About this dataset

Context

The dataset tabulates the Chesterfield County population by year. The dataset can be utilized to understand the population trend of Chesterfield County.

Content

The dataset constitues the following datasets

• Chesterfield County, VA Population Dataset: Yearly Figures, Population Change, and Percent Change Analysis

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/.

About this dataset

Context

The dataset tabulates the Whitestown 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 Whitestown 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 Whitestown was 13,049, a 8.49% increase year-by-year from 2022. Previously, in 2022, Whitestown population was 12,028, an increase of 8.26% compared to a population of 11,110 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Whitestown increased by 12,093. In this period, the peak population was 13,049 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 Whitestown is shown in this column.
• Year on Year Change: This column displays the change in Whitestown 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 Whitestown Population by Year. You can refer the same here

Demographic data for introduced crab from multiple bays along the Central California coast, shallow subtidal (<3 m depth), in 2015. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson acquisition_description=We conducted monthly trappings of invasive European green crabs to gather demographic data from several bays in northern California: Bodega Harbor, Tomales Bay, Bolinas Lagoon, San Francisco Bay, and Elkhorn Slough. All sites were accessed by foot via shore entry. At each of four sites within each bay, we placed 5 baited traps (folding Fukui fish traps) and 5 baited minnow traps in shallow intertidal areas. Traps arrays were set with fish and minnow traps alternating and with each 20 m apart. Traps were retrieved 24 hours later and traps were rebaited and collected again the following day.\u00a0Trapping was continued for three consecutive days with traps removed on the final day.\u00a0Each day, data for crab species, size, sex, reproductive condition, and injuries were collected for all crabs in the field. Following data collection, all crabs were returned to the lab, and frozen overnight prior to disposal.\u00a0

See Turner et al. (2016)\u00a0Biological Invasions\u00a018: 533-548 for additional methodological details:
Turner, B.C., de Rivera, C.E., Grosholz, E.D., & Ruiz, G.M. 2016. Assessing population increase as a possible outcome to management of invasive species. Biological Invasions, 18(2), pp 533\u2013548. doi:10.1007/s10530-015-1026-9 awards_0_award_nid=699764 awards_0_award_number=OCE-1514893 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1514893 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=David L. Garrison awards_0_program_manager_nid=50534 cdm_data_type=Other comment=Demographic data for introduced crab from multiple bays in 2015 PI: Edwin Grosholz (UC Davis) Co-PI: Catherine de Rivera & Gregory Ruiz (Portland State University)
Version: 15 June 2017 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.701751.1 Easternmost_Easting=-121.738422 geospatial_lat_max=38.316968 geospatial_lat_min=36.823953 geospatial_lat_units=degrees_north geospatial_lon_max=-121.738422 geospatial_lon_min=-123.058725 geospatial_lon_units=degrees_east infoUrl=https://www.bco-dmo.org/dataset/701751 institution=BCO-DMO instruments_0_dataset_instrument_description=At each of four sites within each bay, we placed 5 baited traps (folding Fukui fish traps) and 5 baited minnow traps in shallow intertidal areas. instruments_0_dataset_instrument_nid=701774 instruments_0_description=Fukui produces multi-species, multi-purpose collapsible or stackable fish traps, available in different sizes. instruments_0_instrument_name=Fukui fish trap instruments_0_instrument_nid=701772 instruments_0_supplied_name=folding Fukui fish traps metadata_source=https://www.bco-dmo.org/api/dataset/701751 Northernmost_Northing=38.316968 param_mapping={'701751': {'lat': 'master - latitude', 'lon': 'master - longitude'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/701751/parameters people_0_affiliation=University of California-Davis people_0_affiliation_acronym=UC Davis people_0_person_name=Edwin Grosholz people_0_person_nid=699768 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=Portland State University people_1_affiliation_acronym=PSU people_1_person_name=Catherine de Rivera people_1_person_nid=699771 people_1_role=Co-Principal Investigator people_1_role_type=originator people_2_affiliation=Portland State University people_2_affiliation_acronym=PSU people_2_person_name=Gregory Ruiz people_2_person_nid=471603 people_2_role=Co-Principal Investigator people_2_role_type=originator people_3_affiliation=Woods Hole Oceanographic Institution people_3_affiliation_acronym=WHOI BCO-DMO people_3_person_name=Shannon Rauch people_3_person_nid=51498 people_3_role=BCO-DMO Data Manager people_3_role_type=related project=Invasive_predator_harvest projects_0_acronym=Invasive_predator_harvest projects_0_description=The usual expectation is that when populations of plants and animals experience repeated losses to predators or human harvest, they would decline over time. If instead these populations rebound to numbers exceeding their initial levels, this would seem counter-intuitive or even paradoxical. However, for several decades mathematical models of population processes have shown that this unexpected response, formally known as overcompensation, is not only possible, but even expected under some circumstances. In what may be the first example of overcompensation in a marine system, a dramatic increase in a population of the non-native European green crab was recently observed following an intensive removal program. This RAPID project will use field surveys and laboratory experiments to verify that this population explosion results from overcompensation. Data will be fed into population models to understand to what degree populations processes such as cannibalism by adult crabs on juvenile crabs and changes in maturity rate of reproductive females are contributing to or modifying overcompensation. The work will provide important insights into the fundamental population dynamics that can produce overcompensation in both natural and managed populations. Broader Impacts include mentoring graduate trainees and undergraduate interns in the design and execution of field experiments as well as in laboratory culture and feeding experiments. The project will also involve a network of citizen scientists who are involved with restoration activities in this region and results will be posted on the European Green Crab Project website. This project aims to establish the first example of overcompensation in marine systems. Overcompensation refers to the paradoxical process where reduction of a population due to natural or human causes results in a greater equilibrium population than before the reduction. A population explosion of green crabs has been recently documented in a coastal lagoon and there are strong indications that this may be the result of overcompensation. Accelerated maturation of females, which can accompany and modify the expression of overcompensation has been observed. This RAPID project will collect field data from this unusual recruitment class and conduct targeted mesocosm experiments. These will include population surveys and mark-recapture studies to measure demographic rates across study sites. Laboratory mesocosm studies using this recruitment class will determine size specific mortality. Outcomes will be used in population dynamics models to determine to what degree overcompensation has created this dramatic population increase. The project will seek answers to the following questions: 1) what are the rates of cannibalism by adult green crabs and large juveniles on different sizes of juvenile green crabs, 2) what are the consequences of smaller size at first reproduction for population dynamics and for overcompensation and 3) how quickly will the green crab population return to the levels observed prior to the eradication program five years earlier? projects_0_end_date=2016-11 projects_0_geolocation=Europe projects_0_name=RAPID: A rare opportunity to examine overcompensation resulting from intensive harvest of an introduced predator projects_0_project_nid=699765 projects_0_start_date=2014-12 sourceUrl=(local files) Southernmost_Northing=36.823953 standard_name_vocabulary=CF Standard Name Table v55 version=1 Westernmost_Easting=-123.058725 xml_source=osprey2erddap.update_xml() v1.3

