Geology topics

Michael C. Duniway

Publications and source records attributed to Michael C. Duniway.

At least 73 records · Page 4Linked to original sources

USGS2022

Parameterizing an aeolian erosion model for rangelands

Aeolian processes are fundamental to arid and semi-arid ecosystems, but modeling approaches are poorly developed for assessing impacts of management and environmental change on sediment transport rates over meaningful spatial and temporal scales. For model estimates to provide value, estimates of sediment flux that encapsulate intra- and inter-annual and spatial variability are needed. Further, it is important to quantify and communicate transparent estimates of model uncertainty to users. Here, we present a wind erosion and dust emission model parameterized for rangelands using a Generalized Likelihood Uncertainty Estimation framework. Modeled horizontal sediment flux was calibrated using data from five diverse grassland and shrubland sites from the USDA National Wind Erosion Research Network. Observations of wind speed, vegetation height, length of gaps between vegetation, and percent bare ground were used as model inputs. Horizontal sediment flux estimates from 10,000 independently selected parameter sets were compared to flux observations from 44 ∼ month-long collection periods to calculate a likelihood measure for each model. Results show good agreement for individual sampling periods across sites with few observations falling outside prediction bounds and a one-to-one relationship between median predictions and observations. Additionally, combined distributions of sediment flux estimates from all sample periods for a given site closely approximated the probability of observing a given flux at that site. These results suggest AERO effectively represents temporal variability in aeolian transport rates at rangeland sites and provides robust assessments suitable for assessing land health and better predicting changes in air quality and the impacts of land management activities.

Aeolian Research↗

USGS2022

What determines the effectiveness of Pinyon-Juniper clearing treatments? Evidence from the remote sensing archive and counter-factual scenarios

In the intermountain western US, expansion of Pinyon ( Pinus edulis) and Juniper ( Juniperus spp. ) woodlands (PJ) into grasslands and shrublands is a pervasive phenomenon, and an example of the global trend towards enhanced woody growth in drylands. Due to the perceived impacts of these expansions on ecosystem services related to biodiversity, hydrology, soil stability, fire prevention, and livestock forage, mechanical and chemical PJ reduction treatments have been a long-standing practice in the region. More recently, PJ reduction practices have come under enhanced public scrutiny, due to potential impacts on PJ-dependent wildlife, risk of erosion due to soil disturbance, and cost effectiveness due to variable rates of long-term success. Moreover, there is growing interest in understanding the biotic, abiotic, and management conditions under which PJ reduction treatments are effective. Here, we evaluated PJ reduction treatment outcomes leveraging large, curated databases of land treatments, new remotely sensed fractional cover time-series products, gridded climate and soils data, and analytical approaches adopted from the econometric literature. From 302 treatment events and 1569 distinct treatment polygons we found evidence that treatments reduced tree cover and largely increased shrub and perennial herbaceous cover for 10 or more years. However, treatments were also associated with increases in annual, likely non-native, herbaceous cover . Importantly, we noted treatment outcomes varied by landscape context, with some soil and geomorphic settings exhibiting consistent returns to pre-treatment conditions within 10–15 years, and others exhibiting more persistent changes in functional type composition. Despite the overall trends we observed, there was considerable unexplained variability in outcomes from treatment to treatment, highlighting the need for caution and attention to local geomorphic and biological context in planning future treatments.

Arizona, Colorado, New Mexico, Utah↗

USGS2022

Sampling design workflows and tools to support adaptive monitoring and management

On the Ground • Adaptive land management requires monitoring of resource conditions, which requires choices about where and when to monitor a landscape. • Designing a sampling design for a monitoring program can be broken down in to eight steps: identifying questions, defining objectives, selecting reporting units, deciding data collection methods, defining the sample frame, selecting an appropriate design type, deciding stratification and allocation, and identifying the required sampling effort. • Here, we provide descriptions of each step in the process and identify tools and resources to complete each step.

Rangelands↗

USGS2022

Guiding principles for using satellite-derived maps in rangeland management

On the Ground Rangeland management has entered a new era with the accessibility and advancement of satellite-derived maps. Maps provide a comprehensive view of rangelands in space and time, and challenge us to think critically about natural variability. Here, we advance the practice of using satellite-derived maps with four guiding principles designed to increase end user confidence and thereby accessibility of these data for decision-making.

