European countries are experiencing population decline and the tacit assumption in most analyses is that the decline may have detrimental welfare effects. In this paper we use a survey among the population in the Netherlands to discover whether population decline is always met with fear. A number of results stand out: population size preferences differ by geographic proximity: at a global level the majority of respondents favors a (global) population decline, but closer to home one supports a stationary population. Population decline is clearly not always met with fear: 31 percent would like the population to decline at the national level and they generally perceive decline to be accompanied by immaterial welfare gains (improvement environment) as well as material welfare losses (tax increases, economic stagnation). In addition to these driving forces it appears that the attitude towards immigrants is a very strong determinant at all geographical levels: immigrants seem to be a stronger fear factor than population decline.

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

• The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on Earth, which far exceeds the world population of 7.2 billion in 2015. Our estimate based on UN data shows the world's population surpassing 7.7 billion.
• China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.
• The following 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.
• Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.
• In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added yearly.
• This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growth more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Content

• In this Dataset, we have Historical Population data for every Country/Territory in the world by different parameters like Area Size of the Country/Territory, Name of the Continent, Name of the Capital, Density, Population Growth Rate, Ranking based on Population, World Population Percentage, etc. >Dataset Glossary (Column-Wise):
• Rank: Rank by Population.
• CCA3: 3 Digit Country/Territories Code.
• Country/Territories: Name of the Country/Territories.
• Capital: Name of the Capital.
• Continent: Name of the Continent.
• 2022 Population: Population of the Country/Territories in the year 2022.
• 2020 Population: Population of the Country/Territories in the year 2020.
• 2015 Population: Population of the Country/Territories in the year 2015.
• 2010 Population: Population of the Country/Territories in the year 2010.
• 2000 Population: Population of the Country/Territories in the year 2000.
• 1990 Population: Population of the Country/Territories in the year 1990.
• 1980 Population: Population of the Country/Territories in the year 1980.
• 1970 Population: Population of the Country/Territories in the year 1970.
• Area (km²): Area size of the Country/Territories in square kilometers.
• Density (per km²): Population Density per square kilometer.
• Growth Rate: Population Growth Rate by Country/Territories.
• World Population Percentage: The population percentage by each Country/Territories.

About this dataset

Context

The dataset tabulates the White Earth population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of White Earth across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of White Earth was 93, a 0% decrease year-by-year from 2022. Previously, in 2022, White Earth population was 93, a decline of 4.12% compared to a population of 97 in 2021. Over the last 20 plus years, between 2000 and 2023, population of White Earth increased by 28. In this period, the peak population was 99 in the year 2020. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the White Earth is shown in this column.
• Year on Year Change: This column displays the change in White Earth population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for White Earth Population by Year. You can refer the same here

