This project includes the data used for the article ‘Demography: Fast and Slow’, accepted for publication in Population and Development Review. See the paper as well as the Appendix: Supplemental Materials for details.Here is a brief description of the files containing the estimates of PTR and MST used for the paper. The data (in .csv format) can be downloaded from figshare (doi:10.6084/m9.figshare.16751869)..· The file Data_labels.csv contains the country labels used in Figures 2 and 3 in the main paper. · The file Data_paper.csv contains birth, death, immigration and emigration rates, and the derived estimates of country-level PTR and MST used in the paper (five-year intervals between 1990-95 and 2015-20), using Abel-Cohen estimates based on the “Demographic Account Pseudo Bayesian Closed” method. · The file Data_robust.csv is equivalent to Data_paper.csv but it is based on Abel-Cohen “Demographic Account Minimisation Closed” estimates of migratory flows. · The files Data_Italy.csv and Data_Germany.csv contain respectively the data on birth, death, immigration and emigration rates, and the estimates of annual PTR and MST for Italy and Germany.

The slow-fast continuum is known to structure variation in life-history strategies across species. Within populations, it is also assumed to structure individual life histories, yet evidence of its existence remains unclear. We formally assessed the presence of a slow-fast continuum of life histories both within populations and across species using detailed individual-based data for 17 bird and mammal species with contrasting life histories. We estimated adult lifespan, age at first reproduction, breeding frequency and fecundity, and identified the main axes of variation using Principal Component Analyses. The slow-fast continuum was the main axis of life-history variation across species, but within populations individual variation did not follow the slow-fast continuum in any species. This suggests that individual life histories are neither slow nor fast, but rather follow an idiosyncratic pattern across species because of relative differences in the importance of processes such as sto...

This study examines how population change is associated with changes in sociodemographics and economic outcomes across diverse geographic contexts in the United States from 2000 to 2020. Using Census Tract-level data and generalized additive models (GAMs), we found that communities experiencing population growth showed significant improvements in socioeconomic indicators: for example, a 50% population increase in Northeast metropolitan non-coastal areas was associated with a $10,062 rise [95% confidence interval (CI) = $9,181, $10,944] in median household income. Conversely, areas with population decline faced increasing challenges to community composition: communities experiencing a 50% population decline in West coastal metropolitan areas saw their median age increase by 2.556 years (95% CI = 2.23, 2.89 years), indicating an accelerated aging population. We observed a positive relationship between population growth and local economic growth, with areas experiencing population decline or slow growth showing below-average economic growth. While population change alone explained 10.1% of the variance in county-level GDP growth, incorporating sociodemographic shifts alongside population change using a partial least squares regression (PLSR) more than doubled the explanatory power to 21.4%. Overall, we often found the strength of relationships and sometimes the direction varied by geographic context: coastal areas showed distinct patterns from inland regions, and metropolitan areas responded differently than rural ones. For instance, the percentage of owner-occupied housing was negatively associated with population growth in metropolitan areas, but positively associated in non-metropolitan areas. Our research provides valuable insights for policymakers and planners working to address community changes, particularly in the context of anticipated climate-induced migration. The results suggest that strategies for maintaining economic vitality need to consider not just population retention, but also demographic profiles and socioeconomic opportunities across different geographic contexts.

Author: K Swanson, educator, Minnesota Alliance for Geographic EducationGrade/Audience: high schoolResource type: lessonSubject topic(s): populationRegion: worldStandards: Minnesota Social Studies Standards

Standard 5. The characteristics, distribution and migration of human populations on the earth’s surface influence human systems (cultural, economic and political systems).Objectives: Students will be able to:

1. Identify the major demographic indicators that indicate a high or low population growth rate.
2. Compare/contrast the regions of the world using demographic indicators such as growth rate, natural increase, fertility rate, crude birth rates, and crude death rates
3. Identify the regions of the world with the highest and lowest birth rates.
4. Analyze the three means to control population growth: increase death rate, decrease birth rate and government laws.Summary: Students will analyze demographic data from the Population Reference Bureau and determine which areas of the world contain the fastest and slowest population growth rates. Students will determine that the fastest growth rates are in Northern and Eastern Africa and the slowest growth rates are found in Eastern Europe. Students will write an editorial on the best means to control population.