Idaho↗

USGS2022

A quantitative soil-geomorphic framework for developing and mapping ecological site groups

Land management decisions need context about how landscapes will respond to different circumstances or actions. As ecologists’ understanding of nonlinear ecological dynamics has evolved into state-and-transition models (STMs), they have put more emphasis on defining and mapping the soil, geomorphological, and climate parameters that mediate these dynamics. The US Department of Agriculture Natural Resources Conservation Service ecological site descriptions (ESDs) have become the foremost system in classifying lands into ecological units based on STMs. However, an exhaustive inventory of ESDs has proved challenging to complete in the United States, and there have been questions about the consistency of detail in areas completed and the ability to objectively support some assertions made in existing ESDs. To address these issues, this study examines ESDs in the diverse Upper Colorado River region, where ESDs are only partially complete, to look at quantitative approaches to generalizing ecological site concepts based on unifying underlying soil, geomorphology, and climate patterns. Using existing ESDs and vegetation monitoring plot data, results show that a simple hierarchical soil geomorphic unit (SGU) framework based on topographic mediation of moisture, soil salinity, soil depth, slope, rock content, and soil texture can represent much of the ecological dynamics cataloged in ESDs. Analyses of reference plant production data, ecological state attribution, and regional monitoring data show that the new SGUs represent more variation than common climate parameters. This study also included predictively mapping SGUs at 30-m resolution (Kappa of 0.53, 74% agreement with top two predictions in validation). An optimized combination of SGUs with climate zones derived from an aridity index and maximum temperature of the hottest month resulted in an ecological site group framework that condensed over 826 unique ecological site records at various stages of completeness in the regional soil survey down to 35 intuitive and mappable ecological site groups.

Rangeland Ecology and Management↗

USGS2022

Supporting the development and use of native plant materials for restoration on the Colorado Plateau (Fiscal Year 2021 Report)

A primary focus of the Colorado Plateau Native Plant Program (CPNPP) is to identify and develop appropriate native plant materials (NPMs) for current and future restoration projects. Multiple efforts have characterized the myriad challenges inherent in providing appropriate seed resources to enable effective, widespread restoration and have identified a broad suite of research activities to provide the information necessary to overcome those challenges (e.g., Plant Conservation Alliance 2015; Breed et al. 2018; Winkler et al. 2018; McCormick et al. 2021). Many of the most complex information needs relate to identifying the appropriate sources of plant species that can successfully establish in dryland environments, like the Colorado Plateau, where low and highly variable precipitation is standard. Providing this information requires synergistic research efforts in which results from earlier investigations inform the design of subsequent investigations. The U.S. Geological Survey (USGS) Southwest Biological Science Center’s (SBSC’s) research activities to support CPNPP in FY21 followed the FY21 Statement of Work to support a research framework that is continually adapting based on the needs of the restoration community and results from previous investigations; the long-term research framework is outlined in the 2019-2023 5-Year Research Strategy (hereafter referred to as the 5-year plan). This research framework provides support for the National Seed Strategy for Rehabilitation and Restoration (Plant Conservation Alliance 2015), Biden-Harris Administration Executive Order 14008 (Tackling the Climate Crisis at Home and Abroad), and Department of Interior Priority #4 (Working to conserve at least 30% each of our lands and waters by the year 2030). Research activities in FY21 centered on landscape genomics, implementing and monitoring a common garden experiment near Vernal, UT, conducting experimental treatments using the GRID (Germination for Restoration Information and Decision-making) framework, and initiating new genetics projects to investigate the impact of production techniques on plant materials and restoration treatments on native plant communities. These activities were supported by four biological science technicians. The SARS-CoV-2 pandemic delayed some aspects of the FY21 workplan, especially for outside laboratory services. However, goals were largely met, and the overall progress of research remains on track with respect to the 5-year plan. While Dr. Rob Massatti was the only scientist supported by the SBSC-CPNPP agreement in FY21, other scientists, including Drs. John Bradford, Seth Munson, Mike Duniway, Sasha Reed, and Daniel Winkler, spent a considerable amount of time providing expertise and support for individual projects. Work activities performed in support of each 5-year plan goal are discussed in turn. Products resulting from FY21 research activities are reported in Appendix 1.

