Use of genetic methods to estimate effective population size (N^e) is rapidly increasing, but all approaches make simplifying assumptions unlikely to be met in real populations. In particular, all assume a single, unstructured population, and none has been evaluated for use with continuously distributed species. We simulated continuous populations with local mating structure, as envisioned by Wright's concept of neighborhood size (NS), and evaluated performance of a single-sample estimator based on linkage disequilibrium (LD), which provides an estimate of the effective number of parents that produced the sample (N^b). Results illustrate the interacting effects of two phenomena, drift and mixture, that contribute to LD. Samples from areas equal to or smaller than a breeding window produced estimates close to the NS. As the sampling window increased in size to encompass multiple genetic neighborhoods, mixture LD from a two-locus Wahlund effect overwhelmed the reduction in drift LD from i...

The distinction between the effective size of a population (Ne) and the effective size of its neighborhoods (Nn) has sometimes become blurred. Ne reflects the effect of random sampling on the genetic composition of a population of size N, whereas Nn is a measure of within-population spatial genetic structure and depends strongly on the dispersal characteristics of a species. Although Nn is independent of Ne, the reverse is not true. Using simulations of a population of annual plants, it was found that the effect of Nn on Ne was well approximated by Ne=N/(1−FIS), where FIS (determined by Nn) was evaluated population wide. Nn only had a notable influence of increasing Ne as it became smaller (less than or equal to16). In contrast, the effect of Nn on genetic estimates of Ne was substantial. Using the temporal method (a standard two-sample approach) based on 1000 single-nucleotide polymorphisms (SNPs), and varying sampling method, sample size (2–25% of N) and interval between samples (T=1–32 generations), estimates of Ne ranged from infinity to <0.1% of the true value (defined as Ne based on 100% sampling). Estimates were never accurate unless Nn and T were large. Three sampling techniques were tested: same-site resampling, different-site resampling and random sampling. Random sampling was the least biased method. Extremely low estimates often resulted when different-site resampling was used, especially when the population was large and the sample fraction was small, raising the possibility that this estimation bias could be a factor determining some very low Ne/N that have been published.

Estimates are averages of 500 simulations.

Abstract

The Estimating the Size of Populations through a Household Survey (EPSHS), sought to assess the feasibility of the network scale-up and proxy respondent methods for estimating the sizes of key populations at higher risk of HIV infection and to compare the results to other estimates of the population sizes. The study was undertaken based on the assumption that if these methods proved to be feasible with a reasonable amount of data collection for making adjustments, countries would be able to add this module to their standard household survey to produce size estimates for their key populations at higher risk of HIV infection. This would facilitate better programmatic responses for prevention and caring for people living with HIV and would improve the understanding of how HIV is being transmitted in the country.

The specific objectives of the ESPHS were: 1. To assess the feasibility of the network scale-up method for estimating the sizes of key populations at higher risk of HIV infection in a Sub-Saharan African context; 2. To assess the feasibility of the proxy respondent method for estimating the sizes of key populations at higher risk of HIV infection in a Sub-Saharan African context; 3. To estimate the population size of MSM, FSW, IDU, and clients of sex workers in Rwanda at a national level; 4. To compare the estimates of the sizes of key populations at higher risk for HIV produced by the network scale-up and proxy respondent methods with estimates produced using other methods; and 5. To collect data to be used in scientific publications comparing the use of the network scale-up method in different national and cultural environments.

Geographic coverage

National

Analysis unit

• Household
• Individual

Sampling procedure

The Estimating the Size of Populations through a Household Survey (ESPHS) used a two-stage sample design, implemented in a representative sample of 2,125 households selected nationwide in which all women and men age 15 years and above where eligible for an individual interview. The sampling frame used was the preparatory frame for the Rwanda Population and Housing Census (RPHC), which was conducted in 2012; it was provided by the National Institute of Statistics of Rwanda (NISR).

The sampling frame was a complete list of natural villages covering the whole country (14,837 villages). Two strata were defined: the city of Kigali and the rest of the country. One hundred and thirty Primary Sampling Units (PSU) were selected from the sampling frame (35 in Kigali and 95 in the other stratum). To reduce clustering effect, only 20 households were selected per cluster in Kigali and 15 in the other clusters. As a result, 33 percent of the households in the sample were located in Kigali.

