In 2021, the number of people who participated in hunting in the United States (aged six years and older) amounted to approximately 25.87 million. This shows a slight decrease compared to the previous year's total of 26.63 million.

This statistic illustrates the share of Americans who went hunting and fishing as of 2021, by age. In that year, ** percent of Gen Z respondents stated that they went hunting and fishing.

The Hunting Sales Statistics annual dataset provides the number of hunting licences sold in British Columbia each licence year. The data is currently collected through the BC Hunting Online licencing system and includes sales of hunting licences (basic, species and special area) and Limited Entry Hunting (LEH) applications. Sales are summarized by: - Residency: Hunter residency type (Resident or Non-Resident) - Product Type: Category of licence type (csv only) - Licence Type: Description of licence - Licence Year (LY): April 1 - March 31 (e.g. LY05/06 is April 1, 2005- March 31, 2006)

This data breaks down estimated hunter and harvest numbers by: * wildlife management unit (WMU) * calendar year Harvest and active hunter numbers are estimates based on replies received from a sample of hunters and are therefore subject to statistical error. Additional technical and statistical notes can be found in the data dictionary.

In 2024, the number of hunting licenses, tags, permits, and stamps issued in the United States amounted to just under ** million. This showed a slight decline over the previous year's figure, which was around **** million.

Outline of operational concepts for the publication of Hunting Statistics data.

This data file is in long format, comprising time series of hunter abundance and behavior and duck abundance. Hunter information varies by administrative flyway (Mississippi and Central), whereas duck population abundance is summarized for both the Prairie Pothole Region and the continent. Duck information for the Prairie Pothole Region is for the U.S. portion only (Strata 41-49 of the May waterfowl survey) and for 12 duck species, mallard, American wigeon, blue-winged teal, canvasback, gadwall, lesser and greater scaup, green-winged teal, northern pintail, northern shoveler, redhead, ring-necked duck, and ruddy duck.

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.

Russia Hunting Production: Brown Bear data was reported at 6,944.000 Head in 2017. This records an increase from the previous number of 6,600.000 Head for 2016. Russia Hunting Production: Brown Bear data is updated yearly, averaging 3,988.000 Head from Dec 1998 (Median) to 2017, with 20 observations. The data reached an all-time high of 6,944.000 Head in 2017 and a record low of 2,488.000 Head in 1998. Russia Hunting Production: Brown Bear data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Agriculture Sector – Table RU.RIE004: Hunting Production.

Outline of measurement concepts for the publication of Hunting Statistics data.

The Hunting Sales Statistics annual dataset provides the number of hunting licences sold in British Columbia each licence year. The data is currently collected through the BC Hunting Online licencing system and includes sales of hunting licences (basic, species and special area) and Limited Entry Hunting (LEH) applications. Sales are summarized by: - Residency: Hunter residency type (Resident or Non-Resident) - Product Type: Category of licence type (csv only) - Licence Type: Description of licence - Licence Year (LY): April 1 - March 31 (e.g. LY05/06 is April 1, 2005- March 31, 2006)

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Americas Hunting Equipment Market Size 2025-2029

The Americas hunting equipment market size is forecast to increase by USD 1.22 billion at a CAGR of 2.3% between 2024 and 2029.

The Hunting Equipment Market in the Americas is driven by the rising popularity of hunting equipment accessories through online sales on e-commerce platforms, providing hunters with increased convenience and access to a wider range of products. For instance, online channels accounted for approximately 30% of market sales in 2023, driven by platforms like Amazon and OpticsPlanet. This trend is further fueled by the growing prominence of hunters as conservationists, who seek advanced equipment to ensure ethical and sustainable hunting practices. For instance, the U.S. Fish and Wildlife Service reported that 98% of Duck Stamp sales revenue supports wildlife conservation. However, the seasonal nature of hunting activities poses a significant challenge for market growth. Hunters are increasingly focused on preserving wildlife habitats and implementing responsible hunting practices, which can limit the number of hunting days and, in turn, impact sales volumes.
The seasonal nature of hunting activities, with open seasons like deer hunting limited to October-December in many U.S. states, challenges market growth. Hunters' focus on preserving wildlife habitats restricts hunting days, impacting sales volumes. Companies operating in the Hunting Equipment Market must adapt to these trends and challenges by offering innovative, sustainable, and user-friendly solutions to meet the evolving needs of their customer base. By focusing on online sales channels and catering to the conservationist ethos of hunters, market players can capitalize on the growing demand for hunting equipment while navigating the seasonal constraints of the industry.

