South Africa is the sixth African country with the largest population, counting approximately 60.5 million individuals as of 2021. In 2023, the largest city in South Africa was Cape Town. The capital of Western Cape counted 3.4 million inhabitants, whereas South Africa's second largest city was Durban (eThekwini Municipality), with 3.1 million inhabitants. Note that when observing the number of inhabitants by municipality, Johannesburg is counted as largest city/municipality of South Africa.

From four provinces to nine provinces

Before Nelson Mandela became president in 1994, the country had four provinces, Cape of Good Hope, Natal, Orange Free State, and Transvaal and 10 “homelands” (also called Bantustans). The four larger regions were for the white population while the homelands for its black population. This system was dismantled following the new constitution of South Africa in 1996 and reorganized into nine provinces. Currently, Gauteng is the most populated province with around 15.9 million people residing there, followed by KwaZulu-Natal with 11.68 million inhabiting the province. As of 2022, Black African individuals were almost 81 percent of the total population in the country, while colored citizens followed amounting to around 5.34 million.

A diverse population

Although the majority of South Africans are identified as Black, the country’s population is far from homogenous, with different ethnic groups usually residing in the different “homelands”. This can be recognizable through the various languages used to communicate between the household members and externally. IsiZulu was the most common language of the nation with around a quarter of the population using it in- and outside of households. IsiXhosa and Afrikaans ranked second and third with roughly 15 percent and 12 percent, respectively.

As of 2023, South Africa's population increased and counted approximately 62.3 million inhabitants in total, of which the majority inhabited Gauteng, KwaZulu-Natal, and the Western-Eastern Cape. Gauteng (includes Johannesburg) is the smallest province in South Africa, though highly urbanized with a population of over 16 million people according to the estimates. Cape Town, on the other hand, is the largest city in South Africa with nearly 3.43 million inhabitants in the same year, whereas Durban counted 3.12 million citizens. However, looking at cities including municipalities, Johannesburg ranks first. High rate of young population South Africa has a substantial population of young people. In 2024, approximately 34.3 percent of the people were aged 19 years or younger. Those aged 60 or older, on the other hand, made-up over 10 percent of the total population. Distributing South African citizens by marital status, approximately half of the males and females were classified as single in 2021. Furthermore, 29.1 percent of the men were registered as married, whereas nearly 27 percent of the women walked down the aisle. Youth unemployment Youth unemployment fluctuated heavily between 2003 and 2022. In 2003, the unemployment rate stood at 36 percent, followed by a significant increase to 45.5 percent in 2010. However, it fluctuated again and as of 2022, over 51 percent of the youth were registered as unemployed. Furthermore, based on a survey conducted on the worries of South Africans, some 64 percent reported being worried about employment and the job market situation.

Chart and table of population level and growth rate for the Ethekwini, South Africa metro area from 1950 to 2025.

ObjectiveGlycated haemoglobin (HbA1c) is recommended as an additional tool to glucose-based measures (fasting plasma glucose [FPG] and 2-hour plasma glucose [2PG] during oral glucose tolerance test [OGTT]) for the diagnosis of diabetes; however, its use in sub-Saharan African populations is not established. We assessed prevalence estimates and the diagnosis and detection of diabetes based on OGTT, FPG, and HbA1c in an urban black South African population.Research Design and MethodsWe conducted a population-based cross-sectional survey using multistage cluster sampling of adults aged ≥18 years in Durban (eThekwini municipality), KwaZulu-Natal. All participants had a 75-g OGTT and HbA1c measurements. Receiver operating characteristic (ROC) analysis was used to assess the overall diagnostic accuracy of HbA1c, using OGTT as the reference, and to determine optimal HbA1c cut-offs.ResultsAmong 1190 participants (851 women, 92.6% response rate), the age-standardised prevalence of diabetes was 12.9% based on OGTT, 11.9% based on FPG, and 13.1% based on HbA1c. In participants without a previous history of diabetes (n = 1077), using OGTT as the reference, an HbA1c ≥48 mmol/mol (6.5%) detected diabetes with 70.3% sensitivity (95%CI 52.7–87.8) and 98.7% specificity (95%CI 97.9–99.4) (AUC 0.94 [95%CI 0.89–1.00]). Additional analyses suggested the optimal HbA1c cut-off for detection of diabetes in this population was 42 mmol/mol (6.0%) (sensitivity 89.2% [95%CI 78.6–99.8], specificity 92.0% [95%CI: 90.3–93.7]).ConclusionsIn an urban black South African population, we found a high prevalence of diabetes and provide the first evidence for the utility of HbA1c for the diagnosis and detection of diabetes in black Africans in sub-Saharan Africa.

