About this dataset

Context

The dataset tabulates the population of White Deer by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of White Deer across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of male population, with 50.21% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the White Deer is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of White Deer total population. 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 Race & Ethnicity. You can refer the same here

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

For data analysis purposes, Game Management Units (GMUs) are grouped into 7 Data Analysis Units (DAUs) based on deer population characteristics, ecological conditions, and local management considerations. For more information, see: https://idfg.idaho.gov/sites/default/files/plan-deer-white-tailed-2020-25.pdf

This data breaks down estimated hunters as well as antlered, antlerless and total harvest numbers by:

• wildlife management unit (WMU)
• calendar year

Harvest and active hunter numbers are estimates based on replies received from a sample of resident hunters and are therefore subject to statistical error.

Additional technical and statistical notes can be found in the data dictionary.

Areas used to define deer population objectives and monitor deer population status. Parameters important to managing deer populations are trends in population size, fawn recruitment, natural mortality of fawns and adults, hunter harvest rates, and age structure.

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 routes 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.

This dataset consists of occurrence observations for white-tailed deer in the National Park Service Heartland Inventory and Monitoring Network Parks. Because of their impacts on vegetation, disease transmission, visitor health, and vehicle-deer collisions, park managers at Arkansas Post National Memorial, Pea Ridge National Military Park, and Wilson’s Creek National Battlefield identified white-tailed deer as a vital sign for monitoring. Monitoring white-tailed deer populations better positions park management to take action to mitigate concerns involving deer. The overall goals of HTLN white-tailed deer monitoring are to 1) document annual changes in the number of white-tailed deer, as changes could signal presence of illegal deer harvest, disease, or other acute factors of concern for park management; 2) document long-term trends in the number of white-tailed deer to help park management determine if measures need to be taken to maintain herd health, minimize vegetation damage within a park, or alleviate visitor health concerns; and 3) annually map locations of white-tailed deer observed to assist park management in assessing the influences of management actions on deer usage of an area, habitat type, etc.

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.

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.

Reported field study values, parameter values, and references used to construct a population model that was compared with observed trends from deer helicopter surveys in South Texas.

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

Parameter values used to simulate CWD effects on deer population dynamics in South Texas.

Our objectives were to examine the population history of axis deer on Maui, estimate observed population growth, and then use species-specific demographic parameters in a VORTEX population viability analysis to examine removal scenarios that would most effectively reduce the population. Only nine deer were introduced in 1959, but recent estimates of >10,000 deer suggest population growth rates (r) ranging between 0.147 and 0.160 although at least 11,200 have been removed by hunters and resource managers. In the VORTEX simulations, we evaluated an initial population size of 6,000 females and 4,000 males, reflecting the probable 3F:2M sex ratio on Maui because of male biased hunting. Scenarios were modeled over a 10-year period with removal rates of 10%, 20%, and 30% of each annual population estimate, considering both growth and removals. A removal rate of 10% the annual population estimate (1,000 deer in the first year), and an evenly distributed effort that would remove an approximate ratio of 3F:2M resulted in a positive growth rate of 0.103 ± 0.001. A 20% removal rate resulted in only a slight negative growth, while a 30% removal rate dropped the estimate to 2,759 ± 15 deer in 10 years. By increasing the ratio of females removed to 4F:1M in the 30% removals scenario, the rate of decline nearly doubled and resulted in a mean population of 1,086 ± 15 deer. Our results indicate that effectively reducing an axis deer population would require an annual removal of approximately 20–30% of the estimated population and maintaining a ratio of 4F:1M would result in the steepest population decline.

Sex- and age-specific numbers were derived from median values generated in year 7 of simulations based on South Texas data without CWD and without harvest.