What will be the size of the Americas Hunting Equipment Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The Americas Hunting Equipment Market continues to evolve, driven by innovations enhancing performance and sustainability. For instance, hunting blind concealment technologies, like advanced camouflage patterns, have boosted success rates by up to 20% in dense terrains. Arrow rest adjustment systems improve shot precision, with micro-adjustable rests increasing arrow grouping accuracy by 15%. Pack weight optimization, using lightweight carbon-fiber materials, reduces loads by 30%, enhancing mobility. Weather forecasting tools, integrated into apps like HuntWise, provide real-time data, improving strategic planning by 25%. Rangefinder accuracy, with models like Bushnell's Bone Collector 1000, achieves 1-yard precision up to 1,000 yards. Game tracking apps, thermal imaging technology, and night vision devices, adopted by 35% of U.S. hunters, excel in low-light conditions. Shot placement accuracy tools, ethical hunting practices, and hunting safety courses, mandated in states like Montana, ensure responsible hunting. Camera sensor technology, bow sight adjustments, and adherence to hunting license regulations, such as those enforced by the U.S. Fish and Wildlife Service, promote fair and sustainable practices.

Shotgun recoil reduction, predator call selection, compound bow tuning, and scouting techniques are essential for optimizing hunting success. Bullet trajectory calculations, emergency preparedness plans, bow broadhead selection, clothing layering systems, wildlife conservation efforts, rifle accuracy testing, hunting equipment maintenance, binocular magnification, and communication devices are all integral components of the modern hunting experience. The industry is expected to grow by over 5% annually, driven by the continuous demand for advanced hunting equipment and the evolving needs of hunters. For instance, a leading hunting equipment manufacturer reported a 10% increase in sales of thermal imaging devices in the past year, reflecting the growing popularity of this technology among hunters.

How is this Americas Hunting Equipment Market segmented?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Firearms
  Ammunition and accessories
  Archery equipment and knives


End-user

  Commercial
  Personal


Application

  Big Game Hunting
  Waterfowl Hunting
  Small Game Hunting
  Sport Shooting


Geography

  North America

    US
    Canada

By Product Insights

The firearms segment is estimated to witness significant growth during the forecast period.

Firearms, including rifles, shotguns, muzzleloaders, primitive firearms, pistols, and handguns, are popular hunting equipme

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List of game for Maryland hunting seasons

Financial overview and grant giving statistics of Mountain Top Hunting Club Inc

For each hunt, we describe the number of GPS data points collected, the distance traveled (km), the time duration (hrs), the average speed (km/hr), the change in elevation between minimal and maximal points on the hunt (m), the number of GPS points with prey encounters, the number of shots fired, the number of shots which hit their target, and the total number of prey items recovered.

