This statistic shows the population change in Sub-Saharan Africa from 2014 to 2024. Sub-Saharan Africa includes almost all countries south of the Saharan desert. In 2024, Sub-Saharan Africa's population increased by approximately 2.44 percent compared to the previous year.

Graph and download economic data for Population Growth for Developing Countries in Sub-Saharan Africa (SPPOPGROWSSA) from 1961 to 2024 about Sub-Saharan Africa, population, and rate.

As of 2023, the total population of Africa was over 1.48 billion. The number of inhabitants on the continent increased annually from 2000 onwards. In comparison, the total population was around 831 million in 2000. According to forecasts, Africa will experience impressive population growth in the coming years and will close the gap with the Asian population by 2100. Over 200 million people in Nigeria Nigeria is the most populous country in Africa. In 2025, the country’s population exceeded 237 million people. Ethiopia followed with a population of around 135 million, while Egypt ranked third, accounting for approximately 118 million individuals. Other leading African countries in terms of population were the Democratic Republic of the Congo, Tanzania, South Africa, and Kenya. Additionally, Niger, the Democratic Republic of Congo, and Chad recorded the highest population growth rate on the continent in 2023, with the number of residents rising by over 3.08 percent compared to the previous year. On the other hand, the populations of Tunisia and Eswatini registered a growth rate below 0.85 percent, while for Mauritius and Seychelles, it was negative. Drivers for population growth Several factors have driven Africa’s population growth. For instance, the annual number of births on the continent has risen constantly over the years, jumping from nearly 32 million in 2000 to almost 46 million in 2023. Moreover, despite the constant decline in the number of births per woman, the continent’s fertility rate has remained considerably above the global average. Each woman in Africa had an average of over four children throughout her reproductive years as of 2023, compared to a world rate of around two births per woman. At the same time, improved health and living conditions contributed to decreasing mortality rate and increasing life expectancy in recent years, driving population growth.

In 2024, the population of Africa was projected to grow by 2.27 percent compared to the previous year. The population growth rate on the continent has been constantly over 2.5 percent from 2000 onwards, and it peaked at 2.63 percent in 2013. Despite a slowdown in the growth rate after that, the continent's population will continue to increase significantly in the coming years. The second-largest population worldwide In 2023, the total population of Africa amounted to almost 1.5 billion. The number of inhabitants had grown steadily in the previous decades, rising from approximately 831 million in 2000. Driven by a decreasing mortality rate and a higher life expectancy at birth, the African population was forecast to increase to about 2.5 billion individuals by 2050. Africa is currently the second most populous continent worldwide after Asia. However, forecasts showed that Africa could gradually close the gap and almost reach the size of the Asian population in 2100. By that year, Africa might count 3.8 billion people, compared to 4.6 billion in Asia. The world's youngest continent The median age in Africa corresponded to 19.2 years in 2024. Although the median age has increased in recent years, the continent remains the youngest worldwide. In 2023, roughly 40 percent of the African population was aged 15 years and younger, compared to a global average of 25 percent. Africa recorded not only the highest share of youth but also the smallest elderly population worldwide. As of the same year, only three percent of Africa's population was aged 65 years and older. Africa and Latin America were the only regions below the global average of ten percent. On the continent, Niger, Uganda, and Angola were the countries with the youngest population in 2023.

Historical dataset showing Sub-Saharan Africa population growth rate by year from 1961 to 2023.

Nigeria has the largest population in Africa. As of 2025, the country counted over 237.5 million individuals, whereas Ethiopia, which ranked second, has around 135.5 million inhabitants. Egypt registered the largest population in North Africa, reaching nearly 118.4 million people. In terms of inhabitants per square kilometer, Nigeria only ranked seventh, while Mauritius had the highest population density on the whole African continent in 2023. The fastest-growing world region Africa is the second most populous continent in the world, after Asia. Nevertheless, Africa records the highest growth rate worldwide, with figures rising by over two percent every year. In some countries, such as Chad, South Sudan, Somalia, and the Central African Republic, the population increase peaks at over 3.4 percent. With so many births, Africa is also the youngest continent in the world. However, this coincides with a low life expectancy. African cities on the rise The last decades have seen high urbanization rates in Asia, mainly in China and India. African cities are also growing at large rates. Indeed, the continent has three megacities and is expected to add four more by 2050. Furthermore, Africa's fastest-growing cities are forecast to be Bujumbura, in Burundi, and Zinder, Nigeria, by 2035.

