Dromedary camels in the Arabian Peninsula distribute along different geographical and ecological locations, e.g. desert, mountains and coasts. Here, we are aiming to explore the whole genome sequence data of ten dromedary populations from the Arabian Peninsula to assess their genetic structure, admixture levels, diversity and similarity indices. Upon including reference dromedary and Bactrian camel populations from Iran and Kazakhstan, we characterise inter-species and geographic genetic distinction between the dromedary and the Bactrian camels. Individual-based alpha genetic diversity profiles are found to be generally higher in Bactrian camels than dromedary populations, with the exception of five autosomes  at diversity orders (q ≥ 2). The Arabian Peninsula camels are generally homogenous, with a small degree of genetic distinction correlating with three geographic groups: North, Central and West; Southwest; and Southeast of the Arabian Peninsula. No significant variation in diversity or similarity indices are observed among the different Arabian Peninsula dromedary populations. This study contributes to our understanding of the genetic diversity of Arabian Peninsula dromedary camels. It will help conserve the genetic stock of this species and support the design of breeding programmes for genetic improvement of favorable traits. Camelus dromedarius (dromedary camel) and two-humped Camelus bactrianus (Bactrian camel), in addition to the wild two-humped camelus ferus, form the old world Camelini tribe of the Camelidae family. The ancestors of this tribe, which diverged from the new world Lamini tribe about 16.3 million years ago (Mya), reached Eurasia via the Bering land bridge approximately 6.5–7.5 Mya. After the divergence of the one-humped camels from the two-humped animals, around 4.4 Mya, the Bactrian camels were domesticated from their wild ancestors about 5000–6000 years ago, most probably in eastern Asia. This was followed by the domestication of the dromedary camels in the southeastern region of the Arabian Peninsula around 3000–4000 years ago1.The domestic one-humped 2. In several countries, including for instance Iran, India, Turkey and Kazakhstan, both domestic camel types can be found, and anthropogenic hybridization between them can result in animals with high robustness and endurance—commonly used along long-distance trade routes1.Dromedary and Bactrian camels show wide geographical distribution due to their historical use in long-distance trading and transportation. Dromedary camels are predominantly found in the desert and semi-arid regions of Africa, Arabian Peninsula and southwest Asia, while Bactrian camels are mainly distributed throughout eastern and central Asia3. All of the above mentioned camel populations are used for milk and meat production, are hence classified as production camels, and can be found in the north and central area of the Arabian Peninsula. Camels bred specifically for competitive racing are hence known as racing camels, and are mainly from either Oman in the southeast of the Arabian Peninsula, or the north of the Arabian Peninsula where a recognized population known as al-Hurra is popular5.In the Arabian Peninsula, dromedary camels are classified according to various criteria, for example coat color, ecological location and their productivity, however there is no established breeding system informed by genetic analysis. Regarding coat color, camels with dark brown to black coat colors are known as Magaheem, while camels with white coats are known as Wodeh. Sofor and Shual camels have a brown coat color, with Sofor camels mainly characterized as being dark brown to black at the top of their neck, shoulder, hump and tail5.Dromedary camels from the west, e.g. Hadana and Sahlia, and southwest of Arabian Peninsula, e.g. Awadi and Awarik, face different ecological conditions than those from the north and center of the Arabian Peninsula. In contrast to the wide desert covering the north and center of the Arabian Peninsula, Hadana and Awadi camels are populating higher elevations, on the tops mountains, and are hence known as Hill or Mountain camels. By contrast, Sahlia and Awarik camels are mainly found near the Red Sea coast of Saudi Arabia and are known as Beach camels8, while more recent efforts by Bahbahani et al.9 and Ming et al.10 employed genotype-by-sequence (GBS) and whole genome sequence data, respectively. A fundamental challenge in assessing the genetic diversity of dromedary populations concerns accessibility to camel samples representing the different populations, and defining informative statistics with which to evaluate their diversity. Several diversity indices exist that measure species diversity at ecological levels, which can also be used to evaluate the genetic diversity of livestock species12. Shannon’s entropy, Simpson’s index and Renyi’s entropy are examples of such indices, which translated mathematically into number of equivalents called Hill numbers, behave in a way that can be used to express diversity11. All of these indices decompose into two components; alpha and beta diversities, which describe different diversity angles. Alpha diversity measures the average diversity of a single ecological community, while beta diversity measures the relative change in species composition between ecological communities11. An important parameter that differs between the Hill numbers of these two diversity components is the diversity order (q), which determines the sensitivity of the measure to the frequency of the species13. At q = 0, the species frequency, or abundance, is not counted. While, at q = 1, the species are weighed in proportion to their frequency. At q > 1, the Hill number becomes sensitive to the most frequent species13.Assessing the genetic diversity and structure of dromedary camels is the first milestone towards establishing a standard genetically informative breeding programme. Until recently this field of research has largely been confined to the analysis of autosomal microsatellite markers and partial mitochondrial DNA (mtDNA) sequences12 applied these ecological diversity measures to quantify SNP diversity at the level of the individual. These proposed alpha and beta diversity measures are able to account for the uneven, non-random, distribution of SNPs along chromosomes. Ma et al.12 proposed four Hill-number-based similarity measures to compare SNP similarity between populations. These measures investigate SNPs similarities at different levels: local SNP overlap (CqN) that quantifies the proportion of shared SNPs among individuals; regional SNP overlap (UqN) that looks at the proportion of shared SNPs in the pooled population; SNP homogeneity (SqN) that measures SNP evenness between populations; and SNP turnover complement (VqN) that represents the relative rate of SNP turnover per individual.Recently, Ma et al.In this study, we investigate if the different Arabian Peninsula dromedary camel populations can be genetically discriminated based on their geographical distribution using whole genome sequence data. Individual-level diversity and similarity indices are assessed in these populations and compared to reference dromedary and Bactrian camels.The mean depth of coverage of the mapped sequence reads among the dromedary samples ranged from 13.7 to 32.16 ×, with a mean of 25 ×, while among Bactrian camels it ranged from 5.96 to 23.02 × with a mean of 13.3 ×  analysis of all camels dataset 1) reveals clear differentiation between dromedary and Bactrian populations along the first principle component (PC1), explaining 37.8% of the total variation. Whilst, the second principle component (PC2), accounting for 3.2% of the total variation, separates camels from Iran and the Arabian Peninsula populations  reveals structure broadly consistent with the geographic sampling. Dromedaries sampled from the west, southwest and southeast of the Peninsula  are separated from those sampled from the north and center of the Arabian Peninsula populations  along PC1, explaining 6.7% of the total variation. PC2, which accounts for 6.2% of the total variation, shows some separation of the Hurra samples and Awadi camels from the other populations, in addition to an Awarik and a Shual samples   not related to the southwest of Arabian Peninsula. A substantial proportion of the southwest genetic ancestry is observed in camel populations from the west; Hadana (~ 0.21) and Sahili (~ 0.16), and southeast; Omani (~ 0.15), of the Arabian Peninsula. At K = 3, a genetic background specific to Omani camels from the southeast of the Arabian Peninsula is revealed  in Bactrian camel populations than in dromedary camels at all diversity orders, except q = 0  dromedary from the Arabian Peninsula; (2) dromedary from Iran; (3) Bactrian camels from Iran; and (4) Bactrian camels from Khazakhstan. At diversity orders q ≥ 1, We observed higher beta diversity and lower similarity indices when comparing between species , than within species  Figs. . A compa1. Evidence of hybridization between these species can be observed in countries where they coexist, such as Iran, Kazakhstan and Turkey. This genetic hybridization, which explains the introgressed Bactrian genetic ancestry into a dromedary sample from Iran at K = 2 of the dataset 1 admixture analysis, has previously been detected by Ming et al.10, and it is associated with breeding camels that combine the robustness and endurance of both species to withstand long-distance journeys1. The nar  and nar-maya  camels in Kazakhstan, and tulu camel in Turkey are examples of such cross-breeding as reviewed by Dioli14.The inter-species genetic distinction observed between the dromedary and Bactrian camels revealed by both the PCA and admixture analysis likely results from more than 4 M years of divergence between these two speciesThis level of inter-species genetic distinction is also evidenced in the individual-level alpha and beta diversity profiles and similarity indices. However, as the SNPs used in these analyses were derived from aligning sequencing reads to the dromedary camel reference genome assembly (CamDro3), it is inevitable that a greater number of SNPs would be identified in the more divergent species, resulting in higher alpha diversity values in Bactrian camels. Nonetheless, we observed higher alpha diversity profiles in dromedary camels than Bactrians on five autosomes, which may indicate that diversity in the genes on these autosomes may be of functional significance to dromedary camels. This is an interesting observation that warrants further investigation, for example using signatures of selection and gene-wise association analyses with potentially relevant traits.15, and between African and Arabian Peninsula dromedaries9.We observed a degree of differentiation by PCA, admixture