Arizona, Colorado, New Mexico, Utah↗

USGS2021

Drought resistance and resilience: The role of soil moisture–plant interactions and legacies in a dryland ecosystem

In many regions of the world, climate change is projected to reduce water availability through changes in the hydrological cycle, including more frequent and intense droughts, as well as seasonal shifts in precipitation. In water-limited ecosystems, such as drylands, lower soil water availability may exceed the adaptive capacity of many organisms, leading to cascading ecological effects during (concurrent effects) and after drought (legacy effects). The magnitude and duration of concurrent and legacy effects depends on drought intensity, duration and timing as well as the resistance and resilience of the ecosystem. Here, we investigated the effects of drought seasonality and plant community composition on two dominant perennial grasses, Achnatherum hymenoides (C 3 photosynthesis) and Pleuraphis jamesii (C 4 photosynthesis), in a dryland ecosystem. The experiment consisted of three precipitation treatments: control (ambient precipitation), cool-season drought (−66% ambient precipitation November–April) and warm-season drought (−66% ambient precipitation May–October), applied in two plant communities (perennial grasses with or without a large shrub, Ephedra viridis ) over a 3-year period. We examined the concurrent and legacy effects of seasonal drought on soil moisture, phenology and biomass. Drought treatments had strong concurrent and legacy effects on soil moisture, which impacted the phenology and biomass of the two grasses. Drought reduced growing season length by delaying green-up (cool-season drought) or advancing senescence (warm-season drought) and reduced biomass for both species. Biomass and phenology legacy effects from drought emerged in the second and third years of the experiment. While we observed differential sensitivity to drought legacies between the two grasses, we found limited evidence that shrub presence had interactive effects with the drought treatment. Synthesis . The results from this study highlight how abiotic and biotic legacies can develop and influence a community's resistance and resilience to subsequent droughts. When the frequency of repeated extreme events, such as recurring seasonal droughts, exceeds the capacity of organisms or ecosystems to recover (i.e. resilience), persistent drought legacies can reduce the resistance to subsequent drought events. Overall, these results highlight how drought legacies are a product of ecological resistance and resilience to past drought and can influence ecosystem vulnerability to future droughts.

Utah↗

USGS2021

Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty

Operational satellite remote sensing products are transforming rangeland management and science. Advancements in computation, data storage and processing have removed barriers that previously blocked or hindered the development and use of remote sensing products. When combined with local data and knowledge, remote sensing products can inform decision‐making at multiple scales. We used temporal convolutional networks to produce a fractional cover product that spans western United States rangelands. We trained the model with 52,012 on‐the‐ground vegetation plots to simultaneously predict fractional cover for annual forbs and grasses, perennial forbs and grasses, shrubs, trees, litter and bare ground. To assist interpretation and to provide a measure of prediction confidence, we also produced spatiotemporal‐explicit, pixel‐level estimates of uncertainty. We evaluated the model with 5,780 on‐the‐ground vegetation plots removed from the training data. Model evaluation averaged 6.3% mean absolute error and 9.6% root mean squared error. Evaluation with additional datasets that were not part of the training dataset, and that varied in geographic range, method of collection, scope and size, revealed similar metrics. Model performance increased across all functional groups compared to the previously produced fractional product. The advancements achieved with the new rangeland fractional cover product expand the management toolbox with improved predictions of fractional cover and pixel‐level uncertainty. The new product is available on the Rangeland Analysis Platform (https://rangelands.app/), an interactive web application that tracks rangeland vegetation through time. This product is intended to be used alongside local on‐the‐ground data, expert knowledge, land use history, scientific literature and other sources of information when making interpretations. When being used to inform decision‐making, remotely sensed products should be evaluated and utilized according to the context of the decision and not be used in isolation.

Methods in Ecology and Evolution↗

USGS2021

Evaluating natural experiments in ecology: Using synthetic controls in assessments of remotely sensed land treatments

Many important ecological phenomena occur on large spatial scales and/or are unplanned and thus do not easily fit within analytical frameworks that rely on randomization, replication, and interspersed a priori controls for statistical comparison. Analyses of such large‐scale, natural experiments are common in the health and econometrics literature, where techniques have been developed to derive insight from large, noisy observational data sets. Here, we apply a technique from this literature, synthetic control, to assess landscape change with remote sensing data. The basic data requirements for synthetic control include (1) a discrete set of treated and untreated units, (2) a known date of treatment intervention, and (3) time series response data that include both pre‐ and post‐treatment outcomes for all units. Synthetic control generates a response metric for treated units relative to a no‐action alternative based on prior relationships between treated and unexposed groups. Using simulations and a case study involving a large‐scale brush‐clearing management event, we show how synthetic control can intuitively infer treatment effect sizes from satellite data, even in the presence of confounding noise from climate anomalies, long‐term vegetation dynamics, or sensor errors. We find that accuracy depends on the number and quality of potential control units, highlighting the importance of selecting appropriate control populations. Although we consider the synthetic control approach in the context of natural experiments with remote sensing data, we expect the methodology to have wider utility in ecology, particularly for systems with large, complex, and poorly replicated experimental units.

