Dryland ecosystem ecology and restoration

GRASSLAND-SHRUBLAND STATE TRANSITIONS IN ARID RANGELANDS: COMPETITION MATTERS. Nathan A. Pierce*1, Steven Archer2, Brandon T. Bestelmeyer3; 1University of Arizona, Tucson, AZ, 2The University of Arizona, Tucson, AZ, 3USDA-ARS Jornada Experimental Range, Las Cruces, NM

Background: State transition from grassland to shrubland is synonymous with desertification in many arid rangeland systems. Traditional desertification models emphasize abiotic feedbacks that modify the physical environment in ways that promote shrub proliferation and impede grass survival. Inherent in this perspective is the assumption that biotic interactions between grasses and shrubs have little bearing on state transition dynamics. Furthermore, the extent to which density-dependent interactions among shrubs might determine the magnitude and pattern of their cover is unknown. We addressed these assumptions and knowledge gaps over 4 years using selective removal experiments. Shrub-on-Grass Interactions: Grass ANPP responded positively to shrub removal in all years, but more so in years with above-average rainfall and in plots with high shrub abundance. Grass allocation to vegetative reproduction and grass patch area also increased when shrub neighbors were removed. These results demonstrate that biotic interference by shrubs upon grasses can reinforce and magnify abiotic feedbacks during grassland–shrubland transitions. Grass-on-Shrub Interactions: In years with above-average growing season precipitation, ANPP of small shrubs increased when grasses were removed, a result not evident in dry years or in larger shrubs. Grasses may therefore slow the rate at which shrubs attain a physical stature that can modify the physical environment in self-promoting ways. Shrub-Shrub Interactions: Intraspecific interactions between shrubs were not evident in any year, supporting the assumption that abiotic variables rather than competitive interactions constrain maximum shrub cover. Summary: Results from these field experiments provide insights on how shrub-grass interactions amplify or dampen the abiotic drivers of desertification, help explain how woody plants can continue to proliferate despite low or reduced livestock grazing pressure, and generate hypotheses that can help us refine experiments to address the mechanisms of belowground competition at play where grasses and shrubs co-occur in arid ecosystems.

UNDERSTANDING SPATIAL VARIABILITY IN PERENNIAL GRASS RESTORATION FOLLOWING SHRUB REMOVAL IN THE CHIHUAHUAN DESERT: THE RESTORE NEW MEXICO COLLABORATIVE MONITORING PROGRAM
Brandon T. Bestelmeyer*1, Laura M. Burkett1, Leticia Lister2; 1USDA-ARS Jornada Experimental Range, Las Cruces, NM, 2Bureau of Land Management, Las Cruces, NM

Grassland to shrubland transitions are well documented throughout the desert grassland region of the Chihuahuan Desert. These transitions were triggered in the early 20th century by overgrazing of perennial grasses during drought periods, loss of fire regimes, and seed dispersal by livestock. Shrublands dominated by C3 species are highly resilient to natural disturbances, so herbicides specific to the C3 photosynthetic pathway are used to reduce the competitive preemption of soil water resources by shrubs and trigger the recovery of C4 perennial grasses. Anecdotally, there is evidence that shrub management treatments have been effective in some cases, but not in others, creating controversy about their value. As part of the Restore New Mexico partnership, we initiated a region-scale, long-term monitoring experiment embedded within shrub management treatments beginning in 2007. Within shrub management areas, we created treatment and control plots that were matched according to soils/ecological sites and initial state prior to treatments, such that control plots were excluded from treatment. Line-point intercept was used to measure cover prior to and after treatments. In addition, we sampled older treatments (2004 and earlier) using space-for-time substitution assumptions. Our records to date indicate that 1) treatments are generally effective in increasing perennial grass cover, 2) the composition of grasses that increase usually differs from that of putative reference states, creating “novel ecosystems” and 3) soil and climate properties are related to the magnitude of grass response to treatments; specifically, loamy soils and drier climates yield reduced grass response. Experimental monitoring data can be used to refine restoration approaches and resolve controversies about them.