and diversity analyses, between the dromedaries from the Arabian Peninsula and those from Iran, which may further be clarified upon including more dromedary samples from Iran. This differentiation is likely resulted from geographical separation by the sea preventing gene flow. Such geographical-wise genetic distinction, has also been observed among African dromedary populations, from Algeria and Egypt16. Another possible factor contributing to gene flow are the breeding practices of camel owners in the Arabian Peninsula, which are generally panmictic, lacking a structured breeding programme such as that typically observed in other livestock17. The level of admixture between Arabian Peninsula dromedaries obtained here can explain the observed high relatedness between the dromedary samples in the area. Despite this homogeneity, admixture analyses at K = 2 and K = 3 suggest a degree of genetic structure associated with geography, with dromedaries falling into three groups: (1) North , Central (Magaheem), West (Hadana and Sahilia); (2) Southwest (Awarik and Awadi); and (3) Southeast (Omani). Such level of genetic structuring has previously been observed on Arabian Peninsula camels using microsatellite genotyping18.The Arabian Peninsula dromedaries appear to be a homogenous population based on PCA, admixture and diversity analyses. Such genetic homogeneity is likely a consequence of high genetic admixture between the subpopulations sampled, possibly driven by the historical use of dromedaries in trading and transportation along the Arabian Peninsula19, and the coast of the Red Sea. This variable ecology might contribute to genetic differentiation between the dromedary camels populating the different areas. Although the west and southwest camel populations show a degree of genetic differentiation, their close proximity and likelihood of continuous gene flow may explain the shared genetic ancestry proportion between them. For example, the Awadi camels, which are typically found in the mountains of the Jazan region in the southwest, may also be found along the Red sea coast where Sahlia and Awarik camels are distributed.The north and center of the Arabian Peninsula is mainly characterized by plain desert, while the west and southwest of the Arabian Peninsula is characterized by variability in elevation associated with mountains, reaching 3000 m in altitude12.The calculated diversity profiles in this study are associated with different advantages benefiting livestock population genetics studies. First, unlike the well-known population-level diversity measures, e.g. heterozygosity, that require large number of samples per population, they are calculated at individual level solving the problem of low number of samples available per population. Second, both of the alpha and beta profiles assess the distribution pattern of the SNPs along each chromosome, or the whole genome, which other diversity measures do not. Third, these profiles can be calculated using other genetic variations, such as insertions, deletions and inversionsWe have presented here the first whole-genome sequence analysis of the genetic structure and diversity of Arabian Peninsula dromedary camels. By including dromedary and Bactrian reference populations from outside of the Arabian Peninsula, inter-species and geographic genetic differentiations have been revealed. The Arabian Peninsula camel appear to be a homogenous gene pool with a subtle degree of geographic structure. To validate these findings a larger cohort of camels from populations spanning the whole of the Peninsula is necessary. This study is a first step towards understanding the genetic diversity of dromedaries in the Arabian Peninsula, which is known as their center of domestication.20. These samples represent dromedary populations from several geographical locations in the Arabian Peninsula (Table ® Blood and Tissue kit (Qiagen) according to the manufacturer’s protocol.A total of 5 ml blood sampled from the jugular vein was collected from each of 21 dromedary camels using standard techniques approved and in accordance with the relevant guidelines and regulations of the department of veterinary public health and animal husbandry at King Faisal university (Ref: KFU-REC/2018-10-01). All authors compiled with the ARRIVE guidelines 2.0la Table . Genomic10 was downloaded from NCBI’s Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra).Genomic DNA was sequenced using paired-end libraries on an Illumina Hiseq 2000 platform at Macrogen in South Korea. Publicly available whole-genome sequence data for four dromedary camels from Iran, six Bactrian camels from Iran, and six Bactrian camels from Kazakhstan (Supplementary Table 21 was used to trim adapters from the raw sequence reads of the Arabian Peninsula dromedary samples. Raw sequence reads were discarded if: (1) 10% or more of the read bases were uncertain; (2) 40% or more of the read bases were low quality (base Qphred ≤ 20); (3) reads length was shorter than 15 bases; or (4) read complexity was less than 30%. Bases with quality score (Qphred) less than 20 were also filtered out from the reads. The remaining high-quality reads were mapped against the African dromedary reference genome assembly (CamDro3)22 using the BWA-MEM algorithm implemented in Burrows–Wheeler Aligner (BWA)23. Reads were coordinate-sorted using the SortSam option, and PCR-duplicates were marked and excluded using the MarkDuplicates and REMOVE_DUPLICATES = true options in the Picard tools version 1.119 (http://broadinstitue.github.io/picard/index.html). Summary statistics calculated for mapped reads included: the proportion of reference genome coverage via the genomeCoverageBed option implemented in BEDTools software version 2.1724; mean depth of coverage via the DepthOfCoverage tool implemented in GATK version 3.625; and the total number and percentage of mapped reads via the flagstat tool in SAMTools software version 1.1926.The fastp software version 0.22.0HaplotypeCaller tool in GVCF mode. Variants were subsequently combined and genotyped using the GATK CombineGVCFs and GenotypeGVCFs tools in two datasets: dataset 1, which comprised all camel samples; and dataset 2, which comprised only the dromedary camels from the Arabian Peninsula. Quality control filtering criteria were applied on the SNPs of each dataset using the GATK VariantFilteration tool. Parameters included: (1) excluding variants with low quality by depth (QD) (QD < 2); (2) excluding variants with root mean square of mapping quality for all reads of a site less than 40 (MQ < 40.0); (3) excluding variants with base quality score less than 30 (QUAL < 30); (4) excluding variants with high probability of allele-specific strand bias between forward and reverse strand (FS > 60); (5) excluding variants with bias in mapping quality between the reads supporting the reference and alternative alleles (MQRankSum < − 12.5); and (6) excluding variants with bias in the position of the alternative allele towards the ends of the reads (ReadPosRankSum < − 8). SNPs with a depth of coverage ranging between two reads and three standard deviations from the mean depth of coverage across samples were retained. A total of 14,224,566 and 4,945,503 autosomal SNPs were retained for dataset 1 and dataset 2, respectively, which are used to calculate SNP diversity and similarity indices.Single Nucleotide Polymorphisms (SNPs) were called from the mapped sequence reads using the GATK version 4.1.4.0 27. Within each dataset, SNPs were excluded if: (1) they had a minor allele frequency ≤ 5%; (2) had a call rate ≤ 95%; or (3) departed significantly from Hardy–Weinberg equilibrium . SNPs were also filtered to exclude those with a linkage disequilibrium (LD) correlation coefficient r2 value > 0.1, as in Bahbahani et al. (2019), using Plink’s indep-pairwise tool (--indep-pairwise 50 kb 10 kb 0.1) resulting in 44,962 SNPs in dataset 1 and 58,617 SNPs in dataset 2 , SNPs were further filtered with Plink 1.9 95%; or  departedSamples were excluded if: (1) their genotyping call rate was ≤ 95%; or (2) they shared identity-by-state (IBS) ≥ 95% with another sample, in which case that the lowest genotyping call rate was excluded. A single dromedary camel sample from Iran (SRR5563498) was excluded from dataset 1 due to high IBS with another dromedary sample from Iran (SRR5563500).prcomp function of R software28 on datasets 1 and 2 to determine the genetic relationship between the dromedary and Bactrian samples, and among the dromedary samples from the Arabian Peninsula. Local ancestry admixture analyses were conducted on both datasets using ADMIXTURE 1.2329, assuming the number of ancestral clusters (K) ranged from 1 to 13 for dataset 1, and K from 1 to 10 for dataset 2, which reflects the total number of sampled populations in each dataset. A total of 200 bootstrap iterations were performed for each K analysis. The optimal number of clusters was determined as the K value with the lowest cross-validation (CV) error.PCA was conducted using the --relatedness2 tool implemented in VCFtools version 1.1330. This tool implements the Kinship-based INference for Genome-wide association studies (KING) algorithm31 to determine relatedness (Ф). A Ф > 0.354 indicates a duplicate sample or monozygotic twin, 0.177 < Ф < 0.354 indicates first-degree relatives, 0.0884 < Ф < 0.177 indicates second-degree relatives, 0.0442 < Ф < 0.0884 indicates third-degree relatives, while Ф < 0.0442 indicates the sample-pair to be unrelated31.Pairwise relatedness tests were conducted on dataset 2 using the 10. For dataset 1, four populations were defined: (1) dromedary from Arabian Peninsula; (2) dromedary from Iran; (3) Bactrian from Iran; and (4) Bactrian from Kazakhstan. For dataset 2, the ten Arabian Peninsula dromedary populations were treated as separate populations since the admixture analysis did not reveal any clear clustering. Individual-level alpha diversity profiles were calculated at diversity orders q = 0 to 4. The mean alpha diversities were compared between populations using the non-parametric Mann–Whitney U test. For each diversity order, for all pairwise population comparisons in each dataset, we calculated beta diversity in addition to four similarity indices to investigate SNP variation levels: local SNP overlap (Cq); regional SNP overlap (Uq); SNP homogeneity measures (Sq); and SNP turnover complement (Vq).The diversity and similarity profiles, i.e. alpha and beta diversities and similarity indices, of autosomal SNPs per gene were calculated for each autosome in datasets 1 and 2 as detailed by Ming et al.Supplementary Information.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.Supplementary Table S4.Supplementary Table S5.Supplementary Table S6.Supplementary Table S7.Supplementary Table S8.Supplementary Table S9.