United States - Population Growth for Developing Countries in Sub-Saharan Africa was 2.49729 % Chg. at Annual Rate in January of 2023, according to the United States Federal Reserve. Historically, United States - Population Growth for Developing Countries in Sub-Saharan Africa reached a record high of 2.90713 in January of 1986 and a record low of 2.29933 in January of 1960. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Population Growth for Developing Countries in Sub-Saharan Africa - last updated from the United States Federal Reserve on December of 2025.

South Africa ZA: Population: Growth data was reported at 1.245 % in 2017. This records a decrease from the previous number of 1.301 % for 2016. South Africa ZA: Population: Growth data is updated yearly, averaging 2.282 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 2.794 % in 1972 and a record low of 1.047 % in 2008. South Africa ZA: Population: Growth 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.WDI: Population and Urbanization Statistics. Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage . Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.; ; Derived from total population. Population source: (1) United Nations Population Division. World Population Prospects: 2017 Revision, (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

In 2023, Chad ranked first by annual population growth among the 54 countries presented in the ranking. Chad's population growth amounted to 4.57 percent, while South Sudan and Niger, the second and third countries, had records amounting to 4.11 percent and 3.29 percent, respectively.

Historical dataset showing South Africa population growth rate by year from 1961 to 2023.

A flexible model to reconstruct education-specific fertility rates: Sub-saharan Africa case study

The fertility rates are consistent with the United Nation World Population Prospects (UN WPP) 2022 fertility rates.

The Bayesian model developed to reconstruct the fertility rates using Demographic and Health Surveys and the UN WPP is published in a working paper.

Abstract

The future world population growth and size will be largely determined by the pace of fertility decline in sub-Saharan Africa. Correct estimates of education-specific fertility rates are crucial for projecting the future population. Yet, consistent cross-country comparable estimates of education-specific fertility for sub-Saharan African countries are still lacking. We propose a flexible Bayesian hierarchical model to reconstruct education-specific fertility rates by using the patchy Demographic and Health Surveys (DHS) data and the United Nations’ (UN) reliable estimates of total fertility rates (TFR). Our model produces estimates that match the UN TFR to different extents (in other words, estimates of varying levels of consistency with the UN). We present three model specifications: consistent but not identical with the UN, fully-consistent (nearly identical) with the UN, and consistent with the DHS. Further, we provide a full time series of education-specific TFR estimates covering five-year periods between 1980 and 2014 for 36 sub-Saharan African countries. The results show that the DHS-consistent estimates are usually higher than the UN-fully-consistent ones. The differences between the three model estimates vary substantially in size across countries, yielding 1980-2014 fertility trends that differ from each other mostly in level only but in some cases also in direction.

Funding

The data set are part of the BayesEdu Project at Wittgenstein Centre for Demography and Global Human Capital (IIASA, OeAW, University of Vienna) funded from the “Innovation Fund Research, Science and Society” by the Austrian Academy of Sciences (ÖAW).

We provide education-specific total fertility rates (ESTFR) from three model specifications: (1) estimated TFR consistent but not identical with the TFR estimated by the UN (“Main model (UN-consistent)”; (2) estimated TFR fully consistent (nearly identical) with the TFR estimated by the UN ( “UN-fully -consistent”, and (3) estimated TFR consistent only with the TFR estimated by the DHS ( “DHS-consistent”).

For education- and age-specific fertility rates that are UN-fully consistent, please see https://doi.org/10.5281/zenodo.8182960

Variables

Country: Country names

Education: Four education levels, No Education, Primary Education, Secondary Education and Higher Education.