The annual population growth in India increased by 0.1 percentage points (+12.66 percent) in 2023. 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.

About this dataset

Context

The dataset tabulates the Vancouver 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 Vancouver 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 Vancouver was 196,442, a 1% increase year-by-year from 2022. Previously, in 2022, Vancouver population was 194,500, an increase of 0.90% compared to a population of 192,770 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Vancouver increased by 51,483. In this period, the peak population was 196,442 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 Vancouver is shown in this column.
• Year on Year Change: This column displays the change in Vancouver 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 Vancouver Population by Year. You can refer the same here

Demographic data from introduced crab in Seadrift Lagoon (Central California coast, shallow subtidal (<3 m depth)) in 2015. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson acquisition_description=We conducted monthly trapping of invasive European green crabs to gather demographic data in Seadrift Lagoon, Stinson Beach, CA (lat 37.907440 long -122.666169).\u00a0All sites were accessed by either kayak or by foot via shore entry.\u00a0At each of six sites, we placed 10 baited traps (folding Fukui fish traps) in shallow (<2 m) subtidal areas. Traps were retrieved 24 hours later and were rebaited and collected again the following day.\u00a0Trapping was continued for three consecutive days with traps removed on the final day.\u00a0Each day, data for crab species, size, sex, reproductive condition, injuries, and presence of marks were collected for all crabs in the field. Following data collection, all crabs were returned to the lab, frozen overnight disposed of in commercial agricultural compost. \u00a0

For each date and site, crabs from all traps (e.g. 10 traps per site) are pooled for counting and measuring.
Traps Used for each date (some with macroalgae "Ulva"):
02/19/2015\u00a0\u00a0 \u00a010 baited traps + 5 traps with ulva
02/20/2015\u00a0\u00a0 \u00a010 baited traps + 5 with ulva
03/05/2015\u00a0\u00a0 \u00a010 baited traps + 5 traps with ulva per site
03/06/2015\u00a0\u00a0 \u00a010 baited traps + 5 traps with ulva
03/24/2015\u00a0\u00a0 \u00a010 traps/site
04/08/2015\u00a0\u00a0 \u00a010 traps/site
04/15/2015\u00a0\u00a0 \u00a010 baited traps + 4 traps with ulva
04/24/2015\u00a0\u00a0 \u00a010 traps/site
05/27/2015\u00a0\u00a0 \u00a0site 1 & 5 had 10 traps, site 3 had 9 traps
06/23/2015\u00a0\u00a0 \u00a0site 1 & 3 had 15 traps, site 5 had 14 traps
06/24/2015\u00a0\u00a0 \u00a0site 1 & 3 had 15 traps, site 5 had 14 traps
07/21/2015\u00a0\u00a0 \u00a0traps per site: site 1=20, site 2=20, site 3=17, site 4=15, site 5=10, site 6=10, site 7=20
08/25/2017\u00a0\u00a0 \u00a010 traps/site
08/26/2015\u00a0\u00a0 \u00a010 traps/site
08/27/2015\u00a0\u00a0 \u00a010 traps/site
09/01/2015\u00a0\u00a0 \u00a010 traps/site
09/02/2015\u00a0\u00a0 \u00a010 traps/site
09/30/2015\u00a0\u00a0 \u00a010 traps/site
10/01/2015\u00a0\u00a0 \u00a010 traps/site
10/02/2015\u00a0\u00a0 \u00a010 traps/site
12/01/2015\u00a0\u00a0 \u00a010 traps/site
12/02/2015\u00a0\u00a0 \u00a010 traps/site
12/03/2015\u00a0\u00a0 \u00a010 traps/site

