About this dataset

Context

The dataset tabulates the Appalachia population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Appalachia across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Appalachia was 1,387, a 0.64% decrease year-by-year from 2022. Previously, in 2022, Appalachia population was 1,396, a decline of 1.27% compared to a population of 1,414 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Appalachia decreased by 442. In this period, the peak population was 1,829 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

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

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Appalachia is shown in this column.
• Year on Year Change: This column displays the change in Appalachia population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Appalachia Population by Year. You can refer the same here

The counties comprising Appalachia, based on the Appalachian Regional Commission (https://www.arc.gov/appalachian-counties-served-by-arc), plus the counties that fall within a 10-mile buffer of the ARC counties, with 2010 RUCA codes joined. The original source of the counties shapefile was the U.S. Census Bureau's 2020 Cartographic Boundary Files. The original source of the data was the CDC USALEEP (https://www.cdc.gov/nchs/nvss/usaleep/usaleep.html) averaged from the tract level to the county level using the FIPS code.

About this dataset

Context

The dataset tabulates the population of Appalachia by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Appalachia. The dataset can be utilized to understand the population distribution of Appalachia by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Appalachia. 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 Appalachia.

Key observations

Largest age group (population): Male # 45-49 years (99) | Female # 10-14 years (128). 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 Appalachia population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Appalachia is shown in the following column.
• Population (Female): The female population in the Appalachia 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 Appalachia 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 Appalachia Population by Gender. You can refer the same here

This research project was designed to demonstrate the contributions that Geographic Information Systems (GIS) and spatial analysis procedures can make to the study of crime patterns in a largely nonmetropolitan region of the United States. The project examined the extent to which the relationship between various structural factors and crime varied across metropolitan and nonmetropolitan locations in Appalachia over time. To investigate the spatial patterns of crime, a georeferenced dataset was compiled at the county level for each of the 399 counties comprising the Appalachian region. The data came from numerous secondary data sources, including the Federal Bureau of Investigation's Uniform Crime Reports, the Decennial Census of the United States, the Department of Agriculture, and the Appalachian Regional Commission. Data were gathered on the demographic distribution, change, and composition of each county, as well as other socioeconomic indicators. The dependent variables were index crime rates derived from the Uniform Crime Reports, with separate variables for violent and property crimes. These data were integrated into a GIS database in order to enhance the research with respect to: (1) data integration and visualization, (2) exploratory spatial analysis, and (3) confirmatory spatial analysis and statistical modeling. Part 1 contains variables for Appalachian subregions, Beale county codes, distress codes, number of families and households, population size, racial and age composition of population, dependency ratio, population growth, number of births and deaths, net migration, education, household composition, median family income, male and female employment status, and mobility. Part 2 variables include county identifiers plus numbers of total index crimes, violent index crimes, property index crimes, homicides, rapes, robberies, assaults, burglaries, larcenies, and motor vehicle thefts annually from 1977 to 1996.

Identical observations, conducted 1-4 times per year for 15-20 years at two locations in the southern Appalachians, have yielded quantitative data on populations of six species of salamanders. Although the numbers have fluctuated for various reasons, there has been no trend in the numbers of any of the species. The "world-wide decline of amphibian populations" has not occurred in the two localities studied. Please refer to the methodological summary near each graph on the following web page, http://www.unc.edu/~rhwiley/salamandertrends/ The number of salamanders observed in a 1.5 hour search from the creek southward up the slopes 150 m (average of two trips in September each year).

About this dataset

Context

The dataset tabulates the Appalachia 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 Appalachia. The dataset can be utilized to understand the population distribution of Appalachia by age. For example, using this dataset, we can identify the largest age group in Appalachia.

Key observations

The largest age group in Appalachia, VA was for the group of age 10 to 14 years years with a population of 197 (12.22%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Appalachia, VA was the 80 to 84 years years with a population of 12 (0.74%). 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 Appalachia is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Appalachia 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 Appalachia Population by Age. You can refer the same here

Four neochoristoderan vertebral centra are described from the latest Cretaceous of New Jersey. One specimen was recovered from the basal transgressive lag of the Navesink Formation in the area of Holmdel Park, New Jersey, and two others were recovered nearby and likely were derived from the same horizon. The fourth was recovered from the Marshalltown sequence in the vicinity of the Ellisdale Dinosaur Site. These vertebrae expand the geographic range of Late Cretaceous neochoristoderes in North America by over 2000 km further east, and represent the first neochoristoderan remains from the Atlantic coastal plain. To discern whether neochoristodere remains are to be expected in New Jersey, and elucidate why neochoristoderes are apparently so rare in Appalachia, we implemented ecological niche modeling to predict the range of suitable habitat for Champsosaurus, the only known genus of Late Cretaceous neochoristoderes. We found that in Appalachia, the ideal habitat of Champsosaurus likely ex...

