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 Niger, the Democratic Republic of Congo, and Chad, the population increase peaks at over three 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. However, African cities are currently growing at larger rates. Indeed, most of the fastest-growing cities in the world are located in Sub-Saharan Africa. Gwagwalada, in Nigeria, and Kabinda, in the Democratic Republic of the Congo, ranked first worldwide. By 2035, instead, Africa's fastest-growing cities are forecast to be Bujumbura, in Burundi, and Zinder, Nigeria.

The population in Africa is expected to grow by 90 percent by 2050. Among the countries forecast to be the most populated in the continent, Nigeria leads, with an estimated population of over 401 million people. Currently, the nation has already the largest number of inhabitants in Africa. The highest population growth is expected to be measured in Angola, by 143.3 percent between 2019 and 2050. The number of inhabitants in the country is forecast to jump from 31.8 million to 77.4 million in the mentioned period.

The population in Africa was forecast to expand annually by an average of **** percent between 2020 and 2025. Over 20 countries might grow above this rate, with Niger leading by an annual population change of *** percent in the mentioned period. Angola was expected to follow, with an average population growth of **** percent annually. Overall, Africa has recorded a faster population growth compared to other world regions. The continent's population almost doubled in the last 25 years.

Mauritius had the highest population density level in Africa as of 2023, with nearly *** inhabitants per square kilometer. The country has also one of the smallest territories on the continent, which contributes to the high density. As a matter of fact, the majority of African countries with the largest concentration of people per square kilometer have the smallest geographical area as well. The exception is Nigeria, which ranks among the largest territorial countries in Africa and is very densely populated at the same time. After all, Nigeria has also the largest population on the continent.

Total current population for Africa in 2025 is 1,530,031,247, a 2.34% increase from 2024.

<ul style='margin-top:20px;'>

<li>Total population for Africa in 2024 was <strong>1,494,993,924</strong>, a <strong>2.36% increase</strong> from 2023.</li>
<li>Total population for Africa in 2023 was <strong>1,460,481,772</strong>, a <strong>2.37% increase</strong> from 2022.</li>
<li>Total population for Africa in 2022 was <strong>1,426,736,305</strong>, a <strong>2.37% increase</strong> from 2021.</li>
</ul>Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.

Dataset

This dataset was created by Nspaniol

Contents

The average for 2023 based on 53 countries was 50.08 percent. The highest value was in Zimbabwe: 52.38 percent and the lowest value was in the Seychelles: 44.82 percent. The indicator is available from 1960 to 2023. Below is a chart for all countries where data are available.

Africa has the youngest population in the world. Among the 35 countries with the lowest median age worldwide, only three fall outside the continent. In 2023, the median age in Niger was 15.1 years, the youngest country. This means that at this age point, half of the population was younger and half older. A young population reflects several demographic characteristics of a country. For instance, together with a high population growth, life expectancy in Western Africa is low: this reached 57 years for men and 59 for women. Overall, Africa has the lowest life expectancy in the world.

Africa’s population is still growing Africa’s population growth can be linked to a high fertility rate along with a drop in death rates. Despite the fertility rate on the continent, following a constant declining trend, it remains far higher compared to all other regions worldwide. It was forecast to reach 4.12 children per woman, compared to a worldwide average of 2.31 children per woman in 2024. Furthermore, the crude death rate in Africa overall dropped, only increasing slightly during the coronavirus (COVID-19) pandemic. The largest populations on the continent Nigeria, Ethiopia, Egypt, and the Democratic Republic of Congo are the most populous African countries. In 2023, people living in Nigeria amounted to around 224 million, while the number for the three other countries exceeded 100 million each. Of those, the Democratic Republic of Congo sustained the fourth-highest fertility rate in Africa. Nigeria and Ethiopia also had high rates, with 5.24 and 4.16 births per woman, respectively. Although such a high fertility rate is expected to slow down, it will still impact the population structure, growing younger nations.

The northernmost region of the African continent was home to approximately 208 million individuals in 2023. Egypt, the third most populous country in Africa, had roughly 50 percent of the region's population living within its borders, with over 105 million inhabitants. Algeria, Morocco, and Tunisia followed with 46 million, 37 million, and 12 million citizens, respectively.

