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.

Wildlife populations often exhibit unequal catchability between subgroups such as males and females. This heterogeneity of capture probabilities can bias both population size and sex ratio estimates. Several authors have suggested that this problem can be overcome by treating males and females as separate populations and calculating a population estimate for each of them. However, this suggestion has received little testing, and many researchers do not implement it. Therefore, we used two simulations to test the utility of this method. One simulated a closed population, while the other simulated an open population and used the robust design to calculate population sizes. We tested both simulations with multiple levels of heterogeneity, and we used a third simulation to test several methods for detecting heterogeneity of capture probabilities. We found that treating males and females as separate populations produced more accurate population and sex ratio estimates. The benefits of this method were particularly pronounced for sex ratio estimates. When males and females were included as a single population, the sex ratio estimates became inaccurate when even slight heterogeneity was present, but when males and females were treated separately, the estimates were accurate even when large biases were present. Nevertheless, treating males and females separately reduced precision, and this method may not be appropriate when capture and recapture rates are low. None of the methods for detecting heterogeneity were robust, and we do not recommend that researchers rely on them. Rather, we suggest separating populations by sex, age, or other subgroups whenever sample sizes permit.

These detailed tables show sample sizes and population estimates from the 2012 National Survey on Drug Use and Health (NSDUH) Mental Health Detailed Tables. Samples sizes and population estimates are provided age group, gender, race/ethnicity, education level, employment status, county type, poverty level, insurance status, overal health, and geographic area.

We used data from different sources, developed and applied several estimation methods, and confronted the resulting estimates with species experts to achieve breeding size population estimates for all bird species currently breeding in Switzerland. Population size estimates of birds have a wide range of practical conservation uses. As part of the Swiss Breeding Bird Atlas 2013–2016, we aimed to update the national breeding population size estimates for all species. For very rare species and for rare colonial breeders, the estimates are complete enumerations based on annual compilations of all breeding records. For the other species, we applied extrapolation methods of varying complexity, either based on the data from the 2318 one-kilometre squares where territory mapping for the breeding bird atlas was conducted, or based on all breeding period records from 2013 to 2016. In some cases, these data were combined with regional population estimates. For most species, we considered the results of several different approaches to determine the final estimate. Here, we give an overview of the applied procedures, along with some examples. We provide access to code that allows the reproduction of our analyses. We present population size estimates for all 210 species and 4 subspecies of birds breeding in Switzerland. The total population size of all Swiss breeding birds amounts to just over ten million breeding pairs. The largest share consists of species with forest as their main habitat. A main challenge was to decide species by species which approaches lead to the most reliable results. The application of a rule-based selection approach alone is dangerous. Comparing the outcome of different approaches and involvement of species experts are crucial steps to getting sound population size estimates.

Effective population size is a fundamental parameter in population genetics, evolutionary biology and conservation biology, yet its estimation can be fraught with difficulties. Several methods to estimate Ne from genetic data have been developed which take advantage of various approaches for inferring Ne. The ability of these methods to accurately estimate Ne, however, has not been comprehensively examined. In this study, we employ seven of the most cited methods for estimating Ne from genetic data (Colony2, CoNe, Estim, MLNe, ONeSAMP, TMVP, and NeEstimator including LDNe) across simulated datasets with populations experiencing migration or no migration. The simulated population demographies are an isolated population with no immigration, an island model metapopulation with a sink population receiving immigrants, and an isolation by distance stepping stone model of populations. We find considerable variance in performance of these methods, both within and across demographic scenarios, w...