Uranoscopus cognatus) was collected within selected locations in the Indo-West Pacific (IWP). The partial mitochondrial DNA cytochrome c oxidase subunit 1 and the nuclear DNA recombination activating gene 1 were sequenced. Genetic diversity analyses revealed that the populations were moderately to highly diversified  except sampling station (ST) 1 and 14. The low diversity level, however was apparent only in the matrilineal marker , possibly due to stochastic factors or anthropogenic stressors. Population structure analyses revealed a retention of ancestral polymorphism that was likely due to incomplete lineage sorting in U. cognatus, and prolonged vicariance by the Indo-Pacific Barrier has partitioned them into separate stock units. Population segregation was also shown by the phenotypic divergence in allopatric populations, regarding the premaxillary protrusion, which is possibly associated with the mechanism for upper jaw movement in biomechanical feeding approaches. The moderate genetic diversity estimated for each region, in addition to past population expansion events, indicated that U. cognatus within the IWP was still healthy and abundant (except in ST1 and 14), and two stock units were identified to be subjected to a specific resource management program.Benthic species, though ecologically important, are vulnerable to genetic loss and population size reduction due to impacts from fishing trawls. An assessment of genetic diversity and population structure is therefore needed to assist in a resource management program. To address this issue, the two-spined yellowtail stargazer ( In Malaysia, a large amount of bycatch is continuously landed each year, representing the biggest component of the total marine fish landing. Fisheries statistics for 2010–2019 indicate a total of 1,464,917.3 tons (average) of fish landings in Malaysia, of which, 18.4% comprised non-target species2. Of these, 80.1% of the bycatch was landed by trawl nets, followed by purse seines and other seines, suggesting the harmful effect of fishing trawls on fishery resources. Meanwhile, fisheries statistics on the west coast of Thailand (WCT) revealed a slightly higher relative proportion of the bycatch for the same period of the year, where on average, 22.2% of the total marine fish landing  comprised non-target species3.Fishing is one of the most significant causes of decline in populations of ocean species. Catching fish is not harmful to the ocean, except for when we catch so many fish that there are not enough to breed and replenish themselves, including incidental removal of non-target fish species or younger fish. The damage from overfishing and bycatch affects not only the marine environment and socio-economy of the fishermen and their communities, but also billions of people who rely on fish for protein6. Uncontrolled or incidental bycatch activities may kill large numbers of unintended species, leading to a serious threat to marine diversity as a whole5. This includes benthic dwelling marine fish, such as the two-spined yellowtail stargazer (Uranoscopus cognatus). Assessment of its populations in the central-eastern coast of India revealed a high exploitation ratio due to the high discard rates in the bycatch and overfishing4. A high exploitation rate due to overfishing was also reported in the Atlantic stargazer (U. scaber) in the southeastern Black Sea region7. Continuous fishing pressure may ultimately lead these marine species to a high risk of genetic and population bottlenecks, if preventive action is not taken4. U. cognatus is commonly landed as bycatch and discarded in huge quantities in Malaysia and Thailand, as it has a low economic value. Nevertheless, to date, information on its populations particularly from this geographical region, is unavailable. Specifically, there is no available data on the population genetics of this species, despite its vast distribution areas. This species was reported to occur in the eastern Indian Ocean and the west Pacific Ocean from India, east to Northern Australia, south to the Philippines and north to Taiwan9  fishing gear to attract its usual prey of small fishes and crustaceans14. This family comprises seven genera, of which two are monotypic and distributed worldwide in tropical and temperate oceans16. Currently, there are 55 validated species in the family17. The genus Uranoscopus Linnaeus, 1758 is the most specious group of stargazers, consisting of 25 described living species18, with the highest number of species found in the Indian Ocean19. Of these, four species have been recorded in the surrounding seas of Malaysia20, namely U. bicinctus Temminck and Schlegel, 1843, U. cadenati Poll, 1959, U. cognatus Cantor, 1849 and U. oligolepis Bleeker, 1878. Species identification within this genus is very challenging due to the highly overlapping morphological characteristics among species, except for U. cognatus, which has two unique and distinguishing pairs of basipterygial processes with sharp spurs and a short black cirrus on the eyes15. U. cognatus is one of the smallest and most slender species within the genus.Stargazers (Uranoscopidae: Perciformes) are characterized by a heavily armored and flattened head with dorsally directed eyes 21. During the Pleistocene glacial cycles, a series of elevations and depressions in the sea level as low as 120 m below the present level, exposed the Sundaic region (extension of the Southeast Asian region), resulting in the emergence of land barriers that isolated the South China Sea from the Indian Ocean at its southern limit and from the Sulu Sea to the east24. Consequently, the terrestrial, freshwater, and marine taxa were affected by this historical geomorphological rearrangement, shaping the genetic distribution that is apparent today. Several hypotheses on molecular biogeography of multispecies within the IWP have been proposed25; of these, several studies advocate genetic homogeneity or partial panmixia restricted within a particular area of the ocean, for example in moray eels26, Indian mackerel (Rastrelliger kanagurta)27 and Japanese scad (Decapterus maruadsi)28, while others indicate extensive structuring, including in reef-fishes32, invertebrates35, seahorses36 and pelagic fish37.Biogeographically, the Andaman Sea  and South China Sea  are situated within the Indo-Pacific (sometimes known as Indo-West Pacific (IWP)) where the Southeast Asian region lies39. Thus, the multi-locus genetic approach is increasingly favored due to the high success rate in revealing both the contemporary and historical events for targeted species46. Specifically, mitochondrial DNA (mtDNA) markers are maternally inherited and are thus characterized by a low rate of recombination47, relatively rapid rate of evolution, and a deeper and greater range in a bifurcating evolutionary tree49. However, nuclear DNA (nuDNA) markers have a lower substitution rate50, are highly conserved51 and have a larger effective population size compared to mtDNA52. Due to these different characteristics, gene genealogies obtained from individual DNA marker can be different from the actual evolutionary history of a species, thus inferring that both paternal and maternal DNA markers are essential in providing a better understanding of evolutionary history48. The morphological approach is one of the simplest, most cost-effective and most efficient methods to identify fish stock structure and discriminate species, which is determined using differences in body measurements, meristic and anatomical characteristics54. Both genetic and phenotypic-based methods are imperative in designing sound management strategies, thus setting priorities for rational exploitation22.Population genetic tools offer an opportunity to elucidate the genetic level, patterns of dispersal (connectivity), and demographics (past and present), to better understand species responses’ to ecological changes and anthropogenic stressorsU. cognatus is available55, limiting our knowledge on the population genetic diversity of this bioresource. In the study, two samples each from the west coast of Thailand and east coast of Peninsular Malaysia were analyzed. The final partitioned nucleotide alignment consists of 14,098 base pairs, and formed a maximally-supported monophyletic group within the Uranoscopus clade, although clustering between samples collected from Malaysia was not strongly supported55. Herein, the mtDNA CO1 and nuDNA RAG1 genes were sequenced to examine the genetic diversity, phylogenetics and demography of U. cognatus within the IWP, with the inclusion of CO1 sequences retrieved from Indonesia (INDO) and Australia (AUS). A quantitative morphological study was also conducted to investigate whether allopatric U. cognatus differ morphologically. The hypotheses for this study were that a low genetic diversity would be expected due to the high exploitation rate, and a genetic homogeneity for intra-regional populations would be observed due to the absence of a physical barrier, yet a significant population structure between inter-regional populations would be expected. The data obtained from this study will be beneficial for establishing baseline data for stock management.To date, only a single genetic study describing the whole mitogenome of i.e. Hap29–32 were private to the west coast of Thailand (WCT), and the rest were present only on the east coast of Peninsular Malaysia (ECPM). Hap01 (59.13%) and Hap29 (86.96%) were the most common haplotypes in the ECPM and WCT. The nuDNA RAG1 sequence data revealed 22 polymorphic sites (11 parsimoniously informative and 11 singletons), defining 33 putative haplotypes, of which 21 (63.6%) were singletons. Nucleotide substitutions occurred at all codon positions, resulting in 14 non-synonymous amino acid substitutions. The number of haplotypes ranged from three at ST2 to nine at ST3, 9 and 11  defining 43 putative haplotypes, of which 33 (76.7%) were unique sequences. Point mutations occurred at the first and third codon positions, resulting in six non-synonymous amino acid substitutions. The number of haplotypes (nh) ranged from two (sampling station (ST)1) to 13 (ST7)  except for ST1 and 14. Interestingly, the low level of genetic diversity in ST1 and 14 was apparent only in the matrilineal COI marker . A moderate level of genetic diversity was deemed for U. cognatus from each geographical region based on the respective genetic marker, indicated by the moderate-to-high H and relatively low-to-moderate π within the respective regions (Table U. cognatus from ST1 (WCT)  resulted in a reduced level of the overall COI genetic diversity within the WCT . In general, the RAG1 sequences showed a higher H but lower π than the COI data set in most of the sampling stations.Both genetic markers revealed that U. cognatus from Indonesia (INDO) were clustered closely with those from ECPM as they were either very close or totally identical  Fig. a; in concal Fig. a.Figure FCT) was the highest (66.98%) when k = 2, separating the ECPM and WCT. In contrast, SAMOVA based on the RAG1 sequences revealed a much lower genetic subdivision between groups (FCT)  and among populations (FSC) (-1.30–0.61%), yet a higher proportion of genetic variance within populations (FST) (83.98–87.05%). The inter-regional pairwise comparisons ФST (WCT vs. ECPM) based on the COI data set were all statistically significant, however, based on the nuclear DNA data set, five pairwise comparisons were statistically significant (involving only ST1) , and the relatively