In 2023, *** percent of South Dakota residents were American Indian or Alaska Native. A further **** percent of the population were white, and *** percent of South Dakota residents were of two or more races in that same year.

About this dataset

Context

The dataset tabulates the population of South Dakota by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for South Dakota. The dataset can be utilized to understand the population distribution of South Dakota by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in South Dakota. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for South Dakota.

Key observations

Largest age group (population): Male # 10-14 years (32,245) | Female # 5-9 years (30,539). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the South Dakota population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the South Dakota is shown in the following column.
• Population (Female): The female population in the South Dakota is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in South Dakota for each age group.

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 South Dakota Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the population of South Dakota by race. It includes the population of South Dakota across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of South Dakota across relevant racial categories.

Key observations

The percent distribution of South Dakota population by race (across all racial categories recognized by the U.S. Census Bureau): 81.52% are white, 2.24% are Black or African American, 7.73% are American Indian and Alaska Native, 1.40% are Asian, 0.07% are Native Hawaiian and other Pacific Islander, 1.40% are some other race and 5.65% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the South Dakota
• Population: The population of the racial category (excluding ethnicity) in the South Dakota is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of South Dakota total population. 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 South Dakota Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of South Dakota by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of South Dakota across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of male population, with 50.67% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the South Dakota is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of South Dakota total population. 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 South Dakota Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the North Dakota population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for North Dakota. The dataset can be utilized to understand the population distribution of North Dakota by age. For example, using this dataset, we can identify the largest age group in North Dakota.

Key observations

The largest age group in North Dakota was for the group of age 20 to 24 years years with a population of 61,543 (7.90%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in North Dakota was the 80 to 84 years years with a population of 14,488 (1.86%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the North Dakota is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of North Dakota total population. 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 North Dakota Population by Age. You can refer the same here

In 2023, Washington, D.C. had the highest population density in the United States, with 11,130.69 people per square mile. As a whole, there were about 94.83 residents per square mile in the U.S., and Alaska was the state with the lowest population density, with 1.29 residents per square mile. The problem of population density Simply put, population density is the population of a country divided by the area of the country. While this can be an interesting measure of how many people live in a country and how large the country is, it does not account for the degree of urbanization, or the share of people who live in urban centers. For example, Russia is the largest country in the world and has a comparatively low population, so its population density is very low. However, much of the country is uninhabited, so cities in Russia are much more densely populated than the rest of the country. Urbanization in the United States While the United States is not very densely populated compared to other countries, its population density has increased significantly over the past few decades. The degree of urbanization has also increased, and well over half of the population lives in urban centers.

In 2021, just over five percent of the total population of South Dakota was uninsured. The largest part of South Dakota's population was insured through employers. This statistic depicts the health insurance status distribution of the total population in South Dakota in 2021.

The 2015 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The records in this file allow users to map the parts of Urban Areas that overlap a particular county. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint." There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2010.

Household data are collected as of March.

As stated in the Census's "Source and Accuracy of Estimates for Income, Poverty, and Health Insurance Coverage in the United States: 2011" (http://www.census.gov/hhes/www/p60_243sa.pdf):

Estimation of Median Incomes. The Census Bureau has changed the methodology for computing median income over time. The Census Bureau has computed medians using either Pareto interpolation or linear interpolation. Currently, we are using linear interpolation to estimate all medians. Pareto interpolation assumes a decreasing density of population within an income interval, whereas linear interpolation assumes a constant density of population within an income interval. The Census Bureau calculated estimates of median income and associated standard errors for 1979 through 1987 using Pareto interpolation if the estimate was larger than $20,000 for people or $40,000 for families and households. This is because the width of the income interval containing the estimate is greater than $2,500.

We calculated estimates of median income and associated standard errors for 1976, 1977, and 1978 using Pareto interpolation if the estimate was larger than $12,000 for people or $18,000 for families and households. This is because the width of the income interval containing the estimate is greater than $1,000. All other estimates of median income and associated standard errors for 1976 through 2011 (2012 ASEC) and almost all of the estimates of median income and associated standard errors for 1975 and earlier were calculated using linear interpolation.

