Comprehensive demographic dataset for Red Deer, AB, CA including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

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

The number of red deer slaughtered in Argentina registered a decrease of nearly ** percent between 2011 and 2019, with less than *** animals being slaughtered in the latter year. In 2012, that figure stood at nearly ************** heads. The red deer is an exotic species in Argentina, whose population is controlled through an annual hunting season.

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

Population estimates for red deer were assessed by head light counts and observations of game wardens and hunters. Population estimates for wild boar are only locally available (see Table 4 for density data). Sources: Statistic Yearbook Liechtenstein 2011 (Statistical Office Liechtenstein); Swiss hunting statistics (Federal Office for the Environment, FOEN); Swiss Statistics 2011 (Federal Statistical Office, FSO).

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 NameItem NameDescriptionDIGI_CALVSDigital CalvesDIGI = counted from a digital photoSUM_STAGSSUM StagsDIGI + VIS combinedSUM_HINDSSUMHindsDIGI + VIS combinedSUM_CALVESSUM CalvesDIGI + VIS combinedSUM_UNCLSUMUnclassifiedDIGI + VIS combinedUNCL = unclassified – so generally hinds and calves combined.SUM_TOTALSUMTotalOverall 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_IDCOUNT_IDProvides link to accompanying csv file.DIGI_HINDSDigital HindsDIGI = counted from a digital photoVIS_TOTALVisual TotalVIS = counted visually during the countDIGI_UNCLDigital UnclassifiedDIGI = counted from a digital photo UNCL = unclassified – so generally hinds and calves combined.DIGI_TOTALDigital TotalDIGI = counted from a digital photoVIS_STAGVisual StagVIS = counted visually during the countVIS_HINDSVisual HindsVIS = counted visually during the countVIS_CALVSVisual CalvesVIS = counted visually during the countVIS_UNCLVisual UnclassifiedVIS = counted visually during the count UNCL = unclassified – so generally hinds and calves combined.DIGI_STAGDigital StagDIGI = counted from a digital photo

This data product includes two datasets acquired for the study of northern Yellowstone mule deer seasonal movement and survival patterns. One data set “Point Locations of Radio-collared female northern Yellowstone mule deer” provides details about the date and time of relocations of radio-collared female northern Yellowstone mule deer along with location coordinates. Habitat types with which the deer were associated are also included. The other dataset “Records of mule deer found dead or live captured on the northern Yellowstone winter range, 1993-1997” details live captures and radio-collaring of female mule deer on the northern Yellowstone winter range and details of the known deaths of these deer. It also includes records of mule deer of both sexes found dead in the course of field work between 1993 and 1997.

The red deer stock in Hungary increased over the considered period. In 2024, there were ******* red deer in the country compared to ****** in 2010.

Large herbivores at northern latitudes often forage on agricultural farmland. In these populations, presence of both resident and migrant individuals (termed partial migration) is common, but how migrants and residents differ in their selection of farmland is not well understood. Higher access to farmland may provide benefits to residents compensating for not following the ‘green wave’ of emerging vegetation like migrants. According to sexual segregation theory, males and females differ in body-size related nutritional needs and risk-sensitivity associated with farmland. Yet, how the sexes differ in selection of farmland through an annual cycle remains unclear. We quantified seasonal variation in the selection of farmland by partially migratory red deer (Cervus elaphus) at broad, landscape scale and at fine, within-home range scale using 16 years of data (2005-2020) from 329 females and 115 males in Norway. We tested predictions related to the partial migration and sexual segregation th..., The methods comprise fitting research selection functions using use-availability data for red deer (Cervus elaphus) to analyse their selection of farmland on broad landscape scale (second order selection; Johnson, 1980) and on finer, within-home range scale. The data of used locations is collected using GPS collars (Followit, Sweden, and Vectronic, Germany) on male and female adult red deer. The available locations were randomly sampled within each individual's available range (for the second order selection analysis) and seasonal home range (for the third order selection analysis), respectively. The RSF's were fitted using generalised linear mixed-effects models (GLMMs) for the second order selection analysis, and generalised linear models (GLMs) for the third order selection analysis., , # Sex-specific selection of agricultural farmland by a partially migratory ungulate

https://doi.org/10.5061/dryad.m905qfvc6

Description of the data and file structure

Description of the data variables:

• use_avail: Marks whether the data is a "used" or "available" location, based on the definition following the level of selection of the research selection function. 1 = used, 0 = available.
• hab2: Habitat variable with the habitats counted as available habitat for the red deer. Four levels; forest, marsh, mountain, and aafarmland (= farmland).
• sex: Sex of the red deer individual. m = male, f = female.
• visual.assignment: Movement category, with the levels "migratory" and "stationary".
• dens: Red deer population density index. Shot red deer per km2.
• log.dens: The log_e of the variable dens (see above).
• elevation: Altitude of the location, given in metres above sea level.
• log.elevation: The log_w og the variable elevation (se...