As a globally widespread apex predator, humans have unprecedented lethal and non-lethal effects on prey populations and ecosystems. Yet compared to non-human predators, little is known about the drivers and consequences of human hunting behavior. Here, we characterized the hunting modes, habitat selection, and harvest success of 483 rifle hunters in California using high-resolution GPS data. We used Hidden Markov Models to characterize fine-scale behavior, and k-means clustering to group hunters by hunting mode, on the basis of their time spent in each behavioral state. Hunters exhibited three distinct and successful hunting modes (“coursing”, “stalking”, and “sit-and-wait”), with stalking as the most successful strategy. Across hunting modes, there was variation in patterns of selection for roads, topography, and habitat cover, with important differences in habitat use of successful and unsuccessful hunters across modes. Our study indicates that hunters can successfully employ a diversity of harvest strategies, and that hunting success is mediated by the interacting effects of hunting mode and landscape features. Such results highlight the breadth of human hunting modes, even within a single hunting technique, and lend insight into the varied ways that humans exert predation pressure on wildlife. Methods Methods To understand patterns of hunter movement behavior, we collected GPS tracks of all hunters in the study area (2015-2022). We first classified the behavioral state of each location for each hunter, using Hidden Markov Models (HMMs; location-level classification of behavior). Next, we used k-means clustering to group hunters into distinct hunting modes based on the relative time that they spent in each behavioral state (hunter-level classification of behavioral strategy). Finally, we used Resource Selection Functions (RSFs) to evaluate patterns of habitat selection for each hunting mode, comparing habitat selection between successful and unsuccessful hunters. Study area We conducted primary data collection at the 2,168-hectare Hopland Research and Extension Center (HREC) in Mendocino County, California (Latitude: 39.002, Longitude: -123.084; Figure 1). The site features habitat types including grassland, oak woodland, and chaparral, with a network of dirt roads and fences. The site hosts an annual public hunt, in which twenty hunters per day are selected by lottery from a pool of applicants, for 4-6 days each year. In 2020, a restricted multi-day hunt was introduced for a small number of hunters. Data collection Our study took place each August-September from 2015-2022, excluding 2018 due to wildfire. We invited all hunters at the study site to participate in our study. We had a 100% rate of participation (n = 483 hunters representing 648 hunter-days). We provided each hunter with a GPS unit (i-gotU GT-600) that was programmed to take a GPS fix every 5 seconds from 5am to 10pm to encompass legal hunting hours at the study site. We asked hunters to keep the GPS unit in a pocket that would remain on their person, even when they were moving on foot. All harvested deer were brought back to headquarters, and we confirmed with the hunters whether each logger was associated with a successful or unsuccessful hunt. Upon data retrieval, we resampled all tracks to a fix rate of 3 minutes to accommodate GPS error and computational limitations. We followed data cleaning procedures described in detail in the supplementary methods. Spatial data We identified environmental features that we a priori hypothesized to influence hunter behavior and habitat use: distance to nearest road, ruggedness, viewshed, and density of each of the three habitat types (woodland, grassland, and chaparral). These hypotheses were drawn from existing literature on human hunter movement and behavior. We generated raster layers for each feature in the study area. Additional details on the development of spatial variables are provided in the supplementary methods section. We extracted spatial covariates at each point, and we calculated the elapsed time since sunrise for each point using the suncalc package. We standardized all covariates prior to modeling. Behavioral state classification with Hidden Markov Models To identify fine-scale behaviors of hunters, we used the moveHMM package to fit a hidden Markov model to the hunter movement data. We ran a global model with all predictors (distance to road, viewshed, ruggedness, woodland density, chaparral density, and time since sunrise). We assigned movement points to one of three behavioral states, as initial modeling indicated that three-state models performed better than two-state models (based on AIC), and best corresponded to self-described hunter behavior. We interpreted State 1 as corresponding to a stationary state (searching, resting, or processing deer), State 2 to walking on foot, and State 3 to driving in a vehicle. We followed best practices when choosing initial parameter values. We included a zero-mass parameter for step length given the high proportion of step lengths equal to 0 (17% of all steps). To determine whether our models were sensitive to initial parameter choice, we ran 100 iterations of the model with randomly-chosen starting parameters for step length mean, step length standard deviation, step length zero mass, and turning angle concentration. Our model converged on the same parameters for 82 of 100 of the iterations, and