Actual value and historical data chart for South Africa Sex Ratio At Birth Male Births Per Female Births

South Africa ZA: Sex Ratio at Birth: Male Births per Female Births data was reported at 1.030 Ratio in 2016. This stayed constant from the previous number of 1.030 Ratio for 2015. South Africa ZA: Sex Ratio at Birth: Male Births per Female Births data is updated yearly, averaging 1.030 Ratio from Dec 1962 (Median) to 2016, with 20 observations. The data reached an all-time high of 1.030 Ratio in 2016 and a record low of 1.030 Ratio in 2016. South Africa ZA: Sex Ratio at Birth: Male Births per Female Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Population and Urbanization Statistics. Sex ratio at birth refers to male births per female births. The data are 5 year averages.; ; United Nations Population Division. World Population Prospects: 2017 Revision.; Weighted average;

Eastern Cape had the largest share of women in the total population in South Africa. In 2022, 52.7 percent of the people in the province were women. Limpopo and KwaZulu-Natal followed closely, with a share of 52.5 and 52.1 percent, respectively. In absolute terms, Gauteng had the largest number of women residing there, at 8.1 million.

In 2022, women in South Africa represented 51.1 percent of the population. The majority of them were White South African, reaching 51.7 percent of the population. On the other hand, Indian/Asian women had a share of 48.9 percent.

This statistic shows the total population of South Africa from 2013 to 2023 by gender. In 2023, South Africa's female population amounted to approximately 32.46 million, while the male population amounted to approximately 30.75 million inhabitants.

Sirex noctilio is an economically important invasive pest of commercial pine forestry in the Southern Hemisphere. Newly established invasive populations of this woodwasp are characterised by highly male-biased sex ratios that subsequently revert to those seen in the native range. This trend was not observed in the population of S. noctilio from the summer rainfall regions in South Africa, which remained highly male-biased for almost a decade. The aim of this study was to determine the cause of this persistent male-bias. As explanation for this pattern we test hypotheses related to mating success, female investment in male versus female offspring and genetic diversity affecting diploid male production due to complementary sex determination. We found that 61% of females in a newly established S. noctilio population were mated. Microsatellite data analysis showed that populations of S. noctilio from the summer rainfall regions in South Africa are far less genetically diverse than those from the winter rainfall region, with mean Nei’s unbiased gene diversity indexes of 0.056 and 0.273, respectively. These data also identified diploid males at low frequencies in both the winter (5%) and summer (2%) rainfall regions. The results suggest the presence of a complementary sex determination mechanism in S. noctilio, but imply that reduced genetic diversity is not the main driver of the male-bias observed in the summer rainfall region. Among all the factors considered, selective investment in sons appears to have the most significant influence on male-bias in S. noctilio populations. Why this investment remains different in frontier or early invasive populations is not clear but could be influenced by females laying unfertilized eggs to avoid diploid male production in populations with a high genetic relatedness.

In 2020, the sex ratio among those enrolled in secondary education in South Africa amounted to **** points. Between 1989 and 2020, the figure dropped by **** points, though the decline followed an uneven course rather than a steady trajectory.

A table of allele sizes was compiled for 13 microsatellites for 67 males Sirex noctilio from the Western Cape province of South Africa and 77 males from the KwaZulu-Natal province. The dataset also contains (ii) a script for the design of a state-space model predicting the sex-ratio within a population of S. noctilio.