Baseline Clinical and Demographics Characteristics of A Cohort of HIV-Infected Children Failing 1st Line ART in Durban, South Africa Stratified by Initial Treatment Regimen and Presence of Resistance Testing.

Abstract

The Transitions study is conducted by the School of Population and Poverty Studies at the University of Natal, Durban, the Horizons Project, the Policy Research Division of the Population Council, and Focus on Young Adults (FOCUS), and the MEASURE/Evaluation Project of Tulane University. The research is a prospective study of reproductive behavior and sexual health of adolescents in South Africa as well as their education and employment experiences, family and environmental conditions, and other factors in their lives that may influence their sexual behavior and choices.

The study design includes two rounds of data collection from adolescents (ages 14-22), in KwaZulu-Natal (KZN), South Africa, during 1999 and 2001-2002.

Additional data was collected at baseline and follow-up from all schools in the study area regarding the teaching of a Life Skills Programme in those schools. This programme, introduced initially in secondary schools, was a key strategy in the state's response to the HIV/AIDS epidemic in South Africa. The survey data is complemented by data on communities (collected in May and June 2000) and an exploration of some of the principal results from the survey data based on focus groups and other qualitative approaches (carried out in August and September 2000).

Geographic coverage

The survey was carried out in the Durban Metropolitan and Mtunzini Magisterial Districts of Kwazulu-Natal, South Africa.

Analysis unit

Individuals

Universe

The study covered adolescents (ages 14-22), in selected households in the Durban Metropolitan and Mtunzini Magisterial Districts of KwaZulu-Natal (KZN), South Africa.

Kind of data

Longitudinal Survey [ls]

Sampling procedure

Two administrative areas within the province - the Durban Metropolitan and Mtunzini Magisterial Districts - wereselected within KwaZulu-Natal for the study.These administrative areas were selected to ensure a variety of urban, transitional and rural regions within the province. Urban respondents (77 percent of the sample) were taken from the Durban Metro sample as well as those living in urban areas within the Mtunzini Magisterial District. Rural respondents (23 percent) were from the rural areas of Mtunzini.

The study used a modified multi-stage cluster sample approach. The first stage required the random selection of 120 enumeration areas (EAs) from a sampling frame of all EAs in the two districts. At the second stage, field supervisors divided EAs into approximately equal segments of a predetermined size (based on an estimate for the average number of adolescents expected per household, derived from census data). The study team then selected one segment randomly, and interviewers tried (in up to three visits) to find all households within that segment and interview every young person between the ages of 14 and 22 reported to live in those households.

For the study, two rounds of surveys of households and youth were undertaken, in 1999 and 2001-2002. Within each area, all youth 14-22 years of age residing in a segmented, probability sample of Census Enumeration Areas (CEAs) were interviewed in the Wave 1 survey (1999). In Wave 2 (2001), all youth 14-24 years of age residing in the same CEAs were included in the survey, including 2,222 of the 3,052 youth also interviewed in Wave 1.

The individual and household survey data were complemented by surveys with school principals undertaken in 1999 and 2001 and a community survey of the areas undertaken in mid-2000.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire included questions about household members, living conditions, economic shocks, expenditure, government assistance, and discussions about HIV in the household.

Cleaning operations

Data entry and cleaning were done by Policy and Praxis, an independent South African data management organization

Response rate

Interviewers completed interviews with 82.2 percent of the adolescents identified in the selected households. However, response rates varied by population group. Interviewers successfully completed interviews with 90.9 percent of eligible adolescents among Africans in rural areas, 83.6 percent among Africans in urban areas, 69.6 percent among Asians, and 67.5 percent among Whites (the latter two groups were only selected in urban areas). These differences arise from the different patterns of activities among population groups, which keep some youth more than others away from home.

Water-finding and water-maintenance behaviors in Guttural Toads, Sclerophrys gutturalis, from their native population in Durban and an invasive population in Cape Town.