low gene flow (Nm) estimates inferred by both DNA markers .SAMOVA based on the COI sequences revealed that the proportion of the total genetic differences between groups  except for ST2, based on the RAG1 sequences  I   based on 29 morphometric variables discriminated allopatric ce) Fig. , whilst POL Fig. .Figure 4U. cognatus, except for the number of supracleithral spines, where the majority of U. cognatus from the ECPM (83%) and WCT (47.6%) had three and four supracleithral spines, respectively   Table . HistoriU. cognatus spanning the IWP, suggesting a common source of origin or ancestor, yet prolonged habitat isolation, resulting in a significant genetic divergence as detected by a more rapidly mutated genetic marker than the slower one. In particular, the presence of a significant matrilineal structure was found between the WCT and ECPM, and this partially agreed with the nuclear DNA marker. The sharing of nuclear DNA haplotypes in the allopatric populations and an evenly distributed shared polymorphism within the ECPM suggest the retention of ancestral polymorphism because of incomplete lineage sorting62. The nuclear DNA RAG1 gene is known to evolve slower63 than the COI gene, and needs a longer generation time to be fully fixed regionally. Given the massive effective population size of U. cognatus within IWP, even prolonged vicariance for tens of thousands of years can yield ФST values lower than 0.525, as seen in the present study based on RAG1 sequences  and the northern stargazer (Astroscopus guttatus). However, the life cycle and reproductive characteristics of U. cognatus are still poorly known. Although the sex-related dispersal pattern among aquatic organisms has been documented in elasmobranch, horseshoe crab, eels, sea-snake and turtle71, it has never been reported in stargazer. Thus, more studies on uranoscopids should be conducted to uncover more information on this benthic creature.Mitonuclear disparity is often interpreted as a sex-biased dispersal of female philopatry and male-mediated gene flowU. cognatus were bounded by a genetic barrier in the central division  of the Indonesian Archipelago, known as the Indo-Pacific Barrier (IPB), through concomitant changes in oceanographic current, presumably prohibiting gene flow to happen between regions21. This is in concordance with the genetic break of the Indian-Pacific ocean-basin hypothesis21, and in accordance with earlier observations of several marine species including reef fish32, invertebrates35, seahorses36 and pelagic fish37. Nevertheless, several findings on fish that showed low genetic differentiation within the IWP region were also reported and associated with the highly dispersive life history stages that contribute to high rates of gene flow among populations, for instance in moray eels26, Indian mackerel (Rastrelliger kanagurta)27, and Japanese scad (Decapterus maruadsi)28. To better comprehend the evolutionary history of U. cognatus and the level of structuration at the species level, collecting more samples from outside of the WCT and ECPM is therefore highly required.Despite the absence of physical barriers to dispersal in the marine realm, our study showed that the populations of U. cognatus from the ECPM with those from central and west Java, Indonesia was apparent 73, the anemonefish (Amphiprion ocellaris)74, and the Pearse’s mudskipper 75.Interestingly, the genetic proximity of ent Fig. a, sugges77. The present study uncovered the phenotypic divergence of U. cognatus from the WCT and ECPM, characterized mainly by the interorbital fossa length (IFL), upper jaw length (UJL), postorbital length (POL), and the number of supracleithral spines. The IFL and UJL are among the variables with the highest loadings on the PCI, suggesting evolution in the premaxillary protrusion, likely associated with the mechanism for upper jaw movement in relation to biomechanical approaches to feeding (suction feeding). However, the rest of the morphometric and meristic characteristics were indistinct for U. cognatus from the WCT and ECPM, suggesting a close kinship or a shared common ancestor between the individuals, but with some phenotypic modifications for a better adaptation to the benthic lifestyle15. A low morphological divergence was also reported in the Atlantic stargazer (U. scaber) collected from five different seas around the Mediterranean Sea basin78 and in U. marmoratus populations within Palk Bay India53, suggesting specific local adaptations to environmental changes.Fishes are vulnerable to environmental distortion but able to adapt to changing surroundings through modification of their morphology, physiology and behavior. Phenotypic plasticity may, in some instances, reveal a high pliancy in morphological characteristics in response to a different environmental condition, such as availability of prey and water temperatureU. cognatus in this study provide strong evidence for a shared common origin or ancestor of U. cognatus within the IWP and genetic breakdown by the IPB for a long period has likely subdivided the populations into distinct WCT and ECPM lineages, followed by subsequent range expansion within respective regions. The retention of ancestral polymorphism within the IWP was likely due to incomplete lineage sorting in allopatric populations. The moderate genetic diversity estimated for each region, in addition to past population expansion events, indicated that U. cognatus within the scope of this study was still healthy and abundant, except for ST1 and 14. Although a low genetic diversity was apparent only in the matrilineal COI marker, special attention from the authorities is required to prevent further depletion of bioresources. Heterogeneous biotic and/ or abiotic conditions of the WCT and ECPM might have influenced U. cognatus differently, as apparent by morphological modifications to better adapt and survive in the benthic ecosystem. The current results are important for habitat and fisheries resource management, highlighting the effectiveness of incorporating highly conserved vs. rapidly evolving molecular markers, and morphological characteristics in elucidating the population genetics of benthic species within the IWP.The genetic and morphological data of U. cognatus were collected from the west coast of Thailand (WCT) and east coast of Peninsular Malaysia (ECPM) , Institute of Oceanography and Environment (INOS), Universiti Malaysia Terengganu (UMT) with voucher specimen reference numbers UMTGen1318 to UMTGen1323. A small portion of the pectoral fin was cut and cleaned before being preserved in 1.5 mL microcentrifuge tubes containing 95% ethanol solution at room temperature until further analysis.Tissue samples were obtained from dead/preserved specimens, and no experiments on live animals were performed in this study. Random samples of PM) Fig. . Samples79 and amplified at the partial mitochondrial DNA cytochrome c oxidase subunit I (COI) gene using the primers FishF2 and FishR280 and the single-copy nuclear recombination activating gene 1 (RAG1) using the primers RAG1F1 and RAG1R281. The PCR amplification for both DNA markers was carried out in a final volume of 25 µL consisting of 2.0 µl genomic DNA (50 ng/µL), 0.5 µM of each primer, 9.5 µl sterile ultrapure nano water (ddH2O) and 12.5 µl MyTaq DNA Polymerase . The PCR thermal regime for the CO1 consisted of an initial incubation at 95 °C for 2 min (min), followed by 35 cycles of 94 °C denaturation for 30 s (sec), 54 °C annealing for 30 s, and 72 °C extension period for 60 s, followed by a 70 °C final extension for 10 min before termination at 4 °C80. The PCR thermal protocol for RAG1 consisted of an initial incubation at 95 °C for 3 min, followed by 40 cycles of 94 °C denaturation for 30 s, 60 °C annealing for 45 s, and 72 °C extension period for 90 s, followed by a final extension at 72 °C for 10 min, and a final hold at 4 °C . PCR products were visualized on a 1.7% agarose gel stained with 3 µL SYBr safe and were sequenced using the forward primer for the COI and both the forward and reverse primers for RAG1 by the service provider, Apical Scientific Laboratories Sdn. Bhd. Selangor, Malaysia.Total genomic DNA was isolated from fin tissues by a standard salting-out protocol83. A total of 231 and 171 individuals were successfully amplified for the COI (534 base pairs (bp)) and RAG1  genes, respectively. To ensure that the sequences were aligned correctly, they were first translated into a protein sequence (no stop codon or indel was found). Then, the sequence identity was verified through the BOLD Identification System. To examine the degree of heterogeneity among the data sets and to validate whether the two datasets could be combined for analysis, the incongruence length difference for each data set was tested with 1,000 replications implemented in the partition homogeneity test84 using heuristic searches in PAUP* v4.0b1085. The partition homogeneity test returned a significant incongruence in the phylogenetic signal among the CO1 and RAG1 sequences (P = 0.01), suggesting that the data sets should be analyzed separately. All haplotype sequences obtained in this study were deposited in the GenBank database with accession numbers MK728892 to 728934 (COI) and MN649790 to 649822 (RAG1).Multiple sequences were aligned and trimmed using ClustalW implemented in MEGA 6.086. The genetic diversity indices, namely haplotype diversity (H) and nucleotide diversity (π), were calculated in Arlequin v3.587.The aligned sequences were screened for nucleotide variable sites, parsimony informative sites, the number of haplotypes (nh), and amino acid substitutions in DnaSP 5.1088 and Kimura 2-parameter (K2P)89 models, respectively. The phylogenetic relationships of U. cognatus were assessed by constructing a maximum likelihood (ML) tree in MEGA 6.0. The robustness of the statistical support for the ML tree branch was determined by 1,000 bootstrap replicates90. To comprehend the phylogenetic relationship of U. cognatus from a wider geographical range, the available COI haplotypes from Indonesia  and Australia  were included, and U. oligolepis (MK777854) was used as an outgroup taxon. Several validation tests  were conducted on the two sequences from Australia and showed that they were probably misidentification of U. oligolepis, thus, they were excluded from this study. Meanwhile, the only RAG1 GenBank sequence of U. albesca (EU167748) was included as an outgroup taxon in the RAG1 data set. The relationships of haplotypes were further elucidated based on a median-joining calculation using the Minimum Spanning Network (MSN) analysis implemented in Network version 5.0.1.191.The best nucleotide substitution models with the lowest BIC (Bayesian Information Criterion) score for the COI and RAG1 data sets were the Hasegawa-Kishino-Yano + Gamma (HKY + G)92. The partition scheme of groups (k) was determined based on the highest variances among groups (FCT), incorporating information on haplotype divergence and geographical proximity. The population pairwise comparison ФST that calculates the genetic differentiation among sites was determined in Arlequin v3.5.2.2, and statistically significant pairwise comparisons were tested with 10,000 permutations to estimate the departure from the null hypothesis of genetic homogeneity. The significant probability values were adjusted by performing the False Discovery Rate Procedure (FDR) at α = 0.05, which controls the family-wise error rate (FWER), a conservative type I error rate