See Turner et al. (2016)\u00a0Biological Invasions\u00a018: 533-548 for additional methodological details:
Turner, B.C., de Rivera, C.E., Grosholz, E.D., & Ruiz, G.M. 2016. Assessing population increase as a possible outcome to management of invasive species. Biological Invasions, 18(2), pp 533\u2013548. doi:10.1007/s10530-015-1026-9 awards_0_award_nid=699764 awards_0_award_number=OCE-1514893 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1514893 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=David L. Garrison awards_0_program_manager_nid=50534 cdm_data_type=Other comment=Monthly trapping in Seadrift Lagoon in 2015 PI: Edwin Grosholz (UC Davis) Co-PI: Catherine de Rivera & Gregory Ruiz (Portland State University)
Version: 02 June 2017 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.701863.1 Easternmost_Easting=-122.6661694 geospatial_lat_max=37.90744 geospatial_lat_min=37.90744 geospatial_lat_units=degrees_north geospatial_lon_max=-122.6661694 geospatial_lon_min=-122.6661694 geospatial_lon_units=degrees_east infoUrl=https://www.bco-dmo.org/dataset/701863 institution=BCO-DMO instruments_0_dataset_instrument_description=At each of the six sites used for monthly trapping plus three additional sites, we placed 15 baited traps (folding Fukui fish traps) in shallow ( instruments_0_dataset_instrument_nid=701870 instruments_0_description=Fukui produces multi-species, multi-purpose collapsible or stackable fish traps, available in different sizes. instruments_0_instrument_name=Fukui fish trap instruments_0_instrument_nid=701772 instruments_0_supplied_name=Fukui fish traps metadata_source=https://www.bco-dmo.org/api/dataset/701863 Northernmost_Northing=37.90744 param_mapping={'701863': {'lat': 'master - latitude', 'lon': 'master - longitude'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/701863/parameters people_0_affiliation=University of California-Davis people_0_affiliation_acronym=UC Davis people_0_person_name=Edwin Grosholz people_0_person_nid=699768 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=Portland State University people_1_affiliation_acronym=PSU people_1_person_name=Catherine de Rivera people_1_person_nid=699771 people_1_role=Co-Principal Investigator people_1_role_type=originator people_2_affiliation=Portland State University people_2_affiliation_acronym=PSU people_2_person_name=Gregory Ruiz people_2_person_nid=471603 people_2_role=Co-Principal Investigator people_2_role_type=originator people_3_affiliation=Woods Hole Oceanographic Institution people_3_affiliation_acronym=WHOI BCO-DMO people_3_person_name=Shannon Rauch people_3_person_nid=51498 people_3_role=BCO-DMO Data Manager people_3_role_type=related project=Invasive_predator_harvest projects_0_acronym=Invasive_predator_harvest projects_0_description=The usual expectation is that when populations of plants and animals experience repeated losses to predators or human harvest, they would decline over time. If instead these populations rebound to numbers exceeding their initial levels, this would seem counter-intuitive or even paradoxical. However, for several decades mathematical models of population processes have shown that this unexpected response, formally known as overcompensation, is not only possible, but even expected under some circumstances. In what may be the first example of overcompensation in a marine system, a dramatic increase in a population of the non-native European green crab was recently observed following an intensive removal program. This RAPID project will use field surveys and laboratory experiments to verify that this population explosion results from overcompensation. Data will be fed into population models to understand to what degree populations processes such as cannibalism by adult crabs on juvenile crabs and changes in maturity rate of reproductive females are contributing to or modifying overcompensation. The work will provide important insights into the fundamental population dynamics that can produce overcompensation in both natural and managed populations. Broader Impacts include mentoring graduate trainees and undergraduate interns in the design and execution of field experiments as well as in laboratory culture and feeding experiments. The project will also involve a network of citizen scientists who are involved with restoration activities in this region and results will be posted on the European Green Crab Project website. This project aims to establish the first example of overcompensation in marine systems. Overcompensation refers to the paradoxical process where reduction of a population due to natural or human causes results in a greater equilibrium population than before the reduction. A population explosion of green crabs has been recently documented in a coastal lagoon and there are strong indications that this may be the result of overcompensation. Accelerated maturation of females, which can accompany and modify the expression of overcompensation has been observed. This RAPID project will collect field data from this unusual recruitment class and conduct targeted mesocosm experiments. These will include population surveys and mark-recapture studies to measure demographic rates across study sites. Laboratory mesocosm studies using this recruitment class will determine size specific mortality. Outcomes will be used in population dynamics models to determine to what degree overcompensation has created this dramatic population increase. The project will seek answers to the following questions: 1) what are the rates of cannibalism by adult green crabs and large juveniles on different sizes of juvenile green crabs, 2) what are the consequences of smaller size at first reproduction for population dynamics and for overcompensation and 3) how quickly will the green crab population return to the levels observed prior to the eradication program five years earlier? projects_0_end_date=2016-11 projects_0_geolocation=Europe projects_0_name=RAPID: A rare opportunity to examine overcompensation resulting from intensive harvest of an introduced predator projects_0_project_nid=699765 projects_0_start_date=2014-12 sourceUrl=(local files) Southernmost_Northing=37.90744 standard_name_vocabulary=CF Standard Name Table v55 subsetVariables=lagoon,latitude,longitude version=1 Westernmost_Easting=-122.6661694 xml_source=osprey2erddap.update_xml() v1.3

