Geology topics

Donald J. Major

Publications and source records attributed to Donald J. Major.

8 recordsLinked to original sources

USGS2025

Environmental drivers of Greater Sage-grouse population trends over 25 years in Idaho, USA

Greater Sage-grouse ( Centrocercus urophasianus ) populations have been in decline for decades across much of the US Intermountain West. However, findings from 25 years of lek counts in Idaho indicate that some populations are stable or even increasing. After accounting for potential biases in past lek count data, we sought to explain the variability in population trends among all 70 lek clusters (i.e., populations) we identified in the state. For each population, we identified lek count troughs, or low-point years, that occurred between the mid-1990s and 2021 and used a regression slope of those abundance low points to quantify each population's trend over the 25-year time span. We related the 70 populations' slopes to climate, fire, topographic, vegetation, and landcover variables. Our analyses revealed that populations with negative trends tend to occur toward the ends of climate gradients (i.e., extremes of occupied habitats) and in locations with more wildfire, agriculture, and riparian landcover. Populations with positive trends generally occur in landscapes toward the middle of the climate gradient, with high amounts of low sagebrush ( Artemisia arbuscula ) landcover and intermediate amounts of riparian and agricultural landcover. Post hoc analysis indicated that the latter two drivers were strongly associated with high raven occupancy rates, which may contribute to the negative sage-grouse population trends we observed in areas with high riparian or agricultural landcover. When modeled separately for different regions however, various region-specific drivers were identified, including tree cover, annual herbaceous cover, and human development. This information can help guide sage-grouse habitat management decisions and set expectations for population recovery, given the diversity of habitats occupied by the species and the cyclic nature of sage-grouse populations.

Idaho↗

USGS2022

Greater sage-grouse respond positively to intensive post-fire restoration treatments

Habitat loss is the most prevalent threat to biodiversity in North America. One of the most threatened landscapes in the United States is the sagebrush ( Artemisia spp.) ecosystem, much of which has been fragmented or converted to non-native grasslands via the cheatgrass-fire cycle. Like many sagebrush obligates, greater sage-grouse ( Centrocercus urophasianus ) depend upon sagebrush for food and cover and are affected by changes to this ecosystem. We investigated habitat selection by 28 male greater sage-grouse during each of 3 years after a 113,000-ha wildfire in a sagebrush steppe ecosystem in Idaho and Oregon. During the study period, seeding and herbicide treatments were applied for habitat restoration. We evaluated sage-grouse responses to vegetation and post-fire restoration treatments. Throughout the 3 years post-fire, sage-grouse avoided areas with high exotic annual grass cover but selected strongly for recovering sagebrush and moderately strongly for perennial grasses. By the third year post-fire, they preferred high-density sagebrush, especially in winter when sagebrush is the primary component of the sage-grouse diet. Sage-grouse preferred forb habitat immediately post-fire, especially in summer, but this selection preference was less strong in later years. They also selected areas that were intensively treated with herbicide and seeded with sagebrush, grasses, and forbs, although these responses varied with time since treatment. Wildfire can have severe consequences for sagebrush-obligate species due to loss of large sagebrush plants used for food and for protection from predators and thermal extremes. Our results show that management efforts, including herbicide application and seeding of plants, directed at controlling exotic annual grasses after a wildfire can positively affect habitat selection by sage-grouse.

Ecology and Evolution↗

USGS2020

Estimating abiotic thresholds for sagebrush condition class in the western United States

