Graph and download economic data for Resident Population in the Rocky Mountain BEA Region (BEARMPOP) from 1900 to 2024 about Rocky Mountain BEA Region, residents, population, and USA.

Resident Population in the Rocky Mountain BEA Region was 13187.57600 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, Resident Population in the Rocky Mountain BEA Region reached a record high of 13187.57600 in January of 2024 and a record low of 1321.00000 in January of 1900. Trading Economics provides the current actual value, an historical data chart and related indicators for Resident Population in the Rocky Mountain BEA Region - last updated from the United States Federal Reserve on November of 2025.

Comprehensive demographic dataset for Rocky Mountain Industrial Park, Commerce City, CO, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

In 1970, there were 0.19 new cases of Rocky Mountain spotted fever per 100,000 population, compared to 1.6 cases per 100,000 population in 2019. This statistic shows the number of new cases of Rocky Mountain spotted fever per 100,000 population in the U.S. from 1970 to 2019.

Overall, women outnumber men by 240 people. The 0 to 4 years old age cohort exhibits the largest discrepancy with a difference of 35 people between the sexes. Furthermore, majority of the population is between the ages 30 to 34 years old, comprising 7.37 per cent of the population.

We used telemetry data from 55 trumpeter swans in the Rocky Mountain Population captured in summer and winter in the western United States to understand their migratory movements and use of habitats. This dataset includes telemetry data collected between October and March from 35 swans (5 swans captured in Oregon and 14 swans captured by Wyoming Wetlands Society are not included due to data sharing restrictions, and 1 swan captured in Montana is not included because the only data are in July and August). These data do not include telemetry data collected between April and September because those data could be used to infer nesting locations. We used these data to analyze distances traveled by swans, characteristics of swans migrating between Canada and the United States, migratory connectivity, and habitat use of swans.

Over the past two centuries, persecution and habitat loss caused grizzly bears (Ursus arctos) to decline from a population of approximately 50,000 individuals to only 4 fragmented populations within the continental United States. In recent decades, these populations have increased and expanded in size and range due to collaborative conservation efforts and protections under the Endangered Species Act. Today, population estimates exceed 1000 animals each in the Northern Continental Divide Ecosystem (NCDE) and Greater Yellowstone Ecosystem (GYE). The Selkirk Ecosystem (SE) has approximately 50 grizzly bears, and augmentations into the Cabinet-Yaak Ecosystem (CYE) helped boost the population to an estimated 50 – 60 animals. To date, the Bitterroot (BE) and North Cascades Ecosystems (NCE) lack any known permanent residents. Eventual connectivity between populations is a conservation goal, as is establishment of populations in currently unoccupied recovery areas. An understanding of ha ...

description: Sandhill crane flocks were surveyed in the San Luis Valley, Colorado (SLV), during 21-26 October 2016 to assess recruitment (% juveniles (juv.)) in the Rocky Mountain population (RMP) of greater sandhill cranes (Greaters). Fall recruitment surveys of RMP cranes in the SLV have been conducted annually since 1972 (Drewien 2011) and details of survey methodology are described elsewhere (Drewien et al. 1995).; abstract: Sandhill crane flocks were surveyed in the San Luis Valley, Colorado (SLV), during 21-26 October 2016 to assess recruitment (% juveniles (juv.)) in the Rocky Mountain population (RMP) of greater sandhill cranes (Greaters). Fall recruitment surveys of RMP cranes in the SLV have been conducted annually since 1972 (Drewien 2011) and details of survey methodology are described elsewhere (Drewien et al. 1995).

Sustainable management of exploited populations benefits from integrating demographic and genetic considerations into assessments, as both play a role in determining harvest yields and population persistence. This is especially important in populations subject to size-selective harvest, because size selective harvesting has the potential to result in significant demographic, life-history, and genetic changes. We investigated harvest-induced changes in the effective number of breeders ( ) for introduced brook trout populations (Salvelinus fontinalis) in alpine lakes from western Canada. Three populations were subject to three years of size-selective harvesting, while three control populations experienced no harvest. The  decreased consistently across all harvested populations (on average 60.8%) but fluctuated in control populations. There were no consistent changes in  between control or harvest populations, but one harvest population experienced a decrease in  of 63.2%. The /  ratio inc...

Tables showing annual counts of five ungulate populations in the Rocky Mountains region, USA.

Background information and current issues regarding the trumpeter swan translocation project at Bear River Migratory Bird Refuge. Major issues include harvesting of swans in Utah, conflicts between agencies and organizations on the goals and objectives of the project, and the possibility of legal action taken against the USFWS.