ELEVATED DUST EMISSIONS ON THE COLORADO PLATEAU: ROLE OF GRAZING, VEHICLE DISTURBANCE, AND INCREASING ARIDITY. Travis W. Nauman*1, Michael C. Duniway1, Nicholas Webb2, Jayne Belnap1; 1US Geological Survey, Moab, UT, 2USDA-ARS Jornada Experimental Range, Las Cruces, NM

Dust from drylands are of major concern to human society. Dust deposition onto snowpacks can hasten melt rates, resulting in lowered inputs into major rivers. Blowing dust can result in traffic accidents, respiratory disease, and high economic costs. To abate dust emissions, it is necessary to examine suspected sources such as unpaved roads and grazing lands. We measured aeolian sediment mass fluxes in low elevation rangelands and unsurfaced roads on the Colorado Plateau in Utah, USA. Empirical spatial modeling suggests that rangelands are producing ~92-93% of regional dust and roads ~7-8%. Measured ‘hot spots’ in sediment mass flux at 100 cm height rival the highest ever recorded including 7,460 gm-2day-1 (spring 2009) in an off-highway vehicle (OHV) area near Hanksville, UT, but were more commonly 50-2,000 gm-2day-1 in areas with heavy grazing or OHV use. The overall mean measured rangeland sediment mass flux at 100 cm height was 5.14 gm-2day-1, considerably lower than grazed areas (~8-20 gm-2day-1) and OHV areas (414 gm-2day-1). In contrast, a similar area monitored with minimal soil disturbance averaged 1.60 gm-2day-1 from 2007-2015. Annual sediment mass fluxes on all rangeland land use types (light/not grazed, moderately and heavily grazed, and OHV) generally increased with increasing annual temperature, increased winds, and decreasing precipitation. Mass fluxes measured just downwind of unpaved road sites averaged 13.14 gm-2day-1 with a maximum observed seasonal flux of 128.0 gm-2day-1 along a producing oil well access road. Four of the five highest road flux values (n=33 total) measured were adjacent to roads primarily used to access oil or gas well-pads, while one was a road servicing both recreation and well-pads. These findings suggest that predicted future regional mega-droughts may increase dust emissions already elevated due to land management, potentially further compromising air quality, hydrologic cycles, and other ecological services linked to dust.

Livestock production and management in drylands

DEVELOPING REGIONALLY SPECIFIC GRAZING PRACTICES TO PROMOTE PRODUCTION, PROFITABILITY, AND ENVIRONMENTAL QUALITY. Sheri Spiegal*1, Brandon T. Bestelmeyer2, Joel R. Brown3; 1USDA - ARS Jornada Experimental Range, Las Cruces, NM, 2USDA-ARS Jornada Experimental Range, Las Cruces, NM, 3USDA-NRCS, Las Cruces, NM

Rangelands are valued for their capacity to provide diverse suites of ecosystem services, from food production to carbon storage to biological diversity. Although rangelands worldwide share common characteristics, differences among biogeographic regions result in differences in the types of opportunities for ranchers and other rangeland managers to preserve and enhance multiple services. For instance, opportunities to use grazing to promote biodiversity while meeting growing global demand for beef vary among the rangelands of Mediterranean California, the Chihuahuan Desert, and the Great Plains, USA. The drivers of these regional differences are biophysical, social, and economic. Understanding the drivers and their interactions can improve planning for regionally specific grazing practices, land uses, funding programs, and scientific research that optimize the provision of multiple services from U.S. rangelands. We present a model identifying the drivers that differentiate regional opportunities, and use it to compare grazing management options in Mediterranean California with those in other rangeland regions across the United States.

MOTHER-OFFSPRING INTERACTIONS IN RARAMURI CRIOLLO CATTLE ON NEW MEXICO AND CHIHUAHUA RANGELANDS .ShelemiaNyamuryekunge*1, Andres F. Cibils2, Rick E. Estell3, Alfredo Gonzalez3, Octavio R. Estrada4, Felipe A. Rodríguez Almeida4, Sheri Spiegal5; 1Range Science PhD, Las Cruces, NM, 2New Mexico State University, Las Cruces, NM, 3USDA - Agricultural Research Service, Las Cruces, NM, 4Universidad Autonoma de Chihuahua, Rancho Teseachi, Chihuahua, Mexico, 5USDA - ARS Jornada Experimental Range, Las Cruces, NM