originating from multiplicity93.To identify similar groups of populations and to evaluate the amount of genetic variation among the partitions, a spatial analysis of molecular variance was conducted in SAMOVA v1.0ST94 and sequence-based statistics, NST95 and KST* and the significance levels were estimated using permutation tests with 1,000 replicates96 in DnaSP 5.10. The estimates of gene flow (Nm) based on both haplotype-based and sequence-based statistics were derived from the same program. The genetic distance estimates between the sampled stations were calculated in MEGA 6.0.The genetic differentiation among populations was assessed by employing haplotype-based statistics, HD97 and Fu’s Fs98 statistics, mismatch distribution, Harpending’s99 raggedness index (Hri), and the sum of squared deviations (SSD) estimates. The neutrality tests of Tajima’s D and Fu’s Fs were used to evaluate the deviation from the neutral expectation that may arise from historical population range expansion or mutation-drift disequilibrium and were calculated in Arlequin v3.5.2.2. Mismatch distribution indicated whether U. cognatus was demographically stable, expanding, or decreasing over time102 and was estimated in DnaSP 5.10. The Hri and SSD were computed in Arlequin v3.5.2.2 to evaluate if the sequence data significantly diverged from the assumptions of a population expansion model. The historical demographic parameters of τ (relative time since population expansion) were computed in Arlequin v3.5.2.2. The τ value was used to estimate the actual time (T) since population expansion using T = τ/2μk, where μ is the mutation rate per site per generation and k is the sequence length103. In this study, two mutation rates were used for CO1 (i.e. 1.03% and 1.77% per million years), while a mutation rate of 0.097% per million years was used for RAG163.The historical demographic changes in the effective population sizes were assessed based on the neutrality tests Tajima’s 104 and Kishimoto106, except for the head width, interorbital distance, interorbital fossa width and cleithral-spine length, where we followed Gomon and Johnson107, and the mouth (or gape) width was performed as in Rainboth108. The terminology of the morphological features was selected according to Pietsch11, Imamura and Matsumura109, and Vilasri15.Representative samples from the WCT (N = 24) and the ECPM (N = 21) were analyzed for phenotypic variation by using a total of 42 morphometric measurements, and meristic counts were taken on the left side of preserved specimens. Thirty linear measurements Fig. , which ri.e. the relative percentage to the standard or head length110) to equalize the variance among the groups, using the Paleontological Statistics Software Package (PAST) v3.24111. PCA analysis was carried out essentially by calculating the linear combinations of the original variables to create a ‘morphospace’ by plotting the specimens on the first few major axes of the morphological variations (PC axes), which represent the majority of the variability between specimens, while maintaining the relevant variation between the data points112.Principal component analysis (PCA) was carried out on the standardized data (Supplementary Information.


Cities around the globe are embracing the Healthy Cities approach to address urban health challenges. Public awareness is vital for successfully deploying this approach but is rarely assessed. In this study, we used internet search queries to evaluate the public awareness of the Healthy Cities approach applied in Shenzhen, China. The overall situation at the city level and the intercity variations were both analyzed. Additionally, we explored the factors that might affect the internet search queries of the Healthy Cities approach. Our results showed that the public awareness of the approach in Shenzhen was low. There was a high intercity heterogeneity in terms of interest in the various components of the Healthy Cities approach. However, we did not find a significant effect of the selected demographic, environmental, and health factors on the search queries. Based on our findings, we recommend that the city raise public awareness of healthy cities and take actions tailored to health concerns in different city zones. Our study showed that internet search queries can be a valuable data source for assessing the public awareness of the Healthy Cities approach. The internet has increasingly linked the world together. It only took 15 years to increase the number of internet users from 1.1 billion in 2005 to 4.0 billion  by 2019 [Internet search queries account for the lion’s share of data generated by internet users. For example, about 3.5 billion search queries per day are made through Google alone . InterneThe World Health Organization has promoted the Healthy Cities program since 1986 to address modern urban health challenges . The priInternet search queries may provide a cost-effective way to evaluate the public awareness of the Healthy Cities approach. For instance, Google Trend data have been used to evaluate the public’s attention on 46 health-related events in the United States in 2017. Ten of those events were found to be impactful health awareness events . InterneIn this study, we intend to explore the potential of using internet search queries to assess the public awareness of the Healthy Cities approach through a case study in Shenzhen, China. Shenzhen has gone through rapid urbanization in the past four decades. It has changed from a fishing village to the most prominent technology center in China. Its technology-savvy residents are well-connected to the internet. China started a small-scale pilot healthy city program in 1994. However, the program kept a low profile until 2016, when the central government initiated a national program to promote healthy cities. The Shenzhen municipal government set a plan to convert the city into a healthy city in 2019. Since the Healthy Cities approach is relatively new to Shenzhen’s residents, the interest to learn more about the approach should be strong. These combinations made the city an ideal place to conduct the case study. Specifically, we had the following objectives for this study: (1) to reveal the spatiotemporal patterns of search queries on the information relevant to the Healthy Cities approach adopted in Shenzhen and (2) to explore how the demography, environmental conditions, health services, and health status of an area affect the search queries made by the public. Several assumptions were tested using those data: (1) places with higher population densities may have more people interested in health issues, (2) places with a higher percentage of people over 60 years old will make fewer queries, as seniors are less technology-savvy than young people, (3) people living in a better environment may be less worried about the environment’s health risk factors and conduct less queries, (4) the residents’ health status may influence the queries on the Healthy Cities approach, and (5) places with more health institutions will have more search queries, because health workers make the most search queries concerning the Healthy Cities approach.2 roughly between 22°27′–22°52′ N and 113°46′–114°37′ E. Shenzhen consists of 11 districts and 74 subdistricts. Among the districts, the Shenshan Special Cooperation Zone is located in another city but is administered by the Shenzhen government. It is not included in this study. Shenzhen is a highly developed city. By the end of 2018, the built-up area reached 927.96 km2, and the urban population reached 13.03 million. It is the first city in China that has an urbanization rate of 100% [Shenzhen is a megacity in South China. The administrative area of Shenzhen is 1997.47 km of 100% .A list of the keywords that are most relevant to the Healthy Cities approach adopted in China was first generated using two official documents issued by the National Patriotic Health Campaign Committee (NPHCC): Guiding Opinions on Constructing Healthy Cities and Healthy Townships  and the The list of keywords was submitted to Baidu, the most prominent web search engine in China. The daily search queries made through Baidu already reached 4 billion in 2013 . Baidu uAdditionally, data on the demographic, environmental, and health conditions at the subdistrict level were collected. For the demographic data, the population density and the population older than 60 were collected. Areas of green spaces were used as an indicator of the built-up environment. These data and essential geographic data, including the administrative boundaries and building layouts, were obtained from the Shenzhen Research Center of Digital City Engineering, Shenzhen Municipal Bureau of Planning, and Natural Resource Management. The mortality due to noncommunicable diseases for 2016–2018 was used as an indicator of the health status of a place. The data were obtained from the Shenzhen Center for Disease Prevention and Control (CDC). Due to the strict regulation on privacy, Shenzhen CDC only provided mortalities grouped into malignant tumors, heart diseases, cerebrovascular diseases, and endocrine system diseases by age classes at the subdistrict level. In addition to the above data, the point of interest (POI) data in 2018 were obtained from Gaode, one of China’s major map service companies. The POIs for hospitals, community health centers, and public health agencies were extracted as an indicator of the health services.R .Data were preprocessed before the formal analysis. The daily search queries on each keyword were normalized using the total number of search queries to generate the standardized search queries at the subdistrict, district, and city levels. The possible influence of the variations in volumes of internet searches was removed through standardization. The percentage of floor areas of residential buildings in 300 m of a green space larger than 1 ha was calculated as the indicator of green space following the method used in an early study . For morR package funtimes. The time series of search queries on the keyword “healthy city” was also examined, as it is the most specific keyword of the Healthy Cities approach. Additionally, the Shenzhen municipal government’s major events during the health city project were overlaid on top of the time series. We visually checked whether these events impacted the search queries. An effect was assumed when the peak of the search queries occurred immediately after the event.The spatiotemporal patterns of the search queries were analyzed at the city, district, and subdistrict levels. At the city level, the number of search queries for each keyword was first ranked to describe the overall popularity of the keywords. The daily number of search queries on all the keywords was tested for a monotonic time trend using the Mann–Kendall test contained in I values were calculated for the top five keywords to test whether there were spatial clusters for the search queries among the 74 subdistricts. The Moran’s I values were calculated using the spatial autocorrelation function in ArcMap .The total numbers of search queries on each keyword over a year were compared to show the difference in each district’s interest at the district level. The total number of search queries over a year at each subdistrict was also compared to capture interest at the subdistrict level. Besides, Moran’s Hmisc was used for calculating the Spearman correlation coefficients and testing the significance of the correlations.The