About this dataset

Context

The dataset tabulates the Canyon County 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 Canyon County 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 Canyon County was 257,674, a 2.70% increase year-by-year from 2022. Previously, in 2022, Canyon County population was 250,892, an increase of 2.95% compared to a population of 243,710 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Canyon County increased by 124,564. In this period, the peak population was 257,674 in the year 2023. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. 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 Canyon County is shown in this column.
• Year on Year Change: This column displays the change in Canyon County 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 Canyon County Population by Year. You can refer the same here

Trend-based projections

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

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

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

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

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

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

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

GLA 2013 round ethnic group population projections:
Borough: Central

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

Housing linked projections

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

Projection Models:

DCLG-Based Model

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

Capped Household Size Model

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

Household projections are not available from this model.

Development assumptions:

SHLAA housing data

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

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

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

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

Zero-development projections

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

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

Frequently asked question: which projection should I use?

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

The custom-age population tool is here.

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

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

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

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

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

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

About this dataset

Context

The dataset tabulates the Lincoln County 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 Lincoln County 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 Lincoln County was 20,880, a 1.01% increase year-by-year from 2022. Previously, in 2022, Lincoln County population was 20,671, an increase of 2.47% compared to a population of 20,173 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Lincoln County increased by 6,300. In this period, the peak population was 20,880 in the year 2023. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. 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 Lincoln County is shown in this column.
• Year on Year Change: This column displays the change in Lincoln County 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 Lincoln County Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Kimberly 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 Kimberly 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 Kimberly was 5,359, a 5.41% increase year-by-year from 2022. Previously, in 2022, Kimberly population was 5,084, an increase of 4.93% compared to a population of 4,845 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Kimberly increased by 2,762. In this period, the peak population was 5,359 in the year 2023. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. 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 Kimberly is shown in this column.
• Year on Year Change: This column displays the change in Kimberly 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 Kimberly Population by Year. You can refer the same here

In the past four centuries, the population of the United States has grown from a recorded 350 people around the Jamestown colony of Virginia in 1610, to an estimated 331 million people in 2020. The pre-colonization populations of the indigenous peoples of the Americas have proven difficult for historians to estimate, as their numbers decreased rapidly following the introduction of European diseases (namely smallpox, plague and influenza). Native Americans were also omitted from most censuses conducted before the twentieth century, therefore the actual population of what we now know as the United States would have been much higher than the official census data from before 1800, but it is unclear by how much. Population growth in the colonies throughout the eighteenth century has primarily been attributed to migration from the British Isles and the Transatlantic slave trade; however it is also difficult to assert the ethnic-makeup of the population in these years as accurate migration records were not kept until after the 1820s, at which point the importation of slaves had also been illegalized. Nineteenth century In the year 1800, it is estimated that the population across the present-day United States was around six million people, with the population in the 16 admitted states numbering at 5.3 million. Migration to the United States began to happen on a large scale in the mid-nineteenth century, with the first major waves coming from Ireland, Britain and Germany. In some aspects, this wave of mass migration balanced out the demographic impacts of the American Civil War, which was the deadliest war in U.S. history with approximately 620 thousand fatalities between 1861 and 1865. The civil war also resulted in the emancipation of around four million slaves across the south; many of whose ancestors would take part in the Great Northern Migration in the early 1900s, which saw around six million black Americans migrate away from the south in one of the largest demographic shifts in U.S. history. By the end of the nineteenth century, improvements in transport technology and increasing economic opportunities saw migration to the United States increase further, particularly from southern and Eastern Europe, and in the first decade of the 1900s the number of migrants to the U.S. exceeded one million people in some years. Twentieth and twenty-first century The U.S. population has grown steadily throughout the past 120 years, reaching one hundred million in the 1910s, two hundred million in the 1960s, and three hundred million in 2007. In the past century, the U.S. established itself as a global superpower, with the world's largest economy (by nominal GDP) and most powerful military. Involvement in foreign wars has resulted in over 620,000 further U.S. fatalities since the Civil War, and migration fell drastically during the World Wars and Great Depression; however the population continuously grew in these years as the total fertility rate remained above two births per woman, and life expectancy increased (except during the Spanish Flu pandemic of 1918).

