In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

This dataset provides values for POPULATION reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The most important key figures about population, households, population growth, births, deaths, migration, marriages, marriage dissolutions and change of nationality of the Dutch population.

CBS is in transition towards a new classification of the population by origin. Greater emphasis is now placed on where a person was born, aside from where that person’s parents were born. The term ‘migration background’ is no longer used in this regard. The main categories western/non-western are being replaced by categories based on continents and a few countries that share a specific migration history with the Netherlands. The new classification is being implemented gradually in tables and publications on population by origin.

Data available from: 1899

Status of the figures: The 2023 figures on stillbirths and perinatal mortality are provisional, the other figures in the table are final.

Changes as of 23 December 2024: Figures with regard to population growth for 2023 and figures of the population on 1 January 2024 have been added. The provisional figures on the number of stillbirths and perinatal mortality for 2023 do not include children who were born at a gestational age that is unknown. These cases were included in the final figures for previous years. However, the provisional figures show a relatively larger number of children born at an unknown gestational age. Based on an internal analysis for 2022, it appears that in the majority of these cases, the child was born at less than 24 weeks. To ensure that the provisional 2023 figures do not overestimate the number of stillborn children born at a gestational age of over 24 weeks, children born at an unknown gestational age have now been excluded.

Changes as of 15 December 2023: None, this is a new table. This table succeeds the table Population; households and population dynamics; 1899-2019. See section 3. The following changes have been made: - The underlying topic folders regarding 'migration background' have been replaced by 'Born in the Netherlands' and 'Born abroad'; - The origin countries Armenia, Azerbaijan, Georgia, Kazakhstan, Kyrgyzstan, Uzbekistan, Tajikistan, Turkmenistan and Turkey have been assigned to the continent of Asia (previously Europe).

When will the new figures be published? The figures for the population development in 2023 and the population on 1 January 2024 will be published in the second quarter of 2024.

The Gridded Population of the World, Version 3 (GPWv3): Population Density Grid consists of estimates of human population for the years 1990, 1995, and 2000 by 2.5 arc-minute grid cells and associated data sets dated circa 2000. A proportional allocation gridding algorithm, utilizing more than 300,000 national and sub-national administrative Units, is used to assign population values to grid cells. The population density grids are derived by dividing the population count grids by the land area grid and represent persons per square kilometer. The grids are available in various GIS-compatible data formats and geographic extents (global, continent [Antarctica not included], and country levels). GPWv3 is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).

The Gridded Population of the World, Version 3 (GPWv3): Population Density Grid, Future EstimatesFuture Estimates consists of estimates of human population for the years 2005, 2010, and 2015 by 2.5 arc-minute grid cells. A proportional allocation gridding algorithm, utilizing more than 300,000 national and sub-national administrative Units, is used to assign population values to grid cells. The future estimate population values are extrapolated based on a combination of subnational growth rates from census dates and national growth rates from United Nations statistics. All of the grids have been adjusted to match United Nations national level population estimates. The population density grids are derived by dividing the population count grids by the land area grid and represent persons per square kilometer. The grids are available in various GIS-compatible data formats and geographic extents (global, continent [Antarctica not included], and country levels). GPWv3 is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).

Maize (Zea mays), also known as corn, is a crop of world wide importance. Originally domesticated in what is now Mexico, its tolerance of diverse climates has lead to its widespread cultivation. Globally, it is tied with rice as the second most widely grown crop. Only wheat is more widely grown. In Africa it is grown throughout the agricultural regions of the continent from the Nile Delta in the north to the country of South Africa in the south. In sub-Saharan Africa it is relied on as a staple crop for 50% of the population.Dataset SummaryThis layer provides access to a 5 arc-minute (approximately 10 km at the equator) cell-sized raster of the 1999-2001 annual average area of maize harvested in Africa. The data are in units of hectares/grid cell.The SPAM 2000 v3.0.6 data used to create this layer were produced by the International Food Policy Research Institute in 2012. This dataset was created by spatially disaggregating national and sub-national harvest data using the Spatial Production Allocation Model. Link to source metadataFor more information about this dataset and the importance of maize as a staple food see the Harvest Choice webpage.For data on other agricultural species in Africa see these layers:CassavaGroundnut (Peanut)MilletPotatoRiceSorghumSweet Potato and YamWheatData for important agricultural crops in South America are available here.What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop.This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 24,000 x 24,000 pixels which allows access to the full dataset.The source data for this layer are available here.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started follow these links:Landscape Layers - a reintroductionLiving Atlas Discussion Group

Ecologists are increasingly turning to historical abundance data to understand past changes in animal abundance and more broadly the ecosystems in which animals occur. However, developing reliable ecological or management interpretations from temporal abundance data can be difficult because most population counts are subject to measurement or estimation error.