Rangeland beef cows spend approximately six months annually raising their calves. This endeavor is known to significantly alter a dam’s grazing behavior and spatial distribution. The objective of this study was to characterize cow-calf interactions in two herds of RaramuriCriollo (RC) cattle that grazed rangeland pastures in southern New Mexico, USA (NM, 4355 ha) and west-central Chihuahua, Mexico (623 ha) during March and late-April respectively. At each site, five to nine randomly selected mature nursing cows were fitted with Lotek 3300LR GPS collars and Sirtrak proximity loggers. GPS collars recorded animal position at 5-min intervals, whereas proximity loggers were programed to record mother-offspring contact events at <3m distance. All calves were <2 weeks old at the onset of the study. In 2015 and 2016, respectively, collared animals grazed with a herd of 30 and 35 cows at the NM site and with 68 and 87 cows at the Chihuahua site. We computed number of contact events and duration of each event for 24h time periods, and for daytime and nighttime hours for the first three-weeks. ANOVA was used to determine if contact events and duration of each event changed as a function of calf age or time of day (day vs. nighttime). We also determined whether daily area explored by a cow and its calf differed. Day vs. nighttime mother-offspring contact events and contact time were not different (P≤0.05). Dams explored larger areas of the pasture than did their calves on any given day (40.3 vs. 29.2 ha; P=0.03). Cow-calf interactions tended to decrease and mother-offspring difference in daily area explored tended to increase as calves aged. Our results suggest that RC calves follow their dams during daily grazing bouts and are likely to constrain their dams’ feeding site selection the most during the first weeks after calving.

WEIGHT GAIN AND BEHAVIOR OF RARAMURI CRIOLLO VERSUS CORRIENTE STEERS DEVELOPED ON CHIHUAHUAN DESERT RANGELAND
Matthew M. McIntosh*1, Andres F. Cibils1, Rick E. Estell2, Sergio A. Soto-Navarro1, Alfredo Gonzalez2, Shelemia Nyamuryekunge3, Sheri Spiegal4; 1New Mexico State University, Las Cruces, NM, 2USDA - Agricultural Research Service, Las Cruces, NM, 3Range Science PhD, Las Cruces, NM, 4USDA - ARS Jornada Experimental Range, Las Cruces, NM

RaramuriCriollo (RC) steers are commonly raised for beef and finished on rangelands, while Corriente (CR) are often raised for rodeo sports. However, no data exist on weight gains and grazing behavior of rangeland-raised Criollo steers. An experiment was conducted in the Chihuahuan Desert using two cohorts of RC and CR to investigate the effects of biotype on growth performance and landscape utilization. Twenty-two 17-month (cohort 1) steers [10 RC (RC1) and 12 CR (CR1)] were weighed every 60 d between December 2015 and January 2017 and eighteen 8-month (cohort 2) steers [11 RC (RC2) and 7 CR (CR2)] were weighed every 60 d between December 2015 and August 2017 to determine individual body weight (WT), average daily gain (ADG), and body condition score (BCS). Nine steers from each cohort were monitored with Lotek 3300LR GPS collars at 5-min intervals during December 2015 and December 2016, respectively, to compare landscape utilization patterns of 5 RC and 4 CR individuals. Data were analyzed as repeated measures using mixed procedures of SAS. Separate analyses were conducted per age cohort. For cohort 1, ADG was greater (P= 0.03) for RC1 than CR1 (0.18 vs 0.13 ± 0.09 kg). For cohort 2, ADG was greater (P= 0.01) for CR2 than RC2 (0.22 vs 0.12 ± 0.10 kg). Moreover, BCS was not affected (P> 0.58) for either cohort. Distance traveled daily by cohort 1 was not affected (P>0.81) by biotype, but CR2 walked farther during day and night than RC2 (P<0.01). CR1 followed more sinuous trajectories than RC1 counterparts from dusk to midnight (P<0.01). CR2 followed more sinuous movement trajectories than RC2 at night (P<0.01). Although some behavioral differences were found, these results imply that at a young age, CR grow faster than RC, but at an older age RC grow faster than CR.