Urban and regional planners rely on Average Household Size as a foundational indicator for many of their models, calculations, and plans. Average household size (also known as "people per household") is a reflection of many dynamics at play, for example:Age of the population, as many older people tend to live in smaller households (one-person or two-person households)Housing prices in the area, proximity to colleges and universities, and how likely people are to live with roommatesFamily norms and traditions (e.g., multigenerational families are more common in some areas and with some population groups)This feature layer contains the Average Household Size and Population Density for states, counties, and tracts. Data from U.S. Census Bureau's 2014-2018 American Community Survey's 5-year estimates, Tables B25010 and B01001. Population Density was calculated based on the total population and area of land fields, which both came from the U.S. Census Bureau. See the field description for the formula used.This layer is symbolized to show the average household size. Population density, as well as average household size breakdown by housing tenure is presented in the pop-up. Click the Data tab -> Fields list to see all available attributes and their definitions.

Estimating population size by means of mark-resighting counts: theoretical considerations and empirical results

This layer shows Population. This is shown by state and county boundaries. This service contains the 2018-2022 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the point by Population Density and size of the point by Total Population. The size of the symbol represents the total count of housing units. Population Density was calculated based on the total population and area of land fields, which both came from the U.S. Census Bureau. Formula used for Calculating the Pop Density (B01001_001E/GEO_LAND_AREA_SQ_KM). To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2018-2022ACS Table(s): B01001, B09020Data downloaded from: Census Bureau's API for American Community Survey Date of API call: January 18, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

Population size and its distribution, as reflected by age and gender structures and geographical distribution, are essential date for the setting up of economic and social development plans.This statistic represents an estimate of the population by gender and age groups.

Yates_et_al_2016_datasetData associated with quantitative synthesis examining Nb/Nc relationships among three salmonid fishes

An estimation of the size of the self-funding population using regulated community care services in England, using an experimental method. Weighted annual data broken down by geographic variables and care home characteristics.

1. Estimation of national population size can be important for setting conservation priorities but its methodology has received little critical attention. Sites for highly aggregated species are often prioritised if they contain 1% of national or biogeographical populations but the utility of this approach for other species is unclear. 2. To make recommendations for study design, we present methods used to estimate the UK population size of the common nightingale Luscinia megarhynchos. We assess the sensitivity of the population estimate to the analytical method used and identify sites of national importance for this territorial songbird. 3. Survey effort was directed by prior knowledge of the species' distribution and the survey design maximised detectability by focussing on the period of greatest song output. We used three different statistical methods to account for detectability, estimating that 55–65% of the national population was detected during surveys. 4. Birds in areas not known to contain the species accounted for 13–23% of the population estimate. Methods to account for these individuals contributed the greatest uncertainty to the results, due to the difficulty of surveying a very large sample of random sites and consequent need to stratify the sample. 5. The 12 derived estimates ranged between 5094 and 5938 territorial males, with the confidence limits ranging from 4764 to 6534. Site delimitation, using clustering based on nearest-neighbour distances, identified one site clearly of national importance and several others potentially nationally important, depending on the population threshold and clustering distance used. 6. Synthesis and applications. National population estimation is difficult and requires that species-specific variability in detectability and individuals present outside surveyed areas are accurately accounted for through survey design and statistical analysis. Accounting for these sources of error will not always be possible and will hamper efforts to assess true population size and consequently to determine whether sites, however defined, exceed critical thresholds of importance. Resources may be better invested in other activities, for example in generating population trends based on relative indices. The latter are generally easier to produce, potentially more robust and arguably more suitable for many conservation applications.

1.Temporary migration – where individuals can leave and re-enter a sampled population – is a feature of many capture–mark–recapture (CMR) studies of mobile populations which, if unaccounted for, can lead to biased estimates of population capture probabilities and consequently biased estimates of population abundance. 2. We present a method for incorporating radiotelemetry data within a CMR study to eliminate bias due to temporary migration using a Bayesian state-space model. 3. Our results indicate that using a relatively small number of telemetry tags, it is possible to greatly reduce bias in estimates of capture probabilities using telemetry data to model transition probabilities in and out of the sampling area. In a capture–recapture data set for trout Cod in the Murray river, Australia, accounting for temporary migration led to overall higher estimates of capture probabilities than models assuming permanent or zero migration. Also, individual heterogeneity in detectability can be managed through explicit modelling. We show how accounting for temporary migration when estimating capture probabilities can be used to estimate the abundance and size distribution of a population as though it were closed. 4. Our model provides a basis for more complex models that might integrate telemetry data into other CMR scenarios, thus allowing for greater precision in estimates of vital rates that might otherwise be biased by temporary migration. Our results highlight the importance of accounting for migration in survey design and parameter estimation, and the potential scope for supplementing large-scale CMR data sets with a subset of auxiliary data that provide information on processes that are hidden to primary sampling processes.