Thus, use caution when comparing median incomes above $12,000 for people or $18,000 for families and households for different years. Median incomes below those levels are more comparable from year to year since they have always been calculated using linear interpolation. For an indication of the comparability of medians calculated using Pareto interpolation with medians calculated using linear interpolation, see Series P-60, Number 114, Money Income in 1976 of Families and Persons in the United States (www2.census.gov/prod2/popscan/p60-114.pdf).

description: This shapefile contains landscape factors representing human disturbances summarized to local and network catchments of river reaches for the state of South Dakota. This dataset is the result of clipping the feature class 'NFHAP 2010 HCI Scores and Human Disturbance Data for the Conterminous United States linked to NHDPLUSV1.gdb' to the state boundary of South Dakota. Landscape factors include land uses, population density, roads, dams, mines, and point-source pollution sites. The source datasets that were compiled and attributed to catchments were identified as being: (1) meaningful for assessing fish habitat; (2) consistent across the entire study area in the way that they were assembled; (3) representative of conditions in the past 10 years, and (4) of sufficient spatial resolution that they could be used to make valid comparisons among local catchment units. In this data set, these variables are linked to the catchments of the National Hydrography Dataset Plus Version 1 (NHDPlusV1) using the COMID identifier. They can also be linked to the reaches of the NHDPlusV1 using the COMID identifier. Catchment attributes are available for both local catchments (defined as the land area draining directly to a reach; attributes begin with "L_" prefix) and network catchments (defined by all upstream contributing catchments to the reach's outlet, including the reach's own local catchment; attributes begin with "N_" prefix). This shapefile also includes habitat condition scores created based on responsiveness of biological metrics to anthropogenic landscape disturbances throughout ecoregions. Separate scores were created by considering disturbances within local catchments, network catchments, and a cumulative score that accounted for the most limiting disturbance operating on a given biological metric in either local or network catchments. This assessment only scored reaches representing streams and rivers (see the process section for more details). Please use the following citation: Esselman, P., D.M. Infante, L. Wang, W. Taylor, W. Daniel, R. Tingley, J. Fenner, A. Cooper, D. Wieferich, D. Thornbrugh and J. Ross. (April 2011) National Fish Habitat Action Plan (NFHAP) 2010 HCI Scores and Human Disturbance Data (linked to NHDPLUSV1) for South Dakota. National Fish Habitat Partnership Data System. http://dx.doi.org/doi:10.5066/F71834H6; abstract: This shapefile contains landscape factors representing human disturbances summarized to local and network catchments of river reaches for the state of South Dakota. This dataset is the result of clipping the feature class 'NFHAP 2010 HCI Scores and Human Disturbance Data for the Conterminous United States linked to NHDPLUSV1.gdb' to the state boundary of South Dakota. Landscape factors include land uses, population density, roads, dams, mines, and point-source pollution sites. The source datasets that were compiled and attributed to catchments were identified as being: (1) meaningful for assessing fish habitat; (2) consistent across the entire study area in the way that they were assembled; (3) representative of conditions in the past 10 years, and (4) of sufficient spatial resolution that they could be used to make valid comparisons among local catchment units. In this data set, these variables are linked to the catchments of the National Hydrography Dataset Plus Version 1 (NHDPlusV1) using the COMID identifier. They can also be linked to the reaches of the NHDPlusV1 using the COMID identifier. Catchment attributes are available for both local catchments (defined as the land area draining directly to a reach; attributes begin with "L_" prefix) and network catchments (defined by all upstream contributing catchments to the reach's outlet, including the reach's own local catchment; attributes begin with "N_" prefix). This shapefile also includes habitat condition scores created based on responsiveness of biological metrics to anthropogenic landscape disturbances throughout ecoregions. Separate scores were created by considering disturbances within local catchments, network catchments, and a cumulative score that accounted for the most limiting disturbance operating on a given biological metric in either local or network catchments. This assessment only scored reaches representing streams and rivers (see the process section for more details). Please use the following citation: Esselman, P., D.M. Infante, L. Wang, W. Taylor, W. Daniel, R. Tingley, J. Fenner, A. Cooper, D. Wieferich, D. Thornbrugh and J. Ross. (April 2011) National Fish Habitat Action Plan (NFHAP) 2010 HCI Scores and Human Disturbance Data (linked to NHDPLUSV1) for South Dakota. National Fish Habitat Partnership Data System. http://dx.doi.org/doi:10.5066/F71834H6

