The annual population growth in Japan decreased by 0.1 percentage points compared to the previous year. In 2023, the population growth thereby reached its lowest value in recent years. Population growth refers to the annual change in population, and is based on the balance between birth and death rates, as well as migration.Find more key insights for the annual population growth in countries like South Korea and Hong Kong.

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.

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.

ABSTRACTA freely-growing age-structured population was modeled for growth and control by sterile male releases. Equilibrium populations yield critical sterile male release rates that would hold the population at equilibrium. It is shown here that these rates may be different from the release rates required to stop a growing population and bring it to an equilibrium. A computer simulation was constructed of this population and a parameter sensitivity analysis graphed the effects on the required sterile male release rate of fertility, mating delay in adult females, net juvenile survivorship, three adult survivorship curves, the time spent in the juvenile stages, and total life span. The adult survivorship curves had the greatest effect on the required sterile release rate for population elimination. The required release rate was also determined for Ceratitis capitata (Wiedemann) using survivorship and fertility data from a laboratory strain. The concepts of over-flooding ratio and release ratio were discussed and quantified for the cases above.

Graph and download economic data for Infra-Annual Labor Statistics: Working-Age Population Total: From 15 to 64 Years for United States (LFWA64TTUSM647S) from Jan 1977 to Feb 2025 about working-age, 15 to 64 years, population, and USA.

Chart and table of population level and growth rate for the Las Vegas metro area from 1950 to 2025. United Nations population projections are also included through the year 2035.

About this dataset

Context

The dataset tabulates the Belle Plaine 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 Belle Plaine 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 Belle Plaine was 1,452, a 0.48% decrease year-by-year from 2022. Previously, in 2022, Belle Plaine population was 1,459, a decline of 0.34% compared to a population of 1,464 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Belle Plaine decreased by 255. In this period, the peak population was 1,707 in the year 2000. 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 Belle Plaine is shown in this column.
• Year on Year Change: This column displays the change in Belle Plaine 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 Belle Plaine Population by Year. You can refer the same here

This dataset contains data from an analysis of differences in growth rate among three different barnacle populations breeding at different latitudes, described in the paper: Boom, Michiel P., van der Jeugd, H.P., Steffani, B., Nolet, B.A., Larsson, K., & Eichhorn, G. (2021), Postnatal growth rate varies with latitude in range-expanding geese – the role of plasticity and day length. Journal of Animal Ecology.

The postnatal growth period is a crucial life stage, with potential lifelong effects on an animal’s fitness. How fast animals grow depends on their life history strategy and rearing environment, and interspecific comparisons generally show higher growth rates at higher latitudes. However, to elucidate the mechanisms behind this gradient in growth rate, intraspecific comparisons are needed.

Recently, barnacle geese expanded their Arctic breeding range from the Russian Barents Sea coast southwards, and now also breed along the Baltic and North Sea coasts. Baltic breeders shortened their migration, while barnacle geese breeding along the North Sea stopped migrating entirely.

We collected cross-sectional data on gosling tarsus length, head length and body mass, and constructed population-specific growth curves to compare growth rates among three populations (Barents Sea, Baltic Sea and North Sea) spanning 17° in latitude.

Growth rate was faster at higher latitudes, and the gradient resembled the latitudinal gradient previously observed in an interspecific comparison of precocial species. Differences in day length among the three breeding regions could largely explain the observed differences in growth rate. In the Baltic, and especially in the Arctic population, growth rate was slower later in the season, most likely because of the stronger seasonal decline in food quality.

Our results suggest that differences in postnatal growth rate between the Arctic and temperate populations are mainly a plastic response to local environmental conditions. This plasticity can increase the individuals’ ability to cope with annual variation in local conditions, but can also increase the potential to re-distribute and adapt to new breeding environments.

Methods We collected biometric data on growing goslings during long-term studies in colonies from three study-populations: 1) A long-distance migratory population breeding in the Arctic in Kolokolkova Bay along the Barents Sea coast (68°35’N, 52°20’E), data collected in 6 years between 2003 and 2015; 2) A short-distance migratory population breeding on Gotland in the Baltic Sea (57°25’N, 18°53’E) data collected in 15 years between 1986 and 2000; 3) A sedentary population breeding in the Netherlands along the North Sea (51°40’N, 4°14’E) data collected in 5 years between 2004 and 2018 (Larsson et al., 1988; Van der Jeugd et al., 2003, 2009; Eichhorn et al., 2010).

