These data were compiled to monitor potential changes in vegetation and soil properties to indicate recovery of reclaimed oil and gas sites with time since reclamation and allow for the comparison of reclaimed well pads with reference sites of similar site characteristics. Objective(s) of our study were to identify the recovery patterns of (1) individual soil characteristics and (2) holistic multivariate soil recovery as well as (3) determine how site properties and environmental factors affect reclamation outcomes. These data represent observations of 134 reclaimed oil and gas well pads. These data were collected by Assessment Inventory and Monitoring (AIM) certified field crews using field observations and AIM methods on lands impacted by oil and gas development on the Colorado Plateau and New Mexico Plateau of New Mexico, Colorado, and Utah. These data can be used to estimate recovery on reclaimed oil and gas pads.

These data were compiled to assess the recovery of vegetation on reclaimed oil and gas sites. Objective(s) of our study were to assess patterns in reclamation outcomes relative to 1) soil attributes, climate, and time since 39 reclamation and 2) plant and soil reference benchmarks. These data represent observations of vegetation and soil cover from 134 reclaimed oil and gas well pads and 583 AIM reference plots. These data were collected on lands impacted by oil and gas development on the Colorado Plateau as well as Arizona and New Mexico Plateau of New Mexico, Colorado, and Utah. Data was collected from July- September of 2020 and May-September of 2021. These data were collected by Assessment Inventory and Monitoring (AIM) certified field crews using field observations and AIM methods. These data can be used to estimate plant community recovery on reclaimed oil and gas pads.

These data were compiled to assess the recovery of vegetation on reclaimed oil and gas sites. Objective(s) of our study were to assess patterns in reclamation outcomes relative to 1) soil attributes, climate, and time since 39 reclamation and 2) plant and soil reference benchmarks. These data represent observations of vegetation and soil cover from 134 reclaimed oil and gas well pads and 583 AIM reference plots. These data were collected on lands impacted by oil and gas development on the Colorado Plateau as well as Arizona and New Mexico Plateau of New Mexico, Colorado, and Utah. Data was collected from July- September of 2020 and May-September of 2021. These data were collected by Assessment Inventory and Monitoring (AIM) certified field crews using field observations and AIM methods. These data can be used to estimate plant community recovery on reclaimed oil and gas pads.

These data were compiled to support a study of how environmental setting affects the success of well pad reclamation within the region managed by the Bureau of Land Management, Carlsbad Field Office in southeastern New Mexico, USA. The data were collected in 2022 and 2023 and represent vegetation, soil, and climate conditions at 70 reclaimed well pads from 4 to 24 years after the wells were plugged and reclaimed. The data were collected by the U.S. Geological Survey, Southwest Biological Science Center, Moab, UT, Research Station, using field observations and some remotely sensed or mapped products (gridded climate and soil property maps). The data can be used to represent the environmental condition of the well pads at the time of collection, the elapsed time between when wells were plugged (a proxy for the start of the reclamation process) and when data were collected, and the climate at each well pad as represented by the global aridity index. For some data, samples were taken both on well pads and adjacent to them in relatively undisturbed areas and so may be used to compare the condition of pads with adjacent reference conditions.

This data release contains spatial data on the location, number, size and extent of energy-related surface disturbances on the Colorado Plateau of Utah, Colorado, and New Mexico as of 2016. The database includes: 1) polygons of oil and gas pads generated from automated and manual classification of aerial imagery, and 2) polylines of roads derived from the U.S. Census Bureau TIGER/Line Shapefile, supplemented with additional oil and gas access roads digitized from aerial imagery. Pad polygons and road segments are attributed with a "spud year" date based on spud information from the nearest well point. Spudding is the process of beginning to drill a well in the oil and gas industry, and the spud year is a close approximation of when the access roads and pads were cleared for development. The spud year information can be used to develop a chronology of oil and gas surface disturbances across the study region. The remote sensing-based pad mapping captures bright soil of disturbed areas on active pads (not reclaimed areas or other features), and is likely an underestimate of the actual pad size in many areas. The remote sensing mapping methods may also capture areas of bright soils that are not part of a pad, especially in locations surrounded by very bright desert soils.

This data release contains spatial data on the location, number, size and extent of energy-related surface disturbances on the Colorado Plateau of Utah, Colorado, and New Mexico as of 2016. The database includes: 1) polygons of oil and gas pads generated from automated and manual classification of aerial imagery, and 2) polylines of roads derived from the U.S. Census Bureau TIGER/Line Shapefile, supplemented with additional oil and gas access roads digitized from aerial imagery. Pad polygons and road segments are attributed with a "spud year" date based on spud information from the nearest well point. Spudding is the process of beginning to drill a well in the oil and gas industry, and the spud year is a close approximation of when the access roads and pads were cleared for development. The spud year information can be used to develop a chronology of oil and gas surface disturbances across the study region. The remote sensing-based pad mapping captures bright soil of disturbed areas on active pads (not reclaimed areas or other features), and is likely an underestimate of the actual pad size in many areas. The remote sensing mapping methods may also capture areas of bright soils that are not part of a pad, especially in locations surrounded by very bright desert soils.

