Genetic trend was, in part, related to the extent of deployment of new breeding tools in each pipeline, strength of an extensive phenotyping network, and funding stability. Over the past decade, CIMMYT’s breeding pipelines have significantly evolved, incorporating new tools/technologies to increase selection accuracy and intensity, while reducing cycle time. The first pipeline, Eastern Africa Product Profile 1a (EA-PP1a), to implement marker-assisted forward-breeding for resistance to key diseases, coupled with rapid-cycle genomic selection for drought, recorded a genetic trend of 2.46% per year highlighting the potential for deploying new tools/technologies to increase genetic gain.Fostering a culture of continuous improvement through regular monitoring of genetic trends in breeding pipelines is essential to improve efficiency and increase accountability. This is the first global study to estimate genetic trends across the International Maize and Wheat Improvement Center (CIMMYT) tropical maize breeding pipelines in eastern and southern Africa (ESA), South Asia, and Latin America over the past decade. Data from a total of 4152 advanced breeding trials and 34,813 entries, conducted at 1331 locations in 28 countries globally, were used for this study. Genetic trends for grain yield reached up to 138 kg ha Over the past 25 years, maize production has almost doubled, where about half of the increase came from an expansion in the area under maize production and the other half due to increased yields1. However, this production figure masks significant variation between countries and regions. While maize yields have increased globally by over 2.0 t ha-1 in the past 25 years, there is significant disparity in the rate of yield growth observed among the high- and low-income countries2. A major cause for concern is the decline or stagnation in the maize yields of many lower- and lower-middle income countries over the last two decades3. In sub-Saharan Africa (SSA) production gains over the last 50 years are largely associated with an increase in area rather than yields4. At current yield levels, the expansion of maize area is the only way to meet future food security needs5, yet area-based expansion for increased crop production is not sustainable and is also a major driver of biodiversity loss6. Increased production based on improved genetics and agronomy is essential to sustainably meet the needs of future generation.One-third of the global area under maize production is in tropical areas of lower and lower-middle income countries7. Recent modelling studies suggest that anticipated impacts of climate change on maize production will be more pronounced than previously projected, particularly in major maize growing countries, including Mexico8. Although increasing maize productivity and minimizing the impacts of increasing climate variability will require intensive multi-disciplinary efforts, crop genetic improvement has historically played an important role in raising cereal productivity and reducing the impacts of climate variability on food security9.Adding to the problems of maize production in already stress-prone environments, the frequency of climate-induced stresses is increasing in the tropical rainfed regions where maize is predominantly grown in SSA, Asia and Latin America. In South Asia, heat stress currently affects over 50% of the maize area for at least two months of the year−1 yr−110. This gain was initially driven by development of heterotic groups11, the use of reciprocal recurrent selection12, or doubled haploid technologies13, expansion of field-testing networks, improved phenotyping, data management and analytical protocols14. Since the start of the twenty-first century, genetic gains have been largely attributed to the use of molecular technologies to predict and select families and individuals prior to field testing15. Agronomic improvements have also made a significant contribution to yield gains in favourable environments16. Unlike the situation in the Global North, public sector maize breeding, especially through the collaboration between CGIAR and National Agricultural Research System (NARS) remains the primary source of genetic innovations and impactful products for many small- and medium-enterprise (SME) seed companies in the Global South18. Although the application of innovative breeding tools in public sector maize breeding in the Global South is still constrained, in part, by sustainable funding19, there has been a significant shift towards modernizing the breeding programs. Increasing accountability and tracking changes through a continuous improvement plan are essential steps in the reorganisation and modernization of breeding programs20.Over the past 70 years in the US Corn Belt, breeding has increased maize yields by an estimated 0.10 t ha21. However, to date, there are very few estimates of genetic gains in public sector breeding programs19. “Era studies”, whereby varieties released in different years are evaluated in common trials, provide the most unbiased estimates of genetic gain because they avoid differences in agronomic management or climate variability which can confound the genetic trend23; however, these studies require significant investment of resources. The first estimation of genetic gain in CIMMYT’s eastern and southern Africa (ESA) hybrid maize breeding program used 17,018 rows (excluding seed multiplication) 24 which is equivalent to almost two-thirds of the number of trials used at Stage 4  in one product pipeline in southern Africa. This budget could be more effectively allocated towards direct breeding costs to improve genetic gain rather than monitoring through era studies. Sourcing seed and re-assembling old hybrids for era studies is also a logistical challenge. Furthermore, while era studies provide a more accurate estimation of genetic gain, they do not allow real time monitoring, timely diagnosis, and correction of problems .Estimating genetic gain within a breeding program provides an opportunity to monitor breeding efficiency and effectiveness. Genetic gain estimation in maize is routinely implemented by the large multinational seed companies.The primary objectives of this study were (a) to estimate the genetic trends in grain yield in 11 tropical maize breeding pipelines of CIMMYT across SSA, South Asia, and Latin America using the data from historical/advanced trials over the last 10 years to provide a baseline for future investments in tropical maize breeding; and (b) to suggest key elements for routine implementation of genetic trend estimation and improvement of tropical maize−1) were recorded in trials screened under high VPD stress followed by trials evaluated under managed waterlogging stress with more than half of the trials recording grain yield less than 2.5 t ha−1  to 874 (SAHDT). In ESA, an array of abiotic stress environments was used within each breeding pipeline, with over 50% of trials under optimal conditions Fig. . Yield p to 874 (−1 yr−1 (SA-PP1) to 138 kg ha−1 yr−1 (SA-PP2). However, in EA-PP3, there was a negative genetic trend (77 kg ha−1 yr−1) for grain yield. Under managed drought stress, genetic trend ranged from −40 kg ha−1 yr−1 (EA-PP2) to 64 kg ha−1 yr−1 (EA-PP1a). There was no significant trend in grain yield over time under managed low N stress in all three product pipelines  that have low N tolerance in the product profile.Linearized genetic trend for grain yield in each pipeline is presented in Fig. −1 yr−1 under rainfed conditions in both breeding pipelines, except in case of LatAmTM White which recorded 66 kg ha−1 yr−1 grain yield. Estimated genetic trend for grain yield in the LatAmTM Yellow and LatAmTL Yellow breeding pipelines was 143 kg ha−1 yr−1 and 122 kg ha−1 yr−1, respectively, under rainfed conditions. In the LatAmTM White and LatAmTL White pipelines, estimated genetic trend for grain yield was 66 kg ha−1 yr−1 and 117 kg ha−1 yr−1, respectively.In Latin America, estimated genetic trend for grain yield was over 100 kg ha−1 yr−1 under high VPD in the SAHDT pipeline to 118 kg ha−1 yr−1 under low VPD in the same pipeline. There was a small, but significant, negative genetic trend for grain yield in SADT under rainfed (high yielding) conditions. The highest genetic trend in grain yield in South Asia was recorded under managed waterlogging stress in the SAWLDT pipeline at 3.39% per year (84 kg ha−1 yr−1).In South Asia, the estimated genetic trend for grain yield ranged from 6 kg ha20; however, varietal releases alone do not reflect the efficiency of a breeding program nor the impact of a breeding pipeline. This paper presents an overview of genetic progress in CIMMYT’s maize breeding pipelines using historical data from the past decade and is the first-time genetic trends in CIMMYT’s tropical maize breeding pipelines in South Asia and Latin America  are documented. When expressed as a percentage, the highest genetic trend (3.39%) was observed in the SAWLDT pipeline for grain yield under managed waterlogging stress. A similar trend was also observed in the SAHDT pipeline (targeting heat + drought stress tolerance) for trials evaluated under low VPD stress. As nascent product pipelines, with no prior genetic improvement for waterlogging tolerance in Asia prior to 2008 and heat stress before 2010, gains can be expected to be initially high. In addition, the number of trials used for analysis under waterlogging stress were comparatively low (67), primarily due to the limitations on trials that could be handled under this pipeline due to the complexity involved in imposition of the stress, besides limited funding. In this study, trials with low repeatability were removed from the