Our analysis is based on all measured goslings with known age (Sample sizes: Barents Sea = 392; Baltic Sea = 933; North Sea = 116). Sex was determined based on cloacal inspection. Goslings were weighed in a bag using a Pesola spring scale with an accuracy of ± 5 g (if <600 g) or a digital hand scale or Pesola spring scale with an accuracy of ± 10 g (if >600 g). A calliper (± 0.1 mm) was used to measure the outer length of the bent tarsus. Head length was measured using a ruler (± 1 mm).

The number of daylight hours that had accumulated between hatching and capture was calculated for each gosling. Daylight was determined as the period between dawn and dusk, and was calculated based on the coordinates of the three breeding colonies using the R package “Suncalc” (see associated manuscript referenced above).

We calculated relative hatch dates by centralizing hatch dates within each cohort, because years can differ in onset of spring and consequently in timing of breeding and hatching. For the calculation of the relative hatch date for each gosling, we used the mean hatch date of the colonies (not only of the recaptured goslings), as established from nest monitoring (see associated manuscript referenced above for details).

1. Pre-dispersal seed predation is sometimes considered unlikely to dramatically affect plant population growth because plants are generally expected to produce more seeds than there are safe sites for germination. Lupinus constancei is a rare herb of limited distribution, with fewer than 400 reproductive individuals restricted to a single square kilometre of north-western California, USA. In addition to the vulnerability resulting from its extremely small population size, L. constancei faces heavy seed predation by small mammals.
2. As a stop-gap measure to prevent population decline, managers began covering a large number of the reproductive plants with herbivory exclosures in 2003, but the population-level effects of seed predation and the effectiveness of this caging treatment were unknown. We used ten years of demographic data to compare the population dynamics of plants inside herbivory exclosures with those sustaining ambient rodent seed predation.
3. We found that the stochastic population growth rate would be robust without seed predation (λs = 1.17), but without continued human intervention (i.e. use of exclosures), the current rate of predation would result in a decline towards extinction (λs = 0.92).
4. After our study concluded, high mortality due to two extreme winter droughts followed by a wildland fire reduced the number of reproductive plants to ~103, making extinction of L. constancei more likely.
5. Synthesis and applications. The prevalence of consumer-driven population decline is largely unknown, but this study demonstrates that pre-dispersal seed predation by rodents can have powerful population-level effects, and represents one set of conditions under which consumer pressure has the potential to drive plant extinction. However, with continued management to limit the effects of seed predation in the short-term and investigation into the ultimate drivers of this high seed predation rate in the long-term, the Lassics lupine population could be restored to a robust rate of growth.

1. Mechanisms for maintaining biodiversity are still unclear despite considerable research. The classic theory predicts that stable co‐occurrence of competitive species requires niche differentiation. In fact, the co‐occurring species are often differentiated from each other. However, the neutral theory assuming equivalence of the reproductive rate of all individuals regardless of the species in a biological community has successfully recreated the observed patterns of species abundance distribution. This success is based on the unrealistic assumption suggesting that some mechanisms eliminate interspecific differences in the reproductive rates.
2. Here, I present sexual selection as a candidate of the mechanisms by constructing analytical and simulation models. Sexual selection affects the traits that increase mating success even at the expense of fecundity when the species is abundant. By contrast, when the species is at a relatively low density, this negative effect on fecundity is mitigated because less competition for mating occurs in the rare species.
3. The analytical model of this effect on fecundity predicted that sexual organisms stop population growth before exhausting resources due to the effect. This prediction was confirmed by simulation models. The simulations also showed that hundreds of competitive species with interspecific differences in reproductive potential can coexist over 10,000 generations. Moreover, species abundance distributions obtained from the simulations were similar to the patterns observed in field data. Given the generality of sexual reproduction in nature, sexual selection is likely to play a significant role in sustaining biodiversity over a broad range of environments.
4. Synthesis. Evolution does not always maximize population growth rate. This study shows that evolution of sexual selection controls the population growth rate according to density and stabilizes the population size. This stabilizing effect has a potential to rescue endangered species from extinction, prevent overgrowth of common species, promote coexistence of competitive species, and successfully recreate the observed patterns of species abundance distribution.

