@article {JRN55374, title = {Adaptive monitoring in support of adaptive management in rangelands}, journal = {Rangelands}, volume = {44}, year = {2022}, month = {2/2022}, pages = {1-7}, abstract = {On the Ground {\textbullet} Monitoring supports iterative learning about the effectiveness of management actions, information that can help managers plan future actions, facilitate decision-making, and improve outcomes. {\textbullet} Adaptive monitoring is the evolution of a monitoring program in response to new management questions; new or changing environmental or socioeconomic conditions, improved monitoring methods, models, and tools; and experience implementing the monitoring program. {\textbullet} Adaptive monitoring is connected to research and management through the exchange of data; analytical, methodological, and technological developments; information; and understanding. {\textbullet} We review recent advances in adaptive monitoring and discuss new opportunities for both the research and management communities to improve monitoring in the years ahead.}, keywords = {adaptation, co-produ-ction, collaboration, innovation, partnership, rangeland science}, doi = {10.1016/j.rala.2021.07.003}, url = {files/bibliography/22-006.pdf}, author = {Sarah McCord and David Pilliod} } @proceedings {JRN55426, title = {Leveraging standardized monitoring data to support grazing land soil erosion assessments}, year = {2021}, month = {7/31/2021}, abstract = {The LTAR Rangeland Soil Erosion Working Group formed with the objective of coordinating the use of standardized national inventory and monitoring datasets to assess wind and water erosion impacts across US grazing lands. The Rangeland Soil Erosion Working Group is comprised of LTAR scientists, members of USDA-NRCS CEAP Grazing Lands project, as well as collaborators at the Bureau of Land Management and US Geological Survey. The first task of the group was to transform standardized monitoring data, housed in the Landscape Data Commons, into appropriate input formats to parameterize water (RHEM) and wind (AERO) erosion models. To accomplish this task, we coordinated with each modelling team to build accurate data models for each erosion model. This included: 1) identifying each input variable or indicator needed, 2) describing the correct calculation of each variable based on available monitoring data, 3) refining a common species list and functional group list for all species occurring on US grazing lands, and 4) applying the data model to the monitoring data to produce model input and parameterization files. As of July 2021, the data model development process is complete. The data model has been applied to over 60,000 grazing land inventory and monitoring locations nationally, enabling RHEM and AERO applications to those locations. The development of new analyses will support project stakeholders (NRCS CEAP and BLM) and provide erosion indicators across US grazing lands that can be used in the LTAR sustainable intensification indicators framework. }, keywords = {assessments, data, grazing land, Leveraging, monitoring, soil erosion, standardized, support}, author = {Sarah McCord and Nicholas Webb and Kristopher Bonefont and Ericha Courtright and Danielle Duni and Brandon Edwards and David C. Goodrich and Phil Heilman and Mariano Hernandez and Loretta Metz and Mark A. Nearing and Beth Newingham and Nichols, Mary H. and Frederick B. Pierson and John Tatarko and David P. Toledo and Scott Van Pelt and Justin Van Zee and Jason Williams} } @conference {JRN55425, title = {Participatory research and co-production of knowledge: Building on a century of Jornada experience to expand LTAR outcomes and impacts}, booktitle = {LTAR Annual Meeting}, year = {2021}, abstract = {The co-production of knowledge with stakeholders is a unique strength of the Agricultural Research Service. The objective of this poster is to share examples of how co-production has been successfully applied to achieve four very different sets of objectives with national to global impact: (1) developing standardized rangeland indicators and monitoring methods; (2) developing interpretive tools; (3) identifying research gaps and needs for tools and new indicators (e.g., AERO, phenology) to inform conservation planning; and (4) building understanding of management technologies and systems to meet stakeholder land use goals. Collaborative research examples include work with the Malpai Borderlands Group, the BLM{\textquoteright}s {\textquotedblleft}Restore New Mexico{\textquotedblright} program, the development and implementation of BLM AIM and NRCS NRI national inventory and monitoring programs, NRCS CEAP-Grazing Lands model development (AERO; RHEM) and data processing (Landscape Data Commons) for effects assessments of NRCS conservation practices, the Sustainable Southwest Beef Coordinated Agricultural Project, and the development of LandPKS and EDIT. Our approach to co-production is iterative and results from a 25+ year series of diverse engagement approaches (trainings, working groups, day-to-day involvement), which has led to directly applicable research and management tools. Current LTAR efforts, including the Jornada Common Experiment and those of the LTAR Rangeland Soil Erosion, Wind Erosion, Phenology, and Indicators Working Groups, provide opportunities to expand these long-term participatory research and co-production efforts to improve and extend LTAR research impacts. This poster illustrates lessons learned from the Jornada experience and highlights opportunities for broader collaboration across the LTAR network.}, keywords = {building, century, co-production, impacts, Jornada, Long-Term Agricultural Research outcomes, LTAR, Participatory, research}, author = {Sarah McCord and Brandon T Bestelmeyer and Dawn M. Browning and Laura M. Burkett and Brandon Edwards and Jeffrey E. Herrick and Emily Kachergis and Nika Lepak and Leticia Lister and Loretta Metz and Terri Schulz and S. Spiegal and Nicholas Webb} } @article {JRN55283, title = {Provoking a Cultural Shift in Data Quality}, journal = {BioScience}, year = {2021}, month = {March 31, 2021}, abstract = {Ecological studies require quality data to describe the nature of ecological processes and to advance understanding of ecosystem change. Increasing access to big data has magnified both the burden and the complexity of ensuring quality data. The costs of errors in ecology include low use of data, increased time spent cleaning data, and poor reproducibility that can result in a misunderstanding of ecosystem processes and dynamics, all of which can erode the efficacy of and trust in ecological research. Although conceptual and technological advances have improved ecological data access and management, a cultural shift is needed to embed data quality as a cultural practice. We present a comprehensive data quality framework to evoke this cultural shift. The data quality framework flexibly supports different collaboration models, supports all types of ecological data, and can be used to