FishDataSee ReadMe fileDryad.zip

The earliest point where scientists can make reasonable estimates for the population of global regions is around 10,000 years before the Common Era (or 12,000 years ago). Estimates suggest that Asia has consistently been the most populated continent, and the least populated continent has generally been Oceania (although it was more heavily populated than areas such as North America in very early years). Population growth was very slow, but an increase can be observed between most of the given time periods. There were, however, dips in population due to pandemics, the most notable of these being the impact of plague in Eurasia in the 14th century, and the impact of European contact with the indigenous populations of the Americas after 1492, where it took almost four centuries for the population of Latin America to return to its pre-1500 level. The world's population first reached one billion people in 1803, which also coincided with a spike in population growth, due to the onset of the demographic transition. This wave of growth first spread across the most industrially developed countries in the 19th century, and the correlation between demographic development and industrial or economic maturity continued until today, with Africa being the final major region to begin its transition in the late-1900s.

Populations declining toward extinction can persist via genetic adaptation in a process called evolutionary rescue. Predicting evolutionary rescue has applications ranging from conservation biology to medicine, but requires understanding and integrating the multiple effects of a stressful environmental change on population processes. Here we derive a simple expression for how generation time, a key determinant of the rate of evolution, varies with population size during evolutionary rescue. Change in generation time is quantitatively predicted by comparing how intraspecific competition and the source of maladaptation each affect the rates of births and deaths in the population. Depending on the difference between two parameters quantifying these effects, the model predicts that populations may experience substantial changes in their rate of adaptation in both positive and negative directions, or adapt consistently despite severe stress. These predictions were then tested by comparison t..., , , # R code and simulation outputs

All results in this paper are based on simulation. This package includes the simulation code, outputs, and the scripts used in analyses and making figures.

Description of the data and file structure

The basic algorithm is contained in the file QG_full_generalized.R. However, several variants were used throughout the paper. Below is a list of the files used in preparing each figure.

All scripts the analyze simulation data require a path set to the location of a directory include the archived outputs, which are each identified by name (azure, blue, cerulean, no_genes, selection_test, organic).

Each of these simulation data directories contains a table file which primarily serves to associate the initial random number seed with each replicate. For each replicate, a file is generated with the allele effects (e.g., azure_effects_0001.txt) and another file is produced with the complete genotype of each individual in the starting population (e.g., azu...

1. Theoretical and empirical research has shown that increased variability in demographic rates often results in a decline in the population growth rate. In order to reduce the adverse effects of increased variability, life-history theory predicts that demographic rates that contribute disproportionately to population growth should be buffered against environmental variation. To date, evidence of demographic buffering is still equivocal and limited to analyses on a reduced number of age-classes (e.g. juveniles and adults), and on single sex models.
2. Here we used Bayesian inference models for age-specific survival and fecundity on a long-term dataset of wild mountain gorillas. We used these estimates to parameterize two-sex, age-specific stochastic population projection models that accounted for the yearly covariation between demographic rates. We estimated the sensitivity of the long-run stochastic population growth rate to reductions in survival and fecundity on ages belonging to nine sex-age-classes for survival and three age-classes for female fecundity.
3. We found a statistically significant negative linear relationship between the sensitivities and variances of demographic rates, with strong demographic buffering on young adult female survival and low buffering on older female and silverback survival and female fecundity. We found moderate buffering on all immature stages and on prime-age females.
4. Previous research on long-lived slow species has found high buffering of prime-age female survival and low buffering on immature survival and fecundity. Our results suggest that the moderate buffering of the immature stages can be partially due to the mountain gorilla social system and the relative stability of their environment.
5. Our results provide clear support for the demographic buffering hypothesis and its predicted effects on species at the slow end of the slow-fast life history continuum, but with the surprising outcome of moderate social buffering on the survival of immature stages. We also demonstrate how increasing the number of sex-age-classes can greatly improve the detection of demographic buffering in wild populations.

Until the 1800s, population growth was incredibly slow on a global level. The global population was estimated to have been around 188 million people in the year 1CE, and did not reach one billion until around 1803. However, since the 1800s, a phenomenon known as the demographic transition has seen population growth skyrocket, reaching eight billion people in 2023, and this is expected to peak at over 10 billion in the 2080s.