There is now widespread recognition that counts of animal populations are often subject to detection bias. This recognition has led to the development of a general framework for abundance estimation that explicitly accounts for detection bias and its uncertainty, new methods for estimating detection bias, and calls for ecologists to estimate and account for bias and uncertainty when estimating animal abundance. While these methodological developments are now being increasingly accepted and used, there is a wealth of historical population count data in the literature that were collected before these developments. These historical abundance data may, in their original published form, have inherent unrecognised and therefore unaccounted biases and uncertainties that could confound reliable interpretation. Developing approaches to improve interpretation of historical data may therefore allow a more reliable assessment of extremely valuable long-term abundance data.

This dataset contains details of over 200 historical estimates of Adelie penguin breeding populations across the Australian Antarctic Territory (AAT) that have been published in the scientific literature. The details include attributes of the population count (date and year of count, count value, count object, count precision) and the published estimate of the breeding population derived from those attributes, expressed as the number of breeding pairs. In addition, the dataset contains revised population estimates that have been re-constructed using new estimation methods to account for detection bias as described in the associated publication. All population data used in this study were sourced from existing publications.

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 prolongued development arc in Sub-Saharan Africa.

Vulnerable population identified by the nutritional status of children (weight for age and weight for height) as indicators for food security, in sample of households in West Africa study area. Data based on DHS and MICS surveys. In defining vulnerability, WFP (2009) and IFPRI (2012) have been followed and combined with indicators for food security with health indicators that signal vulnerability in a physical sense. IFPRI's Global Hunger Index uses three indicators to measure hunger: the number of adults being undernourished, the number of children that have low weight for age, and child mortality. Other classifications of food security use the variety of the diet as an indicator, combined with anthropometric data on children. However, in the DHS data there were no information available on child mortality, nor on dietary composition. Given these data limitations, data on nutritional status of women (Body Mass Index, BMI) for women and children (weight for age and weight for height) have been used as indicators for food security. These data were combined with data on morbidity among adults and children, specifically the occurrence of malaria, cough, and diarrhea. Combinations of indicators have led to a classification of households as being very vulnerable, vulnerable, nearly vulnerable and not vulnerable.

This data set was produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 5 (WP5). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

This study in WP5 aimed to identify, locate and characterize groups that are vulnerable for climate change conditions in two country clusters; one in West Africa (Benin, Burkina Faso, Côte d’Ivoire, Ghana, and Togo) and one in East Africa (Sudan, South Sudan and Uganda). Data used for the study include the Demographic and Health Surveys (DHS) , the Multi Indicator Cluster Survey (MICS) and the Afrobarometer surveys for the socio-economic variables and grid level data on agro-ecological and climatic conditions.

Data publication: 2013-08-01

Supplemental Information:

ClimAfrica was an international project funded by European Commission under the 7th Framework Programme (FP7) for the period 2010-2014. The ClimAfrica consortium was formed by 18 institutions, 9 from Europe, 8 from Africa, and the Food and Agriculture Organization of United Nations (FAO).

ClimAfrica was conceived to respond to the urgent international need for the most appropriate and up-to-date tools and methodologies to better understand and predict climate change, assess its impact on African ecosystems and population, and develop the correct adaptation strategies. Africa is probably the most vulnerable continent to climate change and climate variability and shows diverse range of agro-ecological and geographical features. Thus the impacts of climate change can be very high and can greatly differ across the continent, and even within countries.

The project focused on the following specific objectives:

1. Develop improved climate predictions on seasonal to decadal climatic scales, especially relevant to SSA;

2. Assess climate impacts in key sectors of SSA livelihood and economy, especially water resources and agriculture;

3. Evaluate the vulnerability of ecosystems and civil population to inter-annual variations and longer trends (10 years) in climate;

4. Suggest and analyse new suited adaptation strategies, focused on local needs;

5. Develop a new concept of 10 years monitoring and forecasting warning system, useful for food security, risk management and civil protection in SSA;

6. Analyse the economic impacts of climate change on agriculture and water resources in SSA and the cost-effectiveness of potential adaptation measures.

The work of ClimAfrica project was broken down into the following work packages (WPs) closely connected. All the activities described in WP1, WP2, WP3, WP4, WP5 consider the domain of the entire South Sahara Africa region. Only WP6 has a country specific (watershed) spatial scale where models validation and detailed processes analysis are carried out.

