AR, HE, PAR; p < 0.05) with a fourfold reduction in effective population size (Ne = 25.7 vs 103.8). There is also strong evidence of genetic differentiation between the island and mainland populations. These results are in accordance with genetic theory for small, isolated, island populations, and most likely the result of low initial diversity and founder effects such as bottlenecks leading to decreased diversity and drift. As the first study to incorporate a large sample set of K’gari dingoes, this provides invaluable baseline data for future research, which should incorporate genetic and demographic monitoring to ensure long-term persistence. Given that human-associated activities will continue to result in dingo mortality, it is critical that genetic factors are considered in conservation management decisions to avoid deleterious consequences for this iconic dingo population.The dingo population on world heritage-listed K’gari-Fraser Island (K’gari) is amongst the most well-known in Australia. However, an absence of population genetic data limits capacity for informed conservation management. We used 9 microsatellite loci to compare the levels of genetic diversity and genetic structure of 175 K’gari dingo tissue samples with 264 samples from adjacent mainland regions. Our results demonstrated that the K'gari population has significantly lower genetic diversity than mainland dingoes ( Canis dingo2) is a highly adaptable canid synonymous with the Australian landscape, and occurs in all habitat types, including subtropical areas, alpine regions and across arid inland Australia3. The taxonomy of the dingo remains under debate, and historical nomenclature has included C. familiaris dingo Blumenbach, 1780; C. antarticus Kerr, 1792; and C. dingo Meyer, 17934. Contemporary taxonomic arguments exist for both C. dingo 5 and C. familiaris 6, and although considerable debate also remains regarding the timing of their arrival to Australia, we adopt C. dingo in this paper due to the unique evolutionary history of the dingo10. Archeological evidence of the dingo’s presence in Australia only extends to 3500 years11 whilst genetic evidence ranges from 500012 to 18,000 years ago13. The latter figure is considered unlikely, given their historical and ongoing absence from Tasmania, which has been geographically isolated from mainland Australia for ~ 12,000 years15.The dingo , New South Wales (NSW) and Victoria, they are also declared pests in all Australian states and territories. In Queensland, they are protected under the Nature Conservation Act 1992 in conservation areas such as national parks but listed as a declared pest under the Rural Lands Protection Act 1985, with an impetus for landowners to cull them under the Biosecurity Act 201517. This contradictory legal status results in the dingo being protected in some areas , but subject to lethal control elsewhere18. Public opinion is similarly polarized, and contested views of dingoes mean that they are both revered and persecuted, and public interest has inevitably led to dingo management becoming politicised and highly emotive20.While the initial geographic radiation of dingoes remains contested, various authors report introduction via a single, small founder event from SE Asia20. The value of dingoes also extends to ecosystem health, with a growing body of evidence confirming their positive influence on native biodiversity and ecosystem functionality25. As a highly interactive, top-order predator, dingoes have a range of direct and indirect effects on the ecosystems in which they inhabit, and many studies report that their removal from ecosystems may have negative consequences over multiple trophic levels28. Although currently unlisted, the dingo was classified as Vulnerable by the IUCN Red List from 2004 to 2018 and the current population trend of ‘pure’ animals is in decline18, and it is generally accepted that the principal threat to the species is loss of genetic integrity through introgression with domestic and feral dogs18. Since 1788, the destruction of dingoes to protect livestock interests has been widespread, with culling associated with bounties on scalps and skins presenting a significant threat to the species18.To many Australians, the dingo is of deep cultural importance, including a deep association with Indigenous culture spanning thousands of years16, although no research has been conducted to support this assertion, and the lack of a definitive test for purity would likely undermine any such efforts. Regardless, the K'gari population is particularly representative of the ecological, aesthetic and cultural value of the species and the challenges faced in contemporary situations of dingo and human co-occurrence. K’gari is the world’s largest sand island (1840 km2) is located off the south-eastern coast of Queensland, Australia and World Heritage listed since 1992. Whilst the timing and exact nature of the dingo’s introduction to the island is unknown, K’gari has been separated from the mainland following the Holocene sea level transgression (~ 6000 YBP)29. The resident dingo population is recognised for its ecological role as a contributor to regulating the island’s biodiversity30. K’gari’s Traditional Owners, the Butchulla People, have longstanding cultural and spiritual connection with wongari (dingoes) and facilitated assisted migration of dingoes from mainland Australia during pre-colonial times. In contemporary times, the K’gari dingo population is also a compelling tourist drawcard as the most publicly recognisable dingo population in Australia31.The iconic dingo population on K'gari-Fraser Island (hereafter K’gari) is reputed to be one of the last remaining reservoirs of genetic purity for the species33. The occurrence of these incidents has necessitated the implementation of management strategies that include non-lethal options, such as temporary area closures, strategic fencing and public education measures. In some instances, where dingoes are deemed to be high-risk to human safety, removal via lethal control occurs. Due to their distinct hierarchical social order, such methods have the potential for far reaching negative effects to the population’s viability34, although this partly depends on the scope and scale of selective removal activities.Negative human-dingo interactions on K’gari arise primarily from human associated activities, such as habitat encroachment, the creation of unnatural food resources and direct or indirect encouragement of dingoes in order to cultivate an interaction with an infamous terrestrial predator. These negative interactions can range in severity from stolen food resources and damage to human property, through to displays of aggression and/or actual physical harm. The worst incident to date occurred in 2001 and resulted in the death of an 11-year-old boy30. As such, these pressures will likely result in a commensurate increase in sought after interactions, alongside the resultant occurrence of negative human-dingo interactions, albeit at a low background rate relative to the nature and scale of human visitation. Management challenges are becoming evident in balancing the conservation requirements for the island’s dingo population, whilst ensuring human safety.The K’gari dingo population has been under heightened pressure since the island was heritage listed and will face continued threats as annual human visitation (currently approximately 400 000) is predicted to increase36), isolated from mainland populations and subject to historical and ongoing culling events. Given the confluence of these factors, we hypothesise that the K’gari dingo population may be suffering negative genetic consequences, which may represent a conservation concern for the species. Therefore, knowledge of the population’s genetic status relative to continental populations is an essential first step in informing ongoing management. The quantification of genetic diversity of threatened species facilitates an interpretation of the manner in which diversity is distributed within and among populations38, leading to an understanding of factors such as gene flow, inbreeding, mutation, selection and genetic drift38. Knowledge of the population genetic structure of a species can provide insight into how a species may be affected by perturbations to these processes, or by historical factors such as founder effects or genetic bottlenecks40. Familiarity with the amount and distribution of genetic variability within a species will therefore increase the precision and effectiveness with which the main priorities for conservation management are identified and implemented, particularly for island populations, which may be more vulnerable to the adverse genetic consequences of isolation and small population size41.The K’gari-Fraser Island population is small  has included K'gari individuals in a discussion of population genetic parameters such as inbreeding and genetic diversity49. This paucity of genetic data will inevitably compromise effective conservation management of an iconic dingo population, which is isolated from the mainland on a World Heritage listed island and is of considerable public interest.Much of the current literature on dingo genetic research relates to the species’ taxonomic status and past radiations into and throughout AustraliaBased on the hypothesis that there may be conservation concerns associated with genetic factors relevant to small, island-bound populations, the primary objective of this study is to address the lack of population genetic data relating to the K'gari dingo population. Specifically, this research aims to compare genetic diversity measures with dingoes from the coastal regions adjacent to the island as well as regional districts up to 500 km inland, and to establish whether genetic drift has occurred. We will frame our results to explore conservation issues that may be associated with our population genetic data.A total of 99 alleles were observed across the 439 analysed dingo samples, with 33 alleles in the K’gari population (175 samples) and 94 in the mainland population (264 samples). While there were 66 private alleles in the mainland population, there were only five private K’gari alleles and a high degree of allelic fixation in this population. For example, two loci (WANV142 and FH3591) were monomorphic in K’gari dingoes but had six and eight alleles, respectively, in the mainland population. For locus FH2168, where 27 alleles were observed in the mainland dingoes, and only 5 alleles in the island population, with two of those alleles found within 