Year: Five-year periods between 1980 and 2015.

ESTFR: Median education-specific total fertility rate estimate

sd: Standard deviation

Upp50: 50% Upper Credible Interval

Lwr50: 50% Lower Credible Interval

Upp80: 80% Upper Credible Interval

Lwr80: 80% Lower Credible Interval

Model: Three model specifications as explained above and in the working paper. DHS-consistent, Main model (UN-consistent) and UN-fully consistent.

List of countries:

Angola, Benin, Burkina Faso, Burundi, Cote D'Ivoire, Cameroon, Central African Republic, Chad, Comoros, Congo, Democratic Republic of Congo, Eswatini, Ethiopia, Gabon, Gambia, Ghana, Guinea, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, Sierra Leone, South Africa, Tanzania, Togo, Uganda, Zambia, Zimbabwe

According to the forecast, Africa's total population would reach nearly 2.5 billion by 2050. In 2025, the continent had around 1.55 billion inhabitants, with Nigeria, Ethiopia, and Egypt as the most populous countries. In the coming years, Africa will experience significant population growth and will close the gap significantly with the Asian population by 2100. Rapid population growth In Africa, the annual growth rate of the population followed an overall increasing trend up to 2013, reaching nearly 2.63 percent. This was followed by a drop to 2.32 percent by 2023. Although population growth was slowing down, it was still growing faster than in all other regions. The reasons behind this rapid growth are various. One factor is the high fertility rate registered in African countries. In 2023, a woman in Somalia, Chad, Niger, the Democratic Republic of Congo, and the Central African Republic had an average of over six children in her reproductive years, the highest rate on the continent. High fertility resulted in a large young population and partly compensated for the high mortality rate in Africa, leading to fast-paced population growth. High poverty levels Africa’s population is concerned with widespread poverty. In 2025, over 438 million people on the continent are extremely poor and live with less than 2.15 U.S. dollars per day. Globally, Africa is the continent hosting the highest poverty rate. In 2025, the countries of Nigeria and the Democratic Republic of the Congo account for over 23 percent of the world's population living in extreme poverty. Nevertheless, the share of the population living in poverty in Africa is forecast to decrease in the coming years.

Abstract

The authors combine data from 84 Demographic and Health Surveys from 46 countries to analyze trends and socioeconomic differences in adult mortality, calculating mortality based on the sibling mortality reports collected from female respondents aged 15-49.

The analysis yields four main findings. First, adult mortality is different from child mortality: while under-5 mortality shows a definite improving trend over time, adult mortality does not, especially in Sub-Saharan Africa. The second main finding is the increase in adult mortality in Sub-Saharan African countries. The increase is dramatic among those most affected by the HIV/AIDS pandemic. Mortality rates in the highest HIV-prevalence countries of southern Africa exceed those in countries that experienced episodes of civil war. Third, even in Sub-Saharan countries where HIV-prevalence is not as high, mortality rates appear to be at best stagnating, and even increasing in several cases. Finally, the main socioeconomic dimension along which mortality appears to differ in the aggregate is gender. Adult mortality rates in Sub-Saharan Africa have risen substantially higher for men than for women?especially so in the high HIV-prevalence countries. On the whole, the data do not show large gaps by urban/rural residence or by school attainment.

This paper is a product of the Human Development and Public Services Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org.

Geographic coverage

We derive estimates of adult mortality from an analysis of Demographic and Health Survey (DHS) data from 46 countries, 33 of which are from Sub-Saharan Africa and 13 of which are from countries in other regions (Annex Table). Several of the countries have been surveyed more than once and we base our estimates on the total of 84 surveys that have been carried out (59 in Sub-Saharan Africa, 25 elsewhere).