This map contains estimates for total population by country from 1950 to 2015. Symbology changes slightly when zoomed in to individual continents (1:50,000,000). Source: United Nations Population Division, Department of Economic and Social Affairs, World Population Prospects: The 2015 Revision, File POP/1-1.Estimates are in thousands. Other fields in the Country Population Estimates layer include: Major Region (e.g. Africa), Minor Region (e.g. Eastern Africa), and UN Documentation Notes. The UN Documentation Notes field corresponds to the following notes:(1) Including Agalega, Rodrigues and Saint Brandon.(2) Including Zanzibar.(3) Including Ascension and Tristan da Cunha.(4) For statistical purposes, the data for China do not include Hong Kong and Macao, Special Administrative Regions (SAR) of China, and Taiwan Province of China.(5) As of 1 July 1997, Hong Kong became a Special Administrative Region (SAR) of China.(6) As of 20 December 1999, Macao became a Special Administrative Region (SAR) of China.(7) The regions Southern Asia and Central Asia are combined into South-Central Asia.(8) Including Sabah and Sarawak.(9) Including Nagorno-Karabakh.(10) Refers to the whole country(11) Including Abkhazia and South Ossetia.(12) Including East Jerusalem.(13) Including Transnistria.(14) Including Crimea(15) Refers to Guernsey, and Jersey.(16) Including Åland Islands.(17) Including Svalbard and Jan Mayen Islands.(18) Refers to the Vatican City State.(19) Including Kosovo.(20) Including Canary Islands, Ceuta and Melilla.(21) The former Yugoslav Republic of Macedonia.(22) Refers to Bonaire, Saba and Sint Eustatius.(23) Including Saint-Barthélemy and Saint-Martin (French part).(24) Including Christmas Island, Cocos (Keeling) Islands and Norfolk Island.(25) Including Pitcairn.

Abstract

Round 1 of the Afrobarometer survey was conducted from July 1999 through June 2001 in 12 African countries, to solicit public opinion on democracy, governance, markets, and national identity. The full 12 country dataset released was pieced together out of different projects, Round 1 of the Afrobarometer survey,the old Southern African Democracy Barometer, and similar surveys done in West and East Africa.

The 7 country dataset is a subset of the Round 1 survey dataset, and consists of a combined dataset for the 7 Southern African countries surveyed with other African countries in Round 1, 1999-2000 (Botswana, Lesotho, Malawi, Namibia, South Africa, Zambia and Zimbabwe). It is a useful dataset because, in contrast to the full 12 country Round 1 dataset, all countries in this dataset were surveyed with the identical questionnaire

Geographic coverage

Botswana Lesotho Malawi Namibia South Africa Zambia Zimbabwe

Analysis unit

Basic units of analysis that the study investigates include: individuals and groups

Kind of data

Sample survey data [ssd]

Sampling procedure

A new sample has to be drawn for each round of Afrobarometer surveys. Whereas the standard sample size for Round 3 surveys will be 1200 cases, a larger sample size will be required in societies that are extremely heterogeneous (such as South Africa and Nigeria), where the sample size will be increased to 2400. Other adaptations may be necessary within some countries to account for the varying quality of the census data or the availability of census maps.

The sample is designed as a representative cross-section of all citizens of voting age in a given country. The goal is to give every adult citizen an equal and known chance of selection for interview. We strive to reach this objective by (a) strictly applying random selection methods at every stage of sampling and by (b) applying sampling with probability proportionate to population size wherever possible. A randomly selected sample of 1200 cases allows inferences to national adult populations with a margin of sampling error of no more than plus or minus 2.5 percent with a confidence level of 95 percent. If the sample size is increased to 2400, the confidence interval shrinks to plus or minus 2 percent.

Sample Universe

The sample universe for Afrobarometer surveys includes all citizens of voting age within the country. In other words, we exclude anyone who is not a citizen and anyone who has not attained this age (usually 18 years) on the day of the survey. Also excluded are areas determined to be either inaccessible or not relevant to the study, such as those experiencing armed conflict or natural disasters, as well as national parks and game reserves. As a matter of practice, we have also excluded people living in institutionalized settings, such as students in dormitories and persons in prisons or nursing homes.