Deer group locations and sizes are used in assessing deer populations living on the 'open range'. 'Open range' generally means open areas of habitat used mainly by red deer (for example, heather moorland). From the outset it is important to be clear that although the terms 'count' or 'census' are used, open range counting enables a population estimate to be made, but with associated error margins. Research has shown that, normally, estimates will vary by between 5 and 16%. In other words if you count 415 deer then the population estimate is at best between 348 and 481 (or at very best between 394 and 435). Open range population counts (and their resulting estimates) are therefore most likely to be useful for setting broad targets or giving an index of deer numbers as opposed to very precise population models. They are also useful for indicating trends in a series of counts. Count information can be obtained by joining table DEER_COUNT_INDEX based on COUNT_ID columns. Both Helicopter and ground counts are included in the data. The majority of the data were collected in 'white ground' conditions where the contrast between deer and the background of snow is maximised enabling deer to be more easily spotted. Summer counts of 'Priority' sites are also included where sites have been counted more intensively. Attribute Name / Item Name / Description DIGI_CALVS / Digital Calves / DIGI = counted from a digital photo SUM_STAGS / SUM Stags / DIGI + VIS combined SUM_HINDS / SUM Hinds / DIGI + VIS combined SUM_CALVES / SUM Calves / DIGI + VIS combined SUM_UNCL / SUM Unclassified / DIGI + VIS combined UNCL = unclassified - so generally hinds and calves combined. SUM_TOTAL / SUM Total / Overall total for that group (not necessarily for the 1km2 as there may be 3 or 4 groups in the 1km2 at that point in time. COUNT_ID / COUNT_ID / Provides link to accompanying csv file. DIGI_HINDS / Digital Hinds / DIGI = counted from a digital photo VIS_TOTAL / Visual Total / VIS = counted visually during the count DIGI_UNCL / Digital Unclassified / DIGI = counted from a digital photo UNCL = unclassified - so generally hinds and calves combined. DIGI_TOTAL / Digital Total / DIGI = counted from a digital photo VIS_STAG / Visual Stag / VIS = counted visually during the count VIS_HINDS / Visual Hinds / VIS = counted visually during the count VIS_CALVS / Visual Calves / VIS = counted visually during the count VIS_UNCL / Visual Unclassified / VIS = counted visually during the count UNCL = unclassified - so generally hinds and calves combined. DIGI_STAG / Digital Stag / DIGI = counted from a digital photo

The Siskiyou mule deer herd migrates from winter ranges primarily north and east of Mount Shasta (i.e., Day Bench, Lake Shastina, Montague, Mount Dome, Mount Hebron, Sheep-Mahogany Mountain, Tionesta, and Wild Horse Mountain) to sprawling summer ranges scattered between the Mount Shasta Wilderness in the west and the Burnt Lava Flow Geological Area in the east. A small percentage of the herd are residents, residing largely within winter ranges across the central and northeast areas of the herd’s annual distribution. The total population size of the Siskiyou herd is unknown, but adult deer densities averaged 6.01 deer per km2 on summer ranges in 2017 and 5.16 deer per km2 on winter ranges in 2019 (Wittmer and others, 2021). Some deer cross major highways during their seasonal migrations (U.S. Highways 97 and 89), and road mortalities of telemetered deer have been recorded. The summer range poses few concerns to the health of the herd, although limited water availability and recent forest fires may affect deer to some extent. Possible risks to deer population health on the winter range are relatively unknown. While recreational (hunting) opportunities have declined, the herd’s status is considered stable. These mapping layers show the location of the winter ranges for mule deer (Odocoileus hemionus) in the Siskiyou population in California. They were developed from 111 sequences collected from a sample size of 66 animals comprising GPS locations collected every 1-13 hours.

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..., ,

After decades of high deer populations, North America forests have lost much of their previous biodiversity. Any landscape-level recovery requires substantial reductions in deer herds, but modern societies and wildlife management agencies appear unable to devise appropriate solutions to this chronic ecological and human health crisis. We evaluated the effectiveness of fertility control hunting in reducing deer impacts at Cornell University. We estimated spring deer populations and planted Quercus rubra seedlings to assess deer browse pressure, rodent attack, and other factors compromising seedling performance. Oak seedlings protected in cages grew well, but deer annually browsed ≥ 60% of unprotected seedlings. Despite female sterilization rates of > 90%, the deer population remained stable. Neither sterilization nor recreational hunting reduced deer browse rates and neither appears able to achieve reductions in deer populations or their impacts. We eliminated deer sterilization and recreational hunting in a core management area in favor of allowing volunteer archers to shoot deer over bait, including at night. This resulted in a substantial reduction in the deer population and a linear decline in browse rates as a function of the spring deer abundance. Public trust stewardship of North American landscapes will require a fundamental overhaul in deer management to may provide for a brighter future, and oak sentinels may be a promising metric to assess changes. These changes will require intense public debate and may require new approaches such as regulated commercial hunting and the natural dispersal or intentional release of important deer predators (e.g., wolves and mountain lions). Such drastic changes in deer management will be highly controversial, and at present, likely difficult to implement in North America. However, the future of our forest ecosystems and associated biodiversity will depend on evidence and guided change in landscape management and stewardship.

Supplementary material for the following publication in the Journal of Wildlife Management:Forsyth D.M., Comte S., Davis N.E., Bengsen A.J., Cote S.D., Hewitt D.G., Morellet N. and Mysterud A. (2022) Methodology Matters When Estimating Deer Abundance: a Global Systematic Review and Recommendations for Improvements. Journal of Wildlife Management

Population estimates using drive counts and mark-resight (MR) methods in a sika deer population on Nakanoshima Island from 1999 to 2006.