Number of people belonging to a visible minority group as defined by the Employment Equity Act and, if so, the visible minority group to which the person belongs. The Employment Equity Act defines visible minorities as 'persons, other than Aboriginal peoples, who are non-Caucasian in race or non-white in colour.' The visible minority population consists mainly of the following groups: South Asian, Chinese, Black, Filipino, Latin American, Arab, Southeast Asian, West Asian, Korean and Japanese.

The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the location where the adverse effects take place.National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. The data products in this publication that represent where people live, reflect 2021 estimates of housing unit and population counts from the U.S. Census Bureau, combined with building footprint data from Onegeo and USA Structures, both reflecting 2022 conditions.The specific raster datasets included in this publication include:Building Count: Building Count is a 30-m raster representing the count of buildings in the building footprint dataset located within each 30-m pixel.Building Density: Building Density is a 30-m raster representing the density of buildings in the building footprint dataset (buildings per square kilometer [km²]).Building Coverage: Building Coverage is a 30-m raster depicting the percentage of habitable land area covered by building footprints.Population Count (PopCount): PopCount is a 30-m raster with pixel values representing residential population count (persons) in each pixel.Population Density (PopDen): PopDen is a 30-m raster of residential population density (people/km²).Housing Unit Count (HUCount): HUCount is a 30-m raster representing the number of housing units in each pixel.Housing Unit Density (HUDen): HUDen is a 30-m raster of housing-unit density (housing units/km²).Housing Unit Exposure (HUExposure): HUExposure is a 30-m raster that represents the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year.Housing Unit Impact (HUImpact): HUImpact is a 30-m raster that represents the relative potential impact of fire to housing units at any pixel, if a fire were to occur. It is an index that incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire.Housing Unit Risk (HURisk): HURisk is a 30-m raster that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density is greater than zero.Additional methodology documentation is provided with the data publication download. Metadata and Downloads: (https://www.fs.usda.gov/rds/archive/catalog/RDS-2020-0060-2).Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.

Over 18 million ha of forests have been destroyed in the past decade in Canada by the mountain pine beetle (MPB) and its fungal symbionts. Understanding their population dynamics is critical to improving modeling of beetle epidemics and providing potential clues to predict population expansion. Leptographium longiclavatum and Grosmannia clavigera are fungal symbionts of MPB that aid the beetle to colonize and kill their pine hosts. We investigated the genetic structure and demographic expansion of L. longiclavatum in populations established within the historic distribution range and in the newly colonized regions. We identified three genetic clusters/populations that coincide with independent geographic locations. The genetic profiles of the recently established populations in northern British Columbia (BC) and Alberta suggest that they originated from central and southern BC. Approximate Bayesian Computation supports the scenario that this recent expansion represents an admixture of individuals originating from BC and the Rocky Mountains. Highly significant correlations were found among genetic distance matrices of L. longiclavatum, G. clavigera, and MPB. This highlights the concordance of demographic processes in these interacting organisms sharing a highly specialized niche and supports the hypothesis of long-term multipartite beetle-fungus co-evolutionary history and mutualistic relationships.

The Canadian Rocky Mountain Population (RMP) Trumpeter Swan (Cygnus buccinator) survey has been completed every 5 years since 1975. It has been used to monitor Trumpeter Swan population and distribution throughout the range of the Canadian RMP, which covers portions of Alberta, British Columbia, Yukon, and Northwest Territories. Up to and including the 2005 survey, a census survey method was used to count Trumpeter Swans on their breeding grounds in late summer. The 2010 and 2015 surveys used a stratified random sampling approach, conducting aerial surveys of selected 1:50K map sheets in late summer. Surveys were conducted by the Canadian Wildlife Service (CWS), the Government of Alberta (AEP), and the US Fish & Wildlife Service (USFWS). The included shapefile is a summary of the last three surveys where adult Trumpeter Swan totals are summarized by 1:50K map sheets. Where the same map sheet was surveyed more than once during the three surveys, only results from the most recent survey are presented. Regional survey reports are presented for 2005. Continental survey reports are presented for 2010 and 2015. Due to the large population expansion, Canadian RMP Trumpeter Swans are no longer a conservation concern. As a result, this costly single species survey has been discontinued.

The aboriginal populations chart shows the composition of the aboriginal population in a municipality.