Bumble bees (Bombus) are vitally important pollinators of wild plants and agricultural crops worldwide. Fragmentary observations, however, have suggested population declines in several North American species. Despite rising concern over these observations in the United States, highlighted in a recent National Academy of Sciences report, a national assessment of the geographic scope and possible causal factors of bumble bee decline is lacking. Here, we report results of a 3-y interdisciplinary study of changing distributions, population genetic structure, and levels of pathogen infection in bumble bee populations across the United States. We compare current and historical distributions of eight species, compiling a database of >73,000 museum records for comparison with data from intensive nationwide surveys of >16,000 specimens. We show that the relative abundances of four species have declined by up to 96% and that their surveyed geographic ranges have contracted by 23–87%, some within the last 20 y. We also show that declining populations have significantly higher infection levels of the microsporidian pathogen Nosema bombi and lower genetic diversity compared with co-occurring populations of the stable (nondeclining) species. Higher pathogen prevalence and reduced genetic diversity are, thus, realistic predictors of these alarming patterns of decline in North America, although cause and effect remain uncertain. Bumble bees (Bombus) are integral wild pollinators within native plant communities throughout temperate ecosystems, and recent domestication has boosted their economic importance in crop pollination to a level surpassed only by the honey bee. Their robust size, long tongues, and buzz-pollination behavior (high-frequency buzzing to release pollen from flowers) significantly increase the efficiency of pollen transfer in multibillion dollar crops such as tomatoes and berries. Disturbing reports of bumble bee population declines in Europe have recently spilled over into North America, fueling environmental and economic concerns of global decline. However, the evidence for large-scale range reductions across North America is lacking. Many reports of decline are unpublished, and the few published studies are limited to independent local surveys in northern California/southern Oregon, Ontario, Canada, and Illinois. Furthermore, causal factors leading to the alleged decline of bumble bee populations in North America remain speculative. One compelling but untested hypothesis for the cause of decline in the United States entails the spread of a putatively introduced pathogen, Nosema bombi, which is an obligate intracellular microsporidian parasite found commonly in bumble bees throughout Europe but largely unstudied in North America. Pathogenic effects of N. bombi may vary depending on the host species and reproductive caste and include reductions in colony growth and individual life span and fitness. Population genetic factors could also play a role in Bombus population decline. For instance, small effective population sizes and reduced gene flow among fragmented habitats can result in losses of genetic diversity with negative consequences, and the detrimental impacts of these genetic factors can be especially intensified in bees. Population genetic studies of Bombus are rare worldwide. A single study in the United States identified lower genetic diversity and elevated genetic differentiation (FST) among Illinois populations of the putatively declining B. pensylvanicus relative to those of a codistributed stable species. Similar patterns have been observed in comparative studies of some European species, but most investigations have been geographically restricted and based on limited sampling within and among populations. Although the investigations to date have provided important information on the increasing rarity of some bumble bee species in local populations, the different survey protocols and limited geographic scope of these studies cannot fully capture the general patterns necessary to evaluate the underlying processes or overall gravity of declines. Furthermore, valid tests of the N. bombi hypothesis and its risk to populations across North America call for data on its geographic distribution and infection prevalence among species. Likewise, testing the general importance of population genetic factors in bumble bee decline requires genetic comparisons derived from sampling of multiple stable and declining populations on a large geographic scale. From such range-wide comparisons, we provide incontrovertible evidence that multiple Bombus species have experienced sharp population declines at the national level. We also show that declining populations are associated with both high N. bombi infection levels and low genetic diversity. This data was used in the paper "Patterns of widespread decline in North American bumble bees" published in the Proceedings of the National Academy of United States of America. For more information about this dataset contact: Sydney A. Cameron: scameron@life.illinois.edu James Strange: James.Strange@ars.usda.gov Resources in this dataset:Resource Title: Data from: Patterns of Widespread Decline in North American Bumble Bees (Data Dictionary). File Name: meta.xmlResource Description: This is an XML data dictionary for Data from: Patterns of Widespread Decline in North American Bumble Bees.Resource Title: Patterns of Widespread Decline in North American Bumble Bees (DWC Archive). File Name: occurrence.csvResource Description: File modified to remove fields with no recorded values.Resource Title: Patterns of Widespread Decline in North American Bumble Bees (DWC Archive). File Name: dwca-usda-ars-patternsofwidespreaddecline-bumblebees-v1.1.zipResource Description: Data from: Patterns of Widespread Decline in North American Bumble Bees -- this is a Darwin Core Archive file. The Darwin Core Archive is a zip file that contains three documents.

The occurrence data is stored in the occurrence.txt file. The metadata that describes the columns of this document is called meta.xml. This document is also the data dictionary for this dataset. The metadata that describes the dataset, including author and contact information for this dataset is called eml.xml.

Find the data files at https://bison.usgs.gov/ipt/resource?r=usda-ars-patternsofwidespreaddecline-bumblebees

Understanding changes in abundance is crucial for conservation, but population growth rates often vary over space and time. We use 40 years of count data (1979–2019) and Bayesian state-space models to assess the African penguin Spheniscus demersus population under IUCN Red List Criterion A. We deconstruct the overall decline in time and space to identify where urgent conservation action is needed. The global African penguin population met the threshold for Endangered with a high probability (97%), having declined by almost 65% since 1989. An historical low of ~17,700 pairs bred in 2019. Annual changes were faster in the South African population (−4.2%, highest posterior density interval, HPDI: −7.8 to −0.6%) than the Namibian one (−0.3%, HPDI: −3.3 to +2.6%), and since 1999 were almost −10% at South African colonies north of Cape Town. Over the 40-year period, the Eastern Cape colonies went from holding ~25% of the total penguin population to ~40% as numbers decreased more rapidly elsew...

Our global population is getting older, largely because of increasing life expectancies and declining birth rates. In 2018 the number of people older than 64 years old surpassed the number of children under 5 years old. This was the first time in history this was the case.