The list of households in each cluster was updated upon arrival of the survey team in the cluster. Once the listing had been updated, a number was assigned to each existing household in the cluster. The supervisor then identified the households to be interviewed in the survey by using a table in which the households were randomly pre-selected. This table also provided the list of households pre-selected for each of the two different definitions of what it means "to know" someone.

For further details on sample design and implementation, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Estimating the Size of Populations through a Household Survey (ESPHS) used two types of questionnaires: a household questionnaire and an individual questionnaire. The same individual questionnaire was used to interview both women and men. In addition, two versions of the individual questionnaire were developed, using two different definitions of what it means “to know” someone. Each version of the individual questionnaire was used in half of the selected households.

Cleaning operations

The processing of the ESPHS data began shortly after the fieldwork commenced. Completed questionnaires were returned periodically from the field to the SPH office in Kigali, where they were entered and checked for consistency by data processing personnel who were specially trained for this task. Data were entered using CSPro, a programme specially developed for use in DHS surveys. All data were entered twice (100 percent verification). The concurrent processing of the data was a distinct advantage for data quality, because the School of Public Health had the opportunity to advise field teams of problems detected during data entry. The data entry and editing phase of the survey was completed in late August 2011.

Response rate

A total of 2,125 households were selected in the sample, of which 2,120 were actually occupied at the time of the interview. The number of occupied households successfully interviewed was 2,102, yielding a household response rate of 99 percent.

From the households interviewed, 2,629 women were found to be eligible and 2,567 were interviewed, giving a response rate of 98 percent. Interviews with men covered 2,102 of the eligible 2,149 men, yielding a response rate of 98 percent. The response rates do not significantly vary by type of questionnaire or residence.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) non-sampling errors, and (2) sampling errors. Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made to minimize this type of error during the implementation of the Rwanda ESPHS 2011, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the ESPHS 2011 is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the ESPHS 2011 sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the ESPHS 2011 is a SAS program. This program uses the Taylor linearization method for variance estimation for survey estimates that are means or proportions.

A more detailed description of estimates of sampling errors are presented in Appendix B of the survey report.

Population size is a main indicator of conservation potential, thought to predict both current and long-term population viability. However, few studies have directly examined the links between the size and the genetic and demographic properties of populations, using metrics that integrate effects across the whole life cycle. In this study, we combined six years of demographic data with SNP-based estimates of genetic diversity from 18 Swedish populations of the orchid Gymnadenia conopsea. We assessed whether stochastic growth rate increases with population size and genetic diversity, and used stochastic LTRE analysis to evaluate how underlying vital rates contribute to among-population variation in growth rate. For each population, we also estimated the probability of quasi-extinction (shrinking below a threshold size) and of a severe (90%) decline in population size, within the next 30 years. Estimates of stochastic growth rate indicated that ten populations are declining, seven increasing, and one population is approximately stable. SLTRE decomposition showed that low mean adult survival and growth characterized strongly declining populations, whereas high mean fecundity characterized strongly increasing populations. Stochastic growth rate increased with population size, mainly due to higher survival in larger populations, but was not related to genetic diversity. One third of the populations were predicted to go extinct and eight populations to undergo a 90% decrease in population size in the coming 30 years. Low survival in small populations most likely reflects a positive association between local environmental conditions and population size. Synthesis: The association between G. conopsea population size and viability was driven by variation in survival, and there was no sign that ongoing declines are due to genetic erosion. This suggests that large populations occur in favourable habitats that buffer effects of climatic variation. The results also illustrate that demographic metrics can be more informative than genetic metrics, regarding conservation priority. Methods The dataset contains six years of demographic data (2017-2022) from each of 18 populations of Gymnadenia conopsea on the island of Öland in Sweden, and the code to run integral projection models in R.

ABOUT

The Global Population Growth Dataset provides a comprehensive record of population trends across various countries over multiple decades. It includes detailed information such as the country name, ISO3 country code, year-wise population data, population growth, and growth rate. This dataset is valuable for researchers, demographers, policymakers, and data analysts interested in studying population dynamics, demographic trends, and economic development.

Key features of the dataset:

✅ Covers multiple countries and regions worldwide
✅ Includes historical and recent population data
✅ Provides year-wise population growth and growth rate (%)
✅ Categorizes data by country and decade for better trend analysis

This dataset serves as a crucial resource for analyzing global population trends, understanding demographic shifts, and supporting socio-economic research and policy-making.