In 1978, the late Denis Marchand launched a project to identify, sample, and analyze soil profiles from seven soil chronosequences in the Western United States. The resulting datasets were compiled as part of a series of reports titled "Soil Chronosequences in the Western United States". Early studies of soil formation highlighted several key factors that together determine the degree of soil pedogenesis, which include climate, organisms (including vegetation), topography, and parent material (Jenny H.; 1941; Factors of Soil Formation, a System of Quantitative Pedology; https://doi.org/10.2134/agronj1941.00021962003300090016x). A soil chronosequence is defined as a series of soils in which all soil-forming factors except time are similar, where time is represented by soil or landform age. This compilation of chronosequences included soils developed on a variety of landforms including alluvial fans, fluvial terraces, glacial moraines, and marine terraces. The estimated age of these soils was based on a variety of chronological dating tools specific to each chronosequence and values range from modern-aged samples to samples that are three hundred thousand years old. At the time preceding this work, it was becoming clear from the marine record that the variations in climate and terrestrial processes were extensive with a paucity of numerical dating techniques applicable to the geologic record. The ubiquitous nature of soils made this project of critical importance to a better understanding of terrestrial processes. These data were originally published in analog form by individual authors in the U.S. Geological Survey Bulletin 1590 series, which was edited by J.W. Harden. Here, data from the original bulletin series including location, land cover, horizon depths, field morphology, color, texture, particle size, bulk density, organic carbon, pH, cation exchange capacity, dithionite extractable iron, major element abundance, trace element abundance, inorganic carbon content, and mineralogy, are compiled together as a single dataset in digital form. In addition, we have also compiled scanned field notes and site photographs that are associated with these publications. Furthermore, the original samples that were collected and analyzed associated with this dataset for the Colorado, Cowlitz, Kane Fans, Merced, Rock Creek, and Ventura chronosequences have been archived via the U.S. Geological Survey Soil Sample Archive (https://doi.org/10.5066/P90KTZW4).

In 1978, the late Denis Marchand launched a project to identify, sample, and analyze soil profiles from seven soil chronosequences in the Western United States. The resulting datasets were compiled as part of a series of reports titled "Soil Chronosequences in the Western United States". Early studies of soil formation highlighted several key factors that together determine the degree of soil pedogenesis, which include climate, organisms (including vegetation), topography, and parent material (Jenny H.; 1941; Factors of Soil Formation, a System of Quantitative Pedology; https://doi.org/10.2134/agronj1941.00021962003300090016x). A soil chronosequence is defined as a series of soils in which all soil-forming factors except time are similar, where time is represented by soil or landform age. This compilation of chronosequences included soils developed on a variety of landforms including alluvial fans, fluvial terraces, glacial moraines, and marine terraces. The estimated age of these soils was based on a variety of chronological dating tools specific to each chronosequence and values range from modern-aged samples to samples that are three hundred thousand years old. At the time preceding this work, it was becoming clear from the marine record that the variations in climate and terrestrial processes were extensive with a paucity of numerical dating techniques applicable to the geologic record. The ubiquitous nature of soils made this project of critical importance to a better understanding of terrestrial processes. These data were originally published in analog form by individual authors in the U.S. Geological Survey Bulletin 1590 series, which was edited by J.W. Harden. Here, data from the original bulletin series including location, land cover, horizon depths, field morphology, color, texture, particle size, bulk density, organic carbon, pH, cation exchange capacity, dithionite extractable iron, major element abundance, trace element abundance, inorganic carbon content, and mineralogy, are compiled together as a single dataset in digital form. In addition, we have also compiled scanned field notes and site photographs that are associated with these publications. Furthermore, the original samples that were collected and analyzed associated with this dataset for the Colorado, Cowlitz, Kane Fans, Merced, Rock Creek, and Ventura chronosequences have been archived via the U.S. Geological Survey Soil Sample Archive (https://doi.org/10.5066/P90KTZW4).