Assessment and monitoring technologies

SCIENCE WITH A SELFIE STICK: PLANT BIOMASS ESTIMATION USING SMARTPHONE BASED “STRUCTURE FROM MOTION” PHOTOGRAMMETRY. Jonathan J. Maynard*1, Jason W. Karl2; 1USDA-ARS, Las Cruces, NM, 2University of Idaho, Moscow, ID

Significant advancements in photogrammetric Structure-from-Motion (SfM) software, coupled with improvements in the quality and resolution of smartphone cameras, has made it possible to create ultra-fine resolution three-dimensional models of physical objects using an ordinary smartphone. Here we present an open-source modeling framework for creating three-dimensional models of vegetation structure using smartphone video of a user-defined sampling space (e.g., 1 m2). Our main objective was to evaluate the accuracy of our SfM sampling method in predicting above-ground biomass relative to traditional estimation techniques. A series of permanent 1-m2 quadrat sampling sites at the Jornada Experimental Range in southern New Mexico were used to develop the SfM sampling method, with each quadrat differing in its proportions of shrub vs. herbaceous vegetation composition. A smartphone mounted on a selfie stick was used to capture video of each 1-m2 quadrat by circumnavigating it while moving the smartphone up and down to capture a range of image angles. Images were extracted from video frames at sampling rate of 4 frames per second and used to create densely reconstructed point clouds using open-source SfM software. Point clouds were georeferenced using a set of 3 control points located on the ends of a 0.25-m3 PVC corner frame placed at each site prior to imaging. Canopy volume was calculated for each point cloud using a foliar canopy approach, and used to model above ground biomass using published linear regression models. These results were compared to traditional nondestructive biomass estimates that were calculated by measuring the dimensions (cover and height) of individual plants or plant parts within each quadrat and converted to aboveground biomass using allometric equations. Preliminary results show a strong correspondence between measured and SfM modeled biomass. Our presentation will describe the details of this method and discuss its potential utility in increasing the accuracy and repeatability of field-based biomass estimation.

MULTI-SCALE WIND EROSION MONITORING AND ASSESSMENT FOR US RANGELANDS
Nicholas Webb*; USDA-ARS Jornada Experimental Range, Las Cruces, NM

Wind erosion is a major resource concern for rangeland managers. Although wind erosion is a naturally occurring process in many drylands, land use activities, and land management in particular, can accelerate wind-driven soil loss – impacting ecosystem dynamics and agricultural production, air quality, human health, biogeochemical cycles, and climate. Wind erosion has large variability in space and time, and as a consequence is difficult to monitor effectively in ways that can support its management. Existing US air quality monitoring programs provide indicators of wind erosion intensity (e.g., particulate matter [PM] concentrations) and associated benchmarks (e.g., Clean Air Act) are regulated federally but provide no information about which landscapes are eroding, but how much, or when. Standardized rangeland monitoring programs like the BLM’s Assessment, Inventory and Monitoring (AIM) Strategy and the NRCS’s National Resources Inventory (NRI) collect information about soil properties (texture) and vegetation (foliar cover, canopy gap size, height) that, coupled with remote sensing products and numerical models, can be used to estimate wind erosion and dust emission rates across spatial scales to better inform management. Here I present an overview of the National Wind Erosion Research Network and ongoing efforts to develop decision-support tools that connect existing soil, vegetation and air quality monitoring programs to provide information to federal agencies and the public about wind erosion rates. I demonstrate how core indicators used in the AIM and NRI monitoring programs can be integrated to assess dust emission rates; enabling air quality, human health and ecosystem impacts of dust to be linked to changes in range condition and benchmarks for rangeland management. I then describe how AIM and NRI data can be used to interpret remote sensing-driven model estimates of wind erosion and understand interactions with land use and management activities across ecological sites.

CHALLENGES AND OPPORTUNITIES WITH STANDARDIZED MONITORING FOR MANAGEMENT DECISION-MAKING. Jason W. Karl*1, Sarah McCord2, Scott W. Miller3; 1University of Idaho, Moscow, ID, 2USDA-ARS Jornada Experimental Range, Las Cruces, NM, 3Bureau of Land Management, Denver, CO

The importance of monitoring for adaptive management of rangelands has been well established. However, the actual use of monitoring data in rangeland management decisions has been modest despite extensive efforts to develop and implement monitoring programs from local to national scales. More effective use of monitoring data is critical to inform adaptive management, to empirically justify management decisions, and to ensure a return on resources invested in monitoring programs like the Bureau of Land Management’s (BLM) Assessment, Inventory, and Monitoring (AIM) program. Several challenges limit the use of monitoring data in management decision making. First, there is often a disconnect between aspects of monitoring (e.g., indicators, sample design, timing) and information needs of managers for making decisions. This can arise from a lack of specific monitoring objectives tied to management decisions or from monitoring indicators being analyzed and presented in forms that do not mesh with land management workflows. Second, in many cases little information exists on how to interpret monitoring results with respect to land potential (e.g., is the amount of bare ground more than what is expected for this type of rangeland?). Third, given limited resources, monitoring data often produce estimates with large confidence intervals, which causes challenges for interpreting whether a change has occurred or a land health standard has been met. Fourth, there is a tension between flexibility to design monitoring around management needs for specific, immediate objectives and maintaining standard monitoring approaches to build long-term datasets. This has the effect of reinforcing short-term monitoring at the expense of investing in standardized efforts that could address multiple objectives over the long term. We describe these challenges in the context of the BLM AIM projects discussed during the symposium. We explore potential opportunities for addressing these challenges and how the AIM program can be leveraged for success.