According to the forecast, Africa's total population would reach nearly 2.5 billion by 2050. In 2023, the continent had around 1.36 billion inhabitants, with Nigeria, Ethiopia, and Egypt as the most populous countries. In the coming years, Africa will experience significant population growth and will close the gap significantly with the Asian population by 2100. Rapid population growth The population of Africa has been increasing annually in recent years, growing from around 818 million to over 1.39 billion between 2000 and 2021, respectively. In the same period, the annual growth rate of the population has been constantly set at roughly 2.5 percent, with a peak of 2.62 percent in 2014. The reasons behind this rapid growth are various. One factor is the high fertility rate registered in African countries. In 2021, a woman in Niger had an average of over 6.8 children in her reproductive years, the highest rate on the continent. High fertility resulted in a large young population and partly compensated for the high mortality rate in Africa, leading to fast-paced population growth. High poverty levels Africa’s population is concerned with widespread poverty. In 2024, over 429 million people on the continent are extremely poor and live with less than 2.15 U.S. dollars per day. Globally, Africa is the continent hosting the highest poverty rate. In 2024, the countries of Nigeria and the Democratic Republic of the Congo account for around 21 percent of the world's population living in extreme poverty. Nevertheless, poverty in Africa is forecast to decrease in the coming years.

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

<iframe title="USA counties (2018) choropleth map Mapping COVID-19 cases by county" aria-describedby="" id="datawrapper-chart-nRyaf" src="https://datawrapper.dwcdn.net/nRyaf/10/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important;" height="400"></iframe><script type="text/javascript">(function() {'use strict';window.addEventListener('message', function(event) {if (typeof event.data['datawrapper-height'] !== 'undefined') {for (var chartId in event.data['datawrapper-height']) {var iframe = document.getElementById('datawrapper-chart-' + chartId) || document.querySelector("iframe[src*='" + chartId + "']");if (!iframe) {continue;}iframe.style.height = event.data['datawrapper-height'][chartId] + 'px';}}});})();</script>

Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

According to Cognitive Market Research, the global Oxygen Cylinder market size is USD 3258.6 million in 2024. It will expand at a compound annual growth rate (CAGR) of 6.0% from 2024 to 2031. North America held the major market share for more than 40% of the global revenue with a market size of USD 1303.44 million in 2024 and will grow at a compound annual growth rate (CAGR) of 4.2% from 2024 to 2031. Europe accounted for a market share of over 30% of the global revenue with a market size of USD 977.58 million. Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 749.48 million in 2024 and will grow at a compound annual growth rate (CAGR) of 8.0% from 2024 to 2031. Latin America had a market share for more than 5% of the global revenue with a market size of USD 162.93 million in 2024 and will grow at a compound annual growth rate (CAGR) of 5.4% from 2024 to 2031. Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 65.17 million in 2024 and will grow at a compound annual growth rate (CAGR) of 5.7% from 2024 to 2031. The Composite Oxygen Cylinders held the highest Oxygen Cylinder market revenue share in 2024. Market Dynamics of Oxygen Cylinder Market Key Drivers for Oxygen Cylinder Market Rising prevalence of chronic respiratory diseases to Increase the Demand Globally The global occurrence of continual respiratory illnesses like Chronic Obstructive Pulmonary Disease (COPD), allergies, and other related ailments is on the rise. This escalation in patient numbers requiring oxygen remedies is considerably boosting the oxygen cylinder marketplace. As the demand for respiratory assistance grows, in particular in the wake of environmental pollution, way of life changes, and getting old populations, the need for portable and dependable oxygen cylinders becomes paramount. This fashion underscores the essential function of the oxygen cylinder marketplace in meeting the escalating healthcare wishes of individuals grappling with global respiratory situations. Aging population to Propel Market Growth With the global population aging, there is a predicted surge in people grappling with age-related respiratory problems. This demographic shift is poised to elevate the call for oxygen remedy and oxygen cylinders. As human beings age, they grow to be more prone to situations like continual obstructive pulmonary disorder (COPD), pneumonia, and other respiratory ailments necessitating respiratory assistance. Consequently, the want for oxygen remedy is expected to intensify, propelling the growth of the oxygen cylinder market. Meeting the respiratory wishes of the growing old populace becomes an increasing number of vital, highlighting the pivotal function of oxygen cylinders in offering vital respiratory assistance to elderly individuals globally. Restraint Factor for the Oxygen Cylinder Market Competition from oxygen concentrators to Limit the Sales The landscape of respiratory assistance is evolving with the advancement of the oxygen concentrator era, posing an aggressive assignment to conventional oxygen cylinders. Oxygen concentrators provide a non-stop oxygen delivery extracted from ambient air, offering a price-powerful alternative over the years. This technological shift is reshaping the marketplace dynamics as healthcare vendors and sufferers more and more opt for the ease and economic benefits of concentrators. While oxygen cylinders stay fundamental in certain situations, along with portability or at some point of electricity outages, the rising recognition of concentrators is altering client possibilities. Thus, the oxygen cylinder market faces intensified competition from those revolutionary devices, prompting stakeholders to evolve strategies to navigate the evolving panorama of respiratory care. Impact of Covid-19 on the Oxygen Cylinder Market The COVID-19 pandemic has appreciably impacted the oxygen cylinder market internationally. The surge in instances strained healthcare structures, main to a vast boom within the call for clinical oxygen to deal with severely ill patients with respiratory misery. This unheard-of call created supply chain demanding situations, inflicting shortages of oxygen cylinders in lots of regions. Manufacturers ramped up manufacturing to satisfy the escalating needs, while governments carried out measures to ensure equitable distribution. Additionally, the pandemic extended the adoption o...

This measure tracks progress toward the recovery of endangered, threatened, or depleted protected species under NMFS’ jurisdiction. The species included in this measure are listed as threatened or endangered under the Endangered Species Act (ESA) or as depleted under the Marine Mammal Protection Act (MMPA). Decreases may occur when species are de-listed or when separate stocks of a listed species are merged. Recovery of threatened, endangered, or depleted species can take decades. It may not be possible to recover or de-list a species in the near term, but progress can be made to stabilize or increase the species population. For some species, this means trying to stop steep population declines, while for others it means trying to increase their numbers.

Over the last decade, Japan’s population has aged more and more, to the point where more than a quarter of Japanese were 65 years and older in 2022. Population growth has stopped and even reversed, since it’s been in the red for several years now.

It’s getting old

With almost 30 percent of its population being elderly inhabitants, Japan is considered the “oldest” country in the world today. Japan boasts a high life expectancy, in fact, the Japanese tend to live longer than the average human worldwide. The increase of the aging population is accompanied by a decrease of the total population caused by a sinking birth rate. Japan’s fertility rate has been below the replacement rate for many decades now, mostly due to economic uncertainty and thus a decreasing number of marriages.

Are the Japanese invincible?

There is no real mystery surrounding the ripe old age of so many Japanese. Their high average age is very likely due to high healthcare standards, nutrition, and an overall high standard of living – all of which could be adopted by other industrial nations as well. But with high age comes less capacity, and Japan’s future enemy might not be an early death, but rather a struggling social network.