Bovine tuberculosis (BTB) is endemic to the African buffalo (Syncerus caffer) of Hluhluwe-iMfolozi Park (HiP) and Kruger National Park, South Africa. In HiP, the disease has been actively managed since 1999 through a test-and-cull procedure targeting BTB-positive buffalo. Prior studies in Kruger showed associations between microsatellite alleles, BTB and body condition. A sex chromosomal meiotic drive, a form of natural gene drive, was hypothesized to be ultimately responsible. These associations indicate high-frequency occurrence of two types of male-deleterious alleles (or multiple-allele haplotypes). One type negatively affects body condition and BTB resistance in both sexes. The other type has sexually antagonistic effects: negative in males but positive in females. Here, we investigate whether a similar gene drive system is present in HiP buffalo, using 17 autosomal microsatellites and microsatellite-derived Y-chromosomal haplotypes from 401 individuals, culled in 2002–2004. We show that the association between autosomal microsatellite alleles and BTB susceptibility detected in Kruger, is also present in HiP. Further, Y-haplotype frequency dynamics indicated that a sex chromosomal meiotic drive also occurred in HiP. BTB was associated with negative selection of male-deleterious alleles in HiP, unlike positive selection in Kruger. Birth sex ratios were female-biased. We attribute negative selection and female-biased sex ratios in HiP to the absence of a Y-chromosomal sex-ratio distorter. This distorter has been hypothesized to contribute to positive selection of male-deleterious alleles and male-biased birth sex ratios in Kruger. As previously shown in Kruger, microsatellite alleles were only associated with male-deleterious effects in individuals born after wet pre-birth years; a phenomenon attributed to epigenetic modification. We identified two additional allele types: male-specific deleterious and beneficial alleles, with no discernible effect on females. Finally, we discuss how our findings may be used for breeding disease-free buffalo and implementing BTB test-and-cull programs.

Sex-ratio of Covid-19 death rates in France and South Africa (Male/Female).

ZA：出生时性别比例：新生儿男女比例在12-01-2016达1.030Ratio，相较于12-01-2015的1.030Ratio保持不变。ZA：出生时性别比例：新生儿男女比例数据按年更新，12-01-1962至12-01-2016期间平均值为1.030Ratio，共20份观测结果。该数据的历史最高值出现于12-01-2016，达1.030Ratio，而历史最低值则出现于12-01-2016，为1.030Ratio。CEIC提供的ZA：出生时性别比例：新生儿男女比例数据处于定期更新的状态，数据来源于World Bank，数据归类于Global Database的南非 – 表 ZA.世界银行：人口和城市化进程统计。

As of 2022, 8.1 million women lived in Gauteng, the most populated province in South Africa. KwaZulu-Natal, Western Cape, and Eastern Cape followed as the provinces with the largest number of women, reaching six, 3.7, and 3.5 million, respectively.

In the African buffalo (Syncerus caffer) population of the Kruger National Park (South Africa) a primary sex-ratio distorter and a primary sex-ratio suppressor have been shown to occur on the Y chromosome. A subsequent autosomal microsatellite study indicated that two types of deleterious alleles with a negative effect on male body condition, but a positive effect on relative fitness when averaged across sexes and generations, occur genome-wide and at high frequencies in the same population. One type negatively affects body condition of both sexes, while the other acts antagonistically: it negatively affects male but positively affects female body condition. Here we show that high frequencies of male-deleterious alleles are attributable to Y-chromosomal distorter-suppressor pair activity and that these alleles are suppressed in individuals born after three dry pre-birth years, likely through epigenetic modification. Epigenetic suppression was indicated by statistical interactions between pre-birth rainfall, a proxy for parental body condition, and the phenotypic effect of homozygosity/heterozygosity status of microsatellites linked to male-deleterious alleles, while a role for the Y-chromosomal distorter-suppressor pair was indicated by between-sex genetic differences among pre-dispersal calves. We argue that suppression of male-deleterious alleles results in negative frequency-dependent selection of the Y distorter and suppressor; a prerequisite for a stable polymorphism of the Y distorter-suppressor pair. The Y distorter seems to be responsible for positive selection of male-deleterious alleles during resource-rich periods and the Y suppressor for positive selection of these alleles during resource-poor periods. Male-deleterious alleles were also associated with susceptibility to bovine tuberculosis, indicating that Kruger buffalo are sensitive to stressors such as diseases and droughts. We anticipate that future genetic studies on African buffalo will provide important new insights into gene fitness and epigenetic modification in the context of sex-ratio distortion and infectious disease dynamics.

In South Africa, children aged up to four made up the largest age group: *** percent of males and *** percent of females. Similarly, people between 30 and 34 years old held the second-largest share of the population. On the other hand, people aged 60 years and older represented a small portion of the population.