Protected areas are important in species conservation, but high rates of human-caused mortality outside their borders and increasing popularity for recreation can negatively affect wildlife populations. We quantified wolverine (Gulo gulo) population trends from 2011 to 2020 in >14 000 km2 protected and non-protected habitat in southwestern Canada. We conducted wolverine and multi-species surveys using non-invasive DNA and remote camera-based methods. We developed Bayesian integrated models combining spatial capture-recapture data of marked and unmarked individuals with occupancy data. Wolverine density and occupancy declined by 39 percent, with an annual population growth rate of 0.925. Density within protected areas was 3 times higher than outside and declined between 2011 (3.6 wolverines/1000 km2) and 2020 (2.1 wolverines/1000 km2). Wolverine density and detection probability increased with snow cover and decreased near development. Detection probability also decreased with human recreational activity. The annual harvest rate of 13% was above the maximum sustainable rate. We conclude that humans negatively affected the population through direct mortality, sub-lethal effects and habitat impacts. Our study exemplifies the need to monitor population trends for species at risk – within and between protected areas - as steep declines can occur unnoticed if key conservation concerns are not identified and addressed.

Wildlife ecologists throughout the world strive to monitor trends in population abundance to help manage wildlife populations and conserve species at risk. Spatial capture-recapture studies are the gold standard for monitoring density, yet they can be difficult to apply because researchers must be able to distinguish all detected individuals. Spatial mark-resight (SMR) models only require a subset of the population to be marked and identifiable. Recent advances in SMR models with radio-collared animals required a two-staged analysis. We developed a one-stage generalized SMR (gSMR) model that used detection histories of marked and unmarked animals in a single analysis. We used simulations to assess the performance of one- and two-stage gSMR models. We then applied the one-stage gSMR with telemetry and remote camera data to estimate grizzly bear (Ursus arctos) abundance from 2012 to 2023 within the Canadian Rocky Mountains. We estimated abundance trends for the population and reproductive females (females with cubs of the year). Simulations suggest one- and two-stage models performed equally well. One-stage models are more dependable as they use exact likelihoods whereas two-stage models have shorter computation times for large datasets. Both methods had > 95% credible interval coverage and minimal bias. Increasing the number of marked animals increased the accuracy and precision of abundance estimates and > 10 marked animals were required to obtain coefficients of variation < 20% in most scenarios. The grizzly bear population increased slightly (growth rate λmean = 1.02) to a 2023 density of 10.4 grizzly bears/1000 km2. Reproductive female abundance had high interannual variability and increased to 1.0 bears/1000 km2. Population density was highest within protected areas, within high quality habitat and far from paved roads. The density of activity centers declined near paved roads over time. Mechanisms of decline may have included direct mortality and shifting activity centers to avoid human activity. Our study demonstrates the influence of human activity on localized density and importance of protected areas for carnivore conservation. Finally, our study highlights the widespread utility of remote camera and telemetry-based spatial mark-resight models for monitoring spatiotemporal trends in abundance. Methods The gSMR models combine remote camera detections of marked animals, unmarked animals, and telemetry data to estimate the baseline detection rate, home range scale parameter, and spatially explicit estimates of density. Our study area encompassed 15,483 km2 and included Banff, Kootenay, and Yoho Nation Parks and the Ya Ha Tinda ecosystem within the Rocky Mountains of Canada. The remote camera data contains detection histories from 25 marked, radio-collared grizzly bears and detections of unmarked grizzly bears recorded at 625 remote cameras from 2012 to 2021. Telemetry data contains daily global positioning system (GPS) locations from fifteen female and ten male grizzly bears. We provide source code to estimate spatial and temporal trends in grizzly bear density as well as the density of female grizzly bears with cubs of the year. We describe each data set and associated attributes in tbl_DataDescription_2023-11-13.csv.

Pika latrine density at 18 sites in the Southern Rocky Mountains.

Geographic range shifts in species’ distributions, due to climate change, imply altered dynamics at both their northern and southern range limits, or at upper and lower elevational limits. There is therefore a need to identify specific weather or climate variable(s), and life stages or cohorts on which they act, and how these affect population growth. Identifying such variables permits prediction of population increase or decline under a changing climate, and shifts in a species’ geographic range. For relatively well studied groups, such as butterflies, geographic range shifts are well documented, but weather variables and mechanisms causing those shifts are not well known. The Holarctic butterfly genus Parnassius (Papilionidae) inhabits northern and alpine environments subject to variable and extreme weather. As such, Parnassius species are vulnerable not only to long-term changes in average conditions but especially to short-term extreme weather events. We use population growth estima...