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.

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.

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

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

his report summarises data from the 2005 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 2005. 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.

This dataset has been developed by the Australian Government as an authoritative source of indigenous location names across Australia. It is sponsored by the Spatial Policy Branch within the Department of Communications and managed solely by the Department of Human Services.

The dataset is designed to support the accurate positioning, consistent reporting, and effective delivery of Australian Government programs and services to indigenous locations.

The dataset contains Preferred and Alternate names for indigenous locations where Australian Government programs and services have been, are being, or may be provided. The Preferred name will always default to a State or Territory jurisdiction's gazetted name so the term 'preferred' does not infer that this is the locally known name for the location. Similarly, locational details are aligned, where possible, with those published in State and Territory registers.

This dataset is NOT a complete listing of all locations at which indigenous people reside. Town and city names are not included in the dataset. The dataset contains names that represent indigenous communities, outstations, defined indigenous areas within a town or city or locations where services have been provided.

Labour force rate and unemployment rate for population aged 15 years and over usually present and resident in the State

A study of the population genetics of the prawn Penaeus monodon in northern and eastern Australian waters. Variations in gene frequencies of allozymes and common proteins (GPI,LGG,LT-1,MDH-1,MDH-2,MPI,PGDH,PGM) 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 7 locations throughout the species range in Australia: Clarence River, Townsville, Cairns, Weipa, Melville Island, Joseph Bonaparte Gulf, De Grey River.A later study was conducted on South Afican samples, see separate metadata record. To estimate connectivity and dispersal between Penaeus monodon populations in northern and eastern Australia. First systematic survey of genetic variation of P. monodon populations over a wide geographic range. Highly significant differences between western and the northern and eastern populations were demonstrated.

The Population Health Area (PHA) data include totals for the Greater Capital City Statistical Areas/ Rest of States/NT, States/ Territories and Australia; and for the Statistical Areas Level 3 and …Show full descriptionThe Population Health Area (PHA) data include totals for the Greater Capital City Statistical Areas/ Rest of States/NT, States/ Territories and Australia; and for the Statistical Areas Level 3 and Level 4. Attribution: Torrens University Australia

Population Usually Resident and Present in their Usual Residence (Number) by Age Group, Type of Household, Disability Type, CensusYear and Sex

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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. Methods For DNA extractions, either the head and legs or the full body without the abdomen was used depending on the samples’ freshness and quality. DNA was extracted using QIAGEN DNeasy® Blood & Tissue Kit following the manufacturer’s protocol with only slight modifications (i.e., overnight incubation of the tissue with lysis buffer and proteinase K at 36 0C and eluting the genomic DNA into 30–50 µL of elution buffer for more concentrated DNA extraction. DNA samples were screened for quality and quantity, using both gel electrophoresis and Qubit assay, and then sent to Diversity Arrays Technology Pty Ltd, Canberra (DArT P/L), for DArTseq high-density genotyping. The restriction enzyme combination, PstI/SphI, was used by DArT P/L for the current B. jarvisi dataset, and the fragments (up to 90bp) were sequenced on an Illumina Hiseq2500 as single end reads.

Abstract

Northern Australia Infrastructure Facility (NAIF) is a development financier to infrastructure projects in the Northern Territory, Queensland, Western Australia and the Australian Indian Ocean Territories. NAIF’s mission is to be an innovative financing partner in the growth of northern Australia. A key focus of any financing is to drive public benefit, economic, population growth, and Indigenous involvement in northern Australia.

This NAIF dataset contains the limit and extent of Northern Australia as defined in the Northern Australia Infrastructure Facility Act 2016 including the Northern Australia Infrastructure Facility Amendment (Extension and Other Measures) Bill 2021 and the Northern Australia Infrastructure Facility Amendment (Miscellaneous Measures) Bill 2023. This is a maintained dataset and is kept updated to reflect any amendments to the legislation.

