In 2008, the Quality Deer Management Association (QDMA) developed a map of white-tailed deer density with information obtained from state wildlife agencies. The map contains information from 2001 to 2005, with noticeable changes since the development of the first deer density map made by QDMA in 2001. The University of Minnesota, Forest Ecosystem Health Lab and the US Department of Agriculture, Forest Service-Northern Research Station have digitized the deer density map to provide information on the status and trends of forest health across the eastern United States. The QDMA spatial map depicting deer density (deer per square mile) was digitized across the eastern United States. Estimates of deer density were: White = rare, absent, or urban area with unknown population, Green = less than 15 deer per square mile, Yellow = 15 to 30 deer per square mile, Orange = 30 to 40 deer per square mile, or Red = greater than 45 deer per square mile. These categories represent coarse deer density levels as identified in the QDMA report in 2009 and should not be used to represent current or future deer densities across the study region. Sponsorship: Quality Deer Management Association; US Department of Agriculture, Forest Service-Northern Research Station; Minnesota Agricultural Experiment Station. Resources in this dataset:Resource Title: Link to DRUM catalog record. File Name: Web Page, url: https://conservancy.umn.edu/handle/11299/178246

The Selkirk White-tailed Deer Management Zone (WDMZ) is home to the largest population of white-tailed deer in the state and consists of seven Game Management Units (GMU; GMUs 105, 108, 111, 113, 117, 121, and 124) located in northeast Washington. Aside from the southern portion of GMU 124, dominated by the metropolitan area of Spokane, Washington, most of these GMUs have similar rural characteristics. Private landowners manage most of the Selkirk WDMZ (77 percent), primarily for commercial timber harvest. The U.S. Forest Service manages 16 percent of the land, and the U.S. Fish and Wildlife Service, Department of Natural Resources, and Bureau of Land Management manage the remaining 7 percent. White-tailed deer used in this analysis were captured on their winter range in GMUs 117 and 121, where the habitat consists of conifer forest (65 percent of the total land cover within the area) and shrub land. Grassland, pasture, and cultivated crops make up the next highest land cover types (altogether comprising nearly 21 percent of the Selkirk WDMZ). Agriculture in the valley supports high densities of deer adjacent to U.S. Highway 395, which bisects the Selkirk WDMZ from north to south. This white-tailed deer population experiences some of the highest rates of deer-vehicle collisions in the state (Myers and others 2008; G. Kalisz, Washington Department of Transportation, written commun.). Currently, there are no crossing mitigations in place along U.S. Highway 395 and State Route 20 to curtail collisions with wildlife. Other wildlife-human management challenges for this herd include mitigating crop damage complaints, maximizing hunting opportunity, and encroaching human development on the deer’s winter range. These mapping layers show the _location of the migration corridors for White-Tailed Deer (odocoileus virginianus) in the Selkirk population in Washington. They were developed from 121 migration sequences collected from a sample size of 43 animals comprising GPS locations collected every 4 hours.

From 1962-2008, White-tailed deer (Odocoileus virginianus) were studied at the SUNY ESF Huntington Wildlife Forest (HWF) and adjacent private and public lands in Essex and Hamilton Counties, New York, USA. Social group membership, migration and dispersal, reproductive biology, and many other objectives were studied over the course of the study period. Deer were captured, individually marked with ear tags or streamers, fitted with radio collars (later, GPS collars), and released to be tracked for a variety of research objectives. Deer were located by visual observation, recapture, and/or their location was estimated with ground, air or tower-based radio telemetry. Physical condition of deer was recorded at capture and at subsequent recapture or visual observation select variables were documented (e.g., deer group size; presence of fawns with does). Physiological, demographic, social organization, home range and behavior data were collected. HWF is a no-hunting area but deer could be harvested if they moved to huntable parts of the study area; there was a managed hunt on HWF in 1966-1970 and in 1984 to meet deer density and forest management objectives at that time. Unmarked deer were incorporated into the dataset if they were roadkilled, harvested or otherwise encountered during field activity; these deer did not receive individual identifications but may have been incorporated into select projects.