In 2024, 34.59 percent of all households in the United States were two person households. In 1970, this figure was at 28.92 percent. Single households Single mother households are usually the most common households with children under 18 years old found in the United States. As of 2021, the District of Columbia and North Dakota had the highest share of single-person households in the United States. Household size in the United States has decreased over the past century, due to customs and traditions changing. Families are typically more nuclear, whereas in the past, multigenerational households were more common. Furthermore, fertility rates have also decreased, meaning that women do not have as many children as they used to. Average households in Utah Out of all states in the U.S., Utah was reported to have the largest average household size. This predominately Mormon state has about three million inhabitants. The Church of the Latter-Day Saints, or Mormonism, plays a large role in Utah, and can contribute to the high birth rate and household size in Utah. The Church of Latter-Day Saints promotes having many children and tight-knit families. Furthermore, Utah has a relatively young population, due to Mormons typically marrying and starting large families younger than those in other states.

In 2021, around ** percent of individuals living in the District of Columbia identified as LGBT. Colorado, Arizona, Nevada, and Oregon also had high rates, exceeding *** percent. Mississippi and North Dakota had the lowest rates of LGBT populations, the only states with less than **** percent.

The 2019 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The records in this file allow users to map the parts of Urban Areas that overlap a particular county. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the ""urban footprint."" There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The generalized boundaries for counties and equivalent entities are as of January 1, 2010.

These data represent an resource selection function (RSF) for translocated sage-grouse in North Dakota during the summer. Human enterprise has led to large‐scale changes in landscapes and altered wildlife population distribution and abundance, necessitating efficient and effective conservation strategies for impacted species. Greater sage‐grouse (Centrocercus urophasianus; hereafter sage‐grouse) are a widespread sagebrush (Artemisia spp.) obligate species that has experienced population declines since the mid‐1900s resulting from habitat loss and expansion of anthropogenic features into sagebrush ecosystems. Habitat loss is especially evident in North Dakota, USA, on the northeastern fringe of sage‐grouse’ distribution, where a remnant population remains despite recent development of energy‐related infrastructure. Resource managers in this region have determined a need to augment sage‐grouse populations using translocation techniques that can be important management tools for coun ...