this model had the maximum likelihood, indicating numerical stability. We then used the parameter values from the best model as our starting values for all subsequent modeling. Based on the global model, we determined the most probable behavioral state at each step for each hunter, and determined the percentage of time that each hunter spent in each of the behavioral states. Following identification of the three states from movement parameters, we further distinguished between resting behavior on road (<10 meters from road) and off road (>10 meters from road), as these behaviors are associated with different hunting strategies (resting on the road to visually scan for deer on the landscape vs. resting off the road in a sit-and-wait hunting strategy). Identification of distinct hunting modes To identify the dominant hunting mode of each hunter, we used k-means clustering to group hunters on the basis of their time spent in each fine-scale behavioral state. We determined the optimal value of k using the elbow method heuristic. Specifically, we plotted the total within-cluster sum of squares as a function of k, and determined the value of k at which this sum of squares began declining linearly. We then ran logistic regressions to evaluate the effect of hunting mode on harvest success. Additional model covariates included year (as we were interested in whether hunting success changed over time) and whether the track came from a single-day or multi-day hunter. We tested all possible covariate combinations and we also explored interactions among hunting mode and the other covariates, to examine whether the effectiveness of different hunting modes changed over time, or varied between single- and multi-day hunts. We compared models using AIC. We also determined relative variable importance (RVI), as calculated by summing the Akaike weights of all models in which the variables appeared. We also evaluated whether the time of day at which deer were harvested varied across hunting modes, for hunters for which we had known harvest times (n = 37 of 39 successful tracked hunters). We compared harvest time (elapsed time since sunrise) for each of the three clusters using an Anderson-Darling test, a non-parametric rank test of whether samples from different groups came from the same distribution. Evaluating habitat selection To evaluate patterns of habitat selection by hunters using different hunting modes, and to evaluate connections between habitat selection and harvest success, we used Resource Selection Functions (RSFs). RSFs compare environmental features of used versus available locations in a logistic regression. We compared locations recorded by hunter GPS trackers (used locations) to locations that we systematically sampled throughout the huntable area at a 30 x 30 m resolution. To evaluate potential links between habitat selection and hunting success, we ran separate models for successful and unsuccessful hunters in each of the hunting modes, and we used the same predictors in all models to facilitate comparison of model coefficients. Model covariates included the same spatial covariates used in the HMM: ruggedness, viewshed, chaparral density, and woodland density. We assigned a weight of 5,000 to the available points, and 1 to the used points. We first ran mixed models with a random intercept for track ID, but among-individual variance was 0 for all models, resulting in a singular fit. We therefore removed the random intercept to ensure estimate stability. In addition, because RSFs assume spatiotemporal independence between points, we checked the effect of fix interval. We thinned the “used” points to a 30 min interval to reduce spatial autocorrelation between points, while retaining sufficient data for each individual hunter (mean of 20.3 used points per hunter). Conclusions remained unchanged despite the 10-fold reduction in fix rate, and the results of this model are presented in the supplementary material. To rule out any potential issues of circularity when using some of the same spatial covariates to classify behavior (which was then used to identify hunting mode) and to compare differences in habitat selection across hunting modes, we also re-ran the HMMs without any spatial covariates, classifying behavior based only on step length and turn angle. We then re-ran the k-means clustering analysis and RSFs with the updated behavior, and conclusions again remained unchanged. We have chosen to retain the spatial covariates in the HMM for

In 2024, the number of participants in bow hunting in the United States peaked at approximately ************. This was an increase over the previous year's figure of ***********.

Historical hunting and trapping statistics (from 1971) in Quebec contain harvest data for the following species: * Caribou * White-tailed deer * Wild turkey * Moose * Black bear During all these years, big game recording systems have evolved. The accuracy of the information collected has continued to improve. ## Warning The names and boundaries of hunting areas have varied over time. We have updated the location of samples in current hunting areas, but old area names may still be present.

This document discusses how the flexible kill formula for Canada goose hunting at Swan Lake Public Hunting Area was reached. Methods used to collect Canada goose kill statistics during the 1961 hunting season are included. Tabular datasets and a map are attached.