About this dataset

Context

The dataset tabulates the White Deer township 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 White Deer township 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 White Deer township was 4,359, a 0.68% decrease year-by-year from 2022. Previously, in 2022, White Deer township population was 4,389, a decline of 0.30% compared to a population of 4,402 in 2021. Over the last 20 plus years, between 2000 and 2023, population of White Deer township increased by 105. In this period, the peak population was 4,633 in the year 2016. 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 White Deer township is shown in this column.
• Year on Year Change: This column displays the change in White Deer township 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 White Deer township Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the White Deer 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 White Deer 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 White Deer was 995, a 9.10% increase year-by-year from 2022. Previously, in 2022, White Deer population was 912, an increase of 1.45% compared to a population of 899 in 2021. Over the last 20 plus years, between 2000 and 2023, population of White Deer decreased by 67. In this period, the peak population was 1,062 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 White Deer is shown in this column.
• Year on Year Change: This column displays the change in White Deer 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 White Deer Population by Year. You can refer the same here

One of the pervasive challenges in landscape genetics is detecting gene flow patterns within continuous populations of highly mobile wildlife. Understanding population genetic structure within a continuous population can give insights into social structure, movement across the landscape and contact between populations, which influence ecological interactions, reproductive dynamics, or pathogen transmission. We investigated the genetic structure of a large population of deer spanning the area of Wisconsin and Illinois, USA, affected by chronic wasting disease. We combined multi-scale investigation, landscape genetic techniques and spatial statistical modeling to address the complex questions of landscape factors influencing population structure. We sampled over 2,000 deer and used spatial autocorrelation and a spatial principal components analysis to describe the population genetic structure. We evaluated landscape effects on this pattern using a spatial auto-regressive model within a model selection framework to test alternative hypotheses about gene flow. We found high levels of genetic connectivity, with gradients of variation across the large continuous population of white-tailed deer. At the fine scale, spatial clustering of related animals was correlated with the amount and arrangement of forested habitat. At the broader scale, impediments to dispersal were important to shaping genetic connectivity within the population. We found significant barrier effects of individual state and interstate highways and rivers. Our results offer an important understanding of deer biology and movement that will help inform the management of this species in an area where over-abundance and disease spread are primary concerns.

High white-tailed deer abundance in the United States represents an ecological and human health threat. Reducing deer populations by lethal means and facilitating return of large predators are two potential, but controversial, management options. We used an online questionnaire to measure perspectives on deer management and predator return among a stratified sample of New York State residents. We found widespread acceptance (>70%) for reducing deer populations using lethal means if doing so would reduce Lyme disease, increase forest regeneration, protect native plants and animals, and improve road safety. Acceptance for shooting more deer was unaffected by ethnicity but strongest among respondents who were older, identified as hunters or conservationists, owned more land, and considered health and safety while answering our questionnaire. Respondents who identified as animal protectionists were least accepting. Restoring regionally extirpated wolves and cougars had limited acceptance..., This dataset contains data from an online questionnaire we used to assess perspectives of New York State residents on deer management and potential return of large predators. Qualtrics LLC (www.qualtrics.com) recruited 1,206 adults (aged 18 or older) living in New York State who answered our questionnaire from 6 - 28 June 2022. To reduce sampling error and increase external validity, we stratified our sample to approximate the population of New York State in terms of age, ethnicity, and gender identity according to the most recent American Community Survey statistics (U.S. Census Bureau, 2020). We oversampled from rural areas to permit more powerful rural-urban comparisons. Respondents reported beliefs about who should participate in deer management; how acceptable it would be for people who shoot deer to use meat and other parts in various ways; how acceptable it would be for land managers to allow shooting more deer if doing so would help achieve various ecological and socioeconomic o..., , # Data from: When dogma meets reality: perspectives of New York State residents to deer management, hunting, and predator reintroductions

https://doi.org/10.5061/dryad.2280gb60s

The spreadsheet contains data from 1,206 respondents (recruited by Qualtrics LLC) to our survey regarding public perceptions of deer management and deer welfare in New York State. We stratified our sample to approximate the population of New York State in terms of age, ethnicity, and gender identity according to the most recent American Community Survey statistics (U.S. Census Bureau, 2020). We oversampled from rural areas to permit more powerful rural-urban comparisons. All respondents provided informed consent and completed a block of demographic questions to ensure they met sample quotas before answering survey questions. Each row of the spreadsheet contains responses from an individual respondent, with columns referring to their demographic information and answers...,