Identifying the specific environmental features and associated density-dependent processes that limit population growth is central to both ecology and conservation. Comparative assessments of sympatric species allow for inference into how ecologically similar species differentially respond to their shared environment, which can be used to inform community-level conservation strategies. Comparative assessments can nevertheless be complicated by interactions and feedback loops among the species in question. We developed an integrated population model based on sixty-one years of ecological data describing the demographic histories of Canvasbacks (Aythya valisineria) and Redheads (Aythya americana), two species of migratory diving ducks that utilize similar breeding habitats and affect each other’s demography through interspecific nest parasitism. We combined this model with a transient life table response experiment to determine the extent that demographic rates, and their contributions to population growth, were similar between these two species. We found that demographic rates and, to a lesser extent, their contributions to population growth covaried between Canvasbacks and Redheads, but the trajectories of population abundances widely diverged between the two species during the end of the 20th century due to inherent differences between the species life-histories and sensitivities to both environmental variation and harvest pressure. We found that annual survival of both species increased during years of restrictive harvest regulations; however, recent harvest pressure on female Canvasbacks may be contributing to population declines. Despite periodic, and often dramatic, increases in breeding abundance during wet years, the number of breeding Canvasbacks declined by 13% whereas the number of breeding Redheads has increased by 37% since 1961. Reductions in harvest pressure and improvements in submerged aquatic vegetation throughout the wintering grounds have mediated the extent to which populations of both species contracted during dry years in the Prairie Pothole Region. However, continued degradation of breeding habitats through climate-related shifts in wetland hydrology and agricultural conversion of surrounding grassland habitats may have exceeded the capacity for demographic compensation during the non-breeding season. Methods DATA COLLECTION We combined a series of long-term data sets into a single integrated population model that provided insights into how variation in seasonal survival (band releases and recoveries) and offspring production (harvest age-ratios) contributed to fluctuations in population growth (breeding survey, harvest estimates) for Canvasbacks and Redheads from 1961–2021. Banding Data – Information regarding the banding and subsequent harvest of ducks was acquired from the GameBirds Database CD (Bird Banding Lab, USGS Patuxent Wildlife Research Center, Laurel MD, USA, version August 2022). Male and female Canvasbacks and Redheads were captured following breeding but prior to the hunting season (Pre-Hunting) as ducklings (Local) or hatch year (HY; fledged juvenile) individuals as well as after hatch year (AHY; adult) individuals or following the hunting season (Post-Hunting) as an undifferentiated mixture of second year (SY) and after second year (ASY) individuals captured and released across North America from 1961–2022. We limited the pre-harvest banding data for both species to include all individuals banded and released alive in areas within the Canadian provinces of Alberta, Manitoba, Saskatchewan, as well as the states of Minnesota, Montana, North Dakota, and South Dakota within the USA (Fig. 1). For the pre-hunting banding group, we retained individuals captured between 1961–2021 during the late summer (Jul 15th – Sep 15th) with a known sex (M or F) and age-class (local, HY or AHY) that were released without any additional markers considered to meaningfully affect survival of an individual (e.g., nasal saddles or dual banding were permissible but telemetered individuals were excluded; Lameris & Kleyheeg, 2017). For post-hunting banding, we limited the spatial boundary of banding efforts to only consider individuals released from the Atlantic, Central, or Mississippi Flyways (Fig. 1). We followed the same data selection procedures, but limited releases to occur between Jan. 1st – March 15th from 1962–2022. Because too few banders differentiated SY from ASY at time of banding, we treated all post-hunting samples as AHY adults. Individuals banded during this period that were reported to be harvested during the winter they were originally banded were censored from the analysis, as the underlying model assumption was that this cohort of individuals had already survived the current hunting season. For both seasonal banding efforts, we only included recoveries of hunter-shot individuals harvested