Age groups: - 0-4 years - 5-14 years - 15-24 years - 24-65 years - 65+ year

Data Source: Age Structure - Our World in Data

Full Data (1950-2021): Population by age group, including UN projections, World

Context

In demographics, the world population is the total number of humans currently living, and was estimated to have reached 7,800,000,000 people as of March 2020. It took over 2 million years of human history for the world's population to reach 1 billion, and only 200 years more to reach 7 billion. The world population has experienced continuous growth following the Great Famine of 1315–1317 and the end of the Black Death in 1350, when it was near 370 million. The highest global population growth rates, with increases of over 1.8% per year, occurred between 1955 and 1975 – peaking to 2.1% between 1965 and 1970.[7] The growth rate declined to 1.2% between 2010 and 2015 and is projected to decline further in the course of the 21st century. However, the global population is still increasing[8] and is projected to reach about 10 billion in 2050 and more than 11 billion in 2100.

Content

Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage . Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. Annual population growth rate. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.

Statistical Concept and Methodology

Total population growth rates are calculated on the assumption that rate of growth is constant between two points in time. The growth rate is computed using the exponential growth formula: r = ln(pn/p0)/n, where r is the exponential rate of growth, ln() is the natural logarithm, pn is the end period population, p0 is the beginning period population, and n is the number of years in between. Note that this is not the geometric growth rate used to compute compound growth over discrete periods. For information on total population from which the growth rates are calculated, see total population (SP.POP.TOTL).

Acknowledgements

Derived from total population. Population source: ( 1 ) United Nations Population Division. World Population Prospects: 2019 Revision, ( 2 ) Census reports and other statistical publications from national statistical offices, ( 3 ) Eurostat: Demographic Statistics, ( 4 ) United Nations Statistical Division. Population and Vital Statistics Reprot ( various years ), ( 5 ) U.S. Census Bureau: International Database, and ( 6 ) Secretariat of the Pacific Community: Statistics and Demography Programme.

Genetic data are useful for detecting sudden population declines in species that are difficult to study in the field. Yet this indirect approach has its own drawbacks, including population structure, mutation patterns, and generation overlap. The ivory gull (Pagophila eburnea), a long-lived Arctic seabird, is currently suffering from rapid alteration of its primary habitat (i.e., sea ice), and dramatic climatic events affecting reproduction and recruitment. However, ivory gulls live in remote areas, and it is difficult to assess the population trend of the species across its distribution. Here we present complementary microsatellite- and SNP-based genetic analyses to test a recent bottleneck genetic signal in ivory gulls over a large portion of their distribution. With attention to the potential effects of population structure, mutation patterns, and sample size, we found no significant signatures of population decline worldwide. At a finer scale, we found a significant bottleneck signal at one location in Canada. These results were compared with predictions from simulations showing how generation time and generation overlap can delay and reduce the bottleneck microsatellite heterozygosity excess signal. The consistency of the results obtained with independent methods strongly indicates that the species shows no genetic evidence of an overall decline in population size. However, drawing conclusions related to the species' population trends will require a better understanding of the effect of age structure in long-lived species. In addition, estimates of the effective global population size of ivory gulls were surprisingly low (approximately 1000 ind.), suggesting that the evolutionary potential of the species is not assured.

United States US: Population: Growth data was reported at 0.713 % in 2017. This records a decrease from the previous number of 0.734 % for 2016. United States US: Population: Growth data is updated yearly, averaging 0.979 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 1.702 % in 1960 and a record low of 0.711 % in 2013. United States US: Population: Growth data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Population and Urbanization Statistics. Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage . Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.; ; Derived from total population. Population source: (1) United Nations Population Division. World Population Prospects: 2017 Revision, (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

The Global Earthquake Total Economic Loss Risk Deciles is a 2.5 minute grid of global earthquake total economic loss risks. A process of spatially allocating Gross Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is utilized. First the proportional contributions of subnational Units to their respective national GDP are determined using sources of various origin. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for the subnational Unit. Once the national GDP has been spatially stratified into the smallest administrative Units available, GDP values for grid cells are derived using Gridded Population of the World, Version 3 (GPWv3) data population distributions. A per capita contribution value is determined within each subnational Unit, and then this value is multiplied by the population per grid cell. Once a GDP value has been determined on a per grid cell basis, then the regionally variable loss rate as derived from the historical records of EM-DAT is used to determine the total economic loss risks posed to a grid cell by earthquake hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking of grid cells based upon the calculated economic loss risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).