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Lia van Wesenbeeck

Resource Contact: Ben Sonneveld

Resource constraints:

copyright

Online resources:

Weight for age <-3DS, % of population - Distribution in sample of households in West Africa

Weight for age -2SD --3SD, % of population - Distribution in sample of households in West Africa

Weight for age -2SD--0, % of population - Distribution in sample of households in West Africa

Weight for age >0SD, % of population - Distribution in sample of households in West Africa

A spatially explicit assessment of specific vulnerabilities of the food system due to climate change and the identification of their causes; Technical report

Scenarios of major production systems in Africa

Climafrica - Climate Change Predictions In Sub-Saharan Africa: Impacts And Adaptations

The Gross Domestic Product per capita (gross domestic product divided by mid-year population converted to international dollars, using purchasing power parity rates) has been identified as an important determinant of susceptibility and vulnerability by different authors and used in the Disaster Risk Index 2004 (Peduzzi et al. 2009, Schneiderbauer 2007, UNDP 2004) and is commonly used as an indicator for a country's economic development (e.g. Human Development Index). Despite some criticisms (Brooks et al. 2005) it is still considered useful to estimate a population's susceptibility to harm, as limited monetary resources are seen as an important factor of vulnerability. However, collection of data on economic variables, especially sub-national income levels, is problematic, due to various shortcomings in the data collection process. Additionally, the informal economy is often excluded from official statistics. Night time lights satellite imagery of NOAA grid provides an alternative means for measuring economic activity. NOAA scientists developed a model for creating a world map of estimated total (formal plus informal) economic activity. Regression models were developed to calibrate the sum of lights to official measures of economic activity at the sub-national level for some target Country and at the national level for other countries of the world, and subsequently regression coefficients were derived. Multiplying the regression coefficients with the sum of lights provided estimates of total economic activity, which were spatially distributed to generate a 30 arc-second map of total economic activity (see Ghosh, T., Powell, R., Elvidge, C. D., Baugh, K. E., Sutton, P. C., & Anderson, S. (2010).Shedding light on the global distribution of economic activity. The Open Geography Journal (3), 148-161). We adjusted the GDP to the total national GDPppp amount as recorded by IMF (International Monetary Fund) for 2010 and we divided it by the population layer from Worldpop Project. Further, we ran a focal statistics analysis to determine mean values within 10 cell (5 arc-minute, about 10 Km) of each grid cell. This had a smoothing effect and represents some of the extended influence of intense economic activity for local people. Finally we apply a mask to remove the area with population below 1 people per square Km.

This dataset has been produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

Data publication: 2014-06-01

Supplemental Information:

ClimAfrica was an international project funded by European Commission under the 7th Framework Programme (FP7) for the period 2010-2014. The ClimAfrica consortium was formed by 18 institutions, 9 from Europe, 8 from Africa, and the Food and Agriculture Organization of United Nations (FAO).

ClimAfrica was conceived to respond to the urgent international need for the most appropriate and up-to-date tools and methodologies to better understand and predict climate change, assess its impact on African ecosystems and population, and develop the correct adaptation strategies. Africa is probably the most vulnerable continent to climate change and climate variability and shows diverse range of agro-ecological and geographical features. Thus the impacts of climate change can be very high and can greatly differ across the continent, and even within countries.

The project focused on the following specific objectives:

1. Develop improved climate predictions on seasonal to decadal climatic scales, especially relevant to SSA;

2. Assess climate impacts in key sectors of SSA livelihood and economy, especially water resources and agriculture;

3. Evaluate the vulnerability of ecosystems and civil population to inter-annual variations and longer trends (10 years) in climate;

4. Suggest and analyse new suited adaptation strategies, focused on local needs;

5. Develop a new concept of 10 years monitoring and forecasting warning system, useful for food security, risk management and civil protection in SSA;

6. Analyse the economic impacts of climate change on agriculture and water resources in SSA and the cost-effectiveness of potential adaptation measures.

The work of ClimAfrica project was broken down into the following work packages (WPs) closely connected. All the activities described in WP1, WP2, WP3, WP4, WP5 consider the domain of the entire South Sahara Africa region. Only WP6 has a country specific (watershed) spatial scale where models validation and detailed processes analysis are carried out.

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Selvaraju Ramasamy

Resource constraints:

copyright

Online resources:

GDP per capita

Project deliverable D4.1 - Scenarios of major production systems in Africa

Climafrica Website - Climate Change Predictions In Sub-Saharan Africa: Impacts And Adaptations

A major goal of invasion biology is to understand global species flows between donor and recipient regions. Our current view of such flows assumes that species are moved directly from their native to their introduced range. However, if introduced populations serve as bridgehead population that generate additional introductions, tracing intercontinental flows between donor and recipient regions misrepresents the introduction history. Our aim was to assess to what extent bridgehead effects distort our view of global species flows. We separately mapped "flows" of 252 alien ant species established on one to six continents, representing a gradient of relatively certain to completely unreliable flows. In 83% of countries, more than 50% of alien ants were established on six continents, indicating that flows to these countries are unreliable. Flows of species established on a single continent were linked to global trade flows, while flows including cosmopolitan species were not linked to global trade. It is crucial to account for bridgehead effects when assessing the biogeography and intercontinental flows of alien species. This is urgent for improving our understanding of how species are moved around the planet.