The graph shows the population growth in China from 2000 to 2024. In 2024, the Chinese population decreased by about 0.1 percent or 1.39 million to around 1.408 billion people. Declining population growth in China Due to strict birth control measures by the Chinese government as well as changing family and work situations of the Chinese people, population growth has subsided over the past decades. Although the gradual abolition of the one-child policy from 2014 on led to temporarily higher birth figures, growth rates further decreased in recent years. As of 2024, leading countries in population growth could almost exclusively be found on the African continent and the Arabian Peninsula. Nevertheless, as of mid 2024, Asia ranked first by a wide margin among the continents in terms of absolute population. Future development of Chinese population The Chinese population reached a maximum of 1,412.6 million people in 2021 but decreased by 850,000 in 2022 and another 2.08 million in 2023. Until 2022, China had still ranked the world’s most populous country, but it was overtaken by India in 2023. Apart from the population decrease, a clear growth trend in Chinese cities is visible. By 2024, around 67 percent of Chinese people lived in urban areas, compared to merely 36 percent in 2000.

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.

According to latest figures, the Chinese population decreased by 1.39 million to around 1.408 billion people in 2024. After decades of rapid growth, China arrived at the turning point of its demographic development in 2022, which was earlier than expected. The annual population decrease is estimated to remain at moderate levels until around 2030 but to accelerate thereafter. Population development in China China had for a long time been the country with the largest population worldwide, but according to UN estimates, it has been overtaken by India in 2023. As the population in India is still growing, the country is very likely to remain being home of the largest population on earth in the near future. Due to several mechanisms put into place by the Chinese government as well as changing circumstances in the working and social environment of the Chinese people, population growth has subsided over the past decades, displaying an annual population growth rate of -0.1 percent in 2024. Nevertheless, compared to the world population in total, China held a share of about 17 percent of the overall global population in 2024. China's aging population In terms of demographic developments, the birth control efforts of the Chinese government had considerable effects on the demographic pyramid in China. Upon closer examination of the age distribution, a clear trend of an aging population becomes visible. In order to curb the negative effects of an aging population, the Chinese government abolished the one-child policy in 2015, which had been in effect since 1979, and introduced a three-child policy in May 2021. However, many Chinese parents nowadays are reluctant to have a second or third child, as is the case in most of the developed countries in the world. The number of births in China varied in the years following the abolishment of the one-child policy, but did not increase considerably. Among the reasons most prominent for parents not having more children are the rising living costs and costs for child care, growing work pressure, a growing trend towards self-realization and individualism, and changing social behaviors.

Abstract of associated article: This paper explores the role of two foods, potatoes and milk, in explaining the increase in economic development experienced throughout the Old World in the 18th and 19th centuries. Nunn and Qian (2011) show the introduction of the potato from the New World has a significant explanatory role for within country population and urbanization growth over this period. I expand on this by considering the role of milk consumption, which is hypothesized to be a complement in diet to potatoes due to a differential composition of essential nutrients. Using a country-level measure for the suitability of milk consumption, the frequency of lactase persistence, I show that the marginal effect of potatoes on post-1700 population and urbanization growth is positively related to milk consumption. As the frequency of milk consumption approaches unity, the marginal effect of potatoes more than doubles in magnitude compared to the baseline estimate of Nunn and Qian.