correlation analysis was conducted to explore the association among the number of search queries and environmental, demographic, and health conditions at the subdistrict level. The Shapiro–Wilk test was first performed to examine the normality of the datasets. Since some data did not pass the test, Spearman’s correlation was used in the analysis. If there was a significant correlation, then the ordinary least-squares model was fit to the pair to examine the influence of the factor on the number of search queries. The R package Between 1 January 2019 and 31 December 2019, a total of 12.34 billion internet search queries were made through Baidu by Shenzhen’s residents. The search queries on the list of keywords relevant to healthy cities were 1.06 million, about 0.001% of the total search queries.At the city level, the number of search queries on the keyword “food safety” was the highest, 545,956 counts, while the number of search queries on the keyword “health impact assessment” was the lowest, with only one count  announced the plan to build a healthy city in a leading position internationally .The number of search queries on all keywords was not significantly associated with the demographic, environmental, and health factors at the subdistrict level . The numThe internet search queries revealed two patterns of public awareness of healthy cities in Shenzhen in 2019: (1) The overall public awareness was low in the city, and (2) public awareness was highly heterogenous at different geographic levels.The total number of internet search queries on all keywords in a year was 1.06 million for the entire city, less than one-third of the mean daily internet search queries (3.38 million) made in Shenzhen. The queries on the specific term “Healthy city” were only 689 for the entire year. These numbers reflected the lack of awareness or lack of interest among Shenzhen’s residents in the Healthy Cities approach. Considering the program is relatively new to Shenzhen’s residents, the former is more likely than the latter. The lack of public awareness could be due to two reasons. First, the healthy city project is just one of many health promotion programs run by the Shenzhen municipal government, so no extra efforts were committed to promoting the project. European cities treat the Healthy Cities approach as a “whole-system” approach that integrates multidisciplinary actions across the risk factors . In ChinSecond, the main channel used to convey information on the healthy city project was the news on TV and in newspapers and official documents posted on the government website. Occasionally, the government holds public outreach events to promote some aspects of the Healthy Cities approach. For example, the spike in the search queries on all keywords on 26 September 2019 was driven by a public outreach event on food safety in school campuses. The search for the keyword “Food safety” jumped to over 30,000 on that day. However, without a consistent and aggressive information campaign, it is unlikely that the project as a whole will capture the residents’ attention. This point was made clear by the low responses to the meetings and events related to the healthy city project in general.While the overall awareness was low at the city level, the public awareness was heterogeneous among districts and subdistricts. The keywords that were queried and the total number of queries all varied substantially. The patterns reflected varied health needs at the district and sub-city levels in Shenzhen. Studies in other cities have also shown that urban communities in cities are facing different health challenges. Besides, their capacities for implementing the health city project are also highly varied ,32. ThisWe tried to explain the variations in internet search queries by looking into the potential influencing factors. Nevertheless, the demographic, environmental, and health factors examined in this study did not show significant correlations with the number of queries on all keywords. Except for the number of health institutions, those factors also did not correlate significantly with the number of queries on the keyword “Healthy city”. The exception was understandable, as healthy cities in China are primarily administered by the health sector so far . HoweverThe failure to identify the factors that significantly affected the internet search queries on the Healthy Cities approach was not surprising. Many human factors can affect internet search behaviors ,35. We cThe findings from our study have some important implications for moving forward the healthy city project in Shenzhen. First, the public awareness of healthy cities needs to be improved significantly. A rigorous public outreach program should be implemented as part of the healthy city project. Second, the divergent needs of people in different communities must be addressed with localized plans and actions. This means that bottom-up actions should be adopted, along with the primarily top-down actions taken by the government so far.The public awareness of the Healthy Cities approach is key to the success of any healthy city project. Nevertheless, public awareness has rarely been assessed in cities where healthy city projects are deployed. The small sample sizes used in cities that have conducted these assessments may not capture the whole picture of public awareness. In this study, we explored the potential of using internet search queries to assess the public awareness of the Healthy Cities approach. We showed that the data was suitable for this task through a case study in Shenzhen. Shenzhen’s residents had a low awareness of healthy cities. There was also a high intercity heterogeneity in public interests on different components of the Healthy Cities approach. Our assessment results provide helpful information for government agencies to assess the healthy city project’s progress and take remedial measures. The method developed in this study can be applied in other cities in China or other countries to assess their healthy city projects. In the future, a direction that is worth pursuing is to combine internet search queries with other types of big data, such as Twitter and Weibo, to better understand the public perception of the Healthy Cities approach in a city.


This study describes the longitudinal changes in bovine leukemia virus (BLV) ELISA antibodies, proviral load (PVL), and blood lymphocyte counts (LC) observed over a 2.5-year period in naturally infected cattle. The dataset utilized was from a BLV intervention field trial on three Midwestern dairy herds. Our analysis showed ELISA false negatives were more likely to occur in cattle with low PVL and normal LC. On average, negligible changes in LC were observed during six-month intervals. Periods of lymphocytosis, defined as >10,000 lymphocytes per uL of blood, were observed in 31.5% (68/216) of BLV test-positive cattle. In BLV test-positive cows, an average increase of 2900 to 3100 proviral copies per 100,000 cells was observed during each subsequent six-month sampling interval. The difference between the minimum and maximum PVL observed for an ELISA-positive cow with 3 or more observations ranged from 0 to 115,600 copies per 100,000 cells . Therefore, following the identification of ELISA-positive cattle and the assessment of PVL and LC, subsequent semiannual tests to assess disease progression may not be needed. Further work is needed to determine how available diagnostic tests can be optimized to design cost-effective testing schemes for BLV control programs. Bovine leukemia virus (BLV) is in the family Retroviridae and genus Deltraretrovirus and is the causative agent of enzootic bovine leukosis. As a retrovirus, the BLV RNA virus is reverse transcribed into a DNA provirus that integrates into the host genome, leading to a persistent lifelong infection. The primary target for BLV is host B-lymphocytes, although it has also been detected in other cells, such as T-lymphocytes and mammary epithelium ,2,3. EndWhen the prevalence is low, BLV can be controlled through the removal or segregation of all infected cattle as was accomplished in many European countries that have achieved disease-free status ,10. For The transmission of BLV among cattle is thought to occur from the transference of BLV-infected cells, which can be found in blood, milk, colostrum, saliva, and nasal secretions ,12,13,14Infection with BLV has classically been thought to develop slowly across three progressive disease states defined by lymphocyte counts (LCs) and tumor development: 1. aleukemic , 2. lymphocytosis (persistently elevated lymphocyte count), and 3. lymphoma or lymphosarcoma (development of tumors) . At any In addition to the three classic disease states, researchers have recently classified BLV-infected cattle by proviral load (PVL), expressed as proviral copies per quantity of cells or DNA ,25. A cuThe development of lymphocytosis is thought to result from the gradual polyclonal expansion of BLV-infected cells ,28. The In another experimental infection study by Forletti et al., the establishment of PVL was associated with the BoLA DRB3 haplotype, which has also been related to high and low PVL disease states in several cross-sectional studies ,33,34. LA greater understanding of the progression to high PVL, high LC disease states is necessary given the potential for BLV control through the removal of these cattle, which are now thought to be the most infectious to herdmates. The objective of this study was to describe the longitudinal changes in BLV ELISA diagnostic results, PVL, and LC in cattle naturally infected with BLV and tested semi-annually over a 2.5-year period. More specifically, we wanted to examine the relationship between observed changes in ELISA test results and both the PVL and LC, to describe the observed fluctuations in the PVL and LC measurements over time, and to assess how changes in these measurements were interrelated.Two or more ELISA tests were available for 68.9% (537/779) of cows in the study database. Consistent ELISA results were observed in 76.0% (408/537) of cows with multiple observations. At least one change in ELISA status, defined as a different ELISA test result than the previous semi-annual test, was observed for 129 cows with two or more observations. Multiple changes (2+) in ELISA status were observed for 20.1% (47/234) of cows with three or more ELISA tests. In fact, 12 cows experienced three changes and four cows experienced four changes in ELISA status.p < 0.001). For ease of interpretation, the marginal probabilities of experiencing a change in ELISA status as lymphocytes increase are provided in In separate GLMM models, increases in LC and PVL were negatively associated with the odds of experiencing a change in ELISA status. The LC for observations with a change in ELISA status ranged from 1800 to 16,600, with 75% of lymphocyte counts being less than 6700 lymphocytes per µL. The odds of a cow experiencing a change in ELISA status were reduced by 23.6% for each increase of 1000 lymphocyte per µL  of cows that had new infections and were subsequently retested. This change was an ELISA false negative for six cows that tested ELISA negative and PCR positive at a subsequent observation. The remaining four cows with new infections experienced changes from ELISA positive to ELISA suspect (n = 3) or suspect to positive (n = 1).p = 0.342) or the semi-annual test (Fisher’s Exact p = 0.486).Aside from those changes that occurred following new