As of June 2023, in the Northern Territory of Australia, about 9.7 percent of the population was between 30 and 34 years old. In comparison, just 0.6 percent of the population was over the age of 85.

Australia Population: Resident: Estimated: Annual: Northern Territory: Greater Darwin data was reported at 148,884.000 Person in 2017. This records an increase from the previous number of 147,102.000 Person for 2016. Australia Population: Resident: Estimated: Annual: Northern Territory: Greater Darwin data is updated yearly, averaging 131,105.500 Person from Jun 2006 (Median) to 2017, with 12 observations. The data reached an all-time high of 148,884.000 Person in 2017 and a record low of 113,461.000 Person in 2006. Australia Population: Resident: Estimated: Annual: Northern Territory: Greater Darwin data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.G002: Estimated Resident Population.

In the Northern Territory in Australia, about 1.66 children were born per woman in the period of 2022-2023. This figure represents a slight increase compared to the previous year.

Australia Population: Resident: Estimated: Northern Territory data was reported at 255,100.000 Person in Jun 2024. This records an increase from the previous number of 254,354.000 Person for Mar 2024. Australia Population: Resident: Estimated: Northern Territory data is updated quarterly, averaging 201,781.000 Person from Jun 1981 (Median) to Jun 2024, with 173 observations. The data reached an all-time high of 255,100.000 Person in Jun 2024 and a record low of 122,616.000 Person in Jun 1981. Australia Population: Resident: Estimated: Northern Territory data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.G002: Estimated Resident Population.

The report provides statistics on all cancers and each cancer site or site group for the entire NT population; for males and females; and for Indigenous and non-Indigenous populations. Equivalent summary statistics for the total Australian population are included for comparison. To allow comparison within the NT population and with the wider Australian population, the incidence and mortality rates are age-adjusted because the age distribution of the NT population is much younger than the total Australian population. Statistical modelling analysis is used to investigate trends of cancer incidence and mortality over time.

BackgroundIndigenous populations globally are disproportionately affected by chronic hepatitis B virus (HBV) infection however contemporary sero-prevalence data are often absent. In the Indigenous population of the Northern Territory (NT) of Australia the unique C4 sub-genotype of HBV universally circulates. There are no studies of the sero-prevalence, nor the impact of the vaccination program (which has a serotype mismatch compared to C4), at a population-wide level.MethodsWe examined all available HBV serology results obtained from the three main laboratories serving NT residents between 1991 and 2011. Data were linked with a NT government database to determine Indigenous status and the most recent test results for each individual were extracted as a cross-sectional database including 88,112 unique individuals. The primary aim was to obtain a contemporary estimate of HBsAg positivity for the NT by Indigenous status.ResultsBased on all tests from 2007–2011 (35,633 individuals), hepatitis B surface antigen (HBsAg) positivity was 3·40% (95%CI 3·19–3·61), being higher in Indigenous (6·08%[5·65%-6·53%]) than non-Indigenous (1·56%[1·38%-1·76%]) Australians, p

Here we provide a catalogue of variants called after sequencing the exomes of 50 Aboriginal individuals from the Northern Territory (NT) of Australia and compare these to 72 previously published exomes from a Western Australian (WA) population of Martu origin. Sequence data for both NT and WA samples were processed using an ‘intersect-then-combine’ (ITC) approach, using GATK and SAMtools to call variants. The data is provided as 2 VCF files, one for the WA population and one for the NT population.

This dataset presents the projected enrolled population at 22 August 2020 for the Northern Territory (NT) by Legislative Assembly (LA) division areas and 2016 Australian Statistical Geography Standards (ASGS) Statistical Area Level 1 (SA1). Projected elector numbers are prepared by the Australian Bureau of Statistics (ABS) according to assumptions reflecting prevailing trends agreed to by the Northern Territory Electoral Commission. This projection is indicative of future population trends and is not official ABS population statistics. In the instance where an SA1 is divided between two or more LA divisions, the SA1 index will appear on multiple rows in the file. An individual row in the file will represent elector numbers for a whole or partial SA1 as it relates to any given LA division boundary. For more information please visit the Northern Territory Government Open Data Portal and read the ABS Projection Assumptions Document. Please note: Members of the Legislative Assembly that reside outside their electoral division are not represented in this dataset.

The aboriginal populations chart shows the composition of the aboriginal population in a municipality.