between September and February in which a known year-of-death could be ascertained. In addition to self-reported recoveries (i.e., reported by the hunter), we included hunter-harvested individuals that were instead reported by federal, state, or provincial entities (e.g., outcomes of hunter check stations or other forms of solicitation). We limited the dataset to only include recoveries of hunter-harvested individuals killed within 15 years of initial banding, which represented > 99% of pre-hunting and post-hunting recoveries. This cut-off was arbitrarily selected but did not meaningfully bias parameter estimation while vastly improving computational efficiency by bypassing the estimation of hundreds of zero-equivalent cell probabilities (personal communication S. Bonner). Harvest Intensity – We used the average number of Canvasbacks or Redheads allowed to be harvested per day (i.e., bag limit; (Appendix S1: Tables S1a-b) across the U.S. portions of the Atlantic, Mississippi, Central, and Pacific flyways during each year of the study as an index of harvest regulatory pressure. Annual harvest restrictions were acquired from the published literature (Péron et al., 2012), the annual release of the Late-Season Migratory Bird Hunting Regulations (e.g., USFWS 2022), and direct requests to the U.S. Fish and Wildlife Service. For these species, liberal harvest regulations were bag limits of two (Canvasbacks) and two to four (Redheads) allowable harvest per day, whereas conservative harvest regulations were either a bag limit of one individual per day or total closure. Harvest Composition – Data describing the age and sex structure of the harvested Canvasback and Redhead populations were derived from the annual Parts Collection surveys conducted by the U.S. Fish and Wildlife Service (USFWS) where a subset of hunters submit a wing from every duck they harvested (Pearse et al. 2014). These data were acquired through a direct request to the U.S. Fish and Wildlife Service. Additionally, estimates of the total number of Canvasbacks and Redheads harvested in the United States and Canada were derived from the Harvest Information Program (Steeg et al., 2002) and Canadian National Harvest Survey (Smith et al., 2022), respectively. Breeding Duck and Pond Densities – The relative number of breeding Canvasbacks and Redheads, as well as the relative amount of their breeding habitat (i.e., flooded ponds) within the Prairies were calculated using count data from the USFWS Waterfowl Breeding Population and Habitat Survey (hereafter BPOP; Smith, 1995), which has conducted an annual survey of breeding waterfowl and their habitats throughout the core part of these species’ breeding ranges (i.e., central Canada and the north-central United States) during the spring from 1961 through 2022 (U.S. Fish and Wildlife Service, 2022). However, BPOP surveys did not occur during 2020 and 2021. For the purposes of this study, we limited the spatial extent of BPOP survey to only include transects flown within Alberta, Manitoba, Saskatchewan, Montana, North Dakota, and South Dakota. Agriculture Development – The amounts of active cropland in the Prairies during each year of the study were estimated from Canada and United States Agriculture Census data (see Buderman et al., 2020). Annual estimates of active cropland acreages were summarized to represent an index of agricultural development during 1961–2021. Although agricultural development is predicted to have greater impact on upland-nesting dabbling ducks (Duncan and Devries 2018), it also impacts the wetland habitats in which Canvasbacks and Redheads forage and nest, as well as the predator communities that can access overwater nesting pochards (Sargeant et al. 1993, Bartzen et al. 2010). Winter Habitat – Winter habitat conditions were assumed to be related to submerged aquatic vegetation (SAV) within the Chesapeake for Canvasbacks and environmental salinity (TDS; total dissolved solids) in the Laguna Madre for Redheads. Although Redheads likely respond to variation in SAV, time series data describing SAV were not available for the Laguna Madre. Therefore, we assumed that annual fluctuations in salinity were an informative proxy of both SAV conditions and osmotic constraints (Quammen and Onuf 1993, Moore 2009), which in turn was representative of winter habitat conditions that simultaneously influenced Redhead food availability and harvest risk (Ballard et al. 2021).. Climate Data – We used the average Pacific/North American (PNA; Leathers et al., 1991) teleconnection pattern from April–July as an index of drought severity or environmental stress during the breeding season throughout the Prairies, and average sea-surface temperatures (SST) from September–March in the Chesapeake and Laguna Madre as an index of winter severity for Canvasbacks and Redheads, respectively (see Data Availability statement).