describe data quality within both short- and long-term ecological studies}, keywords = {Cultural shift, data quality, ecological, ecosystem}, doi = {10.1093/biosci/biab020}, url = {files/bibliography/21-017.pdf}, author = {Sarah McCord and Nicholas Webb and Justin Van Zee and Sarah Burnett and Erica M. Christensen and Ericha Courtright and C. Laney and Lunch, Claire and Connie J. Maxwell and Jason W. Karl and A. L. Slaughter and Nelson Stauffer and Craig Tweedie} } @article {JRN55329, title = {Ten practical questions to improve data quality}, journal = {Rangelands}, year = {2021}, month = {8/24/2021}, pages = {1-12}, abstract = {On the Ground {\textbullet} High-quality rangeland data are critical to supporting adaptive management. However, concrete, cost-saving steps to ensure data quality are often poorly defined and understood. {\textbullet} Data quality is more than data management. Ensuring data quality requires 1) clear communication among team members; 2) appropriate sample design; 3) training of data collectors, data managers, and data users; 4) observer and sensor calibration; and 5) active data management. Quality assurance and quality control are ongoing processes to help rangeland managers and scientists identify, prevent, and correct errors in past, current, and future monitoring data. {\textbullet} We present 10 guiding data quality questions to help managers and scientists identify appropriate workflows to improve data quality by 1) describing the data ecosystem, 2) creating a data quality plan, 3) identifying roles and responsibilities, 4) building data collection and data management workflows, 5) training and calibrating data collectors, 6) detecting and correcting errors, and 7) describing sources of variability. {\textbullet} Iteratively improving rangeland data quality is a key part of adaptive monitoring and rangeland data collection. All members of the rangeland community are invited to participate in ensuring rangeland data quality.}, keywords = {data quality, monitoring, quality assurance, quality control}, doi = {10.1016/j.rala.2021.07.006}, url = {files/bibliography/21-039.pdf}, author = {Sarah McCord and Justin L. Welty and Jennifer Courtright and Catherine Dillon and Alex Traynor and Sarah Burnett and Ericha Courtright and G. Fults and Jason W. Karlgh and Justin Van Zee and Nicholas Webb and Craig Tweedie} } @conference {JRN55239, title = {A data commons approach can help span scales and stakeholders to support ecosystem conservation and land use}, booktitle = {Ecological Society of America}, year = {2020}, month = {02/2020}, address = {Salt Lake City, Utah}, abstract = {Background: Drivers of ecosystem changes include climate change, changing land uses, land management, and invasive species. Research efforts are underway to understand the impacts these drives on ecosystem services and agricultural production systems. At the same time, land managers seek to understand these interactions and implement conservation practices to ensure that management objectives are being met. To address these research and management needs, scientists and land managers have developed a range of disconnected data collection and monitoring initiatives. A strength of these initiatives has been the adoption of a suite of standardized vegetation and soil monitoring methods. These methods measure vegetation cover and structure, as well as soil horizon characteristics across dryland ecosystems from the Chihuahan Desert to the Arctic Tundra to the Mongolian Steppe. Yet, using this multi-scale ecosystem information can be challenging as it is difficult to locate and access these standardized data, ensure that they are trustworthy, and connect them to models and analytical tools. Here we present the Landscape Data Commons, which provides common rangeland datasets, standardized analysis tools and linkages to models that can integrate and add value to the data. The Landscape Data Commons provides a capability to deliver monitoring-derived information to benefit research, policy and land management across the United States and internationally. Methods:Cumulatively, across research institutions, state and federal land management agencies, and non-profit organizations over 60,000 rangeland monitoring locations have been sampled since 1995 in the western US. In aggregate, these data provide a unique and underutilized opportunity to understand rangeland change and ecosystem patterns across physiographic gradients as well as land use and land ownership. In this talk, we will discuss the novel cyberinfrastructure developments which have facilitated the aggregation of these monitoring data. We will also present how the Landscape Data Commons is being leveraged to understand ecosystem change at local and regional scales in combination with wind erosion models, decision frameworks, citizen science and sensor big datasets. We provide community and landscape scale examples of how the Landscape Data Commons is helping to elucidate previously unquantified patterns in soil erosion, invasive species spread to inform land management decisions and further research into ecological dynamics. We will conclude by discussing how the principles of the Landscape Data Commons can be applied to address grand challenges in ecosystem science and management. Paper number $\#$82161.}, author = {Sarah McCord and Nicholas Webb and Kristopher Bonefont and Rachel Burke and Brandon Edwards and Brandon T Bestelmeyer and Jeffrey E. Herrick} } @conference {JRN55202, title = {Terradactyl: an example of modularity and ontologies to ensure the sustainability of open source software}, booktitle = {Society for Range Management}, year = {2020}, month = {02/2020}, address = {Denver, CO}, abstract = {Data-driven rangeland management relies on the ability to flexibly access monitoring data that is relevant to the multitude of challenges on the nation{\textquoteright}s rangelands. Terradactyl is an R package with facilitates the modular use and re-use of rangeland monitoring data. Data are first divided and organized into a tall format which allows for modular data analysis. These data can then be easily analyzed using a modular range of rangeland indicator calculation function. These functions stack together in a way that is increasingly modular. For instance, the pct_cover() function is the foundation for pct_cover_bare_soil(), pct_cover_species(), so that the same code supports functionally similar indicator calculations. This means that terradactyl can be used to support a range of management questions across many different kinds of monitoring data.}, author = {Sarah McCord and Nelson Stauffer} } @conference {JRN54990, title = {Terradactyl: R Package for rangeland core methods data extraction and calculation}, booktitle = {72nd Society for Range Management International Meeting}, year = {2019}, month = {02/2019}, publisher = {Society for Range Management Meeting Abstracts}, organization = {Society for Range Management Meeting Abstracts}, address = {Minneapolis, Minnesota}, abstract = {