97.6% of K’gari individuals.A; p < 0.001, U = 74.50), allelic richness  private allelic richness  and expected heterozygosity . Despite the low allelic diversity for the K’gari dingo population, no statistically significant evidence was found to suggest a recent genetic bottleneck for either population (p > 0.05). The fixation index was close to neutral for K’gari, and not significantly different (p > 0.05) than the low homozygosity excess observed in the mainland dingoes  compared to 103.8 for mainland dingoes .All key genetic diversity measures were significantly lower in the K’gari population Table , includip < 0.001). Except for one individual in the K’gari population that clustered with the mainland individuals, the principal coordinates analysis demonstrated a clear difference in allelic composition between the two populations  with 98% of K’gari individuals being characterised by one genetic cluster and 98% of mainland dingoes characterised by varying proportions of the two remaining genetic clusters . At K = 2, the K’gari dingo that clustered with the mainland population is clearly identified. The evidence of allelic fixation in the island population and genetic differentiation from the mainland population was clearly supported by the PCoA, AMOVA and STRUCTURE analysis.There was evidence of genetic differentiation between the island and mainland population, with AMOVA indicating 38% of the variation due to differences among populations and a relatively high PhiPT value  and also found genetic diversity to be low49. These results are not surprising, as genetic erosion often occurs in geographically-isolated island fauna populations59, including for international taxa such as Arctic Island wolves (Canis lupus arctos)60 and for other threatened Australian native carnivores, such as the northern quoll 62. For example, in the study of northern quolls on Koolan Island, Western Australia, Spencer et al.62 found that despite a relatively high number of alleles present in the resident population, genetic diversity was lower than for mainland populations, and only contained 34% of the species’ allelic richness. Both Cardoso et al.61 and Spencer et al.62 conclude that for many island populations, low diversity results from random selection following a founder event from individuals that only represent a small proportion of the entire species diversity. The net effect is that island populations may be less likely than their mainland counterparts to persist in the long-term, due to a loss of evolutionary potential61. Although rigorously established demographic data for the K’gari dingo population is lacking, pilot studies have estimated either 104–19736 or 73–17335 dingoes to be present on the island, whilst another study estimated a population size of 76–171 in 201263 indicating a small population according to island population genetic theory56. As such, there is strong evidence from our results that the K'gari-dingo population is suffering from the loss of genetic diversity that is typical of small, island populations. Although we found no compelling evidence of recent genetic bottlenecks, it is probable that the initial founder population represented a limited genetic diversity of the wider species, which when coupled with negligible gene flow from mainland dingoes, has led to further erosion of the population’s genetic diversity.Genetic theory predicts, that under most scenarios, island populations will be less genetically diverse than their mainland counterparts64. Cairns et al.49 found extremely high levels of inbreeding present in the K’gari dingo population (FIS = 0.70049). However, those figures should be treated with caution given the low sample size (n = 5), and the potential inclusion of 2–3 siblings . In our study, values for the fixation index were effectively neutral for the K’gari population, and thus we found limited evidence of inbreeding.Another potential consequence of small, island-bound populations is the potential for increased inbreeding65. While there are multiple arguments for an exact minimum viable population (MVP) threshold for effective population size66, recent research suggests ≥ 100/1000 is more realistic than the previous ≥ 50/500 rule67, with the respective figures relating to the likelihood of short and long-term persistence. The K’gari population has an effective population size of 25.7, which is considerably lower than both reported thresholds for short-term persistence (≥ 100/1000 and ≥ 50/500), and hence represents a significant conservation concern. While a lack of population size data (and difference in sampling area) for the mainland dingoes means that limited census vs effective population size comparisons can be made from our results, the fact that the K’gari Ne of 25.7 is considerably lower than the effective population size calculated for the sampled mainland dingoes (Ne = 103.8), also supports our other evidence relating to decreased diversity in the K’gari population. For example, despite the lack of excess heterozygosity indicating no recent population bottleneck, this supports our theory of significant levels of genetic erosion most likely caused by drift following founder effects and/or a historical bottleneck. Although fluctuations in population size on the island have not been monitored, initial founder effects , or whether the founder population was a result of repeated, small-scale, assisted migration events associated with the K’gari’s traditional owners68. Additionally, K’gari has not been immune to the post-colonial persecution of dingoes that occurs throughout Australia, which is best exemplified by the bounty system on dingo scalps that still exists under many jurisdictions in non-protected estate17. For instance, prior to K’gari being gazetted as a national park and achieving World Heritage status, there is documentation of a large-scale culling event  initiated by one person to take advantage of the scalp bounty in the early 1900s68. Selective removal of problematic dingoes still occurs on the island, with 110 dingoes euthanised in the period 2001–201363, which is a relatively high proportion given the low overall population size.Effective population size  is an important consideration for small and isolated populations, and generally positively correlated with a population’s likelihood of persisting and ability to evolve in a changing environment56. In our study there was evidence of genetic differentiation (PhiPt = 0.376) between the K’gari population and the sampled mainland dingoes, which were sourced from adjacent regions in Queensland  and domestic dogs (Canis familiaris) occasionally traverse this distance. Assisted  migration is also a possibility, particularly as this was in the year immediately following the killing of a human child by a dingo, when public opinion regarding dingo management was particularly fraught, and 31 dingoes were euthanised. Despite the possibility of rare immigration events, domestic dogs have been legally excluded from K’gari since 1981 and there is negligible evidence of significant levels of natural gene flow from the mainland. As such, genetic drift has likely occurred, and as a result the K’gari population has become genetically distinct. Whilst this could be interpreted as a highly unique population, it is important to consider that this genetic differentiation is mostly due to reduced genetic diversity and allelic fixation in the K’gari population because of founder effects in a small population (census and effective) following a historical genetic bottleneck.Alongside reduced diversity, island populations often diverge genetically from their mainland founder populationsand Fig. . Both thand Fig.  and 3 deand Fig. . This inand Fig. C on the and Fig. C is only29. In combination with complex, hierarchical pack structures and breeding patterns69 that means that generally only alpha pairs yield offspring that survive past infancy, the genetic consequences have become more pronounced. This is clearly evidenced in the low effective population size, low genetic diversity and high levels of allelic fixation relative to adjacent mainland dingoes. Thus, while we found no evidence of inbreeding there is cumulative alternative evidence to suggest that the current population is suffering negative genetic effects, which may ultimately compromise its evolutionary potential and thereby place the population at greater risk of stochastic extinction.Although assisted migration occurred prior to the colonial era (~ 1840) during due to the Butchulla People’s close links with dingoes, the island has been effectively isolated from the mainland following separation with the increase in sea levels during the Holocene transgression63 due mostly to negative human-dingo interactions. The most significant event occurred in 2001, when 31 dingoes were killed following the death of a young child from a dingo attack, plus 1 additional dingo in an unrelated incident70. There are also many other forms of mortality of K’gari dingoes including attacks by other dingoes and vehicle strike. Given that the high rates of human visitation are likely to continue, human associated forms of mortality will be an ongoing management issue. The authorities responsible for managing K’gari published a Fraser Island Dingo Conservation and Risk Management Strategy (FIDCRMS) which states the “conservation and preservation of a sustainable wild dingo population” as a priority, whilst highlighting that effective management needs to be supported by research that allows the “timely identification of concerning trends regarding impacts to dingo conservation”71.The K’gari dingo population is typical of small, isolated island populations, as exemplified by low genetic diversity, low effective population size, and differentiation from adjacent mainland dingoes. This is likely a product of low initial genetic diversity following an initial founder event and flow-on effects, such as genetic drift, which has been exacerbated by an absence of substantial immigration from outside sources. In total, 110 dingoes were selectively removed on K'gari in the period 2001–201362. Without long-term genetic and demographic monitoring, it is unclear whether increasing human activity will lead to a reduction in population size and further erosion of genetic diversity. Clarification of these points is crucial, as repeated reductions in population size may give rise to numerous demographic and genetic factors which may compromise persistence, and ultimately lead to localised extinction38.Whilst our use of neutral markers precludes the