About this dataset

Context

The dataset tabulates the Aspen 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 Aspen 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 Aspen was 6,612, a 1.77% decrease year-by-year from 2022. Previously, in 2022, Aspen population was 6,731, a decline of 3.12% compared to a population of 6,948 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Aspen increased by 621. In this period, the peak population was 7,776 in the year 2018. 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 Aspen is shown in this column.
• Year on Year Change: This column displays the change in Aspen 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 Aspen Population by Year. You can refer the same here

The data set was created by preparing fine-scale population-specific Species Distribution Models (SDMs) to map revised PHMA and GHMA areas for each of the six greater sage-grouse populations within the current occupied range of Colorado. First, known presence locations of marked greater sage-grouse were used to train Random Forest and Resource Selection Function (RSF) models to estimate seasonal (e.g., breeding, summer-fall and winter) habitat suitability. Secondly, the seasonal model results were classified into high or low habitat suitability categories and subsequently compiled to produce a year-round habitat suitability map. Third, the resulting year-round habitat suitability maps were used to develop revised PHMA and GHMA areas for each population. Finally, the current occupied range for each population were modified to 1) exclude areas identified as unsuitable habitats and 2) include areas outside of current occupied range where evidence of sage-grouse occupancy exists.Data inputs into the RSF and Random Forest Models included presence data from GPS and VHF collar data provided to Olsson from CPW biologists, which was used to refine the models. A combination of vegetative and topographic predictors were employed at multiple scales in assessing the probability of habitat selection for the populations analyzed in this study. The predictors were analyzed at multiple spatial scales, as the literature demonstrates that habitat selection by a species occurs at some scales and not others (Mayor et al. 2009, Acker et al. 2017). The predictors were measured at five scales: 100 meters (m), 400 m, 1000 m, 1600 m, and 3200 m. These were selected to assess a range of local- to landscape-level scales that may influence habitat selection. Furthermore, these scales are comparable to scales assessed in other contemporary studies concerning habitat selection of greater sage-grouse (Doherty et al. 2010; Rice et al. 2016; Walker et al. 2016).Populations were also analyzed to assess utilization of smaller mapped aspen stands as compared to larger continuous forested stands of aspen and/or mixed-conifer. While greater-sage grouse tend to avoid larger forested areas, they will utilize smaller aspen stands (T. Apa pers. comm. 2016-2018). All presence locations for each population were sampled against mapped aspen stands to calculate 1) the rate of selection for aspen stands by the population, and 2) the acreage of each aspen stand utilized. The sampled stand acreages were subsequently graphed and examined to identify natural breaks in the data. Stands with acreages less than the natural break value and not directly adjacent to other forested stands were classified and analyzed separately as isolated aspen polygons which were included as potentially suitable habitat; the remaining aspen stands were classified as forested and integrated with mixed-conifer forests, which were assumed to be non-suitable habitat.Finally, the distance to forested areas was measured as a vegetative predictor using the Euclidean Distance tool in ArcGIS 10.4, excluding all isolated aspen patches and mixed-conifer patches less than 0.5 acres (and see previous paragraph).Vegetation types were derived from the Colorado Vegetation Classification Project (CVCP), a 25 m resolution raster dataset developed by CPW, which mapped landcover conditions through the periods from 1993to 1997. In addition, vegetation types were also derived from the 2001 LANDFIRE Existing Vegetation Type (EVT) layer for areas adjacent to the study area in Utah and Wyoming to provide complete and continuous vegetation cover for populations abutting the state boundary. The LANDFIRE EVT is a 30 m resolution raster dataset developed by the United States Geological Survey (USGS) mapping landcover conditions from 2001 (LANDFIRE 2001). Vegetative types were classified into biologically relevant classes and subsequently measured as percent-proportion by dividing the number of cells for the particular class by the total number of cells within the radii of the five defined scales using ArcGIS 10.4. The assigned classes of vegetative types varied by population and are detailed in the population-specific reports provided to BLM.Topographic predictors were derived from the 10 m resolution National Elevation Dataset (NED) Digital Elevation Model (DEM) developed and maintained by the USGS. Key topographic predictors include aspect, Compound Topographic Index (CTI), elevation, percent slope, slope position and surface roughness. Aspect and percent slope were calculated in ArcGIS 10.4. CTI, slope position and surface roughness were calculated using the Geomorphology and Gradient Metrics toolbox (Evans et al. 2014). In addition, aspect was subsequently transformed using the TRASP method in the Geomorphology and Gradient Metrics toolbox. To develop the