description: The White-faced Ibis (Plegadis chihi) in the Great Basin and surrounding area was listed as a Species of Management Concern (Sharp 1985, USFWS 1995) based on its small population size and vulnerability to breeding habitat loss. Traditionally, most Great Basin ibises have bred in Utah and Nevada with peripheral but growing colonies in Idaho, California, and Oregon (Sharp 1985, Ryder and Manry 1994). After apparently declining precipitously in the 1960's and 1970's (Capen 1977), the Great Basin population was estimated at only 7,500 breeding pairs in 1984 (Sharp 1985). In addition to the Great Basin population (as defined here), small numbers of ibises breed locally in Colorado, Wyoming, Montana, North and South Dakota, and southern Alberta, and large numbers breed in Louisiana, Texas, Mexico, and South America (reviewed in Ryder and Manry 1994). Interchange among these sites and Great Basin colonies has not been investigated. In the arid Great Basin region, ibises breed in semi-permanent wetlands which are susceptible to naturally occurring droughts and floods. Local population fluctuations and colony abandonment reflect this vulnerability. The highly nomadic White-faced Ibis apparently compensates for wetland dynamics by moving among breeding colonies and colonizing new wetlands within and between years (e.g., Ryder 1967, Capen 1977, Ivey et al. 1988, Henny and Herron 1989). The nomadic nature of the White-faced Ibis, like that of several other colonial ciconiiforms, suggests that conservation efforts be undertaken at the landscape level and that population dynamics, distribution, and trends be monitored at the regional or population scale (e.g., Frederick et al. 1996). The status of the Great Basin breeding population has not been reviewed since 1984 (Sharp 1985). Increases in breeding numbers in Oregon, Idaho, and California during the 1980's and 1990's suggested either that ibises were increasing regionally or individuals displaced from flooded Great Salt Lake marshes were colonizing elsewhere (e.g., Ivey et al. 1988, Follansbee and Mauser 1994, Trost and GersteIl1994). An increase in wintering numbers also suggested a population increase (Shuford et al. 1989). Recognizing the need for a comprehensive estimate of the breeding population, U.S. Fish and Wildlife Service (USFWS) coordinated a regional survey of all historic, active, and probable colony sites in 1995. To further assess the 1985-1997 population trend, we compiled available annual survey data for all known colonies. Here we report results of the 1995 survey and annual counts for 1985-1997. The objectives of this report are as follows: I. Document changes in the distribution, abundance, and population trend of White-faced Ibis breeding in the Great Basin and surrounding area during 1985-1997. 2. Interpret population-wide changes in ibis distribution and abundance in relation to wetland dynamics throughout the region. 3. Discuss implications for future monitoring, research, and conservation.; abstract: The White-faced Ibis (Plegadis chihi) in the Great Basin and surrounding area was listed as a Species of Management Concern (Sharp 1985, USFWS 1995) based on its small population size and vulnerability to breeding habitat loss. Traditionally, most Great Basin ibises have bred in Utah and Nevada with peripheral but growing colonies in Idaho, California, and Oregon (Sharp 1985, Ryder and Manry 1994). After apparently declining precipitously in the 1960's and 1970's (Capen 1977), the Great Basin population was estimated at only 7,500 breeding pairs in 1984 (Sharp 1985). In addition to the Great Basin population (as defined here), small numbers of ibises breed locally in Colorado, Wyoming, Montana, North and South Dakota, and southern Alberta, and large numbers breed in Louisiana, Texas, Mexico, and South America (reviewed in Ryder and Manry 1994). Interchange among these sites and Great Basin colonies has not been investigated. In the arid Great Basin region, ibises breed in semi-permanent wetlands which are susceptible to naturally occurring droughts and floods. Local population fluctuations and colony abandonment reflect this vulnerability. The highly nomadic White-faced Ibis apparently compensates for wetland dynamics by moving among breeding colonies and colonizing new wetlands within and between years (e.g., Ryder 1967, Capen 1977, Ivey et al. 1988, Henny and Herron 1989). The nomadic nature of the White-faced Ibis, like that of several other colonial ciconiiforms, suggests that conservation efforts be undertaken at the landscape level and that population dynamics, distribution, and trends be monitored at the regional or population scale (e.g., Frederick et al. 1996). The status of the Great Basin breeding population has not been reviewed since 1984 (Sharp 1985). Increases in breeding numbers in Oregon, Idaho, and California during the 1980's and 1990's suggested either that ibises were increasing regionally or individuals displaced from flooded Great Salt Lake marshes were colonizing elsewhere (e.g., Ivey et al. 1988, Follansbee and Mauser 1994, Trost and GersteIl1994). An increase in wintering numbers also suggested a population increase (Shuford et al. 1989). Recognizing the need for a comprehensive estimate of the breeding population, U.S. Fish and Wildlife Service (USFWS) coordinated a regional survey of all historic, active, and probable colony sites in 1995. To further assess the 1985-1997 population trend, we compiled available annual survey data for all known colonies. Here we report results of the 1995 survey and annual counts for 1985-1997. The objectives of this report are as follows: I. Document changes in the distribution, abundance, and population trend of White-faced Ibis breeding in the Great Basin and surrounding area during 1985-1997. 2. Interpret population-wide changes in ibis distribution and abundance in relation to wetland dynamics throughout the region. 3. Discuss implications for future monitoring, research, and conservation.