direct measurement of genetic ‘health’, we find clear evidence of reduced genetic diversity relative to adjacent mainland populations and a low effective population size. It is well established that both of these factors result in lowered adaptive capacity and increased extinction risk, both of which represent conservation concerns. As such, determining management priorities for the K’gari dingo population will need to be supported by further research. Although the data from this study provides an invaluable baseline, there are precedents of the need for ongoing genetic monitoring, in association with demographic monitoring to maximize the potential for the persistence of small, genetically compromised island populationsTo support the management priorities outlined in the FIDCRMS, we recommend that further genetic analyses be undertaken. Given ongoing advancements in molecular genetic technologies, it is advised that future studies utilise sequencing platforms that will have a higher degree of sensitivity and data resolution, including the potential inclusion of approaches that facilitate the monitoring of genetic markers associated with fitness. This will provide additional highly informative molecular resources for future dingo research as well as clarity on levels of inbreeding and the presence of deleterious allele accumulation in the population . It is also highly recommended that an ongoing genetic and demographic monitoring program is established to more fully understand chronological trends and responses to extrinsic impacts that may compromise the persistence of a robust population.One hundred and seventy-five frozen, viable K’gari dingo tissue samples were provided by Queensland Parks and Wildlife Service (QPWS). These samples were collected between 1999 and 2014 from all areas of K'gari during routine QPWS activities  were utilised to assess population genetic variation by employing PCR and capillary electrophoresis. Forward and reverse primers for each microsatellite loci were used in a 10 μL PCR, consisting of 5 μL Qiagen Multiplex PCR solution , 1 μL Qiagen Q-Solution, 2 μL DNA, 0.2 μm of each primer and DNase-/RNase-free water. Multiplex amplification was performed using an Eppendorf Mastercycler  with cycling conditions as follows: initial denaturation at 95 °C for 15 min, followed by 35 cycles of 30 s at 94 °C, 90 s at 58 °C and 60 s at 72 °C, with a 30 min final extension at 60 °C. PCR products were separated by capillary electrophoresis on an AB 3730 Genetic Analyser (Applied Biosystems) and scored relative to an internal lane standard  using GENEMARKER v. 2.4.0  and checked twice by two independent researchers. Consistent amplification was observed through visualisation using agarose gel electrophoresis and clear electrophoretic signatures were observed following capillary electrophoresis. Individual samples that failed to yield peaks were amplified and genotyped for a second time.Genomic DNA was extracted from the K’gari samples using a DNeasy Blood and Tissue Kit  according to the manufacturer’s instructions. Mainland samples were extracted using the method of Ivanova et al.74. The potential for null alleles was determined using MICRO-CHECKER v2.2.375 and ML-NULLFREQ76, based on 1000 bootstraps. GenAlEx was used to estimate deviation from Hardy–Weinberg equilibrium for each locus77, and an assessment of linkage dis-equilibrium among pairs of loci was undertaken with FSTAT v2.9.3.278. Large allele dropout and stuttering potential was assessed with MICRO-CHECKER v2.2.375, based on 1000 bootstraps and a 95% confidence interval. Evidence of linkage disequilibrium was detected in one loci (FH3591). Although eight of the loci deviated significantly from HWE after Bonferroni correction, the deviation was unsurprising given that isolated, island populations are expected to be affected by selection pressures73. MICRO-CHECKER and ML-NULLFREQ estimated the average potential null allele frequency to be 0.13 ± 0.02, and 0.12 ± 0.02 across all loci, respectively, which can potentially lead to underestimation of diversity and differentiation parameters. Additionally, given the fact that loci from wild populations affected by bottlenecks, inbreeding, genetic drift, founder effects and natural selection are likely to deviate from HWE79, it is unlikely that these deviations will significantly impact subsequent genetic analyses73.Quality control of the allelic data was performed using a range of analyses77  was used to generate allelic frequencies, and to calculate a range of population genetic parameters74 including the mean number of alleles per locus (A), observed and expected heterozygosity (HO and HE), and the fixation index (F) as an estimate of past inbreeding. Allelic richness (AR) and private allelic richness (PAR) per population were calculated in HP-RARE80, using rarefaction to account for unequal sample size. Differences between the diversity and inbreeding measures for the K’gari and mainland populations were analysed using independent sample T-tests  or Mann–Whitney U-tests (skewed data) using SPSS software (SPSS Inc. Chicago).GenAlEx v. 6.50381. The observed level of heterozygosity was compared to the expected HetEQ using the intermediate two-phased model (TPM), due to its suitability for microsatellite data82. The TPM was applied with 95% of the mutations following a two-phase mutation pattern and a variance among multiple steps of 1283, with deviations from equilibrium determined using Wilcoxon’s Sign Rank tests, which are most suitable in instances where less than 20 loci are used81. Effective population size (Ne) was estimated assuming a closed population with discrete generations and random variance in reproductive success using the program NeEstimator v2.0184. The linkage disequilibrium method (LD) was used with the random mating model and results were reported for Pcrit 0.02 which is recommended for greater precision with microsatellite markers85.To detect the likelihood of recent bottlenecks, the program BOTTLENECK was used to test for mutation-drift equilibrium86 was calculated using GenAlEx v. 6.50377, to establish genetic relationships within and among populations. The average pair-wise genetic differentiation between populations (PhiPt) was calculated using multi-locus comparisons based on 999 permutations. The partitioning of genetic variance within and among populations was determined using AMOVA87 with GENALEX version 6.50377.Genetic distance77 using standardised genetic distance measures and we examined population structure further using the Bayesian clustering algorithm implemented in STRUCTURE v2.3.488. We used an admixture model with correlated allele frequencies and 10 independent runs for each value of K (number of clusters)74 between 2 and 6, employing a burn-in of 100 000 followed by 500 000 Markov Chain Monte Carlo (MCMC) steps for each run. The geographic location of samples was not used in the clustering analysis. Results across each run were summarised to determine the optimal value of K using the Evanno89 method, as implemented in STRUCTURE HARVESTER web v0.6.9390, processed using CLUMPP v1.1.291 to determine the optimal alignment of the ten iterations and finally plotted with DISTRUCT v1.192.A principle coordinates analysis (PCoA) was conducted in GENALEX version 6.503Supplementary Information


Using a unique data set containing about 15.06 million truck transportation records in five months, we investigate the highway freight transportation diversity of 338 Chinese cities based on the truck transportation probability  The growing volumes of passenger and freight transport around regionally and globally witness their important role for economic development of different countries ,2,3,4,5.i and j, i (or j), i and j, and In the past decades, the gravity law is the most adopted in understanding transportation networks and predicting transportation fluxes ,9,10,11,t cities , the expt cities , and thet cities , and thet cities .i to city j is obtained as followsi but excluding the source and destination population, and ii to j. Obviously, the data of However, the gravity model has several limitations, especially the requirement of previous traffic data to fit the parameters . To overi and j. The cost-based radiation model has been soon proposed based on the intuition that an individual will choose the site that has the lowest travel cost on the network, where the travel cost can be measured by the path length or travel time from i to j [i to j. Later, to better estimate the fluxes at different spatial scales, a scaling parameter is introduced into the radiation model [In the raw radiation model, m i to j . In thison model . By combon model .i and j and the transportation diversity of a city calculated from Although the radiation model has been adopted in the study of trip distributions ,19,20,21networks ,22,23, rnetworks . The rawIt has been reported that higher social network diversity provides greater access to social and economic opportunities and has a strong correlation with the economic development . With thThe remainder of this work is organized as follows. i to city j. Unloaded trucks are not counted in. Because radiation models do not consider intra-city transportation, we set thatThe data set we analyze was provided by a leading truck logistics company in China, which records the highway truck freight transportation between 338 cities in mainland China over the period from 1 January 2019 to 31 May 2019 . The dathttp://www.chamiji.com, accessed on 18 May 2021), which are publicly available except for a few cities. We supplemented the missing data by searching Baidu Encyclopedias .The GDP and population data for the 338 Chinese cities in 2017 were retrieved online from the Complete Collection of World Population  usually has higher diversity in its industrial structure. These cities usually have higher diversity in highway freight transportation.The strong correlation between transportation diversity and economic development implies a strong association between industry diversity and economic development. Although a causal direction of this relationship cannot be established through our analysis, transportation diversity at least provides a structural signal for the economic development of a city, highlighting the potential benefit of industry-targeted policies for economic development. Further research is required to obtain reliable policy implications. In particular, longitudinal data sets for transportation networks and economic development are required to establish a possible causal relationship.