Since the Second World War, Latin America has replaced Europe as the most common point of origin for migrants, with Hispanic populations growing rapidly across the south and border states. Because of this, the proportion of non-Hispanic whites, which has been the most dominant ethnicity in the U.S. since records began, has dropped more rapidly in recent decades. Ethnic minorities also have a much higher birth rate than non-Hispanic whites, further contributing to this decline, and the share of non-Hispanic whites is expected to fall below fifty percent of the U.S. population by the mid-2000s. In 2020, the United States has the third-largest population in the world (after China and India), and the population is expected to reach four hundred million in the 2050s.

About this dataset

Context

The dataset tabulates the Bonifay 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 Bonifay 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 Bonifay was 2,825, a 1.40% increase year-by-year from 2022. Previously, in 2022, Bonifay population was 2,786, an increase of 0.40% compared to a population of 2,775 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Bonifay decreased by 42. In this period, the peak population was 2,883 in the year 2006. 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 Bonifay is shown in this column.
• Year on Year Change: This column displays the change in Bonifay 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 Bonifay Population by Year. You can refer the same here

Successful wildlife conservation in an era of global change requires understanding determinants of species population growth. However, when populations are faced with novel stressors, factors associated with healthy populations can change, necessitating shifting conservation strategies. For example, emerging infectious diseases can cause conditions previously beneficial to host populations to increase disease impacts. Here, we paired a population dataset of 265 colonies of the federally endangered Indiana bat (Myotis sodalis) with 50.7 logger-years of environmental data to explore factors that affected colony response to white-nose syndrome (WNS), an emerging fungal disease. We found variation in colony responses to WNS, ranging from extirpation to stabilization. The severity of WNS impacts was associated with hibernaculum temperature, as colonies of cold hibernacula declined more severely than those in relatively warm hibernacula, an association that arose following pathogen emergence...., , , # Data from: Drivers of population dynamics of at-risk populations change with pathogen arrival

https://doi.org/10.5061/dryad.3xsj3txqb

This dataset contains population census data from 265 colonies of the Indiana bat (Myotis sodalis) impacted by white-nose syndrome. It additionally contains data on the temperature and humidity conditions of their hibernacula, information used to explore dynamic associations between environmental conditions and population response to pathogen invasion.Â

Description of the data and file structure

The data used in the study is provided in a single .csv file entitled "data.csv." It contains yearly census and population growth data for each of the 265 Indiana bat colonies. Below is a description of the data contained in each column:

• Site: a unique name randomly assigned to each hibernaculum containing an Indiana bat colony.Â
• State: U.S. state of colony.
• County: county of colony.
• Wyear: year census...

About this dataset

Context

The dataset tabulates the Dayton 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 Dayton 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 Dayton was 9,693, a 3.30% increase year-by-year from 2022. Previously, in 2022, Dayton population was 9,383, an increase of 2.41% compared to a population of 9,162 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Dayton decreased by 509. In this period, the peak population was 11,076 in the year 2009. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

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

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Dayton is shown in this column.
• Year on Year Change: This column displays the change in Dayton 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 Dayton Population by Year. You can refer the same here