Methods Species distributions and flows To determine the number of alien ant species that are established in each country, we used the geo-referenced database Antmaps (an authoritative database maintained and updated regularly by experts based on new records from the peer-reviewed scientific literature). The Antmaps database includes information on the native and alien ranges of 252 ant species (https://antmaps.org/?). We did not consider occurrence records that may be dubious (needing taxonomic verification). We kept both indoor and outdoor locations because all parts of the species’ distribution are the consequence of human-mediated dispersal. Populations that occurred at indoor locations were also a possible source of new invasions, for example if material such as potted plants and soil are moved from an indoor location to a different location. The aim of our analyses was not to distinguish between factors (climate, habitat) filtering out species at the establishment stage of the invasion process, but to understand what drives global species movements. As all species records are a reflection of global species flows, we kept all records for the analyses presented in the main part of the manuscript.

We delimited the countries and continents based on the administrative database GADM version 3.6. For mapping, we used the Mollweide projection. We defined a species “flow” as the number of species introduced from one region to another region. To calculate the species flows from donor to recipient regions, we defined the species’ native range as all countries containing native populations according to Antmaps. For species whose native range covers more than one continent, we weighted the flow from each of the continents by the number of political regions where the species is native (i.e., non-overlapping country or sub-country polygons, representing states, counties or islands and which are more homogenous in size than entire countries.

Countries In total, 173 countries worldwide host alien ant species. To compare species flows, we focused on the 41 countries which had both species exotic in only one continent and species exotic in several continents. In that way, we were able to compare the different species flows for all alien species (hereafter ALL species) or species exotic in one continent (Exo1) or two (Exo2), three (Exo3), four (Exo4), five (Exo5) or all continents except Antarctica (Exo6).

Interception data We have sourced previously published interception records for the United States and New Zealand from 1914-2013 (described in detail in Bertelsmeier et al. 2018, PNAS). In total, this dataset contains 69 alien ant species intercepted on cargo, goods, mail and baggage and has information on the country of origin for each interception, and therefore allows calculating the proportion of secondary interceptions for each species (i.e., the proportion of all interceptions of a species which come from a country where the species is not native).

Trade data Most biological invasions arise via human-mediated transport, allowing species to establish in new geographic regions. In particular, accidental transport with traded commodities is an important dispersal pathway for insects in general and especially ants. We used general import flows to represent global flows of potential transport vectors. To calculate import flows to all countries, we used cumulative import data from 1998 to 2017 extracted from the UN Comtrade Database (United Nations Commodity Trade Statistics Database, http://comtrade.un.org/db/ (accessed May 2019)). This dataset contains dyadic trade flows between pairs of countries, given in US dollars per year. Such comprehensive data is not available for earlier periods; as most imports over the last two centuries have occurred during this recent period of globalization, we expect these relatively recent imports to have left their footprint on the flows of ants. Because no import data was available for four previously defined administrative units (Puerto Rico, Christmas Island, Norfolk Island and Marshall Islands), they were excluded from this analysis. The flows to each of the remaining 37 countries were standardized by dividing the flows by the total imports to each country in order to study variations in the proportions of geographic origins of the flows (and not the absolute quantities).

Many species of seabirds, including the only penguin species breeding on the African continent, are threatened with extinction. The world population of the endangered African penguin Spheniscus demersus has decreased from more than 1.5 million individuals in the early 1900s to c.a. 23 000 pairs in 2013. Determining the trophic interactions of species, especially those of conservation concern, is important when declining numbers are thought to be driven by food limitation. By and large, African penguin dietary studies have relied on the identification of prey remains from stomach contents. Despite all the advantages of this method, it has well known biases. We therefore assessed the African penguin’s diet, using stable isotopes, at two colonies in Algoa Bay (south-east coast of South Africa). These represent over 50% of the world population. Various samples (blood, feathers, egg membranes) were collected for carbon and nitrogen stable isotope analyses. Results indicate that the trophic ecology of African penguins is influenced by colony, season and age class, but not adult sex. Isotopic niches identified by standard Bayesian ellipse areas and convex hulls, highlighted differences among groups and variability among individual penguins. Using Bayesian mixing models it was for the first time shown that adults target chokka squid Loligo reynaudii for self-provisioning during particular stages of their annual cycle, while concurrently feeding their chicks primarily with small pelagic fish. This has important ramifications and means that not only pelagic fish, but also squid stocks, need to be carefully managed in order to allow population recovery of African penguin.