Decisions based on trends in population abundance and distribution may fail to protect populations of slow-breeding, long-lived megafauna from irrevocable decline if they ignore demographic constraints. For such taxa, we urge that effort be directed at understanding the interactions among vital rates governing population growth rates, rather than on predicting probabilities of extinction. The proximity of a population to demographic tipping points, i.e., where growth rate switches from positive to negative, can signal vulnerability to perturbation long before numbers drop below a point of no return. We define the “demographic safe space” as the combination of key vital rates that support a non-negative growth rate and illustrate this approach for Asian elephants. Through simulations, we find that even with optimal reproduction, Asian elephant populations cannot tolerate annual female mortality rates exceeding 7.5%. If adult mortality is very low (3%/year), populations can tolerate high annual mortality in calves below age 3 (up to 31.5%/year), or slow female reproduction (primiparity at 30 years or average inter-birth interval of up to 7.68 years). We then evaluate the potential impact of current threats, showing that near-optimal reproduction and high calf survival is necessary to offset even modestly increased mortality among adult female age classes. We suggest that rather than rely on simple counts or “viability” assessments, conservation planners for slow-breeding megafauna should consider demographic tipping points and strive to keep populations within their safe spaces.

1. Aquatic and terrestrial environments display stark differences in key environmental factors and phylogenetic composition but their consequences for the evolution of species’ life history strategies remain poorly understood.

2. Here, we examine whether and how life history strategies vary between terrestrial and aquatic species. We use demographic information for 685 terrestrial and 122 aquatic animal and plant species to estimate key life history traits. We then use phylogenetically corrected least squares regression to explore potential differences in trade-offs between life history traits between both environments. We contrast life history strategies of aquatic vs. terrestrial species in a principal component analysis while accounting for body dimensions and phylogenetic relationships.

3. Our results show that the same trade-offs structure terrestrial and aquatic life histories, resulting in two dominant axes of variation that describe species’ pace-of-life and reproductive s...

Climate change is projected to cause extensive plant range shifts, and in many cases such shifts already are underway. Most long-term studies of range shifts measure emergent changes in species distributions but not the underlying demographic patterns that shape them. To better understand species’ elevational range shifts and their underlying demographic processes, we use the powerful approach of rephotography, comparing historical (1978-82) and modern (2015-16) photographs taken along a 1000 m elevational gradient in theColorado Desert of Southern California. This approach allowed us to track demographic outcomes for 4263 individual plants of 11 long-lived, perennial species over the past ~36 years. All species showed an upward shift in mean elevation (average = 45 m), consistent with observed increasing temperature and severe drought in the region. We found that varying demographic processes underlaid these elevational shifts, with some species showing higher recruitment and some showing higher survival with increasing elevation. Species with faster life history rates (higher background recruitment and mortality rates) underwent larger elevational shifts. Our findings emphasize the importance of demography and life history in shaping range shift responses and future community composition, as well as the sensitivity of desert systems to climate change despite the typical ‘slow motion’ population dynamics of perennial desert plants. Methods We utilized photos originally taken by Dr. Wilbur Mayhew between 1977 and 1982 (Mayhew 1981), which we digitized from 35 mm slides stored at Philip L. Boyd Deep Canyon Desert Research Center (doi:10.21973/N3V66D). We relocated permanently marked sites where historical photos had been taken and rephotographed them using a Canon 5D Mark II camera and tripod in 2015 and 2016. We took one additional set of photos in April 2017 after the end of a multi-year drought, so that we could distinguish dormant from dead individuals of two drought-deciduous species (brittlebush, Encelia farinosa and white bursage, Ambrosia dumosa). We approximated the original view of the original photos as closely as possible in modern photos. For each photo view, we chose a single historical and modern photo for analysis based on resolution, contrast and coloration. The mean timespan between paired historical and modern photos was 36 years. We perfected the alignment between the paired historical and modern photos in Photoshop by making one photo semi-transparent, then rotating and resizing it while maintaining original aspect ratios. Data extraction We extracted data on 11 perennial species that appeared in 5+ sites. We extracted data from the photos in ArcGIS, arranging the paired photos as map layers. We created polygons to delimit a survey area close enough to the camera to identify species; these polygons serve as the “sites” in our subsequent analysis. In some cases, we collected data on larger-bodied or particularly conspicuous species, such as ocotillo (Fouquieria splendens) and creosote (Larrea tridentata), in a larger area including locations farther from the camera than for smaller, less conspicuous species. We recorded whether each plant underwent recruitment (absent historical, alive modern), mortality (alive historical, dead modern) or survival (alive both). We excluded plants that were dead in the historical period or with main stems outside the site polygon. In some cases we consulted other historical and modern photos of the same site to determine species identity or assess whether an individual was alive. We counted and measured clusters of agave (Agave deserti) and Mojave yucca (Yucca schidigera) as single individuals. Rarely, we may have misidentified pygmy cedar (Peucephyllum schottii) for creosote where these species co-occur on steep slopes, since they have similar morphology and are difficult to distinguish from a distance. We measured individual relative change in plant size by measuring the height (perpendicular to the ground) and width (the largest horizontal extent of the plant perpendicular to the camera, i.e. canopy width) of surviving plants in both time periods, using the ruler tool in ArcGIS and focusing on woody biomass. When dead agave rosettes were surrounded by live rosettes, we did not include the width that was dead if it was >20% the total width. We calculated the relative change in height of each plant as (H1–H0) / H0, where H indicates plant height and the subscripts 0 and 1 indicate the historical and modern period, respectively. We used an equivalent equation for relative change in width. For some species at some sites, we could not track the fate of individuals between the two time periods. This most often occurred for narrow-bodied and relatively short-lived species (e.g. teddy bear cholla, Cylindropuntia bigelovii) in photo pairs that were difficult to perfectly align, thereby making it difficult to tell whether plants either survived, or died and were replaced by recruits. It also occurred when a large plant died and a new plant “appeared” in a spot that was previously hidden, such that we were unable to determine whether the second plant was a recruit or a surviving plant. We therefore designated two site types for each species: “trackable” sites – those where we could track the fate of at least one third of individuals of a given species over time, and “count-only” sites – those where we could track fewer than one third of individuals, and instead only counted individuals. Count-only sites were retained for analyses of mean elevation shifts but not demographic rates. Geophysical data We used Google Earth Pro “ground level view” to draw polygons matching the extent of the site polygons outlined in the photos. To do so, we first “stood” at the camera’s locality and angle, then used corresponding features (e.g. washes, large creosote, hills) to find the exact site, and finally dropped pins to mark polygon vertices. We used these polygons to extract data on each site’s size, as well as its mean elevation, aspect, slope and annual solar radiation (“insolation”) using USGS NED Contiguous US 1/3 arc-second digital elevation model (2013) in ArcGIS. We took the cosine of aspect to create linear values ranging from -1 (South) to 1 (North). Additional details Additional details on how these data were collected and processed can be found in the Methods and Supplementary Materials of Skikne et al. 2024. Contrasting demographic processes underlie uphill shifts in a desert ecosystem.