Since its independence in 1945, Indonesia has undergone rapid socioeconomic development. The fastest changes occurred in Java, which is the main island where more than half of the Indonesian population lives.This study aimed to analyze the secular trend in the height of adults living in Java born between 1953 and 1995 by comparing their residence  and considering factors that contribute to adult height.p < 0.05 with a 95% confidence interval (CI).The analysis used the following data: birth year, body height, weight, body mass index, sex, educational attainment, share of household food expenditures, and residence of 20- to 40-year-old men and women collected by the Indonesia Family Life Survey (IFLS) waves 1 to 5. Multiple linear regression was conducted to analyze several factors that influence adult height. Significance was set at The study included 30,656 measurements of individuals born between 1953 and 1995 . Positive secular trends (95% CI) were observed for men and women: 1.3 (1.1–1.4) cm and 0.9 (0.8–1.0) cm per decade, respectively. Multiple linear regression analyses showed that, in addition to the year of birth, the adult height of both males and females was independently associated with level of education and share of household food expenditure. Stratifying the data into residence in rural areas, small cities, and large cities showed that education levels influenced the adult height of men and women living in all regions, whereas the influence of birth year and share of household food expenditure differed between areas and genders.We observed positive secular trends in the height of adults living in Java who were born between 1953 and 1995. The birth year, educational attainment, and share of household food expenditure significantly influenced adult height. A higher education level was consistently associated with taller adult height in both men and women living in rural areas, small cities, and large cities.The online version contains supplementary material available at 10.1186/s12889-022-13144-6. Later generations are almost always taller than their predecessors; this phenomenon is called a secular trend in height . Every cSecular trends in height are influenced by a complex interaction of many factors –7. EnvirIndonesia is an archipelago with a wide diversity of ethnicities and languages. Its population has improved rapidly in terms of socioeconomic conditions since its independence in 1945, although vast inequalities yet remain, such as differences in living conditions between urban and rural areas and other inequalities among its islands. These inequalities are mainly related to the availability of infrastructure, especially the ease of transportation and connections. The largest land mass is Java Island, which is home to the Indonesian capital Jakarta, the most populated and prosperous area of Indonesia. More than half of the Indonesian population lives on Java. In addition, Java has several densely populated cities or even metropolitan areas with international connections through their airports or seaports, which have been ports of entry for foreign visitors for many centuries.Between 1995 and 2015, the population of Java grew from 114 million to 145 million, with a population density ranging from 813 to 15,328 per square kilometer . The perThe Indonesian people are relatively shorter than the rest of the world’s average height, even compared with people from some Southeast Asian countries , 19. Thiwww.rand.org. The surveys were conducted in five waves: 1993–1994, 1997–1998, 2000, 2007, and 2014 [The Indonesia Family Life Survey (IFLS) was conducted by the RAND Corporation in collaboration with Universitas Indonesia and Universitas Gadjah Mada. The data are freely available upon request at and 2014 . The surand 2014 .2), normal weight (18–25 kg/m2), overweight (25–30 kg/m2), and obese (> 30 kg/m2) [We downloaded the data in October 2019. This study included data from individuals aged 20–40 years who lived in Java. At that age range, we assumed that the participant’s final height had been attained before the shrinking in older age occurred. We collected data on birth year, sex, height, weight, body mass index, place of residence, educational attainment, and the household food expenditure share in relation to total expenditure. Body mass index, as a marker for nutritional status, was defined based on the World Health Organization criteria: underweight (< 18 kg/m0 kg/m2) .2), population size , and the presence of ports (airport or seaport) [The environmental backgrounds were determined by the place of residence, i.e., urban or rural. The rural area was distinguished from the urban area by several characteristics defined by the Indonesian Bureau of Statistics: the size of the area, the population’s size and density, the main occupation of its people, and the availability of public facilities. Furthermore, the urban area was divided into small and large cities based on population density (> 1000 population/kmseaport) .Educational attainment was defined as the length of education in years. Education levels were classified as basic education (≤9 years of schooling) and high-level education (> 9 years of education) . The mony = az + bx + c) ; the year 1953 was the minimum birth year for our subjects. The secular trend was then converted to centimeters/decade.We constructed scatter plots of adult height (cm) on the Y-axis against the year of birth on the X-axis, stratified by sex, using standard deviation (SD). The mean and 2 SD score below and above the mean lines emphasize the trend. Assuming linearity of the secular trend, we estimated the trend using the slope of the linear regression lines, with adjustment of age-associated stature loss, stratified by gender. The secular trend in height was calculated from the linear regression formula  followed by the Bonferroni post hoc test to compare the heights of the populations of rural areas, small cities, and large cities in every five-year interval of birth year stratified by gender. For example, birth year “1955” meant those who were born between July 1952 and June 1957; “1960” meant July 1957 to June 1962, and so forth. Multiple linear regressions stratified by gender and residence were conducted to assess the interaction between factors influencing adult height, such as birth year, educational level, nutritional status, and household economic level. Statistical analyses were performed using STATA/MP 14.0 with a 95% confidence interval (CI).The study included 30,656 (54.7%) measurements of individuals who had complete data out of a total 56,085 people aged 20–40 years in the IFLS waves 1, 2, 3, 4, and 5. The proportion of women was between 52.5 and 60.4%, and those living in rural areas, small cities, and large cities remained relatively constant, approximately 66.4–72.3%, 6.1–7.1%, and 21.3–26.5%, respectively. The proportion of those who had finished at least 9 years of schooling steadily increased, with 40.1% in wave 3 and 52.7% in wave 5. No subjects had finished more than 9 years of schooling in waves 1 and 2. The median  share of household food expenditure also decreased steadily, with 0.23  in wave 1 to 0.12  in wave 5 . The secular trend in adult height, based on the formulas, increased 1.3 cm and 0.9 cm/decade for men and women, respectively. After adjusting for various other factors, the adult height increases (95% CI) for men and women were 0.6 (0.5–0.8) and 0.5 (0.4–0.6) cm/decade, respectively . After adjustment, stratified by place of residence, the secular trends (95% CI) of men in rural areas, small cities, and large cities were 0.8 (0.6 to 0.9), 0.4 (− 0.2 to 1.0), and 0.5 (0.2 to 0.9) cm/decade, respectively. The adult height secular trends (95% CI) of women in rural areas, small cities, and large cities were 0.6 (0.4 to 0.7), 0.2 (− 0.2 to 0.7), and 0.6 (0.3 to 0.8) cm/decade, respectively . Previous studies have mentioned that changes in the environment affect growth in boys more than in girls . The devA previous study on the secular trend in Javanese adult height revealed an increase of 3.6 cm in 60 years for men, smaller than that of the Japanese during the same time frame, resulting in the Javanese male being shorter than the Japanese male from 1992 to 1994 . A studyRoche reported that the secular changes in stature differ from country to country . ContrarPopulation density, geography, citizen mobilization, and intercity connectivity have been other factors found to affect adult height , 33–35. Demographic change has taken place in Java. The island of Java became a mega urban island between 2000 and 2010 . As noteNotably, males and females living in large cities had comparable beta coefficients for several factors . Males and females had similar potential for their adult height, although sexual dysmorphism still existed. A previous study in Thailand had found no difference in patterns of secular trends between males and females when comparing urban and rural residents. However, the final adult height indicated that the urban population was taller . Koziel Previous studies have shown that the secular trend in stature occurred at different rates within countries across all socioeconomic groups . Our stuThe final adult height in large cities, small cities, and rural areas was affected the most by education level compared with other factors . The role of education in body height has been evident in previous studies. One study in Korea found that individuals with high education levels were 4 to 5 cm taller than those with no or only elementary education. This difference suggests the influence of educational status as a fundamental factor in South Korea’s height data . Male wGiven that the predictors in our regression models only explain approximately 4–7% of the variability in adult height, there must be other factors that contribute to it, such as pubertal timing, obesity, and demographic transition. Pubertal timing has been shown to be an important factor influencing secular trends . A similObesity in childhood has been reported to influence pubertal timing. Children with obesity tend to have an earlier puberty onset as well as less adolescent height gain . We obseA limitation of our study was the proportion of respondents; the proportion in small cities was not comparable with that in large cities and rural areas, although there was a normal distribution of adult height overall. Furthermore, given this was a cross-sectional study, we cannot conclude that there is a causal connection between level of education and final height. Further study is needed to analyze the correlation between population mobility-interconnectivity and body height. There are various races and ethnicities among the people living on the island of Java. The genetic or ethnic backgrounds of the studied individuals who lived on Java Island were not available. Therefore, we could not analyze genetic or ethnic factors. Further studies are also needed to analyze the secular trend in height among Indonesian children as reported by Cole, which stated that adult height gains were achieved as early as 2 years of age .Between 1955 and 1995, there was a positive secular trend in height among adults living in Java, either in a large city, small city, or in rural areas. Education level, place of residence , share of household food expenditure, and birth year influenced adult height in both men and women. A high education level was the most influential factor for adult height. People who lived in a large city were taller than those in small cities or rural areas. Men and women who lived in a large city had comparable influencing factors for adult height.Additional file 1: Supplementary Table 1. Baseline characteristic of subjects.Additional file 2: Supplementary Figure 1. Infant mortality rate in Indonesia from 1955 to 1995 .