Chronic wasting disease (CWD) is a fatal transmissible spongiform encephalopathy affecting white-tailed deer (Odocoileus virginianus), mule deer (Odocoileus hemionus), Rocky Mountain elk (Cervus elaphus nelsoni), and moose (Alces alces shirasi) in North America. In southeastern Wyoming average annual CWD prevalence in mule deer exceeds 20% and appears to contribute to regional population declines. We determined the effect of CWD on mule deer demography using age-specific, female-only, CWD transition matrix models to estimate the population growth rate (λ). Mule deer were captured from 2010–2014 in southern Converse County Wyoming, USA. Captured adult (≥ 1.5 years old) deer were tested ante-mortem for CWD using tonsil biopsies and monitored using radio telemetry. Mean annual survival rates of CWD-negative and CWD-positive deer were 0.76 and 0.32, respectively. Pregnancy and fawn recruitment were not observed to be influenced by CWD. We estimated λ = 0.79, indicating an annual population decline of 21% under current CWD prevalence levels. A model derived from the demography of only CWD-negative individuals yielded; λ = 1.00, indicating a stable population if CWD were absent. These findings support CWD as a significant contributor to mule deer population decline. Chronic wasting disease is difficult or impossible to eradicate with current tools, given significant environmental contamination, and at present our best recommendation for control of this disease is to minimize spread to new areas and naïve cervid populations.

In a landscape dominated by urban development, the Rocky Mountain Arse:oal (RMA) is an especially important refuge for mule and white-tailed deer. RMA biologists estimate the current deer population exceeds 900 animals (approximately 250-300 white-tailed deer and 650-700 mule deer). The Comprehensive Management Plan for the RMA, completed in 1996, states that the Fish and Wildlife Service (Service) will manage deer populations between 325 and 550 total deer. These goals were generally based on habitat conditions from the early 1990' s and deer research conducted on the refuge in the late 1980's (Matiatos pers. comm. 1999). A specific study evaluating carrying capacity of the habitats on the RMA had never been conducted. The current management approach is a conservative one that attempts to suppress deer populations and minimize habitat degradation by culling females. Culling may have suppressed populations somewhat, but the estimated total population is much higher than the current management goal. The large numerical span between the population estimate and the management goal indicates that habitats on the RMA may be able to support a much higher population of deer than was originally thought. Recognizing this need to evaluate the available habitats and create a more scientifically based method to estimate carrying capacity, the Service initiated this study.

About this dataset

Context

The dataset tabulates the Deer Trail 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 Deer Trail 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 Deer Trail was 1,513, a 9.08% increase year-by-year from 2022. Previously, in 2022, Deer Trail population was 1,387, an increase of 12.76% compared to a population of 1,230 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Deer Trail increased by 912. In this period, the peak population was 1,513 in the year 2023. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. 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 Deer Trail is shown in this column.
• Year on Year Change: This column displays the change in Deer Trail 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 Deer Trail Population by Year. You can refer the same here

Models for US Lake States region created using random forests analysis with explanatory variables listed in order of importance predicted by the models.

