnlp2 mutant, untargeted metabolite profiling revealed changes in intermediates of sugar metabolism, which were opposite to or absent in the nlp7 mutant. Interestingly, such differences were only observed under nonlimiting nitrate conditions, but not under nitrate-limiting conditions. The observation was further supported by the specific downregulation of glycolytic genes in nlp2. Therefore, NLP2 is seemingly important in the integration of nitrate assimilation and carbon metabolism in response to nitrate, which distinguishes NLP2 from NLP7 function. This hypothesis was well addressed by a mixed nitrogen supply experiment (see nlp2 and nlp7 mutants displayed reduced growth, which was additive in the nlp2 nlp7 double mutant. Strikingly, when equivalent nitrogen was supplied in the form of ammonium nitrate, nlp2 and nlp7 showed distinct growth phenotypes. Ammonium nitrate supply partially rescued the nlp2 growth defect but exacerbated that of nlp7. Notably, ammonium nitrate supply to the nlp2 nlp7 double mutant was lethal.Nitrogen and carbon metabolism are closely linked . Light ament see . When niTaken together, NLP2 and other NLP family members share common features in the transcriptional regulation of the PNR network, but also have distinct functions. NLP2 positively regulates carbon metabolism and energy supply, particularly under nonlimiting nitrate supply. Interestingly, NLP2 and NLP7 physically interact in the nucleus after nitrate resupply . The tis