The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2010 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

The 2022 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. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some states and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census and beyond, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

This raster dataset represents 2020 population density from the Gridded Population of the World, Version 4 (GPWv4) dataset, sourced from the Center for International Earth Science Information Network (CIESIN). The data has been clipped to the Northeast USA and normalized to a 0-100 scale to facilitate comparison between population distribution and recreational use of forests. This raster helps identify spatial outliers, where forest recreation is high in areas with low population density, offering insights for land management and conservation planning.Data Source:GPWv4 Population Density, 2020 Revision 11Clipped to the Northeast (ME, NH, VT, NY, MA, CT, RI, PA, NJ)Use Case:Used to compare forest recreation hotspots with population density, revealing areas where recreation is disproportionate to local population, assisting in identifying outliers for focused study or management efforts.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Harvard Forest (HFR) contains human population density measurements in numberPerKilometerSquared units and were aggregated to a yearly timescale.

This statistic shows the number of PET-CT units per million population in Canada in 2019/2020, by province. PET-CT stands for positron emission tomography–computed tomography. In that year, the province of New Brunswick had *** PET-CT units per every million of its population.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Harvard Forest (HFR) contains percent urban population measurements in percent units and were aggregated to a yearly timescale.

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).

Computer tomography (CT) scanners are vital medical technology used in the diagnosis and monitoring of various medical conditions. CT scanner utilize x-ray technology to make images of bones, vessels and other internal organs. As of 2023, Japan had the largest density of CT scanners with 115.7 scanners per million people. The country with the second most scanners at that time was Australia with over 70 scanners per million people. Diagnostic imaging Diagnostic imaging is a branch of medical technology that aims to use advanced technologies to create images of the human body for the purposes of diagnosing and monitoring medical conditions. There are several kinds of imaging available. Magnetic resonance imaging (MRI) is another type of medical imaging common in developed countries. As of 2019 Japan and the U.S. had the largest number of MRI units per million population. Usage of medical imaging also varies significantly among countries with Germany and Austria having the highest rates of examinations by MRI in recent years. Medical technology market globally The medical technology market has been an ever-expanding industry. With segments in diagnostic imaging, cardiology and optometry there is ample opportunities for new technologies to be utilized. The top medical technology segment based on market share was in vitro diagnostics, followed by cardiology and diagnostic imaging. Among medical technology companies Medtronic and Johnson & Johnson were the top two based on worldwide revenue in 2023.

These data were generated to compare different methods of estimating population density from marked and unmarked animal populations. We compare conventional live trapping with two more modern, non-invasive field methods of population estimation: genetic fingerprinting from hair-tube sampling and camera trapping for the European pine marten (Martes martes). We used arrays of camera traps, live traps, and hair tubes to collect the relevant data in the Ring of Gullion in Northern Ireland. We apply marked spatial capture-recapture models to the genetic and live trapping data where individuals were identifiable, and unmarked spatial capture-recapture (uSCR), distance sampling (CT-DS), and random encounter models (REM) to the camera trap data where individual ID was not possible. All five approaches produced plausible and relatively consistent point estimates (0.41 – 0.99 animals per km2), despite differences in precision, cost, and effort being apparent. In addition to the data, we provide novel code for running unmarked spatial capture-recapture (uSCR) and random encounter models (REM) to the camera trap data where individual ID was not possible. Methods All fieldwork was carried out in the Ring of Gullion, Northern Ireland, UK. Cameras Thirty Bushnell HD Trophy Cam 8MP camera traps (model number: 119577) with 8GB SD cards were deployed during June and July 2019. Thirty Bushnell HD Trophy Cam 8MP camera traps (model number: 119577) with 8GB SD cards were deployed during June and July 2019. At the end of the survey period, camera traps were checked and for each detection (the first image in a trigger sequence of an individual pine marten) distance to animal (m) and angle of detection (°) were measured in situ. Noninvasive genetic sampling Twenty hair tubes based on those developed by Mullins et al. (2010), were deployed across the study site between June and July 2019. Hair-tubes were checked weekly and sticky patches and bait were replaced on each visit. Hair samples were frozen at -20oC prior to DNA extraction. Microsatellite analysis to identify individual pine marten was carried out using up to 11 microsatellite markers. Each sample was analysed in duplicate and only samples giving identical results in the replicates were scored. Live traps Twelve Tomahawk 205 live cage traps were deployed along two perpendicular transects spaced approximately 400m apart. Trapping was conducted from August - October 2019 with daily trap checks. Trapped animals were anaesthetised with an intramuscular injection of ketamine (25mg per kg) and midazolam (0.2mg per kg) and scanned for a microchip. Statistical analyses Spatially explicit capture-recapture (SECR) models were used to estimate density for both live trapping and gNIS (Efford & Boulanger, 2019). Occasion lengths for live trapping were one day, whilst for gNIS were one week. For live trapping, we specified a single-use detector type, whilst for gNIS we specified a proximity-based detector type. Density was calculated from camera traps using REM (Rowcliffe et al. 2008), CT-DS (Howe et al. 2017) and uSCR (Chandler & Royle, 2013).