estimation of genetic trend. It is possible that the inclusion of trials with repeatability (< 0.2) could change the estimates of genetic trend. However, trials with low repeatability do not form part of the stage-gate advancement process and are removed from the combined analysis to identify elite hybrids to advance to on-farm testing (Stage 5).Previously public sector breeding programs were mostly assessed by the number of varieties released25 estimated the genetic trend within the LatAmTL White breeding pipeline at 279 kg ha−1 yr−1 during 1994–2002. In the present study, the genetic trend for this pipeline (122 kg ha−1 yr−1) is significantly lower than that estimated by Cordova et al. (2007) 25. One of the major factors could be the significant reduction in funding for the Latin American maize breeding, including the closure of the mid-altitude breeding program in 2004 until 2011 when the Mexican Government started investing in maize improvement through CIMMYT and partners.Cordova et al. (2007)26 reported genetic gain in grain yield of 62.26 kg ha−1 yr−1 (1.24% yr−1) between 1973 to 2012 in the same mega-environment; however, this era study covered old open-pollinated varieties (OPVs) to new hybrids; thus, significantly higher trends could be expected. Over the past decade, funding for the highland maize breeding pipeline (EA-PP3) has remained highly unstable. Until recently, the main source populations were lines developed from Ecuador-573, Kitale-SYN and Pool-9A with limited genetic pool. Nevertheless, in Ethiopia, there has been a sustained increase in maize yield at the country level in the last one decade due to various factors, including replacement of old and obsolete hybrids with improved varieties27.In general, genetic gain under optimal conditions in ESA was greater than 1% per year, although EA-PP3 was an exception to this trend. Kebede et al. (2019)28. The genetic trend for grain yield under drought stress in this pipeline was 2.46% per year (64 kg ha−1 yr−1). Although the present study did not allow partitioning of the gains and relate them to specific breeding tools/technologies used, when expressed as a percentage the genetic trend for grain yield was two-fold greater in EA-PP1a than in EA-PP1b  and SA-PP2 . This result is promising as other breeding pipelines, especially in Africa and Asia, are now mainstreaming genomic selection for improving grain yield under drought stress. In SSA, average fertilizer rate per hectare is estimated at 17.9 kg N ha−129. For many farmers, the yield response to inorganic fertilizer application is often too low and variable to be profitable30, thus increasing the return on investment to fertilizer use through increased yields under low input conditions is essential. Masuka et al. (2017)21 previously found genetic gain for grain yield (20 kg ha−1 yr−1) was the lowest under low N stress but hypothesized increased investment in low N field screening capacity could potentially increase future gains. Surprisingly there was no significant genetic trend for grain yield under low N in breeding pipelines where this is considered as a key trait . Initial investment into breeding for low N focused on the creation of a separate low N breeding pipeline from 2009 to 2016 and this may, in part, account for a slower rate of genetic gain. Phenotyping under low N stress is expensive due to the requirement of growing a depletion crop to maintain plot uniformity; site uniformity is also hard to maintain28. Ertiro et al. (2020)29 previously showed grain yield under low N stress to be amenable to genomic selection; results from the present study suggest this may be a more powerful tool to increase the genetic trend for grain yield under low N within a fixed budget rather than through phenotypic selection alone.Genetic progress in grain yield under managed drought stress in the ESA breeding pipelines was variable. However, yield trends in EA-PP1a, EA-PP1b and SA-PP2 were higher than those reported by Masuka et al. (2017) for the previous decade (2000 to 2010). In EA-PP2 though there was a negative trend for grain yield under drought stress, while no significant change in grain yield under drought stress was recorded in SA-PP1. These results may be a function of the number of sites available for managed drought stress screening, with only one location used in eastern Africa and two locations in southern Africa (there were previously four in this region but two of these sites could not be sustained since 2015 due to budget constraints). Genomic selection for grain yield under drought is now being implemented in ESA breeding pipelines where drought is a priority trait, allowing high selection accuracy under drought where repeatability is low compared to optimum trials. EA-PP1a was the first breeding pipeline to implement genomic selection for grain yield under drought stressTurcicum Leaf Blight and Downy Mildew), and nutritional quality , and later Gray Leaf Spot (GLS) and Banded Leaf and Sheath Blight (BLSB). Breeding pipeline for drought tolerance in Asia was initiated in 2008, with the introgression of drought tolerant (white and yellow) lines from Mexico, and white lines from Africa since 2010. The introgression of drought tolerance from white donor lines into a yellow recipient to derive viable yellow lines eliminates a large number of segregating inbred lines based on colour alone. Thus, large population sizes needed to be screened to be able to derive viable yellow drought tolerant lines, thereby reducing selection intensity for key traits. Interestingly, the genetic trend for grain yield under drought in the SADT pipeline (71 kg ha−1 yr−1) is higher than previously estimated 32.5 to 55 kg ha−1 yr−1 by Masuka et al. (2017)24 under drought stress in ESA. The breeding pipeline SAHDT targets rainfed maize growing environments with the crop experiencing a combination of drought and heat. While heat stress screening was started by CIMMYT in the mid-1980s in Obregon, Mexico, in Asia, breeding for heat tolerance was initiated only in 2012, in partnership with national maize programs and seed companies in Bangladesh, India, Nepal and Pakistan. Genetic gain estimates for this increasingly important trait varied from 0.24% (high VPD), 0.82% (medium VPD) and 2.02% (low VPD). Substantial progress has been made through this pipeline, with successful release and commercialization of heat tolerant maize hybrids across partner countries in South Asia.Compared to CIMMYT’s other maize breeding hubs in Latin America and ESA, the Asia maize breeding program was relatively new, with its establishment in the 1980s in Thailand, from where it was moved to Nepal in 2002, and finally to India in 2003. A regional maize breeding hub was established in Hyderabad, India, in 2008. Until the mid-2000s public maize breeding programs in South Asia focused primarily on high yield potential and disease resistance. In Thailand breeding targets included high yield potential, lodging tolerance, disease resistance  in SSA and in Asia33, and Tar Spot Complex (TSC) in Latin America34, have resulted in the addition of new target traits to CIMMYT’s tropical maize product profiles. These new threats also resulted in significant shifts in the base genetics, and reduced germplasm exchange (mainly due to the risk of transfer of MLN-contaminated seed) among breeding hubs as germplasm movement was restricted35. Key lines in the ESA breeding pipelines, which are widely used in commercial products in eastern Africa, were highly susceptible to MLN35 and required infusion of new genetic variation from other regions. This may have, in part, led to slowing the genetic gain in some breeding pipelines. However, lessons learned in the response to these recent threats will play an important role in reducing the reaction time to future threats. The ability to mobilise relevant germplasm across international borders, and capacity to quickly utilise new breeding tools with a clear vision to deployment are vital. For example, within months of MLN outbreak in Kenya thousands of maize inbred lines from a diverse range of breeding programs worldwide were screened by CIMMYT under artificial inoculation for MLN resistance, and two donor lines identified. Linkage and association mapping approaches were used to identify and subsequently validate genomic regions associated with MLN resistance. A major-effect, recessively inherited QTL (qMLN06_157) on chromosome 6 was identified, accounting for 55–70% of the phenotypic variation for traits related to MLN resistance. Marker-assisted backcrossing (MABC) was used to provide an intermediate step to maintain yields by quickly incorporating six SNPs associated with MLN tolerance into key inbred lines used in commercial products and advanced (pre-release) hybrids. Within 2.5 years, 24 key inbred lines were converted to MLN resistance using MABC36. In parallel, forward breeding was used to enrich populations for MLN resistance prior to field phenotyping in EA-PP1. Lines developed using this approach have now entered Stage 4  testing37. The fast, coordinated adaptation of breeding pipelines, identification of donor lines with favourable QTLs/SNPs, and the release of first set of hybrids with MLN tolerance within three years after disease outbreak in eastern Africa35 were major features of this rapid response to MLN. Similarly, first-generation hybrids with native genetic resistance to FAW were identified and licensed to NARS partners within four years of the pest outbreak33, and genomic analysis of native genetic resistance to FAW was also undertaken38.Emerging threats over the past decade, including Maize Lethal Necrosis (MLN) in eastern Africa39. The level of