Accurate delineation of the urban and rural areas has a broad range of implications on the quality and reliability of agricultural production and socio-economic statistics, design of household survey, establishment of agricultural development strategies and policies, and effective resource allocation. Two most widely-used urban/rural mapping dataset across Africa, GRUMP (Global Rural and Urban Mapping Project; http://sedac.ciesin.columbia.edu/data/collection/grump-v1) and SAGE Urban Extents (https://nelson.wisc.edu/sage/data-and-models/schneider.php), uses the underlying datasets of 2000-2002. There are various pilot studies attempting to update the dataset in major metropolitan areas or specific countries, but no African continent-wide effort has been made to date. To address this, using the GRUMP 2000 data as the baseline, we used a set of recently-published datasets to identify the newly extended urban areas across Africa. Three main data sources were the nightlights data from Defense Meteorological Satellite Program (DMSP) 2010-2013, WorldPop 2010, and the MODIS Global Land Cover 2010-2013. Country-level urban population headcounts and their share of total population were acquired from the World Bank for 2010-2013 and used to control the total size of the urban population from the analysis is consistent with the statistics data at 1 km resolution.

Vulnerable population identified by the prevalence of diseases (malaria, cough and diarrhea) as indicator for food security, in sample of households in West Africa study area. Data based on DHS and MICS surveys. In defining vulnerability, WFP (2009) and IFPRI (2012) have been followed and combined with indicators for food security with health indicators that signal vulnerability in a physical sense. IFPRI's Global Hunger Index uses three indicators to measure hunger: the number of adults being undernourished, the number of children that have low weight for age, and child mortality. Other classifications of food security use the variety of the diet as an indicator, combined with anthropometric data on children. However, in the DHS data there were no information available on child mortality, nor on dietary composition. Given these data limitations, data on nutritional status of women (Body Mass Index, BMI) for women and children (weight for age and weight for height) have been used as indicators for food security. These data were combined with data on morbidity among adults and children, specifically the occurrence of malaria, cough, and diarrhea. Combinations of indicators have led to a classification of households as being very vulnerable, vulnerable, nearly vulnerable and not vulnerable. This data set was produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 5 (WP5). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata. This study in WP5 aimed to identify, locate and characterize groups that are vulnerable for climate change conditions in two country clusters; one in West Africa (Benin, Burkina Faso, Côte d'Ivoire, Ghana, and Togo) and one in East Africa (Sudan, South Sudan and Uganda). Data used for the study include the Demographic and Health Surveys (DHS) , the Multi Indicator Cluster Survey (MICS) and the Afrobarometer surveys for the socio-economic variables and grid level data on agro-ecological and climatic conditions.

Data publication: 2013-08-01

Supplemental Information:

ClimAfrica was an international project funded by European Commission under the 7th Framework Programme (FP7) for the period 2010-2014. The ClimAfrica consortium was formed by 18 institutions, 9 from Europe, 8 from Africa, and the Food and Agriculture Organization of United Nations (FAO).

ClimAfrica was conceived to respond to the urgent international need for the most appropriate and up-to-date tools and methodologies to better understand and predict climate change, assess its impact on African ecosystems and population, and develop the correct adaptation strategies. Africa is probably the most vulnerable continent to climate change and climate variability and shows diverse range of agro-ecological and geographical features. Thus the impacts of climate change can be very high and can greatly differ across the continent, and even within countries.

The project focused on the following specific objectives:

1. Develop improved climate predictions on seasonal to decadal climatic scales, especially relevant to SSA;

2. Assess climate impacts in key sectors of SSA livelihood and economy, especially water resources and agriculture;

3. Evaluate the vulnerability of ecosystems and civil population to inter-annual variations and longer trends (10 years) in climate;

4. Suggest and analyse new suited adaptation strategies, focused on local needs;

5. Develop a new concept of 10 years monitoring and forecasting warning system, useful for food security, risk management and civil protection in SSA;

6. Analyse the economic impacts of climate change on agriculture and water resources in SSA and the cost-effectiveness of potential adaptation measures.

The work of ClimAfrica project was broken down into the following work packages (WPs) closely connected. All the activities described in WP1, WP2, WP3, WP4, WP5 consider the domain of the entire South Sahara Africa region. Only WP6 has a country specific (watershed) spatial scale where models validation and detailed processes analysis are carried out.

