The world's population first reached one billion people in 1803, and reach eight billion in 2023, and will peak at almost 11 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two thirds of the world's population live in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a decade later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.

The impacts of climate change on forest community composition are still not well known. Although directional trends in climate change and community composition change were reported in recent years, further quantitative analyses are urgently needed. Previous studies focused on measuring population growth rates in a single time period, neglecting the development of the populations. Here we aimed to compose a method for calculating the community composition change, and to testify the impacts of climate change on community composition change within a relatively short period (several decades) based on long-term monitoring data from two plots—Dinghushan Biosphere Reserve, China (DBR) and Barro Colorado Island, Panama (BCI)—that are located in tropical and subtropical regions. We proposed a relatively more concise index, Slnλ, which refers to an overall population growth rate based on the dominant species in a community. The results indicated that the population growth rate of a majority of populations has decreased over the past few decades. This decrease was mainly caused by population development. The increasing temperature had a positive effect on population growth rates and community change rates. Our results promote understanding and explaining variations in population growth rates and community composition rates, and are helpful to predict population dynamics and population responses to climate change.

According to latest figures, the Chinese population decreased by 1.39 million to around 1.408 billion people in 2024. After decades of rapid growth, China arrived at the turning point of its demographic development in 2022, which was earlier than expected. The annual population decrease is estimated to remain at moderate levels until around 2030 but to accelerate thereafter. Population development in China China had for a long time been the country with the largest population worldwide, but according to UN estimates, it has been overtaken by India in 2023. As the population in India is still growing, the country is very likely to remain being home of the largest population on earth in the near future. Due to several mechanisms put into place by the Chinese government as well as changing circumstances in the working and social environment of the Chinese people, population growth has subsided over the past decades, displaying an annual population growth rate of -0.1 percent in 2024. Nevertheless, compared to the world population in total, China held a share of about 18 percent of the overall global population in 2022. China's aging population In terms of demographic developments, the birth control efforts of the Chinese government had considerable effects on the demographic pyramid in China. Upon closer examination of the age distribution, a clear trend of an aging population becomes visible. In order to curb the negative effects of an aging population, the Chinese government abolished the one-child policy in 2015, which had been in effect since 1979, and introduced a three-child policy in May 2021. However, many Chinese parents nowadays are reluctant to have a second or third child, as is the case in most of the developed countries in the world. The number of births in China varied in the years following the abolishment of the one-child policy, but did not increase considerably. Among the reasons most prominent for parents not having more children are the rising living costs and costs for child care, growing work pressure, a growing trend towards self-realization and individualism, and changing social behaviors.

Whereas the population is expected to decrease somewhat until 2100 in Asia, Europe, and South America, it is predicted to grow significantly in Africa. While there were 1.5 billion inhabitants on the continent at the beginning of 2024, the number of inhabitants is expected to reach 3.8 billion by 2100. In total, the global population is expected to reach nearly 10.4 billion by 2100. Worldwide population In the United States, the total population is expected to steadily increase over the next couple of years. In 2024, Asia held over half of the global population and is expected to have the highest number of people living in urban areas in 2050. Asia is home to the two most populous countries, India and China, both with a population of over one billion people. However, the small country of Monaco had the highest population density worldwide in 2021. Effects of overpopulation Alongside the growing worldwide population, there are negative effects of overpopulation. The increasing population puts a higher pressure on existing resources and contributes to pollution. As the population grows, the demand for food grows, which requires more water, which in turn takes away from the freshwater available. Concurrently, food needs to be transported through different mechanisms, which contributes to air pollution. Not every resource is renewable, meaning the world is using up limited resources that will eventually run out. Furthermore, more species will become extinct which harms the ecosystem and food chain. Overpopulation was considered to be one of the most important environmental issues worldwide in 2020.

Trend-based projections

Four variants of trend-based population projections and corresponding household projections are currently available to download. These are labelled as High, Central and Low and differ in their domestic migration assumptions beyond 2017. The economic crisis has been linked to a fall in migration from London to the rest of the UK and a rise in flows from the UK to London. The variants reflect a range of scenarios relating to possible return to pre-crisis trends in migration.