Grassland, Shrubland, and Savanna Ecosystems are widely adopted by federal land management agencies, such as the Bureau of Land Management (BLM) and Natural Resources Conservation Service (NRCS), research institutions, and other land management organizations. Within the BLM and NRCS these data are stored in large databases and are generally shared with collaborators and partners via file geodatabase or text file. While a limited suite of indicator calculations is available with these raw data, custom calculations can be complicated given the differences in data structure. Here we present an R package, called terradactyl, which handles data formats from the BLM TerrAdat database, the NRCS Natural Resources Inventory, and the Database for Inventory Monitoring and Assessment. This package provides several groups of functions. The first are a set of tidying functions, which gather data from BLM and NRCS data files into a common long data format to improve calculation efficiency. The second set of functions provide a grammar of rangeland indicator descriptions within which specific classes of indicators (e.g., cover) can be flexibly calculated based on user\&$\#$39;s categorical inputs. The third set of functions provide outputs specifically formatted for the inputs into modelling applications such as AERO, RHEM, and APEX or specified data archiving formats, such as the BLM\’s Terrestrial Indicators table. This R package is used by both the BLM and the NRCS to compute calculations from core methods data and is a powerful tool for integrating core methods data from different datasets.

}, author = {Sarah McCord and Nelson Stauffer} } @proceedings {JRN54856, title = {High quality data: An evaluation of AIM data quality and data quality procedures}, volume = {January 28-February 2, 2018}, year = {2018}, month = {01/28/2018}, publisher = {Society for Range Managment}, address = {Reno, Nevada}, abstract = {