Although generally rare, deleterious alleles can become common through genetic drift, hitchhiking or reductions in selective constraints. Here we present a possible new mechanism that explains the attainment of high frequencies of deleterious alleles in the African buffalo (Syncerus caffer) population of Kruger National Park, through positive selection of these alleles that is ultimately driven by a sex-ratio suppressor. We have previously shown that one in four Kruger buffalo has a Y-chromosome profile that, despite being associated with low body condition, appears to impart a relative reproductive advantage, and which is stably maintained through a sex-ratio suppressor. Apparently, this sex-ratio suppressor prevents fertility reduction that generally accompanies sex-ratio distortion. We hypothesize that this body-condition-associated reproductive advantage increases the fitness of alleles that negatively affect male body condition, causing genome-wide positive selection of these allel...

Tornado-shaped swarms of the hyperiid amphipod Themisto gaudichaudii were observed along the coast of False Bay, South Africa. This is the first time such swarming behaviour has been recorded in the area. The swarms were confirmed to be monospecific, with the majority of the individuals being female. At least 26% of females were gravid and there was no statistical difference in size among males, gravid females, and non-gravid females.

Abstract

The primary objective of the South Africa Demographic and Health Survey (SADHS) 2016 is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the SADHS 2016 collected information on fertility levels; marriage; sexual activity; fertility preferences; awareness and use of contraceptives; breastfeeding practices; nutrition; childhood and maternal mortality; maternal health, including antenatal and postnatal care; key aspects of child health, including immunisation coverage and prevalence and treatment of acute respiratory infection (ARI), fever, and diarrhoea; potential exposure to the risk of HIV infection; coverage of HIV counselling and testing (HCT); and physical and sexual violence against women. Another critical objective of the SADHS 2016 is to provide estimates of health and behaviour indicators for adults age 15 and older, including use of tobacco, alcohol, and codeine-containing medications. In addition, the SADHS 2016 provides estimates of the prevalence of anaemia among children age 6-59 months and adults age 15 and older, and the prevalence of hypertension, anaemia, high HbA1c levels (an indicator of diabetes), and HIV among adults age 15 and older.

The information collected through the SADHS 2016 is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of the country’s population.

Geographic coverage

National

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-59

Universe

The survey covered all de jure household members (usual residents), children age 0-5 years, women age 15-49 years and men age 15-59 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the SADHS 2016 is the Statistics South Africa Master Sample Frame (MSF), which was created using Census 2011 enumeration areas (EAs). In the MSF, EAs of manageable size were treated as primary sampling units (PSUs), whereas small neighbouring EAs were pooled together to form new PSUs, and large EAs were split into conceptual PSUs. The frame contains information about the geographic type (urban, traditional, or farm) and the estimated number of residential dwelling units (DUs) in each PSU. The sampling convention used by Stats SA is DUs. One or more households may be located in any given DU; recent surveys have found 1.03 households per DU on average.

Administratively, South Africa is divided into nine provinces. The sample for the SADHS 2016 was designed to provide estimates of key indicators for the country as a whole, for urban and non-urban areas separately, and for each of the nine provinces in South Africa. To ensure that the survey precision is comparable across provinces, PSUs were allocated by a power allocation rather than a proportional allocation. Each province was stratified into urban, farm, and traditional areas, yielding 26 sampling strata.

The SADHS 2016 followed a stratified two-stage sample design with a probability proportional to size sampling of PSUs at the first stage and systematic sampling of DUs at the second stage. The Census 2011 DU count was used as the PSU measure of size. A total of 750 PSUs were selected from the 26 sampling strata, yielding 468 selected PSUs in urban areas, 224 PSUs in traditional areas, and 58 PSUs in farm areas.

For further details on sample design, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Five questionnaires were used in the SADHS 2016: the Household Questionnaire, the individual Woman’s Questionnaire, the individual Man’s Questionnaire, the Caregiver’s Questionnaire, and the Biomarker Questionnaire. These questionnaires, based on The DHS Program’s standard Demographic and Health Survey questionnaires, were adapted to reflect the population and health issues relevant to South Africa. Input was solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and international donors. After the preparation of the questionnaires in English, the questionnaires were translated into South Africa’s 10 other official languages. In addition, information about the fieldworkers for the survey was collected through a self-administered Fieldworker Questionnaire.