Demographics

Project MAP (Measuring Access and Performance) aims to increase the coverage, quality of coverage, access, and efficiency of social marketing product and service delivery systems. SFH undertook a project MAP study in Johannesburg, Cape Town, and Durban, which are the three main intervention areas of SFHs condom social marketing program. The main goal of this study was to ascertain the coverage, quality of coverage, and access for Trust and Lovers Plus condoms among the general population and among the population residing in selected high transmission areas. The primary sampling unit for the study is sub places (SPs). Only SPs that are classified as urban or suburban were studied. Lot quality assurance sampling was employed, which allows for reasonably accurate estimates of coverage and quality of coverage for an entire supervision area. In each city, a representative samples of high transmission areas (HTAs) and non-high transmission areas (non-HTAs) were randomly selected. Maps for HTAs were acquired from researchers in the South African government. However, where a list of high transmission SPs was not available, an appropriate number of priority HTAs were purposively selected in the same way as hot zones are selected. Within the selected SPs all outlets present were targeted and an audit sheet was administered to collect data. The collected data was entered into an Excel spreadsheet before being processed in SPSS and Microsoft Access.

Abstract

The Asenze Study is a longitudinal, population-based cohort study conducted in a peri-urban area outside Durban in KwaZulu-Natal, South Africa. Waves 1 (2008) and 2 (2012) began with a door-to-door survey to identify all children aged 4-6 years in the study area; to obtain demographic information and to invite the child and the primary caregiver with informed consent to participate in the Asenze study. 87% of those children identified attended an assessment focusing on child neurodevelopment, cognitive function, behavioral problems, and the physical and mental health (including HIV status) of both children and their caregivers. Caregivers had their own height and weight taken, answered questionnaire about the child and their own health, wellbeing and were offered HIV testing.

Geographic coverage

The study collected data in a peri-urban area outside Durban in KwaZulu-Natal, South Africa.

Analysis unit

Individuals

Kind of data

Survey and assessment data

Mode of data collection

Face-to-face

Research instrument

The study used questionnaires and assessment sheets and assessment tools. In addition to questionnaires there was a physical exam, hearing and vision assessment and hematocrit and HIV test (if consent obtained) for the child

This 2008 MAP (Measuring Access and Performance) study allows programmers to assess condom availability and accessibility using pre-defined criteria for measuring coverage and the quality of that coverage. The aim is to improve the efficiency of social marketing product and service delivery systems by providing feedback to programmers. In 2006, SFH conducted a similar MAP study on condom availability in Johannesburg, Cape Town and Durban. The goal of the study was to determine coverage, quality of coverage and access to Lovers Plus and Trust condoms amongst the general population and residents in High Transmission Areas (HTAs) for HIV. Strategic adjustments were made to the condom social marketing delivery system based on these findings. A second MAP study was conducted in 2007 to measure changes in coverage and quality of coverage, using the 2006 MAP results as a baseline. A third round of MAP was completed in late 2008 with the aim of monitoring trends in condom availability since 2006. The specific objectives of the 2008 MAP study were to: 1) Assess the geographical coverage and quality of coverage of Lovers Plus and Trust condoms in Johannesburg, Durban and Cape Town in 2008; 2) Monitor trends in condom availability using the 2006 MAP results as a baseline. Measures of penetration (by outlet type) and price levels of SFH condoms were additional expected outputs. The availability of the free government condoms (Choice) and other brands were also estimated.