The Western Capercaillie (Tetrao urogallus), hereafter capercaillie, is the largest galliform bird present in the boreal and montane forests of the Western Palearctic. Precise and accurate methods for estimating the number of individuals and/or their densities are crucial for the proper management of its free-ranging populations. However, obtaining reliable estimates of the abundance of populations of wild species and, particularly, of birds is not a simple task. In the case of lek-mating birds such as capercaillie, surveys are traditionally based on lek counts, that is, counts of calling males present in their mating areas: the leks. This study was carried out on the Pyrenees at six capercaillie leks where two different lek counting approaches were performed: hide-based and walk-based. The results were compared with those obtained from an estimate of minimum population size (MPE) derived from genotyping all faeces samples found in the lek area, and with a population size estimate deriv..., To estimate the population size (N) of each lek, we use only the male samples that have been correctly analysed i.e., with a QI>0.6. We used the Bayesian approach of this classical M0 model, in which the model is implemented in JAGS code (Plummer 2003) using data augmentation (Kéry and Schaub 2012, Chapter 6.2.1). This model estimated the detection probability on all three sampling occasions based on the assumption that all individuals in each sampling session have equal detection probabilities (see Otis et al., 1978, Kéry and Schaub, 2012 and Supplementary Material S1 for more details and see the Otis Mo model code in R). References: Kéry, M. and Schaub, M. 2012. Bayesian population analysis using WinBUGS. A hierarchical perspective. First edition. - Academic Press. Otis, D.L., Burnham, K. P., White, G. C. and Anderson, D. R. 1978. Statistical inference from capture data on closed animal populations. – Wildl. Monogr. 62:3–135. Plummer, M. 2003. JAGS: a program for analysis of Bayesi..., , # 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 ...

This dataset contains human population density for the state of California and a small portion of western Nevada for the year 2000. The population density is based on US Census Bureau data and has a cell size of 990 meters.

The purpose of the dataset is to provide a consistent statewide human density GIS layer for display, analysis and modeling purposes.

The state of California, and a very small portion of western Nevada, was divided into pixels with a cell size 0.98 km2, or 990 meters on each side. For each pixel, the US Census Bureau data was clipped, the total human population was calculated, and that population was divided by the area to get human density (people/km2) for each pixel.

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...

This map features the World Population Density Estimate 2016 layer for the Caribbean region. The advantage population density affords over raw counts is the ability to compare levels of persons per square kilometer anywhere in the world.

Scottish Marine and Freshwater Science Vol 8 No 4 There is considerable interest in the use of genetic tools to assess the conservation status of Atlantic salmon. Here we assess the feasibility of using genetic methods to estimate effective population size and number of breeders outlining their merits together with potential drawbacks.

Grizzly bear population inventory work has been underway in various population units along the eastern slopes of Alberta. As of the time of the preparation of this report two population units have not been inventoried, the Swan Hills and the Alberta North units. In order to provide a science based estimate of possible grizzly bear population size in the Swan Hills unit we utilized existing data sets from previous DNA inventory work to provide estimates for the purpose of a species status review. We used an objective, multi-scale approach to estimating occupancy-habitat associations based on DNA hair snag information and predicted (extrapolated) occupancy to similar habitats in the Swan Hills grizzly bear management unit.