Ecological Applications↗

USGS2021

Regional ensemble modeling reduces uncertainty for digital soil mapping

Recent country and continental-scale digital soil mapping efforts have used a single model to predict soil properties across large regions. However, different ecophysiographic regions within large-extent areas are likely to have different soil-landscape relationships so models built specifically for these regions may more accurately capture these relationships relative to a ‘global’ model. We ask the question: Is a single ‘global’ model sufficient or are regionally-specific models useful for accurate digital soil mapping? We test this question by modeling soil depth classes across the 432,000 km 2 upper Colorado River Basin in the Western USA using a single global model, multiple ecophysiographic models, and ensembles of the ecophysiographic models. Effective soil depth class observations ( n = 12,194) were derived from multiple soil databases. Fifty-seven environmental covariates were derived from a 30 m digital elevation model, climate data, satellite imagery, and aeroradiometric data. Three independent land classifications were used to stratify the area. Two expert-derived land classifications, USDA Major Land Resource Areas (MLRA) and US-EPA Level III ecoregions, divided the study area into multiple ecophysiographic regions based on vegetation and broad-scale physiographic differences. The third land classification divided the study area into broad landforms. Soil depth observations were split into separate training ( n = 10,470) and validation ( n = 1,724) datasets. First, a ‘global’ random forest model was used to model soil depth classes using all training observations and covariates. ‘Global’ denotes a model built with all training data across the extent of the area, not a model at world extent. Second, the land classifications were used to subset the observations into ecophysiographic sub-datasets and random forest models were refit for each region. Models fit by ecophysiographic region are referred to as regional models. Thirdly, predictions from each regional model were fused into regional-ensemble models. Accuracy, Brier scores, and Shannon’s entropy were used to compare model accuracy and uncertainty. Regional ecophysiographic models were also compared to models built for geographic areas that were defined solely to be approximately equal in area. Training dataset density and the imbalance ratio were investigated to determine if data characteristics influenced regional accuracy/uncertainty metrics. Accuracy for the global model using the validation set was 62.8%. Regional model accuracies ranged between 56.1% and 75.0%. We found: 1) useful inter-regional differences in global model accuracy were revealed when the global model was validated by region, 2) no consistent relationship between training observation density and accuracy/uncertainty metrics, 3) no meaningful differences in accuracy and uncertainty metrics between physiographic and geographic regions, 4) ensembles of regionally-specific models were approximately as accurate as global models, and 5) both region-specific models and ensembles of regional models were less uncertain than the global model. Overall, we recommend the use of soil depth class predictions made from MLRA regional ensemble models because this prediction had higher accuracy than the ecoregion ensemble model prediction, but lower uncertainty than both the global model and the landform ensemble model predictions. We answer our question: Ensembles of regionally-specific models are approximately as accurate as global models, but result in less uncertainty.

Arizona, Colorado, Nevada, New Mexico, Utah, Wyomi↗

USGS2021

Biocrust and the soil surface: Influence of climate, disturbance, and biocrust recovery on soil surface roughness