What to do about areas experiencing political unrest? On the one hand we want to include them because they are politically important. On the other hand, we want to avoid stretching out the fieldwork over many months while we wait for the situation to settle down. It was agreed at the 2002 Cape Town Planning Workshop that it is difficult to come up with a general rule that will fit all imaginable circumstances. We will therefore make judgments on a case-by-case basis on whether or not to proceed with fieldwork or to exclude or substitute areas of conflict. National Partners are requested to consult Core Partners on any major delays, exclusions or substitutions of this sort.

Sample Design

The sample design is a clustered, stratified, multi-stage, area probability sample.

To repeat the main sampling principle, the objective of the design is to give every sample element (i.e. adult citizen) an equal and known chance of being chosen for inclusion in the sample. We strive to reach this objective by (a) strictly applying random selection methods at every stage of sampling and by (b) applying sampling with probability proportionate to population size wherever possible.

In a series of stages, geographically defined sampling units of decreasing size are selected. To ensure that the sample is representative, the probability of selection at various stages is adjusted as follows:

The sample is stratified by key social characteristics in the population such as sub-national area (e.g. region/province) and residential locality (urban or rural). The area stratification reduces the likelihood that distinctive ethnic or language groups are left out of the sample. And the urban/rural stratification is a means to make sure that these localities are represented in their correct proportions. Wherever possible, and always in the first stage of sampling, random sampling is conducted with probability proportionate to population size (PPPS). The purpose is to guarantee that larger (i.e., more populated) geographical units have a proportionally greater probability of being chosen into the sample. The sampling design has four stages

A first-stage to stratify and randomly select primary sampling units;

A second-stage to randomly select sampling start-points;

A third stage to randomly choose households;

A final-stage involving the random selection of individual respondents

We shall deal with each of these stages in turn.

STAGE ONE: Selection of Primary Sampling Units (PSUs)

The primary sampling units (PSU's) are the smallest, well-defined geographic units for which reliable population data are available. In most countries, these will be Census Enumeration Areas (or EAs). Most national census data and maps are broken down to the EA level. In the text that follows we will use the acronyms PSU and EA interchangeably because, when census data are employed, they refer to the same unit.

We strongly recommend that NIs use official national census data as the sampling frame for Afrobarometer surveys. Where recent or reliable census data are not available, NIs are asked to inform the relevant Core Partner before they substitute any other demographic data. Where the census is out of date, NIs should consult a demographer to obtain the best possible estimates of population growth rates. These should be applied to the outdated census data in order to make projections of population figures for the year of the survey. It is important to bear in mind that population growth rates vary by area (region) and (especially) between rural and urban localities. Therefore, any projected census data should include adjustments to take such variations into account.

Indeed, we urge NIs to establish collegial working relationships within professionals in the national census bureau, not only to obtain the most recent census data, projections, and maps, but to gain access to sampling expertise. NIs may even commission a census statistician to draw the sample to Afrobarometer specifications, provided that provision for this service has been made in the survey budget.

Regardless of who draws the sample, the NIs should thoroughly acquaint themselves with the strengths and weaknesses of the available census data and the availability and quality of EA maps. The country and methodology reports should cite the exact census data used, its known shortcomings, if any, and any projections made from the data. At minimum, the NI must know the size of the population and the urban/rural population divide in each region in order to specify how to distribute population and PSU's in the first stage of sampling. National investigators should obtain this written data before they attempt to stratify the sample.

Once this data is obtained, the sample population (either 1200 or 2400) should be stratified, first by area (region/province) and then by residential locality (urban or rural). In each case, the proportion of the sample in each locality in each region should be the same as its proportion in the national population as indicated by the updated census figures.

Having stratified the sample, it is then possible to determine how many PSU's should be selected for the country as a whole, for each region, and for each urban or rural locality.

The total number of PSU's to be selected for the whole country is determined by calculating the maximum degree of clustering of interviews one can accept in any PSU. Because PSUs (which are usually geographically small EAs) tend to be socially homogenous we do not want to select too many people in any one place. Thus, the Afrobarometer has established a standard of no more than 8 interviews per PSU. For a sample size of 1200, the sample must therefore contain 150 PSUs/EAs (1200 divided by 8). For a sample size of 2400, there must be 300 PSUs/EAs.