This project represents the data used in “Influences of potential oil and gas development and future climate on sage-grouse declines and redistribution.” The data sets describe greater sage-grouse (Centrocercus urophasianus) population change, summarized in different boundaries within the Wyoming Landscape Conservation Initiative (WLCI; southwestern Wyoming). Population changes were based on different scenarios of oil and gas development intensities, projected climate models, and initial sage-grouse population estimates. Description of data sets pertaining to this project: Greater sage-grouse population change (percent change) in a high oil and gas development, low population estimate scenario, and with and without effects of climate change. 1. Greater sage-grouse population change (percent change) over 50-years in a high oil and gas development, low population estimate scenario, and with effects of climate change under an RCP 8.5 scenario (2050) 2. Greater sage-grouse population change (percent change) in a low oil and gas development, high population estimate scenario, and with no effects of climate change (2006-2062) 3. Greater sage-grouse population change (percent change) over 50-years in a low oil and gas development, low population estimate scenario, and with effects of climate change under an RCP 8.5 scenario (2050) 4. Greater sage-grouse population change (percent change) in a moderate oil and gas development, high population estimate scenario, and with no effects of climate change (2006-2062) 5. Greater sage-grouse population change (percent change) in a high oil and gas development, low population estimate scenario, and with no effects of climate change (2006-2062) The oil and gas development scenario were based on an energy footprint model that simulates well, pad, and road patterns for oil and gas recovery options that vary in well types (vertical and directional) and number of wells per pad and use simulation results to quantify physical and wildlife-habitat impacts. I applied the model to assess tradeoffs among 10 conventional and directional-drilling scenarios in a natural gas field in southwestern Wyoming (see Garman 2017). The effects climate change on sagebrush were developed using the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM, version 4) climate model and representative concentration pathway 8.5 scenario (emissions continue to rise throughout the 21st century). The projected climate scenario was used to estimate the change in percent cover of sagebrush (see Homer et al. 2015). The percent changes in sage-grouse population sizes represented in these data are modeled using an individual-based population model that simulates dynamics of populations by tracking movements of individuals in dynamically changing landscapes, as well as the fates of individuals as influenced by spatially heterogeneous demography. We developed a case study to assess how spatially explicit individual based modeling could be used to evaluate future population outcomes of gradual landscape change from multiple stressors. For Greater sage-grouse in southwest Wyoming, we projected oil and gas development footprints and climate-induced vegetation changes fifty years into the future. Using a time-series of planned oil and gas development and predicted climate-induced changes in vegetation, we re-calculated habitat selection maps to dynamically modify future habitat quantity, quality, and configuration. We simulated long-term sage-grouse responses to habitat change by allowing individuals to adjust to shifts in habitat availability and quality. The use of spatially explicit individual-based modeling offered an important means of evaluating delayed indirect impacts of landscape change on wildlife population outcomes. This process and the outcomes on sage-grouse population changes are reflected in this data set.

This project represents the data used in “Influences of potential oil and gas development and future climate on sage-grouse declines and redistribution.” The data sets describe greater sage-grouse (Centrocercus urophasianus) population change, summarized in different boundaries within the Wyoming Landscape Conservation Initiative (WLCI; southwestern Wyoming). Population changes were based on different scenarios of oil and gas development intensities, projected climate models, and initial sage-grouse population estimates. Description of data sets pertaining to this project: Greater sage-grouse population change (percent change) in a high oil and gas development, low population estimate scenario, and with and without effects of climate change. 1. Greater sage-grouse population change (percent change) over 50-years in a high oil and gas development, low population estimate scenario, and with effects of climate change under an RCP 8.5 scenario (2050) 2. Greater sage-grouse population change (percent change) in a low oil and gas development, high population estimate scenario, and with no effects of climate change (2006-2062) 3. Greater sage-grouse population change (percent change) over 50-years in a low oil and gas development, low population estimate scenario, and with effects of climate change under an RCP 8.5 scenario (2050) 4. Greater sage-grouse population change (percent change) in a moderate oil and gas development, high population estimate scenario, and with no effects of climate change (2006-2062) 5. Greater sage-grouse population change (percent change) in a high oil and gas development, low population estimate scenario, and with no effects of climate change (2006-2062) The oil and gas development scenario were based on an energy footprint model that simulates well, pad, and road patterns for oil and gas recovery options that vary in well types (vertical and directional) and number of wells per pad and use simulation results to quantify physical and wildlife-habitat impacts. I applied the model to assess tradeoffs among 10 conventional and directional-drilling scenarios in a natural gas field in southwestern Wyoming (see Garman 2017). The effects climate change on sagebrush were developed using the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM, version 4) climate model and representative concentration pathway 8.5 scenario (emissions continue to rise throughout the 21st century). The projected climate scenario was used to estimate the change in percent cover of sagebrush (see Homer et al. 2015). The percent changes in sage-grouse population sizes represented in these data are modeled using an individual-based population model that simulates dynamics of populations by tracking movements of individuals in dynamically changing landscapes, as well as the fates of individuals as influenced by spatially heterogeneous demography. We developed a case study to assess how spatially explicit individual based modeling could be used to evaluate future population outcomes of gradual landscape change from multiple stressors. For Greater sage-grouse in southwest Wyoming, we projected oil and gas development footprints and climate-induced vegetation changes fifty years into the future. Using a time-series of planned oil and gas development and predicted climate-induced changes in vegetation, we re-calculated habitat selection maps to dynamically modify future habitat quantity, quality, and configuration. We simulated long-term sage-grouse responses to habitat change by allowing individuals to adjust to shifts in habitat availability and quality. The use of spatially explicit individual-based modeling offered an important means of evaluating delayed indirect impacts of landscape change on wildlife population outcomes. This process and the outcomes on sage-grouse population changes are reflected in this data set.