The South African commercial real estate market, valued at $9.28 billion in 2025, is projected to experience robust growth, exhibiting a Compound Annual Growth Rate (CAGR) of 7.63% from 2025 to 2033. This expansion is fueled by several key drivers. Increased urbanization and population growth in major cities like Johannesburg, Cape Town, and Durban are creating a surge in demand for office, retail, and industrial spaces. Furthermore, a burgeoning tourism sector and associated hospitality investments are contributing significantly to the market's positive trajectory. The ongoing development of logistics infrastructure to support growing e-commerce activity also plays a crucial role. While potential economic uncertainties and fluctuations in interest rates could pose challenges, the overall market outlook remains positive, underpinned by the continued strength of the South African economy and targeted investments in key sectors. Strong performance is expected across all segments, with the office and logistics sectors likely to see particularly substantial gains due to increasing corporate activity and supply chain optimization strategies respectively. The diverse portfolio of established and emerging property developers in South Africa, including major players like Growthpoint Properties and Amdec Group, further underscores the market's dynamic nature and competitive landscape. The segmentation of the market reveals strong growth potential within specific areas. The substantial investment in modernizing existing commercial infrastructure in Johannesburg and Cape Town will drive significant growth. Furthermore, the expansion of retail spaces in rapidly growing suburban areas will cater to evolving consumer preferences and boost market value in those regions. However, challenges remain, including the need for continued infrastructure development to support sustainable growth in key areas, particularly in logistics and transportation networks. While the overall market exhibits positive momentum, proactive strategies focused on addressing these factors will be crucial to ensure sustained, long-term growth. Careful risk management by investors and developers regarding economic volatility will be critical in navigating potential headwinds. Recent developments include: November 2023: WeWork South Africa announced that it was accelerating its expansion plans as the rise in popularity of hybrid work saw a boost in demand for flexible office spaces.September 2023: Instant Group, a flexible workspace marketplace, acquired property advisor PSA to broaden its reach and grow its business across Africa.. Key drivers for this market are: 4., Urbanization and Population Growth4.; Foreign Direct Investments. Potential restraints include: 4., Urbanization and Population Growth4.; Foreign Direct Investments. Notable trends are: Increasing Demand for Office Space in South Africa.

The South African commercial real estate market, valued at $9.62 billion in 2025, is projected to experience robust growth, with a compound annual growth rate (CAGR) of 10.84% from 2025 to 2033. This expansion is driven by several factors. Firstly, a growing economy and increasing urbanization are fueling demand for office spaces, particularly in major cities like Johannesburg, Cape Town, and Durban. The rise of e-commerce and associated logistics needs are boosting the industrial and logistics sector. Furthermore, a burgeoning tourism sector contributes to the growth of the hospitality segment. However, economic volatility and potential interest rate hikes present challenges. While the office segment is expected to be a major contributor to market growth, the ongoing shift towards hybrid work models could moderate this growth. The retail sector may face some headwinds due to evolving consumer behavior and the increasing popularity of online shopping. Despite these challenges, the overall market outlook remains positive, driven by long-term infrastructural development and continued investment in key cities. Key players like Devmark Property Group, Rabie Property Group, and Growthpoint Properties are well-positioned to benefit from these trends. The segmentation of the market across office, retail, industrial and logistics, and hospitality sectors provides a diversified investment landscape. While Johannesburg, Cape Town, and Durban represent the largest market shares, growth is also anticipated in secondary cities as economic activity expands across the country. The presence of established and well-capitalized real estate companies suggests a mature market with a high level of competition. Future growth will depend on the government's ability to foster economic stability, address infrastructure gaps, and support sustainable urban development. The resilience of the South African economy and its long-term growth potential remain key factors in influencing the success of the commercial real estate sector. Recent developments include: November 2023: WeWork South Africa is accelerating its expansion plans as the rise in popularity of hybrid work sees a boost in demand for flexible office spaces., September 2023: Instant Group, a flexible workspace marketplace, has acquired property advisor PSA to broaden its reach and grow its business across Africa.. Key drivers for this market are: 4., Urbanization and Population growth4.; Foreign direct investments. Potential restraints include: 4., Economic uncertainity4.; Regulatory environment. Notable trends are: Increasing office space demand in South Africa.

SD: standard deviation.Demographics of clients participating in CVCT and completing pre- and post-CVCT surveys stratified by gender, Durban, South Africa, 2013.

Abstract

The South Africa Enterprise Survey was conducted between January and December 2007. Data from 1057 establishments in private manufacturing and services sectors were analyzed. The sample included enterprises with more than four employees (937 companies) as well as micro firms, establishments with less than 5 workers, (120 observations). The survey targeted establishments in Johannesburg, Cape Town, Port Elizabeth and Durban.

The objective of the survey is to obtain feedback from enterprises in client countries on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance. The mode of data collection is face-to-face interviews.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The South Africa Enterprise Survey 2007 included enterprises with more than four employees as well as micro establishments, firms with less than five workers. There are 120 micro establishments in the sample.

The sample for enterprises with more than four employees was designed using stratified random sampling with strata defined by region, sector and firm size.