The definition in the Northern Australia Infrastructure Facility Act 2016 states that Northern Australia means the area that includes the following:

(a) the Northern Territory;

(b) the areas of Queensland and Western Australia that are North of the Tropic of Capricorn other than the Meekatharra Statistical Area level 2;

(c) the areas South of the Tropic of Capricorn of each Statistical Area level 2 that has an area covered by paragraph (b);

(d) the following Statistical Areas level 2:

(i) Gladstone;

(ii) Gladstone Hinterland;

(iii) Carnarvon;

(da) the Territory of Christmas Island;

(db) the Territory of Cocos (Keeling) Islands;

(e) the Local Government Areas of Meekatharra and Wiluna (despite paragraph (b));

(ea) the Local Government Area of Ngaanyatjarraku;

(f) the territorial sea adjacent to areas covered by paragraphs (a) to (db).

Currency

Date modified: 08 September 2023

Modification frequency: As needed

Data Extent

Spatial Extent

West: 95° South: -28° East: 153° North: -8°

Source InformationGeoscience Australia catalog entry: Northern Australia Infrastructure Facility Act 2016 - Northern Australia Definition with Amendment Bill 2021 and Bill 2023

Based on the definition of Northern Australia area in the Northern Australia Infrastructure Facility Act 2016 along with the consultation of NAIF, Geoscience Australia derived this dataset using:

• The latest GDA2020 digital boundary files of Statistical Area Level 2 - 2021 and the Local Government Areas - 2023 downloaded from Australian Bureau of Statistics.

• The Seas and Submerged Lands Act (SSLA) 1973 web service from Geoscience Australia

Lineage Statement

This dataset is a latest update of the limit and extent of Northern Australia to support the Northern Australia Infrastructure Facility Act 2016.

Point of Contact

Geoscience Australia, ClientServices@ga.gov.au

This dataset presents the footprint of the percentage of adults who saw a GP more than 12 times in the preceding 12 months. The data spans the years of 2015-2017 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS).

The data is sourced from the Australian Bureau of Statistics (ABS) 2016-17 Patient Experience Survey, collected between 1 July 2016 and 30 June 2017. It also includes data from previous Patient Experience Surveys conducted in 2013-14, 2014-15 and 2015-16. The Patient Experience Survey is conducted annually by the ABS and collects information from a representative sample of the Australian population. The Patient Experience Survey is one of several components of the Multipurpose Household Survey, as a supplement to the monthly Labour Force Survey. The Patient Experience Survey collects data on persons aged 15 years and over, who are referred to as adults for this data collection.

For further information about this dataset, visit the data source:Australian Institute of Health and Welfare - Patient experiences in Australia Data Tables.

Please note:

• AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

• Percentages are calculated based on counts that have been randomly adjusted by the ABS to avoid the release of confidential data.

• As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the ABS using standard error estimates of the proportion.

• Some of the patient experience measures for 2016-17 have age-standardised rates presented. Age-standardised rates are hypothetical rates that would have been observed if the populations studied had the same age distribution as the standard population.

• Crude rates are provided for all years. They should be used for understanding the patterns of actual service use or level of experience in a particular PHN.

• The Patient Experience Survey excludes persons aged less than 15 years, persons living in non-private dwellings and the Indigenous Community Strata (encompassing discrete Aboriginal and Torres Strait Islander communities).

• Data for Northern Territory should be interpreted with caution as the Patient Experience Survey excluded the Indigenous Community Strata, which comprises around 25% of the estimated resident population of the Northern Territory living in private dwellings.

• Rows that contain a "#" in "Interpret with Caution" indicates that the estimate has a relative standard error of 25% to 50%, which indicates a high level of sampling error relative to its value and must be taken into account when comparing this estimate with other values.

• NP - Not available for publication. The estimate is considered to be unreliable. Values assigned to NP in the original data have been set to null.

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.

This dataset presents the footprint of the crude percentage of adults who have high blood pressure. High blood pressure (or hypertension), is defined as including any of the following; systolic blood pressure greater than or equal to 140 mmHg, or; diastolic blood pressure greater than or equal to 90 mmHg, or; receiving medication for high blood pressure. As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the Australian Bureau of Statistics (ABS) using standard error estimates of the proportion. The data spans the financial year of 2014-2015 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS).