The tradeoff between investment in current reproduction versus future survival is central to life-history theory, and long-lived, iteroparous mammals disproportionately favor their own survival. Previous work has demonstrated that adjustment of reproductive effort in long-lived mammals often occurs after parturition, owing to the greater cost of lactation relative to gestation. Under the right conditions, however, this difference in the relative costs of reproduction may also facilitate another, arguably less intuitive, strategy. Those conditions, which are relatively common among capital-breeding ungulates, include: (1) Females have the capacity to adjust gestation length; (2) Neonatal mortality occurs mostly during the first month of life and is inversely related to birth mass; and (3) The influence of birth mass on the probability of surviving the first month of life is stronger than the influence of autumn body mass on the probability of surviving the first winter of life. Under these circumstances, a female in poor condition in early spring could potentially increase fitness by delaying parturition and increasing investment in gestation, giving birth to a correspondingly larger neonate that has a higher probability of survival during its first month of life, and subsequently reducing investment in lactation to help rebuild somatic reserves. We developed and empirically parameterized a state-dependent model of maternal resource allocation that reflected this strategy. We tested the prediction that population growth would be faster when resource allocation was state-dependent than when gestation length was decoupled from dam condition and adjustment of reproductive investment was largely post-natal. Our results supported this prediction: state-dependent resource allocation by maternal females increased lambda by an average of 4%, leading to larger population sizes after 30 years. Population growth was consistent across a range of winter severities, suggesting that state-dependent resource allocation could help buffer ungulate populations against climatic variation. Our results reveal a potentially general mechanism underpinning intraspecific variation in life-history strategies of long-lived, capital-breeding mammals, and suggest that such variation at the individual level can influence performance outcomes at the population level.

Species' geographic ranges and climatic niches are likely to be increasingly mismatched due to rapid climate change. If a species' range and niche are out of equilibrium, then population performance should decrease from high-latitude "leading" range edges, where populations are expanding into recently ameliorated habitats, to low-latitude "trailing" range edges, where populations are contracting from newly unsuitable areas. Demographic compensation is a phenomenon whereby declines in some vital rates are offset by increases in others across time or space. In theory, demographic compensation could increase the range of environments over which populations can succeed and forestall range contraction at trailing edges. An outstanding question is whether range limits and range contractions reflect inadequate demographic compensation across environmental gradients, causing population declines at range edges. We collected demographic data from 32 populations of the scarlet monkeyflower (Erythr...

The annual population growth in India increased by 0.1 percentage points (+14.71 percent) in 2023 in comparison to the previous year. This was the first time during the observed period that the population growth has increased in India. 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 Nepal and Sri Lanka.

Sea level rise causes habitat loss and is considered to be a key threat to coastal species globally. Sea level rise also reduces habitat quality, potentially threatening populations already before habitat drowns and is lost. The extent and timing of changes in habitat quality for wildlife actively adapting to sea level rise, and how this affects population numbers under different emission scenarios, is unknown. Here, we combine long-term field data with models of sea level rise, marsh geomorphology, adaptive behaviour, and population dynamics to show that habitat quality is already declining on three islands due to increased flooding of shorebird nests. Also, population collapses are projected well before habitat drowns. Habitat loss, a widely used proxy, thus severely underestimates population impacts of sea level rise and coastal species will suffer much sooner than previously thought. Despite shorebirds adapting by moving to higher grounds, sea level rise will result in up to 79% fewer birds in a century, eventually leading to extinction in their prime habitat. Local gas mining exacerbates matters, as deep soil subsidence makes habitat even more vulnerable to sea level rise, effectively halving the window of opportunity for conservation action. Climate change ultimately jeopardizes the biodiversity value of this UNESCO World Heritage Area, and nature management needs to take this long-term perspective on board by in the short-term, boosting the accretion of tidal marshes or developing flood-safe alternative habitat elsewhere.