About this dataset

Context

The dataset tabulates the Brown Deer 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 Brown Deer 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 Brown Deer was 12,553, a 0.39% decrease year-by-year from 2022. Previously, in 2022, Brown Deer population was 12,602, a decline of 0.99% compared to a population of 12,728 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Brown Deer increased by 650. In this period, the peak population was 12,728 in the year 2021. 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 Brown Deer is shown in this column.
• Year on Year Change: This column displays the change in Brown Deer 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 Brown Deer Population by Year. You can refer the same here

Mule deer populations continue to decline across much of the western United States due to loss of habitat, starvation, and severe climate patterns, such as drought. In order to track the home range size and ecological preferences of mule deer, an important species for culture, economy, and ecosystems, the New Mexico Bureau of Land Management Taos Field Office captured mule deer, attached collars to them, and released them into Rio Grande del Norte National Monument. Collected from 2015-2017, each unique entry is one deer during one year, for a total of 23 entries. The point data was then intersected with vegetation data in the area, and the density of points was determined through Kernel Density Estimation (KDE). Reclassified BLM Vegetation Treatment data was used for zonal statistics on the KDE data and offered insights into mule deer response to treatments. This project was conducted as a joint project between the NMBLM TFO, Fort Collins USGS Science Center, and Kent State University’s Biogeography & Landscape Dynamics lab. This dataset includes all spatial data (CPG, DBF, XLSX, PRJ, SBN, SBX, SHP, and SHX) files for the comprehensive location fix shapefile, the convex hulls, the reclassified LANDFIRE EVT raster, the analysis area, the reclassified BLM Vegetation Treatment groups, the Kernel Density Estimation result, and the hill shade and state boundary data.

The Area 7 mule deer population is one of the state’s largest deer herds with an estimated population of about 11,000 in 2019. This deer herd is highly important to Nevada from an economic and ecological perspective. It’s one of the longest distance deer migrations in the state of Nevada with some animals known to migrate over 120 miles during a single migration. A subset of this population, known as the “Pequop” herd, crosses a major highway (US highway 93) and an interstate (Interstate-80) twice annually during their seasonal migration. Several million dollars in wildlife crossing structures have been constructed to help these deer during their migration, yet they still face challenges to connectivity between winter and summer ranges including miles of livestock fencing and a large-scale gold mine operation in close proximity a large stop-over site near Long Canyon. Winter range for this deer herd occurs primarily along the east side of the Pequop Mountains from Sixmile Creek to Ninemile Canyon. The largest stopovers occur along the west side of Snake Mountains near Tabor Creek, Antelope Peak and Bishop Creek areas, north and south of Interstate 80 near Pequop Summit, and the Sixmile Creek to Long Canyon area in the Pequop Mountains. Summer range for this herd primarily occurs between the Owyhee and Bruneau Rivers east of Wildhorse Reservoir. These data provide the location of winter ranges for mule deer (Odocoileus hemionus) in the Pequop Mountains, Nevada. They were developed from Brownian bridge movement models (Sawyer et al. 2009) using 193 winter sequences collected from a sample size of 86 animals comprising GPS locations collected every 1-25 hours.

Recovery of the lower Columbia population of Columbian White-tailed Deer (CWTD) relies on specific population goals. As such, monitoring programs cannot be based on trend analysis or indices, but must yield population estimates. In addition, the primary purpose for JBH is CWTD, and the Refuge is intensively managed to increase or maintain CWTD population levels. Assessing the population is critical to understanding whether our management actions are affecting CWTD numbers. These numbers will also be used to defend the current downlisting proposal any delisting proposal that may occur in the future.