Cleaning operations

All electronic data files for the SADHS 2016 were transferred via the IFSS to the Stats SA head office in Pretoria, where they were stored on a password-protected computer. The data processing operation included secondary editing, which required resolution of computer-identified inconsistencies and coding of open-ended questions. The data were processed by a core group of four people; secondary editing was completed by 11 people. All persons involved in data processing took part in the main fieldwork training, and they were supervised by senior staff from Stats SA with support from ICF. Data editing was accomplished using CSPro software. Secondary editing was initiated in October 2016 and completed in February 2017. Checking inconsistencies in dates of immunisations was aided by the digital images of the immunisation page of the Road-to-Health booklet that had been collected on the tablet by fieldworkers at the time of the interview for that purpose.

Response rate

A total of 15,292 households were selected for the sample, of which 13,288 were occupied. Of the occupied households, 11,083 were successfully interviewed, yielding a response rate of 83%.

In the interviewed households, 9,878 eligible women age 15-49 were identified for individual interviews; interviews were completed with 8,514 women, yielding a response rate of 86%. In the subsample of households selected for the male survey, 4,952 eligible men age 15-59 were identified and 3,618 were successfully interviewed, yielding a response rate of 73%. In this same subsample, 12,717 eligible adults age 15 and older were identified and 10,336 were successfully interviewed with the adult health module, yielding a response rate of 81%. Response rates were consistently lower in urban areas than in nonurban areas.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the SADHS 2016 to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the SADHS 2016 is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the SADHS 2016 sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in Appendix B of the survey final report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months - Height and weight data completeness and quality for children - Completeness of information on siblings - Sibship size and sex ratio of siblings

See details of the data quality tables in Appendix C of the survey final report.

Predicting shifts of species geographical ranges is a fundamental challenge for conservation ecologists given the great complexity of factors involved in setting range limits. Distributional patterns are frequently modelled to “simplify” species responses to the environment, yet the central mechanisms that drive a particular pattern are rarely understood. We evaluated the distributions of two sandhopper species (Crustacea, Amphipoda, Talitridae), Talorchestia capensis and Africorchestia quadrispinosa along the Namibian and South African coasts, encompassing three biogeographic regions influenced by two different oceanographic systems, the Benguela and Agulhas currents. We aimed to test whether the Abundant Centre Hypothesis (ACH) can explain the distributions of these species’ abundances, sizes and sex ratios and examined which environmental parameters influence/drive these distributions. Animals were collected during a once-off survey at 29 sites over c.3500 km of coastline. The ACH was tested using a non-parametric constraint space analysis of the goodness of fit of five hypothetical models. Distance Based Linear Modelling (DistLM) was performed to evaluate which environmental traits influenced the distribution data. Abundance, size and sex ratio showed different patterns of distribution. A ramped model fitted the abundance (Ramped North) and size (Ramped South) distribution for A. quadrispinosa. The Inverse Quadratic model fitted the size distribution of T. capensis. Beach slope, salinity, sand temperature and percentage of detritus found on the shore at the time of collection played important roles in driving the abundance of A. quadrispinosa. T. capensis was mainly affected by salinity and the morphodynamic state of the beach. Our results provided only some support for the ACH predictions. The DistLM confirmed that the physical state of the beach is an important factor for sandy beach organisms. The effect of salinity and temperature suggest metabolic responses to local conditions and a role in small to mesoscale shifts in the range of these populations.