Biocrust communities promote soil surface roughness, a key functional characteristic for soil ecology. However, the spatial scales at which biocrust communities contribute to surface roughness are not well understood. To refine our understanding of the spatial dynamics between biocrust and soil surface roughness, we used mm-resolution terrestrial LiDAR to measure micro-topographic roughness at seven sub-meter, 3-dimensional kernels (spatial scales) for undisturbed and disturbed biocrusts within the cool Great Basin and the hot Chihuahuan Deserts of western North America. This multi-scalar approach applied within the different desert regions allowed us to explore two objectives: 1) assess the relative importance of climate and disturbance on biocrust roughness, and 2) evaluate how soil surface roughness evolves with biocrust recovery. For objective 1, we found that undisturbed cool desert biocrust was up to three times rougher than hot desert biocrust. Much of the difference in roughness between the two desert biocrust communities appeared to be from climate or other regional factors. However, positive correlations between roughness and biocrust indicators, including soil chlorophyll-a and the field-based Level of Development (LOD) index, suggested that differences in roughness at spatial scales ≤ 10 cm are directly related to biocrust development. Mechanical disturbance aimed at removing biocrust resulted in significant reductions in roughness and removed much of the observed differences in roughness between cool and hot desert soils. We evaluated biocrust recovery within the cool desert study area two years after mechanical disturbance and found that the disturbed soil increased in roughness up-to 300%. The increased surface roughness at spatial scales ≤ 10 cm were positively correlated with increased aggregate stability and indicators of biocrust reestablishment. We found that topographic change area was also an important contributor to roughness at all spatial scales, particularly at spatial scales ≥ 20 cm where it was the most important factor evaluated. These results provide insight into how biocrust interacts with other biophysical processes to influence soil surface roughness and how soil surfaces evolve at time scales relevant to soil restoration activities.

New Mexico, Utah↗

USGS2021

Oil and gas reclamation on US public lands: How it works and improving the process with land potential concepts

• There are three general stages of a well's life on US public land: 1) the permitting process to drill, 2) active extraction of fossil fuel resource, and 3) plugging and abandonment of well. • There is no national standard for oil and gas reclamation in the United States similar to mining and therefore current reclamation practices and standards fail to achieve long-term effectiveness across the western United States. • A reclaimed well pad's land potential is determined by 3 properties: static (e.g., climate), dynamic (e.g., soil stability), and process (e.g., water retention). • Understanding a reclaimed well pad's land potential enables federal land agencies to outline surface reclamation goals and requirements consistently and clearly. • Monitoring for land potential increases the capacity of the private industry to practice adaptive management by enabling companies to respond to plant community changes while maintaining long-term progress toward recovery.

Rangelands↗

USGS2021

Supporting the development and use of native plant materials for restoration on the Colorado Plateau (Fiscal Year 2020 Report)

A primary focus of the Colorado Plateau Native Plant Program (CPNPP) is to identify and develop appropriate native plant materials (NPMs) for current and future restoration projects. Multiple efforts have characterized the myriad challenges inherent in providing appropriate seed resources to enable effective, widespread restoration and have identified a broad suite of research activities to provide the information necessary to overcome those challenges (e.g., Plant Conservation Alliance 2015; Breed et al. 2018; Winkler et al. 2018). Many of the most complex information needs relate to identifying the appropriate sources of plant species that can successfully establish in dryland environments, like the Colorado Plateau, where precipitation is generally low and highly variable. Providing this information requires synergistic research efforts that build upon insight gained from earlier investigations. The U.S. Geological Survey (USGS) Southwest Biological Science Center’s (SBSC’s) research activities that supported CPNPP in FY20 followed the FY20 Statement of Work to support a research framework that is continually adapting based on the needs of the restoration community and results from previous investigations; the long-term research framework is outlined in the 2019-2023 5-Year Research Strategy (hereafter referred to as the 5-year plan). This research framework provides support for the National Seed Strategy for Rehabilitation and Restoration (Plant Conservation Alliance, 2015), Department of Interior Secretarial Order #3347 ( Conservation Stewardship and Outdoor Recreation ), and Bureau of Land Management Leadership Priority #1 ( Create a conservation stewardship legacy second only to Teddy Roosevelt ). Research activities in FY20 centered on landscape genomics, implementing a common garden experiment near Vernal, UT, conducting experimental treatments using the GRID (Germination for Restoration Information and Decision-making) framework, and collecting seeds and leaf tissues in the field in preparation for future experiments. These activities were supported by five biological science technicians. The SARS-CoV-2 pandemic delayed some aspects of the FY20 workplan, especially for contract laboratory services and the construction of the GRID garden in Flagstaff, AZ. However, goals were largely met, and the overall progress of research remains on track with respect to the 5-year plan. While Dr. Rob Massatti was the only scientist supported by the SBSC-CPNPP agreement in FY20, other scientists, including Drs. John Bradford, Seth Munson, Mike Duniway, Sasha Reed, Matt Jones, and Daniel Winkler, spent a considerable amount of time providing expertise and support for individual projects. Work activities performed in support of each goal are discussed in turn.

