The spatial distribution of populations and settlements across a country and their interconnectivity and accessibility from urban areas are important for delivering healthcare, distributing resources and economic development. However, existing spatially explicit population data across Africa are generally based on outdated, low resolution input demographic data, and provide insufficient detail to quantify rural settlement patterns and, thus, accurately measure population concentration and accessibility. Here we outline approaches to developing a new high resolution population distribution dataset for Africa and analyse rural accessibility to population centers. Contemporary population count data were combined with detailed satellite-derived settlement extents to map population distributions across Africa at a finer spatial resolution than ever before. Substantial heterogeneity in settlement patterns, population concentration and spatial accessibility to major population centres is exhibited across the continent. In Africa, 90% of the population is concentrated in less than 21% of the land surface and the average per-person travel time to settlements of more than 50,000 inhabitants is around 3.5 hours, with Central and East Africa displaying the longest average travel times. The analyses highlight large inequities in access, the isolation of many rural populations and the challenges that exist between countries and regions in providing access to services. The datasets presented are freely available as part of the AfriPop project, providing an evidence base for guiding strategic decisions.

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available. Data and Resources TIFF South Africa - Population density (2015) DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid...

By 2050, internal climate migrants may account for more than *** percent of the population in North Africa and roughly *** percent of the population of Sub-Saharan Africa. This estimate is based on a pessimistic scenario of high greenhouse gases emission and unequal development. Across the *** regions studied, over *** percent of the population is projected to be displaced by the mid-century. Who are the climate migrants? Climate migrants leave their homes due to unfavorable or harsh environmental changes influenced by global climate change. Estimating or pinpointing new regions where these climate stressors are the causal link for migration requires complex models or calculations. As migration is not solely based on climate change, socio-economic factors, such as political stability, failing economies, or human rights abuses play a substantial role. Concerns of rising temperatures Since the Industrial Revolution, the global average surface temperature has risen by over *** degree Celsius. This temperature rise brings unfamiliar local weather patterns, making some types of extreme weather events more frequent. In recent years, the number of people displaced by weather disasters has increased due to unfavorable conditions such as heatwaves, droughts, increased rainfall, flooding, and sea-level rise.

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available.

This dataset was developed by KTH-dESA and describes settlement patterns relating to electrification in Madagascar. Using the Open Source Spatial Electrification Tool three attributes have been assigned to the settlements retrieved from the Madagascar High Resolution Settlement Layer developed by Facebook Connectivity Lab and CIESIN [1]. The three attributes are as follows:

• Urban or rural status. The urban cutoff level, i.e. the minimum population density per square kilometer, has been calculated so that the urban population matches the official statistics of 35 % in 2015 [2]. The urban cutoff level was calculated to be 683 people/km2, meaning that all settlements above this value are considered urban.

• The number of households in the settlements by 2030. Based on the urban or rural status the future population for the settlements have been estimated by applying a population growth rate to match future population projections according to [3] and [4]. The number of households 2030 have then been calculated using the epected urban and rural household sizes by 2030 of 3.7 and 4.4 people per household respectively [5].

• Modeled household electrification status in 2015 (1 if the household in the cell are considered electrified by the national grid, 2 if electrified by mini-grids and 0 if non-electrified). The algorithm in OnSSET determines which household are likely to be electrified in 2015 to match the current electrification rate of 15% [6], based on meeting certain conditions for night-time light (NTL), population density and distance to the grid and roads. For Madagascar the settlements were calculated to be electrified by the national grid (RI Antananarico, RI Toamasina and RI Fianarantsoa) if they a) where within 5 km from the grid and had a minimum population density of 2287 people/km2 or minimum NTL of 60 or b) within 10 km from the grid and had a minimum population density of 10000 people/km2 or by mini-grids if they c) had a population density above 3882 people/km2 and minimum NTL of 5 or maximum 20 kilometers to major roads.

[1] Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University (2016). High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe https://energydata.info/dataset/madagascar-high-resolution-settlement-layer-2015

[2] United Nations - Economic Commission for Africa. The Demographic Profile of African Countries. (2016).

[3] United Nations, Department of Economic and Social Affairs, Population Division. World Urbanization Prospects: The 2014 Revision. (2014).

[4] Unicef - division of data, research and policy. Generation 2030 | Africa. (2014).

[5] Mentis, D. et al. Lighting the World: the first application of an open source, spatial electrification tool (OnSSET) on Sub-Saharan Africa. Environmental Research Letters. Vol. 12, nr 8. (2017).