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.

}, author = {Sarah McCord and Sarah Burnett and Nicole Cappuccio and Jennifer Courtright} } @proceedings {JRN54665, title = {Analytical approaches to quality assurance and quality control in rangeland monitoring data}, volume = {Jan 1 - Feb 2, 2017, Saint George, Utah}, year = {2017}, month = {01/2017}, publisher = {Society for Range Management}, address = {Saint George, Utah}, abstract = {
Producing quality data to support land management decisions is the goal of every rangeland monitoring program. However, the results of quality assurance (QA) and quality control (QC) efforts to improve data quality are rarely reported. The purpose of QA and QC is to prevent and describe non-sampling errors that introduce noise into monitoring datasets, thereby increasing the repeatability, defensibility, and usability of the data collected. Quality assurance is a proactive process designed to prevent errors from occurring, while QC is a reactive process whereby the number, nature, and implications of errors are identified. Common QA practices include careful design and documentation of the monitoring programs and protocols; training and calibration of data collectors; and structured management of resulting data. Quality control describes errors via data checks for incomplete or invalid values, variance decomposition, and evaluation of signal-to-noise ratios. We analyzed the calibration results and field data of two national monitoring datasets collected between 2011 and 2016. Field and lab calibration data demonstrate that the iterative learning by data collectors which occurs in QA improves field sampling results and can point to areas of future training. We also explored quantitative QC approaches for identifying and visualizing erroneous observations by comparing results to other data sources; and analyzing between-observer variability, patterns of missing data, and seasonal trends in method implementation - all of which help to describe the errors in these national datasets. We conclude that the results of the QA and QC processes can and should be evaluated to improve and document the quality of monitoring data. These evaluations can be used to improve monitoring data collection efforts and to support the role of rangeland monitoring in decision making.\ 
}, author = {Sarah McCord and Jason W. Karl and Justin Van Zee and Ericha Courtright} } @conference {JRN54932, title = {Emerging ecological datasets with application for modeling North American dust emissions}, booktitle = {American Geophysical Union}, year = {2017}, month = {12/2017}, address = {New Orleans, Louisiana}, abstract = {