The countries covered by DHS in Sub-Saharan Africa represent almost 90 percent of the region's population. Outside of Sub-Saharan Africa the DHS surveys we use cover a far smaller share of the population-even if this is restricted to countries whose GDP per capita never exceeds $10,000: overall about 14 percent of the population is covered by these countries, although this increases to 29 percent if China and India are excluded (countries for which we cannot calculate adult mortality using the DHS). It is therefore important to keep in mind that the sample of non-Sub-Saharan African countries we have cannot be thought of as "representative" of the rest of the world, or even the rest of the developing world.

Analysis unit

Country

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Cleaning operations

In the course of carrying out this study, the authors created two databases of adult mortality estimates based on the original DHS datasets, both of which are publicly available for analysts who wish to carry out their own analysis of the data.

The naming conventions for the adult mortality-related are as follows. Variables are named:

GGG_MC_AAAA

GGG refers to the population subgroup. The values it can take, and the corresponding definitions are in the following table:

All - All Fem - Female Mal - Male Rur - Rural Urb - Urban Rurm - Rural/Male Urbm - Urban/Male Rurf - Rural/Female Urbf - Urban/Female Noed - No education Pri - Some or completed primary only Sec - At least some secondary education Noedm - No education/Male Prim - Some or completed primary only/Male Secm - At least some secondary education/Male Noedf - No education/Female Prif - Some or completed primary only/Female Secf - At least some secondary education/Female Rch - Rural as child Uch - Urban as child Rchm - Rural as child/Male Uchm - Urban as child/Male Rchf - Rural as child/Female Uchf - Urban as child/Female Edltp - Less than primary schooling Edpom - Primary or more schooling Edltpm - Less than primary schooling/Male Edpomm - Primary or more schooling/Male Edltpf - Less than primary schooling/Female Edpomf - Primary or more schooling/Female Edltpu - Less than primary schooling/Urban Edpomu - Primary or more schooling/Urban Edltpr - Less than primary schooling/Rural Edpomr - Primary or more schooling/Rural Edltpmu - Less than primary schooling/Male/Urban Edpommu - Primary or more schooling/Male/Urban Edltpmr - Less than primary schooling/Male/Rural Edpommr - Primary or more schooling/Male/Rural Edltpfu - Less than primary schooling/Female/Urban Edpomfu - Primary or more schooling/Female/Urban Edltpfr - Less than primary schooling/Female/Rural Edpomfr - Primary or more schooling/Female/Rural

M refers to whether the variable is the number of observations used to calculate the estimate (in which case M takes on the value "n") or whether it is a mortality estimate (in which case M takes on the value "m").

C refers to whether the variable is for the unadjusted mortality rate calculation (in which case C takes on the value "u") or whether it adjusts for the number of surviving female siblings (in which case C takes on the value "a").

AAAA refers to the age group that the mortality estimate is calculated for. It takes on the values: 1554 - Ages 15-54 1524 - Ages 15-24 2534 - Ages 25-34 3544 - Ages 35-44 4554 - Ages 45-54

Other variables that are in the databases are:

period - Period for which mortality rate is calculated (takes on the values 1975-79, 1980-84 … 2000-04) svycountry - Name of country for DHS countries ccode3 - Country code u5mr - Under-5 mortality (from World Development Indicators) cname - Country name gdppc - GDP per capita (constant 2000 US$) (from World Development Indicators) gdppcppp - GDP per capita PPP (constant 2005 intl $) (from World Development Indicators) pop - Population (from World Development Indicators) hivprev2001 - HIV prevalence in 2001 (from UNAIDS 2010) region - Region

South Africa ZA: Rural Population Growth data was reported at -0.235 % in 2017. This records a decrease from the previous number of -0.168 % for 2016. South Africa ZA: Rural Population Growth data is updated yearly, averaging 1.217 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 2.679 % in 1972 and a record low of -0.329 % in 2008. South Africa ZA: Rural Population Growth 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.WDI: Population and Urbanization Statistics. Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population.; ; World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2018 Revision.; Weighted average;