multi-scale predictors, CTI and percent slope were measured as the mean of all values within the radii of the five defined scales; slope position and surface roughness were calculated using the radii of the five defined scales.The following summary of the step-wise procedure was developed to convert the Random Forest and RSF continuous surface model results into revised Habitat Management Area Prescriptions. Details of these methods follow this list:1. Classify all seasonal Random Forest and RSF model results into high and low habitat suitability layers.2. Ensemble all Random Forest and RSF classified seasonal layers to form a single year-round annual habitat layer designating locations as either high or low habitat suitability.3. Convert all highly suitable locations to Priority Habitat Management Areas (PHMA) and all locations designated as low habitat suitability to General Habitat Management Areas (GHMA).4. Classify all areas within a 0.6-mile radius from lek locations having an active or unknown status designation as PHMA, regardless of habitat suitability classification.5. Identify all irrigated agricultural lands and designate interiors as Undesignated Habitat (UDH).6. Review and apply site-specific manual conversions of initial management prescription designations based on CPW biologist and stakeholder input.7. Remove identified non-habitat areas from Current Occupied Range (COR). Expand COR in areas beyond the current population boundary where evidence exists to demonstrate occupation by greater sage-grouse.The previous habitat layer generated by CPW, only two habitat designations prescribed by the BLM ARMPA exist for assigning management approaches for conservation of the Colorado greater sage-grouse populations; PHMA and GHMA. PHMA have the highest conservation value based on a combination of habitat and sage-grouse population characteristics and are managed to minimize disturbance activities through No Surface Occupancy (NSO) stipulations and implementing capped disturbance allowances. GHMA represent areas with lower greater sage-grouse occupancy and generally have marginal habitat conditions with fewer management restrictions that provide greater flexibility in land use activities.The initial step to applying PHMA and GHMA habitat management prescriptions involves converting all areas classified as highly suitable habitat in the population’s year-round classified habitat layer to PHMA, while the remaining low habitat suitability areas are converted to GHMA. Secondly, all lek locations with a CPW-prescribed active or unknown status designation are buffered with a 0.6-mile radius and the entirety of the interior of the buffer area is converted to PHMA. Third, the most recent mapped irrigated agricultural lands data was acquired from the Colorado Division of Water Resources for all applicable populations, then the following procedure described below were implemented to apply the Undesignated Habitat prescription to the interior of all irrigated agricultural lands.Undesignated HabitatThrough the course of this study, an additional management prescription was established by AGNC to address concerns regarding habitat management on privately held irrigated agricultural lands.An Undesignated Habitat(UDH) management prescription was developed to address concerns surrounding the management of privately held irrigated agricultural lands. The UDH prescription is applicable to all populations, excluding the Parachute-Piceance-Roan population (due to a lack of irrigated agricultural lands). UDH are areas of seasonally irrigated and harvested hay fields. These areas are utilized seasonally by sage-grouse, primarily in the late summer and fall, near edges where irrigated fields are adjacent and abutting sagebrush habitats. UDH is considered effective habitat, but it is the long-term irrigation and haying practices which have created and maintain this habitat type, and thus the unimpeded irrigation, haying operations and maintenance are not considered to be a negative impact to sage-grouse. While utilization of the edges of irrigated agricultural lands by sage-grouse is known to vary from population to population, studying grouse utilization on a population-specific basis proved problematic as most populations lacked adequate telemetry locations within irrigated agricultural lands to yield results with any level of confidence. For this reason, the North Park population was selected to analyze in detail due to the high number of telemetry points located within irrigated agricultural lands. Approximately 20 percent of all summer-fall telemetry locations for the North Park population occur within irrigated agricultural lands, compared to less than 1 percent to 3 percent utilization demonstrated in the remaining populations.All summer-fall telemetry locations occurring within irrigated agricultural lands were sampled to calculate the distance each point occurred from the edges of irrigated fields. The distances for each location were plotted in a histogram and subsequently reviewed by CPW and AGNC team consultants, revealing a natural break occurring in the