About this dataset

Context

The dataset tabulates the Iowa 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 Iowa 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 2024, the population of Iowa was 3.24 million, a 0.72% increase year-by-year from 2023. Previously, in 2023, Iowa population was 3.22 million, an increase of 0.49% compared to a population of 3.2 million in 2022. Over the last 20 plus years, between 2000 and 2024, population of Iowa increased by 313,297. In this period, the peak population was 3.24 million in the year 2024. 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 2024

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2024)
• Population: The population for the specific year for the Iowa is shown in this column.
• Year on Year Change: This column displays the change in Iowa 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 Iowa Population by Year. You can refer the same here

Mark recapture data for introduced crab in Seadrift Lagoon (Central California coast, shallow subtidal (<3 m depth)) in 2015. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson acquisition_description=We conducted monthly trapping of invasive European green crabs to gather demographic data in Seadrift Lagoon, Stinson Beach, CA (lat 37.907440, long -122.6661694). All sites were accessed by either kayak or by foot via shore entry. At each of the six sites used for monthly trapping plus three additional sites, we placed 15 baited traps (folding Fukui fish traps) in shallow (<2 m) subtidal areas. Traps were retrieved 24 hours later and were rebaited and collected again the following day. Trapping was continued for four consecutive days with traps removed on the final day.\u00a0Crabs were marked by clipping two adjacent anterio-lateral spines.\u00a0Each day, data for crab species, size, sex, reproductive condition, injuries, and presence of marks were collected for all crabs in the field. Following data collection, all marked crabs were returned to the lagoon at the same site that the crabs were collected.\u00a0

See Turner et al. (2016)\u00a0Biological Invasions\u00a018: 533-548 for additional methodological details:
Turner, B.C., de Rivera, C.E., Grosholz, E.D., & Ruiz, G.M. 2016. Assessing population increase as a possible outcome to management of invasive species. Biological Invasions, 18(2), pp 533\u2013548. doi:10.1007/s10530-015-1026-9 awards_0_award_nid=699764 awards_0_award_number=OCE-1514893 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1514893 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=David L. Garrison awards_0_program_manager_nid=50534 cdm_data_type=Other comment=Mark recapture data for introduced crab in Seadrift Lagoon in 2015 PI: Edwin Grosholz (UC Davis) Co-PI: Catherine de Rivera & Gregory Ruiz (Portland State University)
Version: 02 June 2017 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.701840.1 Easternmost_Easting=-122.6661694 geospatial_lat_max=37.90744 geospatial_lat_min=37.90744 geospatial_lat_units=degrees_north geospatial_lon_max=-122.6661694 geospatial_lon_min=-122.6661694 geospatial_lon_units=degrees_east infoUrl=https://www.bco-dmo.org/dataset/701840 institution=BCO-DMO instruments_0_dataset_instrument_description=At each of the six sites used for monthly trapping plus three additional sites, we placed 15 baited traps (folding Fukui fish traps) in shallow ( instruments_0_dataset_instrument_nid=701849 instruments_0_description=Fukui produces multi-species, multi-purpose collapsible or stackable fish traps, available in different sizes. instruments_0_instrument_name=Fukui fish trap instruments_0_instrument_nid=701772 instruments_0_supplied_name=Fukui fish traps metadata_source=https://www.bco-dmo.org/api/dataset/701840 Northernmost_Northing=37.90744 param_mapping={'701840': {'lat': 'master - latitude', 'lon': 'master - longitude'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/701840/parameters people_0_affiliation=University of California-Davis people_0_affiliation_acronym=UC Davis people_0_person_name=Edwin Grosholz people_0_person_nid=699768 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=Portland State University people_1_affiliation_acronym=PSU people_1_person_name=Catherine de Rivera people_1_person_nid=699771 people_1_role=Co-Principal Investigator people_1_role_type=originator people_2_affiliation=Portland State University people_2_affiliation_acronym=PSU people_2_person_name=Gregory Ruiz people_2_person_nid=471603 people_2_role=Co-Principal Investigator people_2_role_type=originator people_3_affiliation=Woods Hole Oceanographic Institution people_3_affiliation_acronym=WHOI BCO-DMO people_3_person_name=Shannon Rauch people_3_person_nid=51498 people_3_role=BCO-DMO Data Manager people_3_role_type=related project=Invasive_predator_harvest projects_0_acronym=Invasive_predator_harvest projects_0_description=The usual expectation is that when populations of plants and animals experience repeated losses to predators or human harvest, they would decline over time. If instead these populations rebound to numbers exceeding their initial levels, this would seem counter-intuitive or even paradoxical. However, for several decades mathematical models of population processes have shown that this unexpected response, formally known as overcompensation, is not only possible, but even expected under some circumstances. In what may be the first example of overcompensation in a marine system, a dramatic increase in a population of the non-native European green crab was recently observed following an intensive removal program. This RAPID project will use field surveys and laboratory experiments to verify that this population explosion results from overcompensation. Data will be fed into population models to understand to what degree populations processes such as cannibalism by adult crabs on juvenile crabs and changes in maturity rate of reproductive females are contributing to or modifying overcompensation. The work will provide important insights into the fundamental population dynamics that can produce overcompensation in both natural and managed populations. Broader Impacts include mentoring graduate trainees and undergraduate interns in the design and execution of field experiments as well as in laboratory culture and feeding experiments. The project will also involve a network of citizen scientists who are involved with restoration activities in this region and results will be posted on the European Green Crab Project website. This project aims to establish the first example of overcompensation in marine systems. Overcompensation refers to the paradoxical process where reduction of a population due to natural or human causes results in a greater equilibrium population than before the reduction. A population explosion of green crabs has been recently documented in a coastal lagoon and there are strong indications that this may be the result of overcompensation. Accelerated maturation of females, which can accompany and modify the expression of overcompensation has been observed. This RAPID project will collect field data from this unusual recruitment class and conduct targeted mesocosm experiments. These will include population surveys and mark-recapture studies to measure demographic rates across study sites. Laboratory mesocosm studies using this recruitment class will determine size specific mortality. Outcomes will be used in population dynamics models to determine to what degree overcompensation has created this dramatic population increase. The project will seek answers to the following questions: 1) what are the rates of cannibalism by adult green crabs and large juveniles on different sizes of juvenile green crabs, 2) what are the consequences of smaller size at first reproduction for population dynamics and for overcompensation and 3) how quickly will the green crab population return to the levels observed prior to the eradication program five years earlier? projects_0_end_date=2016-11 projects_0_geolocation=Europe projects_0_name=RAPID: A rare opportunity to examine overcompensation resulting from intensive harvest of an introduced predator projects_0_project_nid=699765 projects_0_start_date=2014-12 sourceUrl=(local files) Southernmost_Northing=37.90744 standard_name_vocabulary=CF Standard Name Table v55 subsetVariables=lagoon,latitude,longitude version=1 Westernmost_Easting=-122.6661694 xml_source=osprey2erddap.update_xml() v1.3