Arizona, Colorado, New Mexico, Utah↗

USGS2020

Ultra‐high‐resolution mapping of biocrusts with Unmanned Aerial Systems

Biological soil crusts (biocrusts) occur in drylands globally where they support ecosystem functioning by increasing soil stability, reducing dust emissions and modifying soil resource availability (e.g. water, nutrients). Determining biocrust condition and extent across landscapes continues to present considerable challenges to scientists and land managers. Biocrusts grow in patches, cover vast expanses of rugged terrain and are vulnerable to physical disturbance associated with ground‐based mapping techniques. As such, remote sensing offers promising opportunities to map and monitor biocrusts. While satellite‐based remote sensing has been used to detect biocrusts at relatively large spatial scales, few studies have used high‐resolution imagery from Unmanned Aerial Systems (UAS) to map fine‐scale patterns of biocrusts. We collected sub‐centimeter, true color 3‐band imagery at 10 plots in sagebrush and pinyon‐juniper woodland communities in a semiarid ecosystem in the southwestern US and used object‐based image analysis (OBIA) to segment and classify the imagery into maps of light and dark biocrusts, bare soil, rock and various vegetation covers. We used field data to validate the classifications and assessed the spatial distribution and configuration of different classes using fragmentation metrics. Map accuracies ranged from 46 to 77% (average 65%) and were higher in pinyon‐juniper (average 70%) versus sagebrush (average 60%) plots. Biocrust classes showed generally high accuracies at both pinyon‐juniper plots (average dark crust = 70%; light crust = 80%) and sagebrush plots (average dark crust = 69%; light crust = 77%). Point cloud density, sun elevation and spectral confusion between vegetation cover explained some differences in accuracy across plots. Spatial analyses of classified maps showed that biocrust patches in pinyon‐juniper plots were generally larger, more aggregated and contiguous than in sagebrush plots. Pinyon‐juniper plots also had greater patch richness and a lower Shannon evenness index than sagebrush plots, suggesting greater soil cover heterogeneity in this plant community type.

Utah↗

USGS2020

Seasonal and individual event-responsiveness are key determinants of carbon exchange across plant functional types

Differentiation in physiological activity is a critical component of resource partitioning in resource-limited environments. For example, it is crucial to understand how plant physiological performance varies through time for different functional groups to forecast how terrestrial ecosystems will respond to change. Here, we tracked the seasonal progress of 13 plant species representing C 3 shrub, perennial C 3 and C 4 grass, and annual forb functional groups of the Colorado Plateau, USA. We tested for differences in carbon assimilation strategies and how photosynthetic rates related to recent, seasonal, and annual precipitation and temperature variables. Despite seasonal shifts in species presence and activity, we found small differences in seasonally weighted annual photosynthetic rates among groups. However, differences in the timing of maximum assimilation ( A net ) were strongly functional group-dependent. C 3 shrubs employed a relatively consistent, low carbon capture strategy and maintained activity year-round but switched to a rapid growth strategy in response to recent climate conditions. In contrast, grasses maintained higher carbon capture during spring months when all perennials had maximum photosynthetic rates, but grasses were dormant during months when shrubs remained active. Perennial grass A net rates were explained in part by precipitation accumulated during the preceding year and average maximum temperatures during the past 48 h, a result opposite to shrubs. These results lend insight into diverse physiological strategies and their connections to climate, and also point to the potential for shrubs to increase in abundance in response to increased climatic variability in drylands, given shrubs’ ability to respond rapidly to changing conditions.

Oecologia↗

USGS2020

A hybrid approach for predictive soil property mapping using conventional soil survey data

Soil property maps are important for land management and earth systems modeling. A new hybrid point-disaggregation predictive soil property mapping strategy improved mapping in the Colorado River Basin, and can be applied to other areas with similar data (e.g. conterminous United States). This new approach increased sample size ~6-fold over past efforts. Random forests related environmental raster layers representing soil forming factors to samples to predict 15 soil properties (pH, texture fractions, rock, electrical conductivity, gypsum, CaCO3, sodium adsorption ratio, available water capacity, bulk density, erodibility, organic matter) at 7 depths, depth to restrictive layer, and surface rock size and cover. Cross-validations resulted in coefficient of determinations averaging 0.52, with a range of 0.20 to 0.76; and mean absolute errors ranged from 3% to 98% of training data averages with a mean of 41%. Uncertainty estimates were also developed by creating relative prediction intervals (RPIs) for the entire study area, which allow end users to evaluate uncertainty relative to original data distributions. Average error increased with higher RPI values (higher uncertainty), and areas with the highest RPI are consistently under-sampled, suggesting that additional sampling in these areas may improve prediction accuracy. Greater uncertainty was also observed in areas with shale parent materials and physiographic settings uncommon relative to the broader study area.