Previously, American black bears (Ursus americanus) were thought to follow the pattern of female philopatry and male-biased dispersal. However, recent studies have identified deviations from this pattern. Such flexibility in dispersal patterns can allow individuals greater ability to acclimate to changing environments. We explored dispersal and spatial genetic relatedness patterns across ten black bear populations—including long established (historic), with known reproduction >50 years ago, and newly established (recent) populations, with reproduction recorded <50 years ago—in the Interior Highlands and Southern Appalachian Mountains, United States. We used spatially-explicit, individual-based genetic simulations to model gene flow under scenarios with varying levels of population density, genetic diversity, and female philopatry. Using measures of genetic distance and spatial autocorrelation, we compared metrics between sexes, between population types (historic and recent), and among simulated scenarios which varied in density, genetic diversity, and sex-biased philopatry. In empirical populations, females in recent populations exhibited stronger patterns of isolation-by-distance (IBD) than females and males in historic populations. In simulated populations, low density populations had a stronger indication of IBD than medium to high density populations; however, this effect varied in empirical populations. Condition dependent dispersal strategies may permit species to cope with novel conditions and rapidly expand populations. Pattern-process modelling can provide qualitative and quantitative means to explore variable dispersal patterns, and could be employed in other species, particularly to anticipate range shifts in response to changing climate and habitat conditions. Code for regression of slopeWe generated a linear regression of genetic distance (Dps) on Euclidean distance for each dyad type, then recorded the slope of the linear model. This file provides code for one scenario, which included each of the ten simulated replicates.SubsamplingRegression_10_01_17.rGenotypes for bears from the Appalachian MountainsFile contains 20-loci genotypes for bears from population sin the Appalachian Mountains. Genotyped by Wildlife Genetics International. Each allele is coded for with three digits.genotypes_appalachian.csvInput for CDPOP simulationsWe used CDPOP v1.2.30 (Landguth and Cushman, 2010) for our simulations. The headers in this file correspond to those required for input into this program. Each line lists one of the individual simulations we ran.cdpop_inputfiles_dryad.csv

Like the Breeding Bird Census, the Winter Bird Population Study (WBPS) is a monitoring program that estimates winter bird densities in specific habitat types throughout North America. In addition, the vegetation of the plot is described. Relatively large areas of a single habitat are preferred for WBPS plots. Censusing methodology follows the Cornell Laboratory of Ornithology guidelines. Two permanent plots (the same two used in our BBCs), a ridge-top south-southwest facing 19.3 ha site in 120-130 year old oak-maple forest (elevation 408-448m), and a low elevation east-facing 16.9 ha site in 120-200 year old, oak-maple forest (elevation 265-347m), have been gridded at 30.5 m intervals in the Sanctuary's forest. The two sites are 750m apart. The habitat in each plot is characterized on a Habitat Classification Form supplied by the Cornell Laboratory of Ornithology. A detailed vegetation survey and vegetation mapping of each plot was conducted in 1989 (F. Watson, unpubl. data).

Summary of several social indicators of the populations in counties around the Appalachian Trail in 2019. The dashboard includes maps of median household income in the last 12 months, the population diversity as the proportion of non-whites plus Hispanic ethnicity (independently of race) and the trend of population aging. Data is based on American Community Survey of 2019 and change rates refers to increases since Decennial Census 2000. Chart of the evolution of income (inflation corrected) compares household's revenue in Decennial Census 2000, 2010 and ACS2019-5yrs respectively.