Throughout most of human history, global population growth was very low; between 10,000BCE and 1700CE, the average annual increase was just 0.04 percent. Therefore, it took several thousand years for the global population to reach one billion people, doing so in 1803. However, this period marked the beginning of a global phenomenon known as the demographic transition, from which point population growth skyrocketed. With the introduction of modern medicines (especially vaccination), as well as improvements in water sanitation, food supply, and infrastructure, child mortality fell drastically and life expectancy increased, causing the population to grow. This process is linked to economic and technological development, and did not take place concurrently across the globe; it mostly began in Europe and other industrialized regions in the 19thcentury, before spreading across Asia and Latin America in the 20th century. As the most populous societies in the world are found in Asia, the demographic transition in this region coincided with the fastest period of global population growth. Today, Sub-Saharan Africa is the region at the earliest stage of this transition. As population growth slows across the other continents, with the populations of the Americas, Asia, and Europe expected to be in decline by the 2070s, Africa's population is expected to grow by three billion people by the end of the 21st century.

Plant demography is a function of both the vital rate characteristics of a species (i.e., survival, growth, and reproduction) and the environmental factors that interact with them to create population dynamics. A more detailed understanding of how local-scale environmental factors and variation in individual vital rates shape population-level demographic patterns is needed to improve predictions of population responses to environmental change and implement successful plant conservation strategies. In this study, we examined how individual vital rates for Shortia galacifolia, an endangered, evergreen herb endemic to the southern Blue Ridge Mountains, USA, change as a function of individual size and resource availability and how that variation affects Shortia demography at four sites representing natural and introduced populations using integral projection models (IPMs). We found that Shortia population growth is positively related to individual size and soil moisture. Changes in soil moisture availability altered the importance of survival and growth in predicting Shortia demography but did not affect the contribution of asexual reproduction for most sites. Moreover, changes in vital rate contributions under a low soil moisture scenario were limited to introduced populations growing outside Shortia’s natural climate envelope. Our study underscores the importance of quantifying the influence of individual state characteristics and environmental variables on different vital rates among natural and introduced populations and demonstrates how the combination of these factors can contribute to the success or failure of rare plant populations.

Much of our understanding of natural invasions is retrospective, based on data acquired after invaders become established. As a consequence, we know little about the characteristics of the early population growth and habitat use of the invaders during establishment. Here we report on experimental introductions of guppies into natural streams in which we conducted monthly censuses of each population. Two of the four introductions were in streams with thinned canopies, which mimics a common form of habitat disturbance. We conducted similar censuses of natural populations to characterize natural population densities and generate a null distribution against which we could test a priori hypotheses about the establishment of the experimental invaders. We constructed a pedigree for one population, which enabled us to quantify lifetime reproductive success. Population simulations predict that the nature of the introduced population's life history, in combination with reduced risk of predation in the introduction sites, will result in explosive population growth; however, populations of introduced invaders instead grew to match densities observed in natural streams with intact canopies. Experimental populations in streams with thinned canopies grew to densities that often exceeded those of natural streams with intact canopies. High population densities were associated with the increased use of marginal habitat. Adult females and males that moved into marginal habitat suffered no apparent fitness loss, suggesting lower population densities found there compensated for lower habitat quality. Our results suggest that the ecological setting in which invasions occur plays a role at least comparable in importance to that of the invader's inherent characteristics in shaping early population growth and habitat use.