In 2011 the US Bureau of Land Management (BLM) established the Assessment, Inventory and Monitoring (AIM) program to monitor the condition of BLM land and to provide data to support evidence-based management of multi-use public lands. The monitoring program shares core data collection methods with the Natural Resources Conservation Service{\textquoteright}s (NRCS) National Resources Inventory (NRI), implemented on private lands nationally. Combined, the two programs have sampled \>30,000 locations since 2003 to provide vegetation composition, vegetation canopy height, the size distribution of inter-canopy gaps, soil texture and crusting information on rangelands and pasture lands across North America. The BLM implements AIM on more than 247.3 million acres of land across the western US, encompassing major dust source regions of the Chihuahuan, Sonoran, Mojave and Great Basin deserts, the Colorado Plateau, and potential high-latitude dust sources in Alaska. The AIM data are publicly available and can be used to support modeling of land surface and boundary-layer processes, including dust emission. While understanding US dust source regions and emission processes has been of national interest since the 1930s Dust Bowl, most attention has been directed to the croplands of the Great Plains and emission hot spots like Owens Lake, California. The magnitude, spatial extent and temporal dynamics of dust emissions from western dust source areas remain highly uncertain. Here, we use ensemble modeling with empirical and physically-based dust emission schemes applied to AIM monitoring data to assess regional-scale patterns of aeolian sediment mass fluxes and dust emissions. The analysis enables connections to be made between dust emission rates at source and other indicators of ecosystem function at the landscape scale. Emerging ecological datasets like AIM provide new opportunities to evaluate aeolian sediment transport responses to land surface conditions, potential interactions with disturbances (e.g., fire) and ecological change (e.g., invasive species), and the impacts of anthropogenic land use and land cover change.

}, author = {Sarah McCord and Nelson Stauffer and S. L. Garman and Nicholas Webb} } @article {JRN54807, title = {Integrating remotely-sensed imagery and existing multi-scale field data to derive rangeland indicators: an application of Bayesian additive regression trees}, journal = {Rangeland Ecology and Management}, volume = {70}, year = {2017}, month = {09/2017}, pages = {644-655}, chapter = {644}, abstract = {

Remotely-sensed imagery at multiple spatial scales is used increasingly in conjunction with field data to estimate rangeland indicators (e.g., vegetation cover) and meet the growing need for landscape-scale monitoring and assessment of rangelands. Remote sensing studies that produce rangeland indicators often require intensive and costly field data collection efforts to produce accurate model predictions. Existing monitoring data, such as those collected by the Bureau of Land Management{\textquoteright}s Assessment, Inventory, and Monitoring (AIM) program, are potentially useful source of field data in remote sensing modeling studies. Due to the small sample sizes of typical AIM field projects compared to other remote sensing data collection efforts, common regression-tree-based modeling approaches may be inadequate for reliably predicting rangeland indicators with these training data. Bayesian models, such as Bayesian additive regression trees (BART), may provide a suitable alternative to traditional regression tree-based modeling approaches to overcome the sample size limitation of the AIM data. In this study, we used 182 AIM field plots together with both high (RapidEye) and moderate (Landsat OLI) spatial resolution satellite imagery to predict bare ground and bare soil, total foliar, herbaceous, woody, and shrub cover indictors on rangelands in a 14,625 km2 area of northeastern California. We demonstrate that a BART model performed similarly to other regression tree approaches when field data and high spatial resolution imagery predictions were combined to predict indicator values using the medium spatial resolution Landsat image. The BART models also provided spatially-explicit uncertainty estimates which allow land managers to more carefully evaluate indicator predictions and to identify areas where future field data collection might be most useful. This study demonstrates that existing field data and freely-available remotely-sensed imagery can be integrated to produce spatially explicit and continuous surface estimates of rangeland indicators across entire landscapes.