[6] USAID. Power Africa in Madagascar | Power Africa | U.S. Agency for International Development. Available at: https://www.usaid.gov/powerafrica/madagascar. (2017).

DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available. DATE OF PRODUCTION: January 2013

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.

HIV is still a major health problem in developing countries. Even though high HIV-risk-taking behaviors have been reported in African fishing villages, local distribution patterns of HIV infection in the communities surrounding these villages have not been thoroughly analyzed. The objective of this study was to investigate the geographical distribution patterns of HIV infection in communities surrounding African fishing villages. In 2011, we applied age- and sex-stratified random sampling to collect 1,957 blood samples from 42,617 individuals registered in the Health and Demographic Surveillance System in Mbita, which is located on the shore of Lake Victoria in western Kenya. We used these samples to evaluate existing antibody detection assays for several infectious diseases, including HIV antibody titers. Based on the results of the assays, we evaluated the prevalence of HIV infection according to sex, age, and altitude of participating households. We also used Kulldorff’s spatial scan statistic to test for HIV clustering in the study area. The prevalence of HIV at our study site was 25.3%. Compared with the younger age group (15–19 years), adults aged 30–34 years were 6.71 times more likely to be HIV-positive, and the estimated HIV-positive population among women was 1.43 times larger than among men. Kulldorff’s spatial scan statistic detected one marginally significant (P = 0.055) HIV-positive and one significant HIV-negative cluster (P = 0.047) in the study area. These results suggest a homogeneous HIV distribution in the communities surrounding fishing villages. In addition to individual behavior, more complex and diverse factors related to the social and cultural environment can contribute to a homogeneous distribution pattern of HIV infection outside of African fishing villages. To reduce rates of transmission in HIV-endemic areas, HIV prevention and control programs optimized for the local environment need to be developed.

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available. Somalia data available from WorldPop here. Data and Resources TIFF Somalia - Population density (2015) DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid...

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata.

DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted.

REGION: Africa

SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator)

PROJECTION: Geographic, WGS84

UNITS: Estimated persons per grid square

MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743.

FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org)

FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available.

Indonesia data available from WorldPop here.

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available.

Here we provide version 1 Flowminder (www.flowminder.org) human mobility models for West Africa, together with WorldPop population density data for the region, to support ongoing efforts to control the ebola outbreak. Before downloading any data, please read the documention carefully as it provides details on the datasets and models provided through the links below. The mobility data refer to estimated patterns before the Ebola outbreak and should be interpreted with caution for Ebola affected countries as mobility patters are known to have changed.

Additional discussion by the authors around the use of mobile operator data for epidemilogical research see: http://currents.plos.org/outbreaks/article/containing-the-ebola-outbreak-the-potential-and-challenge-of-mobile-network-data/

Abstract

The 1998 South Africa Demographic and Health Survey (SADHS) is the first study of its kind to be conducted in South Africa and heralds a new era of reliable and relevant information in South Africa. The SADHS, a nation-wide survey has collected information on key maternal and child health indicators, and in a first for international demographic and health surveys, the South African survey contains data on the health and disease patterns in adults.

Plans to conduct the South Africa Demographic and Health Survey go as far back as 1995, when the Department of Health National Health Information Systems of South Africa (NHIS/SA) committee, recognised serious gaps in information required for health service planning and monitoring.

Fieldwork was conducted between late January and September 1998, during which time 12,247 households were visited, 17,500 people throughout nine provinces were interviewed and 175 interviewers were trained to interview in 11 languages.

The aim of the 1998 South Africa Demographic and Health Survey (SADHS) was to collect data as part of the National Health Information System of South Africa (NHIS/SA). The survey results are intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving health services in the country. A variety of demographic and health indicators were collected in order to achieve the following general objectives:

(i) To contribute to the information base for health and population development programme management through accurate and timely data on a range of demographic and health indicators. (ii) To provide baseline data for monitoring programmes and future planning. (iii) To build research and research management capacity in large-scale national demographic and health surveys.

The primary objective of the SADHS is to provide up-to-date information on: - basic demographic rates, particularly fertility and childhood mortality levels, - awareness and use of contraceptive methods, - breastfeeding practices, - maternal and child health, - awareness of HIV/AIDS, - chronic health conditions among adults, - lifestyles that affect the health status of adults, and - anthropometric indicators.