DIMA.TOOLS: AN R PACKAGE FOR WORKING WITH THE DATABASE FOR INVENTORY, MONITORING, AND ASSESSMENT. Nelson Stauffer*, Sarah McCord; USDA-ARS Jornada Experimental Range, Las Cruces, NM

The Database for Inventory, Monitoring, and Assessment (DIMA) is a Microsoft Access database used to collect, store and summarize monitoring data. This database is used by both local and national monitoring efforts within the National Park Service, the Forest Service, the Bureau of Land Management, Agricultural Research Service, non-profit organizations, and land management agencies globally, including Mongolia. The Access format permits long-term storage of large datasets and enables electronic data capture in the field, sophisticated error checking procedures, and a graphical user interface, however analysis of large amounts of data in one or more DIMAs is difficult. Therefore, further analysis and the production of quality monitoring information often requires interacting with DIMA via other software interfaces such as R.Here we present an package for R, dima.tools, containing functions which simplify direct user access to raw data tables within DIMAs and combining data from multiple DIMAs. Additionally, common data manipulation functions are available in the package for low-level tasks like tidying data and joining data to metadata as well as higher-level functions for tasks like producing standard indicators and quality assurance and quality control checks. Together, these provide a reproducible framework for users to compute their own custom indicators and to complete complex analyses using other functions and packages in R.

EVALUATION OF THE AUTOMATED REFERENCE TOOLSET FOR OIL AND GAS RECLAMATION ON COLORADO RANGELANDS. Sean F. Di Stefano*1, Colby Brungard1, Jason W. Karl2, Nelson Stauffer3, Sarah McCord3; 1New Mexico State University, Las Cruces, NM, 2University of Idaho, Moscow, ID, 3USDA-ARS Jornada Experimental Range, Las Cruces, NM

Rangelands are characterized by low precipitation and low biomass, making them susceptible to disturbance and difficult to reclaim. Considering the widespread and significant impact of oil and gas development on rangelands, effective reclamation is vital. Thus, it is important that land managers understand the ecological context of a reclamation site so that management outcomes can be correctly interpreted. This is often accomplished through comparison of reclamation areas to reference sites which are selected by their similarity to the reclamation area’s pre-disturbance condition, so that the relative condition of a reclamation site can be determined. Reference site selection is normally expert driven on a site-by-site basis, and thus can be inconsistently applied and ineffective in helping to meet reclamation goals over large landscapes. The Automated Reference Tool (ART) was developed to improve the efficiency and efficacy of reference site selection by selecting reference sites of similar land potential to the reclamation area based on soil texture, topography, and geology. However, ART has not been previously evaluated in a management context. Our objectives were to evaluate ART within this context and determine if existing reference sites are appropriate reference sites for well-pads. We applied the ART to oil & gas reclamation sites managed by the Bureau of Land Management’s (BLM) White River Field Office, Colorado, to their reference sites and to nearby sites from the BLM’s Assessment, Inventory, and Monitoring (AIM) program. Both existing reference sites and nearby AIM sites varied in their similarity to reclamation sites according to ART and in terms of vegetation composition from field sampling. Based on these results, ART can complement expert-driven reference site selection, making reference site selection quicker, more quantitative, and defensible, helping land managers better meet their reclamation needs.