These PSUs should then be allocated proportionally to the urban and rural localities within each regional stratum of the sample. Let's take a couple of examples from a country with a sample size of 1200. If the urban locality of Region X in this country constitutes 10 percent of the current national population, then the sample for this stratum should be 15 PSUs (calculated as 10 percent of 150 PSUs). If the rural population of Region Y constitutes 4 percent of the current national population, then the sample for this stratum should be 6 PSU's.

The next step is to select particular PSUs/EAs using random methods. Using the above example of the rural localities in Region Y, let us say that you need to pick 6 sample EAs out of a census list that contains a total of 240 rural EAs in Region Y. But which 6? If the EAs created by the national census bureau are of equal or roughly equal population size, then selection is relatively straightforward. Just number all EAs consecutively, then make six selections using a table of random numbers. This procedure, known as simple random sampling (SRS), will

This horizontal bar chart displays population (people) by country using the aggregation sum in Western Africa. The data is about countries.

In 2023, the population of Africa was projected to grow by 2.34 percent compared to the previous year. The population growth rate on the continent has been constantly over 2.3 percent from 2000 onwards, and it peaked at 2.59 percent between 2012 and 2013. Despite a slowdown in the growth rate, the continent's population will continue to increase significantly in the coming years. The second-largest population worldwide In 2022, the total population of Africa amounted to around 1.4 billion. The number of inhabitants had grown steadily in the previous decades, rising from approximately 810 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 3.9 billion people, compared to 4.7 billion in Asia. The world's youngest continent The median age in Africa corresponded to 18.8 years in 2023. 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 10 percent. On the continent, Niger, Uganda, and Angola were the countries with the youngest population in 2023.

In 2022, India overtook China as the world's most populous country and now has almost 1.46 billion people. China now has the second-largest population in the world, still with just over 1.4 billion inhabitants, however, its population went into decline in 2023. Global population As of 2025, the world's population stands at almost 8.2 billion people and is expected to reach around 10.3 billion people in the 2080s, when it will then go into decline. Due to improved healthcare, sanitation, and general living conditions, the global population continues to increase; mortality rates (particularly among infants and children) are decreasing and the median age of the world population has steadily increased for decades. As for the average life expectancy in industrial and developing countries, the gap has narrowed significantly since the mid-20th century. Asia is the most populous continent on Earth; 11 of the 20 largest countries are located there. It leads the ranking of the global population by continent by far, reporting four times as many inhabitants as Africa. The Demographic Transition The population explosion over the past two centuries is part of a phenomenon known as the demographic transition. Simply put, this transition results from a drastic reduction in mortality, which then leads to a reduction in fertility, and increase in life expectancy; this interim period where death rates are low and birth rates are high is where this population explosion occurs, and population growth can remain high as the population ages. In today's most-developed countries, the transition generally began with industrialization in the 1800s, and growth has now stabilized as birth and mortality rates have re-balanced. Across less-developed countries, the stage of this transition varies; for example, China is at a later stage than India, which accounts for the change in which country is more populous - understanding the demographic transition can help understand the reason why China's population is now going into decline. The least-developed region is Sub-Saharan Africa, where fertility rates remain close to pre-industrial levels in some countries. As these countries transition, they will undergo significant rates of population growth

The global population has grown rapidly since 1950, from 2.5 billion to over eight billion in 2023. The age distribution shows that the number of people within all age groups increased over the period, with the two youngest age groups being the largest in 2023. Population growth driven by development in Asia The increasing global population is explained by economic development and a coinciding improvement in living conditions in several parts of the world, particularly in Asia. Improvements in sanitary conditions, rollout of vaccination programs, and better medical treatment brought down death rates around the world. China saw fast economic development from the early 1980s to the late 2010s, going hand in hand with a rapidly increasing population. Furthermore, the population of India has grown rapidly since it gained independence from the British Empire in the late 1940s, now being the largest in the world. Most of the future population growth will happen in Africa The global population is forecast to continue to increase over the coming decades, set to reach over 10 billion people by 2060. Most of this increase is projected to occur on the African continent as many African countries are expected to experience an improvement in living standards. In 2022, over 40 percent of the population in Sub-Saharan Africa were below 15 years old.

This horizontal bar chart displays population (people) by countries using the aggregation sum in Middle Africa. The data is about countries.