High: In this scenario, the changes to domestic migration flows are considered to be structural and recent patterns persist regardless of an improving economic outlook.

Low: Changes to domestic migration patterns are assumed to be transient and return to pre-crisis trends beyond 2018. Domestic outflow propensities increase by 10% and inflows decrease by 6% as compared to the High variant.

Central: Assumes recent migration patterns are partially transient and partially structural. Beyond 2018, domestic outlow propensities increase by 5% and inflows by 3% as compared to the High variant.

Central - incorporating 2012-based fertility assumptions: Uses the same migration assumptions as the Central projeciton above, but includes updated age-specific-fertility-rates based on 2011 birth data and future fertility trends taken from ONS's 2012-based National Population Projections. The impact of these changes is to increase fertility by ~10% in the long term.

GLA 2013 round trend-based population projections:
Borough: High
Borough: Low
Borough: Central
Borough: Central - incorporating 2012-based NPP fertility assumptions
Ward: Central

GLA 2013 round trend-based household projections:
Borough: High
Borough: Low
Borough: Central

GLA 2013 round ethnic group population projections:
Borough: Central

Updates:
Update 03-2014: GLA 2013 round of trend-based population projections - Methodology
Update 04-2014: GLA 2013 round of trend-based population projections - Results
Data to accompany Update 04-2014
Update 12-2014: GLA 2013 round ethnic group population projections
Data to accompany Update 12-2014

Housing linked projections

Two variants of housing-linked projections are available based on housing trajectories derived from the 2013 Strategic Housing Land Availability Assessment (SHLAA). The two variants are produced using different models to constrain the population to available dwellings. These are referred to as the DCLG-based model and the Capped Household Size model. These models will be explained in greater detail in an upcoming Intelligence Unit Update.

Projection Models:

DCLG-Based Model

This model makes use of Household Representative Rates (HRR) from DCLG’s 2011-based household projections to convert populations by age and gender into households. The models uses iteration to find a population that yields a total number of households that matches the number of available household spaces implied by the development data. This iterative process involves modulating gross migration flows between each London local authority and UK regions outside of London. HRRs beyond 2021 have been extrapolated forward by the GLA. The model also produces a set of household projections consistent with the population outputs.

Capped Household Size Model

This model was introduced to provide an alternative projection based on the SHLAA housing trajectories. While the projections given by the DCLG-Based Model appear realistic for the majority of London, there are concerns that it could lead to under projection for certain local authorities, namely those in Outer London where recent population growth has primarily been driven by rising household sizes. For these boroughs, the Capped Household Size model provides greater freedom for the population to follow the growth patterns shown in the Trend-based projections, but caps average household size at 2012 levels. For boroughs where the DCLG-based SHLAA model gave higher results than the Trend-based model, the projections follow the results of the former.

Household projections are not available from this model.

Development assumptions:

SHLAA housing data

These projections incorporate development data from the 2013 Strategic Housing Land Availability Assessment (SHLAA) database to determine populations for 2012 onwards. Development trajectories are derived from this data for four phases: 2015-20, 2021-25, 2026-30, and 2031-36. For 2012-14, data is taken from the 2009 SHLAA trajectories. No data is included in the database for beyond 2036 and the 2031-36 trajectories are extended forward to 2041. This data was correct as at February 2014 and may be updated in future. Assumed development figures will not necessarily match information in the SHLAA report as some data on estate renewals is not included in the database at this time.

GLA 2013 round SHLAA-based population projections:
Borough: SHLAA-based
Borough: capped SHLAA-based
Ward: SHLAA-based
Ward: capped SHLAA-based

GLA 2013 round SHLAA-based household projections:
Borough: SHLAA-based

GLA 2013 round SHLAA-based ethnic group population projections:
Borough: SHLAA-based

Zero-development projections

The GLA produces so-called zero-development projections for London that assume that future dwelling stocks remain unchanged. These projections can be used in conjunction with the SHLAA-based projections to give an indication of the modelled impact of the assumed development. Variants are produced consistent with the DCLG-based and Capped Household Size projections. Due to the way the models operate, the former assumes no development beyond 2011 and the latter no development after 2012.