Establishments located in Johannesburg, Cape Town, Port Elizabeth and Durban were interviewed.

Following the ISIC (revision 3.1) classification, the following industries were targeted: all manufacturing sectors (group D), construction (group F), retail and wholesale services (subgroups 52 and 51 of group G), hotels and restaurants (group H), transport, storage, and communications (group I), and computer and related activities (sub-group 72 of group K). For establishments with five or more full-time permanent paid employees, this universe was stratified according to the following categories of industry: 1. Manufacturing: Food and Beverages (Group D, sub-group 15), Machinery and Equipment (Group D, sub-group 29), Electrical Machinery and Equipment (Group D, sub-group 31); 2. Manufacturing: Textiles (Group D, sub-group 17), Garment (Group D, sub-group 18), Leather and Footwear (Group D, sub-group 19), Paper and Paper Products (Group D, sub-group 21), Printing and Publishing (Group D, sub-group 22); 3. Manufacturing: Non-Metallic Mineral Products (Group D, sub-group 26), Basic Metals (Group D, sub-group 27), Fabricated Metal Products (Group D, sub-group 28); 4. Manufacturing: Wood and Wood Products (Group D, sub-group 20), Furniture (Group D, sub-group 36) 5. Manufacturing: Refined Petroleum Products (Group D, sub-group 23), Chemical Products (Group D, sub-group 24), Rubber and Plastics (Group D, sub-group 25) 6. Retail Trade: (Group G, sub-group 52); 7. Rest of the universe, including: • Other Manufacturing (Group D excluding sub-groups in strata 1-5); • Construction (Group F); • Wholesale trade (Group G, sub-group 51); • Hotels, bars and restaurants (Group H); • Transportation, storage and communications (Group I); • Computer related activities (Group K, sub-group 72).

Size stratification was defined following the standardized definition used for the Enterprise Surveys: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers.

The implementing agency (EEC Canada) was unable to obtain a satisfactory sample frame from South African statistical agency (STASA) or its Department of Revenue. The best alternative solution was a list obtained from the Department of Trade and Industry Companies and Intellectual Property Registration Office (CIPRO), which contained about 800000 establishments when delineating in-scope cities and industries, but which had incomplete firm characteristics necessary for stratification purposes (e.g. contact information, size). In order to determine the sample frame, EEC Canada randomly drew 9550 units and contacted them.

In South Africa, the survey included panel data collected from establishments surveyed in the 2003 Investment Climate Survey (ICS) of South Africa. That survey included establishments in the manufacturing and the rest of universe strata, distributed across Gauteng (Johannesburg), KwaZulu Natal (Durban), Western Cape (Cape Town) and Eastern Cape (Port Elizabeth) provinces.

In order to collect the largest possible set of panel data, an attempt was made to contact and survey valid establishments (579) in the panel list provided which was part of the Enterprise Survey's scope. Of the 716 establishments provided to EEC Canada from those surveyed in 2003, there were 35 doubles, 8 out-of-scope, 89 excluded from this survey by The World Bank to avoid over representing Construction in a single Residual stratum, and 5 with undefined ISIC codes. This left a total potential of 579 panel establishments. EEC Canada surveyed 231 panel establishments or 40% of the total potential panels without eliminating those establishments which had closed. Once eliminated, this percentage coverage exceeded 55%. Given the non-random nature of panel establishment selection, these establishments are not allocated probability weights in the final dataset.

In this survey, the micro establishment stratum covers all establishments of the targeted categories of economic activity with less than 5 employees located in Johannesburg. The implementing agency selected an aerial sampling approach to estimate the population of establishments and select the sample in this stratum for all states of the survey.

First, to randomly select individual micro establishments for surveying, the following procedure was followed: i) select districts and specific zones of each district where there was a high concentration of micro establishments; ii) count all micro establishments in these specific zones; iii) based on this count, create a virtual list and select establishments at random from that virtual list; and iv) based on the ratio between the number selected in each specific zone and the total population in that zone, create and apply a skip rule for selecting establishments in that zone.

The districts and the specific zones were selected at first according to local sources. The EEC team then went in the field to verify the sources and to count micro establishments. Once the count for each zone was completed, the numbers were sent back to EEC head office in Montreal.