Understanding the influence of environmental variability on population dynamics is a fundamental goal of ecology. Theory suggests that, for populations in variable environments, temporal correlations between demographic vital rates (e.g., growth, survival, reproduction) can increase (if positive) or decrease (if negative) the variability of year-to-year population growth. Because this variability generally decreases long-term population viability, vital rate correlations may importantly affect population dynamics in stochastic environments. Despite long-standing theoretical interest, it is unclear whether vital rate correlations are common in nature, whether their directions are predominantly negative or positive, and whether they are of sufficient magnitude to warrant broad consideration in studies of stochastic population dynamics. We used long-term demographic data for three perennial plant species, hierarchical Bayesian parameterization of population projection models, and stochastic simulations to address the following questions: (1) What are the sign, magnitude, and uncertainty of temporal correlations between vital rates? (2) How do specific pairwise correlations affect the year-to-year variability of population growth? (3) Does the net effect of all vital rate correlations increase or decrease year-to-year variability? (4) What is the net effect of vital rate correlations on the long-term stochastic population growth rate (λS)? We found only four moderate to strong correlations, both positive and negative in sign, across all species and vital rate pairs; otherwise, correlations were generally weak in magnitude and variable in sign. The net effect of vital rate correlations ranged from a slight decrease to an increase in the year-to-year variability of population growth, with average changes in variance ranging from -1% to +22%. However, vital rate correlations caused virtually no change in the estimates of λS (mean effects ranging from -0.01% to +0.17%). Therefore, the proportional changes in the variance of population growth caused by demographic correlations were too small on an absolute scale to importantly affect population growth and viability. We conclude that in our three focal populations and perhaps more generally, vital rate correlations have little effect on stochastic population dynamics. This may be good news for population ecologists, because estimating vital rate correlations and incorporating them into population models can be data-intensive and technically challenging.

In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

While adult sex ratio (ASR) is a crucial component for population management, there is still a limited understanding of how its fluctuation affects population dynamics. To demonstrate mechanisms that hinder population growth under a biased ASR, we examined changes in reproductive success with ASR using a decapod crustacean exposed to female-selective harvesting.

We examined the effect of ASR on the spawning success of females. A laboratory experiment showed that the number of eggs carried by females decreased as the proportion of males in the mating groups increased. Although the same result was not observed in data collected over 25 years in the wild, the negative effect of ASR was suggested when success in carrying eggs was considered as a spawning success. These results indicate that a surplus of males results in females failing to carry eggs, probably due to sexual coercion, and the negative effect of ASR can be detected at the population level only when the bias increases becaus...

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US Population Health Management Market Size 2025-2029

The US population health management (PHM) market size is forecast to increase by USD 6.04 billion, at a CAGR of 7.4% between 2024 and 2029.

Population Health Management (PHM) is a critical aspect of healthcare delivery In the modern era, focusing on improving the health outcomes of large populations. The market is experiencing significant growth, driven by several key trends. One of the primary factors fueling this growth is the increasing adoption of healthcare IT solutions. These technologies enable healthcare providers to collect, manage, and analyze large amounts of patient data, facilitating personalized care and population health improvement. Another trend is the growing adoption of analytics in PHM. Analytics tools help identify patterns and insights from data, enabling early intervention and prevention of diseases. However, the high perceived costs associated with PHM solutions remain a challenge for market growth. Despite this, the benefits of PHM, including improved patient outcomes and reduced healthcare costs, make it a worthwhile investment for healthcare organizations.

What will be the Size of the market During the Forecast Period?

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Population Health Management (PHM) is a proactive healthcare approach focusing on improving the wider determinants of health and addressing health inequalities in various physical, economic, and social contexts. The market reflects the growing recognition of the importance of system-wide outcome focus, local intelligence, and data-driven decision-making in addressing ill health and managing chronic conditions such as cardiovascular disease. PHM integrates qualitative and quantitative data to identify and address the unique needs of populations, enabling personalized interventions and care models. Infrastructure, leadership, and information governance are crucial elements in implementing effective PHM strategies. 
Payment reform and incentives are driving the transformation of healthcare systems towards a more integrated care model, reducing hospitalization and improving overall population health. The market is experiencing significant growth due to the increasing awareness of the importance of addressing the root causes of ill health and the need for a more holistic approach to healthcare. This shift towards PHM is influenced by the economic, social, and demographic changes In the global population, emphasizing the need for a more resource-efficient and sustainable healthcare system.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Software
  Services


Deployment

  Cloud
  On-premises


End-user

  Healthcare providers
  Healthcare payers
  Employers and government bodies


Geography

  US

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.