About this dataset

Context

The dataset tabulates the data for the South Dakota population pyramid, which represents the South Dakota population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for South Dakota, is 32.7.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for South Dakota, is 28.3.
• Total dependency ratio for South Dakota is 61.0.
• Potential support ratio, which is the number of youth (working age population) per elderly, for South Dakota is 3.5.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the South Dakota population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the South Dakota for the selected age group is shown in the following column.
• Population (Female): The female population in the South Dakota for the selected age group is shown in the following column.
• Total Population: The total population of the South Dakota for the selected age group is shown in the following column.

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 South Dakota Population by Age. You can refer the same here

description: The National Wildlife Refuge System s 150 million acres in over 500 refuges representdiverse landscapes with different capacities to provide ecosystem goods and services to society.Natural processes associated with management of national wildlife refuges provide benefits tolocal communities by sustaining production of specific goods and services that are useful topeople. Estimated economic values of these services, such as those presented in this report, can be used to compare refuges in different locations and under different management, climatic, or socio economic conditions. Our estimates of economic benefits from natural ecosystems serve as complements to economic impact analyses, such as the FWS s Banking on Nature studies (Carver and Caudill 2007). This report presents the methods and results from Phase I of our research project. In this report we compare wetlands on four national wildlife refuges to illustrate how existing data can be used to estimate the average annual economic benefits of specific ecosystem services from different types of wetlands. The four sites are Arrowwood National Wildlife Refuge (NWR), North Dakota; Blackwater NWR, Maryland; Okefenokee NWR, Georgia; and Sevilleta and Bosque del Apache NWRs, New Mexico. These four sites were selected to contrast major types of wetlands in terms of physical and social parameters that influence the values of different ecosystem goods and services. We present multiple approaches to assessing ecosystem services benefits. For each of the four refuges, we first consider a purely qualitative assessment of the relative magnitudes of different ecosystem service benefits provided by each refuge. This approach proves to be the most inclusive in terms of our ability to consider ecological data specific to the refuge, and provides a useful tool for broad assessments and comparisons across refuges. However, it does not lead to quantitative estimates of ecosystem service benefits. For these estimates, we use two different benefit transfer techniques: (1) a meta analysis benefit transfer to estimate the economic values of storm protection, water quality provisioning, and support for nursery and habitat for commercial fishing species; and (2) a point transfer approach to estimate the value of stored carbon. Our results suggest that refuge size and the socio demographic characteristics of the surrounding region are important determinates of the estimated per acre value of wetlands in providing ecosystem services. Consistent with economic theory, larger refuges in areas with lower population density tend to have lower per acre values. However, these interaction effects between wetland size, population size and preferences, and ecosystem service values need to be further studied. Our results are an approximation of consumers aggregate willingness to pay to obtain the service provided by the wetlands of a particular NWR. Decision makers can use these numbers to understand how a population might be impacted by a change in distribution of wetlands across a landscape. The most straightforward application of the method we follow concerns estimating the net economic value of a change in an ecosystem service due to a management action which changes a wetland from one type to another. This report represents Phase I of our efforts to estimate the ecosystem service benefits of the National Wildlife Refuge System. The primary focus of the second phase will be the development of a meta analysis benefit transfer model specifically tailored toward wetlands in National Wildlife Refuges.; abstract: The National Wildlife Refuge System s 150 million acres in over 500 refuges representdiverse landscapes with different capacities to provide ecosystem goods and services to society.Natural processes associated with management of national wildlife refuges provide benefits tolocal communities by sustaining production of specific goods and services that are useful topeople. Estimated economic values of these services, such as those presented in this report, can be used to compare refuges in different locations and under different management, climatic, or socio economic conditions. Our estimates of economic benefits from natural ecosystems serve as complements to economic impact analyses, such as the FWS s Banking on Nature studies (Carver and Caudill 2007). This report presents the methods and results from Phase I of our research project. In this report we compare wetlands on four national wildlife refuges to illustrate how existing data can be used to estimate the average annual economic benefits of specific ecosystem services from different types of wetlands. The four sites are Arrowwood National Wildlife Refuge (NWR), North Dakota; Blackwater NWR, Maryland; Okefenokee NWR, Georgia; and Sevilleta and Bosque del Apache NWRs, New Mexico. These four sites were selected to contrast major types of wetlands in terms of physical and social parameters that influence the values of different ecosystem goods and services. We present multiple approaches to assessing ecosystem services benefits. For each of the four refuges, we first consider a purely qualitative assessment of the relative magnitudes of different ecosystem service benefits provided by each refuge. This approach proves to be the most inclusive in terms of our ability to consider ecological data specific to the refuge, and provides a useful tool for broad assessments and comparisons across refuges. However, it does not lead to quantitative estimates of ecosystem service benefits. For these estimates, we use two different benefit transfer techniques: (1) a meta analysis benefit transfer to estimate the economic values of storm protection, water quality provisioning, and support for nursery and habitat for commercial fishing species; and (2) a point transfer approach to estimate the value of stored carbon. Our results suggest that refuge size and the socio demographic characteristics of the surrounding region are important determinates of the estimated per acre value of wetlands in providing ecosystem services. Consistent with economic theory, larger refuges in areas with lower population density tend to have lower per acre values. However, these interaction effects between wetland size, population size and preferences, and ecosystem service values need to be further studied. Our results are an approximation of consumers aggregate willingness to pay to obtain the service provided by the wetlands of a particular NWR. Decision makers can use these numbers to understand how a population might be impacted by a change in distribution of wetlands across a landscape. The most straightforward application of the method we follow concerns estimating the net economic value of a change in an ecosystem service due to a management action which changes a wetland from one type to another. This report represents Phase I of our efforts to estimate the ecosystem service benefits of the National Wildlife Refuge System. The primary focus of the second phase will be the development of a meta analysis benefit transfer model specifically tailored toward wetlands in National Wildlife Refuges.