Life history traits are used to predict asymptotic odds of extinction from dynamic conditions. Less is known about how life history traits interact with stochasticity and population structure of finite populations to predict near-term odds of extinction. Through empirically parameterized matrix population models, we study the impact of life history (reproduction, pace), stochasticity (environmental, demographic), and population history (existing, novel) on the transient population dynamics of finite populations of plant species. Among fast and slow pace and either uniform or increasing reproductive intensity or short or long reproductive lifespan, slow, semelparous species are at the greatest risk of extinction. Long reproductive lifespans buffer existing populations from extinction while the odds of extinction of novel populations decreases when reproductive effort is uniformly spread across the reproductive lifespan. Our study highlights the importance of population structure, pace, a..., We gathered empirically derived stage-based population models from the COMPADRE Plant Matrix Database v6.22.5.0 (created 2022-05-11; Salguero-Gomez et al. 2015) that (1) were ergodic and irreducible, (2) were modelled on an annual time step (Iles et al. 2016), and (3) did not explicitly parse clonal growth into a separate matrix. This subset resulted in 1,606 matrices representing multiple years and/or populations of 317 plant species., , # Data from: Pace and parity predict short-term persistence of small plant populations

Access these datasets on Dryad https://doi.org/10.5061/dryad.2547d7wzv

Empirically derived stage-based population models were collected from the COMPADRE Plant Matrix Database v6.22.5.0 (created 2022-05-11; Salguero-Gomez et al. 2015) that (1) were ergodic and irreducible, (2) were modelled on an annual time step, and (3) did not explicitly parse clonal growth into a separate matrix. This subset resulted in 1,606 matrices representing multiple years and/or populations of 317 plant species.

Life history traits were estimated from the matrix population models using the R package Rage (Jones et al. 2022).

Plant matrix population models were used to simulate asymptotic growth, demographic and environmental stochasticity and test the impact of initial population size, population structure, stochasticity, and life history on the odds of extinction. The impa...