Health risk factors are attributes, characteristics or exposures that increase the likelihood of a person developing a disease or health disorder. Examples of health risk factors include risky alcohol consumption, physical inactivity and high blood pressure. High-quality information on health risk factors is important in providing an evidence base to inform health policy, program and service delivery.

For further information about this dataset, visit the data source: Australian Institute of Health and Welfare - Health Risk Factors in 2014-2015 Data Tables.

Please note:

• AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

• The health risks factors reported are known to vary with age and the different PHN area populations are known to have a range of age structures. As such, comparisons of results between the PHN areas should be made with caution because the crude rates presented do not account for these age differences.

• Adults are defined as persons aged 18 years and over.

• Values assigned to "n.p." in the original data have been removed from the data.

• Data for PHN701 (Northern Territory) should be interpreted with caution as the National Health Survey excluded discrete Aboriginal and Torres Strait Islander communities and very remote areas, which comprise around 28% of the estimated resident population of the Northern Territory living in private dwellings.

This dataset presents the footprint of the percentage of adults who needed to see a GP but did not in the preceding 12 months. The data spans the years of 2013-2017 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS).

The data is sourced from the Australian Bureau of Statistics (ABS) 2016-17 Patient Experience Survey, collected between 1 July 2016 and 30 June 2017. It also includes data from previous Patient Experience Surveys conducted in 2013-14, 2014-15 and 2015-16. The Patient Experience Survey is conducted annually by the ABS and collects information from a representative sample of the Australian population. The Patient Experience Survey is one of several components of the Multipurpose Household Survey, as a supplement to the monthly Labour Force Survey. The Patient Experience Survey collects data on persons aged 15 years and over, who are referred to as adults for this data collection.

For further information about this dataset, visit the data source:Australian Institute of Health and Welfare - Patient experiences in Australia Data Tables.

Please note:

• AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

• Percentages are calculated based on counts that have been randomly adjusted by the ABS to avoid the release of confidential data.

• As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the ABS using standard error estimates of the proportion.

• Some of the patient experience measures for 2016-17 have age-standardised rates presented. Age-standardised rates are hypothetical rates that would have been observed if the populations studied had the same age distribution as the standard population.

• Crude rates are provided for all years. They should be used for understanding the patterns of actual service use or level of experience in a particular PHN.

• The Patient Experience Survey excludes persons aged less than 15 years, persons living in non-private dwellings and the Indigenous Community Strata (encompassing discrete Aboriginal and Torres Strait Islander communities).

• Data for Northern Territory should be interpreted with caution as the Patient Experience Survey excluded the Indigenous Community Strata, which comprises around 25% of the estimated resident population of the Northern Territory living in private dwellings.

• Rows that contain a "#" in "Interpret with Caution" indicates that the estimate has a relative standard error of 25% to 50%, which indicates a high level of sampling error relative to its value and must be taken into account when comparing this estimate with other values.

• NP - Not available for publication. The estimate is considered to be unreliable. Values assigned to NP in the original data have been set to null.

The arid zone of the Northern Territory is one of the worlds more sparsely populated areas, with an estimated population of only 45,000 in 12% of Australia s land mass. The region depends mainly on groundwater for provision of water supplies. Annual water usage for all purposes is estimated at 45,000 ML. Compared with the estimated divertible groundwater resources of 2,870,000 ML, the rate of extraction is insignificant. Problems of resource overuse are of growing concern, due to the spatial distribution of readily usable water sources, and the difficulty of matching these to development requirements and community expectations. Climatic realities make the concept of greening the red heart unattainable, but intensive and sustainable water-dependent development is possible, given rigorous assessment of both water source potential and site economics, and sound resource management.

TRA210 - Extent to which respondents trust people and institutions across OECD participant countries. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Extent to which respondents trust people and institutions across OECD participant countries...