funding for maize breeding in both Latin America and South Asia was however much lower, with greater fluctuations, compared to ESA. Over the past decade there has been a significant effort to modernize CIMMYT maize breeding programs. Different funding levels are also reflected in application of tools/technologies to increase the rate of genetic gain ] to select DH/inbred lines prior to field testing. Marker-assisted forward breeding for selection of favourable haplotypes for resistance to MLN and MSV resulted in a saving of almost US$ 300,000 in phenotyping expenditure during a four-year period. Refinement in genomic selection strategies coupled with a reduction in cost of securing adequate marker density has enabled mainstreaming of genomic selection as an integrated breeding method within eastern Africa42. The use of proximal sensors for key traits used in selection is increasing across breeding pipelines, reducing the time and cost of the measurement of routine traits and allowing money to be diverted towards generation and management of larger populations17. Given the time taken for the key lines developed using new tools and technologies to reach Stage 4 testing, the effects of breeding modernisation are likely to be reflected in genetic trends over the next few years. Genetic trend is a function of the replacement of both female and male parental lines. Our data does not permit decomposing the estimation of genetic trends of female and male parental lines, due to the lack of replication across years. Using an era study composed of hybrids selected by national breeding programs and seed companies for release, Masuka et al. (2017)43 found the replacement of parental female parent inbreds of elite hybrids was slower than that of male parents in ESA. Across pipelines, in general, there has been a reduction in the age of females with increased replacement over time , the use of supplemental irrigation when required, and the smaller number of trials under random stress would have limited the potential to decipher the effect of non-genetic effects related to increased climate variability. Future analyses of genetic trend within breeding pipelines presented in this study should be able to estimate the effect of non-genetic effects related to the environment on genetic trends.The use of historical data to estimate genetic trend can be confounded due to non-genetic trends related to increased climate variability or changes in agronomic management45. However, more checks have cost implications as they increase the size of each trial and reduce testing capacities. The percentage of entries which are checks (check percentage) should strike a balance between the cost of estimating genetic trend and the accuracy of the estimates. Moving forward the check replacement strategy will be harmonized across breeding pipelines to facilitate optimal genetic trend assessment and allow genetic trends to be sub-divided into genetic and non-genetic40.The use of historical data to estimate genetic trend requires connectivity of checks in trials across years. This provides a degree of overlap of different cohorts and avoid confounding of estimates of genetic or breeding values with the year effect. The trials reported in this study were not originally designed with the intention of estimating genetic trend; thus, the connectivity across years and gradual check replacement strategy were not optimal. The connectivity of checks for the five breeding pipelines in ESA are presented in Fig. 42, in several pipelines the genetic trend in yield in the on-station trials were around this target. The high genetic trend for grain yield under drought stress in EA-PP1a, the first breeding pipeline to implement genomic selection in Stage 1, is highly promising; a similar approach may increase genetic gains across the pipelines. Furthermore, over the past decade, there have been significant changes in all the breeding pipelines as new breeding tools/technologies have been utilized to increase selection intensity and accuracy, besides significant exchange of elite germplasm between breeding hubs, and introgression of other sources of genetic diversity . These effects of continuous improvement are expected to manifest in terms of improved genetic trends over the next few years.The present study provides a baseline for future maize breeding efforts in the tropical rainfed areas, especially in the low- and middle-income countries in SSA, Asia and Latin America. While genetic trends for grain yields in CIMMYT’s tropical breeding pipelines were generally slightly lower than the estimated 2.4% per year that was projected as required to meet the future needs47. Genetic gain in breeding pipelines also need to be effectively translated into gains on-farm through active replacement of old and obsolete varieties with improved genetics that can benefit the targeted farming communities, besides improved crop management/agronomy and increase in input use. All this would require stable, committed, and increased funding for tropical maize improvement in SSA, South Asia and Latin America, where the crop is largely cultivated under rainfed conditions by resource-constrained smallholders.While the study showed significant progress in increasing genetic gains in the tropical maize breeding pipelines of CIMMYT, there are certainly opportunities for further improvement. Sustainable funding is needed to aggressively implement modern tools/technologies in improving selection efficiency and accuracy, and to further reduce cycle time, in addition to implementing interdisciplinary approaches for more predictive breeding48 and Gowda et al. (2015)49. Genomic selection was applied at Stage 1 by genotyping all lines with mid-density markers  using the DART genotyping platform. Selection of individuals for advancement was based on genomic estimated breeding values (GEBVs) and genomic best linear unbiased predictions (gBLUPs)41.Selected lines were genotyped, and then testcrossed to either a single-cross or an inbred tester. The testcrosses were then evaluated in relevant mega-environments. In some breeding pipelines, part of the F1 source populations were subjected to backcrossing to introgress specific traits , and the backcross families were further advanced. Lines were advanced based on their testcross performance across multiple locations from Stage 1 to Stage 3. Stage 4 trials include hybrids selected from Stage 3 trials, pre-release varieties nominated by partners i.e., both national agricultural research systems (NARS) and private seed companies, and commercial benchmark varieties, which facilitate advancement decisions. The range of environments was guided by mega-environments and trait requirements of each breeding pipeline. Stage 1–3 trials were conducted by CIMMYT in partnership with NARS, while Stage 4 and Stage 5 trials were jointly conducted by CIMMYT, NARS and seed companies. The final stage of the breeding pipeline was Stage 5 where trials were run in a regional on-farm trial network, under farmer-managed conditions. Stage gate advancement from Stage 3 onwards was based on voting by CGIAR-NARS breeding network team members. Data from both Stage 4 and Stage 5 were used in final product advancement decisions. The number of hybrids, selection intensity applied, and the number of locations per stage varied by breeding pipeline  and the repeatability (H2) were estimated. Data from trials with a Neff lower than 1.25 or H2 lower than 0.2 were excluded from analysis. The trials were laid in α incomplete block designs (IBD) with the exception of those in Latin America where Stage 5 trials were planted in a randomized complete block design. A standard linear mixed model for an IBD was used for individual analysis:For the breeding pipelines in eastern and southern Africa, Stage 4 (on-station) trials were used for the estimation of genetic trend in grain yield. In case of Latin America pipelines Stage 5 (on-farm) data was used due to the larger number of years and locations compared to Stage 4 data. In Asia pipelines, data from stages 2–4 were used as the streamlining of testing strategies is more recent. The average number of consecutive years of trials used for genetic trend estimation was nine Table . FollowiRepeatability was calculated with:44:For the randomized complete block design, block nested in replicate was removed from the model; the replicate effect represents complete-block effect. To remove environmental effects on genotype effects, a linear mixed model was fitted for combined analysis across years. Because the checks, which represent dominant commercial hybrids in the market, were the only genotypes that were replicated across years, they play an important role in the estimation of effects including the year effect. They are the comparison point between genotypes released at different years. The model included year, location, genotypes and their interaction effect plus experimental design effect. Year and genotype were fixed effects, and other effects were considered as random effectsWith a heterogeneous residual error by experiment,The adjusted means of genotypes were estimated from the combined analysis. A linear regression model of genotype adjusted means was then fitted against year of release to estimate the genetic gain. Year of release is the first year a genotype was tested in the set of experiments. From the model, the following statistics were estimated:52. The methodology in this study was selected based on the structure of the data, with limited genotypes screened over consecutive years.There are several ways to estimate genetic gain with historical dataSupplementary Information 1.Supplementary Information 2.