Temporal autocorrelation in demographic processes is an important aspect of population dynamics, but a comprehensive examination of its effects on different life-history strategies is lacking. We use matrix populations models from 454 plant and animal populations to simulate stochastic population growth rates (log λs) under different temporal autocorrelations in demographic rates, using simulated and observed covariation among rates. We then test for differences in sensitivities, or changes, of log λs to changes in autocorrelation among two major axes of life-history strategies, obtained from phylogenetically-informed principal component analysis: the fast-slow and semelparous-iteroparous continua. Fast life histories exhibit highest sensitivities to simulated autocorrelation in demographic rates across reproductive strategies. Slow life histories are less sensitive to temporal autocorrelation, but their sensitivities increase among highly iteroparous species. We provide cross-taxonomic evidence that changes in the autocorrelation of environmental variation may affect a wide range of species, depending on complex interactions of life-history strategies.

• 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.

Human-induced environmental changes have a direct impact on species populations, with some species experiencing declines while others display population growth. Understanding why and how species populations respond differently to environmental changes is fundamental to mitigate and predict future biodiversity changes. Theoretically, species life-history strategies are key determinants shaping the response of populations to environmental impacts. Despite this, the association between species' life-histories and the response of populations to environmental changes has not been tested. In this study, we analysed the effects of recent land-cover and temperature changes on rates of population change of 1,072 populations recorded in the Living Planet Database. We selected populations with at least 5 yearly consecutive records (after imputation of missing population estimates) between 1992 and 2016, and for which we achieved high population imputation accuracy (in the cases where missing values had to be imputed). These populations were distributed across 553 different locations and included 461 terrestrial amniote vertebrate species (273 birds, 137 mammals, and 51 reptiles) with different life-history strategies. We showed that populations of fast-lived species inhabiting areas that have experienced recent expansion of cropland or bare soil present positive population trends on average, whereas slow-lived species display negative population trends. Although these findings support previous hypotheses that fast-lived species are better adapted to recover their populations after an environmental perturbation, the sensitivity analysis revealed that model outcomes are strongly influenced by the addition or exclusion of populations with extreme rates of change. Therefore, the results should be interpreted with caution. With climate and land-use changes likely to increase in the future, establishing clear links between species characteristics and responses to these threats is fundamental for designing and conducting conservation actions. The results of this study can aid in evaluating population sensitivity, assessing the likely conservation status of species with poor data coverage, and predicting future scenarios of biodiversity change.

There are more women than men in Germany, although the number of men has been slowly increasing in recent years, especially since 2015. In 2024, there were around 41.2 million males and 42.3 million females in Germany. Births and deaths Globally, the death rate had been slowly decreasing until 2019, but there was a sharp spike in 2020 and 2021, which can be attributed to the COVID-19 pandemic. The general decline, however, is probably due to medical advancements which mean that many diseases are now treatable or curable, that were not 50 years ago. The birth rate has also been decreasing across the world, but it is lowest in Europe and North America. Future challenges There are a number of challenges facing the German population in the future. Some of the most pressing ones are the growing urban population and especially its ageing structure in combination with slow birth rates, which will put increased pressure on the pension system. Because of this trend, old age security and pensions are already today in the top ten most pressing political issues in Germany.

Sexual selection may act as a promotor of speciation since divergent mate choice and competition for mates can rapidly lead to reproductive isolation. Alternatively, sexual selection may also retard speciation since polygamous individuals can access additional mates by increased breeding dispersal. High breeding dispersal should hence increase gene flow and reduce diversification in polygamous species. Here we test how polygamy predicts diversification in shorebirds using genetic differentiation and subspecies richness as proxies for population divergence. Examining microsatellite data from 79 populations in ten plover species (Genus: Charadrius) we found that polygamous species display significantly less genetic structure and weaker isolation-by-distance effects than monogamous species. Consistent with this result, a comparative analysis including 136 shorebird species showed significantly fewer subspecies for polygamous than for monogamous species. By contrast, migratory behaviour neither predicted genetic differentiation nor subspecies richness. Taken together, our results suggest that dispersal associated with polygamy may facilitate gene flow and limit population divergence. Therefore, intense sexual selection, as occurs in polygamous species, may act as a brake rather than an engine of speciation in shorebirds. We discuss alternative explanations for these results and call for further studies to understand the relationships between sexual selection, dispersal and diversification.