IntroductionThe factors that determine the growth and spread advantages of an alien plant during the invasion process remain open to debate. The genetic diversity and differentiation of an invasive plant population might be closely related to its growth adaptation and spread in the introduced range. However, little is known about whether phenotypic and genetic variation in invasive plant populations covary during the invasion process along invaded geographic distances.MethodsIn a wild experiment, we examined the genetic variation in populations of the aggressively invasive species Erigeron annuus at different geographical distances from the first recorded point of introduction (FRPI) in China. We also measured growth traits in the wild and common garden experiments, and the coefficient of variation (CV) of populations in the common garden experiments.Results and discussionWe found that E. annuus populations had better growth performance (i.e., height and biomass) and genetic diversity, and less trait variation, in the long-term introduced region (east) than in the short-term introduced region (west). Furthermore, population growth performance was significantly positively or negatively correlated with genetic diversity or genetic variation. Our results indicate that there was parallel genetic and phenotypic differentiation along the invaded geographic distance in response to adaptation and spread, and populations that entered introduced regions earlier had consistently high genetic diversity and high growth dominance. Growth and reproduction traits can be used as reliable predictors of the adaptation and genetic variation of invasive plants.

The region of present-day China has historically been the most populous region in the world; however, its population development has fluctuated throughout history. In 2022, China was overtaken as the most populous country in the world, and current projections suggest its population is heading for a rapid decline in the coming decades. Transitions of power lead to mortality The source suggests that conflict, and the diseases brought with it, were the major obstacles to population growth throughout most of the Common Era, particularly during transitions of power between various dynasties and rulers. It estimates that the total population fell by approximately 30 million people during the 14th century due to the impact of Mongol invasions, which inflicted heavy losses on the northern population through conflict, enslavement, food instability, and the introduction of bubonic plague. Between 1850 and 1870, the total population fell once more, by more than 50 million people, through further conflict, famine and disease; the most notable of these was the Taiping Rebellion, although the Miao an Panthay Rebellions, and the Dungan Revolt, also had large death tolls. The third plague pandemic also originated in Yunnan in 1855, which killed approximately two million people in China. 20th and 21st centuries There were additional conflicts at the turn of the 20th century, which had significant geopolitical consequences for China, but did not result in the same high levels of mortality seen previously. It was not until the overlapping Chinese Civil War (1927-1949) and Second World War (1937-1945) where the death tolls reached approximately 10 and 20 million respectively. Additionally, as China attempted to industrialize during the Great Leap Forward (1958-1962), economic and agricultural mismanagement resulted in the deaths of tens of millions (possibly as many as 55 million) in less than four years, during the Great Chinese Famine. This mortality is not observable on the given dataset, due to the rapidity of China's demographic transition over the entire period; this saw improvements in healthcare, sanitation, and infrastructure result in sweeping changes across the population. The early 2020s marked some significant milestones in China's demographics, where it was overtaken by India as the world's most populous country, and its population also went into decline. Current projections suggest that China is heading for a "demographic disaster", as its rapidly aging population is placing significant burdens on China's economy, government, and society. In stark contrast to the restrictive "one-child policy" of the past, the government has introduced a series of pro-fertility incentives for couples to have larger families, although the impact of these policies are yet to materialize. If these current projections come true, then China's population may be around half its current size by the end of the century.

About this dataset

Context

The dataset tabulates the Ann Arbor 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 Ann Arbor 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 Ann Arbor was 119,381, a 0.45% decrease year-by-year from 2022. Previously, in 2022, Ann Arbor population was 119,924, an increase of 0.73% compared to a population of 119,060 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Ann Arbor increased by 4,840. In this period, the peak population was 123,611 in the year 2020. 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 Ann Arbor is shown in this column.
• Year on Year Change: This column displays the change in Ann Arbor 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 Ann Arbor Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Alabama 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 Alabama 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 2024, the population of Alabama was 5.16 million, a 0.78% increase year-by-year from 2023. Previously, in 2023, Alabama population was 5.12 million, an increase of 0.82% compared to a population of 5.08 million in 2022. Over the last 20 plus years, between 2000 and 2024, population of Alabama increased by 706,202. In this period, the peak population was 5.16 million in the year 2024. 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 2024

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2024)
• Population: The population for the specific year for the Alabama is shown in this column.
• Year on Year Change: This column displays the change in Alabama 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 Alabama Population by Year. You can refer the same here

Tethering data for introduced crab for 2015. Experiments were conducted in several bays along Central California coast, shallow subtidal (<3 m depth). access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson acquisition_description=We conducted tethering experiments in several northern California bays: Bodega Harbor, Tomales Bay, Bolinas Lagoon, and Seadrift Lagoon. All sites were accessed by foot via shore entry.\u00a0At each of four sites within each bay, we placed 10 small European green crabs (collected locally) in parallel arrays near the 0.0 tide level. Tethers were retrieved 24 hours later data and scored for presence/absence of crab including missing appendages and or condition of remaining tether line.

