The Rum red deer project is based on the Isle of Rum in the Inner Hebrides off the west coast of Scotland.
Its broad aims are (i) to investigate the regulation of population density and the effects of density independent factors on reproduction and survival; and (ii) to identify the environmental factors, individual characteristics and genetic factors affecting reproductive success and its main components (fecundity or mating success, offspring survival and longevity) in both sexes. This population of red deer (Cervus elaphus) has been the focus of an individual based study in the North Block on the Isle of Rum in the Inner Hebrides since 1971. Since then, over 95% of individuals that use the North Block Study Area have been collared and/or tagged. They have been followed throughout life with information on birth date, birth weight, parental identity, death date and breeding attempts, both successful and unsuccessful, collected. Individual data are also collected on genotype at up to twenty loci / individual and on space use with up to fifty censuses being conduced each year. Detailed weather records and exact estimates of population size and structure exist for each year.

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.

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

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

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.

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

Data used to assess the effect of climate on roaring rate of red deer in Doñana National Reserve, accounting for the population density and the opperational sex ratio.

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.

We analysed more than 600 red deer (Cervus elaphus) from large parts of its European distribution range at 13 microsatellite loci, presenting the first continent-wide study of this species using nuclear markers. Populations were clearly differentiated (overall FST = 0.166, Jost’s Dest = 0.385), and the BAPS clustering algorithm yielded mainly geographically limited and adjacent genetic units. When forced into only three genetic clusters our data set produced a very similar geographic pattern as previously found in mtDNA phylogeographic studies: a western group from Iberia to central and parts of Eastern Europe, an eastern group from the Balkans to Eastern Europe and a third group including the threatened relict populations from Sardinia and Mesola in Italy. This result was also confirmed by a multivariate approach to analysing our data set, a discriminant analysis of principal components (DAPC). Calculations of genetic diversity and effective population sizes (linkage-disequilibrium app...

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 wi...

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 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.

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...

Genome-wide technologies open up new possibilities to clarify questions on genetic structure and phylogeographic history of taxa previously studied with microsatellite loci and mitochondrial sequences. Here, we used 736 individual red deer (Cervus elaphus) samples genotyped at 35,701 single nucleotide polymorphism loci (SNPs) to assess the population structure of the species throughout Europe. The results identified 28 populations, with higher degrees of genetic distinction in peripheral compared to mainland populations. Iberian red deer show high genetic differentiation, with lineages in Western and Central Iberia maintaining their distinctiveness, which supports separate refugial ranges within Iberia along with little recent connection between Iberian and the remaining Western European populations. The Norwegian population exhibited the lowest variability and the largest allele frequency differences from mainland European populations, compatible with a history of bottlenecks and drift..., We collected tissue samples (e.g., fragments of ears, internal organs, or muscles) from culled individuals, immediately kept at 4°C and then stored at -20°C until use. Genomic DNA was isolated from pieces of tissue using the commercial DNA purification kits DNeasy Blood and Tissue Kit (Qiagen, Germantown, MD, USA) according to the manufacturer’s protocol. Due to the number of analyses that had to be carried out on each sample and the minimum requirements needed, it was necessary to obtain high-quality DNA (in terms of high molecular weight, purity, and yield) and carry out quantity and quality controls. Extracted DNA was visualized for integrity and quantified in a 0.8% agarose gel: a 3 ml aliquot of each sample was loaded with 2 ml of Loading Dye. The integrity of DNA samples was verified by comparing them against a DNA Ladder Sibenzyme molecular weight marker (1 Kb). After that, two μl of genomic DNA were measured by NanoDropTM One (Thermo Fisher Scientific Inc, Waltham, Massachusetts..., , # Data from: Genome-wide SNP assessment of contemporary European red deer genetic structure highlights the distinction between peripheral populations and the main admixture zones in Europe

https://doi.org/10.5061/dryad.1rn8pk13b

Data including the genetic information from 490 European red deer at 50,841 SNPs. Populations are coded as in the primary article.

To conduct the analyses included in the primary article, these data were combined with those from McFarlane et al. (2020) and de Jong et al. (2021).

de Jong, J.F., et al. (2020). Fragmentation and Translocation Distort the Genetic Landscape of Ungulates: Red Deer in the Netherlands. Frontiers in Ecology and Evolution, 8. https://doi.org/10.3389/fevo.2020.535715

McFarlane, S.E., et al. (2020). Increased genetic marker density reveals high levels of admixture between red deer and introduced Japanese sika in Kintyre, Scotland. Evolutionary Applications, 13(2), 432–441. https://doi.org/10....

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

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

In 2015, red deer infected with American liver fluke were discovered in the Veldenstein Forest area for the first time. This was the first detection of the fluke in a wild deer population in Bavaria. The aim of the study was to determine the American liver fluke prevalence rates in red deer, roe deer and wild boar in the Veldenstein Forest, as well as factors influencing these rates. Since 2018, the livers of 83% of the red deer culled in the study area have been examined, as well as those of the occasional roe deer and wild boar. The livers are classified by adspection and dissection into four levels of infection. The age of the animals was estimated on the basis of dentition and tooth cementum annuli in the first molar (M1). The livers of 520 red deer, 226 roe deer and 75 wild boar were dissected. All wild boar livers tested negative. 3% of roe deer and 36% of red deer livers were positive. To lower the prevalence, the red deer population was reduced, beginning in 2018. In the following years, it fell significantly. Medium and high levels of infection were initially detected in 61% of adult red deer. Since 2018, the median number of flukes per infected liver has decreased significantly from 17.51 flukes/liver to 10.0 flukes/liver. On driven hunts, significantly more diseased deer were found than during hunting from raised hides. Furthermore, there are close correlations between infectations and the age of the red deer, and infections and the distance from the Pegnitz floodplains. The American liver fluke leads to the extinction of roe deer locally and to a massive infectation and decimation of the red deer population. Without a drastic reduction of the numbers in infected red deer populations, there is a risk of large-scale infection of native deer species across large areas in Bavaria.

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.

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.

To assist with primary health care planning, Alberta Health has developed a series of reports to provide a broad range of demographic, socio-economic and population health statistics considered relevant to primary health care for communities across the province. These community profiles provide information at the Zone and Local Geographic Area (LGA) level for each of the 132 LGAs in Alberta. Each Profile offers an overview of the current health status of residents in the LGA, indicators of the area's current and future health needs, and evidence as to which quality services are needed on a timely basis to address the area's needs. The profiles are intended to highlight areas of need and provide relevant information to support the consistent and sustainable planning of primary health services.