Abstract

The Human Sciences Research Council (HSRC) carried out the Migration and Remittances Survey in South Africa for the World Bank in collaboration with the African Development Bank. The primary mandate of the HSRC in this project was to come up with a migration database that includes both immigrants and emigrants. The specific activities included: · A household survey with a view of producing a detailed demographic/economic database of immigrants, emigrants and non migrants · The collation and preparation of a data set based on the survey · The production of basic primary statistics for the analysis of migration and remittance behaviour in South Africa.

Like many other African countries, South Africa lacks reliable census or other data on migrants (immigrants and emigrants), and on flows of resources that accompanies movement of people. This is so because a large proportion of African immigrants are in the country undocumented. A special effort was therefore made to design a household survey that would cover sufficient numbers and proportions of immigrants, and still conform to the principles of probability sampling. The approach that was followed gives a representative picture of migration in 2 provinces, Limpopo and Gauteng, which should be reflective of migration behaviour and its impacts in South Africa.

Geographic coverage

Two provinces: Gauteng and Limpopo

Limpopo is the main corridor for migration from African countries to the north of South Africa while Gauteng is the main port of entry as it has the largest airport in Africa. Gauteng is a destination for internal and international migrants because it has three large metropolitan cities with a great economic potential and reputation for offering employment, accommodations and access to many different opportunities within a distance of 56 km. These two provinces therefore were expected to accommodate most African migrants in South Africa, co-existing with a large host population.

Analysis unit

• Household
• Individual

Universe

The target group consists of households in all communities. The survey will be conducted among metro and non-metro households. Non-metro households include those in: - small towns, - secondary cities, - peri-urban settlements and - deep rural areas. From each selected household, one adult respondent will be selected to participate in the study.

Kind of data

Sample survey data [ssd]

Sampling procedure

Migration data for South Africa are available for 2007 only at the level of local governments or municipalities from the 2007 Census; for smaller areas called "sub places" (SPs) only as recently as the 2001 census, and for the desired EAs only back so far as the Census of 1996. In sum, there was no single source that provided recent data on the five types of migrants of principal interest at the level of the Enumeration Area, which was the area for which data were needed to draw the sample since it was going to be necessary to identify migrant and non-migrant households in the sample areas in order to oversample those with migrants for interview.

In an attempt to overcome the data limitations referred to above, it was necessary to adopt a novel approach to the design of the sample for the World Bank's household migration survey in South Africa, to identify EAs with a high probability of finding immigrants and those with a low probability. This required the combined use of the three sources of data described above. The starting point was the CS 2007 survey, which provided data on migration at a local government level, classifying each local government cluster in terms of migration level, taking into account the types of migrants identified. The researchers then spatially zoomed in from these clusters to the so-called sub-places (SPs) from the 2001 Census to classifying SP clusters by migration level. Finally, the 1996 Census data were used to zoom in even further down to the EA level, using the 1996 census data on migration levels of various typed, to identify the final level of clusters for the survey, namely the spatially small EAs (each typically containing about 200 households, and hence amenable to the listing operation in the field).

A higher score or weight was attached to the 2007 Community Survey municipality-level (MN) data than to the Census 2001 sub-place (SP) data, which in turn was given a greater weight than the 1996 enumerator area (EA) data. The latter was derived exclusively from the Census 1996 EA data, but has then been reallocated to the 2001 EAs proportional to geographical size. Although these weights are purely arbitrary since it was composed from different sources, they give an indication of the relevant importance attached to the different migrant categories. These weighted migrant proportions (secondary strata), therefore constituted the second level of clusters for sampling purposes.

In addition, a system of weighting or scoring the different persons by migrant type was applied to ensure that the likelihood of finding migrants would be optimised. As part of this procedure, recent migrants (who had migrated in the preceding five years) received a higher score than lifetime migrants (who had not migrated during the preceding five years). Similarly, a higher score was attached to international immigrants (both recent and lifetime, who had come to SA from abroad) than to internal migrants (who had only moved within SA's borders). A greater weight also applied to inter-provincial (internal) than to intra-provincial migrants (who only moved within the same South African province).