Geographic coverage

It was designed principally to produce reliable estimates of demographic rates (particularly fertility and childhood mortality rates), of maternal and child health indicators, and of contraceptive knowledge and use for the country as a whole, the urban and the non-urban areas separately, and for the nine provinces.

Analysis unit

• Household
• Women age 15-49
• Men age 15 and above

Universe

The 1998 South African Demographic and Health Survey (SADHS) covered the population living in private households in the country.

Kind of data

Sample survey data

Sampling procedure

The 1998 South African Demographic and Health Survey (SADHS) covered the population living in private households in the country. The design for the SADHS called for a representative probability sample of approximately 12,000 completed individual interviews with women between the ages of 15 and 49. It was designed principally to produce reliable estimates of demographic rates (particularly fertility and childhood mortality rates), of maternal and child health indicators, and of contraceptive knowledge and use for the country as a whole, the urban and the non-urban areas separately, and for the nine provinces. As far as possible, estimates were to be produced for the four South African population groups. Also, in the Eastern Cape province, estimates of selected indicators were required for each of the five health regions.

In addition to the main survey of households and women 15-49 that followed the DHS model, an adult health module was administered to a sample of adults aged 15 and over in half of the households selected for the main survey. The adult health module collected information on oral health, occupational hazard and chronic diseases of lifestyle.

SAMPLING FRAME

The sampling frame for the SADHS was the list of approximately 86,000 enumeration areas (EAs) created by Central Statistics (now Statistics South Africa, SSA) for the Census conducted in October 1996. The EAs, ranged from about 100 to 250 households, and were stratified by province, urban and non-urban residence and by EA type. The number of households in the EA served as a measure of size of the EA.

CHARACTERISTICS OF THE SADHS SAMPLE

The sample for the SADHS was selected in two stages. Due to confidentiality of the census data, the sampling was carried out by experts at the CSS according to specifications developed by members of the SADHS team. Within each stratum a two stage sample was selected. The primary sampling units (PSUs), corresponded to the EAs and will be selected with probability proportional to size (PPS), the size being the number of households residing in the EA, or where this was not available, the number of census visiting points in the EA. This led to 972 PSUs being selected for the SADHS (690 in urban areas and 282 in non-urban areas. Where provided by SSA, the lists of visiting points together with the households found in these visiting points, or alternatively a map of the EA which showed the households, was used as the frame for second-stage sampling to select the households to be visited by the SADHS interviewing teams during the main survey fieldwork. This sampling was carried out by the MRC behalf of the SADHS working group. If a list of visiting points or a map was not available from SSA, then the survey team took a systematic sample of visiting points in the field. In an urban EA ten visiting points were sampled, while in a non-urban EA twenty visiting points were sampled. The survey team then interviewed the household in the selected visiting point. If there were two households in the selected visiting point, both households were interviewed. If there were three or more households, then the team randomly selected one household for interview. In each selected household, a household questionnaire was administered; all women between the ages of 15 and 49 were identified and interviewed with a woman questionnaire. In half of the selected households (identified by the SADHS working group), all adults over 15 years of age were also identified and interviewed with an adult health questionnaire.

SAMPLE ALLOCATION

Except for Eastern Cape, the provinces were stratified by urban and non-urban areas, for a total of 16 sampling strata. Eastern Cape was stratified by the five health regions and urban and non-urban within each region, for a total of 10 sampling strata. There were thus 26 strata in total.

Originally, it was decided that a sample of 9,000 women 15-49 with complete interviews allocated equally to the nine provinces would be adequate to provide estimates for each province separately; results of other demographic and health surveys have shown that a minimum sample of 1,000 women is required in order to obtain estimates of fertility and childhood mortality rates at an acceptable level of sampling errors. Since one of the objectives of the SADHS was to also provide separate estimates for each of the four population groups, this allocation of 1,000 women per province would not provide enough cases for the Asian population group since they represent only 2.6 percent of the population (according to the results of the 1994 October Household Survey conducted by SSA). The decision was taken to add an additional sample of 1,000 women to the urban areas of KwaZulu-Natal and Gauteng to try to capture as many Asian women as possible as Asians are found mostly in these areas. A more specific sampling scheme to obtain an exact number of Asian women was not possible for two reasons: the population distribution by population group was not yet available from the 1996 census and the sampling frame of EAs cannot be stratified by population group according to SSA as the old system of identifying EAs by population group has been abolished.