The world population surpassed eight billion people in 2022, having doubled from its figure less than 50 years previously. Looking forward, it is projected that the world population will reach nine billion in 2038, and 10 billion in 2060, but it will peak around 10.3 billion in the 2080s before it then goes into decline. Regional variations The global population has seen rapid growth since the early 1800s, due to advances in areas such as food production, healthcare, water safety, education, and infrastructure, however, these changes did not occur at a uniform time or pace across the world. Broadly speaking, the first regions to undergo their demographic transitions were Europe, North America, and Oceania, followed by Latin America and Asia (although Asia's development saw the greatest variation due to its size), while Africa was the last continent to undergo this transformation. Because of these differences, many so-called "advanced" countries are now experiencing population decline, particularly in Europe and East Asia, while the fastest population growth rates are found in Sub-Saharan Africa. In fact, the roughly two billion difference in population between now and the 2080s' peak will be found in Sub-Saharan Africa, which will rise from 1.2 billion to 3.2 billion in this time (although populations in other continents will also fluctuate). Changing projections The United Nations releases their World Population Prospects report every 1-2 years, and this is widely considered the foremost demographic dataset in the world. However, recent years have seen a notable decline in projections when the global population will peak, and at what number. Previous reports in the 2010s had suggested a peak of over 11 billion people, and that population growth would continue into the 2100s, however a sooner and shorter peak is now projected. Reasons for this include a more rapid population decline in East Asia and Europe, particularly China, as well as a prolonged development arc in Sub-Saharan Africa.

Globally, about 25 percent of the population is under 15 years of age and 10 percent is over 65 years of age. Africa has the youngest population worldwide. In Sub-Saharan Africa, more than 40 percent of the population is below 15 years, and only three percent are above 65, indicating the low life expectancy in several of the countries. In Europe, on the other hand, a higher share of the population is above 65 years than the population under 15 years. Fertility rates The high share of children and youth in Africa is connected to the high fertility rates on the continent. For instance, South Sudan and Niger have the highest population growth rates globally. However, about 50 percent of the world’s population live in countries with low fertility, where women have less than 2.1 children. Some countries in Europe, like Latvia and Lithuania, have experienced a population decline of one percent, and in the Cook Islands, it is even above two percent. In Europe, the majority of the population was previously working-aged adults with few dependents, but this trend is expected to reverse soon, and it is predicted that by 2050, the older population will outnumber the young in many developed countries. Growing global population As of 2025, there are 8.1 billion people living on the planet, and this is expected to reach more than nine billion before 2040. Moreover, the global population is expected to reach 10 billions around 2060, before slowing and then even falling slightly by 2100. As the population growth rates indicate, a significant share of the population increase will happen in Africa.

South Africa ZA: Urban Population Growth data was reported at 2.021 % in 2017. This records a decrease from the previous number of 2.090 % for 2016. South Africa ZA: Urban Population Growth data is updated yearly, averaging 2.837 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 3.548 % in 1960 and a record low of 1.930 % in 2008. South Africa ZA: Urban Population Growth 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.WDI: Population and Urbanization Statistics. Urban population refers to people living in urban areas as defined by national statistical offices. It is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects.; ; World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2018 Revision.; Weighted average;

The world's population first reached one billion people in 1805, and reached eight billion in 2022, and will peak at almost 10.2 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two-thirds of the world's population lives in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a few years later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.