Sagebrush ecosystems of the western United States can transition from extended periods of relatively stable conditions to rapid ecological change if acute disturbances occur. Areas dominated by native sagebrush can transition from species-rich native systems to altered states where non-native annual grasses dominate, if resistance to annual grasses is low. The non-native annual grasses provide relatively little value to wildlife, livestock, and humans and function as fuel that increases fire frequency. The more land area covered by annual grasses, the higher the potential for fire, thus reducing the potential for native vegetation to reestablish, even when applying restoration treatments. Mapping areas of stability and areas of change using machine-learning algorithms allows both the identification of dominant abiotic variables that drive ecosystem dynamics and the variables’ important thresholds. We develop a decision-tree model with rulesets that estimate three classes of sagebrush condition (i.e., sagebrush recovery, tipping point [ecosystem degradation], and stable). We find rulesets that primarily drive development of the sagebrush recovery class indicate areas of midelevations (1 602 m), warm 30-yr July temperature maximums (tmax) (30.62°C), and 30-yr March precipitation (ppt) averages equal to 26.26 mm, about 10% of the 30-yr annual ppt values. Tipping point and stable classes occur at elevations that are lower (1 505 m) and higher (1 939 m), respectively, more mesic during March and annually, and experience lower 30-yr July tmax averages. These defined variable averages can be used to understand current dynamics of sagebrush condition and to predict where future transitions may occur under novel conditions.

Arizona, California, Colorado, Idaho, Montana, Neb↗

USGS2019

Validating a time series of annual grass percent cover in the sagebrush ecosystem

We mapped yearly (2000–2016) estimates of annual grass percent cover for much of the sagebrush ecosystem of the western United States using remotely sensed, climate, and geophysical data in regression-tree models. Annual grasses senesce and cure by early summer and then become beds of fine fuel that easily ignite and spread fire through rangeland systems. Our annual maps estimate the extent of these fuels and can serve as a tool to assist land managers and scientists in understanding the ecosystem’s response to weather variations, disturbances, and management. Validating the time series of annual maps is important for determining the usefulness of the data. To validate these maps, we compare Bureau of Land Management Assessment Inventory and Monitoring (AIM) data to mapped estimates and use a leave-one-out spatial assessment technique that is effective for validating maps that cover broad geographical extents. We hypothesize that the time series of annual maps exhibits high spatiotemporal variability because precipitation is highly variable in arid and semiarid environments where sagebrush is native, and invasive annual grasses respond to precipitation. The remotely sensed data that help drive our regression-tree model effectively measures annual grasses’ response to precipitation. The mean absolute error (MAE) rate varied depending on the validation data and technique used for comparison. The AIM plot data and our maps had substantial spatial incongruence, but despite this, the MAE rate for the assessment equaled 12.62%. The leave-one-out accuracy assessment had an MAE of 8.43%. We quantified bias, and bias was more substantial at higher percent cover. These annual maps can help management identify actions that may alleviate the current cycle of invasive grasses because it enables the assessment of the variability of annual grass − percent cover distribution through space and time, as part of dynamic systems rather than static systems.

Rangeland Ecology and Management↗

USGS2016

Cheatgrass percent cover change: Comparing recent estimates to climate change − Driven predictions in the Northern Great Basin

Cheatgrass ( Bromus tectorum L.) is a highly invasive species in the Northern Great Basin that helps decrease fire return intervals. Fire fragments the shrub steppe and reduces its capacity to provide forage for livestock and wildlife and habitat critical to sagebrush obligates. Of particular interest is the greater sage grouse ( Centrocercus urophasianus ), an obligate whose populations have declined so severely due, in part, to increases in cheatgrass and fires that it was considered for inclusion as an endangered species. Remote sensing technologies and satellite archives help scientists monitor terrestrial vegetation globally, including cheatgrass in the Northern Great Basin. Along with geospatial analysis and advanced spatial modeling, these data and technologies can identify areas susceptible to increased cheatgrass cover and compare these with greater sage grouse priority areas for conservation (PAC). Future climate models forecast a warmer and wetter climate for the Northern Great Basin, which likely will force changing cheatgrass dynamics. Therefore, we examine potential climate-caused changes to cheatgrass. Our results indicate that future cheatgrass percent cover will remain stable over more than 80% of the study area when compared with recent estimates, and higher overall cheatgrass cover will occur with slightly more spatial variability. The land area projected to increase or decrease in cheatgrass cover equals 18% and 1%, respectively, making an increase in fire disturbances in greater sage grouse habitat likely. Relative susceptibility measures, created by integrating cheatgrass percent cover and temporal standard deviation datasets, show that potential increases in future cheatgrass cover match future projections. This discovery indicates that some greater sage grouse PACs for conservation could be at heightened risk of fire disturbance. Multiple factors will affect future cheatgrass cover including changes in precipitation timing and totals and increases in freeze-thaw cycles. Understanding these effects can help direct land management, guide scientific research, and influence policy.