Global Volcano Total Economic Loss Risk Deciles is a 2.5 minute grid of global volcano total economic loss risks. First, subnational distributions of Gross Domestic Product (GDP) are computed using a two-fold process. Where applicable, the proportional contribution of subnational Units are determined following the methodology of Sachs et al. (2003) and these proportions are used against World Bank Development Indicators to determine a GDP value for the subnational Unit. Once a national GDP has been spatially stratified into the smallest administrative Units available, it is further distributed based upon Gridded Population of the World, Version 3 (GPWv3) population distributions. A per capita contribution value is determined for each Unit, and this value is multiplied by the population per grid cell. Once the GDP has been determined on a per grid cell basis, then the spatially variable loss rate as derived from EM-DAT historical records is used to determine the total economic loss posed to a grid cell by volcano hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10) ranking of grid cells based upon the calculated economic loss risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).

Amphibian is a diverse terrestrial vertebrate group with more than 8000 species, which is an important part of major terrestrial and freshwater ecosystems. Unfortunately, amphibians are experiencing a worldwide population decline. Trait information is important for understanding the causes of endangerment. Meanwhile, such information is also essential for studying amphibians using trait-based approaches. However, such data of amphibians is the scarcest among terrestrial vertebrate groups. To fill this gap, we collected morphological traits of global amphibians available from the literature and compiled a database to facilitate future studies.

Historical chart and dataset showing Japan population growth rate by year from 1961 to 2023.

The region of present-day China has historically been the most populous region in the world; however, its population development has fluctuated throughout history. In 2022, China was overtaken as the most populous country in the world, and current projections suggest its population is heading for a rapid decline in the coming decades. Transitions of power lead to mortality The source suggests that conflict, and the diseases brought with it, were the major obstacles to population growth throughout most of the Common Era, particularly during transitions of power between various dynasties and rulers. It estimates that the total population fell by approximately 30 million people during the 14th century due to the impact of Mongol invasions, which inflicted heavy losses on the northern population through conflict, enslavement, food instability, and the introduction of bubonic plague. Between 1850 and 1870, the total population fell once more, by more than 50 million people, through further conflict, famine and disease; the most notable of these was the Taiping Rebellion, although the Miao an Panthay Rebellions, and the Dungan Revolt, also had large death tolls. The third plague pandemic also originated in Yunnan in 1855, which killed approximately two million people in China. 20th and 21st centuries There were additional conflicts at the turn of the 20th century, which had significant geopolitical consequences for China, but did not result in the same high levels of mortality seen previously. It was not until the overlapping Chinese Civil War (1927-1949) and Second World War (1937-1945) where the death tolls reached approximately 10 and 20 million respectively. Additionally, as China attempted to industrialize during the Great Leap Forward (1958-1962), economic and agricultural mismanagement resulted in the deaths of tens of millions (possibly as many as 55 million) in less than four years, during the Great Chinese Famine. This mortality is not observable on the given dataset, due to the rapidity of China's demographic transition over the entire period; this saw improvements in healthcare, sanitation, and infrastructure result in sweeping changes across the population. The early 2020s marked some significant milestones in China's demographics, where it was overtaken by India as the world's most populous country, and its population also went into decline. Current projections suggest that China is heading for a "demographic disaster", as its rapidly aging population is placing significant burdens on China's economy, government, and society. In stark contrast to the restrictive "one-child policy" of the past, the government has introduced a series of pro-fertility incentives for couples to have larger families, although the impact of these policies are yet to materialize. If these current projections come true, then China's population may be around half its current size by the end of the century.

The statistic shows the total population of India from 2019 to 2029. In 2023, the estimated total population in India amounted to approximately 1.43 billion people.

Total population in India

India currently has the second-largest population in the world and is projected to overtake top-ranking China within forty years. Its residents comprise more than one-seventh of the entire world’s population, and despite a slowly decreasing fertility rate (which still exceeds the replacement rate and keeps the median age of the population relatively low), an increasing life expectancy adds to an expanding population. In comparison with other countries whose populations are decreasing, such as Japan, India has a relatively small share of aged population, which indicates the probability of lower death rates and higher retention of the existing population.

With a land mass of less than half that of the United States and a population almost four times greater, India has recognized potential problems of its growing population. Government attempts to implement family planning programs have achieved varying degrees of success. Initiatives such as sterilization programs in the 1970s have been blamed for creating general antipathy to family planning, but the combined efforts of various family planning and contraception programs have helped halve fertility rates since the 1960s. The population growth rate has correspondingly shrunk as well, but has not yet reached less than one percent growth per year.