infections, ELISA false negatives were observed in an additional 13 cows. Four cows were observed to have two ELISA false-negative results and one cow was observed to have three ELISA false negatives. Collectively, a total of 25 ELISA false-negative results, from 19 cows, were observed among 609 observations from 254 cows assumed to be BLV test positive based on combined longitudinal ELISA and PCR data. Among the 19 cows, 6 did not have subsequent tests following the false negative, 2 had consecutive false negatives with no subsequent ELISA tests, 7 had one or more ELISA-suspect or positive results after, and 4 had multiple ELISA-negatives with an ELISA-suspect or positive test in between or after the false-negative result(s). The optical density of ELISA false-negative results ranged from 0 to 0.10. The occurrence of ELISA false negatives was not associated with herd  at the time of the false-negative ELISA. Lymphocyte counts associated with ELISA false negatives ranged from 3300 to 10,200 (median: 4900) per µL of blood. A significant association was identified between lymphocyte counts and the odds of testing ELISA negative; for each increase of 1000 lymphocytes, the odds of an ELISA false negative decreased by 28.9% . Lymphocytosis (>7500 lymphocytes per µL of blood) was observed for 40.7% (296/728) of observations, with at least once incident of lymphocytosis observed in 51.5% (167/324) of BLV test-positive cows. Among cows with 2 or more LC observations, 49.1% (106/216) were consistently aleukemic, 30.1% (65/216) were persistently lymphocytotic, 9.3% (20/216) progressed from aleukemic to lymphocytotic, and the remaining 11.6% (25/216) were transiently lymphocytotic. When the definition of lymphocytosis was increased to 10,000 lymphocytes per µL, 23.4% (170/728) of observations were lymphocytotic and 32.7% (106/324) of cows had at least one incident of lymphocytosis. Using a cutoff of 10,000 lymphocyte per µL, 68.5% (148/216) of cows with 2 or more observations were aleukemic, 13.9% (30/216) were persistently lymphocytotic, 9.3% (20/216) progressed from aleukemic to lymphocytotic, and 8.3% (18/216) were transiently lymphocytotic.p = 0.008).Marginal means, which were from a model adjusted for repeated measures, were used to compare lymphocyte counts across ELISA result categories. Marginal LCs were higher among observations with ELISA-positive results (8100 lymphocytes per µL) than ELISA-suspect results (7300 lymphocytes per µL)  marginal increase of 11 (95% CI: −100 to 200) lymphocytes per µL was observed for each subsequent cross-sectional sampling for BLV test-positive cows with three or more leukocyte counts (n = 135). Addition of ELISA status to the model was marginally non-significant (p = 0.085).In an unconditional mean model accounting for repeated measures, 81% of the variation in the observed lymphocyte counts was attributed to between-cow differences. In an unconditional growth model accounting for repeated measures, only 1% of within-cow variation was accounted for by the inclusion of time as a random slope. A non-significant (p < 0.001). This was confirmed using an LMM accounting for repeated measures.Proviral load was measured in DNA extracted from 850 blood samples from 359 BLV test-positive cows. The observed PVL ranged from 0 to almost 180,000 provirus copies per 100,000 cells and was right skewed. Not accounting for repeated measures, PVL was significantly higher in ELISA-positive samples  than in ELISA-suspect samples  .The average change in proviral copies between consecutive cross-sectional samplings was an increase of 2900 copies per 100,000 cells  over the approximately 6-month period b. The mep = 0.001).An unconditional mean model was utilized to partition the observed variation in PVL for cows with 3 or more observations, where 7.6% of variation can be attributed to inter-herd differences while 79.5% is attributed to cow-level differences. The addition of semi-annual test number  as both a fixed and random effect accounted for 19.2% of the cow-level variation. A marginal increase of 3072 proviral copies per 100,000 cells was observed with each subsequent 6-month sampling (p = 0.023). Marginal differences in PVL were not statistically significant between ELISA-suspect and either ELISA-negative  or ELISA-positive  observations. Differences between ELISA positive and ELISA negative, however, were significant (p = 0.033), with a marginal increase of approximately 10,400 copies per 100,000 cells. An increase of 11 proviral copies was observed for every increase of 1000 lymphocytes per µL of blood (p < 0.001). Similar effects were observed in a multivariable model containing sampling number (p = 0.004), ELISA status (p = 0.021), and lymphocyte count (p < 0.001).Independently, ELISA status and lymphocytes per µL of blood were included as predictors in the model that contained time as both fixed and random effects. Overall marginal differences in proviral copies were observed when ELISA status was added to the growth model . PVL testing can be time and labor intensive because it requires the collection of blood samples as compared with milk samples commonly used for ELISA testing. Therefore, control programs typically use milk ELISA testing to screen for BLV and conduct follow-up PVL testing of animals that are ELISA suspect or ELISA positive. Using this sampling scheme, cattle are identified as ELISA suspect or positive as a pre-condition for PVL assays to be performed; therefore, the occurrence of BLV-infected cattle having ELISA false negatives can cause confusion in the operation of a control program. Under typical control program protocols, once a cow is identified as being infected with BLV, it may be tested multiple times over the course of the intervention program to quantify proviral load. Although infection with BLV is known to result in a lifelong infection, given the viral integration into the host genome, a greater understanding of the progression of BLV infection over time and of how proviral load and lymphocyte counts change was needed. This understanding can augment control programs by reducing the frequency, and therefore cost, of repeated samplings.With regard to ELISA tests, we were interested in the cows that had changes in ELISA status, especially going from positive to negative, and their associated proviral load and lymphocyte counts. The ELISA assay was categorized as negative, suspect, or positive based on a numerical optical density (OD) reading. The general purpose of the suspect category was to express caution in the test result because residual milk carryover (milk from the previous cow tested) may cause false positives . HoweverIn this study, the true frequency of ELISA false negatives  was unknown given the way ELISA was used as a screening test. However, because PVL was evaluated for cattle that tested ELISA negative but had a previous test at which they were ELISA suspect, ELISA positive, or PVL positive , we observed that 7.4% (19/258) of cows known to previously be BLV test positive had ELISA false negatives. This proportion is similar to another study, which reported 6.8% (5/73) of cows were serologically negative and PCR positive . The obsWe observed that both the probability of having a change in ELISA and the probability of an ELISA false negative was inversely associated with both the proviral load and lymphocyte count. The observed ELISA false negative with a high PVL  may reflect a diagnostic testing error. In a study by Chen et al., PVL was not examined, but the identification of viral RNA was reported and was associated with elevated lymphocyte counts . Given tThe cows with changes in ELISA status, specifically going from ELISA positive to negative, do not likely pose a great threat to control programs that focus on the removal of cattle with high PVL and LC. In the long term, however, the small subset of cows with repeated ELISA false negatives could prove problematic for BLV eradication programs that rely on serology screening by milk ELISA to initially identify infected cattle.Regarding both the LC and PVL, the main outcome of interest was the observed changes over time. We sought to measure the extent to which LC and PVL increase over time in BLV-infected cows and to determine if the presence of cows with high PVL and lymphocytosis were the result of a gradual disease progression.In this analysis, negligible increases in lymphocytes were typically observed between sequential tests conducted at approximately 6-month intervals. The addition of time into a linear growth model only accounted for a small (1%) portion of within-cow variability. Combined with the observed changes in LC, these results suggest that fluctuations in LC are not the result of gradual disease progression. Given that diagnostic reference intervals for LC typically span an approximate range of 5000 lymphocytes (2500 to 7500 lymphocytes per µL) , the obsSimilar to the lymphocyte counts, changes in PVL were observed between sequential six-month sampling intervals and between the first and last cross-sectional observation. Absolute fluctuations averaged 19,200 proviral copies per 100,000 cells. In an experimental infection study, we observed that PVL typically stabilized, with minor fluctuations after an initial peak following infection . Thus, iThe current thought is that BLV persists through the mitotic replication of infected cells. Our observation of a non-significant increase in LC coupled with a small, yet significant, increase in PVL supports the idea that infected cells survive longer and accumulate more than non-infected cells  or suggeCollectively, our observations indicate that, while variations are observed, the majority of BLV test-positive cows experience minor increases in LC and PVL over time. Once cattle are identified as being infected, and the LC and PVL are quantified, repeated testing as often as 6-month intervals may not be necessary. For ongoing control programs, annual BLV testing may be sufficient. While random intercepts and slopes were included in models of lymphocytes and proviral load, linear mixed models borrow analytical power collectively from all observations, and therefore do not perfectly fit the observed measurements. Thus, it is possible that while the statistical models indicated cows experience negligible to minor increases in LC and PVL with time, there is a subset of cows in which LC and PVL rapidly increase. Although reducing the sampling frequency could fail to identify these cattle, a balance must be identified that allows for cost-effective broad implementation of BLV control programs. Future studies should compare the effectiveness of intervention programs with varying sampling frequency to significantly reduce both the herd BLV prevalence and incidence.Utilizing an existing dataset generated from an intervention field trial leads to limitations for this analysis. Given the series testing design implemented in the field trial, cows needed to have at least one ELISA test with either suspect or positive results for both PVL and LC tests to be performed. Thus, the subset of ELISA-negative cows testing PVL positive  or having lymphocytosis may be greater than reported. Additionally, the intervention field trial focused on the removal of cows with high proviral loads and/or lymphocytosis, which were the disease states of greatest interest. Therefore, our results may be biased because the likelihood of having longitudinal observations on these cattle was reduced. Lastly, the length