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Harvard Forest (HFR) contains percent urban population measurements in percent units and were aggregated to a yearly timescale.

This is a copy of the statewide Census Tract GIS Tiger file. It is used to determine if a census tract (CT) is EDA or not by adding ACS (American Community Survey) Median Household Income (MHI) and Population Density data at the CT level. The IRWM web based DAC mapping tool uses this GIS layer. Every year this table gets updated after ACS publishes their updated estimates. Created by joining 2016 EDA table to 2010 Census Tracts feature class. The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2010 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Harvard Forest (HFR) contains percent urban population measurements in percent units and were aggregated to a yearly timescale.

Accurate and precise assessment of population density plays a critical role in effective wildlife management, but reliable estimates are often difficult to obtain. Camera traps have emerged as valuable non-invasive tools for studying elusive species, offering cost-effective solutions for both marked and unmarked populations. We evaluated the consistency of badger (Meles meles) density estimates obtained from the random encounter model (REM) and camera trap distance sampling (CT-DS) with independent estimates from spatial mark-resight (SMR) models and quantified the bias in CT-DS arising from animals reacting to camera traps. Six camera trap surveys were conducted in Cornwall, UK, in 2019 and 2021, and data were used to estimate badger density using the REM and CT-DS. Four sites were included in a badger vaccination research project, providing an opportunity to mark badgers with uniquely identifiable fur clips to facilitate resighting within an SMR framework. We found consistency in the ..., Data collection Data were collected from six camera trap surveys at five sites in Cornwall, UK, in 2019 and 2021. Data Analysis Badger density was estimated using three methods: The Random Encounter Model (REM), Camera trap Distance Sampling (CT-DS), and Spatially Explicit Mark Resight (SEMR). Details of each method are given below. REM Density Estimation

Density estimates were calculated from encounter rates using an equation involving variables like the number of independent badger encounters (y), temporal survey effort (t), and camera detection zone parameters (r and θ). Model parameters were estimated from camera images, including badger position data, speed, activity level, and detection zone dimensions. Density estimates were obtained using the 'camtools' package, including a nonparametric bootstrap of trap rate errors. Where badgers showed reactive behaviour, 'reactive' sequences were removed from the estimation of animal speed and the camera detection zone.

CT-DS Density Esti..., , # Camera-based badger density estimation using the REM, CT-DS, and SMR

The data and code are provided for three methods used to estimate badger density - the Random Encounter Model (REM), Camera-Trap Distance Sampling (CT-DS), and Spatially-Explicit Mark Resight (SMR).

Description of the data and file structure

For each method, data are organised into separate files representing the different sites (numbered 1-5). Any data containing location information has been omitted in line with privacy-sharing agreements so that participating landholders remain anonymous. As such, we have not included the shapefiles to generate the habitat mask for SMR or the coordinates of camera locations.