GLA 2013 round zero development population projections:
Borough: DCLG zero development
Borough: capped zero development
Ward: DCLG zero development
Ward: capped zero development

Frequently asked question: which projection should I use?

The GLA Demography Team recommends using the Capped Household Size SHLAA projection for most purposes. The main exception to this is for work estimating future housing need, where it is more appropriate to use the trend-based projections.

The custom-age population tool is here.

To access the GLA's full range of demographic projections please click here.

About this dataset

Context

The dataset tabulates the data for the Truth Or Consequences, NM population pyramid, which represents the Truth Or Consequences population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Truth Or Consequences, NM, is 28.7.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Truth Or Consequences, NM, is 42.0.
• Total dependency ratio for Truth Or Consequences, NM is 70.7.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Truth Or Consequences, NM is 2.4.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Truth Or Consequences population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Truth Or Consequences for the selected age group is shown in the following column.
• Population (Female): The female population in the Truth Or Consequences for the selected age group is shown in the following column.
• Total Population: The total population of the Truth Or Consequences for the selected age group is shown in the following column.

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 Truth Or Consequences Population by Age. You can refer the same here

Climate and land-use change are major components of global environmental change with feedbacks between these components. The consequences of these interactions show that land use may exacerbate or alleviate climate change effects. Based on these findings it is important to use land-use scenarios that are consistent with the specific assumptions underlying climate-change scenarios. The Integrated Climate and Land-Use Scenarios (ICLUS) project developed land-use outputs that are based on a downscaled version of the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) social, economic, and demographic storylines. ICLUS outputs are derived from a pair of models. A demographic model generates county-level population estimates that are distributed by a spatial allocation model (SERGoM v3) as housing density across the landscape. Land-use outputs were developed for the four main SRES storylines and a baseline ("base case"). The model is run for the conterminous USA and output is available for each scenario by decade to 2100. In addition to housing density at a 1 hectare spatial resolution, this project also generated estimates of impervious surface at a resolution of 1 square kilometer. This shapefile holds population data for all counties of the conterminous USA for all decades (2010-2100) and SRES population growth scenarios (A1, A2, B1, B2), as well as a 'base case' (BC) scenario, for use in the Integrated Climate and Land Use Scenarios (ICLUS) project.

1- Temporal fluctuations in growth rates can arise from both variation in age-specific vital rates and temporal fluctuations in age structure (i.e., the relative abundance of individuals in each age-class). However, empirical assessments of temporal fluctuations in age structure and their effects on population growth rate are rare. Most research has focused on understanding the contribution of changing vital rates to population growth rates and these analyses routinely assume that: (i) populations have stable age distributions, (ii) environmental influences on vital rates and age structure are stationary (i.e., the mean and/or variance of these processes does not change over time), and (iii) dynamics are independent of density. 2- Here we quantified fluctuations in age structure and assessed whether they were stationary for four populations of free-ranging vertebrates: moose (observed for 48 years), elk (15 years), tawny owls (15 years) and gray wolves (17 years). We also assessed the extent that fluctuations in age structure were useful for predicting annual population growth rates using models which account for density-dependence. 3- Fluctuations in age structure were of a similar magnitude to fluctuations in abundance. For three populations (moose, elk, owls), the mean and the skew of the age distribution fluctuated without stabilizing over the observed time periods. More precisely, the sample variance (interannual variance) of age structure indices increased with the length of the study period which suggests that fluctuations in age structure were non-stationary for these populations – at least over the 15-48 year periods analysed. 4- Fluctuations in age structure were associated with population growth rate for two populations. In particular, population growth varied from positive to negative for moose and from near zero to negative for elk as the average age of adults increased over its observed range. 5- Non-stationarity in age structure may represent an important mechanism by which abundance becomes non-stationary – and therefore difficult to forecast – over time scales of concern to wildlife managers. Overall, our results emphasize the need for vertebrate populations to be modelled using approaches that consider transient dynamics and density-dependence, and that do not rely on the assumption that environmental processes are stationary.