Thailand GDP: NT: Population (1,000 Persons) data was reported at 1,526.102 Person th in 2016. This records an increase from the previous number of 1,513.802 Person th for 2015. Thailand GDP: NT: Population (1,000 Persons) data is updated yearly, averaging 1,512.934 Person th from Dec 1995 (Median) to 2016, with 22 observations. The data reached an all-time high of 1,552.410 Person th in 2000 and a record low of 1,449.702 Person th in 2010. Thailand GDP: NT: Population (1,000 Persons) data remains active status in CEIC and is reported by National Economic and Social Development Board. The data is categorized under Global Database’s Thailand – Table TH.A080: Regional GDP: SNA93: Southern: Current Price (Rev. 4).

125.122 (persons) in 2023.

Climate often drives ungulate population dynamics, and as climates change, some areas may become unsuitable for species persistence. Unraveling the relationships between climate and population dynamics, and projecting them across time, advances ecological understanding that informs and steers sustainable conservation for species. Using pronghorn (Antilocapra americana) as an ecological model, we used a Bayesian approach to analyze long-term population, precipitation, and temperature data from 18 subpopulations in the southwestern United States. We determined which long-term (12 and 24 months) or short-term (gestation trimester and lactation period) climatic conditions best predicted annual rate of population growth (λ). We used these predictions to project population trends through 2090. Projections incorporated downscaled climatic data matched to pronghorn range for each population, given a high and a lower atmospheric CO2 concentration scenario. Since the 1990s, 15 of the pronghorn subpopulations declined in abundance. Sixteen subpopulations demonstrated a significant relationship between precipitation and λ, and in 13 of these, temperature was also significant. Precipitation predictors of λ were highly seasonal, with lactation being the most important period, followed by early and late gestation. The influence of temperature on λ was less seasonal than precipitation, and lacked a clear temporal pattern. The climatic projections indicated that all of these pronghorn subpopulations would experience increased temperatures, while the direction and magnitude of precipitation had high subpopulation-specific variation. Models predicted that nine subpopulations would be extirpated or approaching extirpation by 2090. Results were consistent across both atmospheric CO2 concentration scenarios, indicating robustness of trends irrespective of climatic severity. In the southwestern United States, the climate underpinning pronghorn subpopulations is shifting, making conditions increasingly inhospitable to pronghorn persistence. This realization informs and steers conservation and management decisions for pronghorn in North America, while exemplifying how similar research can aid ungulates inhabiting arid regions and confronting similar circumstances elsewhere. Long-term data from annual aerial surveys of pronghorn subpopulations in Utah, Arizona, New Mexico, and western Texas were used to calculate annual rates of population growth (λ). When subpopulation-specific harvest and translocation data were available, population estimates for calculating λ were adjusted according to the following equation: λt = Nt/(Nt-1 - h - r + a), where λt is population change from time t-1 to t, Nt and Nt-1 are population estimates from current and previous surveys, respectively, h is number of pronghorn harvested, and r and a are number of individuals removed from and released into the population, respectively, through translocations. Only population estimates from surveys conducted in consecutive years were used to calculate λ. If λ = 2, the associated surveys were removed from analyses because λ would be considered to be derived from unreliable or unstandardized population estimates, resulting in biologically unrealistic population growth rates. Monthly climate data (precipitation [mm/day] and mean temperature [degrees C]) were from 14 x 14 km cells from pronghorn range in each subpopulation in Utah, Arizona, New Mexico, and western Texas. Means across grids were calculated to obtain monthly values of precipitation and temperature. Two realistic future global climate scenarios were compared; a lower (Representative Concentrations Pathways 4.5) and a high (Representative Concentrations Pathways 8.5) atmospheric CO2 concentration scenario. Standardized precipitation index for 3-, 6-, 12-, and 24-month periods were calculated from all available monthly precipitation data using program SPI SL 6 (National Drought Mitigation Center 2014). Monthly mean temperature, total precipitation, and mean SPI (3-, 6-, and 12-month periods) were summarized by important periods in an adult female pronghorn's annual reproductive cycle relative to peak fawning (i.e., early, mid-, and late gestation [3 months each] and lactation [4 months]). Mean temperature and total precipitation were also calculated for 12 and 24 months preceding each population survey. Historic pronghorn population trends in relation to temperature and precipitation were assessed using integrated Bayesian population models. All models included a covariate for density effect (i.e., population in the previous year). Precipitation and temperature model comparison sets were run separately, and each model set included a null model (i.e., only density covariate, no climate covariates). These top individual precipitation and temperature covariates were then combined in models (i.e., one precipitation and temperature covariate per model), and these combined models were run including a term for the interaction between precipitation and temperature using the following equation: ln(λt) = Alpha + Beta1XN[t-1] + Beta2Xprec + Beta3Xtemp + Beta4Xprec*temp. Projected climate data for each pronghorn subpopulation was used to predict λt for each year to 2090. An integrated modeling approach was used, whereby the best performing model climatic predictors from historic population trends for each pronghorn subpopulation was embedded in that subpopulation pronghorn population projection model.