New Mexico, Colorado, Wyoming, Utah, Nevada, Arizo↗

USGS2020

Assessment of rangeland ecosystem conditions in Grand Canyon-Parashant National Monument, Arizona

Sustainability of dryland ecosystems depends on the functionality of soil-vegetation feedbacks that affect ecosystem processes, such as nutrient cycling, water capture and retention, soil erosion and deposition, and plant establishment and reproduction. Useful, common indicators can provide information on soil and site stability, hydrologic function, and biotic integrity. Evaluation of rangeland health thus relies on describing the condition and sustainability of these individual, measurable, and observable indicators that are linked to important ecosystem processes. This report focuses on the ~200,000 acres of the Grand Canyon-Parashant National Monument that is administered by the National Park Service (NPS)—one of the largest NPS units where livestock grazing is a permitted land-use activity. Many ecosystems in the monument are characterized by a low degree of resilience to improper grazing because of low and variable precipitation. The monument is marked by a high degree of environmental heterogeneity, including a large elevation gradient, widely differing precipitation patterns, a diversity of geologic substrates, and unique combinations of plant species. The objective of this report is to (1) increase our understanding of the underlying landscape, soil, and climate setting factors that affect Grand Canyon-Parashant National Monument dryland ecosystem structure and function (also referred to as land potential) and (2) characterize the condition of monument ecosystems in relation to management concepts, such as rangeland health. Data were analyzed by elevation zone using both univariate and multivariate approaches. Survey results document the high level of diversity within the study area, including 15 unique soil taxa and 271 species of plants. We collected three new plant species for Grand Canyon-Parashant National Monument and 17 new species for the NPS portion of the monument. Results also document a strong association between rangeland health indicators and elevation, topographic setting, and soils. Soil factors found to explain important variation across plots include the amount of exposed bedrock, soil rockiness, soil texture (and associated hydrologic properties), and soil depth. We also found that dominant species turnover across elevation may represent species’ differences in adaptation to climates, including Larrea tridentata , Coleogyne ramosissima , and Artemisia spp. Bromus rubens is the most common invasive species of concern recorded in this study, but other common invasive species are Bromus tectorum , Erodium cicutarium , and Schismus arabicus . Correlations between an index of cattle use and indicators of rangeland health suggest that areas with high cattle use have increased bare ground, decreased ground cover, increased frequency of Schismus arabicus , decreased cover of Coleogyne ramosissima and Ephedra spp., and increased cover of Gutierrezia spp. The few strong correlations observed between indicators of vascular plant community cover or abundance and indicators of cattle activity support rangeland assessment and monitoring strategies that do not rely solely on plant-based indicators are needed. This work supports management of dryland ecosystems, including Grand Canyon-Parashant National Monument, using concepts of land potential. We conclude the report with recommendations on improving existing land-potential-based classification systems, associated interpretations, and methods for moving forward with a Grand Canyon-Parashant National Monument rangeland monitoring program.

Arizona↗

USGS2020

Bridging the research-management gap: Landscape ecology in practice on public lands in the western United States

The field of landscape ecology has grown and matured in recent decades, but incorporating landscape science into land management decisions remains challenging. Many lands in the western United States are federally owned and managed for multiple uses, including recreation, conservation, and energy development. We argue for stronger integration of landscape science into the management of these public lands. We open by outlining the relevance of landscape science for public land planning, management, and environmental effects analysis, including pertinent laws and policies. We identify challenges to integrating landscape science into public land management, including the multijurisdictional nature and complicated spatial pattern of public lands, the capacity of agencies to identify and fill landscape science needs, and public perceptions about the meaning of landscape approaches to management. We provide several recent examples related to landscape monitoring, restoration, reclamation, and conservation in which landscape science products were developed specifically to support decision-making. We close by highlighting three actions - elevating the importance of science-management partnerships dedicated to coproducing actionable landscape science products, identifying where landscape science could foster efficiencies in the land-use planning process, and developing scenario-based landscape models for shrublands - that could improve landscape science support for public land planners and managers.

Arizona, California, Colorado, Idaho, Montana, New↗