As home to thousands of ethnic groups, hundreds of languages, and numerous religions, a cohesive and broadly-supported effort to reduce population growth is difficult to create. Despite that, India is one country to watch in coming years. It is also a growing economic power; among other measures, its GDP per capita was expected to triple between 2003 and 2013 and was listed as the third-ranked country for its share of the global gross domestic product.

Like many polar animals, emperor penguin populations are challenging to monitor because of the species’ life history and remoteness. Consequently, it has been difficult to establish its global status, a subject important to resolve as polar environments change. To advance our understanding of emperor penguins, we combined remote sensing, validation surveys, and using Bayesian modeling we estimated a comprehensive population trajectory over a recent 10-year period, encompassing the entirety of the species’ range. Reported as indices of abundance, our study indicates with 81% probability that the global population of adult emperor penguins declined between 2009 and 2018, with a posterior median decrease of 9.6% (95% credible interval (CI) -26.4% to +9.4%). The global population trend was -1.3% per year over this period (95% CI = -3.3% to +1.0%) and declines likely occurred in four of eight fast ice regions, irrespective of habitat conditions. Thus far, explanations have yet to be identified regarding trends, especially as we observed an apparent population up-tick toward the end of time series. Our work potentially establishes a framework for monitoring other Antarctic coastal species detectable by satellite, while promoting a need for research to better understand factors driving biotic changes in the Southern Ocean ecosystem.

The Global Earthquake Total Economic Loss Risk Deciles is a 2.5 minute grid of global earthquake total economic loss risks. A process of spatially allocating Gross Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is utilized. First the proportional contributions of subnational Units to their respective national GDP are determined using sources of various origin. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for the subnational Unit. Once the national GDP has been spatially stratified into the smallest administrative Units available, GDP values for grid cells are derived using Gridded Population of the World, Version 3 (GPWv3) data population distributions. A per capita contribution value is determined within each subnational Unit, and then this value is multiplied by the population per grid cell. Once a GDP value has been determined on a per grid cell basis, then the regionally variable loss rate as derived from the historical records of EM-DAT is used to determine the total economic loss risks posed to a grid cell by earthquake hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking of grid cells based upon the calculated economic loss risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).

The Global Landslide Total Economic Loss Risk Deciles is a 2.5 minute grid of global landslide total economic loss risks. A process of spatially allocating Gross Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is utilized. First the proportional contributions of subnational Units to their respective national GDP are determined using sources of various origins. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for the subnational Unit. Once the national GDP has been spatially stratified into the smallest administrative Units available, GDP values for grid cells are derived using Gridded Population of the World, Version 3 (GPWv3) data of population distributions. A per capita contribution value is determined within each subnational Unit, and this value is multiplied by the population per grid cell. Once a GDP value has been determined on a per grid cell basis, then the regionally variable loss rate as derived from the historical records of EM-DAT is used to determine the total economic loss risks posed to a grid cell by landslide hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking of grid cells based upon the calculated economic loss risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).

The Asiatic wild dog or dhole (Cuon alpinus) is a highly elusive, monophyletic, forest dwelling, social canid distributed across south and Southeast Asia. Severe pressures from habitat loss, prey depletion, disease, human persecution and interspecific competition resulted in global population decline in dholes. Despite a declining population trend, detailed information on population size, ecology, demography and genetics is lacking. Generating reliable information at landscape level for dholes is challenging due to their secretive behaviour and monomorphic physical features. Recent advances in non-invasive DNA-based tools can be used to monitor populations and individuals across large landscapes. In this paper, we describe standardization and validation of faecal DNA-based methods for individual identification of dholes. We tested this method on 249 field-collected dhole faeces from five protected areas of the central Indian landscape in the state of Maharashtra, India. Results We tested a total of 18 cross-species markers and developed a panel of 12 markers for unambiguous individual identification of dholes. This marker panel identified 101 unique individuals from faecal samples collected across our pilot field study area. These loci showed varied level of amplification success (57-88%), polymorphism (3-9 alleles), heterozygosity (0.23-0.63) and produced a cumulative misidentification rate or PID(unbiased) and PID(sibs) value of 4.7x10-10 and 1.5x10-4, respectively, indicating a high statistical power in individual discrimination from poor quality samples. Conclusion Our results demonstrated that the selected panel of 12 microsatellite loci can conclusively identify dholes from poor quality, non-invasive biological samples and help in exploring various population parameters. This genetic approach would be useful in dhole population estimation across its range and will help in assessing population trends and other genetic parameters for this elusive, social carnivore.