Bactrocera jarvisi is an endemic Australian fruit fly species (Diptera: Tephritidae). It occurs commonly across tropical and subtropical coastal Australia, from far-northern Western Australia, across the 'Top End' of the Northern Territory, and then down the Queensland east coast. Across this range, its distribution crosses several well-documented biogeographic barriers. In order to better understand factors leading to the divergence of Australian fruit fly lineages, we carried out a population genetic study of B. jarvisi from across its range using genome-wide SNP analysis, utilising adult specimens gained from trapping and fruit rearing. Populations from the Northern Territory (NT) and Western Australia were genetically similar to each other but divergent from the genetically uniform east-coast (=Queensland, QLD) population. Phylogenetic analysis demonstrated that the NT population derived from the QLD population. We infer a role for the Carpentaria Basin as a biogeographic barrier restricting east-west gene flow. The QLD populations were largely panmictic and recognised east-coast biogeographic barriers play no part in north-south population structuring. While the NT and QLD populations were genetically distinct, there was evidence for the historically recent translocation of flies from each region to the other. Flies reared from different host fruits collected in the same location showed no genetic divergence. While a role for the Carpentaria Basin as a barrier to gene flow for Australian fruit flies agrees with existing work on the related B. tryoni, the reason(s) for population panmixia for B. jarvisi (and B. tryoni) over the entire Queensland east coast, a linear north-south distance of >2000km, remains unknown.

This report summarises data from the 2003 Northern Territory (NT) Midwives’ Collection. It includes population characteristics of mothers, maternal health status, antenatal information, conditions and procedures used in labour and childbirth as well as birth outcomes of all births that occurred in 2003. While the NT Midwives’ Collection contains information on both NT resident and interstate residents who gave birth in the NT, the focus of this report is NT residents who gave birth in the NT. Notes and Corrections: On 24 October 2011 an error was observed in table 32. There has been an update to the introduction and to Table 32. An amended version of the document and the previous version are presented below

BACKGROUND:

The diamondback moth (DBM), Plutella xylostella (Lepidoptera: Plutellidae), is a notorious pest of cruciferous plants. In temperate areas, annual populations of DBM originate from adult migrants. However, the source populations and migration trajectories of immigrants remain unclear. Here, we investigated migration trajectories of DBM in China with genome-wide single nucleotide polymorphisms (SNPs) genotyped using double-digest RAD (ddRAD) sequencing. We first analyzed patterns of spatial and temporal genetic structure among southern source and northern recipient populations, then inferred migration trajectories into northern regions using discriminant analysis of principal components (DAPC), assignment tests and spatial kinship patterns.

RESULTS:

Temporal genetic differentiation among populations was low, indicating sources of recipient populations and migration trajectories are stable. Spatial genetic structure indicated three genetic clusters in the southern source populations. Assignment tests linked northern populations to the Sichuan cluster, and central-eastern populations to the South and Yunnan clusters, indicating that Sichuan populations are sources of northern immigrants and South and Yunnan populations are sources of central-eastern populations. First-order (full-sib) and second-order (half-sib) kin pairs were always found within populations, but about 35-40% of third-order (cousin) pairs were found in different populations. Closely related individuals in different populations were in about 35-40% of cases found at distances of 900 to 1500 km, while some were separated by over 2000 km.

CONCLUSION:

This study unravels seasonal migration patterns in the DBM. We demonstrate how careful sampling and population genomic analyses can be combined to help understand cryptic migration patterns in insects.