Shortly after the introduction of cryo-conserved semen in the main farm animal species, gene banks were founded. Safeguarding farm animal genetic diversity for future use was and is the main objective. A sampling of sires was based on their pedigree and phenotypic information. Nowadays, DNA information from cryo-conserved sires and from animals in the living populations has become available. The combination of their DNA information can be used to realize three opportunities: 1) to make the gene bank a more complete archive of genetic diversity, 2) to determine the history of the genetic diversity from the living populations, and 3) to improve the performance and genetic diversity of living populations. These three opportunities for the use of gene bank sires in the genomic era are outlined in this study, and relevant recent literature is summarized to illustrate the great value of a gene bank as an archive of genetic diversity. Genetic diversity is an important characteristic of a population of animals. It creates the opportunity for artificial selection to improve desired traits of the animals. Genetic diversity is important in natural and captive populations because it facilitates their adaptation to a wide variety of environments. Genetic diversity is influenced by genetic drift, selection, migration, and mutation. The loss of genetic diversity within breeds, resulting in inbreeding, is mainly at stake in populations under intense selection for a few traits and in small populations with a high genetic relationship between the animals. The conservation of the genetic diversity between breeds is also relevant because recently many breeds were set aside from the mainstream production chains. These rare breeds may be a source of unique genetic diversity if they still have a sufficient effective population size .Concern about the loss of genetic diversity in farm animals has become widespread, for example, by the activities of the FAO . This loAn example of a gene bank in progress is described by In the era of genomics, the management of gene banks can take their decisions for sampling and for use of the donors of semen based on genomic criteria. In the past, the genetic criteria could only be based on the history of breeds and on pedigree analysis . “MeasurNowadays, when gene bank collections become more or less complete, other questions become relevant : what arConservation of breeds in gene banks requires sampling and freezing of semen and other genetic material. It is labor-intensive when the sires are not used in regular breeding programs. The sampling of breeds and male animals within breeds is based on choices for which genomic similarities between breeds and animals might be decisive. Unique haplotypes and alleles may also influence such decisions. The core set diversity method can be extended to the “safe set +1” approach . The staIn several other studies, the analyzed DNA data originated partly from cryo-conserved males and partly from living animals. For example, The characterization of samples by marker genotypes (SNP) or by Whole Genome Sequencing (WGS) of germplasm can give important additional information to the existing pedigree and phenotype-based information . The selhttp://www.genebankdata.cgn.wur.nl/gencont/gencont.html.Gencont, http://www.nordgen.org, andEVA, http://matesel.une.edu.au.MateSel, The most important aim of a breed’s conservation is to establish a core collection per breed that is sufficiently large enough to reconstitute that breed when it becomes extinct. Within breeds, the main goal is to maximize genetic diversity in the core collection. For a core collection, genomics can be used to select the animals from the living population. The actual number of samples needed in such a core collection, the strategic size consisting of the number of sires per breed and the number of samples per sire, is determined by the objectives of the collection. For the restoration of a population, the number of samples partly depends on the insemination success of the cryo-conserved semen , which are largely absent from Single Nucleotide Polymorphism (SNP) chips. Therefore, WGS data are expected to measure more accurately the genetic relationships in populations. Bos taurus) breeds, including the very small population of Danish Jutland cattle. They established the population structure and genetic diversity within breeds and lineages by genotyping 770 K SNP loci. They included older cryopreserved samples to determine whether the use of cryo-conserved semen is a real opportunity for the re-introduction of lost genetic diversity. They proved the genetic uniqueness of native domestic breeds and emphasized the need for diligent conservation plans, taking into account the unique lineages, in which inbreeding is balanced with carefully designed outcrossing. The use of cryo-conserved semen of founders can indeed support the preservation of traditional genetic characteristics of native domestic breeds.in vitro embryo production with oocytes from elite modern genetic females. The genomic breeding values of these seven bulls indicate that the lost lineages can be re-introduced in one generation using elite dams, resulting in a breed average genetic value for economic merit for the seven bulls. This genetic value was reached through a high genetic merit for fertility, a near-average genetic merit for fat and protein yield, and a below-average genetic merit for udder and physical conformation.Before the era of genomics, there was general consensus on how to manage genetic diversity in livestock populations. Rates of pedigree-based inbreeding and kinship (ΔF and Δf) had to be limited to <1% per generation, and pedigree-based optimal contribution selection (OCS) was the method of choice to do so. Nowadays, rates of genomic-based inbreeding and kinship are the characteristics used to manage diversity. They differ from pedigree based rates of inbreeding used in the past in selection schemes . When daFROH).Runs of homozygosity (ROHs) are frequently used to measure inbreeding. ROHs are long stretches of two homologous chromosomes within the same individual that are identical. They are homozygous for all the loci within these stretches . ROHs reWhen a lot of stakeholders take part in the selection of parents and the number of their offspring, as it is the case in nearly all species except commercial pig and poultry breeding, mean inbreeding coefficients may fluctuate from generation to generation. Populations can suffer from inbreeding effects due to bottle necks in the past, a high selection intensity for a limited number of traits or a popular sire effect. Effects of selection on inbreeding and popular sire effects can be at stake in each generation. This implies that inbreeding rates from generation to generation should be carefully controlled in populations under selection, and in particular in rare breeds with a small effective size.In conclusion, estimations of genetic relationships in small populations carrying rare alleles or carrying alleles rare in the larger breeds should be carried out based on WGS data. When the economic value of traits is changed in a breeding program, gene bank sires may be of value to realize such breeding goals. They can also be used to re-introduce parts of the genomes of lost founders in less controlled breeding programs. ROH’s measure clearly the effects of selection and genetic drift  on inbreeding at the level of the whole genome as well on parts of it. These effects are often transferred into effective population sizes to illustrate better the course of the genetic diversity in the population.Knowledge of pedigrees is the first prerequisite to starting a breeding program. But not all farmers are participants in official pedigree recording programs. Small populations of rare breeds might benefit a lot when the population size can be extended because this can prevent inbreeding. Anecdotal and phenotypic information can indicate that animals belong to a certain breed, but breeding organizations have to be sure of that before these animals can fulfill a role in their breeding program. Genomics gives the opportunity to construct a reference population of individuals whose breed of origin is recorded over several generations. Gene bank sires always have an official registered pedigree and their DNA-composition may offer an important contribution to such a reference population for their breed. It is important that the reference population comprises the total genetic diversity of the breed at stake. The SNP markers can be selected from that “complete” reference population to be able to discriminate accurately amongst the breeds.In the genomic era, the genomic prediction of breeding values is an important, but challenging opportunity for the programs of small local breeds . It offeIn small local populations, it is often impossible to create a reference population of sufficient size. Then data from animals of other breeds has to be added before the genomic prediction can start . But, anMe) was used to estimate the relatedness between individuals from different breeds. It can also be used to prioritize breeds for conservation. The Me is also used an important parameter for the accuracy of genomic prediction. The Me can be estimated both within a population and between two populations or breeds. It is expressed as the reciprocal of the variance of genomic relationships. The six red Dutch rare breeds indicated a high variability of Me. Between breeds, the Me ranges from ∼3,500 to ∼17,400. It indicates the levels of relatedness between the breeds were different. Three clusters of breeds were found: 1) the MRY, Deep Red, and Improved Red; 2) the Dutch Friesian and Dutch Belted; and 3) the Groningen White Headed. The relatedness between breeds within the first two clusters is high. However, across-breed genomic prediction is still hampered due to the low number of genotyped individuals. An increase in this number is very effective. It appeared that for each of the six breeds, 11–133 reference animals from other breeds are needed to achieve the same accuracy of genomic prediction as an additional individual from the same breed.Sometimes a gene bank contains animals with a trait that is not present in a living population, but that trait can have a high value for that population. Then, introgression of the desired allele(s) responsible for that trait in the donor animals into the recipient population can be considered . Here, tGenomic introgression (GI) is a method to introgress a trait from a cryo-conserved donor population into a mainstream recipient population in the case of a complex trait. The genetic architecture of the trait is unidentified, for example, the architecture is complex and many genes determine the trait . ApplyinHistorically, introgression has been used to upgrade and to improve an important trait in breeds. The FecB fecundity QTL is first found in Australian Booroola sheep. This QTL, improving the litter size significantly, has been introgressed into a large number of other breeds .Polledness in cattle has received increasing interest in recent years for welfare reasons. It is based on two different dominant alleles that are situated very close together in the cattle genome . In the Many breeds have experienced migration or introgression in the past. Herdbooks often want or have to register only purebred animals. By selecting animals that minimize genomic co-ancestry between current animals and animals carrying the introgressed parts of the genome, the original genetic background of a breed can be recovered. With genomic information, this expires more efficiently than with pedigree information .In conclusion, gene bank sires have well recorded pedigrees and are used to construct reference populations to test breed purity. In combination with their breeding values for performance traits, they can strengthen the relationships in populations needed in genomic selection schemes. Methods for the introgression of traits from gene bank sires into living populations are well developed and effective. Optimum contribution methodology is available and is used to recover the native genetic background of breeds in which crossbreeding took place in the past.Gene banks can be considered as a sustainable and reliable archive of genetic diversity. Genomics gives three relevant stakeholders important tools to improve their efforts to conserve and use genetic diversity: 1) Gene bank management has the opportunity to use genomics to prioritize breeds and lines and animals within breeds for conservation and to optimize the collections. They may facilitate the creation of reference populations to test animals for breed purity and for genomic selection. At request, they can provide straws for genomic research and for the activities of rare breed associations and commercial companies. 2) rare breed associations have the opportunity to use genomics to add non-recorded animals to their populations, to re-use sires whose genomes are no longer present in the populations, to monitor the relationship and inbreeding over generations and to remove parts of the genome of other introgressed breeds, and 3) commercial breeding companies have the opportunity to use the genotypic and phenotypic data of gene bank sires into their reference populations for genomic selection, may re-use sires when afterwards it reveals that parts of their genome are interesting and no longer present in their current breeding sires, can monitor kinship and inbreeding over generations and can consider the introgression of interesting genes in gene bank sires not present in their current breeding animals.