We suggest that genetic coupling of plasticity and adaptations could promote unusually rapid ‘runaway’ evolution of novel adaptations. The resultant dynamics could facilitate evolutionary rescue, adaptive radiations, the origin of novelties, and other commonly studied processes.There is tantalizing evidence that phenotypic plasticity can buffer novel, adaptive genetic variants long enough to permit their evolutionary spread, and this process is often invoked in explanations for rapid adaptive evolution. However, the strength and generality of evidence for it is controversial. We identify a conceptual problem affecting this debate: recombination, segregation, and independent assortment are expected to quickly sever associations between genes controlling novel adaptations and genes contributing to trait plasticity that facilitates the novel adaptations by reducing their indirect fitness costs. To make clearer predictions about this role of plasticity in facilitating genetic adaptation, we describe a testable genetic mechanism that resolves the problem: genetic covariance between new adaptive variants and trait plasticity that facilitates their persistence within populations. We identify genetic architectures that might lead to such a covariance, including genetic coupling via physical linkage and pleiotropy, and illustrate the consequences for adaptation rates using numerical simulations. Such genetic covariances may also arise from the social environment, and we suggest the indirect genetic effects that result could further accentuate the process of adaptation. We call the latter mechanism of adaptation  This process is akin to West‐Eberhard's concept of phenotypic accommodation: “adaptive adjustment, without genetic change, of variable aspects of the phenotype following a novel input during development” . A full explanation of terms in this expression follows, and Figure zt′ describes a phenotype for which there is an adaptive variant exerting indirect effects on the social environment. The prime indicates that it is interacting individuals carrying the adaptive variant who are causing variation in the expression of other individuals’ phenotypes. The second trait zb represents a phenotype whose expression is responsive to the varied social environment caused by zt′, in turn mitigating negative effects of zt′. The subscriptb is used for illustration to suggest a phenotypically plastic behavioural response to the social environment that permits individuals carrying the adaptive variant of zt to survive and reproduce; a real‐world example is described below. The direct additive genetic effects for each trait are indicated by a, and environmental effects by e. In IGE theory, the effect that interacting social partners have on the expression of traits in focal individuals is captured by the interaction coefficient ψ, where subscripts indicate which trait is affected by which  represents genetic covariance between an adaptive trait and facilitating plasticity. The utility of this expression is that well‐established quantitative genetic frameworks have been developed to estimate all of its components  in Hawaii. A single‐locus genetic variant, flatwing, segregates on the X chromosome and causes male silence. The flatwing variant erases sound‐generating structures on male forewings and protects males from an endoparasitic Tachinid fly (Ormia ochracea) that is attracted to their song and fatally infests their body cavities. The flatwing morphotype was observed to have spread extremely rapidly, and in one population on the island of Kauai has become fixed within the span of ca. 50 generations  developed above describes this genotypic association between socially responsive behavioral plasticity and the rapidly spreading flatwing morphotype in the Hawaiian cricket system. Thus, a direct genetic link between an adaptive variant under selection and plasticity to the subsequently altered social environment may be driving extremely rapid adaptation in this system. Empirical examples from other systems are similarly suggestive of an evolutionary process resembling social drive : De novo or introgressed genetic variants under selection exert pleiotropic effects on other traits.Condition 2 (Plastic mitigation of negative pleiotropy): Negative pleiotropic effects of adaptive genetic variants are offset by facilitating plastic traits.Condition 3 (Heritable plasticity): This facilitating trait plasticity  has a heritable genetic basis.Condition 4 (Genetic coupling): There is genetic covariance between the plastic trait and the adaptive trait under selection.Condition 5 : Facilitating plasticity is caused by IGEs arising from negative pleiotropic changes to the social environment.Prediction 1: We should detect more cases of plasticity facilitating rapid adaptation when facilitating plastic traits are established in a population via other mechanisms prior to the emergence of adaptive variants.Prediction 2: The rate of adaptive evolution will be increased in scenarios following this order: conditions 1–3 are met < conditions 1–4 are met < conditions 1–5 are met.Conditions predisposing a population to rapid adaptation via the mechanism we have described may not be frequently observed, but when genetic coupling does arise it should lead to distinct and rapid dynamics. Thus, as with “magic traits” in speciation . Antipredator behaviors show within‐ and between‐population variation and are influenced by genes, environmental features, and their interactions  is present in the metapopulation at some starting frequency and confers a fitness advantage in HP, but a weak fitness cost in LP (Table To explore the importance of genetic coupling between adaptive mutations and pre‐existing plasticity to evolutionary dynamics, we simulated the above scenario in SLiM (Haller and Messer LP Table .ht, arises in a single diploid individual and directly benefits survival in both populations. However, ht indirectly increases predation risk, neutralizing net fitness benefit in HP, and reducing it in the LP population. If individuals co‐express ht and pc then they retain some net fitness benefit in the HP population, and have twice the net fitness benefit in the LP population (Table ht initially arises in an individual carrying pc, with the probability of recombination between adjacent positions in linkage groups specified as 1e‐8.At the start of the simulation a mutation, on Table . Both mupc starting frequencies and linkage scenarios, with 10,000 runs each, are given in Figure ht. This was particularly the case at lower starting frequencies of pc. The importance of the starting frequency of the plastic allele in modulating the importance of linkage is twofold. First, if the plastic trait is already common in the population, linkage is relatively unimportant as ht is likely to be co‐expressed with pc regardless. Second, when pc is rare, both alleles are under strong positive selection through their combined effects on fitness. Note however that as the starting frequency of pc approaches zero, the probability of the adaptive mutation arising in the same genome becomes vanishingly unlikely.Results of simulations under different All authors made significant contributions to conceptual development and writing of the manuscript. JGR performed simulations. NWB led the writing.No data are associated with this manuscript.The authors declare no conflict of interest.


The characterization of plant genetic resources is a precondition for genetic improvement and germplasm management. The increasing use of molecular markers for DNA-based genotype signature is crucial for variety identification and traceability in the food supply chain. We collected 75 Sicilian hazelnut accessions from private and public field collections, including widely grown varieties from the Nebrodi Mountains in north east Sicily . The germplasm was fingerprinted through nine standardized microsatellites (SSR) for hazelnut identification to evaluate the genetic diversity of the collected accessions, validating SSR discrimination power. We identified cases of homonymy and synonymy among acquisitions and the unique profiles. The genetic relationships illustrated by hierarchical clustering, structure, and discriminant analyses revealed a clear distinction between local and commercial varieties. The comparative genetic analysis also showed that the Nebrodi genotypes are significantly different from the Northern Italian, Iberian, and Turkish genotypes. These results highlight the need and urgency to preserve Nebrodi germplasm as a useful and valuable source for traits of interest employable for breeding. Our study demonstrates the usefulness of molecular marker analysis to select a reference germplasm collection of Sicilian hazelnut varieties and to implement certified plants’ production in the supply chain. Corylus avellana L. is probably native from Asia Minor due to its wide distribution and also as wild forms in Pontus (an ancient province in northern Anatolia). This theory is also supported by the name of hazelnut fruits, Pontikón (káryon) or (karua) Pontika, káryon Pontikón (Ponto nut) given by Greeks ..76].® Plant II commercial kit . Doyle and Doyle’s (1987) method [Young leaf material was sampled from 75 hazelnut accessions. Three extraction protocols were tested to compared DNA extraction yield and purity: (1) modified CTAB-based protocol , (2) mod) method  was modi) method , a CTAB-Hazelnut genetic characterization was performed using 9 SSR markers . The hazThe amplifications were carried out using the following PCR cycling conditions: the first regime repeated for 10 cycles involved denaturation at 94 °C for 5 min, followed by denaturation at 94 °C for 3 min, annealing at 60 °C for 1 min and 30 s, and a minute extension at 72 °C, which was lowered by one-degree centigrade for each cycle; the second thermal regime, repeated for 25 cycles, consisted of a denaturation step at 94 °C for 30 s, an annealing cycle at 60 °C, and an extension phase at 72 °C for one minute. Separation and detection of the PCR products were achieved using a 3130 Genetic Analyzer  loaded with POP-7 polymer (Applied Biosystem) and the size standard ROX-500 Genescan . The determination of the allelic dimensions in terms of base pairs (bp) was carried out using the software GeneMapper Version 4.0.For duplicate identification, the multi-locus approach was used for genotype matching by program GenAlEx 6.502 . The accThe software GenAlEx 6.502  and CERVGenetic relationships between SSR profiles of genotypes were estimated by using Bruvo’s distance  in the pFinally, Bayesian-based clustering was performed using STRUCTURE v.2.3.4  to furthThe most likely K value was processed with STRUCTURE HARVESTER v.0.9.94  and was For hierarchical clustering based on morphological descriptors collected on nuts, the previously generated Euclidean distance matrix was used, and the average linkage method was applied. A heatmap was generated using the heatmap.2 function from the gplots package of R. The Mantel test  was perfThe growing commercial demand for hazelnut derivatives led to the introduction of intensive farming methods that often have a negative impact on the environment. Rural development is still a high priority in many areas worldwide where local communities are mainly dependent on agriculture. Rural communities of Sicilian hazelnut growers require the development of cropping systems based also on the preservation and sustainable use of local agro-biodiversity. Therefore, this study aimed to estimate the genetic diversity of the Sicilian hazelnut germplasm of Nebrodi Mountains and varietal identification by fingerprinting using SSR markers.The genetic analysis identified 30 unique SSR profiles in the Sicilian germplasm, including both commercial and local varieties. In addition, when compared to Italian and European germplasm previously characterized by the same set of SSRs, the distinctness of Sicilian genotypes was highlighted. Further studies on a broader varietal landscape of cultivated hazelnuts could increase the number of unique alleles, enriching the biodiversity of the hazelnuts available in the Nebrodi area of Sicily.Our results may help to establish a reference hazelnut germplasm collection from the Nebrodi territory of Sicily and to provide useful tools to produce certified plants. A certified Sicilian hazelnut food chain could provide valuable support to the growth of the hazelnut sector in the future and, at the same time, meet consumer demand for fresh and processed agri-food authentication and traceability. Further studies could contribute to transparency and food safety and allow producers and retailers to properly promote their products.