Combined data file for analysis in nexus format. (NEX 421 kb)

All unique haplotypes/alleles (COI, CAD, KKV, and ITS2) and GenBank #s. (XLSX 53 kb)

The USGS Central Region Energy Team assesses oil and gas resources of the United States. The onshore and State water areas of the United States comprise 71 provinces. Within these provinces, Total Petroleum Systems are defined and Assessment Units are defined and assessed. Each of these provinces is defined geologically, and most province boundaries are defined by major geologic changes.
The Appalachian Basin Province is located in the eastern United States, encompassing all or parts of the counties in Alabama, Georgia, Kentucky, Maryland, New Jersey, New York, North Carolina, Ohio, Pennsylvania, Tennessee, Virginia, and West Virginia. The main population centers within the study area are Birmingham, Alabama; Buffalo, New York; Cleveland, Ohio; Pittsburgh, Pennsylvania; Chattanooga, Tennessee; and Roanoke, Virginia. The main Interstates are I-20, I-24, I-40, I-59, I-64, I-65, I-66, I-70, I-71, I-75, I-76, I-77, I-78, I-79, I-80, I-81, I-83, I-84, I-87, I-88, and I-90. The Ohi ...

About this dataset

Context

The dataset tabulates the data for the Appalachia, VA population pyramid, which represents the Appalachia 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 Appalachia, VA, is 35.4.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Appalachia, VA, is 25.4.
• Total dependency ratio for Appalachia, VA is 60.9.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Appalachia, VA is 3.9.

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 Appalachia population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Appalachia for the selected age group is shown in the following column.
• Population (Female): The female population in the Appalachia for the selected age group is shown in the following column.
• Total Population: The total population of the Appalachia 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 Appalachia Population by Age. You can refer the same here

Informed conservation of stream fishes requires detailed understanding of the effects of both natural processes and anthropogenic activities on genetic diversity. Brook Trout Salvelinus fontinalis, a salmonid native to eastern North America, typically resides in cold, high-quality stream ecosystems. The species has not only faced historical anthropogenic pressures, but also confronts current and future pressures. In a genetic analysis we used a reduced representation sequencing method (ddRADseq) to characterize 63 individuals from 23 streams where Brook Trout are native in the Appalachian region of Pennsylvania. A total of 2,590 loci passed filtering criteria, and 53% displayed significant association with a major stream drainage basin (Susquehanna or Allegheny; mean FST = 0.085). Mapping of the sequencing reads to the Atlantic Salmon Salmo salar genome revealed no clustering of high interdrainage FST values to specific genome regions. Evidence for genetic heterogeneity within each drainage basin was also detected. Stepwise regression of observed heterozygosity against geographic and environmental features revealed that drainage basin and effective area of watersheds were significant predictors of observed heterozygosity of Brook Trout within streams. Natural features such as waterfalls and major drainage basin, as well as the effects of dams and acid-mine drainage have fragmented habitat and shaped genetic diversity within Brook Trout populations in the Appalachian region of Pennsylvania, overall indicating the vulnerability of this species to increased industrialization.

Data from: Phylogeographic history and future outlook of a flightless, saproxylic beetle within high-elevation southern Appalachian forests

Clayton R. Traylor¹, Michael S. Caterino², Michael D. Ulyshen³, Michael L. Ferro², Joseph V. McHugh¹

¹University of Georgia, Department of Entomology, Athens, Georgia 30602, USA

²Clemson University, Plant and Environmental Sciences Department, Clemson, South Carolina 29634, USA

³USDA Forest Service, Southern Research Station, Athens, Georgia 30602, USA

Abstract

Within the never-glaciated southern Appalachian Mountains, today’s high-elevation forest types are fragmented after contracting from continuous distributions during the last glacial maximum (LGM). Species restricted to high-elevation forests show a genetic history of severe to moderate isolation dictated by dispersal ability, despite historic habitat continuity. However, examples of specialist species with known natural histories are scarce. We investigated the phylogeography of Phellopsis obcordata (Kirby) (Coleoptera: Zopheridae), a flightless, saproxylic beetle that, within the southern Appalachians, depends on old-growth forests at high elevations. We specifically sought to understand drivers of gene flow and genetic diversity to inform conservation efforts today and under future climate change scenarios. We used phylogenetic divergence time estimation and population genetic analyses on mitochondrial DNA (COI) to infer phylogeographic history, and modelled its distribution with Maxent at the LGM, today, and under climate change scenarios in 2070. Phylogenetic analyses recovered five geographically distinct clades, four of which were highly supported. The clades diverged in the late Pliocene/early Pleistocene with several examples of secondary contact in the Pleistocene, including across the Asheville Basin. Additionally, no populations were monophyletic, with intra-clade mixing apparent. Population genetic analyses indicate population stability, high genetic diversity, and modern-day isolation. Distribution models suggest widespread suitability in the southern Appalachians and beyond at the LGM, fragmented suitability only in high elevations today, and range-wide reductions in suitability in 2070 based on both moderate and severe climate change scenarios. Our results indicate that expansion events, likely during glacial maxima, have shifted lineages and allowed connectivity of isolated populations within the southern Appalachian Mountains. Various isolating factors may be responsible, but apparently have been bridged occasionally throughout the Pleistocene by this flightless species. Inclusion of both nuclear DNA and increased geographic sampling are necessary for better insight of admixture and clade distributions. Regardless, our results suggest that unique intraspecific diversity may be at risk with a warming climate.