About this dataset

Context

The dataset tabulates the Impact 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 Impact 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 Impact was 21, a 0% decrease year-by-year from 2022. Previously, in 2022, Impact population was 21, a decline of 0% compared to a population of 21 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Impact decreased by 20. In this period, the peak population was 42 in the year 2009. 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 Impact is shown in this column.
• Year on Year Change: This column displays the change in Impact 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 Impact Population by Year. You can refer the same here

Population growth rates will respond to an environmental driver only if the driver impacts demographic rate(s) and the population is sensitive to impacted demographic rate(s). If populations vary in the sensitivity of population growth rate to demographic rates, the effect of an environmental driver on population growth rate could vary across populations, even if the effect of the driver on demographic rates does not vary across populations. Here, we use five years of demographic data of a common alpine plant, including data from a neighbor removal experiment and a climate warming experiment, to quantify the relative contribution of neighbor effects on demographic rates vs. sensitivity of population growth rate to demographic rates to across-population variation in neighbor effects on population growth rate. We find neighbor effects on population growth rate vary significantly across populations, and this effect is driven primarily by variation in sensitivity of population growth rate t...

Before 2025, the world's total population is expected to reach eight billion. Furthermore, it is predicted to reach over 10 billion in 2060, before slowing again as global birth rates are expected to decrease. Moreover, it is still unclear to what extent global warming will have an impact on population development. A high share of the population increase is expected to happen on the African continent.

Data for "Climate change impacts on population growth across a species’ range differ due to nonlinear responses of populations to climate and variation in rates of climate change "

Changes in climate can alter individual body size, and the resulting shifts in reproduction and survival are expected to impact population dynamics and viability. However, appropriate methods to account for size-dependent demographic changes are needed, especially in understudied yet threatened groups such as amphibians. We investigated individual and population-level demographic effects of changes in body size for a terrestrial salamander using capture-mark-recapture data. For our analysis, we implemented an integral projection model parameterized with capture-recapture likelihood estimates from a Bayesian framework. Our study combines survival and growth data from a single dataset to quantify the influence of size on survival while including different sources of uncertainty around these parameters, demonstrating how selective forces can be studied in populations with limited data and incomplete recaptures. We found a strong dependency of the population growth rate on changes in indivi..., ,

1. Overall impacts of targeted conservation interventions on population growth rate (λ) will depend on within-year and among-year variation in exposure of target individuals to interventions, and in intervention efficacy in increasing vital rates of exposed individuals. Juvenile survival is one key vital rate that commonly varies substantially within and among years, and consequently drives variation in λ. However, within-year, among-year and overall impacts of targeted interventions on population-wide survival probabilities of potentially mobile juveniles are rarely quantified, precluding full evaluation and evidence-based refinement of interventions.

2. We applied multi-state mark-recapture models to eight years of ring-resighting data from a threatened red-billed chough (Pyrrhocorax pyrrhocorax) population to quantify within-year and among-year variation in juvenile exposure to a targeted intervention of supplementary feeding and parasite treatment, and to estimate efficacy in increasing juvenile survival probability. We then combined and up-scaled these estimated effects to evaluate the impact of the eight-year intervention on overall population-wide survival probability and resulting population size.

3. High proportions of surviving juveniles (>70%) were exposed to the intervention across the annual biological cycle in all years. Exposure was associated with higher short-term survival probabilities through the full annual cycle. Consequently, management increased estimated population-wide annual juvenile survival by approximately 0.14. However, such effects were only evident in cohorts with low overall annual survival.

4. Population models projected that these impacts on annual juvenile survival substantially reduced population decline, such that population size at the end of the eight-year intervention was approximately double that without management.