Metanephrops challengeri, which ranges across a vast area of seafloor around New Zealand. This species has limited dispersal capabilities, including larvae with weak swimming abilities and short pelagic duration, while the reptant juvenile/adult stages of the lifecycle are obligate burrow dwellers with limited home ranges. Ninety-one individuals, collected from five scampi fishery management areas around New Zealand, were genotyped using GBS. Using 983 haplotypic genomic loci, three genetically distinct groups were identified: eastern, southern and western. These groups showed significant genetic differentiation with clear source-sink dynamics. The direction of gene flow inferred from the genomic data largely reflected the hydrodynamic particle modelling of ocean current flow around New Zealand. The modelled dispersal during pelagic larval phase highlights the strong connectivity among eastern sampling locations and explains the low genetic differentiation detected among these sampled areas. Our results highlight the value of using a transdisciplinary approach in the inference of connectivity among populations for informing conservation and fishery management.The emergence of high resolution population genetic techniques, such as genotyping-by-sequencing (GBS), in combination with recent advances in particle modelling of larval dispersal in marine organisms, can deliver powerful new insights to support fisheries conservation and management. In this study, we used this combination to investigate the population connectivity of a commercial deep sea lobster species, the New Zealand scampi,  However, our knowledge of the biology of many of these deep sea species is poor due to the significant challenges in undertaking research in the deep sea environment, however, experience indicates these populations are vulnerable to overfishing5. Thus, it is critical to gain insight into these patterns of population connectivity in these targeted species to adopt best practice fisheries management approaches that nurture stock and the environment sustainably in the deep sea. Furthermore, deep sea species frequently have distribution ranges that cover vast areas, and are frequently patchily distributed throughout those areas. This creates potential challenges for maintaining genetic connectivity of the population, especially where the dispersal capacity of the species may be limited. In such instances, broad scale hydrodynamic processes, such as major current systems, can be expected to play a vital role in maintaining connectivity within the broad scale of the population range6.Deep sea species have increasingly become commercial targets due to improving fishing technology and an increasing global demand for seafood that is unfulfilled by declining catches from shallower coastal waters7. Characterising patterns of genetic population structure in part of a species range provides insight into levels and directions of gene flow that could be extrapolated across its distribution, and be useful for fisheries management, such as for designating fishery management areas (FMAs). It can also provide essential information for understanding the drivers of neutral and adaptive genetic diversity within exploited populations15.Patterns of genetic diversity and connectivity are not well understood in deep sea invertebrate species, in stark contrast to the comparative large body of knowledge on their shallow-water and terrestrial counterparts17. Thus, determining source-sink dynamics, including larval dispersal and gene flow patterns, has direct ecological implications for the persistence and structure of populations of deep sea animals that are commercially harvested6. Here we employ the definition that a source is a population in which the net export of individuals or genetic material is greater than the net import, while the reverse is a sink6. A common feature of sink populations, other than their genetic landscape being shaped to a degree by receiving gene flow via larval arrivals from multiple sources, is a tendency for increased levels of genetic diversity18. However, other demographic factors may also contribute to maintaining genetic diversity in a population, such as large local population size19. Thus, it is also possible for large source populations to maintain relatively high levels of genetic diversity, despite elevated levels of self-recruitment.In general, the loss of genetic diversity has the potential to reduce productivity, population persistence and adaptability, posing the risk of increasing the vulnerability of species already under commercial fishing pressure23. In particular, several studies have highlighted the existence of asymmetric patterns of connectivity, revealing source-sink relationships largely determined by currents25. Such relationships are most likely to be observed in species with limited adult and larval dispersal. Arguably, deep sea species with these traits offer an excellent opportunity for evaluating the efficacy of combining population genomics with the modelling of physical dispersal processes that could be expected to be the dominant process for structuring the population, having had less long term anthropogenic disturbance.The results of many marine population genetic studies suggest that larval dispersal, prevailing currents and geographic distance are all important factors that have been implicated in determining patterns of connectivityMetanephrops challengeri), hereafter referred to as scampi, is a deep sea endemic lobster species. Scampi are fished by commercial bottom trawling over extensive areas of the continental shelf of New Zealand below 100 m depth, including around offshore islands26. The fishery is divided into 10 spatial management areas  based only on arbitrary geographic boundaries  of typically less than 11 days, and their very poor swimming abilities throughout the larval stages30. Despite intensive commercial fishing of this species, relatively little else is known about their biology (including their growth) and stock structure. Juveniles and adults of scampi occupy burrows in soft benthic sediment and emerge to scavenge for food in the immediate vicinity of their burrows33. Therefore, it is highly unlikely that the movement of the reptant phase in the lifecycle of scampi is the primary contributor to gene flow within the population range. Given these biological constraints on the extent of gene flow, we predict that there is a relatively high degree of genetic structure within the New Zealand-wide distribution of this species.The New Zealand scampi . Here we examine the population connectivity of the New Zealand scampi by combining genotyping-by-sequencing (GBS) analysis and particle modelling methods to better understand this deep sea invertebrate’s genetic connectivity. Although the known population range of this species is vast, the distribution density is concentrated to certain known areas coinciding with the most heavily fished areas, which are geographically dispersed. We expect that the limited larval dispersal abilities of this species could have constrained connectivity among fishery management areas, with prevailing ocean currents driving the development of different source-sink relationships within the broadly distributed scampi population. The connectivity estimates from the genetic data  will be compared to connectivity estimates from the modelling data . This will allow us to infer connectivity among these dispersed locations and potential source-sink dynamics. We aim to improve our understanding of the resilience of the deep sea scampi population to commercial fishing and contribute towards future management of this fishery.A transdisciplinary method of understanding the patterns of population connectivity among stocks is to combine population genetic analyses, using next-generation DNA sequencing methods, with estimates of animal dispersal using physical modelling of particlesA total of 289,502,412 raw reads were analysed for quality control using FASTQC  reads remained after having been aligned into stacks Table . The CSTThe final haplotypic dataset had a total of 983 loci containing a total of 2,115 SNPs, across 86 individuals that passed QC. The average proportion of missing data per individual and per SNP site was 0.03 (SD = 0.04) and 0.03 (SD = 0.04), respectively Tables  and S4. Only four outliers were detected and only one was identified by all analyses Table . No outlo) and expected  heterozygosity, allelic richness (Ar) and nucleotide diversity (π) and the lowest inbreeding coefficient (FIS)  Table . SCI_7 hST value of 0.036 . Pairwise comparisons of SCIs based on FST values showed that the five SCIs were grouped into three genetic clusters .The AMOVA results complemented the pairwise results, as significant differences were found among all sample partitions analysed except those within SCI_E Table . More thThe Discriminant Analysis of Principal Components (DAPC) provided visualization of the three genetic clusters; SCI_E was separated from SCI_6A and SCI_7 along LD1 and the latter two SCIs were separated along LD2 Fig. . This clThe divMigrate results provide estimates of the relative migration rate, and where the direction in which the relative migration rate is larger indicates which SCI is likely to behave as a sink and the reverse for the source Table . SCI_6A 45 indicated a predominant directional flow of particles between locations following major current systems  Fig.  and S10.47; scallops49; and lobsters50), which are typically influenced by more physically complex coastline and associated hydrodynamic processes compared with generally less complex bathymetry of deep water on continental shelf margins. In contrast, in deep sea species, which frequently have enormous distribution ranges, larval dispersal relies on broad scale hydrographic processes. Although more limited in the literature in comparison with coastal studies, particle modelling has proved to be particularly effective in the investigation of deep sea species population connectivity when combined with population genetics , and as undertaken in this current study.Here we present one of the few cases that have integrated population genomics and particle modelling to