Distance measures are widely used for examining genetic structure in datasets that comprise many individuals scored for a very large number of attributes. Genotype datasets composed of single nucleotide polymorphisms (SNPs) typically contain bi-allelic scores for tens of thousands if not hundreds of thousands of loci. We examine the application of distance measures to SNP genotypes and sequence tag presence-absences (SilicoDArT) and use real datasets and simulated data to illustrate pitfalls in the application of genetic distances and their visualization. The datasets used to illustrate points in the associated review are provided here together with the R script used to analyse the data. Data are either simulated internal to this script or are SNP data generated as part of other studies and included as compressed binary files readily accessable by reading into R using R base function readRDS(). Refer to the analysis script for examples. Methods A dataset was constructed from a SNP matrix generated for the freshwater turtles in the genus Emydura, a recent radiation of Chelidae in Australasia. The dataset (SNP_starting_data.Rdata) includes selected populations that vary in level of divergence to encompass variation within species and variation between closely related species. Sampling localities with evidence of admixture between species were removed. Monomorphic loci were removed, and the data was filtered on call rate (>95%), repeatability (>99.5%) and read depth (5x < read depth < 50x). Where there was more than one SNP per sequence tag, only one was retained at random. The resultant dataset had 18,196 SNP loci scored for 381 individuals from 7 sampling localities or populations – Emydura victoriae [Ord River, NT, n=15], E. tanybaraga [Holroyd River, Qld, n=10], E. subglobosa worrelli [Daly River, NT, n=25], E. subglobosa subglobosa [Fly River, PNG, n=55], E. macquarii macquarii [Murray Darling Basin north, NSW/Qld, n=152], E. macquarii krefftii [Fitzroy River, Qld, n=39] and E. macquarii emmotti [Cooper Creek, Qld, n=85]. The missing data rate was 1.7%, subsequently imputed by nearest neighbour to yield a fully populated data matrix. The data are a subset of those published by Georges et al. (2018, Molecular Ecology 27:5195-5213) for illustrative purposes only. A companion SilicoDArT dataset (silicodart_starting_data.Rdata) is also included. The above manipulations were performed in R package dartR. Principal Components Analysis was undertaken using the glPCA function of the R adegenet package (as implemented in dartR). Principal Coordinates Analysis was undertaken using the pcoa function in R package ape implemented in dartR. To exemplify the effect of missing values on SNP visualisation using PCA, we simulated ten populations that reproduced over 200 non-overlapping generations. Simulated populations were placed in a linear series with low dispersal between adjacent populations (one disperser every ten generations). Each population had 100 individuals, of which 50 individuals were sampled at random. Genotypes were generated for 1,000 neutral loci on one chromosome. We then randomly selected 50% of genotypes and set them as missing data. Principal Components Analysis was undertaken using the glPCA function of the R adegenet package. The R script to implement this is provided (Supplementary_script_for_ms.R). The data for the Australian Blue Mountains skink Eulamprus leuraensis were generated for 372 individuals collected from 17 swamps isolated to varying degrees in the Blue Mountains region of New South Wales. Tail snips were collected and stored in 95% ethanol. The tissue samples were digested with proteinase K overnight and DNA was extracted using a NucleoMag 96 Tissue Kit (MachereyNagel, Duren, Germany) coupled with NucleoMag SEP (Ref. 744900) to allow automated separation of high-quality DNA on a Freedom Evo robotic liquid handler (TECAN, Miinnedorf, Switzerland). SNP data were generated by the commercial service of Diversity Arrays Technology Pty Ltd (Canberra, Australia) using published protocols. A total of 13,496 loci were scored which reduced to 7,935 after filtering out secondary SNPs on the same sequence tag, filtering on reproducibility (threshold 0.99) and call rate (threshold 0.95), and removal of monomorphic loci. The resultant data (Eulamprus_filtered.Rdata) is used to demonstrate the impact of a substantial inversion on the outcomes of a PCA. To test the effect of having closely related individuals (parents and offspring) on the PCoA pattern we ran a simulation using dartR, where we picked up two individuals to become the parents with 2-8 offspring. We ran a PCoA for all of the simulated cases. The R code used is included in the R script uploaded here. Refer to the companion manuscript for links to the literature associated with the above techniques.

Sources: [17], [18], [22], [36], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51].