FILE INFORMATION

The dataset consists of structured records related to country-wise population data, compiled from official sources. Each file contains information on yearly population figures, growth trends, and country-specific data. The structured format makes it useful for researchers, economists, and data scientists studying demographic patterns and changes. The file type is CSV.

COLUMNS DESCRIPTION

• Country – The name of the country.
• ISO3 – The three-letter ISO code of the country.
• Year – The year corresponding to the population data, useful for trend analysis.
• Population – The total population of the country for the given year.
• Population Growth – The absolute increase in population compared to the previous year.
• Growth Rate (%) – The percentage change in population compared to the previous year.
• Decade – The decade classification (e.g., 1990s, 2000s) for grouping long-term trends.

Estimates of effective population size are critical for species of conservation concern. Genetic datasets can be used to provide robust estimates of this important parameter. However, the methods used to obtain these estimates assume that generations are discrete. We used simulated data to assess the influences of overlapping generations on estimates of effective size provided by the linkage disequilibrium method. Our simulations focus on two factors: the degree of reproductive skew exhibited by the focal species and the generation time, without considering sample size or the level of polymorphism at marker loci. In situations where a majority of reproduction is achieved by a small fraction of the population, the effective number of breeders can be much smaller than the per generation effective population size. The linkage disequilibrium in samples of newborns can provide estimates of the former size, while our results indicate that the latter size is best estimated using random samples of reproductively mature adults. Using samples of adults, the downwards bias was less than ~15% across our simulated life histories. As noted in previous assessments, precision of the estimate depends on the magnitude of effective size itself, with greater precision achieved for small populations.

The values of the mean stochastic growth rate (Stoch-r), mean population size (N-all) and the mean time of extinction (Mean TE) for all three populations (Jutland, Zealand and Bornholm) and supplementation simulations (0,10, 20,40,100 and “influx”) with the most recent data from 2021.

Data for: Improving population size estimation at Western Capercaillie leks: lek counts vs. genetic methods

The databases provide the date of each sampling season and the number of individuals genotyped with the number of times each individual has been sampled within each sampling session.

We provide our data in nine databases (the different leks) ready-to-use databases by loading them into the R functions in Supplementary Material S1 for the Otis M0 model. The databases are:

AND1-2018.xlsx survey: 2018-1

AND1-2019.xlsx survey: 2019-1

AND2-2007.xlsx survey: 2017-2

T10-2018.xlsx survey: 2018-2

T17-2016.xlsx survey: 2016-1

T18-2016.xlsx survey: 2016-2

T19-2016.xlsx survey: 2016-3

T20-2016.xlsx survey: 2016-4

T20-2017.xlsx survey: 2017-1

Abbreviations of the database:

date: date the sample was collected (faeces)

indv: label of the genotyped individual

An additional database is:

Y_capercaille.xlsx

This database is a summary of the samples counting for the whole nine leks ...

The dataset contains state-, region- and gender-wise NSS 78th round compiled data on Average Size and Total Number of Households, and Total Number of Persons (by Gender) in India

About this dataset

Context

The dataset tabulates the Italy town population by year. The dataset can be utilized to understand the population trend of Italy town.

Content

The dataset constitues the following datasets

• Italy, New York 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/.

Velocity data for 12 Cape Race streamsVelocity (m/s) measurements measured in the field on cross-sections in 12 streams between 2010 and 2016. Pop; genetically distinct brook trout populations.Cape Race_Velocity.csvIndividual genotypes for 12 brook trout populationsIndividual genotype data from 12 microsatellites in 12 brook trout populations sampled from 2009-2015.Cape Race_Genotypes.csv