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 2022, the population of Brown Deer was 12,609, a 0.93% decrease year-by-year from 2021. Previously, in 2021, Brown Deer population was 12,728, an increase of 1.09% compared to a population of 12,591 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Brown Deer increased by 706. 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 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• 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

A. Population genetic parameters of the red deer administrative management units (AMUs) in North Rhine-Westphalia (NRW). B. Population genetic parameters of the red deer administrative management units (AMUs) in Hesse.

The number of red deer killed by French hunters has been growing steadily since the *****, going from around *** thousand in 1973 to over ** thousand in 2019. Red deer is the animal most killed by hunters in France, after wild boars and roe deer.

The red deer (Cervus elaphus) is the fourth-largest deer species behind moose, elk, and sambar deer.

The threat of isolation to red deer (Cervus elaphus) has been described in numerous European studies. The consequences range from reduced genetic diversity and increased inbreeding to inbreeding depression. It has been shown that the underlying factors cannot be generalised, but vary greatly in their effects depending on local conditions. The aim of this study was to analyse in detail the genetics of red deer in a large German federal state with a population density of 532 inhabitants per km2 and 23.8% settlement and traffic area, in order to generate data for future management of the region. 1199 individual samples of red deer were collected in all 21 Administrative Management Units (AMUs) and compared with existing results from the neighbouring state of Hesse (19 AMUs). All 2400 individuals from both states were clustered using Bayesian methods and connectivity between neighbouring AMUs was quantified. Overall, 30% of the AMUs were found to be highly isolated, mostly with effective po..., Study area, red deer populations and sampling The study area covers the entire region of the federal states of Hesse and North Rhine-Westphalia (NRW) in central and western Germany with a north-south extension of 350 km, a west-east extension of 290 km and a total area of approximately 55,213 km2. Compared to the German average of 237 persons per km2, the population density in 2023 was about 531.7 persons per km2 in North Rhine-Westphalia and 302.6 persons per km2 in Hesse (Statistisches Bundesamt 2024). Hesse consists of different types of land use, mainly forests (42.5%), pastures (13.4%) and agriculture (22.6%). Hesse is the German state with the largest forest area. The corresponding land use proportions for forest, pasture and agriculture in NRW were 26.9%, 12.1% and 31.1, respectively. The AMUs are scattered across the two states and vary considerably in size from 41.3 (RK) to 787.2 km2 (TAU) (Table 1). Forty AMUs were surveyed. The distances between AMUs ranged from 7.64 (DB-SIO)..., , # Data from: High-resolution analysis of red deer (Cervus elaphus) management units in a Central European region of high human population density reveals severe effects on genetic diversity and differentiation

https://doi.org/10.5061/dryad.msbcc2g7v

Description of the data and file structure

A total of 2490 individual hunted red deer from two federal states in Germany were sampled and genotyped with 16 microsatellite markers to analyse population genetic parameters, genetic diversity, and isolation.Â

Files and variables

File: Kopie_von_Genalex_basic_data.xlsx

Description:Â Data are each two alleles of 16 microsatellite markers for 2490 red deer individuals from 40 red deer management units

Variables

• Column A: ID including AMU (red deer administrative management unit) and individual number
• Column B: AMU
• Column C-AH: Pairwise illustration of microsatellite lengths. C: Allele 1 of microsatellite 1; D: Allele 2 ...,

Graph and download economic data for Unemployed Persons in Deer Lodge County, MT (LAUCN300230000000004) from Jan 1990 to Aug 2025 about Deer Lodge County, MT; MT; household survey; persons; unemployment; and USA.