5. Synthesis and applications. Our results show how complex patterns of within-year and among-year variation in exposure and efficacy of targeted conservation interventions can arise and scale up to affect population-level outcomes. We demonstrate positive effects of a major intervention, but also highlight potential routes to improve efficacy, for example through more precise targeting of agricultural management actions in the context of among-year variation in environmental conditions.

Comparative finite rate of population increase (λ), generation time (T, in years) and elasticities (e1 = sum of elasticities of fertility, e2 = sum of juvenile survival and e3 = sum of adult survival) for coastal Carcharhinidae sharks used only natural mortality.

1. The climate on our planet is changing and the range distributions of organisms are shifting in response. In aquatic environments, species might not be able to redistribute poleward or into deeper water when temperatures rise because of barriers, reduced light availability, altered water chemistry, or any combination of these. How species respond to climate change may depend on physiological adaptability, but also on the population dynamics of the species.

2. Density dependence is a ubiquitous force that governs population dynamics and regulates population growth, yet its connections to the impacts of climate change remain little known, especially in marine studies. Reductions in density below an environmental carrying capacity may cause compensatory increases in demographic parameters and population growth rate, hence masking the impacts of climate change on populations. On the other hand, climate-driven deterioration of conditions may reduce environmental carrying capacities, making compensation less likely and populations more susceptible to the effects of stochastic processes.

3. Here we investigate the effects of climate change on Baltic blue mussels using a 17-year data set on population density. Using a Bayesian modelling framework, we investigate the impacts of climate change, assess the magnitude and effects of density dependence, and project the likelihood of population decline by the year 2030.

4. Our findings show negative impacts of warmer and less saline waters, both outcomes of climate change. We also show that density-dependence increases the likelihood of population decline by subjecting the population to the detrimental effects of stochastic processes (i.e., low densities where random bad years can cause local extinction, negating the possibility for random good years to offset bad years).

5. We highlight the importance of understanding, and accounting for both density dependence and climate variation when predicting the impact of climate change on keystone species, such as the Baltic blue mussel. 08-Oct-2020

In 2023, the natural growth rate of the population across China varied between 7.96 people per 1,000 inhabitants (per mille) in Tibet and -6.92 per mille in Heilongjiang province. The national total population growth rate turned negative in 2022 and ranged at -1.48 per mille in 2023. Regional disparities in population growth The natural growth rate is the difference between the birth rate and the death rate of a certain region. In China, natural population growth reached the highest values in the western regions of the country. These areas have a younger population and higher fertility rates. Although the natural growth rate does not include the direct effects of migration, migrants are often young people in their reproductive years, and their movement may therefore indirectly affect the birth rates of their home and host region. This is one of the reasons why Guangdong province, which received a lot of immigrants over the last decades, has a comparatively high population growth rate. At the same time, Jilin, Liaoning, and Heilongjiang province, all located in northeast China, suffer not only from low fertility, but also from emigration of young people searching for better jobs elsewhere. The impact of uneven population growth The current distribution of natural population growth rates across China is most likely to remain in the near future, while overall population decline is expected to accelerate. Regions with less favorable economic opportunities will lose their inhabitants faster. The western regions with their high fertility rates, however, have only small total populations, which limits their effect on China’s overall population size.