To determine the properties of each measure, we repeated simulations of different intensities of step and allele proportion ranges, with varying genome sample size, number of loci, and number of localities. We found that alpha diversity  based measures were ineffective at detecting steps. Further, allelic richness-based beta  measures  were not reliable for detecting steps, but instead detected departures from fixation. The beta diversity measures best able to detect steps were: Shannon Information based measures, GST based measures, a Jost-D related measure, and Bray-Curtis dissimilarity. No one measure was best overall, with a trade-off between those measures with high step detection sensitivity (GST and Bray-Curtis) and those that minimised false positives (a variant of Shannon Information). Therefore, when detecting steps, we recommend understanding the differences between measures and using a combination of approaches.Accurately detecting sudden changes, or steps, in genetic diversity across landscapes is important for locating barriers to gene flow, identifying selectively important loci, and defining management units. However, there are many metrics that researchers could use to detect steps and little information on which might be the most robust. Our study aimed to determine the best measure/s for genetic step detection along linear gradients using biallelic single nucleotide polymorphism (SNP) data. We tested the ability to differentiate between linear and step-like gradients in genetic diversity, using a range of diversity measures derived from the  Identifying a sudden change in genetic diversity (here referred to as a ‘step’) across a landscape is essential for many fields. A landscape geneticist may want to know if a barrier to gene flow is leading to geographical population structure . An evolIn molecular ecology, the identification of a step along a gradient of genetic diversity requires us to detect a significant change in alpha diversity (the genetic diversity of each sampled location) and/or beta diversity (pairwise genetic differentiation/distance between locations). Because beta diversity is, by definition, measuring differences between localities, it is seen to be more effective for detecting steps . HoweverFST, GST and similar measures . Other programs  related measures. These FST/GST/q = 2 related metrics are used as the default despite their much-discussed limitations (see below) and the existence of more robust genetic diversity metrics [Even with the wide variety of techniques and software available to investigate genetic steps , there ias dartR ; GENODIVas dartR ) allow fon model , estimaton model ,15, or mon model ; fastSTRon model ). Howeve metrics . The lac metrics , but morq-profile of diversity measurement–a convenient framework of diversity measurement that unites many of the most commonly used measures [q denotes a variable whose choice determines the “order” of the measure. This profile can be split into three informative values when applied to genetic data: richness of alleles or haplotypes q = 0)( [H-measure”) and a numbers equivalent diversity metric (“D-measure”). While H-measures and D-measures have similar properties for the same value of q, D-measures have the benefit of being on the same scale (“effective numbers”) for all values of q, allowing better comparison of diversity across values of q, studies and systems. Each level of q-profile has different properties, strengths and weaknesses (outlined below), and therefore it has been recommended that all three are used in concert [We arrange the different metrics considered according to the measures . The q d(q = 2) . Each va concert .q = 0 metrics measure the number of allelic types in a population and do not consider the frequencies of the alleles. The alpha diversity metric, allelic richness, is based on the total number of alleles in a population, and when comparing populations (beta diversity), q = 0 represents the number of unshared alleles between populations (variants include Jaccard dissimilarity and Sørensen dissimilarity [q = 0) weight rare alleles equally to common alleles, which has benefits and drawbacks. By weighting rare alleles higher, q = 0 based measures are better at detecting standing variation within samples [q = 0 measures are prone to being biased by genotyping errors . Alleli samples . Howeverlic data ). This ping bias .q = 1 based measures are based on Shannon Information theory and weight each allele by its proportional abundance. The q = 1 measure for alpha diversity is based on the chance that a newly sampled allele will be of a novel type, whereas for beta diversity the mutual information (MI) is the chance that knowing an individual’s allelic type will correctly identify its location of origin . Because measures with q > 0 include information on the proportional abundance of alleles, q = 1 can give greater insight than richness alone. Despite q = 1 based measures being frequently used in studies examining species diversity, for instance Shannon diversity index, they are currently used less in molecular diversity studies [q = 1 measures have fewer sampling error problems than q = 0 measures, and these problems can be eliminated by suitable corrections [q = 1 measures also have an advantage over q = 2 measures because the latter are insensitive to rare alleles (see below [The  studies . By consrections . The q =ee below ).q = 2 measures are the most commonly used measures of alpha and beta genetic diversity. They reflect the chance that two randomly selected alleles from a population are the same type. Familiar q = 2 measures include heterozygosity (He) for alpha diversity, and FST, GST, Jost-D for beta diversity . The q y common . Some q iversity ).q-profile, some are not, but are still seen to have desirable properties, especially when using biallelic data. Specifically, Bray-Curtis (BC) dissimilarity, also known as allele frequency difference , is a bnce , number of loci (L), and localities sampled (K). Even under these conditions, we expect some measures to have low step sensitivity, through being unable to detect steps  or having high rates of false positives. So, we next examine the measures with reasonable properties under standard conditions and ask which are more sensitive under suboptimal conditions. This allows us to differentiate step detection sensitivities between measures. Specifically, we ask how step sensitivity is affected by step steepness, the magnitude of allele proportion difference, proximity to fixation, genome sample size, number of loci, and number of localities. Lastly, we ask if there is a single measure (or set of measures) that has the best step detection over all conditions . All analyses are confined to biallelic loci, such as the currently popular single nucleotide polymorphisms (SNPs).In this paper, we first ask which measures are sensitive to steps under standard conditions, here defined as a steep step with a maximal allele proportion difference, and a large genome sample size  using formulae adapted from Sherwin et. al. [H measures) of alpha diversity we took the allele proportion of the minor allele at a locus (p) and/or the number of different alleles at a locus (S) and entered them into Eqs H or D denote the order of q, while the subscripts denote the diversity measure type .We calculated alpha and beta diversity measures including three levels of the  et. al.  for bial et. al.  . This is the number of equally frequent alleles that would be needed to give the same H value as the alleles identified in the genetic dataset .q = 0, 1 and 2, we used minor allele proportions of each locality (p1 and p2), and the average minor allele proportion across localities (pav). We then used the alpha diversity equation for each value of q (1–3) to calculate the mean alpha diversity of each locus across the two localities (AvLast’ variant) and (2) by averaging mean alpha diversities and gamma diversities over loci (L) before calculating the final beta value (‘AvFirst’ variant). These variants give slightly different means and variances , which is the only case used here.Mutual information: GST Eqs  and 18 ast-D Eqs  and 20 wGST:Jost-D:D scale, the conversion of H measures to D measures can be performed at different stages:D values (Eqs H. to. D represents the relevant Eqs q) and then the results are substituted into Eqs H beta diversity As anotL|p1−p2|In addioportion .d = 0 to d = 1), with equal allele proportions (p) for all loci at the same location along the landscape (d), see pstart) at distance d = 0 and an end allele proportion (pend) at distance d = 1. To simulate different intensities of step, we used the qbeta function in “stats” package in R [qbeta function is unrelated to either the q-profile or beta diversity, instead it refers to the quantile function of a beta distribution). The function can be summarised as being the allele proportion at a certain distance pd, which is the value of x which satisfies d = Ix  is a gamma function. The function was multiplied by the total range of allele proportions (pend—pstart) and added to the starting allele proportion (pstart).x = pdOur simulated data assumed a continuous population across a linear landscape  , where apd) from a specified starting allele proportion (pstart) at distance = 0 to a specified end allele proportion (pend) at distance = 1. When the step parameter was 0, the gradient was linear, and as the step parameter increased, the gradient was increasingly sigmoidal . This resulted in a variable sampled allele proportion for each locus, despite being drawn from identical allele proportions.Each tion pd,  as the pK), starting allele proportion (pstart), end allele proportion (pend), number of loci (L), number of genome samples (n), and intensity of step (step). For each set of simulations, we tested four intensities of step: 0 (linear gradient), 1 (gentle step), 5 (moderate step), 50 (steep step). We also tested five different pstart to pend ranges, allowing us to understand the beta measures’ properties over different magnitudes and positions of allele proportions:Each simulated model took the following variables: number of localities , from 2 to 30 (in increments of 2).Number of genomes sampled (L), from 100 to 2000 (in increments of 100).Number of loci (K), from 4 to 15.Number of evenly spaced localities  have their results of varying genome sample size/loci/localities reported.For the beta diversity measures, there were two possible criteria for detection of a step: (1) if the beta diversity between two adjacent localities was significantly higher than both the beta diversity of the previous adjacent locality pair and the beta diversity of the next adjacent locality pair ; a step was detected at the incorrect location ; no step was detected when no step was present (true negative) and; no step was detected when one was present . We presented true and false positives in our figures and tables, with the remainder of simulations being negatives .p = 0.5, regardless of the intensity of the