An additional sample of 2,000 women was added to Eastern Cape at the request of the Eastern Cape province who funded this additional sample. In Eastern Cape, results by urban and non-urban areas can be given. Results of selected indicators such as contraceptive knowledge and use can also be produced separately for each of the five health regions but not for urban/non-urban within health region.

Result shows the allocation of the target sample of 12,000 women by province and by urban/nonurban residence. Within each province, the sample is allocated proportionately to the urban/non-urban areas.

In the above allocation, the urban areas of KwaZulu-Natal have been oversampled by about 57 percent while those of Gauteng have been oversampled by less than 1 percent. For comparison purposes, it shows a proportional allocation of the 12,000 women to the nine provinces that would result in a completely self-weighting sample but does not allow for reliable estimates for at least four provinces (Northern Cape, Free State, Mpumalanga and North-West).

The number of households to be selected for each stratum was calculated as follows:

• According to the 1994 October Household Survey, the estimated number of women 15-49 per households is 1.2. The overall response rate was assumed to be 80 percent, i.e., of the households selected for the survey only 90 percent would be successfully interviewed, and of the women identified in the households with completed interviews, only 90 percent would have a complete woman questionnaire. Using these two parameters in the above equation, we would expect to select approximately 12,500 households in order to yield the target sample of women.

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Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata.

DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted.

REGION: Africa

SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator)

PROJECTION: Geographic, WGS84

UNITS: Estimated persons per grid square

MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743.

FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org)

FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available.

Equatorial Guinea data available from WorldPop here.

DATASET: Alpha version 2010 and 2014 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/). REGION: Africa SPATIAL RESOLUTION: 0.00833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available. DATE OF PRODUCTION: January 2013

Tropical mountains are global hotspots for birdlife. However, there is a dearth of baseline avifaunal data along eleva-tional gradients, particularly in Africa, limiting our ability to observe and assess changes over time in tropical montane avian communities. In this study, we undertook a multi-year assessment of understory birds along a 1,750 m elevational gradient (1,430-3,186 m) in an Afrotropical moist evergreen montane forest within Ethiopia's Bale Mountains. Analyzing 6 years of systematic bird-banding data from 5 sites, we describe the patterns of species richness, abundance, community composition, and demographic rates over space and time. We found bimodal patterns in observed and estimated species richness across the elevational gradient (peaking at 1,430 and 2,388 m), although no sites reached asymptotic species richness throughout the study. Species turnover was high across the gradient, though forested sites at mid-elevations resembled each other in species composition. We found significant variation across sites in bird abundance in some of the dietary and habitat guilds. However, we did not find any significant trends in species richness or guild abundances over time. For the majority of analyzed species, capture rates did not change over time and there were no changes in species' mean elevations. Population growth rates, recruitment rates, and apparent survival rates averaged 1.02, 0.52, and 0.51 respectively, and there were no elevational patterns in demographic rates. This study establishes a multi-year baseline for Afrotropical birds along an elevational gradient in an under-studied international biodiversity hotspot. These data will be critical in assessing the long-term responses of tropical montane birdlife to climate change and habitat degradation.

Methods Statistical Analyses

Community-level Analyses

To test whether our survey effort had adequately surveyed the local bird community, we calculated rarified species accumulation curves across sampling days for each site, based on observed and expected (sample-based rarefaction) species richness (Colwell et al. 2012) using the “exact” method of the specaccum function from the R package VEGAN (Oksanen et al. 2019). Since our species accumulation curves did not reach asymptotes for species richness, observed species richness likely does not capture true species richness. We, therefore, used sample-size-based rarefaction and extrapolation (R/E) of Hill numbers (the effective number of species, which integrates species richness and relative abundances; Chao et al. 2014). Sample-size-based rarefaction and extrapolation of Hill numbers is an emerging approach used to standardize and compare estimates of diversity between samples (see Cox et al. 2017, Fair et al. 2018, Baumel et al. 2018, Chao et al. 2019, Debela et al. 2020). Specifically, we used this framework to estimate two values of Hill number 0 (i.e. estimated species richness). First, we calculated standardized species richness. We used the function iNEXT from the R package iNEXT (Hsieh et al. 2016) to calculate R/E curves, standardizing our curve parameters to a maximum of 1,000 individual bird captures (endpoint = 1,000), knots = 500, and a bootstrap replication of 1,000 (nboot = 1,000). From these curves, we provide standardized estimates of species richness based on the sampling of 1,000 individuals at each site. We also estimated asymptotic species richness using the function ChaoRichness from the package iNEXT (Hsieh et al. 2016). Although the asymptotic species richness is an estimate of true species richness, in practice, reaching an asymptote can take a long time and a lot of sampling. We then plotted the R/E curves of standardized species richness (i.e. over 1,000 individuals) for each site as a function of sample size using the function ggiNEXT (Hsieh et al. 2016). We also visualized asymptotic species richness by setting the endpoint of the iNEXT function to 10,000 individuals.