About this dataset

Context

The dataset tabulates the South Dakota Hispanic or Latino population. It includes the distribution of the Hispanic or Latino population, of South Dakota, by their ancestries, as identified by the Census Bureau. The dataset can be utilized to understand the origin of the Hispanic or Latino population of South Dakota.

Key observations

Among the Hispanic population in South Dakota, regardless of the race, the largest group is of Mexican origin, with a population of 21,638 (57.52% of the total Hispanic population).

https://i.neilsberg.com/ch/south-dakota-population-by-race-and-ethnicity.jpeg" alt="South Dakota Non-Hispanic population by race">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Origin for Hispanic or Latino population include:

• Mexican
• Black or African American
• Puerto Rican
• Cuban
• Other Hispanic or Latino

Variables / Data Columns

• Origin: This column displays the origin for Hispanic or Latino population for the South Dakota
• Population: The population of the specific origin for Hispanic or Latino population in the South Dakota is shown in this column.
• % of Total Hispanic Population: This column displays the percentage distribution of each Hispanic origin as a proportion of South Dakota total Hispanic or Latino population. 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 South Dakota Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the South Dakota Hispanic or Latino population. It includes the distribution of the Hispanic or Latino population, of South Dakota, by their ancestries, as identified by the Census Bureau. The dataset can be utilized to understand the origin of the Hispanic or Latino population of South Dakota.

Key observations

Among the Hispanic population in South Dakota, regardless of the race, the largest group is of Mexican origin, with a population of 22,841 (55.33% of the total Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Origin for Hispanic or Latino population include:

• Mexican
• Puerto Rican
• Cuban
• Other Hispanic or Latino

Variables / Data Columns

• Origin: This column displays the origin for Hispanic or Latino population for the South Dakota
• Population: The population of the specific origin for Hispanic or Latino population in the South Dakota is shown in this column.
• % of Total Hispanic Population: This column displays the percentage distribution of each Hispanic origin as a proportion of South Dakota total Hispanic or Latino population. 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 South Dakota Population by Race & Ethnicity. You can refer the same here