This layer shows critical winter and summer ranges, fall holding areas, and fawning grounds for deer (Odocoileus hemionus) in CDFG Region 1 (Shasta, Tehama, Lassen, Modoc, Siskiyou, Trinity, and Humboldt Counties). CDFG Wildlife Biologists compiled these data in the 1970s as part of a project to identify Areas of Significant Biological Importance (Jones and Stokes 1979). This project required unit biologists to delineate natural resource boundaries and features on USGS 7.5 and 15 quadrangle maps. Deer range designations were based on biotelemetry studies, personal knowledge, and predicted use of habitats. These data were subsequently digitized to produce this dataset. The purpose of this dataset is to provide general information on the distribution of important deer ranges in Northern California. Range designations may not be current due to anthropogenic impacts or lack of data regarding the locations of seasonal ranges used by deer. CDFG should be consulted for current site-specific information on the designation or usage of seasonal ranges by deer. These maps have not been updated using current GPS techniques and may not include important corridors, reproductive areas, or other ranges important to deer populations. Critical deer winter range can include corridors essential for movement, staging areas where deer temporarily congregate, habitats containing high quality winter forage, or other elements important to the survival of deer in winter. Winter ranges are generally at lower elevations and are far less abundant than summer ranges making them vulnerable to human impacts and often a limiting factor in populations. Deer from different summer ranges may share a common winter range where breeding typically occurs. This mixing of genes on winter ranges contributes to genetically diverse and healthy populations. Critical summer range occurs generally at higher elevations, but can be similar to fall or winter ranges when deer are non-migratory. These ranges are vital to population productivity by providing habitats for parturition and rearing and forage for replenishing nutritional reserves. Summer ranges may be occupied by deer from several distinct winter ranges.. Fall holding areas are used by deer when transitioning to winter ranges. These areas can also be used in mild winters where adequate forage is available and escape from deepening snows is unnecessary. Fawning areas are critical to population productivity. They are generally located within summer ranges but can occur throughout the home ranges of non-migratory deer. Fawning areas are often linked to meadow complexes or riparian communities where adequate cover can hide newborn fawns and herbaceous forage can replenish the nutritional demands of lactation.

Digital map to show harvest totals by town for White-tailed Deer from 2005-present

Estimate, Median Age by Sex, Total Population (5-year estimate) in Deer Lodge County, MT was 49.60000 Years of Age in January of 2023, according to the United States Federal Reserve. Historically, Estimate, Median Age by Sex, Total Population (5-year estimate) in Deer Lodge County, MT reached a record high of 49.60000 in January of 2022 and a record low of 45.10000 in January of 2009. Trading Economics provides the current actual value, an historical data chart and related indicators for Estimate, Median Age by Sex, Total Population (5-year estimate) in Deer Lodge County, MT - last updated from the United States Federal Reserve on June of 2025.

The median value of the mean and standard deviation for the parameters across all units. Values are only listed at the configuration that they were used in the analysis. Thus, season length and the number of hunters are only reported for all units, while all of the limits for the various tag allocation systems are only reported within their respective rows.

The Wenatchee Mountains mule deer herd inhabits a matrix of private and public lands along the eastern slope of the Cascade Range in Chelan and Kittitas Counties in Washington (fig. 24). Historically, the Wenatchee Mountains mule deer herd was separated into two subherds, Chelan and Kittitas; however, recent GPS collar data indicated the mule deer south of U.S. Highway 2 and north of Interstate 90 represent one population. Their high-use winter range extends along the foothills west and south of Wenatchee, Washington and throughout the foothills of the Kittitas Valley near Ellensburg. Their low-use winter range occurs along the foothills west of the Columbia River north of Interstate 90. In the spring, migratory individuals travel west into the Wenatchee Mountains to their summer range, which includes regional wilderness areas. Between 2020 and 2021, collaring efforts focused on the foothills near Wenatchee and in the surrounding foothills near Ellensburg. Collar data analysis indicated the Wenatchee Mountains mule deer population is partially migratory. A high proportion of migratory individuals inhabit the northern winter range of the Wenatchee Mountains, and resident individuals more commonly inhabit the foothills of the Kittitas Valley. In 2022, collaring efforts of mule deer (n=25) in the northern winter range foothills near Wenatchee targeted the higher proportion of the migratory population, to more clearly identify the movement corridors intersecting U.S. Highway 97 near Blewett Pass. The herd has several challenges, including the increasing frequency of large-scale wildfires and residential developments, which continue to degrade and reduce available winter habitat. Disturbance from human recreation on the winter range continues to be a concern. Additionally, U.S. Highway 97 and State Route 970 receive high volumes of traffic in the region and present semipermeable barriers to spring and fall migration. These mapping layers show the location of the winter ranges for mule deer (Odocoileus hemionus) in the Wenatchee population in Washington. They were developed from 151 migration sequences collected from a sample size of 97 animals comprising GPS locations collected every 4 hours.