deliver useful spatial and temporal insights into population connectivity for a deep sea species across a very large distribution range. This complementary approach illustrates how oceanographic drivers have the potential to influence genetic exchange among populations, including those separated by long distances. Many particle modelling studies have focused on the larval dispersal of shallow-water, coastal marine invertebrates  consistently formed a single, relatively homogenous genetic cluster (SCI_E), distinct from both SCI_6A and SCI_7. This combined genetics/hydrodynamics approach identified patterns of gene flow that suggest a likely mechanism for persistence of these local populations. Specifically, modelling and genetic evidence indicates directional migration among stocks, with SCI_6A identified as a source population and SCI_3 identified as the main sink, providing important information to inform fisheries management.53. Significant genetic differences among eastern SCIs  were found when utilizing a single mitochondrial marker54, but this differentiation was not apparent in this current study. The difference could potentially be due to the lower effective population size in mitochondrial DNA  versus nuclear DNA , or due to the genome-wide analyses providing a more accurate evaluation of the stock structure, compared to the stochastically-variable results from a single mitochondrial gene55. It is also feasible that the difference is due to lower dispersal by female scampi, but this is unlikely, as there is no evidence to suggest that male scampi move significantly greater distances as adults33. Furthermore, for marine species which have large effective population sizes and weak population structure, differentiation due to genetic drift is slow to accumulate and can be offset by relatively small amounts of migration56.We found significant genetic differentiation between the SCIs which fall into three clusters . A similar deduction of widespread suitable habitat was made for the Caribbean king crab, despite residing in shallow waters, its biological characteristics are similar to scampi in regards to having a low dispersal potential, no known long distance migrations and a large geographic range58. Sink stocks typically rely on recruitment from source stocks  that have sufficiently large effective population sizes and self-recruitment to also maintain themselves. Overexploitation of source stocks can have implications for the viability of the wider population, as it can decrease not just local recruitment but the recruitment, productivity and persistence of associated sink stocks60.Both genomic and hydrodynamic modelling data indicate a predominantly west to east directional connectivity in the scampi population. The direction of connectivity suggests that some stocks act as net sources or sinks. Both datasets strongly indicate that SCI_6A acts as a net exporter of larvae (source) and that SCI_3 acts as a net recipient of larvae (sink) with the extent of their respective larval supply likely to be shaped by the hydrodynamic regime, as well as the extent of intermediate scampi population involved in a stepping stone transfer process Figs. . Further21 and sponge51), hydrodynamic processes play an important role in population connectivity and particular features  shape the connectivity by directing larvae dispersal and/or increasing or decreasing the dispersal range. The STF appears to permit only a unidirectional gene flow from SCI_6A to SCI_3, while greatly restricting particle movement from SCI_3 to SCI_6A. These results are concordant with previous studies in the species using a single COI marker that suggested this marked genetic differentiation between scampi from the Auckland Islands (SCI_6A) and those east of the main islands of New Zealand is likely due to the STF54. Our results also suggest that the TF also likely plays a role in facilitating gene flow from the west to the east  estimated asymmetric relative rates of migration based on patterns of allelic differentiation among SCIs. The direction of migration is determined by measuring the extent of shared alleles and can become less definitive when the majority of alleles are shared among populations. Hydrodynamic modelling of the movements of particles  provides an independent measure of the likely extent and direction of connectivity among locations within the population range. The particle modelling indicated high levels of exchange between SCI_2 and SCI_3, although favouring particle movement from SCI_2 to SCI_3 versus SCI_3 to SCI_2, which corresponds with the measures provided by the genetic migration method Fig. .The WE a54, or more likely that the two areas are linked through a contiguous distribution of this species that permits stepping stone gene connectivity when supported by hydrodynamic dispersal of larvae ; allowing a species to thrive rather than survive. These factors help to drive source-sink dynamics, but habitat suitability can change via natural or anthropogenic causes68. These changes can be to the detriment (trawling resulting in habitat fragmentation) or advantage  of the population, as changing the habitat size and quality directly affects population stability and persistence69. In regards to species that have a short dispersal period, they would settle ‘near’ to their release, but this limits them to the quality of the habitat in the immediate area and typically strengthens source-sink dynamics69.Habitat suitability can vary due to spatial/environmental heterogeneity, with some habitats being more suitable for particular species than others68. Furthermore, dismissing or failing to identify source-sink dynamics can lead to flawed decision making that can ultimately affect species abundance and distribution70, as identifying connectivity patterns will help determine which local stocks rely on larval retention and/or immigration. This connectivity information allows for each stock’s contribution to the overall population to be characterised, as well as the potential for the population to persist, and can help with potential decision making regarding setting/reassessing stock quota, restocking or replenishment of depleted populations71.Source-sink dynamics are key ecological features with important implications for fisheries stock recruitment/management as connectivity influences both sustainability and yield35. Work on other lobster species has identified that larval supply is dependent on surrounding areas and thus fishers depend on the protection of these sources to protect the stocks72. Furthermore, these results suggested that moderate changes to lobster management areas needed to be made whereby sinks reliant on the same sources are grouped72. Finfish studies have recommended similar management incorporating source-sink dynamics and have shown that a decrease in the exploitation in source populations  has favourable outcomes on adult survival and recruitment in sink populations73.In this study, scampi movement and exchange patterns were used to indicate source-sink dynamics. The potential for fishing to impact on the population connectivity inferred here should be considered in the management of this fishery, along with the variable population vulnerability . When allocating catch quota to SCIs, it is also important to recognise the distinct genetic sources and safeguard them. Source-sink research on scallop populations have argued that when tailoring catch limits by considering contribution to recruitment, there is an added benefit that higher yields could be made available for fishers at a lower risk of over exploitation26. SCI_6A appears to rely on local recruitment to a much greater extent, and behaves as a source population based on inferences of directional gene flow. Furthermore, it has been estimated that individuals from SCI_6A have higher levels of emergence, as they appear to spend less time in their burrows, increasing their catchability by benthic trawl nets74. Both these features potentially make this stock more vulnerable to over-exploitation and more important to ensure its persistence. In contrast, SCI_3, being the main sink, is more dependent on immigration from other stocks. Therefore, differential management should be considered for sources and sinks to avoid major consequences to stocks, as strong sources create strong sinks69.The exploitation of New Zealand scampi sources should be considered in the context of their dependence on high internal recruitment and the importance of providing immigrants that contribute to sustaining the wider population. For example, the two SCIs of greatest fisheries importance, SCI_6A and SCI_3, have similar commercial catch allocations . SCI_6A is 306 t and SCI_3 is 408 t; these two SCIs making up 54% of all TACC allowed for scampi, but have contrasting source-sink features78. These changes can add a layer of difficulty to fisheries management over time, and monitoring will be needed to ensure management accommodates potential alterations in larval dispersal. Modelling has predicted the complexity of ocean circulation changes in the Tasman Sea and it is projected that, by the end of the century, the southern branch of the East Australian Current (EAC) will intensify and that the TF will weaken78. This change may reduce the EAC flow, potentially reducing long-term gene flow to the eastern SCIs. Global climate change has a very real potential of disrupting larval dispersal pathways and impeding population connectivity maintained through ocean currents6. This is not only limited to the changing of ocean currents, but the rise in sea temperature is also of concern among other changes . Larval duration may be reduced, as an increase in larval development rate due to an increase in water temperature, may further alter dispersal capabilities and population connectivity83.Ocean circulation strongly influences genetic connectivity and a shift in current direction and intensity can ultimately change stock connectivity. There is evidence for changes in velocity of some ocean currents due to anthropogenic climate changeThis research has identified areas where more information is required to better understand the scampi population interactions in relation to fishery