Between 1970 and 1989, the Soviet Union's population experienced a rate of natural increase that was consistently higher (sometimes by a significant margin) than that of the United States. In 1970, these increases were fairly similar at 9.2 and 8.8 per 1,000 population respectively, however the margin was considerably larger by the middle of the decade.

Although the Soviet Union's birth and death rates were both higher than those of the U.S. in most of these years, the larger disparity in birth rates is the reason for the USSR's higher rate of natural increase. However, while the USSR had a higher rate of natural increase, this did not mean that the Soviet population grew faster than that of the United States; the U.S. had a much higher net migration rate, which brought population growth rates much closer in the 1970s and 1980s.

Russia Rosstat Forecast: Mean: Natural Increase data was reported at -541,194.000 NA in 2035. This records a decrease from the previous number of -540,267.000 NA for 2034. Russia Rosstat Forecast: Mean: Natural Increase data is updated yearly, averaging -409,061.000 NA from Dec 2017 (Median) to 2035, with 19 observations. The data reached an all-time high of -155,731.000 NA in 2017 and a record low of -541,194.000 NA in 2035. Russia Rosstat Forecast: Mean: Natural Increase data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Global Database’s Russian Federation – Table RU.GD012: Vital Statistics: Forecast: Rosstat: Annual.

In Czechia, the highest natural population increase in the observed period was recorded in 2010 at 10,300. As of 2024, the natural population change amounted to a negative of 27,900. Meaning that the number of live births was lower than the number of deaths.Natural population change is the difference between the number of live births and deaths during a given period.

Russia Rosstat Forecast: Mean: per 1000 Population: Natural Increase data was reported at -3.800 NA in 2035. This records a decrease from the previous number of -3.700 NA for 2034. Russia Rosstat Forecast: Mean: per 1000 Population: Natural Increase data is updated yearly, averaging -2.800 NA from Dec 2017 (Median) to 2035, with 19 observations. The data reached an all-time high of -1.000 NA in 2017 and a record low of -3.800 NA in 2035. Russia Rosstat Forecast: Mean: per 1000 Population: Natural Increase data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Global Database’s Russian Federation – Table RU.GD012: Vital Statistics: Forecast: Rosstat: Annual.

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.

The COVID-19 pandemic increased the global death rate, reaching *** in 2021, but had little to no significant impact on birth rates, causing population growth to dip slightly. On a global level, population growth is determined by the difference between the birth and death rates, known as the rate of natural change. On a national or regional level, migration also affects population change. Ongoing trends Since the middle of the 20th century, the global birth rate has been well above the global death rate; however, the gap between these figures has grown closer in recent years. The death rate is projected to overtake the birth rate in the 2080s, which means that the world's population will then go into decline. In the future, death rates will increase due to ageing populations across the world and a plateau in life expectancy. Why does this change? There are many reasons for the decline in death and birth rates in recent decades. Falling death rates have been driven by a reduction in infant and child mortality, as well as increased life expectancy. Falling birth rates were also driven by the reduction in child mortality, whereby mothers would have fewer children as survival rates rose - other factors include the drop in child marriage, improved contraception access and efficacy, and women choosing to have children later in life.

Means followed by the same letter within rows are not significantly different (P = 0.05, Student-Newman-Keuls sequential test). rm, intrinsic rate of natural increase; R0, net reproductive rate; G, mean generation time (days); λ, finite rate of increase; t, doubling time (days).

Previous SARS-CoV-2 research indicates that antibody levels and corresponding neutralization potential increase with additional exposures (comprising vaccination or infection), and that hybrid immunity resulting from combined vaccination and natural infection is more robust than either alone. However, it is unclear whether or how antibody levels increase or eventually plateau with repeated exposures and how SARS-CoV-2 exposure differs by sex or other demographic factors. Research regarding the association of antibody production with neutralization potential is also limited. We conducted this analysis within the RESPIRA population-based cohort in Costa Rica to investigate relationships between antibody levels and neutralization potential at increasing exposure levels. We examined immunological profiles from systematically defined single-exposure groups (one vaccine dose or one natural infection), double-exposure groups (two vaccine doses or one vaccine dose following a natural infection), and a triple-exposure group (two vaccine doses following a natural infection). We used a S1-RBD-based serological assay for antibody level detection and a pseudovirion assay for neutralization potential quantification. Using linear regression, we compared antibody levels and pseudoneutralization geometric mean titers between exposure groups. For single exposure groups, one vaccine dose was inferior to natural infection, but a second vaccine dose was superior to natural infection. For double exposure groups, those who were vaccinated once after infection developed higher levels of antibodies and higher neutralization potential compared with those who had only two vaccine doses. We note that peak antibody levels following an exposure may plateau after two exposures while neutralization potential continues to increase with a third exposure dose. Response patterns were comparable in males and females and in sensitivity analyses stratified by age, vaccine type, and pandemic wave. These results provide evidence that SARS-CoV-2 vaccination after COVID infection provides immunological benefit and suggest neutralization potential continues to increase after a second vaccine dose despite plateauing of antibody levels.

rm: intrinsic rate of natural increase, GRR: gross reproduction rate, R0: net reproduction rate, Tc: mean generation time (in days), Dt: doubling time (in days), and λ: finite rate of increase.

