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 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.

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

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.

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...,

The white tailed deer herd on St. Vincent Island represents an important part of the island's biotic community. To maintain the integrity of the island's deer population, it is necessary to monitor various aspects of animal's biology, its environmental and socio-economic ramifications. While much of the information relating to deer is collected for other purposes, e.g. census should be part of the refuge wildlife inventory plan and hunting reported as part of public use, these procedures and record keeping systems are consolidated and incorporated as part of this program. This program has been designed primarily as a working tool for the refuge staff. The sections are separated for ease in revision when required.

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

It depends on where you are in the state. White-tailed deer populations are not uniformly distributed statewide; habitat, human development, and hunting all influence deer population abundances locally. Deer are often particularly overpopulated in suburban areas with fertilized lawns and gardens that provide nutrient rich food sources to deer but also lack deer management hunting programs.

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.

The data relate to a study of survival and space use by white-tailed deer fawns in central Iowa. Data include: 1. fawn home range sizes and habitat content for three survival intervals (0-30 days, 0-60 days, 0-7 months), 2. fawn locations in UTM coordinates and associated habitat type, 3. suspected causes of mortality, and 4. files for survival estimation and modeling effects of habitat on survival in three survival intervals.See McGovern PG, Dinsmore SJ, Blanchong JA. Survival of white-tailed deer fawns in central Iowa, PLoS ONE http://dx.doi.org/10.25380/iastate.9827243 for more information about the study.

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.

The letter and enclosed report discusses the health evaluation of 5 deer taken randomly from Pocosin Lakes National Wildlife Refuge population. Herd health appears good based on low numbers of abomosal parasites and other endoparasites, and well as nutritional indices. There is little or no herd immunity to hemorrhagic disease. The letter discusses herd health in relation to population density.

About this dataset

Context

The dataset tabulates the Deer Grove 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 Grove 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 Grove was 37, a 5.71% increase year-by-year from 2022. Previously, in 2022, Deer Grove population was 35, a decline of 2.78% compared to a population of 36 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Deer Grove decreased by 10. In this period, the peak population was 48 in the year 2003. 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 Deer Grove is shown in this column.
• Year on Year Change: This column displays the change in Deer Grove 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 Grove Population by Year. You can refer the same here

The pink lady slipper (Cypripedium acaule) is an iconic understory plant of Massachusetts forests. It is easy to find and identify and is on the United States Forest Service list of white-tailed deer browse. Brian Donahue and members of the Weston Conservation Commission engaged a local garden club to start surveying trails for pink lady slippers. The project was designed to engage the community in a citizen science project that would bring community members into the discussion of conservation land management decisions. It also supports the long-term goal of linking white-tailed deer browse to lady slipper community dynamics.

Predator-prey interactions are important to regulating populations and structuring communities but are affected by many dynamic, complex factors, across larges-scales, making them difficult to study. Integrated population models (IPMs) offer a potential solution to understanding predator-prey relationships by providing a framework for leveraging many different datasets and testing hypotheses about interactive factors. Here, we evaluate the coyote-deer (Canis latrans – Odocoileus virginianus) predator-prey relationship across the state of North Carolina (NC). Because both species have similar habitat requirements and may respond to human disturbance, we considered net primary productivity (NPP) and urbanization as key mediating factors. We estimated deer survival and fecundity by integrating camera trap, harvest, biological and hunter observation datasets into a two-stage, two-sex Lefkovich population projection matrix. We allowed survival and fecundity to vary as functions of urbanizati..., Survival and harvest rates: We used the dynamic N-mixture model of Zipkin et al. (2014) to estimate stage and sex-based survival and harvest rates from stage-at-harvest data collected statewide from 2012-2017 over all 100 counties of North Carolina. The stage-at-harvest data were collected by county each year for two stages for male deer (adults and fawns about to transition to adulthood (i.e., button bucks)) and does. We assumed that all button bucks were fawns and all females were adults. The census took place right before fawns transitioned to adulthood and we considered all fawns to reach adulthood at one year of age. Fawn:doe ratio: To represent hunted populations, we used 2017 hunter observation data from each county of NC. Hunters documented what species they observed on their hunts, given the number of hours they spent hunting, to get an index of abundance. The location of these observations was known only to the county level. Hunters were instructed to report their hunting acti..., ,