implications. Greater confidence in the currently inferred patterns could be achieved by genetically analysing a greater number of individuals at each site, improving the accuracy and statistical power of the estimated genetic parameters. In addition, the degree and extent of stepping stone gene flow could be tested through inclusion of additional, intermediate sampling locations which would provide better inferences across the species’ range. These measures will help add detail to the broad population structure that can aid in fisheries conservation and management decisions.84.This study on the population structure of New Zealand scampi successfully used both genetic data and hydrodynamic particle modelling to independently quantify principal patterns of dispersal and connectivity. The genetic data revealed significant stock differentiation among SCI_E, SCI_6A and SCI_7. Both genetics and hydrodynamic modelling methods reveal a strong predominant pattern of directional dispersal around New Zealand, following the major currents, and showing an overall eastward dispersal of larvae. Hydrodynamic modelling identified SCI_6A as a potential source stock, and SCI_3 was identified as the main sink. Genomic data was less definitive as to the directions of dispersal, but still supported the major patterns identified by the modelling. This combined approach is transferable to other exploited and ecologically important species that rely heavily on hydrodynamic drivers for dispersal and genetic connectivity, and the knowledge gained can assist in better understanding of population connectivity to make more appropriate and effective conservation and fisheries management decisions e.g.,A detailed version of the methods has been included in the supplementary material to ensure reproducibility, which includes the specifics of which functions were used from the respective packages and any parameter changes from the default values.Scampi were collected from five scampi SCIs within New Zealand’s exclusive economic zone, namely from the east: SCI_1, SCI_2 and SCI_3, south: SCI_6A and west: SCI_7, using bottom trawling methods between April 2017 and April 2018 Fig. . The sca85.A special permit (#549) for scampi collection was provided by New Zealand’s Ministry for Primary Industries. The specimens for this study were collected in accordance with approvals under New Zealand’s Animal Welfare Act 1991 approved by the Animal Ethics Committee of the Nelson—Marlborough Institute of Technology (AEC2014-CAW-02). Scampi are taonga (treasured) species for Māori and as such this research is part of a larger project that has a core focus on transdisciplinary research engaging Mātauranga Māori and western scienceDNA extraction was undertaken on scampi tail muscle tissue, using the Gentra Puregene Tissue Kit  following the manufacturer’s instructions. Based on quality  and quantity  assessments from a total of 171 individuals, 91 individuals were selected for GBS. Of these, 18 individual scampi were selected from each of SCI_1, SCI_2 and SCI_3, 17 individuals from SCI_6A, and 20 individuals from SCI_7. Additional DNA extractions from two individual scampi from SCI_7 were used as quality control (QC) replicates. Further preparation and sequencing of the samples for GBS was conducted by AgResearch Ltd . Sequencing was undertaken on an Illumina HiSeq 2500  1 × 101 cycle utilizing v4 chemistry.86 was used to analyse the quality of the raw sequencing data. PROCESS RADTAGS, a module in STACKS v2.487, was used to demultiplex the data and undertake quality control by removing reads with (1) low quality (< 90% probability of being correct), (2) no barcodes or restriction enzyme cut-sites, (3) < 75 bp (reads truncated if > 75 bp), (4) uncalled base and (5) low quality scores. Reads were kept only when a single mismatch occurred in barcodes and restriction enzyme sites. The reads retained were passed to further STACKS modules in succession for de novo genotyping. To ensure only recurring loci were included in subsequent analyses, the loci that were present in at least 80% of individuals in each SCI and present in all SCIs were selected. The catalogue and genotyping of samples were undertaken on the same run.FASTQC v0.11.788 using the STACKS SNP dataset to assess dataset quality. The data were examined for missing data for both individuals and SNP sites, allele counts and frequencies, and sequencing depth per SNP site. The loci underwent further QC in R v4.1.089. The SD and mean locus sequencing depth were calculated, and loci that were two SDs above the mean were excluded from the dataset to remove potential repetitive sequence regions. Remaining loci that had more than five SNPs per locus were also excluded as a precaution against paralogs. These excluded loci were filtered from the STACKS haplotype dataset in order to remove potential false genotypes due to DNA sequencing errors91. Please see supplementary methods for details regarding Hardy–Weinberg equilibrium departure filtering and outlier filtering as to why it was not incorporated.The STACKS haplotypic dataset was used for data filtering and analyses. The QC replicates were assessed and the replicate that was of lower quality was excluded from further analyses. Individual and SNP statistics were produced with VCFTOOLS v0.1.1492. The observed (Ho) and expected  heterozygosity for each SCI was calculated, as well as the inbreeding coefficient (FIS)93. Allelic richness (Ar) was also measured, which uses a rarefied measure of the number of alleles at each locus and in each SCI. The mean nucleotide diversity (π) was estimated per SCI using the POPULATIONS module (STACKS) output data for only those loci that passed QC . The initial statistical results indicated a clustering of SCI_1, SCI_2, and SCI_3 (SCI_E) and so this strata was included in the statistical analyses.Summary statistics were calculated per SCI per locus using HIERFSTAT v0.04-22ST94 values amongst SCIs were calculated  by running a total of 10,000 permutations. An analysis of molecular variance  was undertaken to investigate the portion of variance within and among SCIs to further explore population structure. Significance was assessed using a randomization test based on 100,000 repetitions.Differentiation indices and multivariate statistical approaches were used to investigate genetic differentiation and population structure. Firstly, pairwise and overall F99). This method also enabled the assignment of individuals to clusters and the contribution of individual alleles to population structuring, thereby characterizing population subdivision. Individual assignment utilized the true sampling locations as clusters (SCI priors given). A PCA was also performed to ensure the de novo DAPC population structure reflected an accurate representation of the overall population structure, despite migration rates. It has been found that when migration rates are high and groups are described de novo using a DAPC there may be substantial inaccuracy100.The population structure was analysed by applying the multivariate statistical method of a DAPC, which identifies and describes clusters of genetically related individuals . This model is based on, and calibrated against, more than 25 years of detailed hydrodynamic hindcast data. The model uses high resolution bathymetry available for the shelf waters for the New Zealand region  in combination with higher resolution locally-sourced data) and resolves 50 vertical sigma layers in the water column. The particle tracking hydrodynamic model was used to simulate the dispersal of scampi larvae during their pelagic phase from the sampling locations and the potential inter-generational connectivity trends among the scampi sampling locations, as a proxy for genetic exchange, and assumes the availability of viable intermediate scampi habitat where arriving genetic material can establish and reproduce to further disperse the genetic material. Thus, the particle dispersal model was intentionally run over an extended period (10 years) to account for inter-annual variability in hydrodynamic processes so as to capture dispersal patterns in an analogous fashion to that of genetic connectivity estimates, providing an overall averaged characteristics of circulation20. The model domain covers New Zealand’s Exclusive Economic Zone  and places the open boundaries far enough to fully encompass the TF to the north and the STF to the south whilst also maintaining practicalities of the computational cost for the provision of high resolution models of this scale. Due to the poor swimming abilities of scampi throughout their larval development104 they were treated as passive particles in the model, moved by the 3-dimensional hydrodynamic forces generated from the model and a diffusion coefficient.Modelling of the movement of particles  was undertaken with the open source particle tracking framework OPENDRIFT v1.0106. One hundred representative particles were released on the surface from evenly spaced locations within each sampled area  to extract the data pertaining to that of the pelagic larval phase per season , (2) to track the movement of the particles which could then provide details of the approximate number of generations needed for connectivity among dispersed locations, and (3) enumerating connectivity by calculating the total number of particles arriving at each SCI sample area from each source sample area over the modelling period . Our use of hydrodynamic models for simulating larval dispersal draws on well-established research for a wide range of marine species e.g.,112. However, our approach to the application of the modelling differs in that we are interested in multigenerational connectivity which is consistent with the biological process for migration of genes within a wide ranging population, and it ensured that inter-annual variability in hydrodynamic processes were incorporated into the modelled outcomes, whilst also ensuring there was enough time for particle dispersal to assess possible long distance multigenerational dispersal.We selected particle tracking parameters based on previous hydrodynamic modelling of invertebrate larval dispersal in New ZealandSupplementary Information 1.Supplementary Information 2.


