This map of human habitation was developed, following a modification of Schumacher et al. (2000), by incorporating 2000 U.S Census Data and land ownership. The 2000 U.S. Census Block data and ownership map of the western United States were used to correct the population density for uninhabited public lands. All census blocks in the western United States were merged into one shapefile which was then clipped to contain only those areas found on private or indian reservation lands because human habitation on federal land is negligible. The area (ha) for each corrected polygon was calculated and the 2000 census block data table was joined to the shapefile. In a new field, population density (individuals/ha) corrected for public land in census blocks was calculated . SHAPEGRID in ARC/INFO was used to convert population density values to grid with 90m resolution.

This resource is a member of a series. 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) System (MTS). The MTS 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 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 because of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division 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 Bureau 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.

This is a map of populated areas with population density greater than or equal to 1 individual/ ha (i.e., rural/exurban but including suburban and urban as defined by Marzluff et al. 2001) as determined from U.S. Census data corrected for public lands.

Map containing historical census data from 1900 - 2000 throughout the western United States at the county level. Data includes total population, population density, and percent population change by decade for each county. Population data was obtained from the US Census Bureau and joined to 1:2,000,000 scale National Atlas counties shapefile.

Population density for Idaho compiled by the Department of Geography - Graduate Students Hazard Research Group] at the University of Idaho (GEOG-GSHRG). Data were compiled in 2014.

The 2015 cartographic boundary KMLs 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.

This data set summarizes electrofishing effort, observations, and tissue sampling of aquatic vertebrates near Yellow Pine, Idaho. Sampling was conducted using a backpack electrofishing unit at two reference sites including Cane Creek and Sugar Creek, and at three mine impact sites including Sugar Creek and Cinnabar Creek. Juvenile Bull Trout (Salvelinus confluentus) were present at all five sites and were the primary species targeted during our sampling effort. Additional tissue samples of Sculpin, and amphibians were taken when available. Where possible, a two pass (quantitative depletion) electrofishing survey was conducted at each site for non-USGS stakeholders (Nez Perce Tribe and Idaho Department of Fish and Game) to estimate population density if necessary to fit project needs. Electrofishing efforts were immediately halted when adult spawning Bull Trout were encountered so as to minimize harm to that species and in those cases a two pass quantitative depletion survey was not completed. Both summarized and site specific data included are in tabular form. Data is presented in both .csv and .xlsx format, the content within the Excel workbook is the same content within each individual .csv and .jpeg.

This shapefile contains landscape factors representing human disturbances summarized to local and network catchments of river reaches for the state of Idaho. 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 Idaho. 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 Idaho. National Fish Habitat Partnership Data System. http://dx.doi.org/doi:10.5066/F7RX992S

Understanding reproduction and mating systems is important for managers tasked with conserving vulnerable species. Genetic tools allow biologists to investigate reproduction and mating systems with high resolution and are particularly useful for species that are otherwise difficult to study in their natural environments. We conducted parentage analyses using 19 nuclear DNA microsatellite loci to assess the influence of population density, genetic diversity, and ancestry on reproduction, and to examine the mating system of pygmy rabbits (Brachylagus idahoensis) bred in large naturalized enclosures for the reintroduction and recovery of the endangered distinct population in central Washington, USA. Reproductive output for females and males decreased as population density and individual homozygosity increased. We identified an interaction indicating that male reproductive output decreased as genetic diversity declined at high population densities, but there was no effect at low densities. Males with high amounts (>50%) of Washington ancestry had higher reproductive output than the other ancestry groups, while reproductive output was decreased for males with high northern Utah/Wyoming ancestry and females with high Oregon/Nevada ancestry. Females and males bred with an average of 3.8 and 3.6 mates per year, respectively, and we found no evidence of positive or negative assortative mating with regards to ancestry. Multiple paternity was confirmed in 81% of litters, and we report the first documented cases of juvenile breeding by pygmy rabbits. This study demonstrates how variation in population density, genetic diversity, and ancestry impact fitness for an endangered species being bred for conservation. Our results advance understanding of basic life history characteristics for a cryptic species that is difficult to study in the wild, and provide lessons for managing populations of vulnerable species in captive and free-ranging populations.

DescriptionThe Global Human Footprint Index is the relative human influence in each terrestrial biome expressed as a percentage. The purpose is to provide an updated map of anthropogenic impacts on the environment in geographic projection which can be used in wildlife conservation planning, natural resource management, and research on human-environment interactions. Dataset Summary The Global Human Footprint Index Dataset of the Last of the Wild Project, Version 2, 2005 (LWP-2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1-kilometer grid cells, created from nine global data layers covering human population pressure (population density), human land use and infrastructure (built-up areas, nighttime lights, land use/land cover), and human access (coastlines, roads, railroads, navigable rivers). A value of zero represents the least influenced- the “most wild” part of the biome with value of 100 representing the most influenced (least wild) part of the biome. LimitationsBlank

Adaptation to local environments involves evolution of ecologically important traits and underlying physiological processes. One challenge in studying the genetic architecture of local adaptation is to achieve high marker density to detect candidate genes in natural populations that often have small blocks of linkage disequilibrium. Here, we used low coverage whole-genome resequencing (lcWGR) to identify genome regions involved in thermal adaptation in wild redband trout Oncorhynchus mykiss gairdneri, a subspecies of rainbow trout that inhabits ecosystems ranging from cold montane forests to high elevation deserts.

Data Reuse
License
CC0 1.0
Recommended Citation
Chen, Zhongqi (2021), Allele frequency files for population genomic and phenotypic association analyses, Dryad, Dataset, https://doi.org/10.5061/dryad.bk3j9kd73

Funding
US National Science Foundation and Idaho EPSCoR: OIA-1757324
Bonneville Power Administration