How the three data sources were combined to provide overall scores for EA can be briefly described. First, in each of the two provinces, all local government units were given migration scores according to the numbers or relative proportions of the population classified in the various categories of migrants (with non-migrants given a score of 1.0. Migrants were assigned higher scores according to their priority, with international migrants given higher scores than internal migrants and recent migrants higher scores than lifetime migrants. Then within the local governments, sub-places were assigned scores assigned on the basis of inter vs. intra-provincial migrants using the 2001 census data. Each SP area in a local government was thus assigned a value which was the product of its local government score (the same for all SPs in the local government) and its own SP score. The third and final stage was to develop relative migration scores for all the EAs from the 1996 census by similarly weighting the proportions of migrants (and non-migrants, assigned always 1.0) of each type. The the final migration score for an EA is the product of its own EA score from 1996, the SP score of which it is a part (assigned to all the EAs within the SP), and the local government score from the 2007 survey.

Based on all the above principles the set of weights or scores was developed.

In sum, we multiplied the proportion of populations of each migrant type, or their incidence, by the appropriate final corresponding EA scores for persons of each type in the EA (based on multiplying the three weights together), to obtain the overall score for each EA. This takes into account the distribution of persons in the EA according to migration status in 1996, the SP score of the EA in 2001, and the local government score (in which the EA is located) from 2007. Finally, all EAs in each province were then classified into quartiles, prior to sampling from the quartiles.

From the EAs so classified, the sampling took the form of selecting EAs, i.e., primary sampling units (PSUs, which in this case are also Ultimate Sampling Units, since this is a single stage sample), according to their classification into quartiles. The proportions selected from each quartile are based on the range of EA-level scores which are assumed to reflect weighted probabilities of finding desired migrants in each EA. To enhance the likelihood of finding migrants, much higher proportions of EAs were selected into the sample from the quartiles with the higher scores compared to the lower scores (disproportionate sampling). The decision on the most appropriate categorisations was informed by the observed migration levels in the two provinces of the study area during 2007, 2001 and 1996, analysed at the lowest spatial level for which migration data was available in each case.

Because of the differences in their characteristics it was decided that the provinces of Gauteng and Limpopo should each be regarded as an explicit stratum for sampling purposes. These two provinces therefore represented the primary explicit strata. It was decided to select an equal number of EAs from these two primary strata.

The migration-level categories referred to above were treated as secondary explicit strata to ensure optimal coverage of each in the sample. The distribution of migration levels was then used to draw EAs in such a way that greater preference could be given to areas with higher proportions of migrants in general, but especially immigrants (note the relative scores assigned to each type of person above). The proportion of EAs selected into the sample from the quartiles draws upon the relative mean weighted migrant scores (referred to as proportions) found below the table, but this is a coincidence and not necessary, as any disproportionate sampling of EAs from the quartiles could be done, since it would be rectified in the weighting at the end for the analysis.

The resultant proportions of migrants then led to the following proportional allocation of sampled EAs (Quartile 1: 5 per cent (instead of 25% as in an equal distribution), Quartile 2: 15 per cent (instead

This horizontal bar chart displays population (people) by region using the aggregation sum in Africa. The data is about countries.

The African lion (Panthera leo), listed as a vulnerable species on the IUCN Red List of Threatened Species (Appendix II of CITES), is mainly impacted by indiscriminate killing and prey base depletion. Additionally, habitat loss by land degradation and conversion has led to the isolation of some subpopulations, potentially decreasing gene flow and increasing inbreeding depression risks. Genetic drift resulting from weakened connectivity between strongholds can affect the genetic health of the species. In the present study, we investigated the evolutionary history of the species at different spatiotemporal scales. Therefore, the mitochondrial cytochrome b gene (N = 128), 11 microsatellites (N = 103) and 9,103 SNPs (N = 66) were investigated in the present study, including a large sampling from Tanzania, which hosts the largest lion population among all African lion range countries. Our results add support that the species is structured into two lineages at the continental scale (West-Central vs East-Southern), underlining the importance of reviewing the taxonomic status of the African lion. Moreover, SNPs led to the identification of three lion clusters in Tanzania, whose geographical distributions are in the northern, southern and western regions. Furthermore, Tanzanian lion populations were shown to display good levels of genetic diversity with limited signs of inbreeding. However, their population sizes seem to have gradually decreased in recent decades. The highlighted Tanzanian African lion population genetic differentiation appears to have resulted from the combined effects of anthropogenic pressure and environmental/climatic factors, as further discussed.

This horizontal bar chart displays population (people) by demonym using the aggregation sum in Africa. The data is about countries.