This data package is comprised of three datasets all pertaining to two dominant palmetto species, Serenoa repens and Sabal etonia, at Archbold Biological Station in south-central Florida. The first dataset, palmetto_data, contains survival and growth data across multiple years, habitats and experimental treatments. The second dataset, seedlings_data, follows the fate of marked putative palmetto seedlings in the field to assess survivorship and growth. The final dataset, harvested_palmetto_data, contains size data and estimated dry mass (biomass in grams) of 33 destructively harvested palmetto plants (17 S. repens and 16 S. etonia) of varying sizes and across habitats. Thirty-two of these were used to calculate estimated biomass, using regression equations, for palmettos sampled in the palmetto_data. Below we summarize experimental setup and data collected for each dataset. Palmetto data Demographic data were collected as three separate components. The first component compared growth among habitats. Starting in 1981, equal numbers of both palmetto species were marked across scrubby flatwoods (oak scrub) and flatwoods habitats (3 sites per habitat) for a total of 240 marked plants. These habitats had not burned within the last decade, but historically had experienced a natural fire return interval of 5 - 20 years prior to this studies initiation. The second component added an additional 400 palmettos (200 of each species), which were marked in sand pine scrub (n = 200) in 1985 and sandhill habitat (n = 200) in 1989 on Archbold's Red Hill. At the time of this project's initiation, all Red Hill management units were last burned in 1927 and were considered long unburned. Part of Archbold's management plan included restoring fire into some management units while leaving others long unburned to serve as reference units. Therefore, for our second component, we were able to create a 2x2 factorial design using habitat types on Red Hill and fire management as factors, with 100 palmettos in each category (50 of each species). The third component involved an experiment to examine the factorial effects of clipping and fertilizing on palmetto flowering. We marked 300 palmettos (150 of each species), all in sand pine scrub habitat on Red Hill, and used the 100 palmettos marked in 1985 as controls. Annual data measures included height, canopy length and width (all in cm), number of new and green leaves and flowering scapes. Data were collected continuously (not for all variables or sites) from 1981 through 1997 then again in 2001 and 2017. Data collection is ongoing at 5-year intervals. Data on the 100 plants in the experimental sandhill on Red Hill were not collected in 2017 due to the removal of marked stakes from roller chopping of the site as part of more recent sandhill restoration efforts. A subset of the plants in the clipping and fertilizing experiment were lost in 2013 when a plow line was established to stop the spread of a wildfire. The locations of all remaining plants were taken in 2017 using a Trimble GPS unit and are included as a separate data file (palmetto_location_data) and shapefile (palmetto_shape). Seedling data In January 1989, we marked 100 putative seedlings in flatwoods habitats and 87 in scrubby flatwoods habitats. Putative seedlings typically cannot be identified using morphology as either S. repens or S. etonia so sample sizes of each are unknown. Annual data recorded included survival, standing height (cm) and maximum crown diameter (cm). In 1991, we started measuring basal stem diameter (cm) with calipers. During annual visits, we noted if the species could be identified as S. repens or S. etonia. Data were collected continuously starting in 1989 through 1997, then again in 2001 and 2008. Data collection is not ongoing for this dataset. Harvested Palmetto data Thirty-three palmettos, 17 S. repens and 16 S. etonia, were destructively harvested at three different sites, from two habitats (scrubby flatwoods and sand pine scrub) in 1985. Basic size measures as taken for palmetto demography data were recorded including height, canopy length and width (all in cm) and the number of green leaves. Additional data measures were recorded on the largest leaf blade including maximum length and width of the palmetto leaf and petiole length and width. Finally, basal diameter at the ground level was recorded. Only 32 palmettos were used to develop biomass regressions (17 S. repens and 15 S. etonia). Biomass is the estimated dry mass (g) of each harvested palmetto. Fresh palmettos were divided into leaf and stem (both above- and below-ground), but roots were not harvested since they grow to depths of several meters, making recovery of all root tissues virtually impossible for fresh-mass determination. Subsamples of fresh mass were oven dried at 80C to constant mass for estimation of dry mass equivalent, which in turn was used to estimate the dry mass of the harvested palmettos.

In the Southern Hemisphere, humpback whales (Megaptera novaeangliae) migrate along the extended continental coastlines of Australia, South America, and South Africa. This study reports on photo-identification capture–recapture data from a long-term survey conducted in Hervey Bay, Queensland, where a substantial proportion of the population stop over early in the southern migration. Photo-identification data were collected over 10 weeks per year from 1997 to 2009. The migration through Hervey Bay is dominated and led by females with high fidelity to the site. Mature females, yearlings, and immature whales use the Bay during August, while mature lactating females with calves dominate during September and October. Complex social behaviours occur throughout the season and differ between the early and late cohorts. We argue that the composition of the two cohorts and their distinctively different behaviours indicate that Hervey Bay is not simply a resting site but an area of aggregation that serves important social and biological benefits. A multistate open robust design model was fitted to capture–recapture data to estimate the annual number of whales visiting the Bay, the permanent emigration rate, proportions of the visiting population that do not enter the Bay each year, the number present during each week, and their residency times. The number of annual visitors to the Bay increased approximately linearly from 857 in 1997 to 2175 at the end of sampling in 2009 with two-thirds migrating through during the first half of each season. The population rate of growth may have been slowing by 2009, but there was considerable uncertainty in the trajectory and little basis for projection into the future. While it is desirable to know the current status of the Hervey Bay population and what has occurred since 2009, the cost and effort required make further manual collection and matching of images unlikely. The development of AI algorithmic matching software may enable further research in future.