Rosstat预测：平均值：每1000人：自然增长在12-01-2035达-3.800NA，相较于12-01-2034的-3.700NA有所下降。Rosstat预测：平均值：每1000人：自然增长数据按年更新，12-01-2017至12-01-2035期间平均值为-2.800NA，共19份观测结果。该数据的历史最高值出现于12-01-2017，达-1.000NA，而历史最低值则出现于12-01-2035，为-3.800NA。CEIC提供的Rosstat预测：平均值：每1000人：自然增长数据处于定期更新的状态，数据来源于Федеральная служба государственной статистики，数据归类于全球数据库的俄罗斯联邦 – Table RU.GD012: Vital Statistics: Forecast: Rosstat: Annual。

Following a spike to 2.5 children per woman in the mid-1960s (during the second wave of the post-WWII baby boom), Germany's fertility rate then fell sharply to around 1.5 children per woman in the 1970s, and it has fluctuated between 1.2 and 1.6 children per woman ever since. Germany's fertility rate has been below the natural replacement level of roughly 2.1 children per woman since 1970, meaning that long-term natural population growth is unsustainable. In fact, Germany has experienced a natural population decline in every year since 1972, and its population has only grown or been sustained at its current level through high net immigration rates.Find more statistics on other topics about Germany with key insights such as crude birth rate, life expectancy of women at birth, and total life expectancy at birth.

a See Table 1 for parameter definitions; P-values

Rosstat预测：平均值：自然增长在12-01-2035达-541,194.000NA，相较于12-01-2034的-540,267.000NA有所下降。Rosstat预测：平均值：自然增长数据按年更新，12-01-2017至12-01-2035期间平均值为-409,061.000NA，共19份观测结果。该数据的历史最高值出现于12-01-2017，达-155,731.000NA，而历史最低值则出现于12-01-2035，为-541,194.000NA。CEIC提供的Rosstat预测：平均值：自然增长数据处于定期更新的状态，数据来源于Федеральная служба государственной статистики，数据归类于全球数据库的俄罗斯联邦 – Table RU.GD012: Vital Statistics: Forecast: Rosstat: Annual。

From 1950 to 2024, the cyclone Bhola that hit Bangladesh in 1970 was the deadliest natural disaster in the world. The exact death toll is impossible to calculate, but it is estimated that over 300,000 lives were lost as a result of the cyclone. The Tangshan earthquake in China in 1976 is estimated to have caused the second-highest number of fatalities. The Haiti earthquake The fifth-deadliest natural disaster during this period was the earthquake in Haiti in 2010. However, death tolls vary between 100,000 and 316,000, meaning that some estimates make it the deadliest natural disaster in the world since 1950, and the deadliest earthquake since 1900. Sixty percent of the country’s hospitals and eighty percent of the country’s schools were destroyed. It was the worst earthquake to hit the Caribbean in 200 years, with a magnitude of 7.0 at its epicenter only 25 kilometers away from Haiti’s capital, Port-au-Prince. Poor construction practices were to blame for many of the deaths; Haiti’s buildings were not earthquake resistant and were not built according to building code due to a lack of licensed building professionals. High population density was also to blame for the high number of fatalities. One fourth of the country’s inhabitants lived in the Port-au-Prince area, meaning half of the country’s population was directly affected by the earthquake. Increasing extreme weather As global warming continues to accelerate climate change, it is estimated that natural catastrophes such as cyclones, rainfalls, landslides, and heat waves will intensify in the coming years and decades. For instance, the economic losses caused by natural disasters worldwide increased since 2015. Moreover, it is expected that countries in the Global South will be affected the most by climate change in the coming years, and many of these are already feeling the impact of climate change.

This dataset contains percentage cover of plants, mean numbers of aphids, mean counts of predators and mean counts of herbivores on three crops (field bean, wheat and oilseed rape) within different grassland types (improved, restored and species rich). Data were collected in 2013 on five farms in the Salisbury Plain area of the UK as part of the Wessex Biodiversity and Ecosystem Services Sustainability (BESS) project within the UK Natural Environment Research Council (NERC) BESS programme. This data set was used to provide an assessment of the potential for different grassland types to provide natural pest control ecosystem services. The study uses sentinel plants of the three crops established in the grasslands to monitor herbivorous pest insects, predatory insect occurrence and the population growth rates of artificially established aphids. Full details about this dataset can be found at https://doi.org/10.5285/4c02ae08-5703-46f4-947e-80e5d0a34a28