The Obstetric Quality of Recovery score (ObsQoR-10) is a questionnaire used to assess recovery after cesarean delivery. However, the original ObsQoR-10 is in English and was mainly validated in the Western population. We therefore evaluated the reliability, validity, and responsiveness of the ObsQoR-10-Thai in patients undergoing elective cesarean delivery.The original ObsQoR-10 was translated into Thai, and psychometric validation was performed to evaluate the quality of post-cesarean recovery. The ObsQoR-10-Thai, activities of daily living checklist, and 100-mm visual analog scale of global health (VAS-GH) questionnaires were administered to the study participants before and 24 and 48-h postpartum. Validity, reliability, responsiveness, and feasibility of the ObsQoR-10-Thai were assessed.P < 0.001). The convergent validity between the ObsQoR-10-Thai and VAS-GH was good . The ObsQoR-10-Thai displayed good internal consistency , split-half reliability (0.92), and test–retest reliability . The median time to complete the questionnaire was 2  min.We included 110 patients undergoing elective cesarean delivery. The mean ObsQoR-10-Thai score at baseline and 24 and 48-h postpartum was 83.35 ± 11.15, 56.75 ± 11.6, and 70.96 ± 13.65, respectively. The ObsQoR-10-Thai score differed significantly between the two groups divided based on the VAS-GH (≥ 70 vs. < 70): 75.58 ± 13.81 and 52.56 ± 10.61, respectively .The online version contains supplementary material available at 10.1186/s12871-023-02010-6. Cesarean delivery is one of the most commonly performed surgeries in the world, with nearly 18.5 million cesarean deliveries performed annually , QoR-15 The obstetric quality of recovery score (ObsQoR-11) is an 11-item questionnaire that covers four domains of recovery outcomes: physical comfort, emotional state, physical independence and care of the neonate, and pain . HoweverThis prospective observational study was approved by the ethics committee of Ramathibodi Hospital, Mahidol University, Bangkok, Thailand (ID MURA2020/1939), and was registered on the Thai Clinical Trials Registry, identifier TCTR20210204001, registered on 04/02/2021 (Prospectively registration).All participants provided written informed consent before their inclusion in the study. The study was conducted in accordance with the Declaration of Helsinki and followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines . We inclAfter receiving permission from the authors who created the original ObsQoR-10 to translate and modify, a three-step process to translate the original version into Thai was conducted based on recommendations  and prevAfter obtaining the ObsQoR-10-Thai, the pilot test was conducted on 20 randomly selected patients, who were not included in the study. The questionnaire did not need any further modifications after the pilot test. With this result, the ObsQoR-10-Thai was finalized.The day before surgery, patients were informed about the study and written consents for participation were obtained. Then the patients were asked to complete the ObsQoR-10-Thai, the activities of daily living (ADL) checklist, and a 100-mm visual analog scale of global health (VAS-GH) to evaluate their baseline status. The patients filled the three questionnaires again at 24 and 48-h postpartum.The ADL checklist is a self-evaluation of independence that consists of six basic daily activities: bathing, dressing, toileting, transferring, continence, and feeding. The minimum and maximum ADL scores were 6 and 12, respectively, with higher scores indicating higher levels of independence in performing ADL. The VAS-GH is a global assessment of global health, where 0 and 100 indicate the worst and best health state, respectively. We also recorded the details of patient characteristics, including age, underlying disease, parity, education, type and duration of surgery, and length of hospital stay.The sample size was calculated based on the previous studies , 15, assP < 0.05 was set as significant.Data are presented as mean ± SD, median (interquartile range: IQR), number (%), or 95% CI. The Pearson and Spearman correlation analyses was used for normally and nonnormally distributed variables, respectively, with a correlation coefficient of 0.4–0.7 indicating a moderate correlation and 0.7–1.0 indicating a strong correlation). Reliability was assessed using Cronbach’s alpha (α) and intraclass correlation coefficients. Changes in the ObsQoR-10-Thai score among the three time points were compared using a multilevel mixed-effects linear regression and presented as mean difference and 95% confidence interval. All statistical analyses were performed using STATA version 17.0 . The validity, reliability, and clinical feasibility of the ObsQoR-10-Thai were compared with the ADL questionnaire and VAS-GH.Discriminant validity was measured by comparing the scores of the ObsQoR-10-Thai and the VAS-GH. Good and poor postoperative recovery were defined as VAS-GH of ≥ 70 and < 70 mm, respectively.Convergent validity was measured by analyzing the correlation between the ObsQoR-10-Thai questionnaire with VAS-GH and the ADL questionnaire.Construct validity indicated the associations between the ObsQoR-10-Thai and ASA physical status, operative time, blood loss, perioperative complications, and length of hospital stay.Reliability testing: Reliability was assessed using the internal consistency  of the ObsQoR-10-Thai at the three time points. The correlation between two segments of the ObsQoR-10-Thai was also analyzed. Test–retest reliability was evaluated by repeating the ObsQoR-10-Thai in a subset of 30 patients within 30–60 min. The correlation between measurements was then assessed. Floor and ceiling effects of the ObsQoR-10-Thai were assessed by calculating the proportion respondents that achieved the highest (100) or lowest (0) possible scores (acceptable: < 15%).Acceptability and feasibility were measured by using the time taken for patients to complete the questionnaire (measured and recorded by the investigator), recruitment, and successful completion rate.A total of 110 patients were recruited, and there were no refusals. All patients completed the ObsQoR-10-Thai, ADL, and VAS-GH questionnaires. There were no incomplete or missing data, and no patients were excluded.The mean patient age was 33.6 ± 4.4 years, and 108 patients (98%) were categorized as ASA PS class 2. Fifty-three patients (48%) had a history of previous cesarean delivery. One hundred and five patients (95%) underwent cesarean section under spinal anesthesia. The median length of hospital stay was 4 days  days. The baseline characteristics are presented in Table The mean ObsQoR-10-Thai, ADL, and VAS-GH scores are summarized in Fig. The 24-h median ObsQoR-10-Thai score was 57 , which was the lowest of the three time points. The items performing best to worst over the 48-h postpartum study period are listed in Table P < 0.001).Discrimination validity was assessed by comparing the ObsQoR-10-Thai score in women who had a good (VAS-GH ≥ 70) or poor (VAS-GH < 70) recovery status. The mean ObsQoR-10-Thai scores at 24 h were 75.58 (± 13.81) for good health state vs. 52.56 (± 10.61) for poor health state , 24-h  and 48-h postpartum . However, the ADL was correlated with ObsQoR-10 only at predelivery  and 48-h postpartum   Table .Table 3Cr = 0.14, P = 0.14), operative time , blood loss , perioperative complications , and length of hospital stay .The ObsQoR-10-Thai did not correlate with ASA physical status  min.Our study demonstrates that the ObsQoR-10-Thai has good validity and is consistent with the original ObsQoR-10. The ObsQoR-10-Thai score did not correlate with ASA physical status, perioperative complications, operative time, blood loss, or length of hospital stay. However, the internal consistency measured by Cronbach α, and split-half reliability was excellent, with a high degree of clinical feasibility and responsiveness. Our findings indicate that the ObsQoR-10-Thai provides a valid, reliable, and responsive global assessment of health state after elective cesarean delivery.r = 0.53, P < 0.001) [r = 0.57, 0.51, and 0.45 in Brazilian [r = –0.59 and –0.51 in Brazilian (13) and US (8) populations, respectively). The ObsQoR-10-Thai also had a moderate correlation with the global VAS score (0.45–0.6), suggesting that the ObsQoR-10-Thai is useful for measuring the quality of recovery after elective cesarean delivery in our setting. Compared with ADL, the ObsQoR-10-Thai had higher correlation coefficients at baseline and 24 and 48 h postpartum, which emphasized the ability to measure the quality of recovery better than the ADL score. The ObsQoR-10-Thai also appears to discriminate well between “good” or “poor” recovery, as defined by the global VAS scores of ≥ 70 and < 70, respectively. However, the reliability of the ObsQoR-10-Thai in our setting was at a moderate level, warranting further interdimensional analysis.The ObsQoR-11 was first developed and evaluated for elective cesarean delivery at a single UK university hospital, with a moderate correlation between ObsQoR-11 and global health status ,In our cohort, the median 24-h ObsQoR-10-Thai was 57, which was lower than that reported in the US (median: 77)  and BrazOur data indicated that the ObsQoR-10-Thai did not correlate with maternal age, ASA physical status, operative time, estimated blood loss, perioperative complications, or length of hospital stay. This is consistent with previous studies , 10, 13.Recovery after delivery is a complex process, which depends on patient, surgical, and anesthetic characteristics; moreover, it means different things to different stakeholders . A patieThe strengths of our study are consistent measurement of recovery scores (ObsQoR-10-Thai and ADL) over 48 h postpartum. However, this study has some limitations. First, our main population had low ASA physical status and a low incidence of adverse outcomes . Therefore, ObsQoR-10-Thai failed to correlate with ASA physical status, estimate blood loss, operative time, perioperative complications, and length of hospital stay. Second, our study was performed in a single-center tertiary university hospital. Third, we included only patients undergoing elective cesarean delivery. Future studies should evaluate the ObsQoR-10-Thai in other hospital settings and delivery modes to determine its generalizability. Fourth, the timing of follow-up was not standardized and may have coincided with variable timings depending on when the analgesia was given before or after attempted mobilization. Finally, we did not assess the neonatal health state, although the ability to nurse or hold the baby is included in the ObsQoR-10-Thai.The ObsQoR-10-Thai was found to be a valid, reliable, and clinically feasible patient-reported outcome measurement, which can be administered to Thai-speaking women undergoing elective cesarean delivery. Despite some limitations in construct validity, the ObsQoR-10-Thai performed had good convergent and discriminant validity and was found to be feasible and reliable. Further studies should evaluate the ObsQoR-10-Thai in an emergency setting or vaginal delivery.Additional file 1. Obstetric Quality of Recovery-10 (ObsQoR-10) Questionnaire.Additional file 2: Supplementary2. Description of the perioperative protocol for elective caesarean deliveries.