See Turner et al. (2016) Biological Invasions 18: 533-548 for additional methodological details:
Turner, B.C., de Rivera, C.E., Grosholz, E.D., & Ruiz, G.M. 2016. Assessing population increase as a possible outcome to management of invasive species. Biological Invasions, 18(2), pp 533\u2013548. doi:10.1007/s10530-015-1026-9 awards_0_award_nid=699764 awards_0_award_number=OCE-1514893 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1514893 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=David L. Garrison awards_0_program_manager_nid=50534 cdm_data_type=Other comment=Tethering data for introduced crab for 2015 PI: Edwin Grosholz (UC Davis) Co-PI: Catherine de Rivera & Gregory Ruiz (Portland State University)
Version: 15 June 2017 Note that all Seadrift sites are very close together and thus one lat/lon pair are used to represent all sites within Seadrift. Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.701726.1 Easternmost_Easting=-122.653096 geospatial_lat_max=38.316968 geospatial_lat_min=37.906503 geospatial_lat_units=degrees_north geospatial_lon_max=-122.653096 geospatial_lon_min=-123.058725 geospatial_lon_units=degrees_east infoUrl=https://www.bco-dmo.org/dataset/701726 institution=BCO-DMO metadata_source=https://www.bco-dmo.org/api/dataset/701726 Northernmost_Northing=38.316968 param_mapping={'701726': {'lat': 'master - latitude', 'lon': 'master - longitude'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/701726/parameters people_0_affiliation=University of California-Davis people_0_affiliation_acronym=UC Davis people_0_person_name=Edwin Grosholz people_0_person_nid=699768 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=Portland State University people_1_affiliation_acronym=PSU people_1_person_name=Catherine de Rivera people_1_person_nid=699771 people_1_role=Co-Principal Investigator people_1_role_type=originator people_2_affiliation=Portland State University people_2_affiliation_acronym=PSU people_2_person_name=Gregory Ruiz people_2_person_nid=471603 people_2_role=Co-Principal Investigator people_2_role_type=originator people_3_affiliation=Woods Hole Oceanographic Institution people_3_affiliation_acronym=WHOI BCO-DMO people_3_person_name=Shannon Rauch people_3_person_nid=51498 people_3_role=BCO-DMO Data Manager people_3_role_type=related project=Invasive_predator_harvest projects_0_acronym=Invasive_predator_harvest projects_0_description=The usual expectation is that when populations of plants and animals experience repeated losses to predators or human harvest, they would decline over time. If instead these populations rebound to numbers exceeding their initial levels, this would seem counter-intuitive or even paradoxical. However, for several decades mathematical models of population processes have shown that this unexpected response, formally known as overcompensation, is not only possible, but even expected under some circumstances. In what may be the first example of overcompensation in a marine system, a dramatic increase in a population of the non-native European green crab was recently observed following an intensive removal program. This RAPID project will use field surveys and laboratory experiments to verify that this population explosion results from overcompensation. Data will be fed into population models to understand to what degree populations processes such as cannibalism by adult crabs on juvenile crabs and changes in maturity rate of reproductive females are contributing to or modifying overcompensation. The work will provide important insights into the fundamental population dynamics that can produce overcompensation in both natural and managed populations. Broader Impacts include mentoring graduate trainees and undergraduate interns in the design and execution of field experiments as well as in laboratory culture and feeding experiments. The project will also involve a network of citizen scientists who are involved with restoration activities in this region and results will be posted on the European Green Crab Project website. This project aims to establish the first example of overcompensation in marine systems. Overcompensation refers to the paradoxical process where reduction of a population due to natural or human causes results in a greater equilibrium population than before the reduction. A population explosion of green crabs has been recently documented in a coastal lagoon and there are strong indications that this may be the result of overcompensation. Accelerated maturation of females, which can accompany and modify the expression of overcompensation has been observed. This RAPID project will collect field data from this unusual recruitment class and conduct targeted mesocosm experiments. These will include population surveys and mark-recapture studies to measure demographic rates across study sites. Laboratory mesocosm studies using this recruitment class will determine size specific mortality. Outcomes will be used in population dynamics models to determine to what degree overcompensation has created this dramatic population increase. The project will seek answers to the following questions: 1) what are the rates of cannibalism by adult green crabs and large juveniles on different sizes of juvenile green crabs, 2) what are the consequences of smaller size at first reproduction for population dynamics and for overcompensation and 3) how quickly will the green crab population return to the levels observed prior to the eradication program five years earlier? projects_0_end_date=2016-11 projects_0_geolocation=Europe projects_0_name=RAPID: A rare opportunity to examine overcompensation resulting from intensive harvest of an introduced predator projects_0_project_nid=699765 projects_0_start_date=2014-12 sourceUrl=(local files) Southernmost_Northing=37.906503 standard_name_vocabulary=CF Standard Name Table v55 version=1 Westernmost_Easting=-123.058725 xml_source=osprey2erddap.update_xml() v1.3