}, keywords = {Bayesian additive regression trees, BLM AIM, monitoring, rangelands, remote sensing}, doi = {doi:10.1016/j.rama.2017.02.004}, url = {files/bibliography/17-025.pdf}, author = {Sarah McCord and M. Buenemann and Jason W. Karl and Dawn M. Browning and Hadley, Brian} } @conference {JRN54448, title = {Multiscale ecosystem monitoring: an application of scaling data to answer multiple ecological questions}, booktitle = {68th Annual Society for Range Management Meeting}, year = {2015}, month = {02/2015}, publisher = {Sociiety for Range Management}, organization = {Sociiety for Range Management}, address = {Sacramento, CA}, abstract = {

Standardized monitoring data collection efforts using a probabilistic sample design, such as in the Bureau of Land Management{\textquoteright}s (BLM) Assessment, Inventory, and Monitoring (AIM) Strategy, provide a core suite of ecological indicators, maximize data collection efficiency, and promote reuse of monitoring datasets to address multiple ecosystem concerns at multiple scales. Our objective is to apply multi-scale monitoring data to answer questions related to overall landscape health, post-fire grazing closures, and emergency stabilization and rehabilitation (ES\&R) treatment effectiveness in northern California and northwestern Nevada. To meet this multi-scale objective, we deployed the AIM strategy at three spatial scales and intensities: low-intensity landscape sampling across the study area, medium-intensity sampling of the nine livestock grazing allotments which intersected the Rush Fire of 2012 (610,345 acres), and high-intensity sampling of ES\&R treatments within the fire boundary (2,558 acres). Terrestrial plots were established within a probabilistic sampling framework, and the BLM core indicators were measured at each plot (n=205) in 2013. At the landscape scale, indicator estimates varied with ecological potential, disturbance history, and land use. The sample points were then subset to answer management questions at local scales. ES\&R treatment effectiveness plots (n=28) were found to have high bare ground, low foliar cover, and low perennial plant density. When we applied the data (n=106) to answer post-fire livestock grazing monitoring questions, low perennial grass cover and high bare ground indicate insufficient recovery 1 year after the fire to support extensive livestock grazing. We also found that uncertainty increased with increased scale and decreased sampling density. This multi-scale monitoring approach maximizes efficiency in data collection, analysis, and reporting by acquiring the same ecological information at each scale and reusing data at each scale. This will enable land managers to efficiently and effectively collect monitoring data to inform decision making.

}, author = {Sarah McCord and D. K. James and Wilson, Dereck and Jason W. Karl and Emily Kachergis} } @conference {JRN54447, title = {Using the best available data: integrating field data and remote sensing imagery to monitor rangelands}, booktitle = {68th Annual Society for Range Management Meeting}, year = {2015}, month = {02/2015}, publisher = {Sociiety for Range Management}, organization = {Sociiety for Range Management}, address = {Sacramento, CA}, abstract = {

Monitoring of rangelands poses significant challenges to land managers due to broad extent and many uses of rangelands. The Bureau of Land Management{\textquoteright}s (BLM) Assessment, Inventory, and Monitoring (AIM) program seeks to efficiently collect standard, quantitative monitoring data which is collected once and used multiple times to address a range of management questions. However, the cost of collecting field data to sufficiently monitor landscapes can be prohibitive. Remote sensing image classification provides an opportunity to derive rangeland indicators over large areas and at more frequent intervals than field data collection campaigns. We demonstrate a hierarchical Bayesian approach for maximizing the efficiency of monitoring data collection where existing AIM field plots are used to a) train remote sensing image classification and b) provide finer resolution data in areas of management concern. Where available, high spatial resolution images (e.g., RapidEye) are classified and the results of this classification are used together with field data to improve the moderate spatial resolution image (e.g., Landsat OLS) classification. Results of our classification of 2.7 million acres in northern California show remote sensing-derived estimates of bare ground, shrub cover, and herbaceous cover are the most accurate but other indicators, such as soil aggregate stability, plant density, and vegetation species composition, are best estimated using field collected data. Utilizing AIM field data together with remote sensing enables a strategic rangeland monitoring approach where field data meet multiple monitoring objectives. Combining multiple types of data improves estimates of indicators thus providing higher quality monitoring data to rangeland managers.

}, author = {Sarah McCord and Jason W. Karl} }