Resource quality can have direct or indirect effects on female oviposition choice, offspring growth and survival, and ultimately on body size and sex ratio. We examined these patterns in Sirex noctilio Fabricus, the globally invasive European pine woodwasp, in South African Pinus patula plantations. We studied how tree position as well as natural variation in biotic and abiotic factors influenced sex-specific density, larval size, tunnel length, male proportion, and survival across development. Twenty infested trees divided into top, middle, and bottom sections were sampled at three time points during larval development. We measured moisture content, bluestain fungal colonization, and co-occurring insect density and counted, measured, and sexed all immature wasps. A subset of larval tunnels was measured to assess tunnel length and resource use efficiency (tunnel length as a function of immature wasp size). Wasp density increased from the bottoms to the tops of trees for both males and females. However, the largest individuals and the longest tunnels were found in bottom sections. Male bias was strong (~10:1) and likewise differed among sections, with the highest proportion in the middle and top sections. Sex ratios became more strongly male biased due to high female mortality, especially in top and middle sections. Biotic and abiotic factors such as colonization by Diplodia sapinea, weevil (Pissodes sp.) density, and wood moisture explained modest residual variation in our primary mixed effects models (0-22%). These findings contribute to a more comprehensive understanding of sex-specific resource quality for S. noctilio and of how variation in key biotic and abiotic factors can influence body size, sex ratio, and survival in this economically important woodwasp. Methods Trees were felled and three logs (top middle, and bottom) collected from two P. patula pulp plantations in Mpumalanga, South Africa where moderate to high densities of S. noctilio-infested trees were known to be present. To locate trees infested by S. noctilio, we used the symptoms known to correlate with infestation, specifically the presence of resin drops and yellow or red foliage color (Talbot 1977, Dodds et al. 2010). At three time points throughout the S. noctilio larval life cycle, ten random S. noctilio-infested P. patula trees were selected from each of the two sites. In the sampled area, wasp adults emerge and lay eggs from late October/early November and sampling dates were selected to represent the early (March 2012), late (September 2012), and mid (June 2013) developmental stages. Since the mid-development larval stage was taken from a different larval cohort, only the early and late stages were used to estimate sex-specific patterns in survival and size distribution. Each main stem was visually divided into thirds and a 90 cm section was cut from the approximate middle of each third to represent the bottom, middle and top of each tree. Trees that showed no evidence of active tunneling on the cross-sectional face (top or bottom) of at least one cut log section were discarded and a new tree selected. Diameters of the top and bottom of each log were measured and wood volume calculated using mean log diameter, assuming a perfect cylinder. Four moisture measurements from each log were taken immediately after felling using a Delmhorst RDM-3 moisture probe (species setting: P. radiata [P. patula was not available]) and averaged to represent log moisture. To prevent wasp development prior to log dissection, logs were stored at 4°C at the Forestry and Agricultural Biotechnology Institute (FABI), University of Pretoria. Tunnel sampling and larval extraction Using a hand-operated hydraulic MAC-AFRIC™ 10-ton hydraulic log splitter, logs were carefully split into small fragments (~200 mm long by 5 mm wide) to extract all S. noctilio larvae and pupae. Up to eight randomly selected tunnels per log were meticulously traced back to their origin at the phloem-xylem interface and measured for length and maximum width. Only tunnels within the middle two thirds of log length (60 cm) were selected for measurement to exclude tunnels that continued outside the sampled area and so could not be fully measured. Tunnels were measured as they were uncovered during splitting using digital calipers; lengths were summed across split fragments to provide a total length for each selected gallery. All measured tunnels were separated by at least 10 cm throughout their length. All larvae and pupae were removed from the log, counted, measured, and sexed. Larval sex was determined by the presence of hypopleural organs in cuticular folds on either side of female larvae between the first and second abdominal segments (Gilmour 1965). Also called mycetangia (reflecting their function in collection and storage of arthrospores of the fungal symbiont, A. areolatum) these organs are absent in the first instar. Since all larvae encountered in this study were second instar or greater, this did not represent a source of error. Pupal sex was determined by the presence or absence of an ovipositor, visible through the pupal skin. Measurements included length (from the tip of the head capsule to the tip of the tail spine at the end of the abdomen), head capsule width, and wet and dry body mass. In addition, ten larvae per log section were randomly selected and dissected to confirm the presence or absence of the parasitic nematode, Deladenus siricidicola (Bedding and Akhurst 1974) used as a biocontrol agent in South Africa and elsewhere. Lastly, the number of dead larvae as well as identity and abundance of other insects in the log sections were recorded.

Large variance in reproductive success is the primary factor that reduces effective population size (Ne) in natural populations. In sequentially hermaphroditic (sex-changing) fish, the sex ratio is typically skewed and biased towards the 'first' sex, while reproductive success increases considerably after sex change. Therefore, sex-changing fish populations are theoretically expected to have lower Ne than gonochorists (separate sexes), assuming all other parameters are essentially equal. In this study, we estimate Ne from genetic data collected from two ecologically similar species living along the eastern coast of South Africa: one gonochoristic, the 'santer' sea bream Cheimerius nufar, and one protogynous (female-first) sex changer, the 'slinger' sea bream Chrysoblephus puniceus. For both species, no evidence of genetic structuring, nor significant variation in genetic diversity, was found in the study area. Estimates of contemporary Ne were significantly lower in the protogynous spec...