Next, we assessed the spatial and temporal patterns in observed species richness and guild-specific captures. For guild-specific captures, we identified the primary diet and habitat association of each species using a global dataset of avian ecological traits (Table 1; see Şekercioğlu et al. 2004, 2019 for a description of the dataset) and summed captures for each separate guild based on either primary diet or habitat. We restricted our analyses to guilds that had ≥40 captures and ≥5 species over the study period and modeled each guild independently. We chose a ≥40 capture threshold as our cutoff between infrequently and frequently encountered species. Most species above this threshold were recorded each year and more than once or twice in each year (the few species that were not recorded each year were recorded multiple times in the other years), whereas individuals under this threshold tended to have few captures across more than one year. We chose a ≥5 species threshold for the guild models to ensure that results for these metrics represented more than a few species.

We constructed models comparing each response variable (observed species richness, dietary, and habitat guild-specific captures) as a function of the site, and included the number of survey days per site and year (Table 2) as a covariate to control for the variation in the sampling effort. We used generalized linear models (GLMs) for species richness and guild-specific captures, as these represent count data. Within the GLMs, we used a Poisson error structure for species richness, and for guild-specific captures, we used a quasi-Poisson error structure to account for over-dispersion in the count data. To assess changes in the bird community over time, we ran an additional model for each response variable that contained year and site, with a year * site interaction (error structures were applied as above). We tested the significance of the explanatory variables in the GLMs with an analysis of deviance.

We assessed species dissimilarity between sites along the elevational gradient by calculating the Sørenson dissimilarity index (S8) for pairs of sites adjacent to each other along the elevational gradient, as well as for Chiri-1430 and Dinsho-3186 at either end of the gradient. S8 can range from complete dissimilarity (S8 = 1) to complete similarity (S8 = 0). This dissimilarity can be further decomposed into turnover and nestedness, which we calculated using the function beta.pair in the package betapart (Baselga et al. 2020). Finally, to compare community composition (captures of different species, weighted by abundance), we ran a Principal Coordinate Analysis (PCoA) based on a Bray-Curtis dissimilarity matrix (Legendre and Legendre 2012). A PCoA extracts the greatest orthogonal axes of variation in community composition, plotting them in multidimensional space such that more similar communities are closer to each other in Euclidean space. We extracted the first two axes from the PCoA that represent the greatest variation in community composition.

Species-level Analyses

As a proxy for species abundance (Dulle et al. 2016), we calculated species-specific captures (the number of captured and recaptured individuals of a particular species) per site and year for the most frequently-captured species (≥ 40 captures over the study period). To assess the variation in species’ elevational distributions, we calculated the mean elevation at which each species was detected each year (hereafter “mean elevation”) for frequently-captured species that were detected at least once in every year of the study. Smaller range shifts in tropical birds are more detectable when analyzing mean elevational occurrence rather than the changes in upper or lower range boundaries, as the position of range boundaries is strongly dependent on the sampling effort (Shoo et al. 2006).

We regressed both species-specific captures (in a GLM with a quasi-Poisson error structure) and mean elevation (in a simple linear model) against year. Since the Dinsho-3186 site was located far from the other sites, we decided to re-run the species-level analyses with Dinsho-3186 data removed. The results remained similar with Dinsho-3186 excluded (Supplemental Material Tables S2 and S3) and, therefore, we retained Dinsho-3186 data in the analyses to increase our statistical power. Additionally, we compared our elevational records for banded birds with those reported in the literature for Ethiopia and the Horn of Africa (Ash and Atkins 2009, Dowsett and Dowsett-Lemaire 2015, Rannestad 2016) in order to assess whether any species were detected outside of their recorded elevational distributions. We used an elevational difference of at least 150 m to indicate whether a species had clearly been recorded in our study higher or lower than previously reported in Ethiopia, a distance previously used to signify extralimital records of birds in Ethiopia (Dowsett and Dowsett-Lemaire 2015). A difference of <150 m could result from chance, whereas a difference >150 m is more likely to result from a systematic change in the elevational range.