In the face of rapid demographic transitions, Sub-Saharan African countries stand at a critical juncture where the potential for harnessing a demographic dividend to fuel economic growth is immense. This demographic shift presents both challenges and opportunities, with the right investments in health, education, and employment, countries can turn the growing youth population into a powerful engine for development, driving substantial and sustainable economic progress across the region. This study examines the demographic structure effect on economic growth in the context of structural changes in 26 sub-Saharan African countries. Using data from 1992 to 2019 in the PMG-ARDL, FMOLS, and DOLS estimates, we find that demographic structure has a positive influence on economic growth in the long run, which occurs through effective structural change, that is, structural changes that occur with an increase in labor productivity growth. Indeed, our results show that structural changes are relevant in transforming African youth debt into demographic dividends. The study investigates the impact of demographic structure on economic growth within the context of structural changes in 26 sub-Saharan African countries from 1992 to 2019. It provides a detailed analysis of the impact of demographic transition, characterized by declining fertility rates and an expanding working-age population, on economic growth in sub-Saharan Africa. It highlights the importance of structural changes, such as labor productivity and sectoral composition variations, to transform demographic advantages into sustainable economic growth. Using robust econometric methods (PMG-ARDL, FMOLS, and DOLS), the research demonstrates a significant positive long-term impact of demographic structure on economic development, mediated by effective structural change. The policy implications include promoting family planning and education for young girls, which will help reduce dependency ratios, accelerate demographic transitions, and encourage industrialization and innovation to drive structural change and improve labor productivity. Incorporating demographic characteristics such as education levels and health status into economic planning will help maximize the benefits of demographic transitions. Recommendations include encouraging demographic and sectoral policies to effectively manage demographic transitions and promote structural change and innovation. Future research should include country-specific analyses to address heterogeneity and incorporate additional indicators such as education and health to capture their nuanced impacts on economic growth. The results of this study are significant for policymakers, researchers, and development practitioners working in sub-Saharan Africa. By providing empirical evidence on the interaction between demographic structure and structural change, the study offers valuable insights into strategies for leveraging the demographic dividend to fuel sustainable economic growth in the region. This research contributes to a better understanding of how to navigate demographic transitions and structural changes to achieve long-term economic development.

Objectives: Direct comparative work in morphology and growth on widely dispersed wild primate taxa is rarely accomplished, yet critical to understanding ecogeographic variation, plastic local varia- tion in response to human impacts, and variation in patterns of growth and sexual dimorphism. We investigated population variation in morphology and growth in response to geographic variables (i.e., latitude, altitude), climatic variables (i.e., temperature and rainfall), and human impacts in the vervet monkey (Chlorocebus spp.).

Methods: We trapped over 1,600 wild vervets from across Sub-Saharan Africa and the Caribbean, and compared measurements of body mass, body length, and relative thigh, leg, and foot length in four well-represented geographic samples: Ethiopia, Kenya, South Africa, and St. Kitts & Nevis.

Results: We found significant variation in body mass and length consistent with Bergmann’s Rule in adult females, and in adult males when excluding the St. Kitts & Nevis population, which was more sexually dimorphic. Contrary to Rensch’s Rule, although the South African population had the largest average body size, it was the least dimorphic. There was significant, although very small, variation in all limb segments in support for Allen’s Rule. Females in high human impact areas were heavier than those with moderate exposures, while those in low human impact areas were lighter; human impacts had no effect on males.

Conclusions: Vervet monkeys appear to have adapted to local climate as predicted by Bergmann’s and, less consistently, Allen’s Rule, while also responding in predicted ways to human impacts. To better understand deviations from predicted patterns will require further comparative work in vervets.

Methods The data derive from field collections made over many years using a common protocol: Ethiopia in 1973, Kenya in 1978-79; South Africa in 2002–2008, and several African countries and the Caribbean in 2009– 2011 in collaboration with the International Vervet Research Consortium (Jasinska et al., 2013). The International Vervet Research Consortium is a multidisciplinary research group that has, in addition to morphological variation, studied variation in patterns of growth and development (Schmitt et al., 2018), genetic/genomic (Jasinska, et al., 2013; Schmitt et al., 2018; Svardal et al., 2017; Turner et al. 2016a; Warren et al. 2015) and transcriptomic (Jasinska et al., 2017) variation, SIV immune response (Ma et al., 2013, 2014; Svardal et al., 2017), hor- monal variation (Fourie et al., 2015), C4 isotopes variation in hair (Loudon et al., 2014), gut parasite and disease variation (Gaetano et al., 2014; Senghore et al., 2016), genital morphology and appearance (Cramer et al., 2013; Rodriguez et al., 2015a,b), and other biological parameters within the genus Chlorocebus.