COORDINATING A NATIONAL RANGELAND MONITORING TRAINING PROGRAM: SUCCESSES AND LESSONS LEARNED. Baili M. Foster*1, Sarah McCord2, Emily Kachergis3; 1Bureau of Land Management, Lakewood, CO, 2USDA-ARS Jornada Experimental Range, Las Cruces, NM, 3BLM, Denver, CO

One of the best ways to ensure quality of information gathered in a rangeland monitoring program is through a strong and uniform set of trainings. Curriculum development and delivery of monitoring trainings poses unique challenges that are not seen in academic settings. Participants come from a range of educational and experience backgrounds. Additionally, participants may only have the period of the training to receive the necessary instruction before fully implementing the protocol as part of their job duties. Consequently, careful consideration for content delivery and hands on learning activities is critical for active learning. We present the lessons learned from the Bureau of Land Management’s Assessment Inventory and Monitoring (AIM) training program with the partnership of USDA-ARS Jornada Experimental Range. The successes and lessons were developed from web-based and in-person trainings targeting BLM staff, seasonal staff, non-profit organizations, and academic partners for topics relating to data collection, analysis, database use and more. Consideration was given to the identification and education of both the target audience and the potential non-target audiences while taking into account the unique needs of adult learners. The AIM program has formatted training curriculum that addresses the needs of spatial, auditory, linguistic and kinesthetic learners. Challenges that impact training quality such as inconsistency in staff availability, a seasonal workforce, timing of data needs, phenology, and funding were also factored into the development of the training program. The importance of forming a training cadre and what factors should be assessed in this process has been an additional consideration. The final portion of the process was to review the most effective methods for assessing success. The training program of AIM is continually evolving, building on the success and lessons learned of the past to provide high quality information for rangeland management.

HIGH QUALITY DATA: AN EVALUATION OF AIM DATA QUALITY AND DATA QUALITY PROCEDURES
Sarah E. McCord*1, Sarah Burnett2, Nicole Cappuccio2, Jennifer Courtwright3; 1USDA-ARS, Las Cruces, NM, 2Bureau of Land Management, Denver, CO, 3Utah State University, Logan, UT

The goal of every monitoring program is to collect high-quality data which can then be used to provide information to decision makers. The Bureau of Land Management (BLM) Assessment, Inventory, and Monitoring (AIM) program is one such data set which provides rangeland status, condition, and trend information across BLM rangelands. These points represent aquatic and terrestrial resources across the western United States and Alaska. To date there are over 18,000 terrestrial points and 1400 aquatic points. While these data were collected for specific objectives, they are available for use by other BLM resource managers, other agencies and academic institutions to meet multiple resource questions and objectives. The broad utility of these data is due to the core methods and protocols employed during data collection as well as the quality assurance and quality control protocols employed by the AIM program. Here we evaluate the steps AIM takes to ensure quality, including project planning, core method implementation, observer training and calibration, electronic data capture, automated and manual data checks, and database structures. We describe the terrestrial database, TerrADat, and aquatic database, AquADat, and how these databases can be accessed by non-BLM users. Known quality procedures and evaluation measures improve the AIM data and can help users understand how and when the AIM data or data procedures may be useful to current and future applications.

INTRODUCING VERSION 5 OF INTERPRETING INDICATORS OF RANGELAND HEALTH. Mike Pellant*1, Patrick Shaver2, David A. Pyke3, Jeffrey Herrick4, Fee Busby5, Gregg Riegel6, Nika Lepak7, David Toledo8, Beth A. Newingham9, Emily Kachergis10; 1BLM (retired), Boise, ID, 2NRCS (retired), Monmouth, OR, 3U.S. Geological Survey, Corvallis, OR, 4USDA ARS, Las Cruces, NM, 5Utah State University, Logan, UT, 6USFS, Bend, OR, 7BLM, Boise, ID, 8USDA-ARS, Bismarck, ND, 9USDA-ARS, Reno, NV, 10BLM, Denver, CO

Interpreting Indicators of Rangeland Health was initiated in 1994 as a qualitative, rapid assessment technique to evaluate rangeland health. Seventeen field indicators are used to rate three attributes of rangeland health: 1) soil/site stability, 2) hydrologic function, and 3) biotic integrity. The published versions of this protocol are version 3 (2000) and version 4 (2005). Version 5 represents a continuing effort by an interagency cadre to improve the use of and consistency of results when using this protocol. The revision improves the development or modification of reference sheets and clarifies that the reference for conducting evaluations is the natural range of variability within the natural disturbance regime of the reference state. More emphasis is also placed on the functional/structural worksheet in conducting evaluations. This revision also supports linking qualitative and quantitative measurements, where quantitative measurements may also be used for baseline monitoring. A provisional copy of version 5 will be posted online and field-tested in 2018. Input from field tests will be incorporated into the published technical reference available in early 2019.