Monitoring trends in animal populations is essential for the development of appropriate wildlife management strategies. However, long-term studies are difficult to maintain mainly due to the lack of continuous funding. In this scenario, the collaboration between local stakeholders and researchers can be a fruitful partnership to monitor game species for long periods and vast territories.

We present an experimental framework with the involvement of researchers, local hunters, and game managers for the continuous monitoring of wild ungulate populations. By combining vehicle-based counts with Distance sampling techniques, we implemented and validated a sampling scheme able to provide demographic information for the effective management of wild ungulate populations. Here, we used an Iberian red deer (Cervus elaphus) population as a model.

The project implementation involved 30 participants including 24 stakeholders and 6 field technicians/data analysts with experience in monitoring wild ungulates. A total of eight teams covered 29 itineraries, synchronously, in two periods of ecological relevance for red deer, early summer and early autumn. Density estimates were consistent among sampling periods and characterized by acceptable coefficients of variation (approximately 20%). Our results prove that the application of the proposed framework is feasible (3-4 itineraries per team), cost- and time-effective (one week per sampling period), and produce population estimates fit for management. Being based on direct observations, the method would provide important demographic indicators (e.g., population density, age structure and fawn recruitment, and group size) about wild ungulate populations.

Apart from engaging interested stakeholders, the success of our proposal relies on three key actions including the theoretical and field instruction of participants, the definition of timely and unbiased survey designs, and the maintenance of participants’ motivation. The implementation of rigorous and standardized sampling protocols is pivotal for data integration through time and space. In the absence of continuous funding, the voluntary collaboration between entities should be fostered to study and mitigate the potential threats to wild ungulate populations resulting from disease, unregulated hunting, and environmental changes.

Data set used to assess the effect of climate on the opportunity for sexual selection (Imates) of the red deer population in Doñana National Reserve.

The red deer population inhabiting the north block of the Isle of Rum, Scotland (57°0’N, 6°20’W) has been studied at an individual level since 1971 and was the main focus of this study. After quality control 39,587 autosomal SNPs genotyped in 3046 individuals were retained for analysis. This study also used equivalent genotype data for 157 individuals from a mainland population of red deer from Argyll, Scotland. Data files are in plink readable format (.bed .bim .fam) and include a .txt file with estimated SNP positions in centimorgans (cM). If you plan to analyse the data, we request that you inform us, see README file for more information.

Changing environmental conditions cause changes in the distributions of phenotypic traits in natural populations. However, determining the mechanisms responsible for these changes—and, in particular, the relative contributions of phenotypic plasticity versus evolutionary responses—is difficult. To our knowledge, no study has yet reported evidence that evolutionary change underlies the most widely reported phenotypic response to climate change: the advancement of breeding times. In a wild population of red deer, average parturition date has advanced by nearly 2 weeks in 4 decades. Here, we quantify the contribution of plastic, demographic, and genetic components to this change. In particular, we quantify the role of direct phenotypic plasticity in response to increasing temperatures and the role of changes in the population structure. Importantly, we show that adaptive evolution likely played a role in the shift towards earlier parturition dates. The observed rate of evolution was consistent with a response to selection and was less likely to be due to genetic drift. Our study provides a rare example of observed rates of genetic change being consistent with theoretical predictions, although the consistency would not have been detected with a solely phenotypic analysis. It also provides, to our knowledge, the first evidence of both evolution and phenotypic plasticity contributing to advances in phenology in a changing climate.

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.

In 2011, the Government of Uzbekistan established the Lower Amu Darya State Biosphere Reserve (LABR). This reserve aims to conserve the Tugay, an endangered riparian forest ecosystem straddling the main rivers of Central Asia’s drylands, which is under extreme anthropogenic pressure. The LABR has reintroduced Bukhara red deer (Cervus hanglu bactrianus), a subspecies endemic to Asia whose numbers declined severely over the 20th century. The LABR development project aims to provide operational support to the Uzbek authorities for their application to join the World Network of Biosphere Reserves. GIZ (German Society for International Cooperation) requested CIRAD to provide a science-based estimate of the deer population in the LABR, using an internationally recognized method, and to issue recommendations to ensure ecologically and socio-economically sustainable management. The survey of the Bukhara red deer population was carried out in October 2019. The shared datasets allow (1) to recalculate the 2019 density estimates using Distance software and (2) to replicate the deer survey protocol identically in the future.