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Lia van Wesenbeeck

Resource Contact: Ben Sonneveld

Resource constraints:

copyright

Online resources:

A spatially explicit assessment of specific vulnerabilities of the food system due to climate change and the identification of their causes; Technical report

Scenarios of major production systems in Africa

0 diseases, % of population, % of population - Prevalence of diseases in sample of households in West Africa

1 disease, % of population - Prevalence of diseases in sample of households in West Africa

2 disease, % of population - Prevalence of diseases in sample of households in West Africa

3 disease, % of population - Prevalence of diseases in sample of households in West Africa

CLIMAFRICA – Climate change predictions in Sub-Saharan Africa: impacts and adaptations

Roads and other linear infrastructures are among the largest and most visible human-made artefacts on the planet today and represent a threat for both endangered and common species, mainly due to additional mortality from collisions with vehicles. There is strong evidence that additional non-natural mortality affects many species and a growing number of populations could have increased risk of extinction unless effective mitigation actions are applied. At a global scale, Europe is among the regions with highest transport infrastructures density. Between 1970 and 2000 the kilometres of built roads more than tripled in several countries in Europe (EU-15) reaching up to 3 million km of which around 51 500 km consisted of motorways (1.7%). Currently, 50% of the continent is within 1.5 km of transportation infrastructure which may lead to declines in birds and mammals. We urgently need to advance our understanding of how roads affect biodiversity through two steps: 1) identifying which species and regions are more at risk from infrastructures; and 2) determining where those risks result in impacts (loss of biodiversity). Road ecology as a discipline has largely focused on the first step. In Europe, roadkill rates have been estimated for a wide range of vertebrates with millions of casualties detected each year. However, we still lack estimates for all species or areas, even in well-studied regions. The aim of this study is to determine which species are at risk due to roads and where roads can impact population persistence and biodiversity. We focused on bird and mammalian species in Europe as a case study. First, we developed a predictive model of roadkill rates based on diverse species traits which allowed us to predict rates for all European terrestrial bird and mammal species and to map the potential incidence of roadkills. We fitted trait-based random forest regression models separately for birds and mammals to explain empirical roadkill rates. We used all available roadkill rates and the following predictors: species trait data, multiple characteristics of the study (latitude and longitude and survey interval) to account for species abundance and detectability, and taxonomic order to account for evolutionary relationships. Second, we used a generalized population model to estimate long-term vulnerability to road mortality. We estimated ~194 million birds and ~29 million mammals may be killed each year on the European road network. Overall, species with higher roadkill rates differ from those in which roadkill is likely to affect long-term persistence. Simplified models of species traits and wildlife-roads interactions at a macro scale allow a first assessment of the road mortality on wildlife and implications on population’s persistence. This macroecological approach provide guidance for national road planning, support the definition of target areas for further testing at a finer-scale resolution, and ultimately prioritize site-specific areas where mitigation would be most beneficial.

Variation in age structure influences population dynamics, yet we have limited understanding of the spatial scale at which its fluctuations are synchronised between populations. Using 32 great tit populations, spanning 4○W–33○E and 35–65○N involving >130,000 birds across 67 years, we quantify spatial synchrony in breeding demographic structure (subadult vs. adult breeders) and its drivers. We show that larger clutch sizes, colder winters, and larger beech crops lead to younger populations. We report distance-dependent synchrony of demographic structure, maintained at approximately 650km. Despite covariation with demographic structure, we do not find evidence for environmental variables influencing the scale of synchrony, except for beech masting. We suggest that local ecological and density-dependent dynamics impact how environmental variation interacts with demographic structure, influencing estimates of the environment’s effect on synchrony. Our analyses demonstrate the operation o..., Study systems and data collection The great tit Parus major is a passerine bird found in mixed woodlands across much of the Western Palearctic. Their reproductive lifespan ranges from 1–9, averaging 1.8 years (Bouwhuis et al. 2009; Woodman et al. 2022). Although there are some continuous changes with age (Bouwhuis et al. 2009), the main age effects on individual-level traits and population processes are captured by two age-classes: 1-year-olds (hereafter subadults) and older (hereafter adults, Gosler 1993; Harvey et al. 1979; Perrins 1979; Gamelon et al. 2016, 2019; Woodman et al. 2022). Great tits generally undertake one breeding attempt during a single annual breeding season April–June (in some parts of their range second clutches can occur, Verhulst 1998; Visser et al. 2003). Data used here are from 32 populations, the geographical range of which represents a large part of the species’ breeding range (Sullivan et al. 2009). Generally, data collection at these sites included regular v..., , # Continent-wide drivers of spatial synchrony in breeding demographic structure across wild great tit populations

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Presented here is the raw data ("bred_dem_synchrony.RData") and annotated R Script ("Continent-wide drivers of spatial synchrony in breeding demographic structure across wild great tit populations.R") needed to run analyses for the project "Continent-wide drivers of spatial synchrony in breeding demographic structure across wild great tit populations".

Description of the data and file structure

"bred_dem_synchrony.RData": R-data file needed to run analyses. Descriptions of the three datasets within this file are found in the R script, and also below.

1. "bred_dem_variables" = Base data for annual breeding demographic structure variables of great tit populations. Only includes populations with annual population size equal or greater than 20 individuals and where 25% or more of individuals have be...