We have also included the code for the simulation of animal density using SMR across a range of pID values, reflecting the proportion of identifiable individuals. The values provided are similar to the observed detection conditions of the full dataset. Â

Below we have outlined the methodology...

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Data from Statistics Canada [26].*p-Value from Wilcoxon-Mann-Whitney test;**p-Value from t-test;¶ People 15 years of age and over; SD = Standard Deviation.Sociodemographic characteristics of the rural small towns in which the EDs were located.

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.

AbstractCamera-traps (CTs) are an increasingly popular tool for wildlife survey and monitoring. Estimating relative abundance in unmarked species is often done using detection rate as an index of relative abundance, which assumes a positive linear relationship with true abundance. This assumption may be violated if movement behavior varies with density, but the degree to which movement is density-dependent across taxa is unclear. The potential confounding of population-level relative abundance indices by movement depends on how regularly, and by what magnitude, movement rate and home-range size vary with density. We conducted a systematic review and meta-analysis to quantify relationships between movement rate, home range size, and density, across terrestrial mammalian taxa. We then simulated animal movements and CT sampling to test the effect of contrasting movement scenarios on CT detection rates. Overall, movement rate and home range size were negatively correlated with density and positively correlated with one another. The strength of the relationships varied significantly between taxa and populations. In simulations, detection rates were related to true abundance but underestimated change, particularly for slower moving species with small home ranges. In situations where animal space use changes markedly with density, we estimate that up to thirty percent of a true change in abundance may be missed due to the confounding effect of movement, making trend estimation more difficult. The common assumption that movement remains constant across densities is therefore violated across a wide range of mammal species. When studying unmarked species using CT detection rates, researchers and managers should consider that such indices of relative abundance reflect both density and movement. Practitioners interpreting changes in detection rates should be aware that observed differences may be biased low relative to true changes in abundance, and that further information on animal movement may be required to make robust inferences on population trends. Usage notesData files are contained in the zipped folder BroadleyEtAl_EcolEvol_Data.zip MetaPercent_HRMovt.csv, MetaPercent_DensMovt.csv, and MetaPercent_DensHR.csv, contain data used to graph changes in density, movement rate, and home range size. Indicated are the author, species, each parameter as a percentage of that of the reference population (where the reference is the population with the lowest value for the parameter displayed on the x-axis), and the x-axis parameter as a fold change. MetaForest_HRMovt.csv, MetaForest_DensMovt.csv, and MetaForest_DensHR.csv, contain data used to conduct the meta analysis (i.e., data for each study that provided sufficient statistical information for the given parameters). Indicated are the author and species of the study, percent change in the given parameters for the two populations considered, N, test statistic type and value, and the standardized effect size (as a correlation coefficient). SimulatedHitrateData.csv contains outputs from the movement simulations as described in the paper. The title of each column indicates the number of individuals, the scenario speed, the scenario home range size, and whether the data below represents the sum encounters with cameras, or the hitrate (detections/d). Each row represents an additional instance of the simulation.

** The proportions were calculated for 23 EDs because of 3 missing value.*** The proportions were calculated for 61 EDs because of 1 missing value.*p-Value from Fisher exact test.This is the Table 5. Comparison of 24/7 local access to equipment in rural EDs.

These are the data files associated with Strayer, D.L., Fischer, D.T., Hamilton, S.K., Malcom, H.M., Pace, M.L., and Solomon, C.T. 2019. Long-term variability and density dependence in Hudson River Dreissena populations. Freshwater Biology, and describe the long-term dynamics of populations of zebra mussels and quagga mussels in the Hudson River. Data include population density, body mass, biomass, body condition, shell length, growth, and size structure.

$ The proportions were calculated for 23 EDs because of 3 missing value.& The proportions were calculated for 44 EDs because of 18 missing value.*p-Value from Fisher exact test.Comparison of local 24/7 access to consultants in rural EDs.