Methods Specimen collection and DNA extraction DBM were sampled from potential source population locations in the annual breeding area of southern China. DBM were collected from cabbage and oilseed rape fields, and all sampling was completed before the first observations of DBM in northern China between March and May 1, 2. In order to reduce the likelihood of sampling siblings within populations, third- and fourth-instar larvae of DBM were collected from about 20 sites at each sampling location, each at least 10 m apart. Putative immigrant male adults were collected in northern China by sex pheromone trapping before the presence of first-generation larvae. Trapping of male DBM was conducted in unplanted fields with no greenhouses within 500 m, to reduce the likelihood of trapping individuals overwintering in protected conditions. The distance between traps was at least 50 m. The development of one generation of DBM takes about 30 days in early spring 3. This strategy therefore restricted sampling of genetically related individuals to within three generations between source and recipient populations, and reduced the influence of genomic admixture between immigrants from different sources. This sampling was conducted in 2017 and again in 2018, to examine annual variation in migratory trajectories and temporal variation in population genetic structure. In total, samples were collected from 16 locations in 2017 and 17 locations in 2018, and in 2018 four locations were sampled across multiple months (Fig. 1, Table 1). Twenty individuals from each population (specimens collected at different times from the same location were considered as different populations) were used for genotyping. Genomic DNA for library preparation was extracted from individual specimens using DNeasy Blood and Tissue Kit (Qiagen, Germany). SNP genotyping The ddRAD libraries were prepared following a published protocol 4 for identifying SNPs. Briefly, 120 ng of extracted genomic DNA from each sample was digested by the restriction enzymes NlaIII and AciI (New England Biolabs, USA) 5. The 50 μL digestion reaction was run for 3 hours at 37 °C, followed by DNA cleaning using 1.5× volume of AMPure XP beads (Beckman Coulter, USA) instead of a heat kill step. Next, we ligated each sample to adapters barcoded with a combinatorial index at 16 °C overnight in a 40 μL ligation reaction, labeling each population with a 6-bp index and each individual with a unique 9-bp barcode. After ligation, we pooled uniquely barcoded samples into multiplexed libraries. Fragments between 380-540 bp were selected using BluePippin and a 2% gel cassette (Sage Sciences, USA). Finally, the pooled libraries were enriched with 12 amplification cycles on a Mastercycler Nexus Thermal Cycler (Eppendorf, Germany). PCR products were cleaned with 0.8× volume of beads. We used Qubit 3.0 (Life Invitrogen, USA) and Agilent 2100 Bioanalyzer (Agilent Technology, USA) to check the concentration and size distribution of enriched libraries, respectively. Pooled libraries were sequenced on an Illumina HiSeq 2500 platform to obtain 150-bp paired-end reads, at BerryGenomics Company (Beijing, China). The Stacks v2.3 pipeline 6 was used to call SNPs, linking to the DBM genome (GenBank assembly accession: GCA_000330985.1) as reference 7. FastQC v 0.11.5 was employed to assess read quality and check for adapter contamination 8. Sequence data was demultiplexed and trimmed using process_radtags in Stacks v2.3 6, 9. Low quality reads with a Phred score below 20 were removed as well as any reads with an uncalled base. Reads were trimmed to 140 bp in length. The remaining paired-end reads were aligned to the DBM genome 7 using Bowtie v2.3.5 10. Output reads for all individuals were imported into Stacks pipeline ref_map.pl to call SNPs, requiring a minimum of three identical reads to create a stack. SNPs were called using a maximum likelihood statistical model. Finally, we obtained a catalog with all possible loci and alleles. The exported loci were present in all populations, and in at least 75% of individuals per population. The exported SNPs for populations that were collected in both years were further filtered using the R package vcfR 11 and VCFtools v0.1.16 12 with the following criteria: SNPs with sequencing depth ≤ 3 and in the highest 0.1% depth were removed, as were SNPs with missingness in all samples ≥ 0.05 and those with minimum minor allele count ≤ 20. An additional data matrix was generated by retaining only SNPs separated by at least 500 bp, to reduce linkage among SNPs. Genetic diversity, population structure and assignment tests Global population differentiation was estimated using Weir and Cockerham’s FST with 99% confidence intervals (1000 bootstraps) in diveRsity version 1.9.90. Pairwise FST for all population pairs was estimated using GenePop version 4.7.2 13. Discriminant analysis of principal components (DAPC) was performed in the R package adegenet v2.1.1 14, with the optimal number of clusters determined by the Akaike information criterion (AIC). Assignment tests were performed in assignPOP v1.1.7 15. Source groups of ST (south) and SW (southwest, this group was divided into YN and SC groups in 2018) (see Table 1 and Fig. 1 for locations) were trained using the support vector machine algorithm to build predictive models. For training, we used either 25, 28, or 32 random individuals (2017 samples) or 13, 15 or 17 random individuals (2018 samples) from each group, and loci with the highest 60%, 80% or 100% FST values. Monte-Carlo cross-validation was performed by resampling each training set combination 1000 times. The ratio of assignment probability between the most-likely and second most-likely assigned groups was calculated for each individual 16. When an individual showed an assignment ratio smaller than 2 in more than 30% of the resampling analysis, it was considered unstable and removed in subsequent training. This allowed us to remove individuals from source populations that are not similar enough to other individuals in that source population, thus leaving a set of source populations each comprised of individuals distinctive from those in other populations. Immigrants from the CE (central) and NT (north) regions (see Table 1 and Fig. 1 for locations) were assigned to the trained groups using the support vector machine algorithm. Kinship analysis As a complement to assignment tests (but focusing on the individual level rather than the population level), we investigated spatial patterns of kinship within and between populations. Related individuals were identified following the method of Jasper, Schmidt, Ahmad, Sinkins and Hoffmann 17. First, Loiselle’s K was calculated for all individual pairs using SPAGeDi 18 . Kinship coefficients represent the probability that any allele scored in both individuals is identical by descent, with theoretical mean K values for each kinship category as follows: full‐siblings = 0.25, half-siblings = 0.125, full‐cousins = 0.0625, half‐cousins = 0.0313, second-cousins = 0.0156 and unrelated = 0. To allocate pairs of individuals to relatedness categories across three orders of kinship, maximum‐likelihood estimation in the program ML‐Relate 19 was used to identify first‐order (full‐sibling) and second‐order (half‐sibling) pairs. The K scores of pairs within the full‐sibling and half-sibling data sets were used to calculate standard deviations for these categories. Using the theoretical means and standard deviations of K, we randomly sampled 100,000 simulated K scores from each kinship category. In the initial pool of 40755 pairings (2017) and 89676 pairings (2018), ML‐Relate identified 33 (2017) and 36 (2018) full‐sibling and half‐sibling pairs. Assuming that the data contained twice as many first cousin (full and half) pairings as sibling (full and half) pairings, and twice as many second cousin pairings as first cousin pairings, final sampling distributions were developed as follows: 100,000 unrelated, 320 second-cousins, 80 full‐cousins, 80 half‐cousins, 40