Harvesting and culling are methods used to monitor and manage wildlife diseases. An important consequence of these practices is a change in the genetic dynamics of affected populations that may threaten their long-term viability. The effective population size (Ne) is a fundamental parameter for describing such changes as it determines the amount of genetic drift in a population. Here, we estimate Ne of a harvested wild reindeer population in Norway. Then we use simulations to investigate the genetic consequences of management efforts for handling a recent spread of chronic wasting disease, including increased adult male harvest and population decimation. The Ne/N ratio in this population was found to be 0.124 at the end of the study period, compared to 0.239 in the preceding 14-year period. The difference was caused by increased harvest rates with a high proportion of adult males (older than 2.5 years) being shot (15.2 % in 2005-2018 and 44.8 % in 2021). Increased harvest rates decrease..., Data collectionThe data was collected from the wild reindeer population at Hardangervidda in Southern Norway (60°09’55’’ N, 07°27’58’’ E). The Hardangervidda population is subject to annual harvest before the rut in late summer or the beginning of autumn (August-September). Generally, hunters do not differentiate between female and male calves, and it is also difficult to determine the sex of yearlings (1.5 years old) during hunting. Thus, harvest quotas generally separate between calves (0.5 years old), females (2.5 years and older), yearlings (females and males 1.5 years old), and free licenses (animals of any age and sex). The latter category is typically used to shoot adult males (2.5 years and older), as their size and status as trophy is considered attractive by hunters. Data on the number of harvested animals in each of the six categories (calves, yearlings, and adults of both sexes) were collected as reported by hunters. Four different annual surveys are performed throughout the..., , # Data for: Harvest and decimation affect genetic drift and the effective population size in wild reindeer

https://doi.org/10.5061/dryad.brv15dvh6

These data were used in analyses of the effective population size (Ne) of the wild reindeer population and to investigate the impact of management procedures implemented in the population on Ne using simulation. The management procedures included changes in harvest strategies, such as increases in the proportion of adult males harvested, and population decimations. The harvest strategies and population decimations considered are based on the management's need to handle the recent spread of chronic wasting disease in wild populations of cervids in Norway.

Description of the data and file structure

Two data files are provided, 1) Counts of harvested animals in the age classes calves, yearlings, and adults of both sexes (filename: harvest_counts_hardangervidda_05-21.csv) and 2) Estimated populati...

About this dataset

Context

The dataset tabulates the China town population by year. The dataset can be utilized to understand the population trend of China town.

Content

The dataset constitues the following datasets

• China, Maine 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 Buffalo population by year. The dataset can be utilized to understand the population trend of Buffalo.

Content

The dataset constitues the following datasets

• Buffalo, NY 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 Hawaii population by year. The dataset can be utilized to understand the population trend of Hawaii.

Content

The dataset constitues the following datasets

• Hawaii 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/.

"This study deals primarily with the individual's preferences and opinions on population growth and family planning. Questions asked can be broken down into three categories: 1) family planning, including the ideal number of children, adoption of children, birth control information, abortion and sterilization; 2) social problems that stem from population size such as growth of cities and pollution problems; and 3) perception of population size in U.S. and other countries, including satisfacti on with present community and its size, and the part the government should play in population control."

Effective teaching and learning of population ecology requires integration of quantitative literacy skills. To facilitate student learning in population ecology and provide students with the opportunity to develop and apply quantitative skills, we designed a clicker-based lesson in which students investigate how ecologists measure and model population size. This lesson asks students to "engage like scientists" as they make predictions, plot data, perform calculations, and evaluate evidence. The lesson was taught in three sections of a large enrollment undergraduate class and assessed using a pre/post-test, in-class clicker-based questions, and multiple-choice exam questions. Student performance increased following peer discussion of clicker questions and on post-test questions. Students also performed well on the end-of-unit exam questions.

How population size influences quantitative genetic variation and differentiation among natural, fragmented populations remains unresolved. Small, isolated populations might occupy poor quality habitats and lose genetic variation more rapidly due to genetic drift than large populations. Genetic drift might furthermore overcome selection as population size decreases. Collectively, this might result in directional changes in additive genetic variation (VA) and trait differentiation (QST) from small to large population size. Alternatively, small populations might exhibit larger variation in VA and QST if habitat fragmentation increases variability in habitat types. We explored these alternatives by investigating VA and QST using nine fragmented populations of brook trout varying 50-fold in census size N (179-8416) and 10-fold in effective number of breeders, Nb (18-135). Across 15 traits, no evidence was found for consistent differences in VA and QST with population size and almost no evidence for increased variability of VA or QST estimates at small population size. This suggests that (i) small populations of some species may retain adaptive potential according to commonly adopted quantitative genetic measures and (ii) populations of varying sizes experience a variety of environmental conditions in nature, however extremely large studies are likely required before any firm conclusions can be made.

About this dataset

Context

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

Content

The dataset constitues the following datasets

• Winder, GA 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/.