Vervet monkeys were trapped at locations across sub-Saharan Africa, including South Africa, Botswana, Zambia, Ethiopia, The Gambia, Ghana, and on the Caribbean islands of St. Kitts and Nevis (Figure 1). Trapping in Africa employed individual drop traps as described by Brett, Turner, Jolly, & Cauble (1982) and Grobler and Turner (2010), while trapping in St. Kitts and Nevis was done by local trappers using large group traps (Jasinska et al., 2013). Animals were anesthetized while in the trap and then removed to a processing area. Sex was determined by visual and manual inspection, while age classes were assigned from dental eruption sequences and based on previous observations (Table 2). All animals were weighed with either an electronic or hanging scale, and measured with a tape measure and sliding calipers. Parameters and protocols describing all measurements are available through the Bones and Behavior Working Group (2015; http://www.bonesandbehavior. org/). All animals were released to their social group after sampling and recovery from anesthesia. Observations during trapping allowed us to confirm the animals’ social group and local population affiliation.

For the present study, we chose metrics representative of skeletal size (body length, thigh length, leg length, and foot length) and body mass from a total of 1,613 vervets in four geographically and genomi- cally distinct populations: Ch. aethiops in Ethiopia, Ch. p. hilgerti in Kenya, Ch. p. pygerythrus in South Africa, and Ch. sabaeus on the Carib- bean islands of St. Kitts and Nevis (Table 3). The Caribbean populations are known to be descended from West African Ch. sabaeus brought to the Caribbean several hundred years ago (Warren et al., 2015). Of the whole sample, 288 females and 460 males were dentally immature. Sexual maturity is typically not reached in vervets until near the time of canine tooth eruption, here denoting the beginning of dental age 6 (Cramer et al., 2013; Rodriguez et al., 2015a); although somatic and skeletal growth often continues beyond the emergence of the third molar, which is here denoted as adult (Bolter & Zihlman, 2003). As is common, dental age and skeletal age are presumed to be similarly cor- related across the genus, meaning that comparable dental age implies comparable skeletal developmental age across populations (Seselj, 2013).

All measurements were developed by CJJ and TRT and other measurers (CAS and JDC) were trained directly by TRT. During training, repeated measures of the same individual were conducted in tandem with TRT until concordance was reached.

The location of each trapping site is reported in decimal degrees (Table 1), and for most sites measured using hand-held GPS units. For those trapping sites lacking GPS readings, a general latitude and longi- tude for the trapping area (e.g., game reserve, town) was used. Human impact at each trapping location was assessed according to conditions during the time of trapping using a previously published index devel- oped by Pampush (2010) to study variation in vervet body size, and subsequently used by Loudon et al. (2014) and Fourie et al. (2015) (Table 1). This index includes presence/absence measures of reliable access to (1) agricultural land, (2) human food, (3) rubbish or garbage dumps, and (4) whether animals are regularly provisioned, as well as a three-level scale of human activity within the presumed home range of the group (low, moderate, or high). In the index, point values are assigned to each value, with the lowest tier of human impact each receiving a 1, scaling up by 1 for each level. Added together, these val- ues comprise a human impact group ranging from low (lowest score in each category; index 5 5), to moderate (index 5 6–8), to high (index- 5 9–11). These measures take into account only the ecological impact of humans, and do not address local ecological variables (such as native plant productivity) that might also influence body size and growth. As a proxy for these measures, we collected several climatic variables for trapping sites from the WorldClim 2 database, which has a spatial reso- lution of about 1 km2 (Fick & Hijmans, 2017). Climatic variables consid- ered for inclusion in our models were (1) annual mean temperature (in degrees Celsius), (2) temperature seasonality (measured as the standard deviation of annual mean temperature multiplied by 100), (3) the mini- mum temperature of the coldest month (in degrees Celsius), (4) the mean temperature of the coldest quarter of the year, (5) annual precipi- tation (in mm), and (6) precipitation seasonality (measured as thecoefficient of variation of monthly precipitation). Climate data were accessed via the R package raster v. 2.6-7 (Hijmans & van Etten, 2012), and assigned to trapping sites based on latitude and longitude.