This statistic shows the total population of men and women in Slovakia from 1950 until 2020. The modern-day country of Slovakia was part of the larger country of Czechoslovakia until 1993, and these statistics relate only to the population within the modern boundaries of Slovakia. The population of Slovakia has grew rather steadily from 1950 to 1995, and both the number of females and males increased at a similar level, although the number of women outnumber men by 50 to 100 thousand between 1950 and 1985. From this point onwards both populations begin to stop growing, the gap between men and women widens to 160 thousand people, and both populations plateau in the 2000s, before growing slightly again in the 2010s.

Na = effective size of the ancestral population, Ne pop1 = effective size of the first population, Nepop2 = effective size of the second population, m12 = migration rate from 2 into 1, m21 being the reverse, me12 = effective migration rate in barriers regions, me21 being the same in the reverse, Tsplit = Split time, Tam = time of migration stop, P = proportion of the genome being neutrally exchanged, Q = proportion the genome undergoing linked selection, O = proportion of the genome correctly oriented, hrf = Hill-Roberston factor, indicating the extent of reduction in Ne in region affected by linked selection, b1, b2 = extend of population growth in current population1 and 2 respectively. (XLS)

In 1800, it is estimated that approximately 9.4 million people lived in the region of modern-day South Korea (and 13.8 million on the entire peninsula). The population of this region would remain fairly constant through much of the 19th century, but would begin to grow gradually starting in the mid-1800s, as the fall of the Joseon dynasty and pressure from the U.S. and Japan would end centuries of Korean isolationism. Following the opening of the country to foreign trade, the Korean peninsula would begin to modernize, and by the start of the 20th century, it would have a population of just over ten million. The Korean peninsula was then annexed by Japan in 1910, whose regime implemented industrialization and modernization policies that saw the population of South Korea rising from just under ten million in 1900, to over fifteen million by the start of the Second World War in 1939.

The Korean War Like most regions, the end of the Second World War coincided with a baby boom, that helped see South Korea's population grow by almost two million between 1945 and 1950. However, this boom would stop suddenly in the early 1950s, due to disruption caused by the Korean War. After WWII, the peninsula was split along the 38th parallel, with governments on both sides claiming to be the legitimate rulers of all Korea. Five years of tensions then culminated in North Korea's invasion of the South in June 1950, in the first major conflict of the Cold War. In September, the UN-backed South then repelled the Soviet- and Chinese-backed Northern army, and the frontlines would then fluctuate on either side of the 38th parallel throughout the next three years. The war came to an end in July, 1953, and had an estimated death toll of three million fatalities. The majority of fatalities were civilians on both sides, although the North suffered a disproportionate amount due to extensive bombing campaigns of the U.S. Unlike North Korea, the South's total population did not fall during the war.

Post-war South Korea Between the war's end and the late 1980s, the South's total population more than doubled. In these decades, South Korea was generally viewed as a nominal democracy under authoritarian and military leadership; it was not until 1988 when South Korea transitioned into a stable democracy, and grew its international presence. Much of South Korea's rapid socio-economic growth in the late 20th century was based on the West German model, and was greatly assisted by Japanese and U.S. investment. Today, South Korea is considered one of the world's wealthiest and most developed nations, ranking highly in terms of GDP, human development and life expectancy; it is home to some of the most valuable brands in the world, such as Samsung and Hyundai; and has a growing international cultural presence in music and cinema. In the past decades, South Korea's population growth has somewhat slowed, however it remains one of the most densely populated countries in the world, with total population of more than 51 million people.