step  and half-maximal range (p = 0–0.5) treatments, q = 0 measures correctly detected steep steps , and showed no false positives on linear gradients  treatment  treatment  but detected false positives in some treatments  except the narrow range far from fixation treatment, where a step was detected 51% of the time  were not suitable for step detection due to their high rates of false positives in linear treatments , correctly detecting a moderate step 100% of the time in the half-maximal range and narrow range near fixation treatments and 52% of the time for the narrow range far from fixation treatment , but false positives were detected in the gentle step treatment 8–10% of the time , increasing number of loci sampled (L) and decreasing number of localities sampled  increased with increasing number of genomes sampled  but without detecting false positives . While many have touted the benefit of q = 1 measures more broadly [q = 1 for the detection of geographic genetic steps. Of these measures, there was a clear trade-off between high true positive detection and low false positive detection between the AvFirst and AvLast variants  but were prone to detecting false positives under certain conditions  and be a possible downside to the known property of q = 1 measures being more sensitive to rare alleles than q = 2 measures [The best step detection properties were found in the  broadly –31, our s Tables . Howevers Tables . This un S3.6 in , we founmeasures . HoweverGST as a diversity measure [AvLast variant of GST has one of the best step detection properties of the measures we have tested here  or number of localities (K) sampled is low . This undesirable property was shared with 2Dβ.A.AvFirst and other q = 2 measures, and was an unexpected property of Bray-Curtis. This property can be explained because as the allele proportion of one or both of sites approaches 0 or 1, the range of possible Bray-Curtis values decreases, decreasing the average Bray-Curtis value. Therefore, these biases mean that sampled differentiation as measured by Bray-Curtis is not always equal actual allele proportion difference. Because we found that Bray-Curtis performed about as well as the AvLast variants of q = 1 and GST measures  had goooportion , with dimeasures , and tha2Dβ.A.AvFirst were surprisingly good under standard conditions  and allelic richness (q = 0) measures. When alpha diversity was measured along a linear gradient, diversity peaked at p = 0.5 for all values of q , leadinq = 0) had poor step detection properties in many of our simulated scenarios, so we do not recommend their use for the detection of steps in genetic data. However, we stress that these measures should not be discounted as completely uninformative, because our results confirmed that q = 0 measures are useful for detecting other aspects of genetic diversity such as rates of fixation and changes in small allele proportions. In scenarios where steps were characterised by changes in allele proportion away from fixation (e.g. p = 0.3–0.5), q = 0 measures could not detect even the steepest steps . However, when the steps were closer to fixation (p = 0 or 1), q = 0 measures could detect steep steps and detected false positives (peaks of beta diversity not at the step location) when the step was moderate . When a biallelic locus becomes fixed, the number of the alleles will go from 2 to 1. Therefore, even the smallest change allele proportion to fixation, from p = 0.01 to p = 0 for example, would be detected most strongly with q = 0 measures, followed by q = 1 measures to a lesser extent. However, due to the inevitable incompleteness of sampling, there would rarely be any sampling that would be sufficiently accurate, and there would be many cases of false fixation.Allelic richness related beta measures (st steps . These rOur advice for molecular ecologists is split into two areas: how to design a study to maximise the chances of detecting a step when one is present (true positive rate); and how to choose diversity measures to maximise true positive rate while managing the false positive rate. Firstly, we emphasise the importance of a good sampling strategy in terms of maximising number of genomes sampled, number of loci and the strategic choice of localities. Our advice agrees with current guides for formulating landscape genetic studies ,39. SpecAvLast version of GST, AvLast version of a q = 1 measure, or Bray-Curtis, Tables AvFirst variant of a q = 1 measure , which are not prone to false-positive detection  to better choose the most appropriate beta diversity measure. If possible, prior knowledge of how often certain allele frequency clines (e.g. steep or slight) are in the natural population being studied would also assist practitioners in selecting the most suitable measure.Secondly, when choosing a diversity measure, we recommend the use of multiple measures in concert. Many studies already use multiple diversity measures, and others have recommended this as best practice ,40. Specn Tables  and 4. Lq = 2 measures . Our study has shown that current software would benefit by integrating a wider variety of genetic diversity measures. Specifically, an AvFirst variant of a q = 1 measure could be used to decrease detections of false positives. These tools could also add the sensitive measures of Bray-Curtis and an AvLast variant of q = 1 to maximise the chances of detecting steps over a wide variety of conditions.As mentioned previously, most current methods and software exclusively use, or at least default to In conclusion, when it comes to detecting steps, we show that not all measures are equal, each having its own sensitivities and weaknesses. By using a combination of measures in concert, molecular ecologists will be able to more confidently detect and classify steps in their systems. Understanding these properties is key to reaching the correct conclusions in landscape genetic studies and improving conservation and management outcomes.S1 File(DOCX)Click here for additional data file.S2 FileFigure S2.1—Graphical representation of step detection protocol. Figure S2.2—Dependence of alpha diversity on allele proportion. Figure S2.3—Effect of allele proportion on q = 0 measures.(DOCX)Click here for additional data file.S3 File(DOCX)Click here for additional data file.S4 File(DOCX)Click here for additional data file.


Editorial on the Research TopicBuffalo Genetics and GenomicsBubalus bubalis) are important livestock species with significant contribution to food security for thousands of years as a source of milk, meat, leather, dung, hide, horns, traction power, etc. Buffalo production is almost doubled during the last decades due to the improvement in management and nutrition practices along with advanced breeding approaches. To ensure more food security, it is important to sustain the improvement and efficiency of buffalo production to meet the current and upcoming human needs. Genetic improvement is usually used to achieve this goal by selecting the best individuals and breeding them to pass down their favorable genetic materials to the next generations. In this regard, the merit of an animal is predicted in terms of its estimated breeding values (EBVs) even without knowledge of the genetic control of the relevant traits.Buffalo . Another possible solution to overcome the missing pedigree information is to use genomic data. Even with the availability of pedigree information, genomic methods can provide more accurate prediction than those of traditional estimations. For example, the average accuracies for GBLUP and ssGBLUP were increased by 0.03 and 0.08 units over pBLUP (0.21), respectively for milk production traits in Philippine buffalo . Although these results are promising, the advantage of using genomic information for genetic improvement in buffalo is still lower than what was expected. It would be attribute to the small number of genotyped animals, using animal own performance, and small sample size  profiles of adipose and muscle tissues in buffalo. They have been identified and verified several differentially expressed lncRNAs in adipose and muscle tissues revealing the importance of lncSAMM50 in lipid accumulation of buffalo adipocytes.Recently, it has been reported that incorporating biological information and pre-selected genetic markers can increase the accuracy of prediction . DetectiShao et al. compile the genetic parameters and GWASs for different reproductive traits in both cattle and buffalo populations and highlighted possible options to be implemented for improving buffalo breeding. Recently, the research priorities and strategic plans in developing countries have focused on improving the performance of local breeds to face climate change. Swamp buffalo, which are mainly used for agricultural operations in China and Southeast Asian countries, are currently facing additional challenge of being neglected due to rising farm mechanization. This subspecies can be developed for milk and/or meat production under harsh environments and can be used as a strategic option to secure the income of smallholders. Therefore, the challenges and possible opportunities for improving the productivity of swamp buffalo in the Southeastern Asia are comprehensively discussed by Pineda et al.Since cattle and buffalo are closely related species, it is common to compare the findings from buffalo studies with their relevant ones from previous cattle studies. In this regards,