The data set was created by preparing fine-scale population-specific Species Distribution Models (SDMs) to map revised PHMA and GHMA areas for each of the six greater sage-grouse populations within the current occupied range of Colorado. First, known presence locations of marked greater sage-grouse were used to train Random Forest and Resource Selection Function (RSF) models to estimate seasonal (e.g., breeding, summer-fall and winter) habitat suitability. Secondly, the seasonal model results were classified into high or low habitat suitability categories and subsequently compiled to produce a year-round habitat suitability map. Third, the resulting year-round habitat suitability maps were used to develop revised PHMA and GHMA areas for each population. Finally, the current occupied range for each population were modified to 1) exclude areas identified as unsuitable habitats and 2) include areas outside of current occupied range where evidence of sage-grouse occupancy exists.Data inputs into the RSF and Random Forest Models included presence data from GPS and VHF collar data provided to Olsson from CPW biologists, which was used to refine the models. A combination of vegetative and topographic predictors were employed at multiple scales in assessing the probability of habitat selection for the populations analyzed in this study. The predictors were analyzed at multiple spatial scales, as the literature demonstrates that habitat selection by a species occurs at some scales and not others (Mayor et al. 2009, Acker et al. 2017). The predictors were measured at five scales: 100 meters (m), 400 m, 1000 m, 1600 m, and 3200 m. These were selected to assess a range of local- to landscape-level scales that may influence habitat selection. Furthermore, these scales are comparable to scales assessed in other contemporary studies concerning habitat selection of greater sage-grouse (Doherty et al. 2010; Rice et al. 2016; Walker et al. 2016).Populations were also analyzed to assess utilization of smaller mapped aspen stands as compared to larger continuous forested stands of aspen and/or mixed-conifer. While greater-sage grouse tend to avoid larger forested areas, they will utilize smaller aspen stands (T. Apa pers. comm. 2016-2018). All presence locations for each population were sampled against mapped aspen stands to calculate 1) the rate of selection for aspen stands by the population, and 2) the acreage of each aspen stand utilized. The sampled stand acreages were subsequently graphed and examined to identify natural breaks in the data. Stands with acreages less than the natural break value and not directly adjacent to other forested stands were classified and analyzed separately as isolated aspen polygons which were included as potentially suitable habitat; the remaining aspen stands were classified as forested and integrated with mixed-conifer forests, which were assumed to be non-suitable habitat.Finally, the distance to forested areas was measured as a vegetative predictor using the Euclidean Distance tool in ArcGIS 10.4, excluding all isolated aspen patches and mixed-conifer patches less than 0.5 acres (and see previous paragraph).Vegetation types were derived from the Colorado Vegetation Classification Project (CVCP), a 25 m resolution raster dataset developed by CPW, which mapped landcover conditions through the periods from 1993to 1997. In addition, vegetation types were also derived from the 2001 LANDFIRE Existing Vegetation Type (EVT) layer for areas adjacent to the study area in Utah and Wyoming to provide complete and continuous vegetation cover for populations abutting the state boundary. The LANDFIRE EVT is a 30 m resolution raster dataset developed by the United States Geological Survey (USGS) mapping landcover conditions from 2001 (LANDFIRE 2001). Vegetative types were classified into biologically relevant classes and subsequently measured as percent-proportion by dividing the number of cells for the particular class by the total number of cells within the radii of the five defined scales using ArcGIS 10.4. The assigned classes of vegetative types varied by population and are detailed in the population-specific reports provided to BLM.Topographic predictors were derived from the 10 m resolution National Elevation Dataset (NED) Digital Elevation Model (DEM) developed and maintained by the USGS. Key topographic predictors include aspect, Compound Topographic Index (CTI), elevation, percent slope, slope position and surface roughness. Aspect and percent slope were calculated in ArcGIS 10.4. CTI, slope position and surface roughness were calculated using the Geomorphology and Gradient Metrics toolbox (Evans et al. 2014). In addition, aspect was subsequently transformed using the TRASP method in the Geomorphology and Gradient Metrics toolbox. To develop the multi-scale predictors, CTI and percent slope were measured as the mean of all values within the radii of the five defined scales; slope position and surface roughness were calculated using the radii of the five defined scales.The following summary of the step-wise procedure was developed to convert the Random Forest and RSF continuous surface model results into revised Habitat Management Area Prescriptions. Details of these methods follow this list:1. Classify all seasonal Random Forest and RSF model results into high and low habitat suitability layers.2. Ensemble all Random Forest and RSF classified seasonal layers to form a single year-round annual habitat layer designating locations as either high or low habitat suitability.3. Convert all highly suitable locations to Priority Habitat Management Areas (PHMA) and all locations designated as low habitat suitability to General Habitat Management Areas (GHMA).4. Classify all areas within a 0.6-mile radius from lek locations having an active or unknown status designation as PHMA, regardless of habitat suitability classification.5. Identify all irrigated agricultural lands and designate interiors as Undesignated Habitat (UDH).6. Review and apply site-specific manual conversions of initial management prescription designations based on CPW biologist and stakeholder input.7. Remove identified non-habitat areas from Current Occupied Range (COR). Expand COR in areas beyond the current population boundary where evidence exists to demonstrate occupation by greater sage-grouse.The previous habitat layer generated by CPW, only two habitat designations prescribed by the BLM ARMPA exist for assigning management approaches for conservation of the Colorado greater sage-grouse populations; PHMA and GHMA. PHMA have the highest conservation value based on a combination of habitat and sage-grouse population characteristics and are managed to minimize disturbance activities through No Surface Occupancy (NSO) stipulations and implementing capped disturbance allowances. GHMA represent areas with lower greater sage-grouse occupancy and generally have marginal habitat conditions with fewer management restrictions that provide greater flexibility in land use activities.The initial step to applying PHMA and GHMA habitat management prescriptions involves converting all areas classified as highly suitable habitat in the population’s year-round classified habitat layer to PHMA, while the remaining low habitat suitability areas are converted to GHMA. Secondly, all lek locations with a CPW-prescribed active or unknown status designation are buffered with a 0.6-mile radius and the entirety of the interior of the buffer area is converted to PHMA. Third, the most recent mapped irrigated agricultural lands data was acquired from the Colorado Division of Water Resources for all applicable populations, then the following procedure described below were implemented to apply the Undesignated Habitat prescription to the interior of all irrigated agricultural lands.Undesignated HabitatThrough the course of this study, an additional management prescription was established by AGNC to address concerns regarding habitat management on privately held irrigated agricultural lands.An Undesignated Habitat(UDH) management prescription was developed to address concerns surrounding the management of privately held irrigated agricultural lands. The UDH prescription is applicable to all populations, excluding the Parachute-Piceance-Roan population (due to a lack of irrigated agricultural lands). UDH are areas of seasonally irrigated and harvested hay fields. These areas are utilized seasonally by sage-grouse, primarily in the late summer and fall, near edges where irrigated fields are adjacent and abutting sagebrush habitats. UDH is considered effective habitat, but it is the long-term irrigation and haying practices which have created and maintain this habitat type, and thus the unimpeded irrigation, haying operations and maintenance are not considered to be a negative impact to sage-grouse. While utilization of the edges of irrigated agricultural lands by sage-grouse is known to vary from population to population, studying grouse utilization on a population-specific basis proved problematic as most populations lacked adequate telemetry locations within irrigated agricultural lands to yield results with any level of confidence. For this reason, the North Park population was selected to analyze in detail due to the high number of telemetry points located within irrigated agricultural lands. Approximately 20 percent of all summer-fall telemetry locations for the North Park population occur within irrigated agricultural lands, compared to less than 1 percent to 3 percent utilization demonstrated in the remaining populations.All summer-fall telemetry locations occurring within irrigated agricultural lands were sampled to calculate the distance each point occurred from the edges of irrigated fields. The distances for each location were plotted in a histogram and subsequently reviewed by CPW and AGNC team consultants, revealing a natural break occurring in the

Oystershell scale (OSS; Lepidosaphes ulmi L.) is an invasive insect that threatens sustainability of aspen (Populus tremuloides Michx.) in the southwestern United States. OSS invasions have created challenges for land managers tasked with maintaining healthy aspen ecosystems for the ecological, economic, and aesthetic benefits they provide. Active management is required to suppress OSS populations and mitigate damage to aspen ecosystems, but before management strategies can be implemented, critical knowledge gaps about OSS biology and ecology must be filled. This study sought to fill these gaps by addressing 3 questions: (i) What is the short-term rate of aspen mortality in OSS-infested stands in northern Arizona, USA? (ii) What are the short-term rates of OSS population growth on trees and OSS spread among trees in aspen stands? (iii) What is the phenology of OSS on aspen and does climate influence phenology? We observed high levels of aspen mortality (annual mortality rate = 10.4%) and found that OSS spread rapidly within stands (annual spread rate = 10–12.3%). We found first, second, and young third instars throughout the year and observed 2 waves of first instars (i.e., crawlers), one throughout the summer and a second in mid-winter. The first wave appeared to be driven by warming seasonal temperatures, but the cause of the second wave is unknown and might represent a second generation. We provide recommendations for future OSS research, including suggestions for more precise quantification of OSS phenology, and discuss how our results can inform management of OSS and invaded aspen ecosystems.