At the head office, the count by zone was converted into one list of sequential numbers for the whole survey region, and a computer program performed a random selection of the determined number of establishments from the list. Then, based on the number that the computer selected in each specific zone, a skip rule was defined to select micro establishments to survey in that zone. The skip rule for each zone was sent back to the EEC field team.

In Johannesburg, enumerators were sent to each zone with instructions how to apply the skip rule defined for that zone as well as how to select replacements in the event of a refusal or other cause of non-participation.

For complete information about sampling methodology, refusal rate and weighting please review "South Africa Enterprise Survey 2007 Implementation Report" in "Technical Documents" folder.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Core Questionnaire + Manufacturing Module [ISIC Rev.3.1: 15-37] - Core Questionnaire + Retail Module [ISIC Rev.3.1: 52] - Core Questionnaire [ISIC Rev.3.1: 45, 50, 51, 55, 60-64, 72] - Micro

Characteristics of the total study population by sex (n = 1190).

Abstract

The survey was initiated in 1998 by the IJPR and the Kaplan Centre and conducted using face-to-face interviews with a sample of adults from Jewish families in Cape Town, Durban, Johannesburg, and Pretoria.

Geographic coverage

The survey covered selected Jewish households in Cape Town, Durban, Johannesburg, and Pretoria.

Analysis unit

Households and individuals

Universe

The target population of the survey consists of Jewish South Africans.

Kind of data

Sample survey data

Mode of data collection

Face-to-face [f2f]

Research instrument

A single household questionnaire was used for the survey

Urban areas can be attractive to certain species because of increased food abundance and nesting availability which in turn may increase productivity or breeding rates. However, there are also potential costs associated with urban living such as higher nest failure, poorer body condition or increased prevalence of disease. These costs may result in species trading off the number of young produced against the condition of their young. African Crowned Eagles (Stephanoaetus coronatus) are a rare example of large, powerful apex predators that breed in some urban areas in Africa. In this study, we explored the breeding performance of these eagles across an urbanization gradient in KwaZulu-Natal Province, South Africa, over seven breeding seasons. We predicted that living in an urban environment would increase productivity through an increase in breeding rate (shifting from typically biennial breeding to annual breeding). We then explored if there were any hidden costs associated with such a change in breeding strategy by examining the body condition of chicks from pairs which had successfully bred in the previous year. We found that pairs in more urban areas were more likely to breed annually, resulting in higher breeding rates, but were also less likely to successfully fledge a chick (i.e., lower breeding success). These two contrasting responses counteracted each other and resulted in similar productivity across the urbanization gradient. For those eagles that bred in consecutive years, annual breeding did not appear to have a negative cost on chick condition. The switch to annual breeding is thought to be a response to improved or more constant food sources in urban areas, while higher failure rates might be because of increased nest disturbances from anthropogenic sources (e.g., vegetation clearing, development of industrial areas, human and car traffic). However, although urbanization negatively affected the breeding success of African Crowned Eagles, they are able to persist and thrive in this highly transformed environment likely through an increased breeding rate.

Methods Study Area and Data Collection

The study area covered ~20,000 km2 in southern KwaZulu-Natal Province, South Africa, centered on the metropoles of Durban and Pietermaritzburg, and extended to several coastal towns both north and south of Durban (Figure 1; McPherson et al. 2016a, b, 2019). Crowned Eagle nesting sites were initially found by networking with interested individuals/groups (local birding experts, Birdlife and Falconry club members, and online community groups), and accessing unpublished databases from Durban Natural History Museum, eThekwini Municipality, and SABAP2, as well as by direct searching in suitable habitat or where territorial displays were observed.

Crowned Eagle nest monitoring was conducted from August through January the following year for the years 2011 to 2017 (i.e., 7 breeding seasons), which covers their peak annual breeding period in the area (McPherson et al. 2016a, b). Territories were visited regularly, at least twice in the first month, in order to assess occupation (e.g., nest building, incubation or brooding behavior). A nest was classified as active if nest building or fresh green leaves were seen on the nest or if the adults were present in either of these first two nest visits. A nest was classified as having a breeding attempt if incubation or brooding behavior was seen. Nests with a breeding attempt were then monitored during 2-3 nest site visits until conclusion of the breeding event (i.e., until the chicks were around 70±5 days old). Breeding success was defined as having a chick survive until banding age (70 ± 5 days old). After this age, failures in this species and most other large raptors are relatively low (Brown, 1976). Nests were observed from vantage points generally 50 – 200 m away from the nest (see details in McPherson et al. 2016a).