Greater sage-grouse (Centrocercus urophasianus) are at the center of state and national land use policies largely because of their unique life-history traits as an ecological indicator for health of sagebrush ecosystems. These data represent an updated population trend analysis and Targeted Annual Warning System (TAWS) for state and federal land and wildlife managers to use best available science to help guide current management and conservation plans aimed at benefitting sage-grouse populations range-wide. This analysis relied on previously published population trend modeling methodology from Coates and others (2021, 2022) and includes population lek count data from 1960-2023. Bayesian state-space models estimated 2.8 percent average annual decline in sage-grouse populations across their geographical range, which varied among subpopulations at the largest scale of analysis, termed climate clusters (2.1-3.1). Cumulative declines were 41.1, 64.5, and 78.4 percent range-wide during Period 5 (19 years), Period 3 (35 years), and Period 1 (55 years), respectively. Mean extirpation probabilities calculated across all neighborhood clusters at approximately 18, 37, and 55 years in the future were 0.15 (SD of 0.25), 0.22 (SD of 0.27), and 0.26 (SD of 0.29), respectively. We also present updated results to the TAWS which models rates of change in abundance from spatially structured populations and identifies when local declines fall out of synchrony with trends at larger spatial scales. The TAWS framework provides signals that alert managers to the categorical significance of observed declines while avoiding signals where declines result from drivers operating at larger spatial scales (for example, periodic reductions in primary productivity owing to drought). Definitions: Watch: Assigned to populations that exhibit evidence of population decline below those of their respective climate cluster (slow signal) over 2 consecutive years. Warning: Assigned to populations that experienced slow signals in 3 out of 4 consecutive years OR a relatively strong magnitude (fast signal) of evidence for 2 out of 3 years. Watches may identify the need for intensive monitoring whereas warnings may identify the need for management intervention aimed at stabilizing populations. References: Coates, P.S., Prochazka, B.G., O’Donnell, M.S., Aldridge, C.L., Edmunds, D.R., Monroe, A.P., Ricca, M.A., Wann, G.T., Hanser, S.E., Wiechman, L.A., and Chenaille, M.P., 2021, Range-wide greater sage-grouse hierarchical monitoring framework-Implications for defining population boundaries, trend estimation, and a targeted annual warning system: U.S. Geological Survey Open-File Report 2020-1154, 243 p., https://doi.org/10.3133/ofr20201154. Coates, P.S., Prochazka, B.G., Aldridge, C.L., O’Donnell, M.S., Edmunds, D.R., Monroe, A.P., Hanser, S.E., Wiechman, L.A., and Chenaille, M.P., 2022, Range-wide population trend analysis for greater sage-grouse (Centrocercus urophasianus)-Updated 1960-2021: U.S. Geological Survey Data Report 1165, 16 p., https://doi.org/10.3133/dr1165

In 1800, the population of Japan was just over 30 million, a figure which would grow by just two million in the first half of the 19th century. However, with the fall of the Tokugawa shogunate and the restoration of the emperor in the Meiji Restoration of 1868, Japan would begin transforming from an isolated feudal island, to a modernized empire built on Western models. The Meiji period would see a rapid rise in the population of Japan, as industrialization and advancements in healthcare lead to a significant reduction in child mortality rates, while the creation overseas colonies would lead to a strong economic boom. However, this growth would slow beginning in 1937, as Japan entered a prolonged war with the Republic of China, which later grew into a major theater of the Second World War. The war was eventually brought to Japan's home front, with the escalation of Allied air raids on Japanese urban centers from 1944 onwards (Tokyo was the most-bombed city of the Second World War). By the war's end in 1945 and the subsequent occupation of the island by the Allied military, Japan had suffered over two and a half million military fatalities, and over one million civilian deaths.

The population figures of Japan were quick to recover, as the post-war “economic miracle” would see an unprecedented expansion of the Japanese economy, and would lead to the country becoming one of the first fully industrialized nations in East Asia. As living standards rose, the population of Japan would increase from 77 million in 1945, to over 127 million by the end of the century. However, growth would begin to slow in the late 1980s, as birth rates and migration rates fell, and Japan eventually grew to have one of the oldest populations in the world. The population would peak in 2008 at just over 128 million, but has consistently fallen each year since then, as the fertility rate of the country remains below replacement level (despite government initiatives to counter this) and the country's immigrant population remains relatively stable. The population of Japan is expected to continue its decline in the coming years, and in 2020, it is estimated that approximately 126 million people inhabit the island country.