This map shows the change in particulate matter 2.5 (PM 2.5) air quality data for the US between 2010 and 2016 based on NASA SEDAC gridded data. The color indicates better or worse air quality, and the size of the symbol indicates population growth.This map shows particulate matter in the air sized 2.5 micrometers of smaller (PM 2.5). The data is aggregated from NASA Socioeconomic Data and Applications Center (SEDAC) gridded data into state, county, congressional district (116th) and 50 km hex bins. The unit of measurement is micrograms per cubic meter.The data is averaged for each year and over the the 19 years to provide an overall picture of air quality in the United States, including Puerto Rico. A space time cube was performed on a multidimensional mosaic version of the data in order to derive an emerging hot spot analysis. The county and state layers provide a population-weighted PM 2.5 value to emphasize which areas have a higher human impact. Each layer has been enriched with a set of 2019 US demographic attributes (excluding Puerto Rico) apportioned to the geography in order to map patterns alongside each other. Citations:van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2018. Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4ZK5DQS. Accessed 1 April 2020van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2016. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites. Environmental Science & Technology 50 (7): 3762-3772. https://doi.org/10.1021/acs.est.5b05833.Boundaries:50km hex bins generated using the Generate Tessellation toolStates and counties come from 2018 TIGER boundaries with coastlines clipped116th Congressional Districts come from this ArcGIS Living Atlas layerData processing notes:NASA's GeoTIFF files for 19 years (1998-2016) were first brought into ArcGIS Pro 2.5.0 and put into a multidimensional mosaic dataset.For each geography level, the following was performed: Zonal Statistics were run against the mosaic as a multidimensional layer.A Space Time Cube was created to compare the 19 years of PM 2.5 values and detect hot/cold spot patterns. To learn more about Space Time Cubes, visit this page.The Space Time Cube is processed for Emerging Hot Spots where we gain the trends and hot spot results.The Enrich tool was run to add 2019 Esri demographic and 2014-2018 ACS attributes to the geographies. Attributes such as population, poverty, minority population, and others were added to the layer.To create the population-weighted attributes on the state and county layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average PM 2.5 were multiplied.The hex bins were converted into centroids and summarized within the state and county boundaries.The summation of these values were then divided by the total population of each state/county.

Demographic correlations are pervasive in wildlife populations and can represent important secondary drivers of population growth. Empirical evidence suggests that correlations are in general positive for long-lived species, but little is known about the degree of variation among spatially segregated populations of the same species in relation to environmental conditions. We assessed the relative importance of two cross-season correlations in survival and productivity, for three Atlantic puffin (Fratercula arctica) populations with contrasting population trajectories and non-overlapping year-round distributions. The two correlations reflected either a relationship between adult survival prior to breeding on productivity or a relationship between productivity and adult survival in the subsequent year. Demographic rates and their correlations were estimated with an integrated population model, and their respective contributions to variation in population growth were calculated using a transient life table response experiment. For all three populations, demographic correlations were positive at both time lags, although their strength differed. Given the difference in year-round distributions of these populations, this variation in the strength of population-level demographic correlations points to environmental conditions as an important driver of demographic variation through life-history constraints. Consequently, the contributions of variances and correlations in demographic rates to population growth rates differed among puffin populations, which has implications for – particularly small – populations' viability under environmental change as positive correlations tend to reduce the stochastic population growth rate.

Rapid land use transformations and increased climatic uncertainties challenge potential sustainable development pathways for communities and wildlife in regions with strong economic reliance on natural resources. In response to the complex causes and consequences of land use change, participatory scenario development approaches have emerged as key tools for analyzing drivers of change to help chart the future of socio-ecological systems. We assess stakeholder perspectives of land use and land cover change (LULCC) and integrate co-produced scenarios of future land cover change with spatial modeling to evaluate how future LULCC in the wider Serengeti ecosystem might align or diverge with the United Nations’ Sustainable Development Goals and the African Union’s Agenda 2063. Across the wider Serengeti ecosystem, population growth, infrastructural development, agricultural economy, and political will in support of climate change management strategies were perceived to be the key drivers of future LULCC. Under eight scenarios, declines in forest area as a proportion of total land area ranged from 0.1% to 4% in 2030 and from 0.1% to 6% in 2063, with the preservation of forest cover linked to the level of protection provided. Futures with well-demarcated protected areas, sound land use plans, and stable governance were highly desired. In contrast, futures with severe climate change impacts and encroached and degazetted protected areas were considered undesirable. Insights gained from our study are important for guiding pathways toward achieving sustainability goals while recognizing societies’ relationship with nature. The results highlight the usefulness of multi-stakeholder engagement, perspective sharing, and consensus building toward shared socio-ecological goals.