Ages chart illustrates the age and gender trends across all age and gender groupings. A chart where the the covered area is primarily on the right describes a very young population while a chart where the the covered area is primarily on the left illustrates an aging population.

Estimated resident population (ERPs) is the official measure of the Australian population, published quarterly by the ABS. This dataset contains quarterly ERP by age, at state/territory and Australia level.\r

As of June 2023, there were approximately 8.33 million residents in the New South Wales region in Australia. In comparison, there were around 252 thousand residents in the Northern Territory region.

33,4 (years) in 2021.

A study of the population genetics of the prawn Penaeus monodon in northern and eastern Australian waters. Mitochondrial D-loop DNA (and Restriction Fragment Length Polymorphism - RFLP) were used to …Show full descriptionA study of the population genetics of the prawn Penaeus monodon in northern and eastern Australian waters. Mitochondrial D-loop DNA (and Restriction Fragment Length Polymorphism - RFLP) were used to estimate connectivity and dispersal between populations which range through locations in Western Australia, Northern Territory, Queensland and New South Wales. Statistical analyses and clustering procedures were carried out.Collection of samples were from 6 locations throughout the species range in Australia: Townsville, Cairns, Weipa, Melville Island, Joseph Bonaparte Gulf, De Grey River.Some comparison was made with Indonesian and South African samples, see separate metadata record.Microsatellite markers were used in a further study of genetic variation among the Australian populations above. To estimate connectivity and dispersal between Penaeus monodon populations in northern and eastern Australia.To compare results with genetic analyses using allozymes. Separate metadata records apply for data relating to the genetic analyses using allozymes of Penaeus monodon from Australian waters and South Africa.