Idaho, Montana, Nevada, Oregon, Utah, Washington, ↗

USGS2015

Mapping and monitoring cheatgrass dieoff in rangelands of the Northern Great Basin, USA

Understanding cheatgrass (Bromus tectorum) dynamics in the Northern Great Basin rangelands, USA, is necessary to effectively manage the region’s lands. This study’s goal was to map and monitor cheatgrass performance to identify where and when cheatgrass dieoff occurred in the Northern Great Basin and to discover how this phenomenon was affected by climatic, topographic, and edaphic variables. We also examined how fire affected cheatgrass performance. Land managers and scientists are concerned by cheatgrass dieoff because it can increase land degradation, and its causes and effects are not fully known. To better understand the scope of cheatgrass dieoff, we developed multiple ecological models that integrated remote sensing data with geophysical and biophysical data. The models’ R 2 ranged from 0.71 to 0.88, and their root mean squared errors (RMSEs) ranged from 3.07 to 6.95. Validation of dieoff data showed that 41% of pixels within independently developed dieoff polygons were accurately classified as dieoff, whereas 2% of pixels outside of dieoff polygons were classified as dieoff. Site potential, a long-term spatial average of cheatgrass cover, dominated the development of the cheatgrass performance model. Fire negatively affected cheatgrass performance 1 year postfire, but by the second year postfire performance exceeded prefire levels. The landscape-scale monitoring study presented in this paper helps increase knowledge about recent rangeland dynamics, including where cheatgrass dieoffs occurred and how cheatgrass responded to fire. This knowledge can help direct further investigation and/or guide land management activities that can capitalize on, or mitigate the effects of, cheatgrass dieoff.

Northern Great Basin↗

USGS2013

The integration of geophysical and enhanced Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index data into a rule-based, piecewise regression-tree model to estimate cheatgrass beginning of spring growth

Cheatgrass exhibits spatial and temporal phenological variability across the Great Basin as described by ecological models formed using remote sensing and other spatial data-sets. We developed a rule-based, piecewise regression-tree model trained on 99 points that used three data-sets – latitude, elevation, and start of season time based on remote sensing input data – to estimate cheatgrass beginning of spring growth (BOSG) in the northern Great Basin. The model was then applied to map the location and timing of cheatgrass spring growth for the entire area. The model was strong ( R 2 = 0.85) and predicted an average cheatgrass BOSG across the study area of 29 March–4 April. Of early cheatgrass BOSG areas, 65% occurred at elevations below 1452 m. The highest proportion of cheatgrass BOSG occurred between mid-April and late May. Predicted cheatgrass BOSG in this study matched well with previous Great Basin cheatgrass green-up studies.

Great Basin↗

USGS2012

Ecosystem performance monitoring of rangelands by integrating modeling and remote sensing

Monitoring rangeland ecosystem dynamics, production, and performance is valuable for researchers and land managers. However, ecosystem monitoring studies can be difficult to interpret and apply appropriately if management decisions and disturbances are inseparable from the ecosystem's climate signal. This study separates seasonal weather influences from influences caused by disturbances and management decisions, making interannual time-series analysis more consistent and interpretable. We compared the actual ecosystem performance (AEP) of five rangeland vegetation types in the Owyhee Uplands for 9 yr to their expected ecosystem performance (EEP). Integrated growing season Normalized Difference Vegetation Index data for each of the nine growing seasons served as a proxy for annual AEP. Regression-tree models used long-term site potential, seasonal weather, and land cover data sets to generate annual EEP, an estimate of ecosystem performance incorporating annual weather variations. The difference between AEP and EEP provided a performance measure for each pixel in the study area. Ecosystem performance anomalies occurred when the ecosystem performed significantly better or worse than the model predicted. About 14% of the Owyhee Uplands showed a trend of significant underperformance or overperformance ( P <0.10). Land managers can use results from weather-based rangeland ecosystem performance models to help support adaptive management strategies.

Oregon;Idaho↗