    Crowned Eagle chicks were banded when their estimated age was 65 - 75 days, a time window recommended by experts (S. Thomsett and B. Hoffman pers. comm.). The age estimates used in this study were ascertained by photo reference material of pulli of known age (McPherson et al. 2017) and were based on size and plumage development. During banding, chicks were weighed (with an electronic hanging scale to the nearest 5 g) and the total length and unfurled length of the 8th primary feather was taken (with a straight ruler to the nearest 1 mm). All measurements were done in accordance with the SAFRING user manual (de Beer et al. 2001).

Urbanization Score

In order to establish the percentage of urbanization around each nest site, we used the LandCover 2014 raster (GEOTERRAIMAGE, 2015), which classifies land use into 72 different categories. We chose a circular buffer area of 10 km2 (radius = 1784.1 m) based on the mean home range size of the species during the breeding season from four telemetered adults in the study area (McPherson et al. 2019). Once the percentage of each land class around each nest site had been calculated, the values for all land classes containing sealed surface (see Rose et al. 2017) were used to calculate an urban score (%) for each nest. Examples of what land classes constituted sealed surface are urban residential, industrial, townships, and mines. In territories where there was more than one nest, the mean urban score was taken to represent the territory.

Statistical Analyses

All analyses were conducted in R version 3.5.1 (R Core Team 2018) with the packages ‘lme4’ (Bates et al., 2015), ‘car’ (Fox and Weisberg, 2018), and ‘effects’ (Fox, 2003). All means are presented with standard deviations. Generalized Linear Models (GLMs) or Linear Mixed Models (LMMs) were used to analyze the data. An initial model selection for the GLMs considered both the linear or quadratic relationship between urbanization and our response variables, as a quadratic relationship could reveal changed breeding demography at intermediate levels of urbanization. In all cases, the linear relationship had the best model fit (lowest AIC) and thus only linear relationships were considered in the final analyses.

    We explored how urbanization affected several Crowned Eagle breeding parameters over seven breeding seasons. GLMs with a binomial distribution were used to investigate the effect of urbanization on three key breeding parameters using the cbind function. These three variables were i) breeding rates: modelled as the total number of attempts and number of non-attempts (i.e., no nest building activity (nest lining, mating behaviour, incubation etc.) at a previously occupied nest) across the years a territory was monitored; ii) breeding success: modelled as the total number of successful breeding attempts and number of failures across the years in which a territory was active; and iii) breeding continuity: modelled as the number of continuous breeding attempts (i.e., no gap between breeding attempts) and the number of non-continuous breeding attempts (i.e., with at least 1 year gap between breeding attempts) for the total number of years monitored. This binomial approach also accounted for differences in the number of years of data for each territory, by effectively weighting each sample according to the total number of years monitored (models i and iii) or total number of active years (model ii). Additionally, a different GLM was used to investigate Crowned Eagle productivity in relation to urbanization. Here the response variable was the total number of young fledged across all the years each territory was monitored. Models were fitted with a Poisson distribution, with an offset specified as the log of the number of years monitored.

    An LMM was used to explore whether a Crowned Eagle breeding attempt or, more importantly, a breeding success in the previous year, had an influence on the body condition of chicks. For this LMM, the response variable was the condition of each chick (n = 72), where chick condition was the residual from a linear regression of weight against the length of the 8th primary feather. The explanatory variable was either attempt (t-1), where 0 = no attempt previous year, and 1 = attempt the previous year; we also ran the same model but specifying success (t-1), where 0 = no successful chick produced in the previous year, and 1 = chick successfully produced in the previous year. ‘Year’ and ‘Territory Identity’ were included as random terms to account for the repeated measures from the same territory and from different territories in the same year. As Crowned Eagles only fledge 1 chick per breeding attempt we did not need to control for the number of chicks in a nest.

Description of the population at baseline, overall and by whether blood was drawn for CD4+ count on the day of diagnosis, 459 newly-diagnosed HIV+ women and men, Durban, South Africa, 2010–2012.