At the population level, we used Pradel models (Pradel 1996) implemented with the package RMark (Laake and Rexstad 2012) to estimate the rates of apparent survival (φ), recruitment (F), and realized population growth (λ) while controlling for encounter probabilities (p). φ is the rate at which individuals remain in the population; F is the rate at which new individuals join the population via birth or immigration; and λ is the combined effect of survival and recruitment. A population does not change in size when λ = 1, declines when λ <1, and grows when λ >1. These mark-recapture models cannot distinguish movement in and out of a study area (immigration/emigration) from true birth and survival. However, birds living in tropical mountains are known to have small range sizes (Orme et al. 2006), and tropical

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available.

DATASET: Alpha version 2010, 2014, and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and 2010 and 2015 stimates of numbers of people per grid square remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.00833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available. DATE OF PRODUCTION: January 2013

Patterns of genetic variation in human populations across the African continent are still not well studied in comparison with Eurasia and America, despite the high genetic and cultural diversity among African populations. In population and forensic genetic studies a single sample is often used to represent a complete African region. In such a scenario, inappropriate sampling strategies and/or the use of local, isolated populations may bias interpretations and pose questions of representativeness at a macrogeographic-scale. The non-recombining region of the Y-chromosome (NRY) has great potential to reveal the regional representation of a sample due to its powerful phylogeographic information content. An area poorly characterized for Y-chromosomal data is the West-African region along the Bight of Benin, despite its important history in the trans-Atlantic slave trade and its large number of ethnic groups, languages and lifestyles. In this study, Y-chromosomal haplotypes from four Beninese populations were determined and a global meta-analysis with available Y-SNP and Y-STR data from populations along the Bight of Benin and surrounding areas was performed. A thorough methodology was developed allowing comparison of population samples using Y-chromosomal lineage data based on different Y-SNP panels and phylogenies. Geographic proximity turned out to be the best predictor of genetic affinity between populations along the Bight of Benin. Nevertheless, based on Y-chromosomal data from the literature two population samples differed strongly from others from the same or neighbouring areas and are not regionally representative within large-scale studies. Furthermore, the analysis of the HapMap sample YRI of a Yoruban population from South-western Nigeria based on Y-SNPs and Y-STR data showed for the first time its regional representativeness, a result which is important for standard population and forensic genetic applications using the YRI sample. Therefore, the uniquely and powerful geographical information carried by the Y-chromosome makes it an important locus to test the representativeness of a certain sample even in the genomic era, especially in poorly investigated areas like Africa.

This map shows the percentage of people who identify as something other than non-Hispanic white throughout the US according to the most current American Community Survey. The pattern is shown by states, counties, and Census tracts. Zoom or search for anywhere in the US to see a local pattern. Click on an area to learn more. Filter to your area and save a new version of the map to use for your own mapping purposes.The Arcade expression used was: 100 - B03002_calc_pctNHWhiteE, which is simply 100 minus the percent of population who identifies as non-Hispanic white. The data is from the U.S. Census Bureau's American Community Survey (ACS). The figures in this map update automatically annually when the newest estimates are released by ACS. For more detailed metadata, visit the ArcGIS Living Atlas Layer: ACS Race and Hispanic Origin Variables - Boundaries.The data on race were derived from answers to the question on race that was asked of individuals in the United States. The Census Bureau collects racial data in accordance with guidelines provided by the U.S. Office of Management and Budget (OMB), and these data are based on self-identification. The racial categories included in the census questionnaire generally reflect a social definition of race recognized in this country and not an attempt to define race biologically, anthropologically, or genetically. The categories represent a social-political construct designed for collecting data on the race and ethnicity of broad population groups in this country, and are not anthropologically or scientifically based. Learn more here.Other maps of interest:American Indian or Alaska Native Population in the US (Current ACS)Asian Population in the US (Current ACS)Black or African American Population in the US (Current ACS)Hawaiian or Other Pacific Islander Population in the US (Current ACS)Hispanic or Latino Population in the US (Current ACS) (some people prefer Latinx)Population who are Some Other Race in the US (Current ACS)Population who are Two or More Races in the US (Current ACS) (some people prefer mixed race or multiracial)White Population in the US (Current ACS)Race in the US by Dot DensityWhat is the most common race/ethnicity?