STAT3 and p-STAT3 are often overexpressed in various human tumours and participate in cancer development and progression. However, whether STAT3/p-STAT3 expression is associated with clinicopathologic characteristics and has prognostic significance for people suffering from ovarian cancer remains controversial. We conducted a systematic review and meta-analyses to clarify the associations between STAT3/p-STAT3 expression and clinicopathologic characteristics and prognostic factors of ovarian cancer.A systematic electronic search in the PubMed, Embase, CNKI, and Wanfang databases was conducted to identify relevant articles published before 3 April 2021. All statistical analyses were performed using Stata 15.1.p < 0.00001; OR = 9.08, P < 0.00001; OR = 4.01, p < 0.00001, respectively). STAT3/p-STAT3 overexpression was significantly correlated with FIGO stage (I-II vs. III-IV) , tumour grade (G1 + G2 vs. G3)  and lymph node metastasis (yes vs. no) . High STAT3/p-STAT3 expression was correlated with poorer prognosis of ovarian cancer patients for both overall survival (OS)  and progression-free survival (PFS) .We included 16 eligible studies incorporating 1747 ovarian cancer patients. The expression of STAT3/p-STAT3 was upregulated in ovarian cancer samples versus normal ovarian tissue, benign tumours and borderline tumours (OR = 10.14, The present meta-analysis indicated that high STAT3/p-STAT3 expression is likely predictive of an unfavourable prognosis in ovarian cancer patients. Nonetheless, prospective trials are required to confirm these associations.The online version contains supplementary material available at 10.1186/s13048-021-00918-6. Ovarian cancer constitutes the most lethal gynaecological tumour type, accounting for approximately 125,000 deaths annually worldwide . CurrentThe signal transducers and activators of transcription (STAT) family comprises seven members encoded by different genes: STAT1, STAT2, STAT3, STAT4, STAT5a, STAT5b, and STAT6, among which STAT3 is closely related to carcinogenesis . The speSeveral recently published meta-analyses have suggested that the expression levels of STAT3 and p-STAT3 are promising prognostic biomarkers for glioma , breast A comprehensive literature search of PubMed, Embase, CNKI, and Wanfang was performed. Scientific papers reporting associations between STAT3/p-STAT3 expression and pathological characteristics of ovarian cancer and survival outcomes published prior to 3 April 2021 in English or Chinese in peer-reviewed journals were selected. We used the following keywords to search for publications (ovarian OR ovary): AND (cancer OR carcinoma OR tumour OR neoplasm) AND .Studies meeting the following inclusion criteria were included in our systematic review: (1) participants were diagnosed with ovarian cancer through pathological or clinical diagnosis methods; (2) determination of STAT3 and p-STAT3 in neoplastic tissue was performed using immunohistochemical (IHC) staining; (3) the study provided data on the association between STAT3/p-STAT3 expression and survival outcomes, including hazard ratios (HRs), 95% confidence intervals (CIs), or Kaplan–Meier survival curves; (4) the study provided data on the relevance of STAT3/p-STAT3 expression to clinicopathological features, including histological type, pathological type, FIGO stage, tumour grade, and lymph node metastasis; (5) when the results of a study were published in two or more journals, we selected the most complete or the latest version; and (6) all studies were restricted to English or Chinese language publications.The exclusion criteria were as follows: (1) reviews, expert opinions, abstracts, case reports, conference reports, systematic reviews, meta-analyses, letters, ongoing studies; and (2) studies related to cell lines, tissue culture, or animal models.Two independent researchers (GS and ZWY) screened all titles and abstracts, and if necessary, screened the full text to determine relevant studies. Any difference in opinion was resolved through discussion by the two reviewers until consensus was reached. If the data in the text were incomplete or missing, we contacted the primary authors by email or fax to obtain the necessary data.The extracted data comprised the name of the first author, year of publication, study country, number of patients, age, level of STAT3 or p-STAT3 expression, pathological type, histological type, FIGO stage, tumour grade, lymph node metastasis, antibody, scoring method, cut-off value, follow-up, survival analysis, and HR estimate. The quality of the included studies was evaluated using the Newcastle–Ottawa Quality Assessment Scale (NOS) . The NOShttps://github.com/markummitchell/engauge-digitizer/releases) and the Excel program file  provided by Tierney et al. [2 random effects model was used to conduct the meta-analysis if the Q test P-value was < 0.10 or I 2 > 50% [P < 0.05 denoted statistical significance.The relationships between STAT3/p-STAT3 expression and clinicopathologic features of ovarian cancer were assessed with odds ratios (ORs) and 95% CIs. A combination of HRs and 95% CIs was used to estimate the impact of STAT3/p-STAT3 expression in ovarian cancer on progression-free survival (PFS) and OS. When studies reported only Kaplan–Meier curves , we estimated HRs and their corresponding 95% CIs using Engauge Digitizer version 11.2 . These studies revealed that STAT3/p-STAT3 expression was significantly higher in ovarian carcinoma tissue than in normal tissue   , 28, 34 2 = 1.7%, p = 0.417). These studies revealed that STAT3/p-STAT3 expression was significantly higher in ovarian carcinoma than in benign ovarian tumours . We obtained consistent outcomes in the ovarian cancer biomarker subgroups (STAT3 and p-STAT3)  . These studies revealed that STAT3/p-STAT3 expression was significantly higher in ovarian carcinoma than in borderline ovarian tumours  , a random effects model was employed. The results demonstrated that compared with stage III-IV ovarian cancer patients, stage I–II ovarian cancer patients had significantly lower STAT3/p-STAT3 expression levels . We obtained consistent outcomes for the ovarian cancer biomarker subgroups (STAT3 and p-STAT3)  . STAT3/p-STAT3 expression in G1 + G2 was significantly lower than that in G3 . We obtained consistent outcomes in the ovarian cancer biomarker subgroups (STAT3 and p-STAT3)  . The results showed significantly lower STAT3/p-STAT3 expression in lymphatic metastasis patients than in those without lymph node metastasis . We obtained consistent outcomes in the ovarian cancer biomarker subgroups (STAT3 and p-STAT3)  . There was no significant difference in STAT3/p-STAT3 expression between serous and nonserous ovarian carcinoma patients   . STAT3/p-STAT3 expression was significantly lower in patients with mucinous ovarian cancer than in patients with nonmucinous ovarian cancer   . There was no significant difference in STAT3/p-STAT3 expression between clear cell and non-clear cell ovarian carcinoma patients   Fig. . Four st96) Fig. . A sensip = 0.743, I2 = 0%). STAT3/p-STAT3 expression was significantly associated with worse overall survival in patients . Consistent outcomes were found in the ovarian cancer biomarker subgroups (STAT3 and p-STAT3)   Fig. . A sensip = 0.362, I2 = 1.6%). STAT3/p-STAT3 expression was found to be significantly associated with worse PFS   Fig. . A sensip = 0.024, Begg: p = 0.016). In evaluating the relevance of p-STAT3/STAT3 expression in OS, the funnel plot revealed no proof of asymmetry, and Egger’s test with the Begg test suggested no potential publication bias   Fig. .Fig. 14FIn the current meta-analysis, we assessed the influence of STAT3/p-STAT3 expression on the clinicopathological features and prognosis of ovarian cancer patients, and 16 published articles were included. The outcomes demonstrated that high STAT3/p-STAT3 expression in ovarian cancer tissue was correlated with a higher FIGO stage, a more advanced tumour grade, a higher risk of lymph node metastasis, non-mucinous ovarian cancer, shorter overall survival, and shorter progression-free survival. Min, Xiao, Gao, and Wu, , 25–27 f. Persistent STAT3 activation promotes tumour progression and metastasis in various cancers [In this study, we investigated the relationship between STAT3/p-STAT3 expression and 3 major pathological characteristics . Rosen  found no cancers –40. Our p = 0.01) but not with any of the other clinicopathologic variables tested. In the present study, we found that STAT3/p-STAT3 expression was significantly different in patients with serous vs. non-serous, endometrioid vs. non-endometrioid, and clear cell vs. non-clear cell ovarian cancer subtypes. There was no significant difference in STAT3/p-STAT3 expression between mucinous vs. non-mucinous ovarian cancer subtypes.The histologic subtypes of ovarian cancer are classified into serous, mucinous, endometrioid, and clear-cell carcinoma. Rosen  found anIn addition, we comprehensively analysed the relationship of STAT3/p-STAT3 expression with OS and PFS. High STAT3/p-STAT3 expression was significantly associated with poor OS and unfavourable PFS in ovarian cancer patients. Cui  reportedTo the best of our knowledge, this is the first meta-analysis to systematically explore the relevance of STAT3/p-STAT3 expression on the prognosis and clinicopathological characteristics of ovarian cancer. The included 16 studies covering 1747 ovarian cancer patients led to more reliable and stable results compared with those of the individual studies. However, our study also has some limitations. First, most populations included in our analysis were Asian, with only 4 studies carried out with Caucasian patients. Therefore, our results should be confirmed with additional research in other ethnicities. Second, our research strategy was restricted to articles from four databases  and English and Chinese publications only. Therefore, selection bias in the outcomes cannot be entirely excluded. Third, different studies used different scoring methods to define high STAT3/p-STAT3 expression. Some studies relied on both the intensity of staining and the percentage of stained cells to evaluate STAT3/p-STAT3 expression, while others relied on the percentage of positive cells alone. Fourth, different antibodies, dilutions, and cut-off values may have resulted in heterogeneity and affected the study results. Therefore, uniform criteria must be applied when determining STAT3/p-STAT3 expression to more reliably interpret its significance in ovarian cancer. Fifth, due to the obvious heterogeneity in the FIGO stage analysis, we conducted subgroup analysis based on year of publication, scoring method, sample size, and primary antibody to identify the source of heterogeneity. However, the heterogeneity remained and its source could not be identified. Sixth, the funnel plot, Egger’s test, and the Begg test indicated publication bias in the FIGO stage, leading to overestimating the effect sizes. Finally, several original studies did not report HRs with their 95% CIs or the estimated HRs and their 95% CIs from Kaplan–Meier survival curves, which may have introduced bias.Here, we summarized all relevant studies and performed a meta-analysis to assess the value of STAT3/p-STAT3 expression as a prognostic indicator for OC patients. Despite the aforementioned limitations, the results of this meta-analysis indicated the prognostic value and clinicopathological significance of STAT3 expression in ovarian cancer. Our findings show that STAT3 expression has potential as a specific biomarker in patients with ovarian cancer, and its increased expression indicates poor patient prognosis. These results may contribute to future explorations of the pathogenesis, diagnosis, anti-STAT3 therapy, and prognosis in ovarian cancer.Additional file 1: Figure S1. Sensitivity analysis for ovarian carcinoma vs. normal ovarian tissue. Figure S2. Sensitivity analysis for ovarian carcinoma vs. benign ovarian tumour. Figure S3. Sensitivity analysis for ovarian carcinoma vs. borderline ovarian tumours. Figure S4. Sensitivity analysis for FIGO stage. Figure S5. Sensitivity analysis for tumour stage. Figure S6. Sensitivity analysis for lymphatic metastasis. Figure S7. Sensitivity analysis for histological type (serous vs. non-serous) (A). Sensitivity analysis for histological type (mucinous vs. non-mucinous) (B). Sensitivity analysis for histological type (endometrioid vs. non-endometrioid) (C). Sensitivity analysis for histological type (clear cell vs non- clear cell)(D). Figure S8. Sensitivity analysis for overall survival. Figure S9. Sensitivity analysis for progression‐free survival.