Population Health Management (PHM) software is a crucial tool In the US healthcare sector, collecting and analyzing patient data from various healthcare systems to predict health conditions and improve overall patient care. Advanced data analytics, including data visualizations and business intelligence, enable PHM software to identify health risks within communities and promote value-based care. The adoption of PHM software is on the rise due to the increasing prevalence of chronic conditions and the demand for efficient, cost-effective healthcare. PHM software also facilitates system-wide outcome focus, integrating qualitative and quantitative data, local intelligence, and decision-making to redesign care services for at-risk groups.

The US healthcare transformation prioritizes PHM, with NHS England, NHS trusts, Public health, VCSE organizations, and Integrated Care Systems (ICSs) utilizing PHM software to address health inequalities and improve health outcomes. PHM software's infrastructure, leadership, information governance, and digital infrastructure support the integration of interventions, care models, hospitalization incentives, payment reforms, and integrated care systems. PHM software plays a vital role in addressing health issues such as cardiovascular disease (CVD) and improving overall population health.

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Market Dynamics

Our US Population Health Management (PHM) Market researchers analyzed the data with 2024 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

What are the key market drivers leading to the rise in adopti

Aim: When alien species are introduced to new ranges, climate or trait mismatches may initially constrain their population growth. However, inter- and intraspecific selection in the new environment should cause population growth rates to increase with residence time. Using a species-for-time approach, we test whether with increasing residence time (a) negative effects of climatic mismatches between the species' new and native range on population growth weaken, and (b) functional traits converge towards values that maximize population growth in the new range.

Location: Germany.

Time period: 12,000 years BP to present.

Major taxa studied: 46 plant species of the Asteraceae family.

Methods: We set up a common-garden mesocosm-experiment using annual plant species with a wide range of residence times (7-12,000 years) and followed their population dynamics over two years. We calculated climatic distance between the common garden and the species' native range. We also measured key functional traits of each species to analyse trait-demography relationships and test trait convergence with increasing residence time.

Results: We found no support for the hypothesis that negative effects of climatic mismatches on population growth weaken with residence time. However, seed mass had a clear negative effect on population growth. As expected under such strong directional selection between or within species, increasing residence time led seed mass to converge to low values that increase population growth. Accordingly, population growth tended to increase with residence time.

Main conclusions: We identify trait but not climatic mismatches as important constraints on population growth of invaders. Understanding how inter- and intraspecific selection shapes functional traits of alien species should improve the predictability of future invasions and help understanding limits to the population growth and spread of invaders already present. In a broader context, this study contributes to the conceptual integration of invasion biology with community, functional, and population ecology.

About this dataset

Context

The dataset tabulates the Tucson 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 Tucson 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 Tucson was 547,239, a 0.14% increase year-by-year from 2022. Previously, in 2022, Tucson population was 546,500, an increase of 0.98% compared to a population of 541,217 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Tucson increased by 60,134. In this period, the peak population was 548,961 in the year 2019. 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 Tucson is shown in this column.
• Year on Year Change: This column displays the change in Tucson 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 Tucson Population by Year. You can refer the same here

Mexico MX: Completeness of Death Registration with Cause-of-Death Information data was reported at 99.000 % in 2011. This records an increase from the previous number of 93.900 % for 2009. Mexico MX: Completeness of Death Registration with Cause-of-Death Information data is updated yearly, averaging 93.900 % from Dec 1992 (Median) to 2011, with 5 observations. The data reached an all-time high of 99.000 % in 2011 and a record low of 90.100 % in 1992. Mexico MX: Completeness of Death Registration with Cause-of-Death Information data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mexico – Table MX.World Bank.WDI: Population and Urbanization Statistics. Completeness of death registration is the estimated percentage of deaths that are registered with their cause of death information in the vital registration system of a country.; ; World Health Organization, Global Health Observatory Data Repository/World Health Statistics (http://apps.who.int/gho/data/node.main.1?lang=en).; Weighted average;