Remote sensing/land classification

TESTING THE ECOLOGICAL SITE GROUP CONCEPT
Shawn W. Salley*1, Jonathan J. Maynard1, Travis W. Nauman2, Curtis J. Talbot3, Joel R. Brown3; 1USDA-ARS, Las Cruces, NM, 2US Geological Survey, Moab, UT, 3USDA-NRCS, Las Cruces, NM

The 2016 “Ecological Sites for Landscape Management” special issue of Rangelands recommended an update to our thinking of Ecological Sites, suggesting that in our desire to make Ecological Sites more quantitative, we abandoned consideration of Ecological Sites’ spatial context. In response, Ecological Site Groups (ESGs) and associated general state-and-transition modelwere proposed as a framework for describing landscape-level processes occurring across multiple ecological sites, and thus integrating multiple ecological sites from similar landscapes into common behavioral units. We hypothesized that the spatial distribution of ESGs could be predicted using readily available geospatial data due to the theoretical association between ESGs and landscape biophysical properties. Here we test ESG concepts with a spatial modeling framework using machine learning algorithms, a SSURGO modified NASIS point dataset, and a suite of remote sensing-based spatial covariates (e.g., hyper-temporal remote sensing, terrain attributes, climate data, land-cover, and lithology). Our modeling approach was tested on two Major Land Resource Area (MLRA) study areas within the western U.S., representing 6.1 million ha within MLRA 35 and 7.5 million ha within MLRA 42. Results show our approach was effective in mapping ESGs, with a 64% correct classification based on 1,406 point observations across 8 expertly-defined ESG classes in MLRA 35 and a 75% correct classification based on 2,626 point observations across 9 expertly-defined ESG classes in MLRA 42. National coverage of the training and covariate data used in this pilot study provides opportunities for a consistent national-scale mapping effort of ESGs.

DEVELOPING A GRAZING STUDY VEGETATION MAP WITH REMOTE SENSING IN THE NORTHERN GREAT PLAINS
Fatima G. Continanza*1, Jennifer Muscha2, Caitriana M. Steele3, Mark K. Petersen2, Andrew J. Roberts4, Andres F. Cibils1; 1New Mexico State University, Las Cruces, NM, 2USDA-ARS Fort Keogh, Miles City, MT, 3USDA-ARS Jornada Exp. Range, Las Cruces, NM, 4USDA-ARS Fort Keogh, Miles city, MT

Accurate mapping of rangeland vegetation is critical in studies of livestock spatial distribution. Our objective was to develop a detailed vegetation map to help model the drivers of beef heifer grazing behavior on Northern Great Plains rangeland at the USDA-ARS Fort Keogh LARRL, Miles City, MT. Remote sensing technologies for mapping vegetation are less time-intensive than traditional on-the-ground techniques and cover larger areas. However, map classes should correspond with vegetation classes of interest, and the accuracy of a final map product should be known. Unsupervised classification of National Agricultural Imagery Program (NAIP) imagery guided ground data collection for training / testing supervised classification. Two line point transects were read at 15 sites within each class to determine plant cover and species composition. A 2 m belt transect was read along each line point transect to determine shrubs density. We discriminated four classes. Class 1: Badlands - sparsely vegetated hills; 57.7 ± 3.8 % bare ground (BG) and 998.7 ± 151.4 shrubs/ha. Class 2: Mixed grass prairie, dominated by cool season grasses; 13.7 ± 2.5 % BG and 227.5 ± 65.1 shrubs/ha. Class 3: Shrublands with mixed grass prairie understory; 27.1 ± 3.3 % BG and 765.1 ± 124.8 shrubs/ha. Class 4: Cool season grasses/legumes and shrubs; 8.5 ± 3.1 % BG and 1098.6 ± 268.4 shrubs/ha. Training data class separability was evaluated using Jeffries-Matusita and transformed divergence methods (ENVI software). Values of 1.6 – 1.9 indicated that classes were sufficiently separable. We used ENVI’s maximum likelihood algorithm to classify combined NAIP, Landsat 8 Operational Land Imager and digital elevation datasets (spatial resolution sampled to 15 m). Testing data were used to develop a confusion matrix and to calculate Kappa. Overall accuracy was 89.1 % with Kappa = 0.8. We conclude that these results are acceptable for the scope of our study.