Genetic admixture is supposed to be an important trigger of species expansions because it can create the potential for selection of genotypes suitable for new climatic conditions. Up until now, however, no continent-wide population genetic study has performed a detailed reconstruction of admixture events during natural species expansions. To fill this gap, we analysed the postglacial history of Alnus glutinosa, a keystone species of European swamp habitats, across its entire distribution range using two molecular markers, cpDNA and nuclear microsatellites. CpDNA revealed multiple southern refugia located in the Iberian, Apennine, Balkan and Anatolian Peninsulas, Corsica and North Africa. Analysis of microsatellites variation revealed three main directions of postglacial expansion: (i) from the northern part of the Iberian Peninsula to Western and Central Europe and subsequently to the British Isles, (ii) from the Apennine Peninsula to the Alps and (iii) from the eastern part of the Balkan Peninsula to the Carpathians followed by expansion towards the Northern European plains. This challenges the classical paradigm that most European populations originated from refugial areas in the Carpathians. It has been shown that colonizing lineages have met several times and formed secondary contact zones with unexpectedly high population genetic diversity in Central Europe and Scandinavia. On the contrary, limited genetic admixture in southern refugial areas of A. glutinosa renders rear-edge populations in the Mediterranean region more vulnerable to extinction due to climate change.

Population connectivity and gene flow in near shore Antarctic Echinoids (Sterechinus neumayeri, Abatus nimrodi and Abatus ingens) was investigated in East Antarctica. This data set consists of microsatellite genotype data from 11 novel loci and mitochondrial DNA sequences from two gene region, COI and 16S. In addition, to determine if changes in temperature and salinity impacted fertilisation success in S. neumayeri, and to determine the appropriate sperm to egg ratio for this type of experiment, a fertilisation experiment was completed using various combinations of temperature, salinity and sperm to egg ratio. Samples were collected near two Australian Antarctic research stations, Casey and Davis, during the 08/09 and 09/10 summer field seasons.

To generate the microsatellite data set, a total of 545 adults, nuemayeri and 26 echinoplutei were collected. Spatial replication was achieved by comparing adult populations between two regions (Casey and Davis). These two regions are separated by approximately 1400 km. Sampling in the Casey region was done at two locations 9 km apart and in the Davis region at five locations separated by 5 - 30 km. Within each location 25-50 individuals were collected from up to three sites approximately 0.5 km apart. Within each site, all individuals were collected within an area less than 50 m2. Adult urchins were collected by dip nets, snorkel or scuba depending on location. Echinoplutei were collected from the water column in two locations in the Davis region using a purpose built plankton net. DNA was extracted using QiagenDNeasy Blood and Tissue extraction kits as per the manufacturer's protocols. PCR amplification was carried out in four multiplex reactions and analysis of the PCR product was carried out on a CEQ 8000 (Beckman Coulter) automated sequencer by capillary separation, and alleles scored as fragment size using CEQ 8000 Genetic Analysis System software (ver. 8.0).

Data available: Data consists of 571 individual genotypes at 11 loci in an excel spreadsheet following the GenAlEx v 6.41 layout. Sites from the Davis region are; Old Wallow 1 (OW1), Old Wallow 2 (OW2), Boyd Island (BO1), Ellis Fjord 1 (EL1), Ellis Fjord 2 (EL2), Ellis Fjord 3 (EL3), Trigwell Island 1 (TR1), Trigwell Island 2 (TR2), Trigwell Island 3 (TR3), Zappit Point 1 (ZP1), Zappit Point 2 (ZP2), Zappit Point 3 (ZP3). Sites from the Casey region are; Browning Peninsula 1 (CB1), Browning Peninsula 2 (CB2), Browning Peninsula 3 (CB3), Sparkes Bay 1 (CS1), Sparkes Bay 2 (CS2).Echinoplutei samples are Hawker Island (D1); Kazak Island 1 (K1); Kazak Island 2 (K2) Data is coded as fragment length, with a zero value representing no data.

To generate the mtDNA sequence data, a total of 24 S. neumayeri individuals were sequenced for the COI gene region with two haplotypes found. For the 16S gene region, 25 individuals were sequenced with three haplotypes founds. For Abatusingens, 51 individuals were sequenced with six CO1 haplotypes and five 16S haplotypes. For Abatus nimrodi (n = 48) there were two CO1 haplotypes and eight 16S haplotypes. In addition, eight A. shackeltoni, four A. philippii and one A. cavernosus sample were included from the Davis region.

Data available: data are available in four FASTA text format files, one for Abatus COI data, one forAbatus 16S data, one for Sterechinus COI data. Individuals are coded with the first two letters representing species (SN = S. neumayeri, AN = A. nimrodi, AI = A. ingens, AS = A. shackletoni, AC= A. cavernosus) the next two representing gene region (CO = COI, 16 = 16S) and either three or four more digits for Davis region samples or five digits beginning with 41 for Casey region samples.