Key words: biogeography, deadwood, fungus beetle, glacial refugia, montane spruce-fir forest, old-growth forest

This data set was developed to provide a depiction of Designated Campsites along the Appalachian National Scenic Trail in an easily transferable format so they can be correctly represented on digital and printed maps and to assist land managers, partners, trail-maintaining clubs, and others with planning activities. The Appalachian National Scenic Trail is a footpath over 2,190 miles in length that traverses the Appalachian Mountains from Maine to Georgia. It passes through 14 states, approximately 241 jurisdictions, and links some 75 national and state parks and forests. Virtually every mile is within easy access of a major population center and some portion of the trail is within a day's drive of 2/3rds of the U.S. population. The idea for an Appalachian Trail was conceived by forester Benton MacKaye in 1921. In 1925, he formed the Appalachian Trail Conference (now called the Appalachian Trail Conservancy), a private not-for-profit organization whose mission is to protect, preserve, manage, and promote the Appalachian Trail. By 1937, an Appalachian Trail footpath was considered complete and open for all to enjoy. In 1968, Congress passed the National Scenic Trails Act that created a system of national scenic trails, starting with the Appalachian Trail and Pacific Crest Trail. Today, the trail and its associated lands are managed by the National Park Service Appalachian Trail Park Office and Appalachian Trail Conservancy, in conjunction with 30 affiliated trail clubs and several other partners including the USDA Forest Service and numerous state park and state forest agencies.

This data set was developed to provide a depiction of Vistas along the Appalachian National Scenic Trail in an easily transferable format so they can be correctly represented on digital and printed maps and to assist land managers, partners, trail-maintaining clubs, and others with planning activities. The Appalachian National Scenic Trail is a footpath over 2,190 miles in length that traverses the Appalachian Mountains from Maine to Georgia. It passes through 14 states, approximately 241 jurisdictions, and links some 75 national and state parks and forests. Virtually every mile is within easy access of a major population center and some portion of the trail is within a day's drive of 2/3rds of the U.S. population. The idea for an Appalachian Trail was conceived by forester Benton MacKaye in 1921. In 1925, he formed the Appalachian Trail Conference (now called the Appalachian Trail Conservancy), a private not-for-profit organization whose mission is to protect, preserve, manage, and promote the Appalachian Trail. By 1937, an Appalachian Trail footpath was considered complete and open for all to enjoy. In 1968, Congress passed the National Scenic Trails Act that created a system of national scenic trails, starting with the Appalachian Trail and Pacific Crest Trail. Today, the trail and its associated lands are managed by the National Park Service Appalachian Trail Park Office and Appalachian Trail Conservancy, in conjunction with 30 affiliated trail clubs and several other partners including the USDA Forest Service and numerous state park and state forest agencies.

We examined the reach-scale distributions of three fish species to determine which biotic and abiotic factors are influential in the fishes distributions.

Human populations are rapidly expanding and encroaching on previously undisturbed habitats. Stream salamanders in the southern Appalachian Mountains are a diverse and abundant group threatened by rapid exurban development in high-elevation watersheds. Previous research has demonstrated the sensitivity of salamanders to urbanization, but little research exists describing the mechanisms behind population declines and extirpations. Appalachian stream salamanders are adapted to forested streams with dense overstory and little light, yet following urbanization, light gaps associated with land clearing emerge. Light avoidance behaviors may alter movement behaviors of salamanders, fragmenting populations on opposite sides of light gaps. To study the effects on riparian disturbance on salamanders we established 6 experimental sites with canopy gaps ranging from 13m to 85m in stream length and 2 control sites lacking canopy gaps in May of 2010. Animals were collected within these plots, marked, and translocated to the plot on the opposite side of the gap. To establish detection probabilities in the absence of translocation, we established an additional 10m plot in the forest at each site where individuals were captured, marked, and re-released within this area. Recaptured individuals were measured and in some cases re-marked if those individuals had returned to their capture location.