In subarctic Sweden, recent decadal colonization and expansion of aspen (Populus tremula L.) were recorded. Over the past 100 years, aspen became c. 16 times more abundant, mainly as a result of increased sexual regeneration. Moreover, aspen now reach tree-size (>2 m) at the alpine treeline, an ecotone that has been dominated by mountain birch (Betula pubescens ssp. czerepanovii) for at least the past 4000 years. We found that sexual regeneration in aspen probably occurred seven times or more within the last century. Whereas sexual regeneration occurred during moist years following a year with an exceptionally high June-July temperature, asexual regeneration was favored by warm and dry summers. Disturbance to the birch forest by cyclic moth population outbreaks was critical in aspen establishment in the subalpine area. At the treeline, aspen colonization was less determined by these moth outbreaks, and was mainly restricted by summer temperature. If summer warming persists, aspen spread may continue in subarctic Sweden, particularly at the treeline. However, changing disturbance regimes, future herbivore population dynamics and the responses of aspen's competitors birch and pine to a changing climate may result in different outcomes.

DOCTORATE DISSERTATION: A four year study was made of the introduced beaver on Sagehen Creek, a tributary of the Little Truckee River, in the northern Sierra Nevada. The purpose of the investigation was to examine and compare utilization of willow and aspen in colonies where the supplies were strikingly different. Aspen, which arose following logging fires, was cut by the beaver as long as available. The heaviest cutting occurred close to shore and was very light at distances of more than 100 feet from the water's edge. The amount of aspen, measured in terms of weight of bark and twigs, used by a beaver was correlated roughly with availability, being highest where aspen was most available, lowest where least available. The beavers cut most aspen during the late summer and early fall when they were storing their winter food supplies. In addition to this major rhythm in activity, an unexplained coincidence of minor spurts and lags in cutting was noted in two colonies over five miles apart. Although aspen sprouts after cutting, the new shoots were eaten by sheep which grazed the Sagehen basin during the summer. Consequently, the supply is constantly diminishing, primarily because of beaver cutting, secondarily because sheep kill the new sprouts. As aspen diminished in abundance the beavers cut increasing amounts of willow. Some willow, however, was cut even in areas where aspen was still abundant. Willow cutting exceeded willow growth in two of the three beaver colonies studied. Although it is a vigorous sprouter, willow may be locally exhausted by heavy use and the beaver then gradually shift to adjacent sites. Willow on the original sites can then recover. Following the elimination of aspen, the beaver population on this stream will probably stabilize itself at a level considerably below its peak and will subsist on willow, shifting its sites of cutting on a type of rotational sustained yield basis as local sites are exhausted.

The beaver (Castor canadensis) is a large, primarily nocturnal semi-aquatic rodent used as an indicator of the conditions in freshwater ecosystems due to its role as a keystone species and an ecosystem engineer. The potential for beaver colonization within Kouchibouguac National Park is considerable due to the quantity of first and second order streams within its borders. In addition, the most important and preferred food source available to beavers in the Park is the trembling aspen (Populous tremuloides); though this tree species colonizes disturbed areas and is usually replaced by conifers or shade-tolerant hardwoods in long-term succession. Hence, the population dynamics of beavers can reflect large-scale changes in forest ecosystems. Beavers are also an important component of biodiversity and ecological integrity because of the positive effects that the creation and maintenance of wetland areas have on a large number of animal and plant species. The purpose of the beaver monitoring program is to determine the total number of active sites in order to evaluate the abundance and distribution of families as an indication of population status. The methods for this measure involve a total ground census of all watercourses, conducted every ten years during the summer months from June to August. Site locations are recorded with a global positioning system along with noticeable signs of beaver activity (e.g., recent maintenance of dam/hut; territorial scent mounds; trails; freshly cut trees, branches or twigs; visual observation). Active sites are each considered as separate families while inactive or abandoned sites are noted but not measured as part of the count.