To generate the fertilisation data set, S. neumayeri were collected from Ellis Fjord prior to ice breakout. A total of 12 individuals were screened for the fertilisation experiment, seven males and five females to ensure a suitable cross where greater than 90% fertilisation success was achievable. Sperm were activated with FSW at -1.8 degrees C and sperm concentration determined using a haemocytometer. Three temperature treatments, (-1.8 degrees C, 1 degrees C and 3 degrees C), three salinity treatments (35ppt, 30ppt and 25ppt), and five sperm to egg ratios (50:1, 100:1, 500:1, 1500:1 and 2500:1) were used during fertilisation, with four replicates at each temperature:salinity:sperm to egg ratio combination. After 30 min, three to five drops of 10% formalin were added to each vial to fix eggs and to prevent further fertilisation from occurring. To determine percentage fertilisation, the first 100 eggs encountered from each vial were scored as either fertilised or unfertilised based on the presence or absence of an elevated fertilisation membrane.

Data available: Data are available as an excel file, with three spreadsheets, one for each temperature treatment. Each spreadsheet consists of three tables, one for each salinity treatment. Each salinity treatment table consists of five columns. From left to right these are; sperm : egg ratio - Sperm to egg ratio, rep. No. - replicate number,
Fert. - number of fertilised eggs counted Unfert. - number of unfertilised eggs counted Mean- mean number of fertilised eggs counted

Geographic Fields:FID: A unique identifier for each record in the dataset name: Name of the country or region continent: The continent where the country/region is located region: A more specific subregion within the continent (e.g., "Southeast Asia") iso_3166_1: ISO 3166-1 country code for standardized identificationFood Security and Energy Fields:Average dietary energy requirement (kcal/cap/day) (FS_Average):The average energy requirement per person per day, measured in kilocalories per capita per day (kcal/cap/day). This value represents the baseline energy needed for a healthy population.Coefficient of variation of habitual caloric consumption distribution (real number) (FS_Coeffic):A statistical measure (unitless, as a real number) that indicates variability in caloric consumption within the population. Higher values suggest greater inequality in food access.Dietary energy supply used in the estimation of prevalence of undernourishment (kcal/cap/day) (FS_Dietary):The average dietary energy supply available for consumption, measured in kilocalories per capita per day (kcal/cap/day). Lower values indicate potential undernourishment.Incidence of caloric losses at retail distribution level (percent) (FS_Inciden):The percentage of calories lost at the retail and distribution stage of the food supply chain. Measured in percent (%).Minimum dietary energy requirement (kcal/cap/day) (FS_Minimum):The minimum energy needed for survival and basic metabolic functions, measured in kilocalories per capita per day (kcal/cap/day).Per capita food supply variability (kcal/cap/day) (FS_Per_cap):The variation in food supply per person over time, measured in kilocalories per capita per day (kcal/cap/day). This reflects stability or instability in food availability.Spatial Data:Shape_Area: The total area of the spatial feature (e.g., country or region), measured in square unitsShape_Length: The perimeter length of the spatial feature, measured in linear units

In 2025, nearly 11.7 percent of the world population in extreme poverty, with the poverty threshold at 2.15 U.S. dollars a day, lived in Nigeria. Moreover, the Democratic Republic of the Congo accounted for around 11.7 percent of the global population in extreme poverty. Other African nations with a large poor population were Tanzania, Mozambique, and Madagascar. Poverty levels remain high despite the forecast decline Poverty is a widespread issue across Africa. Around 429 million people on the continent were living below the extreme poverty line of 2.15 U.S. dollars a day in 2024. Since the continent had approximately 1.4 billion inhabitants, roughly a third of Africa’s population was in extreme poverty that year. Mozambique, Malawi, Central African Republic, and Niger had Africa’s highest extreme poverty rates based on the 2.15 U.S. dollars per day extreme poverty indicator (updated from 1.90 U.S. dollars in September 2022). Although the levels of poverty on the continent are forecast to decrease in the coming years, Africa will remain the poorest region compared to the rest of the world. Prevalence of poverty and malnutrition across Africa Multiple factors are linked to increased poverty. Regions with critical situations of employment, education, health, nutrition, war, and conflict usually have larger poor populations. Consequently, poverty tends to be more prevalent in least-developed and developing countries worldwide. For similar reasons, rural households also face higher poverty levels. In 2024, the extreme poverty rate in Africa stood at around 45 percent among the rural population, compared to seven percent in urban areas. Together with poverty, malnutrition is also widespread in Africa. Limited access to food leads to low health conditions, increasing the poverty risk. At the same time, poverty can determine inadequate nutrition. Almost 38.3 percent of the global undernourished population lived in Africa in 2022.