of infection was unknown for cattle in the dataset, hindering the ability to associate changes in diagnostic measurements with length of infection, which could potentially provide better insight to the rate of natural disease progression.The data used for this analysis were from a previously published field trial, which aimed to reduce the prevalence and incidence of BLV using PVL and LC measurements as indicators of the infection potential of cows that could then be targeted for culling or for segregation until culling at a later date . Three mMilk and serum antibody ELISA testing were performed by CentralStar Cooperative  using a modified commercial gp51 antibody capture ELISA test  as previously described ,47. ELISBlood leukocyte differentials were performed using an early version of the QScout blood leukocyte differential machine  as previously described . The difProviral load was quantified using the CoCoMo qPCR method   as previAll descriptive and analytical statistics were performed using Stata 15 .A change in ELISA status was defined as a cow having different ELISA test results  reported at consecutive semi-annual sample times. Changes in ELISA status were attributed to new infections, false negatives, and false positives based on the criteria presented in Only the observations from cattle classified as BLV “test positive” by combined ELISA and PCR results contributed to the descriptive distributions of both PVL and LC. In evaluating the LC of BLV test-positive cows, persistent lymphocytosis was defined by all observed LC being greater than the specified cutoff; transient lymphocytosis was defined by the observance of both normal and lymphocytic results over time in a BLV test-positive cow. Cut-offs of both 7500 and 10,000 lymphocytes per µL of blood were examined and used to define lymphocytosis based on diagnostic laboratory reference intervals and BLV-related literature, respectively ,45.A generalized linear mixed model (GLMM) approach was used to examine how PVL and LC affected changes in ELISA status in BLV test-positive cattle across semi-annual sampling points. Using a binomial distribution and a logit link, two separate outcomes were examined: any change in ELISA status and a change associated with an ELISA false negative. The linearity of continuous variables in the logit was assessed using the Box-Tidwell statistic. The low frequency of false negatives resulted in categorical variables with zero counts that could not be included in bivariable logistic regression models because of perfect prediction. Therefore, the associations between categorical predictors and ELISA false negatives were assessed using Fisher’s exact tests.Changes in LC and PVL over time in BLV test-positive cattle were evaluated using linear mixed models (LMMs). Models of PVL included random effects at both the herd and cow level. The PCR assay number was included as a random crossed effect in PVL models to account for systematic variation in proviral load that may have been the result of inter-plate variability. Potential explanatory variables in PVL models were LC and ELISA status. Models of LC included a random effect for each cow. The standard errors of herd-level random effects could not be estimated in the unconditional lymphocyte model and, therefore, herd was not included as a random effect. Only ELISA status was included as an explanatory variable in LC models. In addition, linear growth models were created by including the semi-annual test number as both a fixed and random effect slope to estimate both the average and individual change over time. Only BLV test-positive cows with three or more outcome measurements were included in the linear growth models. All LMMs were built using restricted maximum likelihood estimation using Kenward-Rodger adjustment for degrees of freedom. The normality of residuals was visually assessed by histograms and Q-Q plots. Levene’s test was used to evaluate the homogeneity of variance across categorical predictors. Wald’s test was used to assess the statistical significance of independent variables. The statistical significance of marginal comparisons was adjusted using the Bonferroni correction for multiple comparisons.In this analysis, we identified that a small portion of ELISA-positive cattle became ELISA negative when re-tested at 6-month sampling periods. These cattle tended to have low proviral loads and normal lymphocyte counts, and therefore likely pose little transmission risk to herd mates. Examination of the longitudinal changes in LC and PVL identified that, although both measures varied over time, most cows do not experience large changes that may be indicative of disease progression or an increase in their potential to infect susceptible herd mates. Confirmation is needed, but our results suggest that PVL and/or LC testing at 6-month intervals may be more frequent than necessary for most disease control programs.


Human impact is noticeable around the globe, indicating that a new era might have begun: the Anthropocene. Continuing human activities, including land‐use changes, introduction of non‐native species and rapid climate change, are altering the distributions of countless species, often giving rise to human‐mediated hybridization events. While the interbreeding of different populations or species can have detrimental effects, such as genetic extinction, it can be beneficial in terms of adaptive introgression or an increase in genetic diversity. In this paper, I first review the different mechanisms and outcomes of anthropogenic hybridization based on literature from the last five years (2016–2020). The most common mechanisms leading to the interbreeding of previously isolated taxa include habitat change (51% of the studies) and introduction of non‐native species . These human‐induced hybridization events most often result in introgression (80%). The high incidence of genetic exchange between the hybridizing taxa indicates that the application of a genic view of speciation (and introgression) can provide crucial insights on how to address hybridization events in the Anthropocene. This perspective considers the genome as a dynamic collection of genetic loci with distinct evolutionary histories, giving rise to a heterogenous genomic landscape in terms of genetic differentiation and introgression. First, understanding this genomic landscape can lead to a better selection of diagnostic genetic markers to characterize hybrid populations. Second, describing how introgression patterns vary across the genome can help to predict the likelihood of negative processes, such as demographic and genetic swamping, as well as positive outcomes, such as adaptive introgression. It is especially important to not only quantify how much genetic material introgressed, but also what has been exchanged. Third, comparing introgression patterns in pre‐Anthropocene hybridization events with current human‐induced cases might provide novel insights into the likelihood of genetic swamping or species collapse during an anthropogenic hybridization event. However, this comparative approach remains to be tested before it can be applied in practice. Finally, the genic view of introgression can be combined with conservation genomic studies to determine the legal status of hybrids and take appropriate measures to manage anthropogenic hybridization events. The interplay between evolutionary and conservation genomics will result in the constant exchange of ideas between these fields which will not only improve our knowledge on the origin of species, but also how to conserve and protect them. The exact start of the Anthropocene has been a matter of debate and its onset has been connected to different events, such as the rise of deforestation and agriculture  to identify the most common mechanisms and outcomes of human‐mediated hybridization events  with transgressive phenotypes could expand into more harsh environments with higher food availability  and grizzly bears (U. arctos) in the Arctic  in Italy  that originated from the interbreeding of two Italian species (S. aethnensis and S. chrysanthemifolius) in British gardens at the turn of the 18th century  and variegate darters (E. variatum) are replacing the now endangered candy darter  and hybrids show higher population growth rates than the parental taxa, genetic swamping might occur in which the parental taxa are replaced by hybrids  and the invasive Madagascar ragwort (S. madagascariensis). Hybridization rates between these species are very high, but hybrids are not viable. As the invasive plant increases in abundance, the native plant continues to waste reproductive effort and will start declining in numbers  that could lead to the decline of the latter species  and hybridization rates are high, parental taxa will waste reproductive effort when hybridizing. This situation can lead to demographic swamping where parental taxa decline due to decreased population growth rates  which focuses on reproductive isolation between diverging lineages Mayr, . This coThis new perspective is nicely illustrated by recent studies that showed how genetic differentiation between diverging lineages is heterogeneously distributed across the genome, often concentrated in a few genomic regions, so‐called “islands of differentiation” , second‐generation hybrids (F2), backcrosses, etc. For example, a mixture of different generational hybrids indicates the formation of a hybrid swarm, whereas the absence of backcrosses and second‐generation hybrids suggests selection against hybrids. Most conservation genetic studies use a set of variable genetic markers  to quantify the genetic make‐up of different individuals using software packages such as The use of a few genetic markers works well to identify early‐generation hybrids and backcrosses, but can run into several issues  and American black ducks (Anas rubripes) might lead to genetic extinction of the latter species  and Japanese sika (C. nippon). The RADseq data were able to identify more advanced backcrosses compared to the microsatellites, leading to a more fine‐grained picture of introgression dynamics between these species.These two issues—setting a realistic admixture threshold and the presence of homozygous markers—can often be solved by adding more markers. Indeed, genomic studies can more confidently identify hybrids and later‐generation backcrosses, leading to more sound conclusions about the hybridization dynamics  and twaite shad  up to the third generation. Similar marker sets and sequencing protocols have been developed for other study systems  and tundra bean goose (A. f. serrirostris) resulted in a largely homogeneous genomic landscape, but a few genomic islands of differentiation seem to prevent these taxa from merging  adapted to high levels of water pollution through introgressive hybridization with the non‐native Atlantic killifish (F. heteroclitus). And a study on hybridization between the local moth species Helicoverpa zea and the invasive H. armigera revealed that an insecticide‐resistant locus introgressed into the local species and increased in frequency  in the United States which lists and protects vulnerable species and subspecies of plants and animals  in the San Francisco Estuary  to theoretical considerations, such as patterns of introgression, ancient hybridization events, and the reticulated nature of evolution.From a conservation perspective, the unpredictability of (adaptive) introgression dynamics (Taylor & Larson, None declared.